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"bootstrap",
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"BufferExposure",
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"build_chain",
"build_sequence",
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"buttbp",
"buttbs",
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"buttlp",
"bvcchy",
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"calibrated_rec",
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"calibratedrec",
"calibratedrecommendations",
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"calibration_kl",
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"candidate_sequence",
"CandidateEquilibrium",
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"CanopyGaps",
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"cascade",
"categorical_loss",
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"causal_e_value",
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"causal_mediation_baron_kenny",
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"cbs_statistic",
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"CCepstrum",
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"CCRSO_TABLE1_RELEASES",
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"centering_parameter",
"centering_steps",
"central_moment",
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"cgnonl_beta_fletcher_reeves",
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"chain_entry",
"ChainRule",
"Chameleon",
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"changepoint_move_probabilities",
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"cheatsheet",
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"chgsen",
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"chin1",
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"chisqmc",
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"choose_k",
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"classification_features",
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"cles",
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"clrnt",
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"cohens_d",
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"coherence",
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"commands_by_category",
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"compare_forms",
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"Compvar",
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"concentrated_loglik",
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"conditional_from_joint",
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"ConditionalTurningBands",
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"Configmodel",
"confounding_check",
"confusion_error",
"ConleyVcov",
"Connectivity",
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"ConstituentLoad",
"ConstrainedHierarchical",
"ConstructionNoise",
"Contam",
"ContaminatedNormalOutliers",
"ContConv",
"ContConvAlt",
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"ConvexHull",
"Convgs",
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"ConvolveRunoff",
"cooccurrence",
"CooksD",
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"copynm",
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"correlation",
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"corrng",
"corrupt",
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"cosine_cutoff",
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"cosine_similarity",
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"CostDistance",
"Counterfactual",
"counterfactual_mean",
"CountModelIc",
"covariance_from_scale_rotation",
"covariate_kink_test",
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"covLst",
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"cox_reid_loglik",
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"cramers_v",
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"cross_validate",
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"crypto_shred",
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"css",
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"d_to_nnt",
"d_to_or",
"d_to_r",
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"dagostino_pearson",
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"DarcyFlow",
"data_adaptive_parameter",
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"DayEveningNight",
"Dbeta",
"Dbinom",
"DbscanClusters",
"Dccgrch",
"Dchisq",
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"Ddimst",
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"de_bruijn_graph",
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"decode",
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"DeepF",
"DeepGpSample",
"deepSVDD_cheatsheet",
"Deepw",
"Deepwk",
"default_learners",
"Defdtr",
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"DeformationFieldSimulate",
"Deftef",
"Degmat",
"DegreeHeatingWeeks",
"Deitkd",
"Delaunay",
"Delaunay3D",
"DelaunayTriangulation",
"delete_d_jackknife",
"DeltaArea",
"DeltaDecomp",
"DeltaLim",
"Denclue",
"denoise",
"density_expectation",
"density_is_view_independent",
"density_kink_test",
"DensProb",
"DensProbC",
"DeprivationIndex",
"DepthH",
"DepthInvariantIndex",
"DepthP",
"DepthS",
"dequantize",
"derivatives",
"derive_context_keys",
"deseq2",
"deseq2_de",
"deseq2_differential",
"Desigeff",
"design_effect",
"design_from_prior",
"design_mf",
"detect_overfitting",
"detokenize",
"Detrbb",
"Detrnd",
"Devresid",
"Dexp",
"dfa",
"Dfbetas",
"Dfbetb",
"Dffit",
"Dffitsols",
"Dft",
"DftConv",
"DftK",
"DftRI",
"DftTw",
"DftX",
"Dgamma",
"Dgi",
"Diana",
"Dic",
"dice_loss",
"DicNotch",
"DictCode",
"did_estimate",
"did_forest",
"DieVar",
"Difent",
"Diff1",
"Diff2",
"DiffEnt",
"difference",
"differential_expression",
"Diffevol",
"DiffP",
"DiffQuot2",
"DiffQuotN",
"DiffusionStCovariance",
"Difmh",
"Difpst",
"Difsib",
"Dimensionality",
"dimrd",
"Dimredrate",
"DinfFlowDirection",
"Dinmlt",
"Dinocenter",
"Dinoloss",
"Dinov2",
"Diophs",
"DiopT",
"dipole_moment",
"DiracDelta",
"DirectionalVariogram",
"DirectSampling",
"dirichlet_predictive",
"DirichletFitMom",
"DirichletSample",
"Diripr",
"disaggregate",
"DiscountUtility",
"discover_levels",
"DiscreteMoranTest",
"discretize_zoh",
"discrimination_loss",
"Disctime",
"dispersion_gene_wise",
"dispersion_trend",
"Disproportionality",
"DissectionIndex",
"DissimilarityCheck",
"DissimilarityIndex",
"distance_matrix",
"DistanceBandWeights",
"DistanceSampling",
"DistanceTransform",
"Distilkl",
"DistM",
"distortion",
"distribution_mean",
"DistrictCompactness",
"DistrictCompetitiveness",
"dit_block",
"DivAv",
"Divergence",
"divergence_curve",
"diversity",
"diversity_prior",
"Divgvs",
"Divrate",
"DixonQ",
"DlaAggregate",
"Dnnmt",
"Dnorm",
"DNotchSmth",
"Doctide",
"DoSaturation",
"DotProd",
"double_q_target",
"Dpacct",
"Dpbayes",
"Dpedt",
"Dpgem",
"Dpgmm",
"Dpineq",
"Dpld",
"Dpll",
"Dpmem",
"Dpois",
"Dpparit",
"Dprime",
"Dpsbm",
"Dpsing",
"Dpvar",
"DrainWaterTable",
"draw_projections",
"Drbnk",
"Drbsze",
"Drcef",
"Drchange",
"Drclt",
"Drctf",
"Drdidblock",
"Drdidboot",
"Drdiddyn",
"Drdidsplit",
"Drhtg",
"Drlp1",
"Drnpc",
"Dropmask",
"Drrct",
"Drspa",
"Drspr",
"Drsta",
"Drvst",
"Drweights",
"Dssoot",
"DsspAssign",
"DsspHbondEnergy",
"Dt",
"Dtft",
"DtftZ",
"dual_averaging_update",
"dual_objective",
"dueling_aggregate",
"dueling_q",
"dueling_step",
"DunnIndex",
"DurantonOverman",
"dwnmn",
"Dwt",
"Dwt2Tap",
"dynamic_mask_head",
"Dyntmt",
"e_gcn",
"e_step",
"e_value",
"e_value_d",
"e_value_hr",
"e_value_or",
"e_value_rr",
"Eaiprl",
"Eapinfo",
"EarthMoversDistance",
"easy_only_fit",
"Ebfmi",
"EbGlobal",
"EbLocal",
"Ec25Correction",
"ecccen",
"EcefToGeodetic",
"Ecfp4",
"Ecfp6",
"ecgdet",
"EcgEmgCpl",
"EcgFeat",
"EcgWaveShp",
"EchoCep",
"EchoImp",
"EchoLogPsd",
"EchoLogSp",
"EchoPsd",
"EchoSeries",
"EchoSig",
"EchoSpec",
"EchoZ",
"EcologicalRegression",
"Edf",
"edge_encoding",
"edge_message",
"edgrn_effective_library_size",
"edgrn_exact_test",
"edgrn_moderate_dispersion",
"edgrn_nb_variance",
"edgrn_ql_f_test",
"edgrn_tmm_factor",
"Editdist",
"EDivisive",
"EdrSignal",
"EegAcf",
"EegAdapt",
"EegBands",
"EfaNfactors",
"effect_curve",
"effect_size_result",
"effective_reproduction_number",
"effective_sample_size",
"EffectiveNumberParties",
"efficacy_test_and_release",
"efficiency_check",
"Effmod",
"EganEgg",
"egcl",
"EggFeat",
"egnn_layer",
"egnnlayer",
"Eigcent",
"Eigengap",
"EigenvectorFiltering",
"EkfF",
"elbo",
"Elbow",
"ElectoralSwing",
"ElevationProfile",
"EllipticScan",
"EllisonGlaeser",
"elmo",
"elmo_mix",
"elmo_representation",
"elmorepresentation",
"Elnetr",
"Elomatch",
"Elorating",
"em_imputation",
"EmAlgo",
"Emaxr",
"EmbeddingQuality",
"EmdEns",
"EmdSpec",
"Emdtsm",
"EmgFreq",
"Emisinv",
"Emkfst",
"Empbnp",
"empirical_bayes_moran",
"EmpiricalBayesianKriging",
"EmpiricalBreakdownPoint",
"EmpiricalQuantileMap",
"EmpiricalVariogramBins",
"Empproc",
"encode",
"encode_box_prompt",
"encode_bpe",
"encode_mask_prompt",
"encode_point_prompt",
"encoder_block",
"endemic_equilibrium",
"Enetobj",
"enforce_invertibility",
"EngCap",
"EnsAvg",
"ensemble_average",
"ensemble_variance",
"EnsMean",
"entropy",
"EntryGame",
"EofAnalysis",
"Epbias",
"Epeols",
"Epetheor",
"Epicur",
"Epsgreedy",
"epsilon_data_independent",
"epsilon_squared",
"Eqmm",
"Eqms",
"EquivalentLevel",
"equivariantgnn",
"equivariantgraphconv",
"ErdErs",
"Erdosg",
"erf",
"Ergmod",
"ErlangC",
"ErlangRenewal",
"ErpArtifact",
"ErpFeat",
"ErrBound",
"error_cache",
"ErrorOperator",
"Erstst",
"escape_regex",
"escape_whitespace",
"esl_total_sum_squares",
"Esllso",
"Esmoeg",
"Esnnts",
"ess",
"EstebanRayIndex",
"estimate_ate",
"estimate_ate_gcomputation",
"estimate_eic",
"estimate_pi0",
"estimate_pliv",
"estimate_plr",
"EstimateHaze",
"estimator_score",
"EstradaIndex",
"EsumConv",
"EsumIid",
"Esumtv",
"eta_squared",
"Etsmod",
"EuclP",
"Euler",
"eulerian_path",
"EuphoticDepth",
"Evalgate",
"Evaltw",
"Evalu",
"Evangia",
"Evdec",
"EvenOdd",
"EvenPart",
"Evespot",
"Evhpvr",
"Evhrid",
"EvidenceAccumulation",
"Evmrlp",
"Evmsexp",
"Evpdfn",
"Evpot",
"Evstud",
"Evtlmom",
"Evtrecords",
"Evtsthr",
"Evvarpot",
"ExactHalfHeads",
"exceptional_law_share",
"ExcessScoreFactor",
"ExerEcgSt",
"Exp3",
"expand",
"expand_polynomials",
"ExpectationLinear",
"expected_clusters",
"expected_features",
"expected_improvement",
"ExpectedCounts",
"ExpIntP",
"Expkern",
"ExpKerTfd",
"explain_file",
"explain_known_files",
"ExpLinApx",
"explor",
"ExplPow",
"Expmc",
"ExpMean",
"ExpMom2",
"ExponentialCrossingTime",
"ExponentialWaitingDensity",
"exposure_summary",
"ExposureIndex",
"ExpTaylor",
"Exptest",
"EXSquared",
"extend_one",
"external_validate",
"extract",
"extract_spans",
"extvm",
"FacilityLocation",
"Facov",
"factor_analysis_ml",
"FactorialKrige",
"fallback_procedure",
"Fano",
"Fanova",
"Fanva",
"Farsig",
"FastGreedyModularity",
"FastIca",
"Fastm",
"fasttext",
"favor_attention",
"favor_features",
"FayHerriot",
"FcaAccessibility",
"Fcfp4",
"Fdaclust",
"FDiff",
"FDiffFr",
"fdiffm",
"FDiffMag",
"FDiffPh",
"FDiffTf",
"FDiverg",
"FdPsd",
"FdVag",
"Fdwarp",
"feature_augmentation",
"feature_combination",
"FecgNmf",
"Ferror",
"FetalEcg",
"Fevdc",
"Fhar",
"Fhn",
"Fiedlercut",
"field_aware_fm",
"fieldawarefm",
"FillSinks",
"FilteredKrige",
"FilterKernel",
"FilterSim",
"Finalsz",
"find_homologous_segments",
"find_reasonable_epsilon",
"Finegray",
"FiniteMixtureGibbs",
"FireflyAlgorithm",
"FiringRate",
"FirstPassageTime",
"FishCrit",
"fisher_combined",
"fisher_exact_test",
"FishLda",
"FiskDist",
"Fistalasso",
"fit_aol",
"fit_cdae",
"fit_ffm",
"fit_fm",
"fit_msm",
"fit_power_law",
"fit_seed_state",
"fit_stages",
"Fitcgp",
"FitVariogram",
"FitVariogramWls",
"fixed_effects_meta",
"fixed_length_ci",
"fixed_sequence",
"FixedCircleScan",
"FixedRankKriging",
"FlameClustering",
"fleiss_kappa",
"FleissKappa",
"flexible_iptw",
"FlexScan",
"FlexZones",
"Flmgcr",
"FloatingAlgaeIndex",
"FloodFrequency",
"flow_forward",
"flow_log_density",
"flow_logprob",
"FlowAccumulation",
"FlowCapturingLocation",
"FlowDurationCurve",
"Flowmm",
"Flsh2",
"FmSig",
"FnDist",
"Fnested",
"Fnlm",
"focal_loss",
"FocalFilter",
"FocalStatistics",
"fold_in",
"FoldedNormal",
"FoliageAttenuation",
"folkrank",
"FoodDesert",
"forces_from_energy",
"forecast",
"forecast_summary",
"forest_taxon_diversity",
"forest_weights",
"ForestFragmentation",
"format_dataframe",
"format_number",
"FormFactor",
"forward_corrupt",
"Fourier",
"fourier_terms",
"Fours",
"FparFromNdvi",
"Fpc",
"Fpca",
"FpeOrder",
"fraction_missing_information",
"FractionalVegetationCover",
"frank_wolfe_qp",
"Frdbnd",
"FrechetDist",
"Freg",
"FreqResp",
"FriedF",
"friedman_test",
"frisch_potential",
"FrmlD",
"from_pure_dp",
"front_door",
"frontdoor_adjustment",
"frontdoor_criterion",
"Frtaxd",
"Frwol2",
"FtConv",
"fu_hepatocytes",
"fu_microsomes",
"full_diagnostics",
"FunctionalNorm",
"FunnelControlLimits",
"FuzzyCmeans",
"Fwlfwd",
"Fxidf",
"fzbrd",
"fzcvm",
"fzedg",
"fzhdc",
"fzhok",
"fzkdf",
"fzksm",
"fzlst",
"fzmis",
"fzmrb",
"fzmrl",
"fzqnt",
"fzsgn",
"fzsrv",
"fzwlc",
"g_computation",
"g_formula_pooled",
"g_formula_sequential",
"Ga_opt",
"gabc_log_likelihood",
"gail",
"gail_occupancy_measure",
"GaKriging",
"GaloisGroup",
"gam_smoother",
"Gamelog",
"gamfit",
"gamma_generation_time",
"GammaFromLambdaDelta",
"Ganomscore",
"Gat",
"gated_rnn_equivalent",
"gated_update",
"Gatemd",
"GatLayer",
"GatV2",
"GaussApx",
"GaussCount",
"GaussDom",
"gaussian_approx_2n",
"gaussian_approx_biased",
"gaussian_approx_n",
"gaussian_approximation",
"gaussian_expansion",
"gaussian_mechanism",
"gaussian_splatting",
"gaussian_sum_density",
"GaussianHmm",
"GaussianMlClassify",
"GaussianPlume",
"GaussianPuff",
"gaussiansplatting",
"Gausslik",
"Gaussm",
"GaussOverlap",
"GaussTail",
"Gblupeq",
"Gblupr",
"Gbtcom",
"gcd_span",
"Gcnchb",
"Gcnemd",
"GcnII",
"GcnL",
"GcnLayer",
"GComp",
"Gdpf",
"Geglu",
"Gekdfbias",
"Gekdfmise",
"Gekdfvar",
"GelmanKingIncumbency",
"gelu",
"GeneFlowNm",
"GeneralizedProcrustes",
"generate_walks",
"GenLambda",
"GenomicReliability",
"genre_distribution",
"Genvdm",
"Genvxt",
"GeodeticToEcef",
"GeodeticToEnu",
"GeographicProfile",
"GeoidHeight",
"GeomExp",
"Gesd",
"GetisFilter",
"Getisordg",
"Gevcdf",
"gflops",
"Ghk",
"Ghosalabsolutecontinuity",
"Ghosaladaptthm",
"Ghosalbayesruleinfinite",
"Ghosalbbcensored",
"Ghosalbernsteinfeller",
"Ghosalbetaprocdef",
"Ghosalbmprior",
"Ghosalbpcont",
"Ghosalbpdiscrete",
"Ghosalbppathgen",
"Ghosalcoxbvm",
"Ghosalcoxbvmsp",
"Ghosalcoxmodel",
"Ghosalcoxpost",
"Ghosalcredsetcov",
"Ghosaldpbvm",
"Ghosaleffinflfn",
"Ghosalepgp",
"Ghosalexplink",
"Ghosalfbmprior",
"Ghosalfrsbinreg",
"Ghosalfrsdensity",
"Ghosalfrspoireg",
"Ghosalfrsreg",
"Ghosalfuncreg",
"Ghosalgpadaptthm",
"Ghosalgpbinregcrt",
"Ghosalgpcrtthm",
"Ghosalgpdefrkhs",
"Ghosalgpdenscrt",
"Ghosalgpincreasingprior",
"Ghosalgplaplace",
"Ghosalgppriordef",
"Ghosalhistogramprior",
"Ghosalinfdimbvm",
"Ghosalmixbp",
"Ghosalmixturebasisprior",
"Ghosalmodselbic",
"Ghosalnpbinaryreg",
"Ghosalnpnormalreg",
"Ghosalnppoissonreg",
"Ghosalntrbvm",
"Ghosalntrconsist",
"Ghosalntrdef",
"Ghosalntrlevy",
"Ghosalparamnpbf",
"Ghosalparamrate",
"Ghosalpriorposteriorupdate",
"Ghosalrandombasisexpansion",
"Ghosalrescalgp",
"Ghosalrkhsnorm",
"Ghosalrlprocess",
"Ghosalrndseriespr",
"Ghosalselfsimgp",
"Ghosalsemiparabvm",
"Ghosalsemiparaeff",
"Ghosalseriesgp",
"Ghosalsmhazgp",
"Ghosalstatgpspec",
"Ghosalstrictsbvm",
"Ghosalstrongapxdp",
"Ghosalsurvdpkm",
"Ghosalsurvdppost",
"Ghosaltwomodeladp",
"Ghosalunivweights",
"Ghosalwnadapt",
"Ghosalwnfullbvm",
"Ghosalwnlinbvm",
"GhoseDrugFilter",
"GHSecant",
"gibbons_are_scale_tests",
"gibbs_bigram_topic",
"gibbs_feature_update",
"Gibbsm",
"GibbsPseudolikelihood",
"Ginisimp",
"GirvanNewman",
"giss",
"Gkcov",
"Gkgeoext",
"Gkrawbv",
"gktau",
"glass_delta",
"Glivenko",
"GlmmResiduals",
"glmpoi",
"GlmProfCI",
"GlmStdRes",
"glove_loss",
"glove_weight",
"Glpopt",
"Glr",
"glrtest",
"Gmcenter",
"gmconj",
"GmErrorHet",
"GMErrorSAR",
"GmrfSimulate",
"Gmsbound",
"gnnEx_cheatsheet",
"gnnEx_computation_graph",
"gnnEx_conditional_entropy",
"gnnEx_explain_node",
"gnnEx_mask_objective",
"Gnsetenr",
"GnsML",
"Gnsres",
"Goalcond",
"Goenr",
"gp_fit",
"gp_posterior",
"gp_posterior_gradient",
"gp_predict",
"Gpcgs",
"Gpcla",
"GpClassify",
"Gpcm",
"GpCovariance",
"Gpdcdf",
"GpdD",
"GpdDistribution",
"Gpdkl",
"Gpdsh",
"gpfit",
"Gpfit",
"GpFit",
"Gphtr",
"Gpkern",
"GpLoo",
"Gpmlt",
"Gpmoe",
"Gppost",
"GpPredict",
"Gpreg",
"Gpregb",
"Gpresid",
"gps_abc",
"GpSample",
"GpSparse",
"Gpsps",
"Gpsvi",
"Gpt2",
"Gptas",
"Gpvar",
"Gpvbo",
"grad.Fun",
"Gradclip",
"Gradds",
"gradient",
"gradient_inner_product",
"graph_transformer",
"graph_transformer_layer",
"GraphBetweenness",
"Graphcmp",
"GraphDegreeCentrality",
"GraphDiameter",
"Graphlap",
"Graphlet",
"GraphModularity",
"graphormer",
"graphormer_attention",
"graphsage",
"graphtransformer",
"GravityAccessibility",
"GravityDistribution",
"GravityPPML",
"GreatCircleDistance",
"greedy_action",
"greedy_decode",
"GreyMorphology",
"GreyWolfOptimizer",
"GridContiguity",
"Grmsam",
"gross",
"GroundAttenuation",
"group_align",
"group_delay",
"group_privacy",
"group_time_att",
"GroupAvg",
"GroverS",
"grow_forest",
"GrowingDegreeDays",
"GrowingNeuralGas",
"GrowingSeasonLength",
"GrpDelay",
"gru_step",
"gru4rec",
"GrubbsT",
"gruforrecommendation",
"GS2SLSSAC",
"Gstabwt",
"GtDorfman",
"Gtfd",
"GtPrev",
"GtRegEM",
"GtwrFit",
"guide_tree",
"GumbelDist",
"GWRBasic",
"gwrcoef",
"GwrCollinearity",
"gwrcv",
"gwrdlt",
"gwreg",
"GwrFTests",
"gwrfwl",
"gwrhat",
"GWRKernelWeights",
"gwrlgt",
"gwrpois",
"GwrPredict",
"gwrres",
"gwrstd",
"gwrsur",
"gwrtst",
"GwSummary",
"Gxeblup",
"H2est",
"HaarDwt",
"HaarDwt2d",
"HaarMra",
"HaarMra2d",
"HabitatSuitabilityIndex",
"HadamardInverse",
"HadamardTransform",
"HadleyEdge",
"hal_fit",
"hal_predict",
"HalfHeads",
"HammingW",
"Hampel",
"Hampw",
"han_forward",
"HannFilt",
"HannFr",
"HannFrs",
"HannImp",
"HannMag",
"HannPh",
"HannTf",
"HannW",
"HannZ",
"HanskiConnectivity",
"harmonic_mean_p",
"HarmonicMeanEvidence",
"HarmonicOzoneDlm",
"HarmonicRegression",
"HarrisHawksOptimizer",
"hazard_ratio_table",
"Hazratio",
"hcepst",
"HClust",
"Hcoreg",
"Hdecmom",
"hdecon",
"Hdpgmm",
"Hdplda",
"Hdpmix",
"HealthConcentrationIndex",
"HeatEquationSeries",
"Hecea",
"hedges_g",
"HeightAboveDrainage",
"hellie",
"Hellinger",
"hellngd",
"HelmertTransform",
"Hendmme",
"HerfindahlIndex",
"hermitS",
"Hertbg",
"hess.Fun",
"Hetero",
"Hetlc",
"Hetmix",
"hexagonal_grid",
"HexagonalGridSample",
"Hexgrd",
"hfd",
"Hffdsg",
"HhGate",
"HhModel",
"hierarchical_bonferroni",
"HierarchicalClustering",
"Hiermodel",
"Hillq",
"Hillshade",
"hindsr",
"HistogramTransform",
"history_match",
"Hits",
"HitsR",
"Hittime",
"hkdf",
"Hmcsam",
"HmcSpatial",
"Hmicl",
"Hmmprf",
"HmmTag",
"Hmstrn",
"hochberg",
"hockey_stick",
"HockStick",
"Hodprc",
"holiday_matrix",
"holm",
"holm_sidak",
"Holmadj",
"HomDeconv",
"hommel",
"HomoFilt",
"HomPred",
"honest_forest",
"honest_tree",
"HooghoudtSpacing",
"horowitz_fredholm_eq",
"hosmer_lemeshow_test",
"hot_cold_spots",
"Hotcld",
"HotellingModel",
"HotellingPriceLocation",
"Hpdint",
"HrFromCnt",
"HrvAr",
"hrvfd",
"HrvFreq",
"hrvnl",
"HrvRatio",
"hrvtd",
"HrvTime",
"HrvTv",
"Hrzaul",
"hrzb1",
"hrzb2",
"Hrzboxc",
"Hrzbr5",
"hrzc1",
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"hrzd1",
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"hrzi1",
"hrzi2",
"hrzk1",
"hrzk2",
"hrzk3",
"hrzm1",
"hrzn1",
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"Hrznpiv",
"hrzp1",
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"Hrzph",
"Hrzphd",
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"Hrzplrq",
"hrzq1",
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"hrzt1",
"hrzt2",
"Hrztf",
"Hrztiku",
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"Hrztpar",
"hrzw1",
"hrzw2",
"Hrzweib",
"hsbkrg",
"Hsirt",
"HSoundId",
"Hsumcdf",
"Htebias",
"Htpfn",
"Htprd",
"Htvar1",
"Huberw",
"Hubrho",
"Hubsa",
"HullMetrics",
"HurricaneTrack",
"hwetst",
"HydrographSummary",
"hypercube_instruments",
"HypercubeQueue",
"hypergeometric_pmf",
"HyperLim",
"hyperparameter_design",
"hyperplane_side",
"Hzbnds",
"i_squared",
"Ibdmtx",
"ibp_log_probability",
"Ibp3par",
"IcaClean",
"IcaFix",
"Icarbm",
"Icc1",
"Icc12c",
"Icc2",
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"Idealf",
"identified_set",
"IdfFit",
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"IdftRI",
"idlpt",
"Idw",
"IdwCv",
"IdwPowerSearch",
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"Igravfe",
"Igravlm",
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"Igravvf",
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"Imf",
"ImperviousIndices",
"implausible",
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"impsm",
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"incidence_rate_difference",
"inclusion_proof",
"InclusionExclusion3",
"IncompleteCholesky",
"Incrtio",
"indicator_basis",
"IndicatorCcdf",
"IndicatorVariogram",
"indkr",
"InfantCry",
"Infcrt",
"Infcrv",
"Infgnt",
"Infiltration",
"infinitesimal_jackknife",
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"Infobtl",
"InfomapPartition",
"Infomax",
"infonce",
"InformationCriteria",
"inject_special_codes",
"install_cli",
"instance_norm",
"instruction_prompt",
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"Intanl",
"integrate_marginals",
"intervention_mechanism",
"Intf",
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"IntMag",
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"IntPh",
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"Intradiv",
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"inv_logit",
"invariance_error",
"invbpr",
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"InvPredCI",
"iou",
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"Ipferd",
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"iptw_ate",
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"Irfun",
"Irlsfn",
"IrrigationWaterQuality",
"irt_variance_legislator",
"Irt1pl",
"Irt4pl",
"Irtgpc",
"Irtgrm",
"Irtmh1",
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"irtsp",
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"is_forecastable",
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"is_positive_semidefinite",
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"IsolationIndex",
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"isotn",
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"Iwdid",
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"JoinCountMulti",
"joint_probability",
"JointDensityFactorizes",
"JointIndependent",
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"Jsdiv",
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"JtcCalibrate",
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"JtcSurface",
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"Jzdiff",
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"Kdfr1",
"Kdfr2",
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"kendall_tau",
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"Kerncvm",
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"Kernks",
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"kfd",
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"Klmsm1",
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"KMeans",
"kmeans2",
"KmeansClassify",
"KmeansCvFolds",
"KmeansLloyd",
"Knn",
"knn_trainer",
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"koleo",
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"Krfgrp",
"krftst",
"Krig",
"KrigCl",
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"KrigeCV",
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"KrigingCovariance",
"KrigingEfficiency",
"KrigingExceedance",
"KrigingQuantile",
"KrigingSystem",
"Krigsm",
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"KrugmanIndex",
"kruskal_wallis",
"ks_confidence_set",
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"ks_statistic",
"ks_test_one_sample",
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"Ksstat",
"Kstest1",
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"Ktrend",
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"KumaraswamyWarp",
"kurtosis_coeff",
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"Kvquant",
"L1med",
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"label",
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"Lacgear",
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"lambda_star",
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"laplacian",
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"Ldiff",
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"Ldprun",
"leaf_of",
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"LeastCostPath",
"leave_one_out_cv",
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"left_ordered_form",
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"levene_test",
"Levinson",
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"Lftrt",
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"Lgtnpdf",
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"Likfit",
"lilef",
"lilliefors_test",
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"LinconBT",
"LinConv",
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"LinDSep",
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"linear_fluctuation_unsafe",
"linear_strain_space",
"LinearAssignment",
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"LinModelInv",
"linsys",
"Lints",
"LipinskiDescriptors",
"LipinskiRuleOfFive",
"LisaClust",
"LisaI",
"Lisamoran",
"littles_mcar_test",
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"ll3way",
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"LogitVote",
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"loglik",
"loglik_profile",
"LogMaxPh",
"logmeanexp",
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"Logor",
"Logrank",
"LogReg",
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"LogSep",
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"loss",
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"louvain",
"lower_confidence_bound",
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"Lpc",
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"Lpdual",
"Lppd",
"LqGini",
"lrgtst",
"Lrresid",
"Lrtest",
"Lrwald",
"lsa_decompose",
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"LsiPar2",
"LsiParH",
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"LsiSer",
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"lvrgh",
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"m_step",
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"Ma8Imp",
"Ma8Rec",
"Ma8RecTf",
"Ma8Sinc",
"Ma8Tf",
"MaAutocorrelation",
"mabay",
"mac3",
"MaccsFingerprint",
"macum",
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"madMov",
"madsc",
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"MAF",
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"mafft_anchors_from",
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"mafft_default_raw",
"mafft_degap",
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"mafft_nw",
"mafft_peaks",
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"mafft_weights",
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"MaFir",
"mafix",
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"Mafrti",
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"magpa",
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"Mahaldep",
"mahg",
"mahks",
"mahsj",
"mai2",
"mainf",
"MajorityCompare",
"MajorityTournament",
"make_blocks",
"malab",
"Malapportionment",
"malor",
"malrr",
"Mamh",
"MangroveVegetationIndex",
"Manh2h",
"ManifestoScales",
"Manlmm",
"mann_whitney_u",
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"Manski",
"manski_bounds",
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"MantelM2",
"MantelTest",
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"map_invert",
"MapEquation",
"Mapeto",
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"MapProject",
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"MarkDependenceTest",
"MarkovChainSimulate",
"MarkVariogram",
"marndm",
"marpct",
"MarshallEb",
"Martcons",
"Martingale",
"martingale_check",
"martingale_variance",
"mask_loss",
"mask_units",
"MASS_ginv",
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"matau2pi",
"Matccd",
"MatchedFilt",
"Matern",
"matern52",
"Maternvg",
"MaTf",
"Matgame",
"math2",
"MatnK",
"matr",
"Matrim",
"MatrixFactorizationAls",
"MatrixSymbolic",
"matrl",
"max_step",
"max_step_size",
"MaxentFeatures",
"MaxentFit",
"MaxentPredict",
"MaxEntropyDiscrete",
"MaximalCorrelation",
"MaximalCovering",
"maximise_acquisition",
"maxmin_selection",
"Maxpl",
"MaxPRegions",
"MaxStableField",
"maxsum_selection",
"Mbconv",
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"McGillInteractionInformation",
"mcint_crude",
"McIntegrate",
"mcmc_sample",
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"mcnemar_test",
"McpHomeRange",
"McStandardError",
"McStratified",
"Mctsbackup",
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"Mctsnode",
"Mctsq",
"McZonal",
"MdlOrder",
"Mdppol",
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"MdsBootstrap",
"MdsFlip",
"MdsJackknife",
"mdspl",
"MdsPolarity",
"MdsReflect",
"mdvtr",
"mean_period",
"mean_scale",
"mean_squared_error",
"MeanderMetrics",
"MeanExc",
"MeanShift",
"MeanSquaredDisplacement",
"MeanSum",
"MEcgFilt",
"MedCI",
"MedianLines",
"MedianParty",
"MedianVoter2d",
"mediation_analysis",
"mediation_formula",
"Medint",
"Medmsm",
"MedPolish",
"mehtad_residuals",
"memb",
"Mercerchk",
"merge_panels",
"MergeSplitParties",
"merkle_root",
"meta_level",
"Meta1l",
"Metapath",
"metapath_neighbours",
"Meteor",
"MfAcf",
"Mfcc",
"MfdFlowAccumulation",
"MfiltH",
"MfImpEeg",
"MfImpulse",
"MfInput",
"MfMaxSnr",
"MfNoiseIn",
"MfNoiseOut",
"MfOutput",
"MfPeak",
"MfPsd",
"MfRatio",
"MfSnr",
"MfTf",
"MfTfEeg",
"mgamrf",
"Mgkbias",
"Mgkde",
"Mgkvar",
"mgwraic",
"MgwrBackfit",
"mgwrbk",
"mgwrbw",
"mgwrcof",
"mgwrcv",
"mgwrdg",
"mgwrfit",
"mgwrhat",
"MgwrLocalT",
"mgwrres",
"mgwrsig",
"mgwrstd",
"mgwrtst",
"mhmcmc",
"Mhors",
"Mhrate",
"MhSpatial",
"mi_pmm",
"Middleout",
"midranks",
"miexp",
"Mifmi",
"miiv",
"MillsConditionalMean",
"mimc",
"miml",
"min_loss_over_masks",
"Mincutsw",
"MinimalRangeCoalition",
"minorm",
"MinPhase",
"mint_P",
"mint_reconcile",
"miols",
"miorig",
"miprgr_branch_and_bound",
"miprgr_cheatsheet",
"miprgr_enumerate_integer",
"miprgr_fractional_variable",
"miprgr_round_relaxation",
"miprgr_simplex",
"miprgr_solve_relaxation",
"mirand",
"mirror_pad",
"Mirt2",
"Mirt3",
"MissinM",
"Misspecbd",
"mistr_apply_rope",
"mistr_attention_span",
"mistr_cheatsheet",
"mistr_grouped_query_attention",
"mistr_mistral_block",
"mistr_rms_norm",
"mistr_rope_angles",
"mistr_sliding_window_mask",
"mistr_swiglu",
"mivar",
"Mivbound",
"MixCmp",
"mixed",
"MixedLogitVote",
"MixedUtility",
"mizval",
"Mktest",
"MlFac",
"Mlfit",
"Mlloglik",
"MlpBp",
"Mlpv",
"Mlwz",
"Mmaxn",
"MnfTransform",
"mnpbt",
"Mnpenlik",
"model_comparison_table",
"Modelrl",
"Modlar",
"ModularityMatrix",
"ModularityScore",
"Modulq",
"Moelayer",
"Moeswitch",
"Moetop",
"MolecularProperties",
"moment_check",
"moment_estimate",
"moment_vectors",
"momento_cheatsheet",
"momento_harmonise",
"momento_mask_patches",
"momento_masked_loss",
"momento_reconstruction_curve",
"momento_task_mask",
"moments",
"moments_accountant",
"Momprior",
"monte_carlo_spatial_test",
"MoranEigenvectorFilter",
"MoranEigenvectors",
"MoranPermutationTest",
"MoranRes",
"MoranScatter",
"MorganEnvironments",
"morie_aalen_johansen",
"morie_abcgp",
"morie_abcrej",
"morie_abod",
"morie_acceptance_rate_diagnostic",
"morie_acf_estimate",
"morie_aci",
"morie_acigls",
"morie_actuarial_lifetable",
"morie_adagrad",
"morie_adam",
"morie_adamw_step",
"morie_adf_test",
"morie_adfur",
"morie_adida",
"morie_adjacency_matrix",
"morie_adjustment_variances",
"morie_aes",
"morie_aft",
"morie_aft_generalized_gamma",
"morie_aft_log_logistic",
"morie_aft_residuals",
"morie_aft_weibull",
"morie_agalfsy_rl_pose_search",
"morie_age_crime_aggregate",
"morie_age_crime_panel_cov",
"morie_age_crime_type_cov",
"morie_agenda_setter_power",
"morie_aggregate",
"morie_aggregateatt",
"morie_airl",
"morie_aitchison_balance",
"morie_akp_effect_size",
"morie_alammar_ann_search",
"morie_alammar_augmented_sbert",
"morie_alammar_bag_of_words",
"morie_alammar_bertopic_pipeline",
"morie_alammar_bio_tagging",
"morie_alammar_c_tfidf",
"morie_alammar_chain_prompting",
"morie_alammar_chat_template",
"morie_alammar_chosen_rejected_template",
"morie_alammar_classification_head",
"morie_alammar_contextualized_embedding",
"morie_alammar_continued_pretraining",
"morie_alammar_conversation_buffer_memory",
"morie_alammar_cosine_similarity_loss",
"morie_alammar_document_embedding_pool",
"morie_alammar_embedding_classifier",
"morie_alammar_greedy_decoding",
"morie_alammar_grouped_query_attention",
"morie_alammar_hdbscan_cluster",
"morie_alammar_image_captioning",
"morie_alammar_infonce_loss",
"morie_alammar_instruction_data_template",
"morie_alammar_kv_cache_lookup",
"morie_alammar_layer_freezing",
"morie_alammar_lda_topic_distribution",
"morie_alammar_llm_as_judge",
"morie_alammar_mean_reciprocal_rank",
"morie_alammar_mteb_benchmark_score",
"morie_alammar_multi_head_attention",
"morie_alammar_multi_query_attention",
"morie_alammar_multi_query_retrieval",
"morie_alammar_multiple_negatives_ranking",
"morie_alammar_ndcg_at_k",
"morie_alammar_negative_sampling_skipgram",
"morie_alammar_ner_token_head",
"morie_alammar_openclip_contrastive",
"morie_alammar_output_verification",
"morie_alammar_react_agent_loop",
"morie_alammar_recall_at_k",
"morie_alammar_recursive_chunking",
"morie_alammar_reward_model_bt",
"morie_alammar_sampling_decoding",
"morie_alammar_sbert_triplet_loss",
"morie_alammar_sdp_attention",
"morie_alammar_setfit_twostep",
"morie_alammar_simcse_dropout_aug",
"morie_alammar_sliding_window_attention",
"morie_alammar_t5_classify",
"morie_alammar_token_embedding_lookup",
"morie_alammar_tokenization_pipeline",
"morie_alammar_tokenizer_vocab_overlap",
"morie_alammar_tsdae_objective",
"morie_alammar_umap_projection",
"morie_alammar_vit_patch_embedding",
"morie_alammar_zero_shot_classification",
"morie_alf3df",
"morie_alf3df_augment",
"morie_alf3df_cheatsheet",
"morie_alf3df_rotation",
"morie_alf3df_sample",
"morie_alf3df_schedule",
"morie_alf3df_step",
"morie_alfbnp_af3_sample",
"morie_alfesf_esmfold_confidence",
"morie_alfmpv",
"morie_alfmpv_msa_pairing",
"morie_alfomg",
"morie_alfomg_bias",
"morie_alfomg_cheatsheet",
"morie_alfomg_opm",
"morie_alfomg_row_attention",
"morie_alfomg_softmax",
"morie_alfqud",
"morie_alfqud_actions",
"morie_alfqud_cheatsheet",
"morie_alfqud_correctness",
"morie_alfqud_run",
"morie_alfqud_step",
"morie_alfqud_text",
"morie_alfrf2",
"morie_alfrf2_cheatsheet",
"morie_alfrf2_ideal",
"morie_alfrf2_kabsch",
"morie_alfrf2_noise",
"morie_alfrf2_rmsd",
"morie_alfrf2_schedule",
"morie_ali_mikhail_haq_copula",
"morie_alloc_optimal",
"morie_alpha_from_gamma",
"morie_alteration_ramsey",
"morie_anamorphosis",
"morie_aniso",
"morie_anisotropy_test",
"morie_anm_direction",
"morie_ann_forward",
"morie_ann_gradients",
"morie_ann_train",
"morie_anominate_ideal_points",
"morie_anova_one_way",
"morie_anova_twoway",
"morie_antithetic_variates",
"morie_approx_dp",
"morie_approx_ladder",
"morie_ar_spectrum",
"morie_ar1_climate",
"morie_arccos_kernel",
"morie_arch_in_mean",
"morie_arch_lm_test",
"morie_are_nonparametric",
"morie_are_orthogonal",
"morie_arsau_analyze_aggregate_summary",
"morie_arsau_analyze_detailed_dataset",
"morie_arsau_analyze_individual_records",
"morie_arsau_analyze_main_records",
"morie_arsau_analyze_probe_cycle_records",
"morie_arsau_analyze_weapon_records",
"morie_arsau_available_datasets",
"morie_arsau_available_years",
"morie_arsau_ckan_url",
"morie_arsau_describe",
"morie_arsau_download",
"morie_arsau_fetch_sidecar",
"morie_arsau_load_aggregate_summary",
"morie_arsau_load_detailed_dataset",
"morie_arsau_load_individual_records",
"morie_arsau_load_main_records",
"morie_arsau_load_probe_cycle_records",
"morie_arsau_load_weapon_records",
"morie_arsau_read_markdown_dictionary",
"morie_arsau_read_sidecar",
"morie_arsau_read_xlsx_dictionary",
"morie_arsau_registry_df",
"morie_arsau_sidecar_schema",
"morie_arsau_sidecar_to_frame",
"morie_ask_percy",
"morie_asmnvr",
"morie_assign_latent_time",
"morie_astle_balding_grm",
"morie_ate_potential_outcomes",
"morie_ate_sensitivity",
"morie_att_weights",
"morie_attenuation_two_regressor",
"morie_attnq_scaled_dot_product_attention",
"morie_attractiveness_contraction",
"morie_attrInf",
"morie_audit_all_variables",
"morie_audit_arsau_variables",
"morie_audit_categories",
"morie_audit_otis_variables",
"morie_audit_public_outputs",
"morie_augment",
"morie_autocorf",
"morie_autocorrelation",
"morie_autoencoder_anomaly",
"morie_autoform",
"morie_avalon",
"morie_avalon_cheatsheet",
"morie_avalon_features",
"morie_avalon_fnv",
"morie_avalon_h",
"morie_avalon_parse",
"morie_avalon_rings",
"morie_avalon_tanimoto",
"morie_average_derivative",
"morie_average_derivative_hat",
"morie_awltrn",
"morie_azuma_bound",
"morie_backdoor_adjustment",
"morie_backdoor_criterion",
"morie_backdoor_sets",
"morie_backfitting",
"morie_backlog_censoring",
"morie_backlog_published",
"morie_backpropagation",
"morie_balanced_twophase",
"morie_bandwidth_mise",
"morie_barerp",
"morie_baron_kenny",
"morie_bartels_rank_test",
"morie_baseline_hazard",
"morie_basEvap",
"morie_basic_composition",
"morie_batch_norm_forward",
"morie_bats",
"morie_bayes",
"morie_bayes_binomial",
"morie_bayes_compare",
"morie_bayes_continue",
"morie_bayes_cpi_genomic",
"morie_bayes_density",
"morie_bayes_diagnostics",
"morie_bayes_ess",
"morie_bayes_factor_savage_dickey",
"morie_bayes_gblup",
"morie_bayes_geweke",
"morie_bayes_lm",
"morie_bayes_plot",
"morie_bayes_rhat",
"morie_bayes_ridge_gibbs",
"morie_bayes_risk_dpi",
"morie_bayesian_horseshoe",
"morie_bayesian_ideal_points",
"morie_bayesian_kernel_blup",
"morie_bayesian_lasso_full",
"morie_bayesian_ridge_regression",
"morie_bayhmc",
"morie_bayisr",
"morie_baynav",
"morie_bayoptr",
"morie_bayreg2_student_t_regression",
"morie_baysmplr",
"morie_baysmplr_cheatsheet",
"morie_baysmplr_choose",
"morie_baysmplr_ess",
"morie_baysmplr_gibbs",
"morie_baysmplr_hmc",
"morie_baysmplr_mh",
"morie_baysmplr_nuts",
"morie_baytsm_dlm_local_level",
"morie_bcoinbf",
"morie_bcoinlik",
"morie_bcoinpri",
"morie_bcq",
"morie_bdtrns",
"morie_bekk_garch",
"morie_bell_number",
"morie_bellman_value",
"morie_bet_proof_interval",
"morie_bet_violation",
"morie_beta_schedule_values",
"morie_betti_numbers_gf2",
"morie_bibd_parameters",
"morie_bic_model_average",
"morie_bicdag",
"morie_big_add",
"morie_big_binom",
"morie_big_cmp",
"morie_big_divmod_small",
"morie_big_factorial",
"morie_big_fits_double",
"morie_big_mul",
"morie_big_ndigits",
"morie_big_pow",
"morie_big_sub",
"morie_bigint",
"morie_binary_mediation",
"morie_binary_metrics",
"morie_binomial_dist",
"morie_binomial_inference",
"morie_binseg",
"morie_binsumga",
"morie_bipartite_matching",
"morie_bkprp_backpropagation",
"morie_bl_distance",
"morie_blast",
"morie_blast_pvalue",
"morie_blastp",
"morie_blinkg",
"morie_blinkg_bin_filter",
"morie_blinkg_cheatsheet",
"morie_blinkg_ld_filter",
"morie_blinkg_scan",
"morie_blinkg_select",
"morie_blipqf",
"morie_blocking_crossover",
"morie_blomqvist_beta",
"morie_blstn",
"morie_blue_blup_v",
"morie_bmtme_conditionals",
"morie_bnd_imbens_manski",
"morie_bnd_lp",
"morie_bnd_manski",
"morie_bnd_moment_inequality",
"morie_bnd_polya_tree",
"morie_bndapp",
"morie_bndest",
"morie_bnfwd_batch_norm_forward",
"morie_bnppct",
"morie_bnppct_cdf",
"morie_bnppct_cheatsheet",
"morie_bnppct_expand",
"morie_bnppct_quantile",
"morie_bnppvl",
"morie_bnppvl_cheatsheet",
"morie_bnppvl_counts",
"morie_bnppvl_draw",
"morie_bnppvl_log_prior",
"morie_bnppvl_loglik",
"morie_bns_jump_test",
"morie_bnsadt",
"morie_bnsadt_cheatsheet",
"morie_bnsadt_fit",
"morie_bnsadt_group_counts",
"morie_bnsadt_loglik",
"morie_bnsadt_median_unbiased",
"morie_bnsadt_p",
"morie_boot",
"morie_boot_basic_ci",
"morie_boot_ci",
"morie_boot_nonoverlap_block",
"morie_boot_run",
"morie_bootss",
"morie_bootstrap_ci",
"morie_bootstrap_sample",
"morie_boryis",
"morie_bound_missing_outcome",
"morie_bounds_confidence",
"morie_bowarc",
"morie_boxcox",
"morie_boxplot_outliers",
"morie_boyd_backtracking",
"morie_boyd_newton",
"morie_boyd_newton_decrement",
"morie_boyd_proximal",
"morie_bprMF",
"morie_bracken_abundance",
"morie_breslow_tie_correction",
"morie_breusch_pagan",
"morie_bricklayer",
"morie_bridge_covariance",
"morie_bridge_observations",
"morie_bridgs",
"morie_brier",
"morie_brr_balanced",
"morie_brr_gibbs",
"morie_brr_variance",
"morie_brunner_dette_munk",
"morie_brunner_munzel",
"morie_brute_force_max_weight",
"morie_bspline_basis",
"morie_bt_632",
"morie_bt_bias",
"morie_bt_ci_ratio",
"morie_bt_iid",
"morie_bt_jackknife",
"morie_bt_oob",
"morie_bt_var",
"morie_btarsv",
"morie_btcalib",
"morie_btdbl",
"morie_btiseq",
"morie_budget_optimum",
"morie_budget_rule",
"morie_build_outputs_manifest",
"morie_build_prompt",
"morie_build_tables",
"morie_builtin_db",
"morie_burg_method",
"morie_burkov_add_k",
"morie_burkov_backoff",
"morie_burkov_bce_gradients",
"morie_burkov_binary_cross_entropy",
"morie_burkov_bits_per_character",
"morie_burkov_categorical_cross_entropy",
"morie_burkov_computational_graph",
"morie_burkov_cosine_similarity",
"morie_burkov_dot_product",
"morie_burkov_elman_rnn",
"morie_burkov_interpolation",
"morie_burkov_kneser_ney",
"morie_burkov_laplace",
"morie_burkov_layer1_output",
"morie_burkov_layer2_output",
"morie_burkov_linear_function",
"morie_burkov_linear_vector",
"morie_burkov_logistic",
"morie_burkov_mse_cost",
"morie_burkov_next_token",
"morie_burkov_ngram_mle",
"morie_burkov_repetition_penalty",
"morie_burkov_squared_error",
"morie_burkov_term_frequency",
"morie_burkov_tf_idf",
"morie_burkov_unit_vector",
"morie_burkov_vector_norm",
"morie_burkov_weight_tying",
"morie_burnside_orbit_count",
"morie_bvdecomp",
"morie_cache_clear",
"morie_cache_dir",
"morie_cache_file",
"morie_cache_list",
"morie_cache_load",
"morie_cache_store",
"morie_calculate_ebac",
"morie_calculate_ipw_weights",
"morie_calfeat",
"morie_calibration",
"morie_calibration_chi2",
"morie_calibration_flip",
"morie_calibration_weights",
"morie_calonico_cattaneo_titiunik",
"morie_caltbR",
"morie_canonical_collection",
"morie_canonicalize_cpads_data",
"morie_capsule_bundle",
"morie_capsule_compare",
"morie_capsule_drift",
"morie_capsule_manifest",
"morie_capsule_sign",
"morie_capsule_verify",
"morie_capsule_write",
"morie_catalan_number",
"morie_catalog_coverage",
"morie_catstop",
"morie_caus_aipw",
"morie_caus_did_sun_abraham",
"morie_caus_dml_partial_lin",
"morie_caus_iv_2sls",
"morie_caus_iv_dag",
"morie_caus_iv_late",
"morie_caus_iv_liml",
"morie_causal_caliper_matching",
"morie_causal_did_three_way",
"morie_causal_falsification_test",
"morie_causal_forest",
"morie_causal_forest_bootstrap",
"morie_causal_forest_predict",
"morie_causal_impact",
"morie_causal_iptw_atoweights",
"morie_causal_mahalanobis_match",
"morie_causal_mediation",
"morie_causal_overlap_diagnostic",
"morie_causal_robust_se",
"morie_causal_rosenbaum_bound",
"morie_causal_survival_blp",
"morie_causal_survival_forest",
"morie_causal_weighting",
"morie_causdidcs",
"morie_causdiddc",
"morie_causdiddc_did_m",
"morie_causdiddc_twfe",
"morie_causdiddc_weights",
"morie_causdidev",
"morie_cause_specific_hazard",
"morie_causrdd",
"morie_causrddc",
"morie_causrddc_cheatsheet",
"morie_causrddc_rd_bandwidth",
"morie_causrddf",
"morie_causrddh",
"morie_causrung",
"morie_causscd",
"morie_cbcapx",
"morie_ccc_multivariate_garch",
"morie_cdaeRC",
"morie_cdaeRC_decode",
"morie_cdp_subgaussian_amplification",
"morie_censoring_at_risk_weight",
"morie_censoring_survival",
"morie_cepstrum_pitch",
"morie_cgnonl",
"morie_change_of_variance",
"morie_check_buffer",
"morie_check_plugin_license",
"morie_cheeger_bound",
"morie_chemsc",
"morie_chemsc_block",
"morie_chemsc_cheatsheet",
"morie_chemsc_clash",
"morie_chemsc_hbond",
"morie_chemsc_lipophilic",
"morie_chemsc_metal",
"morie_chemsc_rot",
"morie_chemsc_score",
"morie_chemsc_smooth_block",
"morie_chemsc_torsion",
"morie_chen_transform",
"morie_chernoff_bound",
"morie_chgseg",
"morie_chi_square_test",
"morie_choice_based_max_score",
"morie_choice_based_shares",
"morie_chrF",
"morie_christoffersen_cc",
"morie_chronos",
"morie_ckan_search",
"morie_class_metrics",
"morie_classify_variable",
"morie_clausius_clapeyron",
"morie_cli",
"morie_cli_dump_catalog",
"morie_cliff_delta",
"morie_closure",
"morie_clpm_coefficients",
"morie_clpopt",
"morie_clrgrf",
"morie_clrnt",
"morie_clusmd",
"morie_clusmd_butina_clustering",
"morie_clusmd_butina_clusters",
"morie_clusmd_cluster_summary",
"morie_clusmd_neighbour_lists",
"morie_cluster",
"morie_cluster_batch",
"morie_cluster_robust_effect",
"morie_cluster_sample",
"morie_cluster_twostage",
"morie_cluster_variance",
"morie_cma_es",
"morie_cmlmer_compressed_lmm",
"morie_cmuti",
"morie_cnn_genomic",
"morie_cnn1d_conv1d_forward",
"morie_cnn2d_conv2d_forward",
"morie_cnvlfc",
"morie_coarsen_interval",
"morie_cochran_q",
"morie_cohens_d",
"morie_coherence",
"morie_coin_independence",
"morie_coin_oneway",
"morie_coin_wilcoxon",
"morie_cokrg",
"morie_cokrig",
"morie_cokriging",
"morie_collider",
"morie_collider_arrest",
"morie_comet",
"morie_compare_nested_logistic_models",
"morie_compare_sets",
"morie_competing_risks_fg",
"morie_composition_budget",
"morie_compute_design_weights",
"morie_concentration_decompose",
"morie_concentration_dispersion",
"morie_concentration_distinct_growth",
"morie_concentration_gini",
"morie_concentration_null_bound",
"morie_concordance_corr",
"morie_concordance_incomplete",
"morie_concordance_index",
"morie_conditional_indirect_effect",
"morie_conditional_prior_uniform",
"morie_condorcet_winner",
"morie_confidence_set",
"morie_conflicts",
"morie_confusion_matrix_metrics",
"morie_contagion_branching",
"morie_contagion_extinction",
"morie_contagion_moment_witness",
"morie_contaminated_bounds",
"morie_contingency_coefficient",
"morie_contraction_conditions",
"morie_control_comparison",
"morie_control_median_test",
"morie_control_variates",
"morie_conv1d_forward",
"morie_conv2d",
"morie_conv2d_forward",
"morie_convex_hull",
"morie_convlayer",
"morie_copod",
"morie_copula_cdf",
"morie_copula_estimation",
"morie_copula_fit",
"morie_copula_sample",
"morie_copula_spearman",
"morie_copula_survival",
"morie_copula_tau",
"morie_cor_pearson_cpp",
"morie_corrections_uof_resource_ids",
"morie_count_edges",
"morie_count_glm",
"morie_court_backlog",
"morie_covariate_balance",
"morie_covariate_balance_check",
"morie_coverage_by_region",
"morie_cox_breslow_step",
"morie_cox_dfbeta_influence",
"morie_cox_frailty",
"morie_cox_martingale_residuals",
"morie_cox_overdispersion",
"morie_cox_partial_loglik",
"morie_cox_ph",
"morie_cox_schoenfeld_residuals",
"morie_cox_score_process",
"morie_cox_stratified",
"morie_cox_time_varying",
"morie_cpads_canonicalize_frame",
"morie_cpads_contract",
"morie_cpads_has_raw_columns",
"morie_cpads_infer_file_format",
"morie_cpads_missing_variables",
"morie_cpads_validate_frame",
"morie_cpd_all_analyses",
"morie_cqr",
"morie_cqtmpl",
"morie_cramers_v",
"morie_cressie_hawkins",
"morie_crfflt_cf_filter",
"morie_crfflt_frequency_response",
"morie_crfflt_ideal_weights",
"morie_crfsel",
"morie_crim_etas",
"morie_crim_hawkes_multivariate",
"morie_crim_near_repeat",
"morie_crim_risk_terrain",
"morie_crkbsg_cokriging",
"morie_crosstab_verify",
"morie_croston",
"morie_crsfmr_attention",
"morie_crsfmr_complexity",
"morie_crsfmr_cross_dimension_stage",
"morie_crsfmr_cross_time_stage",
"morie_crsfmr_dsw_embed",
"morie_crsfmr_segment_merge",
"morie_crsfmr_two_stage_attention",
"morie_crsfst",
"morie_crsfst_cheatsheet",
"morie_crsfst_folds",
"morie_crsfst_rmst",
"morie_crypto_chacha20_poly1305_decrypt",
"morie_crypto_chacha20_poly1305_encrypt",
"morie_crypto_hkdf_sha256",
"morie_crypto_hqc_decaps",
"morie_crypto_hqc_encaps",
"morie_crypto_hqc_keygen",
"morie_crypto_hybrid_container_version",
"morie_crypto_hybrid_decrypt",
"morie_crypto_hybrid_encrypt",
"morie_crypto_hybrid_keygen",
"morie_crypto_keystore_create",
"morie_crypto_keystore_list",
"morie_crypto_keystore_load",
"morie_crypto_keystore_public_key",
"morie_crypto_keystore_store",
"morie_crypto_lamport_keygen",
"morie_crypto_lamport_sign",
"morie_crypto_lamport_verify",
"morie_crypto_liboqs_available",
"morie_crypto_liboqs_version",
"morie_crypto_mldsa65_keygen",
"morie_crypto_mldsa65_sign",
"morie_crypto_mldsa65_verify",
"morie_crypto_mlkem768_decaps",
"morie_crypto_mlkem768_encaps",
"morie_crypto_mlkem768_keygen",
"morie_crypto_pqc_inventory",
"morie_crypto_random_bytes",
"morie_crypto_slhdsa_keygen",
"morie_crypto_slhdsa_sign",
"morie_crypto_slhdsa_verify",
"morie_crypto_sodium_available",
"morie_crypto_sodium_version",
"morie_csr_nn_test",
"morie_cssant",
"morie_cthrgr",
"morie_cthrgr_cheatsheet",
"morie_cutting_plane_sphere",
"morie_cvm_statistic",
"morie_cycle_index_necklaces",
"morie_cypin",
"morie_cypin_cheatsheet",
"morie_cypin_descriptors",
"morie_cypin_fit",
"morie_cypin_logistic",
"morie_cypin_predict",
"morie_dag",
"morie_dag_estimate",
"morie_dag_identify",
"morie_dag_refute",
"morie_dark_figure_bounds",
"morie_dark_figure_breakdown",
"morie_dark_figure_comparable",
"morie_dark_figure_hierarchy",
"morie_dark_figure_no_interaction",
"morie_dark_figure_three_list",
"morie_dark_figure_two_source",
"morie_data_variance",
"morie_dataset_catalog",
"morie_dataset_column_profile",
"morie_dataset_detect_role",
"morie_dataset_infer_level",
"morie_dataset_info",
"morie_dataset_load",
"morie_dataset_portal_catalog",
"morie_dataset_portal_catalog_clear_cache",
"morie_dataset_profile",
"morie_dataset_profile_summary_table",
"morie_dataset_profile_to_list",
"morie_dataset_suggest_plan",
"morie_dataset_summarize_column",
"morie_datasets_arcgis_item_by_id",
"morie_datasets_arcgis_item_metadata",
"morie_datasets_arsau_aggregate_summary",
"morie_datasets_arsau_detailed_dataset",
"morie_datasets_arsau_uof_individual_records",
"morie_datasets_arsau_uof_main_records",
"morie_datasets_arsau_uof_probe_cycle_records",
"morie_datasets_arsau_uof_weapon_records",
"morie_datasets_bigquery",
"morie_datasets_browse",
"morie_datasets_calgary_community_crime_stats",
"morie_datasets_calgary_fire_response_calls",
"morie_datasets_calgary_fire_stations",
"morie_datasets_calgary_open_crime_adjacent_layers",
"morie_datasets_calgary_opendata_bulk_layers",
"morie_datasets_calgary_socrata_by_id",
"morie_datasets_chicago_arrests",
"morie_datasets_chicago_community_areas",
"morie_datasets_chicago_crime",
"morie_datasets_chicago_crime_map",
"morie_datasets_chicago_crime_odata",
"morie_datasets_chicago_crime_resolved",
"morie_datasets_chicago_crime_soql",
"morie_datasets_chicago_iucr_codes",
"morie_datasets_chicago_neighborhoods",
"morie_datasets_chicago_opendata_bulk_layers",
"morie_datasets_chicago_police_beats",
"morie_datasets_chicago_police_districts",
"morie_datasets_chicago_socrata_by_id",
"morie_datasets_chicago_wards",
"morie_datasets_cihi_data_tables",
"morie_datasets_ckan_package",
"morie_datasets_ckan_search",
"morie_datasets_corrections_uof_ethnic_origin",
"morie_datasets_corrections_uof_incident_type",
"morie_datasets_corrections_uof_incidents",
"morie_datasets_corrections_uof_indigenous",
"morie_datasets_corrections_uof_inmate_incident",
"morie_datasets_corrections_uof_inmate_participant",
"morie_datasets_corrections_uof_institution_summary",
"morie_datasets_corrections_uof_location_summary",
"morie_datasets_corrections_uof_race",
"morie_datasets_corrections_uof_religion",
"morie_datasets_corrections_uof_select_incident_summary",
"morie_datasets_corrections_uof_staff_incident",
"morie_datasets_cpads",
"morie_datasets_cpd_public_arrests",
"morie_datasets_edmonton_fire_stations",
"morie_datasets_edmonton_open_crime_adjacent_layers",
"morie_datasets_edmonton_opendata_bulk_layers",
"morie_datasets_edmonton_police_stations",
"morie_datasets_edmonton_socrata_by_id",
"morie_datasets_external_socrata_layers",
"morie_datasets_load_by_key",
"morie_datasets_montreal_ckan_resource",
"morie_datasets_montreal_justice_safety_layers",
"morie_datasets_montreal_opendata_bulk_layers",
"morie_datasets_montreal_sim_intervention_types",
"morie_datasets_montreal_sim_interventions",
"morie_datasets_namus_missing_persons",
"morie_datasets_nibrs",
"morie_datasets_nist_rds",
"morie_datasets_nyc_boroughs",
"morie_datasets_nyc_boundaries_catalog",
"morie_datasets_nyc_community_districts",
"morie_datasets_nyc_council_districts",
"morie_datasets_nyc_ntas_2020",
"morie_datasets_nyc_nypd_arrests_historic",
"morie_datasets_nyc_nypd_arrests_ytd",
"morie_datasets_nyc_nypd_boro_crosswalk",
"morie_datasets_nyc_nypd_by_key",
"morie_datasets_nyc_nypd_complaint_historic",
"morie_datasets_nyc_nypd_complaint_ytd",
"morie_datasets_nyc_nypd_hate_crimes",
"morie_datasets_nyc_nypd_law_books",
"morie_datasets_nyc_nypd_layers",
"morie_datasets_nyc_nypd_offense_codes",
"morie_datasets_nyc_nypd_resolved",
"morie_datasets_nyc_nypd_uof_incidents",
"morie_datasets_nyc_nypd_uof_subjects",
"morie_datasets_nyc_nypd_vehicle_stops",
"morie_datasets_nyc_opendata_bulk_layers",
"morie_datasets_nyc_police_precincts",
"morie_datasets_nyc_school_districts",
"morie_datasets_nyc_socrata_by_id",
"morie_datasets_nyc_stop_and_frisk",
"morie_datasets_nyc_zctas",
"morie_datasets_ontario_ckan_by_key",
"morie_datasets_ontario_ckan_layers",
"morie_datasets_otis_a01",
"morie_datasets_otis_a01_restrictive_confinement",
"morie_datasets_otis_b01_segregation_detailed",
"morie_datasets_otis_b02_segregation_total_days",
"morie_datasets_otis_b03_seg_alerts_by_institution",
"morie_datasets_otis_b04_seg_consecutive_by_region",
"morie_datasets_otis_b05_seg_consecutive_lengths",
"morie_datasets_otis_b06_seg_reason_by_institution",
"morie_datasets_otis_b07_seg_alerts_by_gender",
"morie_datasets_otis_b08_seg_consecutive_by_institution",
"morie_datasets_otis_b09_seg_n_times",
"morie_datasets_otis_c01_individuals_total",
"morie_datasets_otis_c02_individuals_by_institution",
"morie_datasets_otis_c03_individuals_race_by_gender",
"morie_datasets_otis_c04_individuals_race_by_region",
"morie_datasets_otis_c05_individuals_religion_by_region",
"morie_datasets_otis_c06_individuals_age_by_region",
"morie_datasets_otis_c07_individuals_alerts",
"morie_datasets_otis_c08_individuals_religion_by_gender",
"morie_datasets_otis_c09_individuals_age_by_gender",
"morie_datasets_otis_c10_aggregate_durations_by_institution",
"morie_datasets_otis_c11_aggregate_lengths",
"morie_datasets_otis_c12_aggregate_durations_by_region",
"morie_datasets_otis_d01_deaths_in_custody",
"morie_datasets_otis_d02_deaths_by_gender",
"morie_datasets_otis_d03_deaths_by_race",
"morie_datasets_otis_d04_deaths_by_religion",
"morie_datasets_otis_d05_deaths_by_age_category",
"morie_datasets_otis_d06_cause_by_alert",
"morie_datasets_otis_d07_alerts_by_housing_unit",
"morie_datasets_ottawa_open_crime_adjacent_layers",
"morie_datasets_ottawa_opendata_bulk_layers",
"morie_datasets_siu_director_reports",
"morie_datasets_siu_report_fields",
"morie_datasets_siu_report_text",
"morie_datasets_statcan_ccjs_cubes",
"morie_datasets_statcan_cube_metadata",
"morie_datasets_statcan_full_csv_url",
"morie_datasets_statcan_vectors",
"morie_datasets_summary",
"morie_datasets_toronto_ambulance_stations",
"morie_datasets_toronto_asr_miscellaneous",
"morie_datasets_toronto_open_ckan_resource",
"morie_datasets_toronto_open_crime_adjacent_layers",
"morie_datasets_toronto_opendata_bulk_layers",
"morie_datasets_toronto_zoning_per_neighbourhood",
"morie_datasets_tps_2008_firs",
"morie_datasets_tps_2009_firs",
"morie_datasets_tps_2010_firs",
"morie_datasets_tps_2011_firs",
"morie_datasets_tps_2012_firs",
"morie_datasets_tps_2013_firs",
"morie_datasets_tps_administrative",
"morie_datasets_tps_arcgis_hub_by_id",
"morie_datasets_tps_arcgis_hub_download",
"morie_datasets_tps_arcgis_hub_layers",
"morie_datasets_tps_arrested_and_charged_persons",
"morie_datasets_tps_arrests_and_strip_searches",
"morie_datasets_tps_assault",
"morie_datasets_tps_automobile_ksi",
"morie_datasets_tps_autotheft",
"morie_datasets_tps_bicycle_thefts",
"morie_datasets_tps_bicycletheft",
"morie_datasets_tps_breakandenter",
"morie_datasets_tps_budget_2020",
"morie_datasets_tps_budget_2021",
"morie_datasets_tps_budget_2022",
"morie_datasets_tps_budget_2023",
"morie_datasets_tps_budget_2024",
"morie_datasets_tps_budget_2025",
"morie_datasets_tps_budget_2026",
"morie_datasets_tps_budget_by_command",
"morie_datasets_tps_calls_for_service_attended",
"morie_datasets_tps_community_safety_indicators",
"morie_datasets_tps_complaint_dispositions",
"morie_datasets_tps_cyclist_ksi",
"morie_datasets_tps_dispatched_calls_by_division",
"morie_datasets_tps_facilities",
"morie_datasets_tps_fatals_ksi",
"morie_datasets_tps_firearms_top_calibres",
"morie_datasets_tps_gross_expenditures_by_division",
"morie_datasets_tps_gross_operating_budget",
"morie_datasets_tps_hatecrimes",
"morie_datasets_tps_homicide",
"morie_datasets_tps_homicides",
"morie_datasets_tps_intimate_partner_family_violence",
"morie_datasets_tps_investigated_alleged_misconduct",
"morie_datasets_tps_killed_and_seriously_injured",
"morie_datasets_tps_layers",
"morie_datasets_tps_major_crime",
"morie_datasets_tps_mha_apprehensions",
"morie_datasets_tps_miscellaneous_calls_for_service",
"morie_datasets_tps_miscellaneous_data",
"morie_datasets_tps_miscellaneous_firearms",
"morie_datasets_tps_motorcylist_ksi",
"morie_datasets_tps_neighbourhood_crime_rates",
"morie_datasets_tps_passenger_ksi",
"morie_datasets_tps_patrol_zone",
"morie_datasets_tps_pedestrian_ksi",
"morie_datasets_tps_personnel_by_command",
"morie_datasets_tps_personnel_by_rank",
"morie_datasets_tps_personnel_by_rank_by_division",
"morie_datasets_tps_persons_in_crisis_calls_for_service_attended",
"morie_datasets_tps_police_divisions",
"morie_datasets_tps_psdp_resolved",
"morie_datasets_tps_regulated_interactions",
"morie_datasets_tps_reported_crimes",
"morie_datasets_tps_robbery",
"morie_datasets_tps_search_of_persons",
"morie_datasets_tps_shooting_firearm_discharges",
"morie_datasets_tps_shootings",
"morie_datasets_tps_staffing_by_command",
"morie_datasets_tps_theft_from_motor_vehicle",
"morie_datasets_tps_theft_over",
"morie_datasets_tps_tickets_issued",
"morie_datasets_tps_top_20_offences_of_firearm_seizures",
"morie_datasets_tps_total_public_complaints",
"morie_datasets_tps_traffic_collisions",
"morie_datasets_tps_use_of_force_call_for_service_types",
"morie_datasets_tps_use_of_force_call_sources_by_month",
"morie_datasets_tps_use_of_force_gender_composition",
"morie_datasets_tps_use_of_force_location_of_occurrences",
"morie_datasets_tps_use_of_force_occurrence_category",
"morie_datasets_tps_use_of_force_time_of_day_trends",
"morie_datasets_tps_use_of_force_use_of_force_types_and_perceived_weapons",
"morie_datasets_tps_victims_of_crime",
"morie_datasets_vancouver_community_centres",
"morie_datasets_vancouver_community_food_markets",
"morie_datasets_vancouver_disability_parking",
"morie_datasets_vancouver_fire_halls",
"morie_datasets_vancouver_graffiti",
"morie_datasets_vancouver_homeless_shelters",
"morie_datasets_vancouver_noise_control_areas",
"morie_datasets_vancouver_opendata_bulk_layers",
"morie_datasets_vancouver_opendata_by_id",
"morie_datasets_vancouver_opendata_layers",
"morie_datasets_vancouver_property_use_inspection_districts",
"morie_datasets_vancouver_public_art",
"morie_datasets_vic_cache_dir",
"morie_datasets_vic_catalog",
"morie_datasets_vic_sheets",
"morie_datasets_vic_table",
"morie_datasets_vpd_crime",
"morie_datasets_vpd_legal_disclaimer",
"morie_datasette_databases",
"morie_datasette_read",
"morie_db_connect",
"morie_db_create_indexes",
"morie_dbscan_clustering",
"morie_dcc_garch",
"morie_dcc_multivariate_garch",
"morie_ddpest",
"morie_ddpest_cheatsheet",
"morie_ddpest_check_marginals",
"morie_ddpest_correlation",
"morie_ddpest_dependence_kind",
"morie_ddpest_predict_density",
"morie_ddpest_single_atoms",
"morie_ddpest_single_weights",
"morie_ddqn",
"morie_decision_tree_split",
"morie_decode_labelled",
"morie_deconv_normality",
"morie_deconv_rate",
"morie_deconvolution",
"morie_deep_learning_genomic",
"morie_deepar_fit",
"morie_deepar_forecast",
"morie_deepar_gaussian_loglik",
"morie_deepar_negative_binomial_loglik",
"morie_deepar_sample",
"morie_deepar_scale_factor",
"morie_default_synthetic_name_map",
"morie_default_workflow_map",
"morie_deff_cluster",
"morie_density",
"morie_dependent_censoring_hazard",
"morie_derangement_rounding",
"morie_derangements",
"morie_desc_atkinson",
"morie_desc_cramers_v",
"morie_desc_gini",
"morie_desc_kappa",
"morie_desc_winsorize",
"morie_describe",
"morie_describe_by_name",
"morie_design_effect",
"morie_design_effect_direction",
"morie_design_sensitivity",
"morie_det_rng",
"morie_det_rng_sha_hex",
"morie_detection_rate_shift",
"morie_deterrence_design_check",
"morie_deterrence_logcost",
"morie_deterrence_response",
"morie_deviance_residual_cox",
"morie_deviance_residuals",
"morie_dfbeta_cox",
"morie_dfl_reweight",
"morie_diagnostic_prevalence",
"morie_did",
"morie_did_2x2",
"morie_did_aggregate_gt_att",
"morie_did_bacon_decomposition",
"morie_did_borusyak",
"morie_did_chaisemartin_dhaultfoeuille",
"morie_did_continuous_treatment",
"morie_did_diagnostics",
"morie_did_did2s",
"morie_did_doubly_robust",
"morie_did_event_study",
"morie_did_fuzzy",
"morie_did_group_time_att",
"morie_did_heterogeneous",
"morie_did_honest_sensitivity",
"morie_did_panel_fe",
"morie_did_parallel_trends_data",
"morie_did_placebo_test_group",
"morie_did_placebo_test_outcome",
"morie_did_placebo_test_time",
"morie_did_repeated_cross_section",
"morie_did_sensitivity_analysis",
"morie_did_staggered",
"morie_did_sun_abraham",
"morie_did_synthdid_estimate",
"morie_did_synthetic",
"morie_did_test_parallel_trends",
"morie_did_triple_difference",
"morie_did_twoway_fe_weights",
"morie_did_wild_cluster_bootstrap",
"morie_didfst_aggregate_att",
"morie_didfst_did_estimate",
"morie_didfst_did_forest",
"morie_didfst_group_time_att",
"morie_didfst_panel_differences",
"morie_didfst_placebo_did",
"morie_dif_delta_mh",
"morie_dif_sample_size",
"morie_difference_sign_test",
"morie_diffRC_denoise",
"morie_diffRC_forward_corrupt",
"morie_diffRC_importance_weights",
"morie_diffRC_noise_schedule",
"morie_diffRC_posterior_mean",
"morie_diffser",
"morie_diffu_diffusion_forward",
"morie_diffu_heat_diffusion",
"morie_diffusion_forward",
"morie_diffusion_score_matching",
"morie_digest",
"morie_digest2int",
"morie_dilworth_decomposition",
"morie_dimensionality_test",
"morie_dimNet_angle_between",
"morie_dimNet_bessel_basis",
"morie_dimNet_directional_message_pass",
"morie_dimNet_spherical_harmonic_basis",
"morie_dimNet_triplet_count",
"morie_direct_discrete",
"morie_dirmulti",
"morie_dirrec",
"morie_discrete_wavelet_anomaly",
"morie_disparity_benchmark",
"morie_disparity_decomposition",
"morie_disparity_exposure_bounds",
"morie_dist_cdf",
"morie_dist_integrate",
"morie_dist_moment",
"morie_dist_pdf",
"morie_dist_quantile",
"morie_dist_sum",
"morie_distq",
"morie_distribution",
"morie_dits16",
"morie_dml_clustered",
"morie_dmlqs",
"morie_docalc",
"morie_dointerv",
"morie_dominant_singularity_growth",
"morie_download",
"morie_download_bootstrap",
"morie_dp_adam",
"morie_dp_changepoint",
"morie_dp_count",
"morie_dp_covariance",
"morie_dp_exponential_mechanism",
"morie_dp_fedavg",
"morie_dp_gan",
"morie_dp_gaussian_mechanism",
"morie_dp_gaussian_mixture",
"morie_dp_histogram",
"morie_dp_kmeans",
"morie_dp_laplace_mechanism",
"morie_dp_logistic",
"morie_dp_mean",
"morie_dp_median",
"morie_dp_minmax",
"morie_dp_pca",
"morie_dp_predictive",
"morie_dp_quantile",
"morie_dp_release_calibration",
"morie_dp_sgd",
"morie_dp_sum",
"morie_dp_survival",
"morie_dp_synthetic_data",
"morie_dp_unit_definition",
"morie_dpgrf",
"morie_dpoF",
"morie_dpss",
"morie_dqm_check",
"morie_dqnv",
"morie_dr_did",
"morie_dr_learner",
"morie_dr_overlap_weighted",
"morie_dreamr",
"morie_dreamr_cheatsheet",
"morie_dreamr_imagine",
"morie_dreamr_lambda_return",
"morie_dreamr_value_update",
"morie_drift_forecast",
"morie_drop_edges",
"morie_droPDSI_palmer_pdsi",
"morie_dropout_forward",
"morie_droSPI",
"morie_drpfw_dropout_forward",
"morie_dseptest",
"morie_dsp_acf_from_psd",
"morie_dsp_alpha_trimmed_mean",
"morie_dsp_amplitude_histogram",
"morie_dsp_arc_length",
"morie_dsp_band_power",
"morie_dsp_baseline_correlation",
"morie_dsp_centroidal_time",
"morie_dsp_coherence",
"morie_dsp_coherence_spectrum",
"morie_dsp_comb",
"morie_dsp_complex_cepstrum",
"morie_dsp_complex_demodulation",
"morie_dsp_crest_factor",
"morie_dsp_cross_correlation",
"morie_dsp_cross_correlation_cpp",
"morie_dsp_csd",
"morie_dsp_cv",
"morie_dsp_derivative_detect",
"morie_dsp_dicrotic_notch",
"morie_dsp_ensemble_average",
"morie_dsp_entropy_histogram",
"morie_dsp_even_odd",
"morie_dsp_fbm_synthesis",
"morie_dsp_form_factor",
"morie_dsp_fractal_dim_psd",
"morie_dsp_hann_filter",
"morie_dsp_higuchi_fd",
"morie_dsp_hilbert_envelope",
"morie_dsp_hjorth",
"morie_dsp_hjorth_activity",
"morie_dsp_hjorth_complexity",
"morie_dsp_hjorth_mobility",
"morie_dsp_homomorphic",
"morie_dsp_hr_from_rr",
"morie_dsp_integrated_emg",
"morie_dsp_katz_fd",
"morie_dsp_lms",
"morie_dsp_lms_cpp",
"morie_dsp_matched",
"morie_dsp_mean_abs",
"morie_dsp_mean_frequency",
"morie_dsp_median_filter",
"morie_dsp_median_filter_cpp",
"morie_dsp_median_frequency",
"morie_dsp_min_phase",
"morie_dsp_moving_average",
"morie_dsp_myopulse_rate",
"morie_dsp_nlms",
"morie_dsp_nlms_cpp",
"morie_dsp_notch",
"morie_dsp_onset_detect",
"morie_dsp_pan_tompkins",
"morie_dsp_parzen_pdf",
"morie_dsp_psd_bartlett",
"morie_dsp_psd_periodogram",
"morie_dsp_psd_to_db",
"morie_dsp_psd_welch",
"morie_dsp_qrs_features",
"morie_dsp_rls",
"morie_dsp_rls_cpp",
"morie_dsp_rms",
"morie_dsp_ruler_fd",
"morie_dsp_shannon_energy",
"morie_dsp_shape_factor",
"morie_dsp_slope_sign_changes",
"morie_dsp_snr",
"morie_dsp_snr_improvement",
"morie_dsp_spectral_edge",
"morie_dsp_spectral_entropy",
"morie_dsp_spectral_flatness",
"morie_dsp_spectral_kurtosis",
"morie_dsp_spectral_moment",
"morie_dsp_spectral_ratio",
"morie_dsp_synchronized_average",
"morie_dsp_t_wave",
"morie_dsp_teager_energy",
"morie_dsp_template_match",
"morie_dsp_threshold_detect",
"morie_dsp_turning_points",
"morie_dsp_turns_count",
"morie_dsp_variance_ratio",
"morie_dsp_waveform_length",
"morie_dsp_waveform_length_norm",
"morie_dsp_wiener_filter",
"morie_dsp_wiener_hopf",
"morie_dsp_willison_amplitude",
"morie_dsp_window",
"morie_dsp_zero_crossing",
"morie_dssm",
"morie_dssm_click_posterior",
"morie_dssm_collision_rate",
"morie_dssm_cosine_similarity",
"morie_dssm_letter_ngrams",
"morie_dssm_word_hash",
"morie_dual_frame_total",
"morie_dueldqn",
"morie_dueling_q",
"morie_dunnett_test",
"morie_durbin_watson",
"morie_dynamic_wnominate",
"morie_dynamical_fit",
"morie_e_div",
"morie_e_value",
"morie_eccentricity_centrality",
"morie_ecfp4",
"morie_ecfp6",
"morie_ecod",
"morie_ecological_bounds",
"morie_ecological_decompose",
"morie_ecsTCR",
"morie_ecsTCR_co2_forcing",
"morie_ecsTCR_integrate",
"morie_eda_as_vector",
"morie_eda_compare",
"morie_eda_dataset",
"morie_eda_index",
"morie_eda_join",
"morie_eda_plot",
"morie_eda_summary",
"morie_eda_table",
"morie_edgrn",
"morie_effective_dose",
"morie_effective_sample_size",
"morie_effective_sample_size_bayes",
"morie_effective_sample_size_bulk",
"morie_effective_sample_size_tail",
"morie_effects_comparisons",
"morie_effects_emmeans",
"morie_effects_predictions",
"morie_effects_slopes",
"morie_effects_tidy",
"morie_efficiency_bound_ate",
"morie_efron_tie_correction",
"morie_eg_coint",
"morie_egarch_model",
"morie_egcn",
"morie_egnnL",
"morie_egnnL_equivariance_error",
"morie_egregious_loss_forest",
"morie_ehhdec",
"morie_elmo",
"morie_elod",
"morie_em_lmm",
"morie_emd",
"morie_emissions_carbon_intensity",
"morie_emissions_start",
"morie_emissions_stop",
"morie_emissions_track",
"morie_emissions_verify",
"morie_empirical_bayes",
"morie_empirical_bayes_dp",
"morie_empirical_sup_norm",
"morie_ensemble_average",
"morie_ensure_extras",
"morie_entheo_analyze_subject",
"morie_entheo_available_subjects",
"morie_entheo_clone_dmt_imaging",
"morie_entheo_dataset_overview",
"morie_entheo_dynamic_functional_connectivity",
"morie_entheo_load_eeg_region",
"morie_entheo_load_fmri_subject",
"morie_entheo_lz_complexity",
"morie_entheo_spectral_band_power",
"morie_entropy_balancing",
"morie_entropy_integral",
"morie_envhealth_attributable_fraction",
"morie_envhealth_burden",
"morie_envhealth_burden_by_fsa",
"morie_envhealth_cheatsheet",
"morie_envhealth_crf_no2",
"morie_envhealth_crf_pm25",
"morie_envhealth_equity",
"morie_envhealth_mortality_displaced",
"morie_envhealth_sensitivity",
"morie_epsilon_dp",
"morie_equating_haebara",
"morie_equating_stocking_lord",
"morie_erdos_ko_rado",
"morie_erdos_szekeres_check",
"morie_esl_adaboost",
"morie_esl_adaboost_predict",
"morie_esl_aic_score",
"morie_esl_archetypes",
"morie_esl_backprop",
"morie_esl_basis_fit_se",
"morie_esl_bayes_basis_posterior",
"morie_esl_bic_score",
"morie_esl_boltzmann",
"morie_esl_bootstrap_err",
"morie_esl_cross_entropy",
"morie_esl_curds_whey",
"morie_esl_cv_score",
"morie_esl_decision_tree",
"morie_esl_dirichlet_posterior",
"morie_esl_dirichlet_proc",
"morie_esl_dropout",
"morie_esl_effective_dof",
"morie_esl_em_gmm",
"morie_esl_f_test",
"morie_esl_fda",
"morie_esl_forward_stagewise",
"morie_esl_fwer",
"morie_esl_gam",
"morie_esl_gaussian_mixture",
"morie_esl_gbm",
"morie_esl_gbm_multiclass",
"morie_esl_gbm_predict",
"morie_esl_gcv",
"morie_esl_ggm_fit",
"morie_esl_graphical_lasso",
"morie_esl_hme",
"morie_esl_ica",
"morie_esl_indicator_regression",
"morie_esl_ising_fit",
"morie_esl_isomap",
"morie_esl_iwls",
"morie_esl_kernel_density",
"morie_esl_l1_logistic",
"morie_esl_l1_margin",
"morie_esl_lasso",
"morie_esl_lda_disc",
"morie_esl_least_angle_reg",
"morie_esl_linear_prediction_error",
"morie_esl_lle",
"morie_esl_local_linear",
"morie_esl_local_logistic",
"morie_esl_logistic_reg",
"morie_esl_mallows_cp",
"morie_esl_markov_rf",
"morie_esl_mars",
"morie_esl_mda",
"morie_esl_mdl",
"morie_esl_median_nn_radius",
"morie_esl_multi_output_ls",
"morie_esl_multinomial_l1",
"morie_esl_multinomial_logit",
"morie_esl_mvn_em_missing",
"morie_esl_nadaraya_watson",
"morie_esl_naive_bayes",
"morie_esl_natural_spline",
"morie_esl_neural_net",
"morie_esl_nmf",
"morie_esl_ols_normal_equations",
"morie_esl_one_se_rule",
"morie_esl_oob_632",
"morie_esl_parametric_bootstrap",
"morie_esl_partial_dependence",
"morie_esl_pca_svd",
"morie_esl_pca_transform",
"morie_esl_pcr",
"morie_esl_penalized_logistic",
"morie_esl_precision_regression",
"morie_esl_principal_curve",
"morie_esl_prodenica",
"morie_esl_projection_pursuit",
"morie_esl_prototype_lvq",
"morie_esl_qda",
"morie_esl_random_forest",
"morie_esl_rbf_network",
"morie_esl_residual_sum_squares",
"morie_esl_residual_variance",
"morie_esl_ridge",
"morie_esl_score_match",
"morie_esl_se_beta",
"morie_esl_self_organize",
"morie_esl_sis_screening",
"morie_esl_smoothing_spline",
"morie_esl_sparse_pca",
"morie_esl_supervised_pc",
"morie_esl_svc",
"morie_esl_svm_kernel",
"morie_esl_svr",
"morie_esl_test_r2",
"morie_esl_thin_plate_spline",
"morie_esl_tree_predict",
"morie_esl_var_beta_hat",
"morie_esl_varying_coef",
"morie_esl_vc_bound",
"morie_esl_wavelet_smooth",
"morie_esl_weight_decay",
"morie_esl_z_score",
"morie_ess_autocorrelation",
"morie_ess_ipseq",
"morie_estimate_aipw",
"morie_estimate_atc",
"morie_estimate_ate",
"morie_estimate_att",
"morie_estimate_cate",
"morie_estimate_double_ml",
"morie_estimate_dr_forest",
"morie_estimate_g_computation",
"morie_estimate_gate",
"morie_estimate_gumbel",
"morie_estimate_irm",
"morie_estimate_late",
"morie_estimate_propensity_scores",
"morie_eta_squared",
"morie_etwfe",
"morie_euler_characteristic",
"morie_evalue",
"morie_evidmix",
"morie_evratio",
"morie_evt_bayes_gev",
"morie_evt_chi",
"morie_evt_chibar",
"morie_evt_dedh",
"morie_evt_extremal_intervals",
"morie_evt_extremal_runs",
"morie_evt_extremal_sliding",
"morie_evt_gev_cdf",
"morie_evt_gev_lmoments",
"morie_evt_gev_loglik",
"morie_evt_gev_logpdf",
"morie_evt_gev_mle",
"morie_evt_gev_pwm",
"morie_evt_gev_quantile",
"morie_evt_gev_trend",
"morie_evt_gpd_cdf",
"morie_evt_gpd_loglik",
"morie_evt_gpd_mle",
"morie_evt_gpd_pwm",
"morie_evt_gpd_quantile",
"morie_evt_hill",
"morie_evt_hill_alias",
"morie_evt_madogram",
"morie_evt_pickands",
"morie_evt_return_level",
"morie_evt_return_level_ci",
"morie_evt_return_level_pot",
"morie_evt_xi_ci_profile",
"morie_ewma_volatility",
"morie_exact_conditional",
"morie_exp3",
"morie_expandcv",
"morie_experiment_anova",
"morie_explor",
"morie_expmc",
"morie_exponential_dist",
"morie_exposure_positivity",
"morie_extremal_index_clusters",
"morie_extreme_value_copula",
"morie_extreme_value_gev",
"morie_f_change",
"morie_face_smooth",
"morie_fairness_apply_profile",
"morie_fairness_average_odds_difference",
"morie_fairness_base_rate_bounds",
"morie_fairness_bias_amplification",
"MORIE_FAIRNESS_CANONICAL_FIELDS",
"morie_fairness_city_profile",
"morie_fairness_column_map",
"morie_fairness_compare_groups",
"morie_fairness_ctgan_debiaser",
"morie_fairness_demographic_parity",
"morie_fairness_disparate_impact",
"morie_fairness_equalized_odds",
"morie_fairness_get_city",
"morie_fairness_gini",
"morie_fairness_implied_fpr",
"morie_fairness_list_cities",
"morie_fairness_noisy_or_detection",
"morie_fairness_predpol_aggregate_areas",
"morie_fairness_predpol_calibration_audit",
"morie_fairness_predpol_score_disparity",
"morie_fairness_predpol_temporal_audit",
"morie_fairness_rates",
"morie_fairness_register_city",
"morie_fairness_simulate_biased_crime_data",
"morie_fairness_spatial_gan",
"morie_fairness_true_rate",
"morie_fairness_xai_ale",
"morie_fairness_xai_ceteris_paribus",
"morie_fairness_xai_partial_dependence",
"morie_fairness_xai_permutation_importance",
"morie_fairness_xai_shap_values",
"morie_fairRC",
"morie_faithchk",
"morie_farmlmm",
"morie_fast_available",
"morie_fastxt",
"morie_fauzi_assumptions_b1_b5",
"morie_fauzi_b1_coefficient",
"morie_fauzi_b2_coefficient",
"morie_fauzi_b2_coefficient_kdfe",
"morie_fauzi_b3_coefficient",
"morie_fauzi_b4_coefficient",
"morie_fauzi_b4_coefficient_mrl",
"morie_fauzi_b5_coefficient_mrl",
"morie_fauzi_bdfree_density_from_cdf",
"morie_fauzi_bdfree_kdfe_test",
"morie_fauzi_berry_esseen_quantile",
"morie_fauzi_bias_reduced_kdfe",
"morie_fauzi_boundary_free_kde",
"morie_fauzi_c1_coefficient",
"morie_fauzi_c2_coefficient",
"morie_fauzi_chung_smirnov",
"morie_fauzi_conditions_c1_c6",
"morie_fauzi_conditions_d1_d5",
"morie_fauzi_cov_surv_est1",
"morie_fauzi_cov_surv_est2",
"morie_fauzi_cumulative_survival_1",
"morie_fauzi_cumulative_survival_2",
"morie_fauzi_cvm_smoothed",
"morie_fauzi_cvm_statistic",
"morie_fauzi_ecdf",
"morie_fauzi_edgeworth_quantile",
"morie_fauzi_g_theta_distribution",
"morie_fauzi_gamma_kde",
"morie_fauzi_gn_edgeworth_correction",
"morie_fauzi_h_decomposition",
"morie_fauzi_higher_order_kernel",
"morie_fauzi_kde",
"morie_fauzi_kdfe",
"morie_fauzi_kdfe_mise",
"morie_fauzi_kdfe_properties",
"morie_fauzi_kernel_quantile",
"morie_fauzi_kernel_quantile_asymptotic",
"morie_fauzi_ks_smoothed",
"morie_fauzi_ks_statistic",
"morie_fauzi_l_statistic",
"morie_fauzi_lemma_3_1",
"morie_fauzi_mise",
"morie_fauzi_mise_computation",
"morie_fauzi_modified_gamma_kde",
"morie_fauzi_moment_ineq_ustat",
"morie_fauzi_mrl_asymptotic",
"morie_fauzi_mrl_boundary_free",
"morie_fauzi_mrl_boundary_free_2",
"morie_fauzi_mrl_naive",
"morie_fauzi_muller_kernel",
"morie_fauzi_naive_kernel_cvm",
"morie_fauzi_naive_kernel_ks",
"morie_fauzi_order_m_kernel",
"morie_fauzi_quantile_amse",
"morie_fauzi_quantile_asymp_var",
"morie_fauzi_quantile_bw_condition",
"morie_fauzi_r1_integral",
"morie_fauzi_r2_integral",
"morie_fauzi_sample_quantile",
"morie_fauzi_sign_moments",
"morie_fauzi_smoothed_sign",
"morie_fauzi_smoothed_wilcoxon",
"morie_fauzi_survival_kernel",
"morie_fauzi_theorem_4_3",
"morie_fauzi_theorem_4_4",
"morie_fauzi_theorem_4_5",
"morie_fauzi_theorem_4_6",
"morie_fauzi_thm1_1_bias_mgkde",
"morie_fauzi_thm1_2_var_mgkde",
"morie_fauzi_thm1_3_mise_mgkde",
"morie_fauzi_thm1_4_asympnorm_mgkde",
"morie_fauzi_thm1_5_consistency_mgkde",
"morie_fauzi_thm2_1_expected_kdfe",
"morie_fauzi_thm2_2_bias_brdkdfe",
"morie_fauzi_thm2_3_var_brdkdfe",
"morie_fauzi_thm2_4_mise_brdkdfe",
"morie_fauzi_thm4_1_surv_bias_var",
"morie_fauzi_thm4_2_surv2_bias_var",
"morie_fauzi_thm5_1_naive_kernel_equiv",
"morie_fauzi_thm5_2_bdfree_kdfe_bv",
"morie_fauzi_thm5_3_bdfree_normality",
"morie_fauzi_thm5_5_bdfree_kde_bv",
"morie_fauzi_thm5_6_bdfree_ks_equiv",
"morie_fauzi_thm5_7_bdfree_cvm_equiv",
"morie_fauzi_thm5_8_smoothed_convergence",
"morie_fauzi_thm5_9_edgeworth_wilcoxon",
"morie_fauzi_wilcoxon_moments",
"MORIE_FCFP_FEATURE_CLASSES",
"morie_fcfp4",
"morie_fda_basis_deriv",
"morie_fda_beta_function",
"morie_fda_bic",
"morie_fda_design",
"morie_fda_env_interaction",
"morie_fda_fit",
"morie_fda_loocv",
"morie_fdr_qvalues",
"morie_feedback_loop_bound",
"morie_feedback_loop_envelope",
"morie_feedback_loop_limit",
"morie_feedback_loop_meanfield",
"morie_feedback_loop_sim",
"morie_feedback_loop_urn_law",
"morie_felsen",
"morie_fetch",
"morie_fetch_arcgis",
"morie_fetch_ckan",
"morie_fetch_csv",
"morie_fetch_html",
"morie_fetch_json",
"morie_fetch_naps",
"morie_fetch_parquet",
"morie_fetch_siu",
"morie_fetch_tps",
"morie_fetch_unified",
"morie_fetch_xml",
"morie_ffmFM",
"morie_fgam_functional_gam",
"morie_find_project_root",
"morie_fine_gray_subdistribution_hazard",
"morie_fir_filter",
"morie_first_moment_ramsey",
"morie_first_passage_time",
"morie_fisher_exact_test",
"morie_fit_indices",
"morie_flexrd",
"morie_flexrd_cheatsheet",
"morie_flexrd_chi",
"morie_flexrd_energy",
"morie_flexrd_rotate",
"morie_flow_an",
"morie_forsnp",
"morie_forward_pass_dense",
"morie_forwsr",
"morie_forwsr_consistency_factor",
"morie_forwsr_forward_plot",
"morie_forwsr_forward_search",
"morie_forwsr_lms_start",
"morie_forwsr_ols_fit",
"morie_fourfeat",
"morie_frechet_check",
"morie_frequentist_confidence_set",
"morie_frfgrf",
"morie_frgrow",
"morie_frgrow_cheatsheet",
"morie_frgrow_dg",
"morie_frgrow_ge",
"morie_frgrow_le",
"morie_frgrow_metrics",
"morie_friedman_urn",
"morie_funBand",
"morie_funBand_cheatsheet",
"morie_funBand_gcv_score",
"morie_funBand_influence_matrix",
"morie_funBoot",
"morie_funCA_functional_cca",
"morie_funcal",
"morie_funcal_assign_orthologs",
"morie_funcal_cheatsheet",
"morie_funcal_seed_orthologs",
"morie_funcal_transfer_terms",
"morie_functional_band",
"morie_functional_delta",
"morie_funkM",
"morie_funkM_imputed_svd_error",
"morie_funkM_rmse",
"morie_funmix_functional_mixture",
"morie_fwpas_forward_pass_dense",
"morie_fwxF",
"morie_g_formula",
"morie_gam",
"morie_gamma_bound",
"morie_gamma_frailty_cox",
"morie_gan_an",
"morie_gan_loss",
"morie_ganls_gan_loss",
"morie_garch_fit",
"morie_garch_recursion",
"morie_garch_spec_fit",
"morie_garch_var_es",
"morie_gate",
"morie_gaussian_approx",
"morie_gblup",
"morie_gblup_full",
"morie_gblup_model",
"morie_gbtcom",
"morie_gchpost",
"morie_gee_regression",
"morie_genemt",
"morie_genemt_cheatsheet",
"morie_genemt_conditional_set_test",
"morie_genemt_gene_covariates",
"morie_genemt_gene_set_regression",
"morie_genemt_gene_statistic",
"morie_genemt_ld_principal_components",
"morie_generalized_gamma_aft",
"morie_generalized_pareto",
"morie_generate_ar_coefficients",
"morie_generate_synthetic_data",
"morie_generate_var_coefficients",
"morie_generate_view",
"morie_genmol",
"morie_genmol_cheatsheet",
"morie_genmol_elbo",
"morie_genmol_kl",
"morie_genmol_optimise",
"morie_genmol_sample",
"morie_genmol_validity",
"morie_genomic_cross_validation",
"morie_genotype_probabilities",
"morie_gentmt",
"morie_geods",
"morie_geographically_weighted_regression",
"morie_geostat_krige",
"morie_geostat_variogram",
"morie_geron_1cycle_schedule",
"morie_geron_a2c",
"morie_geron_a3c",
"morie_geron_action_value_function",
"morie_geron_actor_critic_advantage",
"morie_geron_adaboost",
"morie_geron_adaboost_weight_update",
"morie_geron_adagrad",
"morie_geron_adagrad_update",
"morie_geron_adam",
"morie_geron_adam_update",
"morie_geron_adamax",
"morie_geron_adamw",
"morie_geron_adamw_decoupled_weight_decay",
"morie_geron_agglomerative",
"morie_geron_aic",
"morie_geron_aic_gmm",
"morie_geron_albert",
"morie_geron_alexnet",
"morie_geron_anomaly_autoencoder",
"morie_geron_anomaly_gmm",
"morie_geron_arima",
"morie_geron_arima_forecast",
"morie_geron_auc_roc",
"morie_geron_auc_roc_trapezoid",
"morie_geron_autoencoder",
"morie_geron_autoencoder_reconstruction_loss",
"morie_geron_autograd",
"morie_geron_autograd_chain_rule",
"morie_geron_auxiliary_task_pretraining",
"morie_geron_backprop_through_time",
"morie_geron_backpropagation",
"morie_geron_backpropagation_gradient",
"morie_geron_bagging",
"morie_geron_bagging_predictor",
"morie_geron_bahdanau_attention",
"morie_geron_bart",
"morie_geron_batch_gd_grad",
"morie_geron_batch_gradient_descent",
"morie_geron_batch_learning",
"morie_geron_batch_normalization",
"morie_geron_batch_normalization_paper",
"morie_geron_batch_size_heuristic",
"morie_geron_beam_search",
"morie_geron_beam_search_decoder",
"morie_geron_bert",
"morie_geron_bert_finetune",
"morie_geron_bert_mlm_loss",
"morie_geron_bert_nsp_loss",
"morie_geron_bf16",
"morie_geron_bf16_range",
"morie_geron_bias_variance_tradeoff",
"morie_geron_bic",
"morie_geron_bic_gmm",
"morie_geron_bidirectional_combine",
"morie_geron_bidirectional_rnn",
"morie_geron_binary_classification",
"morie_geron_biological_neuron",
"morie_geron_birch",
"morie_geron_blip",
"morie_geron_blip_itm_itc",
"morie_geron_blip2",
"morie_geron_block_feed_forward",
"morie_geron_block_layer_norm",
"morie_geron_block_multi_head_attention",
"morie_geron_block_params",
"morie_geron_box_iou",
"morie_geron_bpe_merge",
"morie_geron_bpe_tokenizer",
"morie_geron_cart_algorithm",
"morie_geron_cart_split_cost",
"morie_geron_causal_mask",
"morie_geron_center_gram",
"morie_geron_ch4_cross_entropy_gradient_vector",
"morie_geron_ch4_elastic_net_cost_function",
"morie_geron_ch4_lasso_regression_cost_function",
"morie_geron_ch4_linear_regression_prediction",
"morie_geron_ch4_logistic_regression_prediction",
"morie_geron_ch4_mse_gradient_vector",
"morie_geron_ch4_normal_equation",
"morie_geron_ch4_simple_linear_life_satisfaction",
"morie_geron_ch4_softmax_function",
"morie_geron_char_rnn",
"morie_geron_classification_localization",
"morie_geron_classification_mlp",
"morie_geron_classification_mlp_output",
"morie_geron_classification_tree",
"morie_geron_clip",
"morie_geron_clip_contrastive_loss",
"morie_geron_computational_graph",
"morie_geron_conditional_p",
"morie_geron_confusion_matrix",
"morie_geron_confusion_matrix_labeled",
"morie_geron_contrastive_infonce",
"morie_geron_contrastive_learning",
"morie_geron_conv_autoencoder_trained",
"morie_geron_conv_output_size",
"morie_geron_conv2d_forward",
"morie_geron_convolutional_autoencoder",
"morie_geron_cosine_annealing",
"morie_geron_credit_assignment",
"morie_geron_cross_attention",
"morie_geron_cross_attention_report",
"morie_geron_cross_entropy_cost",
"morie_geron_cross_entropy_gradient",
"morie_geron_cross_validation_score",
"morie_geron_curse_dimensionality",
"morie_geron_dalle",
"morie_geron_dalle_autoregressive_token",
"morie_geron_dataloader",
"morie_geron_dataloader_minibatch",
"morie_geron_dbscan",
"morie_geron_dbscan_core_point",
"morie_geron_dcgan",
"morie_geron_dcgan_generator",
"morie_geron_ddim",
"morie_geron_ddim_sampling_step",
"morie_geron_ddpg",
"morie_geron_ddpm",
"morie_geron_ddpm_forward_process",
"morie_geron_ddpm_reverse_step",
"morie_geron_ddpm_simple_loss",
"morie_geron_decision_boundary",
"morie_geron_decoder_only",
"morie_geron_deep_rnn",
"morie_geron_deit",
"morie_geron_deit_distillation_loss",
"morie_geron_denoising_autoencoder",
"morie_geron_denoising_autoencoder_train",
"morie_geron_detr",
"morie_geron_detr_hungarian_matching",
"morie_geron_diffusion_forward",
"morie_geron_diffusion_reverse",
"morie_geron_digamma",
"morie_geron_dino",
"morie_geron_dino_self_distillation",
"morie_geron_discounted_return",
"morie_geron_distilbert",
"morie_geron_double_center",
"morie_geron_double_dqn",
"morie_geron_double_dqn_target",
"morie_geron_dpo",
"morie_geron_dpo_loss",
"morie_geron_dqn",
"morie_geron_dqn_loss",
"morie_geron_dropout",
"morie_geron_dropout_alt",
"morie_geron_dueling_dqn",
"morie_geron_dueling_dqn_alt",
"morie_geron_dynamic_quantization",
"morie_geron_dynamic_quantization_alt",
"morie_geron_early_stopping",
"morie_geron_early_stopping_alt",
"morie_geron_elastic_net",
"morie_geron_elastic_net_cost",
"morie_geron_elbo",
"morie_geron_elu",
"morie_geron_embedding_lookup",
"morie_geron_encoder_decoder_nmt",
"morie_geron_encoder_decoder_seq2seq",
"morie_geron_encoder_decoder_transformer",
"morie_geron_encoder_only",
"morie_geron_encoder_params",
"morie_geron_entropy_impurity",
"morie_geron_epsilon_greedy",
"morie_geron_epsilon_greedy_alt",
"morie_geron_error_analysis",
"morie_geron_explained_variance_ratio",
"morie_geron_explained_variance_ratio_alt",
"morie_geron_exploding_gradients",
"morie_geron_extra_trees",
"morie_geron_f1_score",
"morie_geron_f1_score_alt",
"morie_geron_fashion_mnist",
"morie_geron_fcn",
"morie_geron_fcn_upsample",
"morie_geron_feature_importance_mdi",
"morie_geron_feature_map",
"morie_geron_feature_map_dim",
"morie_geron_few_shot",
"morie_geron_filter_kernel",
"morie_geron_finetune_lm",
"morie_geron_fit_ab",
"morie_geron_flamingo",
"morie_geron_flamingo_cross_modal_attn",
"morie_geron_flash_attention",
"morie_geron_flash_attention_tile",
"morie_geron_forward_autodiff",
"morie_geron_forward_mode_autodiff",
"morie_geron_fp16_mixed_precision",
"morie_geron_fp16_quant",
"morie_geron_fp32",
"morie_geron_gan",
"morie_geron_gan_minimax",
"morie_geron_gan_mode_collapse_metric",
"morie_geron_gaussian_mixture",
"morie_geron_gaussian_rand_projection",
"morie_geron_gaussian_random_projection",
"morie_geron_gelu",
"morie_geron_gini_impurity",
"morie_geron_gini_impurity_grgin",
"morie_geron_glorot_init",
"morie_geron_glorot_xavier_init",
"morie_geron_gmm_em_step",
"morie_geron_gmm_log_likelihood",
"morie_geron_gmm_n_params",
"morie_geron_googlenet",
"morie_geron_gpt_autoregressive_loss",
"morie_geron_gpt1",
"morie_geron_gpt2",
"morie_geron_gpt3",
"morie_geron_gradient_boosting",
"morie_geron_gradient_boosting_residual",
"morie_geron_gradient_clipping",
"morie_geron_gradient_clipping_grgcl",
"morie_geron_grid_search",
"morie_geron_grid_search_cv",
"morie_geron_gru",
"morie_geron_gru_cell",
"morie_geron_hard_voting",
"morie_geron_he_init",
"morie_geron_he_init_hmhei",
"morie_geron_heaviside",
"morie_geron_heaviside_step",
"morie_geron_hebb_rule",
"morie_geron_hebb_rule_grhbb",
"morie_geron_hf_pipelines",
"morie_geron_hf_trainer",
"morie_geron_hidden_layers_heuristic",
"morie_geron_histogram_gradient_boosting",
"morie_geron_hyperparameter_tuning",
"morie_geron_image_segmentation",
"morie_geron_imputation_median",
"morie_geron_in_context_learning",
"morie_geron_in_context_learning_hmicl",
"morie_geron_incremental_pca",
"morie_geron_information_gain",
"morie_geron_information_gain_hmigr",
"morie_geron_instance_based",
"morie_geron_int8_quant",
"morie_geron_int8_quantization",
"morie_geron_isomap",
"morie_geron_johnson_lindenstrauss",
"morie_geron_johnson_lindenstrauss_bound",
"morie_geron_kernel_pca_from_gram",
"morie_geron_kernel_pca_poly",
"morie_geron_kernel_pca_rbf",
"morie_geron_kernel_pca_rbf_hmkprbf",
"morie_geron_kernel_pca_sigmoid",
"morie_geron_kfold",
"morie_geron_kfold_cv",
"morie_geron_kl_divergence_gaussian",
"morie_geron_kmeans",
"morie_geron_kmeans_limits",
"morie_geron_kmeans_objective",
"morie_geron_kmeans_plus_plus",
"morie_geron_kmeans_pp_seeding",
"morie_geron_knowledge_distillation",
"morie_geron_knowledge_distillation_loss",
"morie_geron_kv_cache_compress",
"morie_geron_kv_cache_compression",
"morie_geron_l1_regularization",
"morie_geron_l2_regularization",
"morie_geron_lasso_cost",
"morie_geron_lasso_cost_hmlaso",
"morie_geron_layer_norm_rnn",
"morie_geron_layer_normalization",
"morie_geron_layer_normalization_hmlntr",
"morie_geron_layer_norms",
"morie_geron_leaky_relu",
"morie_geron_learning_curves",
"morie_geron_learning_curves_hmlcv",
"morie_geron_learning_rate_heuristic",
"morie_geron_learning_rate_schedule",
"morie_geron_lenet5",
"morie_geron_linear_layer_forward",
"morie_geron_linear_regression_life",
"morie_geron_linreg_mse_cost",
"morie_geron_linreg_mse_cost_grmse",
"morie_geron_linreg_pytorch",
"morie_geron_local_outlier_factor",
"morie_geron_local_outlier_factor_hmlof",
"morie_geron_locally_linear_embedding",
"morie_geron_logistic_cost",
"morie_geron_logistic_cost_gradient",
"morie_geron_logistic_cross_entropy_cost",
"morie_geron_logistic_gradient",
"morie_geron_logistic_probability",
"morie_geron_logistic_regression_probability",
"morie_geron_lr_cosine_annealing",
"morie_geron_lr_exponential",
"morie_geron_lr_exponential_schedule",
"morie_geron_lstm",
"morie_geron_lstm_cell",
"morie_geron_mae",
"morie_geron_mae_grmae",
"morie_geron_masked_lm",
"morie_geron_matmul_order",
"morie_geron_max_norm",
"morie_geron_max_norm_regularization",
"morie_geron_max_pool",
"morie_geron_max_pooling",
"morie_geron_mc_dropout",
"morie_geron_mdp",
"morie_geron_mds",
"morie_geron_mean_shift",
"morie_geron_memory_cell",
"morie_geron_min_max_scaling",
"morie_geron_minibatch_gd",
"morie_geron_minibatch_gradient_descent",
"morie_geron_minibatch_kmeans",
"morie_geron_minmax_scaler",
"morie_geron_mish",
"morie_geron_mistral7b",
"morie_geron_mixed_precision",
"morie_geron_mlp",
"morie_geron_mlp_forward",
"morie_geron_mode_collapse",
"morie_geron_model_based",
"morie_geron_model_context_protocol",
"morie_geron_model_parallelism",
"morie_geron_momentum",
"morie_geron_momentum_update",
"morie_geron_mps_acceleration",
"morie_geron_multi_head_attention",
"morie_geron_multihead_attention",
"morie_geron_multilabel",
"morie_geron_multilabel_classification",
"morie_geron_multinomial_logistic",
"morie_geron_multioutput",
"morie_geron_nadam",
"morie_geron_ncsn",
"morie_geron_nesterov",
"morie_geron_nesterov_accelerated_gradient",
"morie_geron_neurons_per_layer",
"morie_geron_next_sentence_prediction",
"morie_geron_nmf",
"morie_geron_nmf_objective",
"morie_geron_normal_equation",
"morie_geron_normal_equation_grnorm",
"morie_geron_novelty_detection",
"morie_geron_numerical_diff",
"morie_geron_numerical_differentiation",
"morie_geron_one_class_svm",
"morie_geron_one_cycle",
"morie_geron_one_hot_encoding",
"morie_geron_one_hot_encoding_grohe",
"morie_geron_one_shot",
"morie_geron_one_vs_one",
"morie_geron_one_vs_one_hm",
"morie_geron_one_vs_rest",
"morie_geron_one_vs_rest_hm",
"morie_geron_online_learning",
"morie_geron_onnx_export",
"morie_geron_oob_score",
"morie_geron_optics",
"morie_geron_ordinal_encoding",
"morie_geron_ordinal_encoding_grord",
"morie_geron_overfitting",
"morie_geron_overfitting_gap",
"morie_geron_padding",
"morie_geron_pairwise_distances",
"morie_geron_pasting",
"morie_geron_pca_projection",
"morie_geron_pca_variance",
"morie_geron_peephole_lstm",
"morie_geron_peephole_lstm_cell",
"morie_geron_perceiver",
"morie_geron_perceiver_io",
"morie_geron_perceiver_io_hm",
"morie_geron_perceptron",
"morie_geron_permutation_masks",
"morie_geron_pipeline_parallelism",
"morie_geron_policy",
"morie_geron_policy_gradient",
"morie_geron_polynomial_features",
"morie_geron_polynomial_features_hm",
"morie_geron_positional_encoding",
"morie_geron_ppo",
"morie_geron_ppo_clipped_objective",
"morie_geron_precision",
"morie_geron_precision_hm",
"morie_geron_precision_recall_curve",
"morie_geron_precision_recall_curve_hm",
"morie_geron_predict_tree",
"morie_geron_prelu",
"morie_geron_pretrained_embeddings",
"morie_geron_principal_components",
"morie_geron_prioritized_experience_weight",
"morie_geron_prioritized_replay",
"morie_geron_pvt",
"morie_geron_pyramid_vit_stage",
"morie_geron_pytorch_tensor",
"morie_geron_q_learning",
"morie_geron_q_learning_update",
"morie_geron_q_value_iteration",
"morie_geron_quantization_aware_training",
"morie_geron_quantization_aware_training_hm",
"morie_geron_quantize",
"morie_geron_quantize_symmetric",
"morie_geron_random_forest",
"morie_geron_random_patches",
"morie_geron_random_subspaces",
"morie_geron_randomized_pca",
"morie_geron_randomized_search",
"morie_geron_randomized_search_cv",
"morie_geron_recall",
"morie_geron_recall_hm",
"morie_geron_recurrent_neuron",
"morie_geron_regression_mlp",
"morie_geron_regression_mlp_output",
"morie_geron_regression_mlp_pytorch",
"morie_geron_regression_tree",
"morie_geron_reinforce",
"morie_geron_reinforce_policy_gradient",
"morie_geron_reinforcement_learning",
"morie_geron_relu",
"morie_geron_reparameterization_trick",
"morie_geron_resnet",
"morie_geron_resnet_skip",
"morie_geron_reverse_autodiff",
"morie_geron_reverse_mode_autodiff",
"morie_geron_revnet",
"morie_geron_reward_function",
"morie_geron_ridge_cost",
"morie_geron_ridge_cost_grridg",
"morie_geron_ridge_normal",
"morie_geron_ridge_normal_equation",
"morie_geron_rlhf",
"morie_geron_rlhf_reward_kl_objective",
"morie_geron_rmse",
"morie_geron_rmse_grrmse",
"morie_geron_rnn_visual_attention",
"morie_geron_roberta",
"morie_geron_roc_curve",
"morie_geron_roc_curve_hm",
"morie_geron_run_graph",
"morie_geron_sac",
"morie_geron_save_load_pytorch",
"morie_geron_scaled_dot_product",
"morie_geron_scaled_dot_product_attention",
"morie_geron_score_matching_loss",
"morie_geron_self_attention",
"morie_geron_self_attention_modules",
"morie_geron_self_supervised",
"morie_geron_selu",
"morie_geron_semantic_segmentation",
"morie_geron_semisupervised",
"morie_geron_semisupervised_cluster",
"morie_geron_senet",
"morie_geron_senet_squeeze_excite",
"morie_geron_sentiment_analysis",
"morie_geron_sentiment_binary",
"morie_geron_separable_params",
"morie_geron_seq2seq",
"morie_geron_sft",
"morie_geron_sft_objective",
"morie_geron_sgd_classifier",
"morie_geron_sgd_update",
"morie_geron_shannon_entropy",
"morie_geron_sigmoid",
"morie_geron_sigmoid_grsig",
"morie_geron_silhouette",
"morie_geron_silhouette_score",
"morie_geron_simple_imputer",
"morie_geron_simple_rnn",
"morie_geron_simple_rnn_cell",
"morie_geron_sinusoidal_positional_encoding",
"morie_geron_soft_voting",
"morie_geron_softmax_cost_gradient",
"morie_geron_softmax_cross_entropy_cost",
"morie_geron_softmax_function",
"morie_geron_softmax_probability",
"morie_geron_softmax_score",
"morie_geron_softmax_score_grsmxs",
"morie_geron_span_corrupt",
"morie_geron_sparse_autoencoder",
"morie_geron_sparse_rand_projection",
"morie_geron_spectral_clustering",
"morie_geron_stacked_autoencoder",
"morie_geron_stacked_autoencoder_modules",
"morie_geron_stacking",
"morie_geron_stacking_predictor",
"morie_geron_standardization",
"morie_geron_standardization_grstd",
"morie_geron_state_value_function",
"morie_geron_static_ptq",
"morie_geron_static_quantization_ptq",
"morie_geron_stochastic_gradient_descent",
"morie_geron_stratified_sampling",
"morie_geron_stride",
"morie_geron_supervised_learning",
"morie_geron_svd_pseudoinverse",
"morie_geron_swin",
"morie_geron_swin_window_attention",
"morie_geron_swish",
"morie_geron_symbolic_diff",
"morie_geron_symbolic_differentiation",
"morie_geron_symd_evaluate",
"morie_geron_symd_parse",
"morie_geron_symd_to_string",
"morie_geron_t5",
"morie_geron_t5_restore",
"morie_geron_tanh",
"morie_geron_tanh_activation",
"morie_geron_td_learning",
"morie_geron_td_zero_update",
"morie_geron_td3",
"morie_geron_temperature_sampling",
"morie_geron_tensor_parallelism",
"morie_geron_threshold_logic_unit",
"morie_geron_time_series_forecast",
"morie_geron_tlu",
"morie_geron_topk_sampling",
"morie_geron_torch_compile",
"morie_geron_torchscript",
"morie_geron_trace",
"morie_geron_train_logreg",
"morie_geron_transfer_learning",
"morie_geron_transformer",
"morie_geron_transformer_decoder_block",
"morie_geron_transformer_encoder_block",
"morie_geron_transformer_feedforward",
"morie_geron_tree_classification_leaf",
"morie_geron_tree_high_variance",
"morie_geron_tree_regression_leaf",
"morie_geron_tree_regularization",
"morie_geron_tree_sensitivity_scale",
"morie_geron_trl_finetune",
"morie_geron_tsne",
"morie_geron_umap",
"morie_geron_underfitting",
"morie_geron_unsupervised_learning",
"morie_geron_unsupervised_pretraining",
"morie_geron_vae",
"morie_geron_vae_elbo",
"morie_geron_vae_loss_and_grads",
"morie_geron_value_function",
"morie_geron_value_iteration",
"morie_geron_vanishing_gradients",
"morie_geron_variational_bayes_gmm",
"morie_geron_videobert",
"morie_geron_vilbert",
"morie_geron_vision_transformer",
"morie_geron_vit_patch_embedding",
"morie_geron_voting_hard",
"morie_geron_vq_vae",
"morie_geron_warm_restarts",
"morie_geron_weight_pruning",
"morie_geron_weight_pruning_hm",
"morie_geron_word_embeddings",
"morie_geron_wordpiece_tokenizer",
"morie_geron_wordpiece_tokenizer_score",
"morie_geron_xception",
"morie_geron_xgboost",
"morie_geron_xgboost_gain",
"morie_geron_xlnet",
"morie_geron_yolo",
"morie_geron_yolo_grid_loss",
"morie_geron_zero_shot",
"morie_gh_crm_laplace_gamma",
"morie_gh_dirichlet_moments",
"morie_gh_discrete_hazard",
"morie_gh_dp_median_cdf",
"morie_gh_dp_ndistinct",
"morie_gh_dp_posterior",
"morie_gh_dp_predictive",
"morie_gh_eps_dp_size",
"morie_gh_ewens_log",
"morie_gh_hellinger2",
"morie_gh_ibp_expected_dishes",
"morie_gh_kl",
"morie_gh_ncrm_laplace",
"morie_gh_pt_bits",
"morie_gh_pt_posterior_density",
"morie_gh_py_eppf_log",
"morie_gh_renyi",
"morie_gh_stick_breaking",
"morie_gh_wn_posterior",
"morie_ghosal_adaptation",
"morie_ghosal_bernstein_von_mises",
"morie_ghosal_contraction_rate",
"morie_ghosal_dirichlet_posterior",
"morie_ghosal_dpmixture_density",
"morie_ghosal_empirical_bayes",
"morie_ghosal_gp_matern",
"morie_ghosal_gp_squared_exponential",
"morie_ghosal_hierarchical_bayes",
"morie_ghosal_log_density",
"morie_ghosal_moment_matching",
"morie_ghosal_neutral_right",
"morie_ghosal_np_classification",
"morie_ghosal_np_regression",
"morie_ghosal_np_testing",
"morie_ghosal_posterior_consistency",
"morie_ghosal_sieve_prior",
"morie_ghosal_stick_breaking_trunc",
"morie_ghosal_survival_beta_process",
"morie_ghosal_wavelet_prior",
"morie_gibbs_slice",
"morie_gini_gamma",
"morie_giss_anomaly",
"morie_glides",
"morie_glides_cheatsheet",
"morie_glides_coulomb",
"morie_glides_hbond",
"morie_glides_lipo",
"morie_glides_score",
"morie_glides_vdw",
"morie_gllsuff",
"morie_glm",
"morie_glm_nb",
"morie_glm_predict",
"morie_glmbay_bayesian_glm",
"morie_glmm",
"morie_glove",
"morie_glr_test",
"morie_glrtest",
"morie_gm_error_sar",
"morie_gmm_log_pdf",
"morie_gms_critical_value",
"morie_goldsc",
"morie_goldsc_cheatsheet",
"morie_goldsc_hbond",
"morie_goldsc_lj",
"morie_goldsc_split",
"morie_goldsc_torsion",
"morie_goldsc_vdw",
"morie_goodman_minimum",
"morie_goodman_triangles",
"morie_goto",
"morie_gp_density_rate",
"morie_gpl_compatible_licenses",
"morie_gr_dual",
"morie_grace",
"morie_gradient_boosting_ensemble",
"morie_gradient_boosting_genomic",
"morie_gradient_descent_update",
"morie_gradient_descent_vanilla",
"morie_granger_test",
"morie_graphcontrastive",
"morie_graphic_matroid",
"morie_graphsage",
"morie_grclus",
"morie_greedy_independent_set",
"morie_greg",
"morie_greg_variance",
"morie_grid_search_cv",
"morie_grm",
"morie_grm_vanraden",
"morie_groebn",
"morie_gross_error_sensitivity",
"morie_grouptimeatt",
"morie_grphmr_centrality",
"morie_grphmr_edge",
"morie_grphmr_sp",
"morie_grphmr_spatial",
"morie_gru_cell",
"morie_gru4r_bpr",
"morie_gru4r_gru",
"morie_gru4r_mrr",
"morie_gru4r_recall",
"morie_gru4r_top1",
"morie_grucl_gru_cell",
"morie_gsageemd_aggregate",
"morie_gsageemd_embed",
"morie_gsageemd_layer",
"morie_gsageemd_loss",
"morie_gsageemd_sample",
"morie_gsplat_composite",
"morie_gsplat_covariance",
"morie_gsplat_density",
"morie_gsplat_project",
"morie_gsplat_psd",
"morie_gtrf_attention",
"morie_gtrf_lap_pe",
"morie_gtrf_laplacian",
"morie_gtrf_layer",
"morie_gtrf_sign_flip",
"morie_gtrf_sparse_attention",
"morie_gvar_relu",
"morie_gvar_sigmoid",
"morie_gwasem",
"morie_gwasem_gc",
"morie_gwasem_gower",
"morie_gwasem_kinship",
"morie_gwasem_kinship_ibs",
"morie_gwasem_reml",
"morie_gwPot",
"morie_gwrcal",
"morie_gwrcal_global_aicc",
"morie_gwrcal_prepare",
"morie_gxe_blup",
"morie_gxe_interaction_model",
"morie_gxe_multitrait",
"morie_hadcrut",
"morie_hadcrut_area_mean",
"morie_hadcrut_blend",
"morie_hadcrut_cheatsheet",
"morie_hadcrut_coverage_error",
"morie_hadcrut_weights",
"morie_hajek_mean",
"morie_haldane",
"morie_halft",
"morie_halft_cheatsheet",
"morie_halft_rates",
"morie_hall_condition",
"morie_hamming_bound",
"morie_hansch_qsar",
"morie_hapblk",
"morie_happy_ending_quadrilateral",
"morie_hardy_ramanujan_partitions",
"morie_hardy_weinberg",
"morie_harrell_davis",
"morie_has",
"morie_has_clique",
"morie_hawkes_baseline_integral_cpp",
"morie_hawkes_fit",
"morie_hawkes_kernel_cdf_cpp",
"morie_hawkes_kernel_density_cpp",
"morie_hawkes_ll_exp_const_cpp",
"morie_hawkes_ll_gamma_const_cpp",
"morie_hawkes_ll_lomax_const_cpp",
"morie_hawkes_ll_weibull_const_cpp",
"morie_hawkes_pair_excitation_sum_cpp",
"morie_hawkes_st_fit",
"morie_hawkes_st_intensity",
"morie_hawkes_st_loglik",
"morie_hawkes_st_simulate",
"morie_hazard_selection",
"morie_hbos",
"morie_hbvMod",
"morie_hdbsc",
"morie_he_initialization",
"morie_hedges_g",
"morie_heinz_he_initialization",
"morie_hellinger_distance",
"morie_helly_intervals",
"morie_hermite_basis",
"morie_heteroskedastic_scales",
"morie_hetgnn",
"morie_hiatus",
"morie_hibrid_hibrid_prediction",
"morie_hierF",
"morie_hits",
"morie_hlm_components",
"morie_hlmgr",
"morie_hmac",
"morie_hntfst",
"morie_hockey_stick",
"morie_holt_linear",
"morie_holt_winters",
"morie_holt_winters_additive",
"morie_holt_winters_mult",
"morie_hook_lengths",
"morie_horowitz_additive_model",
"morie_horowitz_additive_nonid_link",
"morie_horowitz_additive_quantile",
"morie_horowitz_additive_unknown_link",
"morie_horowitz_average_derivative",
"morie_horowitz_bandwidth_bootstrap",
"morie_horowitz_bias_reduction_deconv",
"morie_horowitz_binary_response",
"morie_horowitz_binary_response_model",
"morie_horowitz_both_nonpar_transform",
"morie_horowitz_box_cox",
"morie_horowitz_bw_cv_sim",
"morie_horowitz_censored_regression",
"morie_horowitz_confidence_bands",
"morie_horowitz_control_function",
"morie_horowitz_curse_dimensionality",
"morie_horowitz_deconvolution",
"morie_horowitz_dimension_reduction",
"morie_horowitz_duration_model",
"morie_horowitz_empirical_cf",
"morie_horowitz_index_model",
"morie_horowitz_kernel_density",
"morie_horowitz_kernel_regression",
"morie_horowitz_likelihood_ratio_test",
"morie_horowitz_local_ate",
"morie_horowitz_local_linear",
"morie_horowitz_manski_bounds",
"morie_horowitz_mixture_model",
"morie_horowitz_multiple_index_model",
"morie_horowitz_nonparametric_iv",
"morie_horowitz_npiv_model",
"morie_horowitz_parametric_T",
"morie_horowitz_ph_discrete_obs",
"morie_horowitz_ph_frailty_nonpar",
"morie_horowitz_ph_heterogeneity",
"morie_horowitz_plr_bandwidth",
"morie_horowitz_plr_estimator",
"morie_horowitz_plr_identification",
"morie_horowitz_plr_quantile",
"morie_horowitz_proportional_hazards",
"morie_horowitz_quantile_regression",
"morie_horowitz_rate_beta_estimation",
"morie_horowitz_rate_G_estimation",
"morie_horowitz_sample_selection",
"morie_horowitz_semipar_mle_binary",
"morie_horowitz_series_unknown_T",
"morie_horowitz_sim_id_discrete_x",
"morie_horowitz_sim_identification",
"morie_horowitz_sim_quantile",
"morie_horowitz_single_index_model",
"morie_horowitz_smoothed_maximum_score",
"morie_horowitz_sms_confidence",
"morie_horowitz_thm4_1_id_median",
"morie_horowitz_tikhonov_unknown_T",
"morie_horowitz_transformation_model",
"morie_horowitz_treatment_effect",
"morie_horowitz_weibull_heterogeneity",
"morie_horowitz_wild_bootstrap",
"morie_horvitz_thompson",
"morie_hosted_datasets",
"morie_hosted_manifest",
"morie_hot",
"morie_hotspot_shrinkage",
"morie_hotspot_threshold",
"morie_hsic",
"morie_hsicstat",
"morie_ht_elephant",
"morie_ht_estimators",
"morie_hte_blp_test",
"morie_hurst_r",
"morie_hybrid_gibbs_slice",
"morie_hyper2",
"morie_hyper2_cheatsheet",
"morie_hyper2_kernel",
"morie_hyper2_logml",
"morie_hyper2_slice",
"morie_hypercube_instruments",
"morie_hypergeometric_dist",
"morie_ibdmtx",
"morie_ibpfa",
"morie_icc_rho",
"morie_ichimura",
"morie_ideal_fourths",
"morie_ideal_point_model",
"morie_ideal_point_recovery",
"morie_idealf_iqr",
"morie_identified_set",
"morie_identified_set_interval",
"morie_identify_estimate_refute",
"morie_ihstst",
"morie_impala_vtrace",
"morie_impFB",
"morie_importance_sampling",
"morie_imputation",
"morie_impute_column",
"morie_impute_untreated",
"morie_in_identified_set",
"morie_incapacitation",
"morie_incapacitation_career",
"morie_inception_module",
"morie_incidence_check",
"morie_indep_test",
"morie_index_regression",
"morie_indicator_kriging",
"morie_indirect_effect_sobel",
"morie_infer_measurement_level",
"morie_infmax",
"morie_infmer",
"morie_infmer_cheatsheet",
"morie_informer_cheatsheet",
"morie_informer_complexity",
"morie_informer_full_attention",
"morie_informer_kl_from_uniform",
"morie_informer_probsparse_attention",
"morie_informer_select_queries",
"morie_informer_sparsity_measure",
"morie_ingest_a2aj_citation_edges",
"morie_ingest_a2aj_coverage",
"morie_ingest_a2aj_download",
"morie_ingest_a2aj_fetch",
"morie_ingest_a2aj_gaps",
"morie_ingest_a2aj_load",
"morie_ingest_a2aj_search",
"morie_ingest_a2aj_url",
"morie_ingest_bigquery_build_sql",
"morie_ingest_bigquery_query",
"morie_ingest_bigquery_table",
"morie_ingest_canlii_case",
"morie_ingest_canlii_case_id",
"morie_ingest_canlii_cases",
"morie_ingest_canlii_citator",
"morie_ingest_canlii_databases",
"morie_ingest_canlii_legislation",
"morie_ingest_canlii_legislation_databases",
"morie_ingest_canlii_legislations",
"morie_ingest_chicago_crime",
"morie_ingest_chicago_crime_bigquery",
"morie_ingest_chicago_resources",
"morie_ingest_chicago_socrata",
"morie_ingest_cihi_xlsx",
"morie_ingest_ckan_fetch_package_csvs",
"morie_ingest_ckan_package_search",
"morie_ingest_ckan_package_show",
"morie_ingest_ckan_read_resource",
"morie_ingest_ckan_resource_show",
"morie_ingest_ckan_search_packages",
"morie_ingest_forensics_namus_missing",
"morie_ingest_forensics_nibrs",
"morie_ingest_forensics_nist_rds",
"morie_ingest_statcan_cansim",
"morie_ingest_statcan_csv",
"morie_ingest_statcan_vectors",
"morie_ingest_tps_feature_layer",
"morie_ingest_tps_fetch",
"morie_ingest_tps_layers",
"morie_inspect_output",
"morie_inspection_game",
"morie_install_extras",
"morie_instrument_check",
"morie_instrumental_inequality",
"morie_interracial_rates",
"morie_interval_map",
"morie_interval_mapping",
"morie_interventional_effects",
"morie_invariance_compare",
"morie_inverse_haldane",
"morie_ipcw_interval",
"morie_ipwgrf",
"morie_irt_2pl",
"morie_irt_eap",
"morie_irt_grm",
"morie_irt_marginal_reliability",
"morie_irt_spatial",
"morie_irt_theta_se",
"morie_is_backdoor_admissible",
"morie_is_latin_square",
"morie_is_matroid",
"morie_is_over_legal_limit",
"morie_isolation_forest",
"morie_isotonic_regression",
"morie_itrans",
"morie_itrgrf",
"morie_its",
"morie_iv_2sls",
"morie_iv_anderson_rubin",
"morie_iv_anderson_rubin_ci",
"morie_iv_conditional_lr",
"morie_iv_control_function",
"morie_iv_cragg_donald",
"morie_iv_cue_gmm",
"morie_iv_diagnostics",
"morie_iv_durbin_wu_hausman",
"morie_iv_first_stage_diagnostics",
"morie_iv_gmm",
"morie_iv_hansen_j",
"morie_iv_hausman",
"morie_iv_jive",
"morie_iv_kleibergen_paap",
"morie_iv_liml",
"morie_iv_montiel_olea_pflueger",
"morie_iv_natural_bounds",
"morie_iv_panel",
"morie_iv_probit",
"morie_iv_residual_analysis",
"morie_iv_sargan",
"morie_iv_split_sample",
"morie_iv_stock_yogo",
"morie_iv_tsls",
"morie_iv_wald",
"morie_jackknife_estimate",
"morie_jackknife_estimator",
"morie_jacquez_knn",
"morie_jnpnt",
"morie_johansen_cointegration",
"morie_joint_convergence",
"morie_joseph_croston_intermittent",
"morie_joseph_mint_reconciliation",
"morie_joseph_naive_forecast",
"morie_joseph_simple_exponential_smoothing",
"morie_joseph_ts_outlier_detection",
"morie_json_stringify",
"morie_jsonlt",
"morie_jsonlt_base64_dec",
"morie_jsonlt_base64_enc",
"morie_jsonlt_base64url_dec",
"morie_jsonlt_base64url_enc",
"morie_jsonlt_flatten",
"morie_jsonlt_from_json",
"morie_jsonlt_minify",
"morie_jsonlt_parse_json",
"morie_jsonlt_prettify",
"morie_jsonlt_rbind_pages",
"morie_jsonlt_read_json",
"morie_jsonlt_serialize",
"morie_jsonlt_stream_in",
"morie_jsonlt_stream_out",
"morie_jsonlt_to_json",
"morie_jsonlt_unbox",
"morie_jsonlt_unserialize",
"morie_jsonlt_validate",
"morie_jsonlt_write_json",
"morie_judge_iv",
"morie_judge_iv_population",
"morie_judge_monotonicity",
"morie_judge_slope_test",
"morie_kalman_filter",
"morie_kamath_3h_alignment",
"morie_kamath_adalora_rank_allocation",
"morie_kamath_alibi_bias",
"morie_kamath_attention_output",
"morie_kamath_attention_score",
"morie_kamath_attention_softmax",
"morie_kamath_autoprompt_gradient_search",
"morie_kamath_bertscore",
"morie_kamath_best_of_n_sampling",
"morie_kamath_bleu_score",
"morie_kamath_bm25_score",
"morie_kamath_bradley_terry_preference",
"morie_kamath_ch3_back_translation_prob",
"morie_kamath_ch3_cloze_prompt_template",
"morie_kamath_ch3_dante_cloze",
"morie_kamath_ch3_prefix_prompt_template",
"morie_kamath_ch3_prefix_tuning_obj",
"morie_kamath_ch3_prompt_label_mapping",
"morie_kamath_ch3_prompt_search_argmax",
"morie_kamath_ch3_prompt_softmax_label",
"morie_kamath_ch3_qa_trigger_template",
"morie_kamath_ch3_t5_template_obj",
"morie_kamath_ch3_top1_prompt_metric",
"morie_kamath_ch3_translate_prefix_prompt",
"morie_kamath_ch4_full_finetune_obj",
"morie_kamath_ch4_krona_efficient",
"morie_kamath_ch4_krona_output",
"morie_kamath_ch4_krona_tuned_weights",
"morie_kamath_ch4_kronecker_product",
"morie_kamath_ch4_loftq_objective",
"morie_kamath_ch4_lora_forward",
"morie_kamath_ch4_lora_obj",
"morie_kamath_ch4_parallel_adapter",
"morie_kamath_ch4_series_adapter",
"morie_kamath_ch4_vera_forward",
"morie_kamath_ch5_bradley_terry_pref",
"morie_kamath_ch5_dpo_loss",
"morie_kamath_ch5_dpo_pref_simplified",
"morie_kamath_ch5_dpo_pref_substituted",
"morie_kamath_ch5_dpo_reward_optimal",
"morie_kamath_ch5_ppo_loss",
"morie_kamath_ch5_pref_sigmoid_form",
"morie_kamath_ch5_reward_kl_penalty",
"morie_kamath_ch5_reward_loss_pairwise",
"morie_kamath_ch5_rlhf_objective",
"morie_kamath_ch5_rlhf_optimal_policy",
"morie_kamath_ch5_rm_bradley_terry",
"morie_kamath_ch6_affect_lm",
"morie_kamath_ch6_alignment_function",
"morie_kamath_ch6_alignscore_total_loss",
"morie_kamath_ch6_cat_metric",
"morie_kamath_ch6_cbs_variance",
"morie_kamath_ch6_ceat_random_effects",
"morie_kamath_ch6_co_occurrence_bias",
"morie_kamath_ch6_cps_metric",
"morie_kamath_ch6_debias_regularizer",
"morie_kamath_ch6_demographic_representation",
"morie_kamath_ch6_differential_privacy",
"morie_kamath_ch6_ear_entropy_reg",
"morie_kamath_ch6_emt_metric",
"morie_kamath_ch6_factscore",
"morie_kamath_ch6_gedi_combined_loss",
"morie_kamath_ch6_gender_direction",
"morie_kamath_ch6_gender_projection_reg",
"morie_kamath_ch6_honest_score",
"morie_kamath_ch6_log_prob_ratio_attr",
"morie_kamath_ch6_lpbs_bias",
"morie_kamath_ch6_lstm_chain_rule",
"morie_kamath_ch6_lstm_softmax_word",
"morie_kamath_ch6_perplexity_leakage",
"morie_kamath_ch6_pii_likelihood",
"morie_kamath_ch6_pll",
"morie_kamath_ch6_self_diagnosis_prob",
"morie_kamath_ch6_sgs_invariance",
"morie_kamath_ch6_stereotypical_assoc",
"morie_kamath_ch6_toxic_fraction",
"morie_kamath_ch6_toxicity_probability",
"morie_kamath_ch6_weat_effect_size",
"morie_kamath_ch6_weat_function",
"morie_kamath_ch6_weat_similarity",
"morie_kamath_ch7_answer_relevance",
"morie_kamath_ch7_faithfulness_metric",
"morie_kamath_ch8_bertscore_f1",
"morie_kamath_ch8_bertscore_precision",
"morie_kamath_ch8_bertscore_recall",
"morie_kamath_ch8_bleu_final",
"morie_kamath_ch8_bleu_n_geom_mean",
"morie_kamath_ch8_bleu_precision",
"morie_kamath_ch8_brevity_penalty",
"morie_kamath_ch8_geval_score",
"morie_kamath_ch8_moverscore_distance",
"morie_kamath_ch8_ngram_embedding",
"morie_kamath_ch8_ngram_weight",
"morie_kamath_ch8_pass_at_k",
"morie_kamath_ch8_perplexity",
"morie_kamath_ch8_rouge_n",
"morie_kamath_ch8_smd",
"morie_kamath_ch8_wmd",
"morie_kamath_ch9_clip_contrastive_total",
"morie_kamath_ch9_clip_image_to_text",
"morie_kamath_ch9_clip_text_to_image",
"morie_kamath_ch9_flamingo_dataset_mix",
"morie_kamath_ch9_flamingo_factorized",
"morie_kamath_ch9_fom_loss",
"morie_kamath_ch9_input_alignment_loss",
"morie_kamath_ch9_input_projector",
"morie_kamath_ch9_itg_loss",
"morie_kamath_ch9_itm_hard_negative",
"morie_kamath_ch9_itm_loss",
"morie_kamath_ch9_ldm_loss",
"morie_kamath_ch9_llm_signal_tokens",
"morie_kamath_ch9_mm_instr_predict",
"morie_kamath_ch9_mml_vlm_loss",
"morie_kamath_ch9_mmllm_autoregressive",
"morie_kamath_ch9_moc_loss",
"morie_kamath_ch9_modality_encoder",
"morie_kamath_ch9_output_alignment",
"morie_kamath_ch9_output_projector_mse",
"morie_kamath_ch9_simvlm_mlm",
"morie_kamath_ch9_simvlm_prefixlm",
"morie_kamath_chain_of_thought",
"morie_kamath_chinchilla_compute_optimal",
"morie_kamath_christiano_deep_rl_feedback",
"morie_kamath_clm_loss",
"morie_kamath_constitutional_ai_loop",
"morie_kamath_context_simplest",
"morie_kamath_context_vector",
"morie_kamath_corrective_rag",
"morie_kamath_cross_encoder_rerank",
"morie_kamath_crowspairs_bias",
"morie_kamath_decoder_state",
"morie_kamath_decoder_token_distribution",
"morie_kamath_dense_passage_retrieval",
"morie_kamath_dequantize_nf4",
"morie_kamath_differential_privacy",
"morie_kamath_double_quantization",
"morie_kamath_dpo_loss",
"morie_kamath_emergent_abilities",
"morie_kamath_encoder_state",
"morie_kamath_expert_capacity_factor",
"morie_kamath_factscore",
"morie_kamath_fasttext_subword",
"morie_kamath_few_shot_exemplar_selection",
"morie_kamath_ffn_relu",
"morie_kamath_g_eval",
"morie_kamath_glove_cost",
"morie_kamath_glove_weight",
"morie_kamath_gpt_combined",
"morie_kamath_gpt_unsupervised",
"morie_kamath_groundedness_reward",
"morie_kamath_houlsby_adapter",
"morie_kamath_hybrid_retrieval_fusion",
"morie_kamath_hyde_hypothetical_doc",
"morie_kamath_image_text_contrastive",
"morie_kamath_image_text_matching",
"morie_kamath_in_context_learning_prob",
"morie_kamath_instruction_tuning_loss",
"morie_kamath_kl_reward_shaping",
"morie_kamath_layer_norm",
"morie_kamath_layer_norm_rows",
"morie_kamath_llava_visual_instruction",
"morie_kamath_lora_weight_update",
"morie_kamath_mamba_ssm",
"morie_kamath_masked_attention",
"morie_kamath_medusa_heads",
"morie_kamath_membership_inference",
"morie_kamath_memorization_exposure",
"morie_kamath_mixtral_moe",
"morie_kamath_mlm_loss",
"morie_kamath_moe_load_balance_loss",
"morie_kamath_moe_output",
"morie_kamath_moe_router_softmax",
"morie_kamath_moe_top_k_gating",
"morie_kamath_moe_topk_gating",
"morie_kamath_moverscore",
"morie_kamath_multihead_concat",
"morie_kamath_multihead_head_i",
"morie_kamath_multimodal_mae",
"morie_kamath_nextgpt_any2any",
"morie_kamath_nf4_datatype",
"morie_kamath_ngram_language_model",
"morie_kamath_normal_quantile",
"morie_kamath_nsp_loss",
"morie_kamath_nucleus_sampling",
"morie_kamath_p_tuning_v2",
"morie_kamath_pass_at_k",
"morie_kamath_perplexity",
"morie_kamath_pet_loss",
"morie_kamath_positional_cos",
"morie_kamath_positional_sin",
"morie_kamath_post_ln_transformer",
"morie_kamath_ppo_rlhf_objective",
"morie_kamath_pre_ln_transformer",
"morie_kamath_prefix_tuning",
"morie_kamath_prompt_tuning",
"morie_kamath_q_former",
"morie_kamath_qlora_4bit",
"morie_kamath_ragas_answer_relevance",
"morie_kamath_ragas_context_relevance",
"morie_kamath_ragas_faithfulness",
"morie_kamath_reciprocal_rank_fusion",
"morie_kamath_rejection_sampling_finetune",
"morie_kamath_retnet_retention",
"morie_kamath_reward_model_training_loss",
"morie_kamath_rlaif_objective",
"morie_kamath_rlhf_pipeline",
"morie_kamath_rms_norm",
"morie_kamath_rotary_positional_embedding",
"morie_kamath_rouge_n",
"morie_kamath_rrf_score",
"morie_kamath_rtd_loss",
"morie_kamath_rwkv_time_mix",
"morie_kamath_scaled_dot_attention",
"morie_kamath_scaled_dot_score",
"morie_kamath_scaling_laws",
"morie_kamath_self_consistency",
"morie_kamath_self_rag",
"morie_kamath_sentencepiece_tokenizer",
"morie_kamath_seq2seq_cross_entropy",
"morie_kamath_softmax_element",
"morie_kamath_span_loss",
"morie_kamath_speculative_decoding",
"morie_kamath_ssl_loss",
"morie_kamath_step_back_prompting",
"morie_kamath_stereoset_bias",
"morie_kamath_summarize_from_feedback",
"morie_kamath_swiglu_activation",
"morie_kamath_swish",
"morie_kamath_t5_span_corruption",
"morie_kamath_temperature_sampling",
"morie_kamath_tlm_loss",
"morie_kamath_toxigen_score",
"morie_kamath_tree_of_thoughts",
"morie_kamath_unigram_lm_tokenizer",
"morie_kamath_unigram_loglik",
"morie_kamath_vera_adapter",
"morie_kamath_verbalizer_mapping",
"morie_kamath_viterbi_segment",
"morie_kamath_word_movers_distance",
"morie_kamath_word_ngrams",
"morie_kamath_word2vec_skipgram",
"morie_kamath_yarn_context_extrapolation",
"morie_kaplan_meier",
"morie_karlin_altschul",
"morie_karpV",
"morie_karpV_adjusted",
"morie_karpV_depth",
"morie_karpV_evaluate",
"morie_karpV_ramped",
"morie_karpV_raw_fitness",
"morie_karpV_size",
"morie_karpV_to_string",
"morie_kcusum",
"morie_kde_h",
"morie_kde2d",
"morie_kendall_tau",
"morie_kendall_tau_partial",
"morie_kernel_blup_gxe",
"morie_kernel_blup_replicated",
"morie_kernel_eigen_design",
"morie_kernel_matrix",
"morie_kernel_pca",
"morie_kernel_quantile",
"morie_khatri_rao_rows",
"morie_km_hadamard",
"morie_km_native",
"morie_kmeans_clustering",
"morie_kmt_bound",
"morie_knn_entropy",
"morie_konig_theorem",
"morie_kosorok_bootstrap_empirical",
"morie_kosorok_bracketing_number",
"morie_kosorok_censoring_survival",
"morie_kosorok_counting_process",
"morie_kosorok_cox_partial_likelihood",
"morie_kosorok_donsker_class",
"morie_kosorok_efficient_score",
"morie_kosorok_empirical_process",
"morie_kosorok_glivenko_cantelli",
"morie_kosorok_influence_function",
"morie_kosorok_information_bound",
"morie_kosorok_m_estimator",
"morie_kosorok_maximal_inequality",
"morie_kosorok_multiplier_bootstrap",
"morie_kosorok_nelson_aalen",
"morie_kosorok_one_step_estimator",
"morie_kosorok_profile_likelihood",
"morie_kosorok_tangent_space",
"morie_kosorok_vc_dimension",
"morie_kosorok_z_estimator",
"morie_kriging",
"morie_kriging_pred_error",
"morie_krpkrg_ordinary_kriging",
"morie_kruskal_wallis_test",
"morie_ks_limit",
"morie_ksr01_kosorok_empirical_process",
"morie_ksr02_kosorok_donsker_class",
"morie_ksr03_kosorok_glivenko_cantelli",
"morie_ksr04_kosorok_vc_dimension",
"morie_ksr05_kosorok_bracketing_number",
"morie_ksr06_kosorok_maximal_inequality",
"morie_ksr07_kosorok_bootstrap_empirical",
"morie_ksr08_kosorok_multiplier_bootstrap",
"morie_ksr09_kosorok_z_estimator",
"morie_ksr10_kosorok_m_estimator",
"morie_ksr11_kosorok_efficient_score",
"morie_ksr12_kosorok_information_bound",
"morie_ksr13_kosorok_tangent_space",
"morie_ksr14_kosorok_profile_likelihood",
"morie_ksr15_kosorok_one_step_estimator",
"morie_ksr16_kosorok_influence_function",
"morie_ksr17_kosorok_counting_process",
"morie_ksr18_kosorok_nelson_aalen",
"morie_ksr19_kosorok_cox_partial_likelihood",
"morie_ksr20_kosorok_censoring_survival",
"morie_kupiec_var_test",
"morie_lagfeat",
"morie_lamb",
"morie_laniyonu_actuarial_risk_disparity",
"morie_laniyonu_gentrification_policing",
"morie_laniyonu_smi_force_disparity",
"morie_laplace_noise",
"morie_laplc",
"morie_lars",
"morie_lasso_path",
"morie_latent_time",
"morie_latin_hypercube",
"morie_latin_square",
"morie_lattice_convex_hull",
"morie_lbfgsm",
"morie_lcfsdq",
"morie_lcfsdq_cheatsheet",
"morie_lcfsdq_clark_evans",
"morie_lcfsdq_nn",
"morie_lcg_normal",
"morie_lcmm_latent_class",
"morie_lcs_length",
"morie_lcwphr_latent_class_weighted",
"morie_ld",
"morie_ld50",
"morie_ldiff",
"morie_learning_curve",
"morie_least_squares",
"morie_levene_test",
"morie_lewbel_binary",
"morie_lggvls",
"morie_license_metadata",
"morie_likemc",
"morie_lil_ratio",
"morie_limmav",
"morie_linear_corr_model",
"morie_linear_regression_ols",
"morie_linevid",
"morie_linkhae",
"morie_linkmm",
"morie_linkqp",
"morie_linprm",
"morie_linwlr",
"morie_linwlr_blip",
"morie_linwlr_cheatsheet",
"morie_list_datasets",
"morie_list_morie_modules",
"morie_llavx",
"morie_llm_agent_available",
"morie_llm_ask",
"morie_llm_ask_multi",
"morie_llm_backend",
"morie_llm_chat",
"morie_llm_config",
"morie_llm_detect_provider",
"morie_llm_login",
"morie_llm_logout",
"morie_llm_models",
"morie_llm_ollama_models",
"morie_llm_probe_ollama",
"morie_llm_provider_set",
"morie_llm_provider_show",
"morie_llm_provider_unset",
"morie_llm_request_completion",
"morie_lloyd_max_codebook",
"morie_lm",
"morie_lm_scaling",
"morie_lmm",
"morie_lmm_loglik",
"morie_lms_error",
"morie_load_cpads",
"morie_load_cpads_data",
"morie_load_dataset",
"morie_load_hosted_dataset",
"morie_local_dp_randomised_response",
"morie_local_linear",
"morie_local_outlier_factor",
"morie_local_shift",
"morie_locdp",
"morie_locdp_cheatsheet",
"morie_locfdr_estimate",
"morie_locp",
"morie_locS",
"morie_loess",
"morie_logistic_effects",
"morie_logistic_fit",
"morie_logistic_wald",
"morie_logit_link",
"morie_logit_rescale",
"morie_logit_separation",
"morie_logrank_test",
"morie_logtrans",
"morie_longrd",
"morie_longrd_align",
"morie_longrd_cheatsheet",
"morie_longrd_pileup",
"morie_longrd_poa",
"morie_longrd_rle",
"morie_longrd_unrle",
"morie_lord_chisq",
"morie_loss_stream_variance",
"morie_louv",
"morie_lovasz_local_lemma",
"morie_lrprod",
"morie_lsa",
"morie_lstm_cell",
"morie_lstmc_lstm_cell",
"morie_lvrgh",
"morie_lyapun",
"morie_lyapun_embed",
"morie_m_normality",
"morie_ma_hedges_g",
"morie_mabay",
"morie_mac3",
"morie_macum",
"morie_mad",
"morie_mad_median_rule",
"morie_mad_rescaled",
"morie_madAd",
"morie_madMov",
"morie_madn",
"morie_madsc",
"morie_mafft",
"morie_mafix",
"morie_magpa",
"morie_mahks",
"morie_mahsj",
"morie_mai2",
"morie_mainf",
"morie_make_raw",
"morie_malab",
"morie_malor",
"morie_malrr",
"morie_mambss",
"morie_manfd",
"morie_manfd_cheatsheet",
"morie_manfd_jacobi",
"morie_manfd_knn",
"morie_manfd_l2",
"morie_manfd_paths",
"morie_manfd_scaling",
"morie_mann_whitney_test",
"morie_manski",
"morie_manskif",
"morie_mantel_number",
"morie_mapets",
"morie_mard",
"morie_marginal_integration",
"morie_marginals_verify",
"morie_marker_variance",
"morie_marndm",
"morie_marpct",
"morie_mask_features",
"morie_masrcn",
"morie_matau2pi",
"morie_matching_abadie_imbens_kernel_cpp",
"morie_matching_abadie_imbens_se",
"morie_matching_atc_matched",
"morie_matching_ate_matched",
"morie_matching_att_matched",
"morie_matching_balance",
"morie_matching_balance_table",
"morie_matching_cardinality",
"morie_matching_cem",
"morie_matching_cem_strata_cpp",
"morie_matching_common_support",
"morie_matching_doubly_robust",
"morie_matching_entropy_balance",
"morie_matching_estimate_propensity",
"morie_matching_euclidean_pairs_cpp",
"morie_matching_exact",
"morie_matching_full",
"morie_matching_genetic",
"morie_matching_longitudinal",
"morie_matching_love_plot_data",
"morie_matching_mahalanobis",
"morie_matching_mahalanobis_pairs_cpp",
"morie_matching_multi_treatment",
"morie_matching_nearest_neighbor",
"morie_matching_nn_select_cpp",
"morie_matching_optimal_pair",
"morie_matching_overlap",
"morie_matching_quality",
"morie_matching_rosenbaum_bounds",
"morie_matching_subclassify",
"morie_matching_trim_propensity",
"morie_matching_variable_ratio",
"morie_math2",
"morie_matheron_estimator",
"morie_matr",
"morie_matrl",
"morie_matroid_bases",
"morie_matroid_circuits",
"morie_matroid_dual",
"morie_matroid_rank",
"morie_matrxP",
"morie_matthews_corrcoef",
"morie_max_flow_min_cut",
"morie_maximum_score",
"morie_maxpool_forward",
"morie_mcc",
"morie_mcc_counts",
"morie_mcd_outlier",
"morie_mcdrop",
"morie_mctsr",
"morie_mdlpost",
"morie_mdppol",
"morie_mdpval",
"morie_mds_spatial_map",
"morie_mean_cpp",
"morie_mectest",
"morie_median_se",
"morie_median_voter",
"morie_median_voter_ci",
"morie_mediation_difference",
"morie_mediation_product",
"morie_mediation_sensitivity",
"morie_meglt",
"morie_mehtad",
"morie_memb",
"morie_meta_convert",
"morie_meta_dl_bias",
"morie_meta_effect_sizes",
"morie_meta_hksj",
"morie_meta_pool",
"morie_meta_random_effects",
"morie_meta_rma",
"morie_metabd",
"morie_metropolis_hastings",
"morie_metsem",
"morie_metsem_cheatsheet",
"morie_metsem_graph",
"morie_metsem_kmers",
"morie_metsem_n50",
"morie_metsem_unitigs",
"morie_mfird",
"morie_mfovsm",
"morie_mfovsm_cheatsheet",
"morie_mhatf_multi_head_attention_full",
"morie_midas_regression",
"morie_midegf",
"morie_miefcl",
"morie_mienco",
"morie_miest1",
"morie_mini_batch_gradient",
"morie_minimax_regret_constant",
"morie_minimum_spanning_tree",
"morie_miord2",
"morie_miprgr_branch_and_bound",
"morie_miprgr_enumerate_integer",
"morie_miprgr_fractional_variable",
"morie_miprgr_round_relaxation",
"morie_miprgr_solve_relaxation",
"morie_misclass_correct",
"morie_misclass_count",
"morie_misclassified_contrast",
"morie_misclassified_rd",
"morie_mistr_apply_rope",
"morie_mistr_attention_span",
"morie_mistr_grouped_query_attention",
"morie_mistr_mistral_block",
"morie_mistr_rms_norm",
"morie_mistr_rope_angles",
"morie_mistr_sliding_window_mask",
"morie_mistr_swiglu",
"morie_ml_apply_smote",
"morie_ml_assess",
"morie_ml_bake",
"morie_ml_center",
"morie_ml_eval_robustness",
"morie_ml_impute",
"morie_ml_load",
"morie_ml_model",
"morie_ml_prep",
"morie_ml_recipe",
"morie_ml_resample",
"morie_ml_save",
"morie_ml_scale",
"morie_ml_split",
"morie_ml_train",
"morie_ml_train_time",
"morie_ml_tune",
"morie_mlogit_probs",
"morie_mmd_test",
"morie_mme",
"morie_mobius_inversion",
"morie_model_assisted",
"morie_module_names",
"morie_mom_estimator",
"morie_momento_harmonise",
"morie_momento_mask_patches",
"morie_momento_masked_loss",
"morie_momento_reconstruction_curve",
"morie_momento_task_mask",
"morie_monotone_causal_forest",
"morie_monte_carlo_integration",
"morie_moran_trend",
"morie_morans_i",
"morie_morans_i_test",
"morie_morrisM",
"morie_motfsr",
"morie_motfsr_bayes_threshold",
"morie_motfsr_log_odds_matrix",
"morie_motfsr_mm_fit",
"morie_motfsr_score_sequence",
"morie_motiff",
"morie_moving_average",
"morie_mpfn_is_permutation_invariant",
"morie_mpfn_message",
"morie_mpfn_message_passing",
"morie_mpfn_readout",
"morie_mpfn_update_gru",
"morie_mqtmpl_hmm_genotype_probabilities",
"morie_mqtmpl_imputation_weights",
"morie_mqtmpl_lod_support_interval",
"morie_mqtmpl_method_status",
"morie_mqtmpl_permutation_threshold",
"morie_mqtmpl_sample_genotypes",
"morie_mqtmpl_scanone",
"morie_mrcv_glmm",
"morie_mrm_dags",
"morie_mrm_estimate_causal_effect",
"morie_mrm_load_si_dataset",
"morie_mrm_reconcile",
"morie_mrm_report",
"morie_msglen",
"morie_msm_accelerated_failure",
"morie_msm_cox_marginal",
"morie_msm_gmm_estimator",
"morie_msm_linear",
"morie_msm_logistic",
"morie_msm_negative_binomial",
"morie_msm_poisson",
"morie_msm_time_varying_exposure",
"morie_msp_exact",
"morie_mtdrl",
"morie_mtdrl_bandit_tasks",
"morie_mtdrl_history_features",
"morie_mtr2sx",
"morie_multi_head_attention_full",
"morie_multi_horizon_ks",
"morie_multi_trait_gblup",
"morie_multicategory_logit",
"morie_multinomial_coef",
"morie_multinomial_loglik",
"morie_multinomial_pmf",
"morie_multinomial_probit_spatial",
"morie_multinomial_probs",
"morie_multitrait",
"morie_multitrait_ridge",
"morie_mupostsg",
"morie_muzero",
"morie_mvn_with_covariance",
"morie_mvnorm_pmv",
"morie_mvnorm_sample",
"morie_mvrnorm",
"morie_mwem",
"morie_mxetA",
"morie_mxpol_maxpool_forward",
"morie_myopic_example",
"morie_nashq",
"morie_nb_matches_zip",
"morie_nbeats_basis",
"morie_ncfRS_cheatsheet",
"morie_ncfRS_fit_gmf",
"morie_ncfRS_gmf",
"morie_ncfRS_log_loss",
"morie_ncfRS_mlp_layers",
"morie_ncfRS_neumf",
"morie_neff_cluster",
"morie_nelson_aalen",
"morie_nesterov",
"morie_netcms",
"morie_netsts",
"morie_neural_kantorovich_map",
"morie_newey_west_vcov",
"morie_ngnest",
"morie_nhits",
"morie_nhitsnet",
"morie_nls",
"morie_nls_weight_function",
"morie_nonbacktracking_matrix",
"morie_nonresponse_region",
"morie_nonstationary_covariance",
"morie_normal_pdf_cpp",
"morie_normal_scores",
"morie_np_kernel_reg",
"morie_npiv_operator",
"morie_npiv_quantile",
"morie_nw_regression",
"morie_nypd_all_analyses",
"morie_nystrom",
"morie_odds_ratio_check",
"morie_odds_ratio_ci",
"morie_ols",
"morie_ols_simple",
"morie_ols_two_iv",
"morie_omega_squared",
"morie_one_sample_coverage",
"morie_one_sample_t_test",
"morie_one_step_efficient",
"morie_one_step_m",
"morie_one_way",
"morie_oneway_test",
"morie_opthr",
"morie_optimal_alpha",
"morie_optimal_alpha_att",
"morie_optimal_classification",
"morie_optimal_overlap",
"morie_optimal_overlap_weight",
"morie_order_statistic_laws",
"morie_ordered_alternatives_test",
"morie_ordered_categories",
"morie_ordered_max_score",
"morie_ordinal_logit_ca",
"morie_ordinal_probit_gibbs",
"morie_ordinal_probs",
"morie_ordinary_kriging",
"morie_ospats",
"morie_otis_aipw_ate",
"morie_otis_aipw_superlearner",
"morie_otis_alert_state_combo",
"morie_otis_all_analyses",
"morie_otis_analyze_a01",
"morie_otis_analyze_a01_dlrm",
"morie_otis_analyze_a01_mrm",
"morie_otis_analyze_a01_mrm_alt_age",
"morie_otis_analyze_a01_mrm_alt_gender",
"morie_otis_analyze_a01_mrm_alt_toronto",
"morie_otis_analyze_a01_mrm_per_year",
"morie_otis_analyze_a01_mrm_subgroup_female",
"morie_otis_analyze_a01_mrm_subgroup_male",
"morie_otis_analyze_a01_ruhela_alt_age",
"morie_otis_analyze_a01_ruhela_alt_gender",
"morie_otis_analyze_a01_ruhela_alt_toronto",
"morie_otis_analyze_a01_ruhela_formulations",
"morie_otis_analyze_a01_ruhela_per_year",
"morie_otis_analyze_a01_ruhela_subgroup_female",
"morie_otis_analyze_a01_ruhela_subgroup_male",
"morie_otis_analyze_a01_with_csi_context",
"morie_otis_analyze_all",
"morie_otis_analyze_b01",
"morie_otis_analyze_b01_dlrm",
"morie_otis_analyze_b01_mrm",
"morie_otis_analyze_b01_mrm_alt_age",
"morie_otis_analyze_b01_mrm_alt_gender",
"morie_otis_analyze_b01_mrm_alt_toronto",
"morie_otis_analyze_b01_mrm_per_year",
"morie_otis_analyze_b01_mrm_subgroup_female",
"morie_otis_analyze_b01_mrm_subgroup_male",
"morie_otis_analyze_b01_ruhela_alt_age",
"morie_otis_analyze_b01_ruhela_alt_gender",
"morie_otis_analyze_b01_ruhela_alt_toronto",
"morie_otis_analyze_b01_ruhela_formulations",
"morie_otis_analyze_b01_ruhela_per_year",
"morie_otis_analyze_b01_ruhela_subgroup_female",
"morie_otis_analyze_b01_ruhela_subgroup_male",
"morie_otis_analyze_b02",
"morie_otis_analyze_b02_dlrm",
"morie_otis_analyze_b02_mrm",
"morie_otis_analyze_b02_mrm_alt_age",
"morie_otis_analyze_b02_mrm_alt_region",
"morie_otis_analyze_b02_ruhela_alt_age",
"morie_otis_analyze_b02_ruhela_alt_region",
"morie_otis_analyze_b02_ruhela_formulations",
"morie_otis_analyze_b03",
"morie_otis_analyze_b03_mrm_aggregate",
"morie_otis_analyze_b03_ruhela_aggregate",
"morie_otis_analyze_b04",
"morie_otis_analyze_b04_mrm_aggregate",
"morie_otis_analyze_b04_ruhela_aggregate",
"morie_otis_analyze_b05",
"morie_otis_analyze_b05_mandela_classification",
"morie_otis_analyze_b05_ruhela_aggregate",
"morie_otis_analyze_b06",
"morie_otis_analyze_b06_mrm_aggregate",
"morie_otis_analyze_b06_ruhela_aggregate",
"morie_otis_analyze_b07",
"morie_otis_analyze_b07_mrm_aggregate",
"morie_otis_analyze_b07_ruhela_aggregate",
"morie_otis_analyze_b08",
"morie_otis_analyze_b08_mrm_aggregate",
"morie_otis_analyze_b08_ruhela_aggregate",
"morie_otis_analyze_b09",
"morie_otis_analyze_b09_mrm_aggregate",
"morie_otis_analyze_b09_ruhela_aggregate",
"morie_otis_analyze_c_chi2",
"morie_otis_analyze_c01",
"morie_otis_analyze_c01_mrm_aggregate",
"morie_otis_analyze_c01_mrm_aggregate_region_cluster",
"morie_otis_analyze_c01_ruhela_aggregate",
"morie_otis_analyze_c01_ruhela_aggregate_region_cluster",
"morie_otis_analyze_c02",
"morie_otis_analyze_c02_mrm_aggregate",
"morie_otis_analyze_c02_ruhela_aggregate",
"morie_otis_analyze_c03",
"morie_otis_analyze_c03_mrm_aggregate",
"morie_otis_analyze_c03_ruhela_aggregate",
"morie_otis_analyze_c04",
"morie_otis_analyze_c04_mrm_aggregate",
"morie_otis_analyze_c04_mrm_aggregate_region_cluster",
"morie_otis_analyze_c04_ruhela_aggregate",
"morie_otis_analyze_c04_ruhela_aggregate_region_cluster",
"morie_otis_analyze_c05",
"morie_otis_analyze_c05_mrm_aggregate",
"morie_otis_analyze_c05_ruhela_aggregate",
"morie_otis_analyze_c06",
"morie_otis_analyze_c06_mrm_aggregate",
"morie_otis_analyze_c06_ruhela_aggregate",
"morie_otis_analyze_c07",
"morie_otis_analyze_c07_mrm_aggregate",
"morie_otis_analyze_c07_ruhela_aggregate",
"morie_otis_analyze_c08",
"morie_otis_analyze_c08_mrm_aggregate",
"morie_otis_analyze_c08_ruhela_aggregate",
"morie_otis_analyze_c09",
"morie_otis_analyze_c09_mrm_aggregate",
"morie_otis_analyze_c09_ruhela_aggregate",
"morie_otis_analyze_c10",
"morie_otis_analyze_c10_mrm_aggregate",
"morie_otis_analyze_c10_ruhela_aggregate",
"morie_otis_analyze_c11",
"morie_otis_analyze_c11_mandela_classification",
"morie_otis_analyze_c11_mrm_aggregate",
"morie_otis_analyze_c11_ruhela_aggregate",
"morie_otis_analyze_c12",
"morie_otis_analyze_c12_mrm_aggregate",
"morie_otis_analyze_c12_ruhela_aggregate",
"morie_otis_analyze_d_chi2",
"morie_otis_analyze_d01",
"morie_otis_analyze_d02",
"morie_otis_analyze_d02_mrm_aggregate",
"morie_otis_analyze_d02_ruhela_aggregate",
"morie_otis_analyze_d03",
"morie_otis_analyze_d03_mrm_aggregate",
"morie_otis_analyze_d03_ruhela_aggregate",
"morie_otis_analyze_d04",
"morie_otis_analyze_d04_mrm_aggregate",
"morie_otis_analyze_d04_ruhela_aggregate",
"morie_otis_analyze_d05",
"morie_otis_analyze_d05_mrm_aggregate",
"morie_otis_analyze_d05_ruhela_aggregate",
"morie_otis_analyze_d06",
"morie_otis_analyze_d07",
"morie_otis_analyze_otis_mandela_provincial_vs_federal",
"morie_otis_analyze_ruhela_grid",
"morie_otis_analyze_ruhela_master",
"morie_otis_analyze_ruhela_per_year",
"morie_otis_analyzers",
"morie_otis_astcmb",
"morie_otis_atc",
"morie_otis_balance",
"morie_otis_causal_grid",
"morie_otis_churn_analyze_all",
"morie_otis_classify_mandela_combo",
"morie_otis_descriptives",
"morie_otis_disciplinary_medical_overlap",
"morie_otis_dml",
"morie_otis_embedding_distribution",
"morie_otis_figures",
"morie_otis_gcomputation",
"morie_otis_intra_year_transition_matrix",
"morie_otis_ipw_ate",
"morie_otis_irm_dml",
"morie_otis_irr_glmm_vm",
"morie_otis_load",
"morie_otis_make_pair_a",
"morie_otis_make_pair_alert_to_volatility_a01",
"morie_otis_make_pair_alert_to_volatility_all",
"morie_otis_make_pair_alert_to_volatility_naive",
"morie_otis_make_pair_alert_to_volatility_ruhela",
"morie_otis_make_pair_b",
"morie_otis_make_pair_c",
"morie_otis_mortification_cooccurrence",
"morie_otis_otdesc",
"morie_otis_otdml",
"morie_otis_path_complexity_gini",
"morie_otis_per_year_irm_dml",
"morie_otis_plr",
"morie_otis_psm",
"morie_otis_psm_subclass",
"morie_otis_rc_trends",
"morie_otis_rctrnd",
"morie_otis_regC_demog_contingency",
"morie_otis_region_alert_state_richness",
"morie_otis_regional_placement",
"morie_otis_repeat_placement_concentration",
"morie_otis_rplace",
"morie_otis_tps_analyze_all",
"morie_otis_tps_composite_overlay",
"morie_otis_tps_per_region_rollup",
"morie_otis_tps_yoy_correlation",
"morie_otis_volat",
"morie_otis_volatility",
"morie_otis_within_year_placement_count",
"morie_otis_within_year_region_diversity",
"morie_outcome_test",
"morie_over",
"morie_oversample_factor",
"morie_owate_weights",
"morie_p_ibs_given_ibd",
"morie_pace",
"morie_pace_local_linear",
"morie_pace_local_linear_2d",
"morie_pacfts",
"morie_painn_equivariance_error",
"morie_pair_loss",
"morie_paired_t_test",
"morie_pan_tompkins_update",
"morie_panel_cointegration",
"morie_panel_deconvolution",
"morie_panel_densities",
"morie_panel_max_score",
"morie_parse",
"morie_parse_nypd_law_code",
"morie_partial_cor",
"morie_partial_cor_test",
"morie_partial_permutations",
"morie_partial_tau",
"morie_partially_linear",
"morie_partition_count",
"morie_partitions_into_parts",
"morie_partitions_of",
"morie_party_alignment",
"morie_party_problem",
"morie_party_unity",
"morie_patchts",
"morie_paths",
"morie_pbos",
"morie_pca",
"morie_pca_dimension_reduction",
"morie_pcg_filter",
"morie_pcm",
"morie_pelt",
"morie_penalised_tmle",
"morie_penalisedtmle",
"morie_penalized_multinomial",
"morie_penalized_poisson",
"morie_penalized_regression",
"morie_penalized_spline",
"morie_percentage_bend_correlation",
"morie_percentile_modified_rank",
"morie_performance_check_collinearity",
"morie_performance_check_model",
"morie_performance_check_outliers",
"morie_performance_r2",
"morie_perK",
"morie_permutation_attention_masks",
"morie_permutation_lm_loss",
"morie_permutation_test_general",
"morie_permutation_valid",
"morie_pesdol_ardl_bounds",
"morie_pgdsdg",
"morie_phacf3",
"morie_phacf3_bin",
"morie_phacf3_canonical",
"morie_phacf3_cheatsheet",
"morie_phacf3_space",
"morie_phacf3_tanimoto",
"morie_phylby",
"morie_phylby_chain_temperature",
"morie_phylby_cheatsheet",
"morie_phylby_clade_credibility",
"morie_phylby_log_posterior",
"morie_phylby_nni_neighbours",
"morie_phylby_splits_of",
"morie_phylby_swap_acceptance",
"morie_phylby_topology_key",
"morie_phylml",
"morie_pibmd_prior_informativeness_bias_diagnostic",
"morie_pick_theorem",
"morie_pinball",
"morie_placebo_dr_did",
"morie_plcbsc",
"morie_plncF",
"morie_plrgrf",
"morie_plsa",
"morie_plsqs_pls_regression",
"morie_pmf_convolve",
"morie_pmf_moments",
"morie_pmfsc",
"morie_pmfsc_bin",
"morie_pmfsc_cheatsheet",
"morie_pmfsc_derive",
"morie_pmfsc_score",
"morie_pmfsc_shell",
"morie_pmpfit",
"morie_pmpfit_cheatsheet",
"morie_pmpfit_check",
"morie_pmpfit_expected",
"morie_pmpfit_predictive",
"morie_pmpfit_tail",
"morie_point_biserial_r",
"morie_pointwise_mutual_info",
"morie_poisson_chernoff",
"morie_poisson_dist",
"morie_poisson_loglinear",
"morie_poisson_spatial_glm",
"morie_poissp",
"morie_poissp_car_precision",
"morie_poissp_cheatsheet",
"morie_poissp_rho_bounds",
"morie_polarization_index",
"morie_polkrn",
"morie_polqnt",
"morie_polr",
"morie_poltrx",
"morie_polya_tree_density",
"morie_polya_tree_mixture",
"morie_polya_tree_rate",
"morie_polyak",
"morie_polynomial_regression",
"morie_portmanteau_hosking",
"morie_posab_positional_encoding_abs",
"morie_poschk",
"morie_positional_encoding_abs",
"morie_posspr",
"morie_post_lasso",
"morie_posterior_hpd",
"morie_postgapx",
"morie_postodds",
"morie_potM",
"morie_potM_cheatsheet",
"morie_power_prop_test",
"morie_power_t_test",
"morie_power_ttest_crim",
"morie_ppcor_partial",
"morie_ppcor_semipartial",
"morie_pps_sample",
"morie_pps_variance",
"morie_pps_with_replacement",
"morie_pratt",
"morie_pratt_attention_entropy",
"morie_pratt_cheatsheet",
"morie_prediction_accuracy",
"morie_predictive_recursion",
"morie_predpol_aggregate_areas",
"morie_predpol_calibration_audit",
"morie_predpol_score_disparity",
"morie_predpol_temporal_audit",
"morie_pretprep",
"morie_primal",
"morie_privacy_amplification",
"morie_private_accuracy_tradeoff",
"morie_private_ci",
"morie_private_release",
"morie_prob_rules",
"morie_probability_of_necessity",
"morie_profile_dataset",
"morie_progeny_required",
"morie_prophe",
"morie_prophet_components",
"morie_propinf_fcnn_predict",
"morie_propinf_flat_representation",
"morie_propinf_permute_hidden_layer",
"morie_propinf_property_inference",
"morie_propinf_set_representation",
"morie_propinf_sorted_representation",
"morie_propinf_train_fcnn",
"morie_proportion_ci",
"morie_prphet_design",
"morie_prphet_fit",
"morie_prphet_fourier_terms",
"morie_prphet_holiday_matrix",
"morie_prphet_piecewise_trend",
"morie_prphet_predict",
"morie_prphet_trend_matrix",
"morie_prsclm_prs_cs_clump",
"morie_prsLL",
"morie_prsLR",
"morie_prsPEG_and_",
"morie_prsPEG_choice",
"morie_prsPEG_lit",
"morie_prsPEG_not_",
"morie_prsPEG_opt",
"morie_prsPEG_packrat_parse",
"morie_prsPEG_parse",
"morie_prsPEG_plus",
"morie_prsPEG_seq",
"morie_prsPEG_star",
"morie_prtcl_effective_sample_size",
"morie_prtcl_kalman_filter_1d",
"morie_prtcl_particle_filter",
"morie_prtcl_systematic_resample",
"morie_prxgms_lasso_fista",
"morie_prxgms_prox_gradient",
"morie_prxgms_soft_threshold",
"morie_psdmt",
"morie_psm_balance",
"morie_psy_eap_theta",
"morie_psy_icc1k",
"morie_psy_icc2k",
"morie_psy_icc3k",
"morie_psy_ma_loo",
"morie_psy_ma_paule_mandel",
"morie_psy_ma_reml",
"morie_psy_ma_rve",
"morie_psy_map_theta",
"morie_psy_mle_theta",
"morie_psy_theta_eap",
"morie_psy_theta_map",
"morie_psy_wle_theta",
"morie_psymet_alpha",
"morie_psymet_alphadel",
"morie_psymet_ave",
"morie_psymet_bartlett",
"morie_psymet_cr",
"morie_psymet_discrimination",
"morie_psymet_itemtotal",
"morie_psymet_kmo",
"morie_psymet_omega",
"morie_psymet_parallel",
"morie_psymet_splithalf",
"morie_ptmcmc",
"morie_qbcfgs",
"morie_qbcfgs_cheatsheet",
"morie_qbcfgs_smd",
"morie_qbcfgs_strata",
"morie_qjlcrn",
"morie_qlearn",
"morie_qlrtst",
"morie_qmDS",
"morie_qpdual",
"morie_qrF",
"morie_qrntcq_efficacy_test_and_release",
"morie_qrntcq_gamma_generation_time",
"morie_qrntcq_optimal_duration",
"morie_qrntcq_quarantine_efficacy",
"morie_qrntcq_relative_utility",
"morie_qrntcq_utility",
"morie_qsfrgr",
"morie_qsfrgr_cheatsheet",
"morie_qsfrgr_forest",
"morie_qsfrgr_km",
"morie_qsfrgr_logrank",
"morie_qsfrgr_quantile",
"morie_qsfrgr_weights",
"morie_qte_firpo",
"morie_quanrg",
"morie_quantile_causal_forest",
"morie_quantile_function",
"morie_quantile_hadamard",
"morie_quantile_reg",
"morie_quantize_angles",
"morie_quantize_with_codebook",
"morie_quantreg",
"morie_r3post",
"morie_ragRet_ivf_index",
"morie_ragRet_ivf_search",
"morie_ragRet_marginalise",
"morie_ragRet_normalise",
"morie_ragRet_recall_at_k",
"morie_ragRet_top_k",
"morie_rake",
"morie_ramsey_lower_bound_probabilistic",
"morie_ramsey_number",
"morie_ramsey_upper_bound",
"morie_ramsey_witness",
"morie_randIE_decompose",
"morie_randIE_interventional_mean",
"morie_randIE_mediator_distribution",
"morie_randIE_randomized_interventional_effect",
"morie_random_forest_ensemble",
"morie_random_forest_genomic",
"morie_random_search_cv",
"morie_random_walk_exposure",
"morie_randomized_response_dp",
"morie_randtests_bartels",
"morie_randtests_runs",
"morie_randtests_turning_point",
"morie_rangayyan_adaptive_filter",
"morie_rangayyan_approximate_entropy",
"morie_rangayyan_ar_burg",
"morie_rangayyan_coherence",
"morie_rangayyan_correlation_dimension",
"morie_rangayyan_dfa",
"morie_rangayyan_eeg_bands",
"morie_rangayyan_emg_rms",
"morie_rangayyan_envelope",
"morie_rangayyan_fir_filter",
"morie_rangayyan_higuchi_fd",
"morie_rangayyan_hrv",
"morie_rangayyan_iir_filter",
"morie_rangayyan_lyapunov",
"morie_rangayyan_psd",
"morie_rangayyan_qrs_detect",
"morie_rangayyan_sample_entropy",
"morie_rangayyan_stft",
"morie_rangayyan_wavelet_denoise",
"morie_rangayyan_zero_crossing",
"morie_rank_based_test",
"morie_rank_exact_null",
"morie_rank_index",
"morie_rank_order_statistics",
"morie_rank_persistence_null",
"morie_rank_placements",
"morie_rank_tie_variance",
"morie_ranking_resolution",
"morie_ranova",
"morie_raoscot",
"morie_rapaf_ar_confidence_interval",
"morie_rapaf_levin_ar",
"morie_rapaf_partial_ar",
"morie_rapaf_population_attributable_risk",
"morie_rapaf_rate_ratios_from_logit",
"morie_rappor",
"morie_rappor_decode",
"morie_rappor_epsilon",
"morie_rappor_star",
"morie_rate_check",
"morie_ratio_estimator",
"morie_rational_gf_coefficients",
"morie_rct_tests",
"morie_rd_mserd_bandwidth",
"morie_rdd",
"morie_rdd_bandwidth_cct",
"morie_rdd_bandwidth_ik",
"morie_rdd_bandwidth_rot",
"morie_rdd_bandwidth_sensitivity",
"morie_rdd_bias_corrected",
"morie_rdd_cattaneo_density",
"morie_rdd_covariate_balance",
"morie_rdd_density_test",
"morie_rdd_discrete",
"morie_rdd_donut",
"morie_rdd_fuzzy",
"morie_rdd_geographic",
"morie_rdd_kernel_epanechnikov",
"morie_rdd_kernel_gaussian",
"morie_rdd_kernel_triangular",
"morie_rdd_kernel_uniform",
"morie_rdd_kink",
"morie_rdd_local_polynomial",
"morie_rdd_local_randinf",
"morie_rdd_local_randomisation",
"morie_rdd_mccrary",
"morie_rdd_placebo_cutoff",
"morie_rdd_plot_data",
"morie_rdd_power",
"morie_rdd_power_calc",
"morie_rdd_sample_size",
"morie_rdd_sharp",
"morie_rdkfp",
"morie_rdp_compose",
"morie_rdp_gaussian",
"morie_rdp_laplace",
"morie_rdp_sampled_gaussian",
"morie_rdpc",
"morie_rdpcomp",
"morie_rdrobu",
"morie_read_outputs_manifest",
"morie_read_parquet",
"morie_recmulti",
"morie_recommended_pair_test",
"morie_recommended_summary",
"morie_reconcile_hierarchy",
"morie_recording_map",
"morie_recording_regimes",
"morie_regime_switching",
"morie_regime_value",
"morie_regmlm",
"morie_regression_estimator",
"morie_regression_to_mean",
"morie_regularization_path",
"morie_reinfc",
"morie_reinfc_cheatsheet",
"morie_reinfc_expected_update",
"morie_rejct",
"morie_relabel_forensics",
"morie_relative_risk_from_or",
"morie_relmae",
"morie_reml_loglik",
"morie_remlfn",
"morie_renyi_dp_composition",
"morie_repcn",
"morie_repcpb",
"morie_replwt",
"morie_reportm",
"morie_residual_connection",
"morie_residual_edf",
"morie_respwt",
"morie_return_level",
"morie_rfppos",
"morie_rfppos_angle",
"morie_rfppos_cheatsheet",
"morie_rfppos_dihedral",
"morie_rfppos_distance",
"morie_rfppos_warhead",
"morie_rgs_functional_regression",
"morie_ri_clpm_coefficients",
"morie_ridge",
"morie_ridge_estimator",
"morie_ridgrg",
"morie_rif_gap",
"morie_rif_quantile",
"morie_ripley_csr_test",
"morie_ripley_f_function",
"morie_ripley_g_function",
"morie_ripley_k",
"morie_ripley_k_function",
"morie_risk_difference_ci",
"morie_risk_ratio_ci",
"morie_rkhs_fit",
"morie_rkhs_full",
"morie_rkhs_kernel_regression",
"morie_rkhs_mixed_equations",
"morie_rkhs_predict",
"morie_rkmeans",
"morie_rlearn",
"morie_rlhfRS",
"morie_rlhfRS_cheatsheet",
"morie_rlhfRS_mrr",
"morie_rlhfRS_ndcg",
"morie_rlhfRS_offpolicy",
"morie_rlhfRS_precision",
"morie_rlhfRS_rollout",
"morie_rlm",
"morie_rls_phi",
"morie_rminsamp",
"morie_rmrl",
"morie_rmrl_cheatsheet",
"morie_rmrl_machine_step",
"morie_rmrl_qlearn_flat",
"morie_rmrl_reward_machine",
"morie_rmrl_reward_machine_run",
"morie_rmsdtr",
"morie_rmse",
"morie_rmsprop",
"morie_rmsse",
"morie_rmst",
"morie_rmst_difference",
"morie_rnacov",
"morie_rnacov_cheatsheet",
"morie_rnacov_counts",
"morie_rnacov_mi",
"morie_rnacov_nussinov",
"morie_rnacov_parse",
"morie_rndnet",
"morie_rndnet_combine_returns",
"morie_rnn_genomic",
"morie_rob_huber",
"morie_rob_m",
"morie_rob_mm",
"morie_rob_mm_alias",
"morie_rob_qn",
"morie_rob_s",
"morie_rob_sens_slope",
"morie_rob_sn",
"morie_rob_tau",
"morie_rob_theil_sen",
"morie_robpca",
"morie_robust_se",
"morie_robust_vcov",
"morie_roc_auc_score",
"morie_roll_call_analysis",
"morie_rollfeat",
"morie_rosenb",
"morie_rotary_position_embedding",
"morie_rotrp_rotary_position_embedding",
"morie_rouge",
"morie_rouge_cheatsheet",
"morie_rouge_l",
"morie_rouge_n",
"morie_rouge_w",
"morie_rpgad",
"morie_rq",
"morie_rqtmpl",
"morie_rrblpr_rr_blup",
"morie_rsample_bootstraps",
"morie_rsk_correspondence",
"morie_rsk_insert",
"morie_rsk_inverse",
"morie_rslnk_residual_connection",
"morie_rtwall",
"morie_ruin_probability",
"morie_run_ebac_selection_ipw_analysis",
"morie_run_length_bound",
"morie_run_morie_module",
"morie_run_morie_modules",
"morie_run_pipeline",
"morie_run_propensity_ipw_analysis",
"morie_run_treatment_effects_analysis",
"morie_run_weighted_logistic_analysis",
"morie_run_workflow_step",
"morie_runs_pmf",
"morie_runs_test",
"morie_runs_updown",
"morie_S_function",
"morie_sa_opt",
"morie_sacc",
"morie_sacrb",
"morie_sacrb_bleu",
"morie_safe_factor",
"morie_safe_recode",
"morie_safe_relabel",
"morie_safrl",
"morie_safrl_cmdp_returns",
"morie_safrl_worst_case_violation",
"morie_saigeg",
"morie_sam2vd_cheatsheet",
"morie_sam2vd_memory_attention",
"morie_sam2vd_memory_bank",
"morie_sam2vd_propagate",
"morie_sam2vd_push_memory",
"morie_sam2vd_temporal_embedding",
"morie_samdec",
"morie_sample",
"morie_sample_acf",
"morie_sample_pacf",
"morie_sample_size",
"morie_sample_size_domain",
"morie_sample_size_logistic",
"morie_sample_size_proportion",
"morie_sar_lag",
"morie_sarimax",
"morie_sarsa",
"morie_sasimi",
"morie_sax_breakpoints",
"morie_sax_mindist",
"morie_saxR",
"morie_sbmdg2",
"morie_sbmest",
"morie_scaled_dot_product_attention",
"morie_scaled_schoenfeld",
"morie_scan_interval",
"morie_sccsno",
"morie_sccsno_build_intervals",
"morie_sccsno_cheatsheet",
"morie_sccsno_check_assumptions",
"morie_sccsno_fit",
"morie_sccsno_loglik",
"morie_sccsno_relative_incidence",
"morie_scfhop",
"morie_scfhop_cats",
"morie_scfhop_cheatsheet",
"morie_scfhop_murcko",
"morie_scfhop_signature",
"morie_scfhop_similarity",
"morie_scfhop_types",
"morie_scintg",
"morie_score_distribution",
"morie_screen_design",
"morie_sctraj",
"morie_sctraj_average_curve",
"morie_sctraj_cheatsheet",
"morie_sctraj_cluster_distances",
"morie_sctraj_cosine_cdf",
"morie_sctraj_lineages_from_tree",
"morie_sctraj_minimum_spanning_tree",
"morie_sctraj_principal_curve",
"morie_sctraj_shrinkage_weight",
"morie_sctsne",
"morie_scumap",
"morie_scumap_cheatsheet",
"morie_scumap_fit_ab",
"morie_scumap_fuzzy_simplicial_set",
"morie_scumap_smooth_knn_dist",
"morie_scumap_spectral_layout",
"morie_scumap_umap_singlecell",
"morie_sd_forms",
"morie_sdcfst",
"morie_sdcfst_cheatsheet",
"morie_sdcfst_forest",
"morie_sdcfst_logistic",
"morie_sdcfst_predict",
"morie_sdne",
"morie_sdne_cheatsheet",
"morie_sdxlcd_aspect_ratio_buckets",
"morie_sdxlcd_cheatsheet",
"morie_sdxlcd_condition_vector",
"morie_sdxlcd_crop_conditioning",
"morie_sdxlcd_discarded_fraction",
"morie_sdxlcd_fourier_embedding",
"morie_sdxlcd_sample_crop",
"morie_sdxlcd_size_conditioning",
"morie_se3T",
"morie_se3T_cheatsheet",
"morie_se3T_check_equivariance",
"morie_se3T_invariant_features",
"morie_se3T_radial_kernel",
"morie_se3T_rotation_matrix",
"morie_se3T_se3_attention",
"morie_seasnaive",
"morie_secaead",
"morie_secaead_aead_decrypt",
"morie_secaead_aead_encrypt",
"morie_secaead_chacha20",
"morie_secaead_chacha20_block",
"morie_secaead_cheatsheet",
"morie_secaead_poly1305_key_gen",
"morie_secaead_poly1305_mac",
"morie_secarg_argon2",
"morie_secarg_compress",
"morie_secarg_parameter_advice",
"morie_secarg_prehash",
"morie_secarg_variable_hash",
"morie_sechsh_build_chain",
"morie_sechsh_chain_entry",
"morie_sechsh_inclusion_proof",
"morie_sechsh_merkle_root",
"morie_sechsh_verify_chain",
"morie_sechsh_verify_inclusion",
"morie_seckdf_derive_context_keys",
"morie_seckdf_expand",
"morie_seckdf_extract",
"morie_seckdf_hkdf",
"morie_second_moment_threshold",
"morie_secrtt_crypto_shred",
"morie_secrtt_generate_dek",
"morie_secrtt_open_record",
"morie_secrtt_rotate_dek",
"morie_secrtt_rotate_kek",
"morie_secrtt_rotation_cost",
"morie_secrtt_seal_record",
"morie_secrtt_unwrap_dek",
"morie_secrtt_wrap_dek",
"morie_selective_labels",
"morie_selective_labels_judges",
"morie_semipar_efficiency",
"morie_semipartial_cor",
"morie_sensitivity_amplification",
"morie_sensitivity_evalue",
"morie_sensitivity_konfound",
"morie_sensitivity_omitted_var_bias",
"morie_sensitivity_rosenbaum",
"morie_sensitivity_tipping_point",
"morie_sentence_effect_bounds",
"morie_sentence_effect_mtr",
"morie_sentence_effect_mts",
"morie_sentpc_decode",
"morie_sentpc_encode_bpe",
"morie_sentpc_escape_whitespace",
"morie_sentpc_train_bpe",
"morie_sentpc_unescape_whitespace",
"morie_sentpc_viterbi_segment",
"morie_sepset",
"morie_sequence_frequencies",
"morie_sequence_probabilities",
"morie_serial_mediation",
"morie_series_regression",
"morie_seriesdecomp",
"morie_setT_set_transformer",
"morie_setT_setT",
"morie_sexbeta",
"morie_sexdfdt",
"morie_sexfsol",
"morie_sgd_momentum",
"morie_sgd_update",
"morie_sgflrt_spatial_glmm_fit",
"morie_sgolay_smooth",
"morie_sgt_closeness_centrality",
"morie_sgtcoml",
"morie_sgtdiff",
"morie_sgthits",
"morie_sgtkem",
"morie_sgtsbpd",
"morie_sgtspn",
"morie_sha1",
"morie_shapiro_wilk_test",
"morie_shrinkage_factor_error",
"morie_shrinkage_loss",
"morie_shrunk_targeting_unsafe",
"morie_si_estimators",
"morie_sibtest",
"morie_sieve_basis",
"morie_sieve_iv",
"morie_sigevid",
"morie_sign_test_power",
"morie_simple_random_sample",
"morie_simpleboot_two",
"morie_simplicial_complex_faces",
"morie_simulate_gene",
"morie_simulate_longitudinal_panel",
"morie_single_marker",
"morie_singleton_bound",
"morie_singularity_transfer",
"morie_siu_all_analyses",
"morie_siu_anomaly_check",
"morie_siu_audit_case",
"morie_siu_audit_columns",
"morie_siu_audit_panel",
"morie_siu_by_police_service",
"morie_siu_by_year",
"morie_siu_cache_path",
"morie_siu_case_counts",
"morie_siu_charges_by_year_chi2",
"morie_siu_classify_mandela",
"morie_siu_cli",
"morie_siu_compare",
"morie_siu_decision_timing",
"morie_siu_demographics",
"morie_siu_fetch_cases",
"morie_siu_fetch_dataframe",
"morie_siu_fetch_report",
"morie_siu_html_to_text",
"morie_siu_index",
"morie_siu_index_url",
"morie_siu_llm_extract",
"morie_siu_mental_health_race_indicators",
"morie_siu_panel",
"morie_siu_parse_html",
"morie_siu_parse_news_html",
"morie_siu_parse_report",
"morie_siu_parse_reports",
"morie_siu_record_correction",
"morie_siu_refresh_manifest",
"morie_siu_report_url",
"morie_siu_reports",
"morie_siu_resolve_so",
"morie_siu_sanity_check",
"morie_siu_schema",
"morie_siu_sprott_doob_feb2021",
"morie_siu_sprott_doob_iftene_table1",
"morie_siu_sprott_doob_iftene_table10",
"morie_siu_sprott_doob_iftene_table15",
"morie_siu_sprott_doob_iftene_table9",
"morie_siu_sprott_doob_table11",
"morie_siu_sprott_doob_table12",
"morie_siu_sprott_doob_table13",
"morie_siu_sprott_doob_table15",
"morie_siu_sprott_doob_table19",
"morie_siu_sprott_doob_table22",
"morie_siu_sprott_doob_table23",
"morie_siu_sprott_doob_table4",
"morie_siu_strip_boilerplate",
"morie_siu_to_iso_date",
"morie_siu_translate",
"morie_siu_translate_fr_to_en",
"morie_siu_verify_chi2",
"morie_siu_verify_published_chi_squares",
"MORIE_SIUIAP_AFFIDAVITS",
"morie_siuiap_cite",
"MORIE_SIUIAP_CRIMSL_REPORTS",
"MORIE_SIUIAP_ORIGINAL_PANEL_2019_2020",
"MORIE_SIUIAP_PANEL_MANDATE",
"MORIE_SIUIAP_PANEL_MEMBERS",
"morie_siuiap_panel_summary",
"MORIE_SIUIAP_REPORTS",
"MORIE_SIUIAP_URL",
"morie_slbpdg",
"morie_slbpdg_cheatsheet",
"morie_slbpdg_density",
"morie_slbpdg_weights",
"morie_slice",
"morie_slice_chain",
"morie_slice_sample_1d",
"morie_slidecv",
"morie_smape",
"morie_smatch_poisson_design",
"morie_smatch_power",
"morie_smatch_relative_efficiency",
"morie_smatch_sample_size",
"morie_smatch_sccs_poisson_fit",
"morie_smfd",
"morie_smoothed_fU",
"morie_sms_rate",
"morie_smwgrp",
"morie_snht",
"morie_snm_cox",
"morie_snmcox",
"morie_snmcox_blip_down",
"morie_snmcox_cheatsheet",
"morie_snmcox_gest_score",
"morie_snp_blup",
"morie_snpest",
"morie_snpest_cheatsheet",
"morie_snpest_predictive",
"morie_snpest_t_logpdf",
"morie_snpqc1",
"morie_snpqc1_call_rates",
"morie_snpqc1_cheatsheet",
"morie_snpqc1_heterozygosity",
"morie_snpqc1_hwe_pvalue",
"morie_snpqc1_ibd_moments",
"morie_snpqc1_ibs_given_ibd",
"morie_snpqc1_kinship_matrix",
"morie_snpqc1_ld_prune",
"morie_snpqc1_maf",
"morie_snpqc1_pihat_matrix",
"morie_snpqc1_sex_check",
"morie_sobel_test",
"morie_sobol_sequence",
"morie_soft_value_iteration",
"morie_solve_kinetics",
"morie_sparse_kernel_design",
"morie_spatial",
"morie_spatial_2sls",
"morie_spatial_agreement",
"morie_spatial_ar_combined",
"morie_spatial_ar_error",
"morie_spatial_ar_error_model",
"morie_spatial_ar_lag",
"morie_spatial_ar_lag_model",
"morie_spatial_autocorrelation",
"morie_spatial_block_kriging",
"morie_spatial_classical_mds_cpp",
"morie_spatial_cluster",
"morie_spatial_combined",
"morie_spatial_coords",
"morie_spatial_cross_validation",
"morie_spatial_crs",
"morie_spatial_distance",
"morie_spatial_emirt_theta_update_cpp",
"morie_spatial_error_model",
"morie_spatial_glm",
"morie_spatial_krige",
"morie_spatial_lag_model",
"morie_spatial_leaflet_spec",
"morie_spatial_mixed_model",
"morie_spatial_moran",
"morie_spatial_neighbors",
"morie_spatial_nominate_iterate_cpp",
"morie_spatial_omission",
"morie_spatial_sample",
"morie_spatial_smacof_step_cpp",
"morie_spatial_transform",
"morie_spatial_trend_surface",
"morie_spatial_variogram",
"morie_spatial_variogram_fit",
"morie_spatial_voting_aldrich_mckelvey",
"morie_spatial_voting_alpha_nominate",
"morie_spatial_voting_anchoring_vignettes",
"morie_spatial_voting_bayesian_am",
"morie_spatial_voting_bayesian_irt_likelihood",
"morie_spatial_voting_bayesian_irt_posterior",
"morie_spatial_voting_bayesian_mds",
"morie_spatial_voting_bayesian_unfolding",
"morie_spatial_voting_blackbox",
"morie_spatial_voting_cjr_irt",
"morie_spatial_voting_classical_mds",
"morie_spatial_voting_cutting_lines",
"morie_spatial_voting_double_centering",
"morie_spatial_voting_dw_nominate",
"morie_spatial_voting_dynamic_irt",
"morie_spatial_voting_em_irt",
"morie_spatial_voting_ideal_point_recovery",
"morie_spatial_voting_indscal",
"morie_spatial_voting_mds_fit_stats",
"morie_spatial_voting_mlsmu6",
"morie_spatial_voting_nominate_bootstrap",
"morie_spatial_voting_nominate_loglik",
"morie_spatial_voting_nominate_utility",
"morie_spatial_voting_nominate_vote_prob",
"morie_spatial_voting_nonmetric_mds",
"morie_spatial_voting_nonparametric_bootstrap",
"morie_spatial_voting_normal_vectors",
"morie_spatial_voting_optimal_classification",
"morie_spatial_voting_ordered_oc",
"morie_spatial_voting_ordinal_irt",
"morie_spatial_voting_procrustes",
"morie_spatial_voting_smacof",
"morie_spatial_voting_smacof_unfolding",
"morie_spatial_voting_unfolding_stress",
"morie_spatial_voting_wordfish",
"morie_spatial_weights",
"morie_spatial_wordfish_omega_update_cpp",
"morie_spatiotemporal_autocovariance",
"morie_spatiotemporal_kriging",
"morie_spatiotemporal_variogram",
"morie_spearman_rho",
"morie_specs_from_df",
"morie_spectral_bandwidth",
"morie_spectral_cluster",
"morie_spectral_density",
"morie_sperner_lemma_triangle",
"morie_sperner_width",
"morie_spillover_effects",
"morie_spillover_exposure",
"morie_spillover_exposure_probs",
"morie_spillover_ht",
"morie_spillover_ht_variance",
"morie_spillover_ring_bound",
"morie_spline_logit",
"morie_sschin_chained_imputation",
"morie_sse4r",
"morie_stahdo",
"morie_standard_tableaux_count",
"morie_stars_bars",
"morie_state_space_model",
"morie_std_coef",
"morie_steady_state_velocity",
"morie_steiner_triple_system",
"morie_stirling_first",
"morie_stirling_second",
"morie_stirling_series_error",
"morie_stl_decompose",
"morie_stlAn",
"morie_stldecomp",
"morie_stratified_proportion",
"morie_stratified_sample",
"morie_stratified_sampling",
"morie_stratsplt",
"morie_strmkr_strauss_process",
"morie_stsi_estimators",
"morie_subcompositional_incoherence",
"morie_sucrule",
"morie_suggest_analysis_plan",
"morie_sukhatme_test",
"morie_sum_density",
"morie_summarize_output_audit",
"morie_survey_calibrate",
"morie_survey_categorical",
"morie_survey_complex_glm",
"morie_survey_design",
"morie_survey_glm",
"morie_survey_hajek_mean",
"morie_survey_ht_total",
"morie_survey_mean",
"morie_survey_poststratify",
"morie_survey_ratio",
"morie_survey_subpop",
"morie_survey_variances",
"morie_survival_aft",
"morie_survival_cif",
"morie_survival_compare_parametric",
"morie_survival_concordance",
"morie_survival_cox",
"morie_survival_coxsnell",
"morie_survival_deviance",
"morie_survival_finegray",
"morie_survival_hr",
"morie_survival_km",
"morie_survival_landmark",
"morie_survival_left_truncated_km",
"morie_survival_logrank",
"morie_survival_martingale",
"morie_survival_nelsonaalen",
"morie_survival_parametric",
"morie_survival_psi",
"morie_survival_rmst",
"morie_survival_rmst_diff",
"morie_survival_schoenfeld",
"morie_survival_turnbull",
"morie_survnnr_baseline_hazard",
"morie_survnnr_cheatsheet",
"morie_survnnr_concordance",
"morie_survnnr_fit",
"morie_survnnr_forward",
"morie_survnnr_partial_loglik",
"morie_survnnr_risk_score",
"morie_survnnr_survival_function",
"morie_survroc",
"morie_survroc_auc_at",
"morie_survroc_kaplan_meier",
"morie_survroc_roc_at",
"morie_survroc_sensitivity",
"morie_survroc_specificity",
"morie_survrsf",
"morie_survrsf_best_split",
"morie_survrsf_c_index",
"morie_survrsf_cheatsheet",
"morie_survrsf_conservation_check",
"morie_survrsf_conservation_residuals",
"morie_survrsf_conserve_statistic",
"morie_survrsf_ensemble_chf",
"morie_survrsf_forest",
"morie_survrsf_grow_tree",
"morie_survrsf_logrank_score_statistic",
"morie_survrsf_logrank_scores",
"morie_survrsf_logrank_statistic",
"morie_survrsf_mortality",
"morie_survrsf_nelson_aalen",
"morie_survrsf_predict_tree",
"morie_survrsf_rule_status",
"morie_survrsf_vimp",
"morie_survvae",
"morie_survvae_cheatsheet",
"morie_survvae_concordance",
"morie_survvae_elbo",
"morie_survvae_exact_loglik",
"morie_survvae_fit_competing",
"morie_survvae_gates",
"morie_survvae_log_pdf",
"morie_survvae_log_survival",
"morie_survvae_predict_survival",
"morie_survvae_PRIMITIVES",
"morie_survvae_risk_score",
"morie_surxgb",
"morie_surxgb_aft_gradient_hessian",
"morie_surxgb_aft_loss",
"morie_surxgb_boost",
"morie_surxgb_cdf",
"morie_surxgb_cheatsheet",
"morie_surxgb_concordance",
"morie_surxgb_ddpdf",
"morie_surxgb_dpdf",
"morie_surxgb_leaf_weight",
"morie_surxgb_pdf",
"morie_surxgb_predict",
"morie_surxgb_split_gain",
"morie_sutvachk",
"morie_sv_dl",
"morie_sv_dl_build_sv_graph",
"morie_sv_dl_cheatsheet",
"morie_sv_dl_classify_pair",
"morie_sv_dl_deletion_type_reference",
"morie_sv_dl_gotoh_score_vectors",
"morie_sv_dl_insert_size_stats",
"morie_sv_dl_kmer_diagonals",
"morie_sv_dl_maximal_clique",
"morie_sv_dl_optimal_split",
"morie_sv_dl_paired_end_calls",
"morie_sv_dl_refine_breakpoint",
"morie_sv_dl_split_read_consensus",
"morie_sv_dl_structural_variant",
"morie_svdd",
"morie_svdpp",
"morie_svm_beta",
"morie_svm_decision",
"morie_svm_dual_objective",
"morie_svm_fit_dual",
"morie_svm_genomic",
"morie_svm_hinge_primal",
"morie_svm_intercept",
"morie_svm_kernel_trick",
"morie_svm_label_matrix",
"morie_svycox",
"morie_svyrcq_survey_quantile_regression",
"morie_sxrhrt_sex_specific_h2",
"morie_sync_rng",
"morie_synth_control",
"morie_synth_corrections_uof",
"morie_synth_otis",
"morie_synth_otis_all",
"morie_t_quantile",
"morie_t5enc",
"morie_table_inertia",
"morie_tagRC",
"morie_tail_ratio",
"morie_taphonomy_bhm",
"morie_taphonomy_clr",
"morie_taphonomy_decay_absorption",
"morie_taphonomy_decay_chain",
"morie_taphonomy_decay_delta",
"morie_taphonomy_decay_simulate",
"morie_taphonomy_evidence_loglik",
"morie_taphonomy_fetch_usgs_soil",
"morie_taphonomy_ilr",
"morie_taphonomy_likelihood_ratio",
"morie_taphonomy_morphosource_fetch",
"morie_taphonomy_morphosource_search",
"morie_taphonomy_pmi_schema",
"morie_taphonomy_preservation_delta",
"morie_taphonomy_preservation_lr",
"morie_taphonomy_schema",
"morie_taphonomy_simulate_pxrf",
"morie_tarone_ware",
"morie_tau_to_theta",
"morie_taulep",
"morie_tcc",
"morie_tcls",
"morie_tcmech",
"morie_tcmech_cheatsheet",
"morie_tcmech_compose",
"morie_tcmech_eps",
"morie_tcmech_floor",
"morie_tcmech_rho",
"morie_tcmech_rho_from_pure",
"morie_tcmech_rho_from_sigma",
"morie_tcmech_sigma",
"morie_tdcvar",
"morie_tdcvar_cheatsheet",
"morie_terry_hoeffding_test",
"morie_test_information",
"morie_text_ate",
"morie_tfidf",
"morie_tftnet",
"morie_tgarch_model",
"morie_theta_method",
"morie_thin_plate_spline",
"morie_thinning_identification",
"morie_thomp",
"morie_threshold",
"morie_threshold_autoregression",
"morie_thrtmt",
"morie_tide",
"morie_tightness_check",
"morie_tikhonov_iv",
"morie_timeRS",
"morie_timeRS_cheatsheet",
"morie_timeRS_deviation",
"morie_timeRS_fit_time_bias",
"morie_timeRS_item_bias",
"morie_timeRS_predict_time",
"morie_timeRS_time_bin",
"morie_timeRS_timesvd",
"morie_timeRS_timesvdpp",
"morie_timeRS_user_bias",
"morie_timesf",
"morie_timesfm",
"morie_timesfm_causal_mask",
"morie_timesfm_cheatsheet",
"morie_timesfm_horizon_plan",
"morie_timesfm_input_patches",
"morie_timesfm_rollout_steps",
"morie_tipsne",
"morie_tipsne_cheatsheet",
"morie_tl1step",
"morie_tlbandt",
"morie_tlboot",
"morie_tlclust",
"morie_tlctmle",
"morie_tlcvnp",
"morie_tldapar",
"morie_tlgcmp",
"morie_tlhal",
"morie_tlhaltm",
"morie_tlheic",
"morie_tlhoest",
"morie_tlhoest_cheatsheet",
"morie_tlhoest_rate_requirement",
"morie_tlhoest_remainder_order",
"morie_tlnetlg_cheatsheet",
"morie_tlnetlg_community_estimand",
"morie_tlnetlg_exposure_summary",
"morie_tlnetlg_longitudinal_network_gcomp",
"morie_tlnetlg_network_summary",
"morie_tlnetlg_network_variance",
"morie_tloilr",
"morie_tlonsl",
"morie_tlonsl_cheatsheet",
"morie_tlonsl_online_super_learner",
"morie_tlonsl_sequential_risk",
"morie_tlonsl_summary_measure",
"morie_tlonsl_update_weights",
"morie_tlseqsl_cheatsheet",
"morie_tlseqsl_cv_folds",
"morie_tlseqsl_cv_risk",
"morie_tlseqsl_discrete_super_learner",
"morie_tlseqsl_ensemble_super_learner",
"morie_tlseqsl_sequential_super_learner",
"morie_tlsieve",
"morie_tlsurvy_adaptive_survey_tmle",
"morie_tlsurvy_cheatsheet",
"morie_tlsurvy_design_efficiency",
"morie_tlsurvy_draw_sample",
"morie_tlsurvy_horvitz_thompson",
"morie_tlsurvy_inclusion_probabilities",
"morie_tmlcds",
"morie_tmlcen",
"morie_tmlcic",
"morie_tmlcic_adaptive_prespecification",
"morie_tmlcic_candidate_tmle",
"morie_tmlcic_cheatsheet",
"morie_tmlcic_cluster_weights",
"morie_tmlcic_default_library",
"morie_tmlcic_influence_curve",
"morie_tmlcic_tmle_cluster_ic",
"morie_tmlcic_tmle_hierarchical",
"morie_tmlcic_variance_estimate",
"morie_tmlcll",
"morie_tmlcmp",
"morie_tmlcontinuoustreatment",
"morie_tmlcou",
"morie_tmlcountoutcome",
"morie_tmlcps",
"morie_tmldgp",
"morie_tmldgp_cheatsheet",
"morie_tmldta",
"morie_tmldta_cheatsheet",
"morie_tmldyk",
"morie_tmldyk_cheatsheet",
"morie_tmldyn",
"morie_tmldyn_cheatsheet",
"morie_tmle_ate",
"morie_tmle_censoring",
"morie_tmle_cross_lagged",
"morie_tmle_data_adaptive",
"morie_tmle_diff_kernel",
"morie_tmle_doubly_robust_pen",
"morie_tmle_dynamic_regime",
"morie_tmle_effective_pi",
"morie_tmle_late",
"morie_tmle_longitudinal",
"morie_tmle_mediation",
"morie_tmle_propensity_only",
"morie_tmle_quantile",
"morie_tmle_rmst",
"morie_tmle_sensitivity",
"morie_tmle_time_varying",
"morie_tmle_truncation_sweep",
"morie_tmledataadaptive",
"morie_tmlediffkernel",
"morie_tmledynamicregime",
"morie_tmlefp",
"morie_tmlefp_cheatsheet",
"morie_tncomp",
"morie_to_hood_crosswalk",
"morie_to_neighbourhoods",
"morie_toa_radiation_balance",
"morie_tokenizer_decode",
"morie_tokenizer_encode",
"morie_tokenizer_new",
"morie_tokenizer_vocab_size",
"morie_tolerance_beta",
"morie_tolerance_limits",
"morie_topological_torsion",
"morie_topological_torsions",
"morie_toptor",
"morie_toptor_cheatsheet",
"morie_torsion_similarity",
"morie_tox_antemortem_lr",
"morie_tox_calibration",
"morie_tox_ethanol_congeners",
"morie_tox_left_censor_impute",
"morie_tox_matrix_reliability",
"morie_tox_matrix_schema",
"morie_tox_pmr_ratio",
"morie_tps_add_hood_140_from_158",
"morie_tps_add_hood_158_from_140",
"morie_tps_aggregate_158_to_140",
"morie_tps_analyze_all",
"morie_tps_analyze_assault",
"morie_tps_analyze_autotheft",
"morie_tps_analyze_bicycletheft",
"morie_tps_analyze_breakandenter",
"morie_tps_analyze_communitysafetyindicators",
"morie_tps_analyze_csi_from_dataframes",
"morie_tps_analyze_hatecrimes",
"morie_tps_analyze_homicides",
"morie_tps_analyze_intimatepartnerandfamilyviolence",
"morie_tps_analyze_neighbourhoodcrimerates",
"morie_tps_analyze_one",
"morie_tps_analyze_robbery",
"morie_tps_analyze_shootingandfirearmdiscarges",
"morie_tps_analyze_theftfrommovingvehicle",
"morie_tps_analyze_theftover",
"morie_tps_arima_forecast",
"morie_tps_assert_hood_version",
"morie_tps_available_formats",
"morie_tps_bivariate_moran",
"morie_tps_bivariate_morans_i",
"morie_tps_category_correlation_matrix",
"morie_tps_changepoint_detection",
"morie_tps_compare_hawkes_kernels",
"morie_tps_composite_index",
"morie_tps_crime_compare",
"morie_tps_criminal_network_graph",
"MORIE_TPS_CSI_CATEGORIES",
"morie_tps_csi_per_neighbourhood",
"morie_tps_csi_per_year",
"morie_tps_csi_weight",
"morie_tps_data_dir",
"morie_tps_dbscan_clusters",
"morie_tps_disaggregate_140_to_158",
"morie_tps_district_for_centroid",
"morie_tps_fetch_category",
"morie_tps_fetch_dataframe",
"morie_tps_figures",
"morie_tps_fokker_planck_grid",
"morie_tps_getis_ord_g_star",
"morie_tps_gini_concentration",
"morie_tps_hawkes_advanced_fit",
"morie_tps_hawkes_markovian_vs_nonmarkovian",
"morie_tps_hawkes_temporal_fit",
"morie_tps_inspection_game_phase",
"morie_tps_kde_density",
"morie_tps_langevin_simulate",
"morie_tps_layer_urls",
"MORIE_TPS_LAYER_URLS",
"morie_tps_levy_flight_alpha",
"morie_tps_list_categories",
"morie_tps_list_datasets",
"morie_tps_list_formats",
"morie_tps_load",
"morie_tps_load_dataset",
"morie_tps_load_tps",
"morie_tps_load_tps_dataset",
"morie_tps_local_morans_i",
"morie_tps_lotka_volterra_police_crime",
"morie_tps_moran_sweep_heatmap",
"morie_tps_morans_i_neighbourhood",
"morie_tps_neighbourhood_concentration",
"morie_tps_offence_summary",
"morie_tps_polygon_morans_i",
"morie_tps_pretty_label",
"morie_tps_project_xy",
"morie_tps_psdp_layers",
"MORIE_TPS_REGISTRY",
"morie_tps_render_choropleth",
"morie_tps_render_dbscan",
"morie_tps_render_district_proportional",
"morie_tps_render_points",
"morie_tps_render_quad",
"morie_tps_render_satscan_panel",
"morie_tps_render_yearly_grid",
"morie_tps_resolve_hood_col",
"morie_tps_ripley_k",
"morie_tps_sarima_forecast",
"morie_tps_sdb_reaction_diffusion",
"morie_tps_sdb_turing_demo",
"morie_tps_seasonal_pattern",
"morie_tps_spatial_summary",
"morie_tps_statphysics_analyze_all",
"MORIE_TPS_SUPPORTED_FORMATS",
"morie_tps_temporal_summary",
"MORIE_TPS_TORONTO_POPULATION_BY_YEAR",
"MORIE_TPS_TOTAL_CSI_WEIGHTS",
"morie_tps_urban_scaling_beta",
"morie_tps_use_of_force",
"MORIE_TPS_VIOLENT_CSI_WEIGHTS",
"morie_tps_year_over_year_trend",
"morie_tps_year_to_hood_version",
"morie_tps_yoy_panel",
"morie_tpsuof",
"morie_tqang",
"morie_tqang_angular_difference",
"morie_tqang_cheatsheet",
"morie_tqang_quantize_angles",
"morie_tqang_wrap_angle",
"morie_tqipb",
"morie_tqipb_cheatsheet",
"morie_tqlld",
"morie_tqlld_cheatsheet",
"morie_tqlld_lloyd_max_codebook",
"morie_tqlld_quantize_with_codebook",
"morie_transfer_entropy_gaussian",
"morie_transfer_function",
"morie_transfer_verify",
"morie_transform_asymptotics",
"morie_transform_prediction",
"morie_transform_T_F",
"morie_transformer_block",
"morie_transformer_genomic",
"morie_trend_vector",
"morie_trfbl_transformer_block",
"morie_trimmed_mean",
"morie_trimmed_mean_anova",
"morie_trimmed_mean_ci",
"morie_trimmed_mean_se",
"morie_trkassign",
"morie_trmRew_cheatsheet",
"morie_trmRew_complete",
"morie_trmRew_critical_pairs",
"morie_trmRew_decides",
"morie_trmRew_is_confluent",
"morie_trmRew_is_locally_confluent",
"morie_trmRew_is_terminating",
"morie_trmRew_joinable",
"morie_trmRew_lpo_greater",
"morie_trmRew_normal_form",
"morie_trmRew_positions",
"morie_trmRew_replace_at",
"morie_trmRew_rewrite_step",
"morie_trmRew_rule",
"morie_trmRew_subterm_at",
"morie_trmRew_term_rewriting",
"morie_trnsfr",
"morie_trnsfr_balancing_weights",
"morie_trnsfr_cheatsheet",
"morie_trnsfr_transfer_msm",
"morie_trnsfr_transport_ate",
"morie_trnsfr_transport_weights",
"morie_truncated_normal_interval",
"morie_trupek",
"morie_trupek_trust_region",
"morie_ts",
"morie_ts_accuracy",
"morie_ts_acf",
"morie_ts_arima",
"morie_ts_backtest",
"morie_ts_decompose",
"morie_ts_difference",
"morie_ts_forecast",
"morie_ts_select",
"morie_ts_stationarity",
"morie_ts_trim_forecast",
"morie_ts_undifference",
"morie_tsbF",
"morie_tsbF_cheatsheet",
"morie_tsbF_croston_forecast",
"morie_tsbF_demand_classification",
"morie_tsbF_intermittent_forecast",
"morie_tsbF_sba_forecast",
"morie_tsbF_tsb_forecast",
"morie_tsboot",
"morie_tsimpute",
"morie_tsmixer",
"morie_tsne_reduction",
"morie_tsregmat",
"morie_ttrace",
"morie_ttsAn",
"morie_ttsplit",
"morie_turan_graph",
"morie_turan_number",
"morie_turboquant_angle_quantization",
"morie_turning_point_test",
"morie_tv_denoise_1d",
"morie_tvtsplit",
"morie_twelvefold_way",
"morie_two_boot",
"morie_two_group_binomial",
"morie_two_point_bound",
"morie_two_sample_coverage",
"morie_two_sample_t_test",
"morie_two_step_additive",
"morie_two_way_fixed_effects",
"morie_two_way_mundlak",
"morie_twostage_design",
"morie_twoT_batch_softmax_loss",
"morie_twoT_cheatsheet",
"morie_twoT_corrected_logits",
"morie_twoT_retrieve",
"morie_twoT_streaming_frequency",
"morie_twoT_tower_embedding",
"morie_typset",
"morie_u_process",
"morie_ucbb",
"morie_ukfF",
"morie_ukfF_cheatsheet",
"morie_ukrig",
"morie_unclr_alr",
"morie_unclr_alr_inv",
"morie_unclr_dft_amp",
"morie_unclr_lr_impute",
"morie_unclr_phi",
"morie_unclr_Phi",
"morie_unclr_sym_norm",
"morie_unfolding_analysis",
"morie_unifAlg_app",
"morie_unifAlg_apply_subst",
"morie_unifAlg_compose",
"morie_unifAlg_const",
"morie_unifAlg_disagreement",
"morie_unifAlg_factor_through",
"morie_unifAlg_is_var",
"morie_unifAlg_match",
"morie_unifAlg_occurs",
"morie_unifAlg_substitute",
"morie_unifAlg_unify",
"morie_unifAlg_var",
"morie_unifAlg_variables",
"morie_uniform_matroid",
"morie_union_bound_exists",
"morie_universal_kriging",
"morie_unobserved_components",
"morie_urnpost",
"morie_urnpred",
"morie_userguide",
"morie_vae_elbo",
"morie_vaenc_vae_elbo",
"morie_validate_cpads_data",
"morie_validate_outputs_manifest",
"morie_van_der_waerden_test",
"morie_vanr1",
"morie_var_backtest",
"morie_var_cpp",
"morie_varcal",
"morie_variable_importance",
"morie_variogram_composite_likelihood",
"morie_variogram_design",
"morie_variogram_estimation",
"morie_variogram_fitting",
"morie_varqc1",
"morie_varqc1_cheatsheet",
"morie_varqc1_hard",
"morie_varqc1_logpdf",
"morie_varqc1_mixture",
"morie_vcomp",
"morie_vcov_cl",
"morie_vcov_hac",
"morie_vcov_hc",
"morie_vcov_robust",
"morie_vdcal",
"morie_vdcal_cheatsheet",
"morie_vdcal_fut",
"morie_vdcal_oie_tozer",
"morie_vecm",
"morie_veil_of_darkness",
"morie_velocity",
"morie_vepan",
"morie_vepan_annotate",
"morie_vepan_cheatsheet",
"morie_vepan_consequence_impact",
"morie_vepan_consequence_rank",
"morie_vepan_most_severe_consequence",
"morie_vepan_pick",
"morie_vepan_transcript_sequence",
"morie_vepan_vep_annotation",
"morie_verify_pollution",
"morie_verify_statistical_output",
"morie_vertex_access_token",
"morie_vertex_ask_gemini",
"morie_vertex_health_check",
"morie_vertex_resolve_config",
"morie_vic_indigenous_ratio",
"morie_vic_lga_rates",
"morie_vic_offence_trend",
"morie_vidgen_as_image_model",
"morie_vidgen_attention_cost",
"morie_vidgen_cheatsheet",
"morie_vidgen_reconstruction_guidance",
"morie_vidgen_space_only_conv",
"morie_vidgen_spatial_attention",
"morie_vidgen_temporal_attention",
"morie_vif",
"morie_vif_tolerance",
"morie_vine_copula",
"morie_vit2lf",
"morie_vit2lf_cheatsheet",
"morie_vit2lf_entropy",
"morie_vit2lf_log_coords",
"morie_vit2lf_logits",
"morie_vit2lf_relative_bias",
"morie_vit2lf_softmax",
"morie_vol_garman_klass",
"morie_vol_harmonic",
"morie_vol_noise",
"morie_vol_parkinson",
"morie_volsabr",
"morie_voting_power_index",
"morie_vpc",
"morie_vpd_download_instructions",
"morie_vqgdec_adaptive_weight",
"morie_vqgdec_cheatsheet",
"morie_vqgdec_decode",
"morie_vqgdec_decode_indices",
"morie_vqgdec_patch_discriminator",
"morie_vqgdec_sliding_windows",
"morie_wald_iv",
"morie_walkfwd",
"morie_warpL_alpha_weights",
"morie_warpL_cheatsheet",
"morie_warpL_estimate_rank",
"morie_warpL_rank_weight",
"morie_warpL_sample_violation",
"morie_warpL_warp_loss",
"morie_warpL_warp_step",
"morie_wasserman_aic",
"morie_wasserman_bic",
"morie_wasserman_boosting",
"morie_wasserman_bootstrap_percentile",
"morie_wasserman_bootstrap_pivotal",
"morie_wasserman_char_fn",
"morie_wasserman_chebyshev_ineq",
"morie_wasserman_chi_sq_gof",
"morie_wasserman_clt",
"morie_wasserman_covariance",
"morie_wasserman_cramer_rao",
"morie_wasserman_credible_interval",
"morie_wasserman_delta_method",
"morie_wasserman_directed_graph",
"morie_wasserman_dkw_cb",
"morie_wasserman_em_algorithm",
"morie_wasserman_empirical_cdf",
"morie_wasserman_empirical_quantile",
"morie_wasserman_entropy",
"morie_wasserman_expectation",
"morie_wasserman_fisher_info",
"morie_wasserman_gibbs_sampler",
"morie_wasserman_gmm_em",
"morie_wasserman_graphical_model",
"morie_wasserman_hmm_forward",
"morie_wasserman_hoeffding",
"morie_wasserman_influence_function",
"morie_wasserman_kernel_regression",
"morie_wasserman_kfold_cv",
"morie_wasserman_kmeans",
"morie_wasserman_kullback_leibler",
"morie_wasserman_lasso",
"morie_wasserman_least_squares",
"morie_wasserman_likelihood",
"morie_wasserman_lln",
"morie_wasserman_local_polynomial",
"morie_wasserman_log_likelihood",
"morie_wasserman_log_linear",
"morie_wasserman_logistic_regression",
"morie_wasserman_markov_ineq",
"morie_wasserman_mcmc_metropolis",
"morie_wasserman_mgf",
"morie_wasserman_minimax",
"morie_wasserman_mle_asymptotic",
"morie_wasserman_mutual_info",
"morie_wasserman_nonparametric_boot",
"morie_wasserman_odds_ratio",
"morie_wasserman_parametric_boot",
"morie_wasserman_pca",
"morie_wasserman_poisson_regression",
"morie_wasserman_posterior",
"morie_wasserman_posterior_mean",
"morie_wasserman_relative_risk",
"morie_wasserman_ridge",
"morie_wasserman_smoothing_spline",
"morie_wasserman_svm",
"morie_wasserman_undirected_graph",
"morie_wasserman_variance",
"morie_wasserman_viterbi",
"morie_wasserman_white_huber",
"morie_wasserstein_test",
"morie_wavelet_time_series",
"morie_wayback_url",
"morie_weight_cbps",
"morie_weight_diagnostic",
"morie_weight_entropy",
"morie_weight_ow",
"morie_weight_ps",
"morie_weight_stabilized",
"morie_weight_super",
"morie_weight_trimming",
"morie_weighted_logrank",
"morie_weighted_moments",
"morie_weighted_quantile",
"morie_weightit",
"morie_weights_bootstrap",
"morie_weights_brr",
"morie_weights_calibrate_to_totals",
"morie_weights_combined",
"morie_weights_deff",
"morie_weights_design",
"morie_weights_detect_extreme",
"morie_weights_diagnostics",
"morie_weights_ess",
"morie_weights_fay_brr",
"morie_weights_greg",
"morie_weights_jackknife",
"morie_weights_multiframe",
"morie_weights_nonresponse",
"morie_weights_normalize",
"morie_weights_poststratify",
"morie_weights_propensity_nonresponse",
"morie_weights_rake",
"morie_weights_replicate_variance",
"morie_weights_sdr",
"morie_weights_smooth",
"morie_weights_totals",
"morie_weights_trim",
"morie_welch_test",
"morie_wenge",
"morie_wenge_cheatsheet",
"morie_wenge_mediation_functional",
"morie_wenge_weight_based_mediation",
"morie_wilcox_test",
"morie_wilcoxon_power",
"morie_wilcoxon_signed_rank_test",
"morie_winkler",
"morie_winsorize",
"morie_winsorized_correlation",
"morie_winsorized_mean",
"morie_winsorized_regression",
"morie_winsorized_variance",
"morie_within_between_decomposition",
"morie_wnominate",
"morie_wnominate_estimate",
"morie_wnominate_fit",
"morie_word_hits",
"morie_wrd2v",
"morie_wrd2v_analogy",
"morie_wrd2v_cheatsheet",
"morie_wrd2v_noise_distribution",
"morie_wrd2v_subsample_probability",
"morie_wrd2v_training_complexity",
"morie_wrd2v_wrd2v",
"morie_write_audit_markdown",
"morie_write_parquet",
"morie_write_synthetic_data",
"morie_wsm_admissible",
"morie_wsm_bagging",
"morie_wsm_bootstrap",
"morie_wsm_importance_sampling",
"morie_wsm_kde",
"morie_wsm_mle",
"morie_wsm_plug_in",
"morie_xavier_initialization",
"morie_xavir_xavier_init",
"morie_xgboost_objective",
"morie_xml_sax",
"morie_xpehh1",
"morie_yangr",
"morie_yolovx",
"morie_yolovx_box_iou",
"morie_yolovx_center_sampling",
"morie_yolovx_cheatsheet",
"morie_yolovx_decode_box",
"morie_yolovx_decoupled_head",
"morie_yolovx_encode_box",
"morie_yolovx_simota_assign",
"morie_yuen_paired",
"morie_yuen_test",
"morie_yule_walker",
"morie_zap_link",
"morie_zap_loglik",
"morie_zap_mean_variance",
"morie_zap_mle",
"morie_zap_predict",
"morie_zfmech",
"morie_zip_vuong_boundary",
"morie_zonal_ebm",
"Morieclrp",
"MorinFactorial",
"MorisitaIndex",
"MorphologicalOpening",
"Motfom",
"motfsr_alphabet_of",
"motfsr_bayes_threshold",
"motfsr_cheatsheet",
"motfsr_lambda_grid",
"motfsr_log_component",
"motfsr_log_odds_matrix",
"motfsr_mm_fit",
"motfsr_mu",
"motfsr_normalise_windows",
"motfsr_prepare",
"motfsr_run",
"motfsr_score_sequence",
"motfsr_theta_from_subsequence",
"motfsr_uniform_theta",
"MothFlameOptimizer",
"MotionArt",
"MovementHmm",
"MovementNetwork",
"MovingLeastSquares",
"mpfn_cheatsheet",
"mpfn_is_permutation_invariant",
"mpfn_message",
"mpfn_message_passing",
"mpfn_readout",
"mpfn_sig",
"mpfn_update_gru",
"MPursuit",
"mqtmpl_cheatsheet",
"mqtmpl_check_method",
"mqtmpl_genotype_probabilities",
"mqtmpl_haldane",
"mqtmpl_hmm_genotype_probabilities",
"mqtmpl_imputation_weights",
"mqtmpl_kw_n_imp",
"mqtmpl_lod_support_interval",
"mqtmpl_method_status",
"mqtmpl_permutation_threshold",
"mqtmpl_sample_genotypes",
"mqtmpl_scan_cim",
"mqtmpl_scan_imp",
"mqtmpl_scanone",
"mqtmpl_single_marker",
"Mra",
"Mrlb4",
"Mrlb5",
"mrm_admissions",
"mrm_adp",
"mrm_adp_from_counts",
"mrm_alos",
"mrm_anova_bonferroni",
"mrm_anova_oneway",
"mrm_anova_power",
"mrm_assumptions_check",
"mrm_causal_design",
"mrm_check_balancing",
"mrm_check_overlap",
"mrm_classify_mandela",
"mrm_clt_demo",
"mrm_factorial_2k",
"mrm_fractional_factorial",
"mrm_gentrification_panel",
"mrm_graeco_latin",
"mrm_latin_square",
"mrm_mc_power",
"mrm_median_causal_effect",
"mrm_morans_i",
"mrm_oneprop_test",
"mrm_otis_mandela_spectrum",
"mrm_otis_mortification_cooccurrence",
"mrm_otis_placement_concentration",
"mrm_otis_region_locality",
"mrm_otis_seg_duration_km",
"mrm_otis_stock_flow",
"mrm_period_days",
"mrm_perm_block",
"mrm_pit",
"mrm_qq_plot",
"mrm_random_latin",
"mrm_rcbd",
"mrm_response_surface",
"mrm_siu_case_to_decision_km",
"mrm_siu_outcome_classifier",
"mrm_siu_per_service_rate",
"mrm_spatial_spillover_decomposition",
"mrm_standardised_difference",
"mrm_stay_summary",
"mrm_stock_flow",
"mrm_synthetic_area_exposure",
"mrm_threshold_coefficient",
"mrm_threshold_specific_ordinal",
"mrm_tps_kulldorff_scan",
"mrm_tps_levy_scaling",
"mrm_tps_lisa",
"mrm_tps_load_hawkes_refit",
"mrm_tps_moran_clustering",
"mrm_tps_neighbourhood_recurrence_km",
"mrm_tps_polygon_moran_per_year",
"mrm_two_treatment_test",
"mrm_twoprop_test",
"mrm_uof_data_quality_audit",
"mrm_uof_demographic_disparity",
"mrm_uof_force_concentration",
"mrm_uof_region_locality",
"mrm_uof_weapon_diversity",
"mrm_uof_yoy_change",
"mrm_var_test",
"Mrr",
"mrr_at_k",
"Msc",
"Mscmcl",
"MsCoh",
"MseGrad",
"Msetst",
"Mskbnd2",
"Msm001",
"Msm002",
"Msm003",
"Msm004",
"Msm005",
"Msm006",
"Msm007",
"Msm008",
"Msm009",
"Msm010",
"Msm011",
"Msm012",
"Msm013",
"Msm014",
"Msm019",
"Msm026",
"Msm027",
"Msm028",
"Msm029",
"Msm030",
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"Msm043",
"Msm044",
"Msm045",
"Msm047",
"Msm057",
"Msm058",
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"Msm077",
"Msm083",
"Msm084",
"Msm092",
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"Msm098",
"Msm099",
"Msm101",
"Msm102",
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"Msm109",
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"Msm236",
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"Msm315",
"Msm316",
"Msm317",
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"Msm319",
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"Msm322",
"Msm323",
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"Msm325",
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"Msm327",
"Msm328",
"Msm329",
"Msm330",
"Msm331",
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"Msm333",
"Msm334",
"mtdrl_bandit_tasks",
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"Mtrbnd",
"Mtrbound",
"Mtsbnd",
"Mtsbound",
"Mtsmtv",
"MuapModel",
"Multboot",
"multi_head_attention",
"MultiDepotVrp",
"MultiExp",
"Multindex",
"Multinom",
"MultinomialDist",
"multiplier_bootstrap",
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"MultM",
"MultModel",
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"MurmSpec",
"MuscleAlign",
"Mutinf",
"Mutinfo",
"MuY",
"muzero_add_noise",
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"muzero_Node",
"muzero_search",
"muzero_select",
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"Mvedet",
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"MvSkewNorm",
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"mwem",
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"Ncut",
"Ndcg",
"Nde",
"NdviEmissivity",
"NearestFacility",
"NearRepeatTable",
"NeedBasedAllocation",
"Neffsrs",
"negbinom_offspring",
"Negctc",
"NeighbourCardinality",
"neighbours",
"NelderMead",
"ner_decode",
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"nested_cross_validate",
"NestedDecomposition",
"NestedGrid",
"NestedRandomEffects",
"nestvc",
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"Netbtw",
"Netclocent",
"Netdeg",
"Neteigcent",
"Netparf",
"NetworkConnectivity",
"NetworkPolarization",
"NetworkRobustness",
"NetworkSummary",
"Newraf",
"newton_direction",
"Neymal",
"Neyman",
"NeymanScottProcess",
"ngcf_cheatsheet",
"ngcf_laplacian_coefficient",
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"ngcf_propagate",
"ngcf_score",
"ngcf_stack_layers",
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"Nie",
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"nlsgn",
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"NmixtureModel",
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"Nnet1lay",
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"nnlsq",
"no_u_turn",
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"node2v_alpha_pq",
"node2v_cheatsheet",
"node2v_check_pq",
"node2v_dist",
"node2v_skipgram_pairs",
"node2v_transition_probabilities",
"node2v_walk",
"NodeVulnerability",
"noise_power",
"noise_psd",
"noise_schedule",
"Noisealpha",
"NoiseAnnoyance",
"NoiseHealthRisk",
"NoiseMap",
"NoiseModel",
"NoiseZones",
"noisy_argmax",
"nonlinear_cg",
"NonstationaryKriging",
"NormalGammaDlm",
"NormalGravity",
"normalise_bbox",
"normality_suite",
"normalized_similarity_matrix",
"normalizer",
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"NormalScore",
"NormalVote",
"NormDist",
"Normlap",
"normtl",
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"Notch60",
"Novlt",
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"Npbcl",
"Npbcox",
"Npbqr",
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"Nprphet",
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"ntrtau",
"nuclear_norm",
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"number_needed_to_harm",
"number_needed_to_treat",
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"numerical_derivative",
"NutsSpatial",
"nw_regression",
"Nystromap",
"OaeFeat",
"Objfair",
"observed_clint_u",
"ObsReal",
"OccupancyModel",
"OceanChlorophyll",
"Ocmtmd",
"ocrwit_cheatsheet",
"ocrwit_mask_units",
"ocrwit_normalise_bbox",
"ocrwit_patch_of_box",
"ocrwit_segment_layout_boxes",
"OddPart",
"odds_ratio",
"odds_ratio_table",
"OddsRat",
"OdenIpop",
"OdeSymbolic",
"OdMatrix",
"offlrl",
"offlrl_as_dist",
"offlrl_cheatsheet",
"offlrl_logsumexp",
"offlrl_lookup",
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"OilSpillDrift",
"okrig",
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"omega_squared",
"Omegah",
"omitted_variable_bias",
"OmpFit",
"one_minus_km",
"one_proportion_ztest",
"one_sample_ttest",
"one_way_anova",
"OneMExp",
"Onestep",
"OniIndex",
"online_tmle_series",
"open_record",
"optcl",
"opthr",
"OpticsOrdering",
"optimal_alpha",
"optimal_alpha_att",
"optimal_duration",
"optimal_policy",
"optimal_rule",
"OptimalCircularSearch",
"OptimalCuttingLines",
"OptimalEffortAllocation",
"optimise_branch",
"Optionshrl",
"Opttre",
"or_to_d",
"or_to_r",
"OralBioavailabilityRules",
"order_is_graph_determined",
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"Ordprobs",
"OrdRep",
"OrdSubs",
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"OrInt",
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"orthogonal_moment",
"orthogonal_random_forest",
"OsFilt",
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"Otbar",
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"Otfgw",
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"Otsinkit",
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"Otws",
"Otws2",
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"Outbrkdet",
"outer_loop_schedule",
"outlyingness",
"Outmad",
"OutMah",
"Outms",
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"Ovbias",
"Ovbnk",
"overlap_tiles",
"owate_weights",
"owl_weights",
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"OxygenSolubility",
"Paa",
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"panel_differences",
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"PanelWithin",
"PanSharpen",
"PanterDiteBound",
"papadk",
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"parallel_scan",
"ParallelSweepPod",
"parametric_bootstrap",
"Parasurv",
"Pareff",
"ParetoSet",
"Parseval",
"partial_correlation",
"partial_eta_squared",
"partial_permutations",
"PartialMantel",
"particle_filter_simple",
"PartisanGerrymanderMeasures",
"PartitionedCovariance",
"PartyDivergence",
"PartyInfluence",
"PartyNicheness",
"PartyPositions",
"PartySystemIndices",
"parzen_kernel",
"PascalId",
"patch_grid",
"patch_of_box",
"patchify",
"PatchMetrics",
"PatchStructure",
"patchtst_encode",
"pate",
"PatrolCarRequirement",
"PBCI",
"Pbeta",
"Pbinom",
"pburg",
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"PcaIdealPoints",
"Pcaprx",
"PcaSig",
"Pcasnps",
"PCenter",
"PCepRel",
"PCepstrum",
"PCepSum",
"Pcf",
"Pcfunc",
"PcgAr",
"PcgEeg",
"pcgenv",
"PcgEnvAvg",
"pcgmur",
"PcgParts",
"pcgseg",
"PcgSeg",
"PcgSyncAvg",
"Pcgxe",
"Pchisq",
"pchnrm",
"PdfKurt",
"PdfMean",
"PdfMS",
"PdfOverlap",
"PdfSkew",
"PdfVar",
"Pdic",
"PdMonitor",
"PdoIndex",
"pearson_correlation",
"Pearsonr",
"Pelt",
"penalty_path",
"PenaltyMin",
"Percn",
"PerConv",
"PeriodicVrp",
"Permanova",
"permt",
"permutation_fdr",
"permutation_fwer",
"permutation_test",
"permutations_count",
"Perplex",
"perplexity",
"PerronFrobenius",
"Persample",
"PersistenceLandscape",
"Pertarget",
"Perturb",
"Pettitt",
"pettitt_changepoint",
"Pexp",
"Pf",
"pfd",
"PfieldSimulate",
"Pgamma",
"Pgram",
"Pgrank",
"PgSigmas",
"ph_assumption_test",
"phase1",
"PhaseResp",
"Pheno2",
"phi",
"phi_coefficient",
"phmmsr_cheatsheet",
"phmmsr_gumbel_pvalue",
"phmmsr_msv_score",
"phmmsr_search_pipeline",
"phmmsr_sparse_rescale",
"phmmsr_striped_layout",
"Phylog",
"Phylotr",
"PickupDeliveryInsertion",
"piecewise_trend",
"Piepar",
"PiidInt",
"PivotedCholesky",
"PivotPoints",
"placebo_did",
"plackt",
"planar_flow",
"PLineNotch",
"Pliv",
"Plncf",
"PlotEstimate",
"Plrident",
"PlumeConcentration",
"PluralityCompetition",
"Pmedex",
"PMedian",
"PmfSd",
"PmfSumConvolution",
"PmfVar",
"pmiwd",
"PnaAgg",
"Pnie",
"Pnorm",
"point_biserial_correlation",
"PointSourceLevel",
"Poisdev",
"Poisdir",
"PoisExactCI",
"PoisGauss",
"Poislo",
"PoisMean",
"PoisMode",
"PoisNorm",
"PoisPeak",
"Poispen",
"PoisPmf",
"Poispred",
"poisson_binomial_peak_ratio",
"poisson_mean_rate",
"poisson_small_interval",
"poisson_zero_series",
"PoissonEcological",
"PoissonGammaEb",
"PoissonProcessFit",
"PoisStirC",
"PoisStirl",
"PoisSum1",
"PoisVar",
"PoisZero",
"PolarizationIndices",
"PolarizationMeasures",
"PolarizationTrend",
"PolarTransform",
"PoleZero",
"policy_iteration",
"policy_values",
"policy_walk",
"Policyhead",
"PolicyMotivatedEquilibrium",
"PollardRho",
"PolrFit",
"polrz",
"poltrx_continuity_regime",
"poltrx_eps_from_key",
"poltrx_finite_tree",
"poltrx_gamma",
"poltrx_level_parameters",
"poltrx_partition_index",
"poltrx_set_probability",
"poltrx_tree_density",
"PolyExpand",
"PolyFactorModP",
"PolygonArea",
"PolygonBoolean",
"PolygonCompactness",
"PolygonContiguity",
"Polykern",
"pool",
"pooled_features",
"PopulationDensitySurface",
"popvar",
"PopVar",
"positional_encoding",
"positivity_check",
"PossibilisticCmeans",
"PossibilisticFcm",
"post_lasso",
"posterior_mean",
"postprocessing",
"Postrt",
"Poststs",
"PotthoffWhittinghill",
"PottsExact",
"PottsGibbs",
"PowerAnova",
"PowerDist",
"PowerIndices",
"Powering",
"PowerLawField",
"PowerPropTest",
"PowerTTest",
"PowExpAp2",
"PowExpApx",
"PowLogSer",
"Powsrv",
"Ppcheck",
"Ppcrep",
"PpgFeat",
"PpgWtDen",
"Ppmean",
"Ppoclip",
"Ppois",
"Ppsamp",
"Ppssamp",
"ppswrs",
"Pptest",
"Pptrend",
"Ppunit",
"Ppv",
"PracticalSalinity",
"Prdur",
"precision_recall",
"PrecK",
"Predacc",
"Predcomp",
"predict_naive",
"predict_next",
"prediction_accuracy",
"prediction_interval",
"PredictionImprovement",
"predictive_density",
"preference_vector",
"Prefixev",
"Prehay",
"preliminary_estimates",
"Prevratio",
"prewhiten_var",
"PrewhitenedMannKendall",
"prgrl",
"private_ci",
"private_release",
"Prnkpg",
"probabilistic_bias_analysis",
"probabilisticlsa",
"ProbabilisticVote",
"probability_of_control",
"probability_of_improvement",
"ProbabilityKriging",
"ProbabilityMap",
"ProbabilityRatio",
"ProbitVote",
"ProbOrExclusive",
"ProbOrGeneral",
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"ProcrustesFit",
"ProcrustesOblique",
"ProductVariance",
"profile_beta",
"progressive_align",
"project_ball",
"project_box",
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"project_nonneg",
"project_patches",
"project_to_llm",
"projected_gradient",
"projection_matrix",
"promptable_segment",
"Propal",
"Propalloc",
"prophe_additive_components",
"prophe_cheatsheet",
"prophe_component_shares",
"prophet_design",
"prophet_predict",
"PropMd",
"Propme",
"ProportionalSeats",
"ProspectiveScan",
"ProteinDisorder",
"ProxGrad",
"ProximityBands",
"ProximityMetrics",
"ProximityVote",
"prphet_cheatsheet",
"Prsmtd",
"prtcl_cheatsheet",
"Prtdid",
"prxgms_cheatsheet",
"PsdAcf",
"PsdHz",
"Pseudo",
"pseudo_outcome",
"Pseudor2",
"PsgStage",
"psis_loo",
"Psoop",
"pspln",
"Pt",
"Ptmcmc",
"Ptnulltst",
"Ptotal",
"PTukey",
"Puctsel",
"purity_completeness",
"PvcBayes",
"PvcLinDf",
"PWaveDet",
"Pwpgt",
"PwrRTest",
"PysrRegression",
"PzForm",
"PzFormZ",
"PzMag",
"PzPhase",
"PzResp",
"q_values",
"Qasyvar",
"Qbebnd",
"Qbeta",
"Qbinom",
"QboIndex",
"Qbwcheck",
"Qchisq",
"Qcritset",
"Qda",
"Qedgew",
"Qexp",
"Qf",
"qformer_attend",
"Qgamma",
"qini_coefficient",
"qini_curve",
"Qlearn",
"Qmds",
"Qnorm",
"QnScale",
"qpdual",
"qpdual_cheatsheet",
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"Qrf",
"qrntcq_cheatsheet",
"qrs_duration",
"QrsDeriv",
"QrsDeriv1",
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"QrsDerivMx",
"QrsDerivOp",
"QrsDetect",
"QrsDrvSmth",
"QrsHPassDf",
"QrsHPassIo",
"QrsHPassTf",
"QrsHpLpDf",
"QrsHpLpTf",
"QrsLPassDf",
"QrsLPassTf",
"QrsMwInt",
"QrsSpkiUpd",
"QrsThresh",
"QrsWSqDrv",
"qsarh",
"Qt",
"QTukey",
"quadratic_program",
"quadratic_spectral_kernel",
"QuadraticRoots",
"QuadraticUtilityConstrained",
"QuadraticUtilityLagrange",
"QuadratTest",
"QuadtreeGrid",
"QuadUtility",
"quantile_bins",
"QuantileRegressionLp",
"quantize",
"quarantine_efficacy",
"query_tokens",
"quntf",
"QuotaSolution",
"r_convention",
"r_effect_size",
"r_squared",
"r_to_d",
"r_to_or",
"R0",
"R0bayse",
"R2FTest",
"RadialBasis",
"RadiationFlows",
"radiative_forcing_co2",
"RadiometricCorrection",
"Radj",
"RainfallAdequacy",
"Raklng",
"RampFilt",
"ramsey_reset_test",
"Ramsw",
"RandicIndex",
"random_effects_meta",
"random_sign_flip",
"random_walk_kernel",
"RandomPhaseField",
"RandomSpatialSample",
"Randres",
"RandW",
"Randwk",
"RangeCondition",
"rank_biserial_correlation",
"rank_by_similarity",
"rank_masks",
"RankedProbabilityScore",
"Ranksum",
"RaoScott",
"Rasscale",
"RasterAggregate",
"RasterDisaggregate",
"RasterizePoints",
"RasterMask",
"RasterResample",
"rate",
"rate_condition",
"rate_ratio",
"rate_test",
"Ratedist",
"Ratioest",
"RationalCancel",
"RatZ",
"raw_moment",
"ray_points",
"RayleighDist",
"rbf_basis",
"RbfGrid",
"RbfInterpolate",
"Rbfk",
"Rbfkern",
"RbfLoocv",
"RbfMultiscale",
"Rbfn",
"rcall",
"RcMemb",
"Rddmanip",
"Rdfzzy",
"rdgr",
"Rdkfp",
"Rdkkin",
"Rdksrn",
"Rdmcbw",
"Rdpc",
"reassigned_spectrogram",
"recall_at_k",
"recapTOA",
"recommend",
"reconstruct",
"recover_bias",
"Redcap",
"RedEdgePosition",
"Redund",
"reference_free",
"Reffec",
"RefinedSolve",
"RefPattern",
"regimen_value",
"RegimeWeights",
"register_stat_command",
"Reglu",
"regression_table",
"RegressionToMeanFactor",
"RegularGrid",
"rejct",
"relative_efficiency",
"relative_error",
"relative_utility",
"Relbt",
"ReliefFactor",
"Reluact",
"remainder_bound",
"RemlComponents",
"Remlik",
"RemovalEfficiency",
"Renent",
"renyi_divergence_gaussian",
"ReosFilter",
"repeated_cv",
"repeated_measures_anova",
"RepeatedKfoldIndices",
"RepetitionErrorApprox",
"Replaypack",
"replicated_pfilter",
"Reportm",
"Resaln",
"resample",
"Resblock",
"rescale",
"residual_forest",
"residual_loss",
"residual_map",
"ResidualMoran",
"residue_vectors",
"ResistanceFromSuitability",
"ResolutionRefutation",
"resolve_stat_command",
"resolve_synthetic_name_map",
"RespFeat",
"RespSound",
"Retention",
"RetestIQ",
"RetestR",
"retlv",
"retrieve_augment",
"reversible_jump_mcmc",
"RevSlope",
"Rexp",
"Rfcomp",
"Rfkrn",
"Rfmdi",
"Rfmlt",
"Rfpmi",
"rgadp",
"rgapn",
"rgarb",
"rgcoh",
"rgcrl",
"rgdfa",
"rgeeg",
"rgemg",
"rgemgf",
"rgemgfd",
"rgenv",
"rgfir",
"rghfd",
"rghrv",
"rgiir",
"rgisint",
"rgkneejt",
"rglyp",
"rgpsd",
"rgqrs",
"rgsam",
"rgstf",
"rgvmg",
"rgwav",
"rgzcr",
"rhobin",
"RhoBounds",
"RhumbLine",
"Riccati",
"RiceDist",
"ridge_fit",
"Ridgeobj",
"Ridgesol",
"ridgrg",
"RipF",
"RipG",
"Ripk",
"RipsPersistence",
"RischIntegration",
"risk_difference",
"risk_ratio",
"Riskdf",
"RiskLayers",
"Riskrt",
"RiskTerrain",
"rj_log_acceptance",
"rkd_estimate",
"Rkhsbayes",
"rkhsc",
"Rkhsmt",
"Rkhsnorm",
"rKL",
"RlsApriori",
"RlsFilt",
"RlsLattice",
"RlsMonitor",
"RlsNormal",
"RlsObj",
"RlsUpdate",
"Rms",
"rms_value",
"Rmsetst",
"Rmsnorm",
"Rmst",
"Rmstdiff",
"rND",
"Rnorm",
"Robcov",
"RobinsonProject",
"RobustLda",
"RobustSoliton",
"Roc",
"RoemerPune",
"RoganGladen",
"roi_align",
"roi_pool",
"RollcallCohesion",
"RollcallFilter",
"RollcallMatrix",
"RollcallSummary",
"Rolloutmc",
"root_mean_squared_error",
"Rootnoise",
"Ropedy",
"Rosenb",
"rosenbaum_bounds",
"rotate_dek",
"rotate_kek",
"RotatedGrid",
"RotatedPole",
"Rotatesc",
"rotation_cost",
"RotationScore",
"round_trip",
"Rownorm",
"rpmcor",
"Rpnlt",
"Rrand",
"rRD",
"rrexct",
"rrint",
"RstressMds",
"Rtrenew",
"RtSi",
"rubins_rules",
"RubinsteinBargaining",
"rule_value_seq",
"run_egnn",
"run_stat_command",
"RunInt",
"RunIntAll",
"RunlengthCapacity",
"RunoffWinner",
"runs_test",
"RuppertRefine",
"Rusle",
"RweqWindErosion",
"Rwlap",
"S_function",
"S2slsLag",
"s6_layer",
"Sacboot",
"Sacc",
"Sacconv",
"Sacdet",
"Sacimp",
"Sacjac",
"Saclrt",
"Sacmod",
"Sacres",
"Sacrob",
"Sactv",
"Sacvar",
"Sacwald",
"sage",
"sage_layer",
"SalopCircle",
"SalvoExchange",
"sampen",
"SampfordDesign",
"sample_acf",
"sample_bound",
"sample_ibp",
"sample_mean",
"sample_neighbors",
"sample_pdf",
"sample_urn",
"SampleLmoments",
"SampleVariogram",
"SampleVariogramNd",
"SampMean",
"SampR",
"SampVar",
"SampVarVar",
"Sarboot",
"SarCovariance",
"Sardet",
"Sarerr",
"Sarfilt",
"Sarimp",
"Sarjac",
"sarla",
"Sarlag",
"Sarlrt",
"Sarmix",
"SarNegbin",
"SarOrderedProbitGibbs",
"SarPoisson",
"SarPoissonLmTest",
"SarProbitGibbs",
"Sarr2",
"sarre",
"Sarres",
"Sarsa",
"Sarsc",
"Sarsim",
"Sarspil",
"SarTobitGibbs",
"Sarvar",
"Sarwald",
"SarZinb",
"SarZip",
"SaScore",
"sasimi_cosine",
"sasimi_counts",
"sasimi_dice",
"sasimi_distance",
"sasimi_fingerprint",
"sasimi_nearest_neighbours",
"sasimi_similarity_matrix",
"sasimi_tanimoto",
"sasimi_tversky",
"SatorraBentler",
"SavingsRoutes",
"SaxtonRawls",
"Sbcrank",
"sbmdg2",
"sbmest",
"scalar_vector_message",
"scale_to_liver",
"scaling_comparison",
"Scalogram",
"scan_depth",
"Scanstat",
"ScanZones",
"Scbsft",
"Scdisp",
"Scfd",
"ScheffeCI",
"Schoenfeld",
"Schoenres",
"schomg",
"SchwarzC",
"SchwarzR",
"Scnblrt",
"scnbmf",
"score_data_quality",
"score_separation",
"score_test",
"Scoredist",
"Scoretest",
"Scpboot",
"Scpflx",
"scpmf",
"scpprd",
"scpwld",
"ScsRunoff",
"SdAvgBin",
"SdBernoulli",
"SdBinom",
"Sdcdis",
"SdCoinSum",
"SdemML",
"Sdemres",
"SdFairCoinAvg",
"SdFromVar",
"sdid",
"SdIidSum",
"Sdmboot",
"Sdmcf",
"Sdmconv",
"Sdmdet",
"Sdmflt",
"Sdmjac",
"Sdmlrt",
"SdmML",
"Sdmolsi",
"Sdmr2",
"Sdmres",
"Sdmvar",
"Sdmwald",
"Sdmwx",
"SdOfMean",
"SdOfMeanHetero",
"sdpwts_barrier",
"sdpwts_central_path_gap",
"sdpwts_cheatsheet",
"sdpwts_is_psd",
"sdpwts_lmi",
"sdpwts_min_eigenvalue_sdp",
"sdpwts_solve_sdp",
"SdScale",
"SdSum",
"SdXbar",
"seal_record",
"SeaLevelSemiEmpirical",
"SearchCost",
"Searchhoriz",
"SearchPod",
"SearchSuccess",
"seasonal_harmonics",
"second_choice",
"SedimentTransport",
"segment_layout_boxes",
"SegregationEvenness",
"SegregationTest",
"Seiarp",
"Seirep",
"SeizCohere",
"SeizDet",
"SeizDict",
"SeizWt",
"select_bandwidth",
"selective_scan",
"selective_ssm_step",
"SelectVariogramModel",
"self_attention",
"self_distillation_loss",
"Selfconsis",
"SelfDualCodeCheck",
"Sem",
"semantic_attention",
"Semboot",
"Semconv",
"Semflt",
"semi_partial_correlation",
"SemiparKernels",
"Semjac",
"Semlm",
"Semlrt",
"Semres",
"Semsc",
"Semspec",
"Semsro",
"Semthe",
"Semvar",
"Semwald",
"Sens",
"sensitivity_curve",
"sensitivity_rosenbaum",
"sensitivity_summary",
"SeparablePrewhiten",
"separation_weight_map",
"Sepcons",
"SepIndex",
"sequential_blips",
"sequential_g_formula",
"sequential_neural_likelihood",
"sequential_scan",
"SequentialInhibition",
"SequentialLinearProgramming",
"SequentialQuadraticProgramming",
"serial_interval_draw",
"SerialCorrelation",
"series_g",
"ServiceCoverage",
"ServR",
"session_parallel_batches",
"set_transformer",
"SetCoveringLocation",
"setT",
"SetterOutcome",
"Sfbnds",
"sfgetis",
"sfloc",
"sfmemb",
"sfmi",
"sforth",
"sfredu",
"sgcar",
"Sgcrh",
"SgcRidge",
"Sgdmomstep",
"sglm",
"Sglm",
"Sglss",
"Sgntest",
"sgolay",
"Sgrwn",
"SgsBlockSimulate",
"SgsCrossValidation",
"SgsSimulate",
"Sgtadj",
"Sgtcheegerbound",
"Sgtcheegerconstant",
"sgtcoml",
"sgtdiff",
"Sgteigcent",
"Sgtgrn",
"sgthits",
"sgtkem",
"Sgtlap2",
"Sgtleid",
"Sgtmix",
"Sgtmodq",
"Sgtnbe",
"Sgtpgr",
"Sgtrwk",
"Sgtsbnd",
"sgtsbpd",
"Sgtsck",
"sgtspn",
"Sgtwlk",
"Shanent",
"Shannon",
"shapiro_wilk",
"ShapleyOwen",
"ShepardPredict",
"Shewh",
"Shfflm",
"Shfrm",
"ShiftInt",
"ShiftSchedule",
"ShorFactoring",
"ShortCrimeLattice",
"ShortCrimePde",
"shortest_path_matrix",
"ShortestPath",
"ShortestPathLengths",
"ShortwaveAlbedo",
"shrink_covariance",
"shrinkage_msm",
"ShrinkPred",
"shrunk_targeting_unsafe",
"ShuntingYard",
"Siaepi",
"sidak",
"Sieg",
"Siepid",
"Sietrt",
"Sift",
"Sifting",
"Sigbasis",
"SigEnergy",
"SigFeatures",
"Siglip",
"sigma_for_rho",
"SignlessL",
"SilhouetteWidths",
"silverman_bandwidth",
"Simbrate",
"Simbwcv",
"simes_combined",
"Simgrate",
"Simident",
"Simidentd",
"SimpleEpidemicDuration",
"simplex",
"SimplexLP",
"Simquant",
"simulate_outbreak",
"simulate_utility_shocks",
"SimulateWalk",
"SimulationSensitivity",
"SimulationSummary",
"SincKern",
"SinCosTest",
"Sindex",
"SineCosineAlgorithm",
"Singgw",
"Singsd",
"singular_value_adjust",
"SinhArcsinh",
"Sinkdist",
"Sinkhlog",
"Sinkhlowr",
"sinkhorn_knopp",
"SinkhornWeights",
"Sinkhwarm",
"Sirepi",
"Sirstn",
"Sirtdy",
"SisMarkovBayes",
"SisSimulate",
"site_likelihood",
"SitePercolation",
"sixtuple_distance",
"size_factors",
"Skater",
"skewness_coeff",
"skewness_correction",
"skip_concat",
"skipgram_pairs",
"Slope1",
"SlopeFromCov",
"SlopeProd",
"Slxboot",
"Slxflt",
"SLXRegression",
"Slxres",
"Slxwx",
"SmacofIndDiff",
"SmacofMds",
"SmartsMatch",
"smcopt",
"smcsam",
"Smean",
"smfd_bspline_basis",
"smfd_bspline_one",
"smfd_difference_matrix",
"smfd_knot_sequence",
"SmilesHba",
"SmilesHbd",
"SmilesMolecularWeight",
"SmilesRotatableBonds",
"SmilesTpsa",
"smixd",
"smo",
"smo_platt",
"smoothed_parameter",
"smoothing_bias",
"SmPgram",
"Smplqc",
"Smpqnt",
"Smrind",
"Smsciband",
"Smthconv",
"Smthedge",
"SmtSolver",
"smwgrp",
"Snaivefc",
"SnesimSimulate",
"Snmlin",
"Snpblr",
"Snpblup",
"snpeff",
"Snr",
"Snr2",
"Snr2u",
"SnrFilt",
"SnScale",
"Sntmn",
"SobelFilter",
"sobls",
"sobol_sequence",
"Sobolidx",
"Socpts",
"soft_threshold",
"soft_value_iteration",
"softmax_attention",
"softplus",
"SoilCarbon",
"SoilChemistry",
"SolomonVrptw",
"solve_lp",
"solve_pair",
"sort_pooling",
"SoundExposureLevel",
"SpaceTimeInteractionTest",
"SpaceTimeK",
"SpaceTimeSample",
"SpAcf",
"span_f1",
"spanis",
"Sparseattn",
"SparseCode",
"Sparsegp",
"SparseGpKrige",
"SparseVector",
"spatial_bias",
"spatial_cure_rate",
"spatial_dbscan",
"spatial_filter",
"spatial_frailty",
"spatial_sir",
"SpatialAccessibility",
"SpatialBargaining",
"SpatialBayesGibbs",
"SpatialBPTest",
"SpatialConcentration",
"SpatialCorrelogram",
"SpatialGini",
"SpatialGlmmFit",
"SpatialGlmmPredict",
"SpatialGlmmSimulate",
"SpatialHeart",
"SpatialImpacts",
"SpatialJTest",
"SpatialKde",
"SpatialLogit",
"SpatialLogitGmm",
"SpatialLrTest",
"SpatialPanelFe",
"SpatialPanelMl",
"SpatialPanelRe",
"SpatialPanelReLag",
"SpatialPca",
"SpatialPoisson",
"SpatialProbit",
"SpatialProbitGmm",
"SpatialQuantileIv",
"SpatialRegressionML",
"SpatialThinning",
"SpatialTwoStageLS",
"SpatialWaldTest",
"SpatialWeights",
"SpatialZip",
"Spatrend",
"SpautolmFit",
"spbayr",
"spbesf",
"spbino",
"spblk",
"spblup",
"Spbrown",
"spbym",
"spcar",
"spcgm",
"spcirc",
"spcmp",
"spcont",
"spconv",
"spcovf",
"spcrs",
"spcsr",
"SpdePrecisionGrid",
"spearman_correlation",
"Spec",
"SpecAnom",
"Specclus",
"SpecClust",
"SpecDec",
"SpecEntropy",
"specification_curve",
"SpecMoments",
"SpecRad",
"SpecRes",
"spectral_radius",
"SpectralAngleClassify",
"SpectralGRF",
"SpectralIndex",
"Spectrogram",
"SpeechFeat",
"SpErrMod",
"spexp",
"spffun",
"SpGam",
"spgaus",
"spgfun",
"spglmk",
"spglmm",
"spgls",
"spglsv",
"spgwr",
"spgwrb",
"spgwrk",
"sphet",
"SphVario",
"spicar",
"SpikeInfo",
"spiso",
"SpKappa",
"spkcrs",
"spkfnn",
"spkfun",
"spkrnv",
"spkwt",
"splfun",
"splgk",
"splgtmf",
"splgtml",
"split_sample",
"split_specific_tmle",
"SplitWindowSst",
"Splrtest",
"spmatr",
"spmenv",
"Spmi",
"spmidw",
"spml",
"Spmlebin",
"spmsd",
"spmsim",
"spmwst",
"spnest",
"spnpsv",
"spnscl",
"spnsr",
"spnst",
"spnug",
"spols",
"SpPanelDynamic",
"sppbe",
"sppboot",
"sppcov",
"sppdiag",
"sppdyn",
"spperiod",
"sppfe",
"sppgmm",
"spphaus",
"sppiv",
"spplm",
"sppois",
"sppow",
"sppql",
"sppre",
"sppres",
"spprmf",
"spprml",
"spprprd",
"sppsac",
"sppsar",
"sppsdm",
"sppsem",
"SprawlEntropy",
"spreml",
"sprfss",
"sprmfdi",
"spsar",
"spsemv",
"spskrg",
"spspec",
"spsph",
"spssoc",
"spstcn",
"spstcv",
"spstp",
"spstvg",
"sptag",
"sptau",
"sptent",
"sptfx",
"sptgk",
"spthom",
"sptrn",
"sptrx",
"spwkth",
"spwls",
"squared_exponential",
"SquareRootLaw",
"Srfintp",
"Srms",
"Srstotal",
"Srvbv1",
"Srvbv2",
"Srvcov1",
"Srvcov2",
"Sscompv",
"sse4r_cheatsheet",
"Ssello",
"Ssgnmom",
"Ssintc",
"Sstrlf",
"StAcf",
"stacv",
"StaffingRequirement",
"stage_game_type",
"stage_one_objectives",
"standard_form",
"StandardiseRealisations",
"standardized_coefficients",
"StandardizedRatio",
"StandDensityIndex",
"StarBars",
"StarBRec",
"StarFit",
"StarmaFit",
"StarmaForecast",
"StarsAndBars",
"start_allowed",
"starting_models",
"stat_bridge_exec",
"stat_bridge_fn_info",
"stat_bridge_fn_search",
"stat_bridge_help",
"stat_bridge_main",
"stat_bridge_registry_json",
"stat_bridge_verify",
"stat_command",
"state_matrices",
"StationaryBootstrap",
"stats_free_interp",
"Stbciw",
"StBlockKriging",
"StCokriging",
"STCovariance",
"StCovarianceFamily",
"Stefanbz",
"StehfestInverse",
"step_function_loglik",
"StepSeq",
"StewartPotential",
"StFit",
"StftParam",
"StGetisOrd",
"StGFunction",
"stick_breaking",
"Stickpost",
"sticks_for_tolerance",
"Stickw",
"StirlingGammaRatio",
"StJFunction",
"StKFunction",
"stkrg",
"STKriging",
"STKrigingCV",
"StKrigingDiagnostics",
"StLag",
"StLeaveHOut",
"StLinearCombination",
"StLocalKriging",
"StModelVariogram",
"stochastic_intervention",
"StochasticRouteCost",
"StoneTest",
"storey_q",
"stouffer_combined",
"StPacf",
"StPortmanteau",
"StPredictGrid",
"strat",
"Stratdes",
"StrategicVoteShares",
"StratifiedEstimate",
"Stratmean",
"Strdis",
"StreamOrder",
"StreamPowerIndex",
"StreamSegments",
"strec_attention_weights",
"strec_cheatsheet",
"strec_cross_entropy",
"strec_mlp_cell",
"strec_session_average",
"strec_stamp_scores",
"strec_trilinear",
"StripMean",
"Strtwt",
"structural_distance",
"StructureInducedEquilibrium",
"sts_score",
"StSimulate",
"Stsmod",
"StSmoothness",
"StTrendSurface",
"Studrs",
"StUniversalKriging",
"stvar",
"STVariogram",
"Subcomp",
"SubmaxComparisons",
"SubmaxTest",
"subsampling",
"subscale_ea_ave",
"subscale_ea_composite_rel",
"subscale_ee_ave",
"subscale_ee_composite_rel",
"subscale_er_ave",
"subscale_er_composite_rel",
"subscale_ua_ave",
"subscale_ua_composite_rel",
"substitution_matrix",
"subwords",
"Suffstat",
"SuitFullHouseProbability",
"SumDens",
"SumDensP",
"SumInts",
"summary_statistics_table",
"summing_matrix",
"SumOfPairsScore",
"super_learner",
"Superlrn",
"surepi_c1_mild",
"surepi_c2_medium",
"surepi_c3_ultra",
"surepi_cheatsheet",
"surepi_compound_smoothing",
"surepi_ears_detect",
"surepi_salmonella_cusum",
"Surpris",
"Survbri",
"Survci2",
"survcind",
"Survip",
"Survipa",
"Survlnk",
"Survrls",
"Survtdc",
"SuspendedSolids",
"Svariog",
"svdd",
"Svgp",
"Svm",
"Svmep",
"Svmhp",
"SvmKern",
"Svmsl",
"Svmwolfe",
"svt",
"Svymed",
"Svyqtl",
"Svytbl",
"swadapt",
"swblk",
"swdist",
"SweepWidth",
"swgab",
"swiglu",
"Swiglu",
"Swilmom",
"Swinmw",
"swinv",
"switching",
"swkern",
"swknn",
"swlag",
"swlag2",
"swlagf",
"swpath",
"swpower",
"swspars",
"swsph",
"Swt",
"SwtDen",
"swtri",
"symbolic_representation",
"SymbolicDiff",
"SymbolicIntegrate",
"SymbolicLimit",
"SymmetrizeWeights",
"SyncAvg",
"SyncSum",
"synthesize",
"synthesize_marginals",
"synthesize_noisy",
"synthetic_abc",
"synthetic_log_likelihood",
"synthetic_required_keys",
"Sysamp",
"Sysrs",
"systematic_resample",
"SystematicSample",
"t3bfs",
"t3expit",
"t3golden",
"t3nodes",
"t3ols",
"t3relu",
"t5enc_cheatsheet",
"t5enc_format_regression",
"t5enc_parse_prediction",
"t5enc_relative_bucket",
"t5enc_span_corruption",
"t5enc_task_prefix",
"table1",
"tagRC_adapted_pagerank",
"tagRC_cheatsheet",
"tagRC_preference_vector",
"tagRC_tripartite_graph",
"TalbotInverse",
"TangoTest",
"TaperedKriging",
"TaperedSimulate",
"target_dimension",
"targeted_bootstrap",
"targeted_loss",
"TasseledCap",
"Taxass",
"TbBandConvergence",
"TbEnsemble",
"td_error",
"td3",
"teacher_votes",
"TeachingLearningOptimizer",
"TelegraphProcess",
"Tempdecay",
"temperature_ladder",
"TemperedSpatial",
"Template",
"temporal_combination",
"temporal_validate",
"TemporalStratifiedSample",
"term_weighting",
"TerrainIndices",
"test_variant",
"Testcond",
"tetranucleotide_frequency",
"Tfcells",
"Tfidf",
"TheilDecomposition",
"theorem3_moment",
"ThinPlateSpline",
"ThomasPcf",
"ThomasSimulate",
"Thomp",
"thrtmt_blip_function",
"tie_probability",
"time_weights",
"tippett_combined",
"tipping_point_analysis",
"Tldepl",
"Tldepu",
"tlltmle_cheatsheet",
"tlltmle_clever_covariate",
"tlltmle_fluctuate",
"tlltmle_influence_curve_se",
"tlltmle_ltmle",
"tlltmle_tmle_point",
"Tmlbas",
"Tmldis",
"tmle_cdrs",
"Tmle2stage",
"Tmleboot",
"Tmlecat",
"Tmlecde",
"Tmleext",
"Tmleinf",
"Tmleor",
"Tmleoutcomeonlyregr",
"Tmlepool",
"Tmleqs",
"Tmlerd",
"Tmlerob",
"Tmlerr",
"Tmlestab",
"Tmlevar",
"Tmlfed",
"Tmlhrz",
"Tmlitr",
"Tmlmct",
"TmlMd",
"Tmlmda",
"Tmlmlt",
"Tmlmnl",
"Tmlmpc",
"Tmlmpi",
"Tmlmrk",
"Tmlnde2",
"Tmlnsm",
"Tmlnte",
"Tmlper",
"Tmlphd",
"Tmlpse",
"Tmlqlc",
"Tmlrct",
"Tmlrec",
"Tmlres",
"Tmlric",
"Tmlsbg",
"Tmlsbs",
"Tmlspl",
"Tmltrn",
"Tmscore",
"Tnie",
"to_approx_dp",
"ToaReflectance",
"toc_curve",
"tokenize",
"top1_loss",
"Topdown",
"TopographicCorrection",
"TopographicWetnessIndex",
"topological_torsions",
"ToroidalShiftTest",
"torsion_similarity",
"TostCorrelation",
"total_compute",
"TotalDissolvedSolids",
"tpspn",
"Tqdist",
"Tqhs",
"Tqmom",
"Tqorth",
"Tqrot",
"TrackSteps",
"TrafficAssignment",
"train_bpe",
"train_flow",
"trainable_fraction",
"training_stage",
"TrajectoryMetrics",
"Transent",
"TransfiniteGrid",
"transform_to_real",
"TransformedField",
"transition_probabilities",
"TransitionMatrix",
"translate",
"TransportationProblem",
"TravellingSalesman",
"trclrn_cheatsheet",
"trclrn_fit_tree",
"trclrn_predict_rule",
"trclrn_rule_value",
"trclrn_tree_rules",
"Trcwgt",
"treatment_effect_table",
"treatment_weights",
"tree_paths",
"TreeBiomass",
"Treedepth",
"TreeTops",
"trend_matrix",
"Triangle",
"TriangleQuality",
"TriangularGrid",
"TrimCI",
"Trimit",
"TrimSE",
"tripartite_graph",
"TripGeneration",
"triplet_loss",
"TripNoRep",
"Trmwgt",
"Trpostep",
"Trsub",
"truncated_dp",
"TruncatedProductPvalue",
"truncation_error",
"Tsallen",
"Tsalls",
"TspTour",
"TuckerHooi",
"tukey_hanning_kernel",
"TukeyKramer",
"TukeyLambda",
"Tukeyw",
"Tukrho",
"Tukrr",
"Turnbull",
"TurningBands",
"TurningBandsSpherical",
"TurnsCount",
"Tvdi",
"TvLsi",
"Tvsbn",
"TwaEmd",
"TwaSpectr",
"TWaveDet",
"TwidConj",
"TwidCS",
"Twiddle",
"TwidPer",
"two_proportion_ztest",
"two_sample_ttest",
"two_way_anova",
"two_way_block",
"TwoCoinVar",
"Twostage",
"Twostg",
"Ucbb",
"UhiIntensity",
"ukrig",
"ULearnerCate",
"UnderwaterPropagation",
"unescape_whitespace",
"unfdl",
"uniform_bins",
"unit_weights",
"UnitHydrograph",
"Unitl",
"univariate_mcd",
"unmeasured_conf_bias",
"UnobservedEffectsTest",
"Unobts",
"unscale",
"unsupervised_loss",
"unwrap_dek",
"upsample",
"urn_weights",
"UsdaTexture",
"UStatisticSensitivity",
"Ustep",
"utility",
"UtilityMax",
"UtmZone",
"Vaccthresh",
"Vaean",
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"LmcCosimulate",
"NestedDecomposition",
"RefinedSolve",
"SimulationSensitivity",
"TaperedSimulate",
"WendlandTaper"
]
},
{
"page": "BandwidthDiggle",
"title": "Berman-Diggle cross-validated kernel bandwidth",
"topics": [
"BandwidthDiggle"
]
},
{
"page": "Barte",
"title": "BART text-infilling denoising step",
"topics": [
"Barte"
]
},
{
"page": "Bartkw",
"title": "Triangular lag weights that keep a long-run variance non-negative",
"topics": [
"Bartkw"
]
},
{
"page": "bartlett_test",
"title": "Bartlett's test for equality of variances",
"topics": [
"bartlett_test"
]
},
{
"page": "BasalArea",
"title": "Forest inventory and LiDAR canopy analysis",
"topics": [
"AdaptiveClusterEstimate",
"BasalArea",
"CanopyGaps",
"CanopyHeightModel",
"CarbonStock",
"CrownSegmentation",
"LineIntersectVolume",
"PlotEstimate",
"RasterizePoints",
"StandDensityIndex",
"StratifiedEstimate",
"TreeBiomass",
"TreeTops"
]
},
{
"page": "Basevap",
"title": "FAO-56 Penman-Monteith reference evapotranspiration (Eq. 6)",
"topics": [
"Basevap"
]
},
{
"page": "Basisexp",
"title": "Linear basis expansion fitted by least squares (ESL eqs. 2.30, 2.43)",
"topics": [
"Basisexp"
]
},
{
"page": "BatAlgorithm",
"title": "Bat algorithm",
"topics": [
"BatAlgorithm"
]
},
{
"page": "Baujat",
"title": "Baujat plot coordinates for a meta-analysis",
"topics": [
"Baujat"
]
},
{
"page": "BayesExp",
"title": "Bayes theorem in explicit form",
"topics": [
"BayesExp"
]
},
{
"page": "bayesian_fmi",
"title": "E-BFMI: energy Bayesian fraction of missing information",
"topics": [
"bayesian_fmi"
]
},
{
"page": "BayesLinearHalfcauchy",
"title": "Gibbs samplers for Bayesian regression, mixtures and genomic prediction",
"topics": [
"BayesA",
"BayesB",
"BayesLinearHalfcauchy",
"ContaminatedNormalOutliers",
"FiniteMixtureGibbs",
"GenomicReliability"
]
},
{
"page": "Bayeslogit",
"title": "Posterior mode and normal approximation for logistic regression",
"topics": [
"Bayeslogit"
]
},
{
"page": "Bayesocp",
"title": "Bayesian online changepoint detection (re-export)",
"topics": [
"Bayesocp"
]
},
{
"page": "BayesOutbreak",
"title": "Spatial epidemiology and disease surveillance",
"topics": [
"BayesOutbreak",
"BufferExposure",
"Bym2Structure",
"CusumSurveillance",
"EcologicalRegression",
"KernelExposure",
"LerouxPrecision"
]
},
{
"page": "Bayisr",
"title": "Sampling-importance-resampling (SIR)",
"topics": [
"Bayisr"
]
},
{
"page": "Baynet",
"title": "Discrete Bayesian network inference by variable elimination",
"topics": [
"bayes_network",
"Baynet"
]
},
{
"page": "Baytrace",
"title": "MCMC trace summaries: running mean and running quantile bands",
"topics": [
"Baytrace"
]
},
{
"page": "Bcdblk",
"title": "Cyclic block coordinate descent, exact minimisation per block",
"topics": [
"Bcdblk"
]
},
{
"page": "Bcelo",
"title": "Binary cross-entropy (logistic) loss",
"topics": [
"Bcelo"
]
},
{
"page": "bcppcc",
"title": "Box-Cox lambda by the probability plot correlation",
"topics": [
"bcppcc"
]
},
{
"page": "Beggtest",
"title": "Begg and Mazumdar's rank correlation test for publication bias",
"topics": [
"Beggtest"
]
},
{
"page": "benjamini_hochberg",
"title": "Benjamini-Hochberg FDR control",
"topics": [
"benjamini_hochberg",
"bh"
]
},
{
"page": "benjamini_yekutieli",
"title": "Benjamini-Yekutieli FDR control under arbitrary dependence",
"topics": [
"benjamini_yekutieli",
"by_fdr"
]
},
{
"page": "BerkeleyEarth",
"title": "Berkeley Earth regional temperature average",
"topics": [
"BerkeleyEarth"
]
},
{
"page": "bernoulli_variance",
"title": "Elementary probability results of Morin (2016)",
"topics": [
"bernoulli_variance",
"binomial_expansion",
"binomial_pmf_vector",
"binomial_variance",
"classify_events",
"conditional_from_joint",
"conditional_subset",
"density_expectation",
"gaussian_approx_2n",
"gaussian_approx_biased",
"gaussian_approx_n",
"gaussian_sum_density",
"hockey_stick",
"hypergeometric_pmf",
"partial_permutations",
"permutations_count",
"poisson_binomial_peak_ratio",
"poisson_mean_rate",
"poisson_small_interval",
"poisson_zero_series"
]
},
{
"page": "Bernstein",
"title": "Bernstein's concentration inequality",
"topics": [
"Bernstein"
]
},
{
"page": "Bertscore",
"title": "BERTScore precision, recall and F1",
"topics": [
"Bertscore"
]
},
{
"page": "BestConst",
"title": "Best constant predictor under squared error",
"topics": [
"BestConst"
]
},
{
"page": "Betabinom",
"title": "Beta-binomial conjugate updating",
"topics": [
"Betabinom"
]
},
{
"page": "Betavae",
"title": "beta-VAE objective with an optional capacity target",
"topics": [
"Betavae"
]
},
{
"page": "Bezout",
"title": "Bezout coefficients by the extended Euclidean algorithm",
"topics": [
"Bezout"
]
},
{
"page": "BfgsMinimize",
"title": "BFGS minimisation",
"topics": [
"BfgsMinimize"
]
},
{
"page": "Bfgsupd",
"title": "BFGS secant update of the Hessian and its inverse",
"topics": [
"Bfgsupd"
]
},
{
"page": "bias_adjusted_estimate",
"title": "Bias-adjusted treatment effect (Ding & VanderWeele 2016)",
"topics": [
"bias_adjusted_estimate"
]
},
{
"page": "Biasvardec",
"title": "Bias-variance decomposition of the expected prediction error",
"topics": [
"Biasvardec"
]
},
{
"page": "bic_posterior_probs",
"title": "Posterior model probabilities from BIC",
"topics": [
"bic_posterior_probs"
]
},
{
"page": "Bicarp",
"title": "BIC order selection for an autoregression, on the Schwarz penalty",
"topics": [
"Bicarp"
]
},
{
"page": "BicubicSpline",
"title": "Spatial interpolation extras",
"topics": [
"BarrierIdw",
"BicubicSpline",
"StewartPotential"
]
},
{
"page": "Bimid",
"title": "Biweight midvariance",
"topics": [
"Bimid"
]
},
{
"page": "BinaryGlm",
"title": "Spatial binary-choice models",
"topics": [
"BinaryGlm",
"DiscreteMoranTest",
"SpatialLogitGmm",
"SpatialProbitGmm"
]
},
{
"page": "BinghamDens",
"title": "Bingham distribution on the circle and the sphere",
"topics": [
"BinghamDens"
]
},
{
"page": "BinomCoef",
"title": "Unordered subgroups: the binomial coefficient",
"topics": [
"BinomCoef"
]
},
{
"page": "BinomCtr",
"title": "Centred fair-coin binomial",
"topics": [
"BinomCtr"
]
},
{
"page": "BinomCtrF",
"title": "Factorial form of the centred binomial",
"topics": [
"BinomCtrF"
]
},
{
"page": "BinomDie",
"title": "Binomial pmf for a b-sided die",
"topics": [
"BinomDie"
]
},
{
"page": "BinomExp",
"title": "Binomial expansion of a shifted power",
"topics": [
"BinomExp"
]
},
{
"page": "BinomMean",
"title": "Binomial mean",
"topics": [
"BinomMean"
]
},
{
"page": "BinomMom2",
"title": "Binomial second moment",
"topics": [
"BinomMom2"
]
},
{
"page": "BinomPeak",
"title": "Peak height of the binomial distribution",
"topics": [
"BinomPeak"
]
},
{
"page": "BinomPeq",
"title": "Binomial success probability equalising P(0) and P(1)",
"topics": [
"BinomPeq"
]
},
{
"page": "BinomPmf",
"title": "Binomial probability mass function",
"topics": [
"BinomPmf"
]
},
{
"page": "BinomVar",
"title": "Binomial variance",
"topics": [
"BinomVar"
]
},
{
"page": "BinPoisLim",
"title": "Binomial against its Poisson limit",
"topics": [
"BinPoisLim"
]
},
{
"page": "Binseg",
"title": "Binary segmentation changepoint search",
"topics": [
"Binseg"
]
},
{
"page": "bivariate_morans_i",
"title": "Bivariate Moran I between two variables",
"topics": [
"bivariate_morans_i"
]
},
{
"page": "bkelim",
"title": "Backward elimination by the partial F statistic",
"topics": [
"bkelim"
]
},
{
"page": "bkrnig",
"title": "Bayesian kriging with a normal-inverse-gamma prior",
"topics": [
"bkrnig"
]
},
{
"page": "BlackboxTranspose",
"title": "Blackbox-transpose scaling of a respondent-by-stimulus rating matrix",
"topics": [
"BlackboxTranspose"
]
},
{
"page": "BlissPoints",
"title": "Bliss points from preference ratings",
"topics": [
"BlissPoints"
]
},
{
"page": "block_bootstrap",
"title": "Block bootstrap for dependent / time-series data",
"topics": [
"block_bootstrap"
]
},
{
"page": "BlockBootstrapGrid",
"title": "Resampling for spatially dependent data",
"topics": [
"BlockBootstrapGrid",
"BootstrapBands",
"StationaryBootstrap",
"ToroidalShiftTest"
]
},
{
"page": "BlockDiscretize",
"title": "Discretisation of a rectangular block",
"topics": [
"BlockDiscretize"
]
},
{
"page": "BlockWeights",
"title": "Block weights from group labels",
"topics": [
"BlockWeights"
]
},
{
"page": "Blupint",
"title": "BLUP of a random intercept",
"topics": [
"Blupint"
]
},
{
"page": "Blupslope",
"title": "BLUP of random intercept and slope",
"topics": [
"Blupslope"
]
},
{
"page": "bnappx",
"title": "Normal approximation to the binomial distribution function",
"topics": [
"bnappx"
]
},
{
"page": "Bndapp",
"title": "Manski-Pepper MTR-MTS bounds (the returns-to-schooling application)",
"topics": [
"Bndapp"
]
},
{
"page": "Bndcvr",
"title": "Empirical coverage of replicated intervals, tested against nominal",
"topics": [
"Bndcvr"
]
},
{
"page": "Bndfre",
"title": "Imbens-Manski interval from replicated estimates of the two bounds",
"topics": [
"Bndfre"
]
},
{
"page": "Bndinf",
"title": "Test-inversion confidence set for an interval-identified scalar",
"topics": [
"Bndinf"
]
},
{
"page": "Bndlgt",
"title": "Bounds on the causal odds ratio for a binary outcome",
"topics": [
"Bndlgt"
]
},
{
"page": "Bndlmm",
"title": "Intersection bounds from many candidate lower and upper bounds",
"topics": [
"Bndlmm"
]
},
{
"page": "Bndlpm",
"title": "Balke-Pearl sharp ATE bounds by linear programming",
"topics": [
"Bndlpm"
]
},
{
"page": "Bndmoq",
"title": "Sharp quantile bounds for a selectively observed outcome",
"topics": [
"Bndmoq"
]
},
{
"page": "Bndngt",
"title": "Negative-only treatment bound (decreasing monotone treatment response)",
"topics": [
"Bndngt"
]
},
{
"page": "Bndnmt",
"title": "Bound the complier effect when defiers are not ruled out",
"topics": [
"Bndnmt"
]
},
{
"page": "Bndnpr",
"title": "Nonparametric (kernel) worst-case regression bound",
"topics": [
"Bndnpr"
]
},
{
"page": "Bndnvg",
"title": "Naive gross bound on the average treatment effect",
"topics": [
"Bndnvg"
]
},
{
"page": "Bndpos",
"title": "Positive-only treatment bound (increasing monotone treatment response)",
"topics": [
"Bndpos"
]
},
{
"page": "Bndsbs",
"title": "Coordinate projection of a d-dimensional identified set",
"topics": [
"Bndsbs"
]
},
{
"page": "Bndsdo",
"title": "ATE bound under monotone treatment selection",
"topics": [
"Bndsdo"
]
},
{
"page": "Bndtfm",
"title": "Worst-case bounds for a monotonically transformed outcome",
"topics": [
"Bndtfm"
]
},
{
"page": "Bndvld",
"title": "Validity check for a collection of bound assumptions",
"topics": [
"Bndvld"
]
},
{
"page": "Bnrunstat",
"title": "Batch-normalisation running statistics and inference transform",
"topics": [
"Bnrunstat"
]
},
{
"page": "Bnscbo",
"title": "Compound-outcome worst-case bound",
"topics": [
"Bnscbo"
]
},
{
"page": "Bnscnf",
"title": "Confidence set for a partially identified parameter (alias)",
"topics": [
"Bnscnf"
]
},
{
"page": "Bnscom",
"title": "Complier means, principal-strata shares and the resulting ATE bound",
"topics": [
"Bnscom"
]
},
{
"page": "Bnscrf",
"title": "Confidence interval for a partially identified parameter (alias)",
"topics": [
"Bnscrf"
]
},
{
"page": "Bnsiii",
"title": "Identified set as the zero-level set of the moment-inequality criterion",
"topics": [
"Bnsiii"
]
},
{
"page": "Bnsipv",
"title": "Intersection of the instrument bound and the shape bound",
"topics": [
"Bnsipv"
]
},
{
"page": "Bnssel",
"title": "Sample-selection bound for a selectively observed outcome",
"topics": [
"Bnssel"
]
},
{
"page": "Bnstst",
"title": "Test a null value against the Imbens-Manski confidence interval",
"topics": [
"Bnstst"
]
},
{
"page": "Bnstvr",
"title": "Intersection bounds on the ATE under an exclusion restriction",
"topics": [
"Bnstvr"
]
},
{
"page": "Bocpd",
"title": "Bayesian online changepoint detection (Gaussian run model)",
"topics": [
"Bocpd"
]
},
{
"page": "bonferroni",
"title": "Bonferroni FWER correction",
"topics": [
"bonferroni"
]
},
{
"page": "BookMeans",
"title": "Means of the book's five-point data set",
"topics": [
"BookMeans"
]
},
{
"page": "bootstrap",
"title": "Nonparametric bootstrap inference",
"topics": [
"bootstrap"
]
},
{
"page": "bootstrap_632",
"title": ".632 and .632+ bootstrap estimators for prediction error",
"topics": [
"bootstrap_632"
]
},
{
"page": "bootstrap_effect_size_ci",
"title": "Generic bootstrap CI wrapper for any effect-size function",
"topics": [
"bootstrap_effect_size_ci"
]
},
{
"page": "bootstrap_validate",
"title": "Bootstrap .632 / .632+ validation",
"topics": [
"bootstrap_validate"
]
},
{
"page": "Bottomup",
"title": "Bottom-up reconciliation of a hierarchical forecast",
"topics": [
"Bottomup"
]
},
{
"page": "bound_monotone_test",
"title": "Kitagawa (2015) test of instrument validity",
"topics": [
"bound_monotone_test"
]
},
{
"page": "BoundedDistanceNoise",
"title": "Coding-theory results from MacKay",
"topics": [
"BoundedDistanceNoise",
"CodeLengthDecomposition",
"McGillInteractionInformation",
"RepetitionErrorApprox",
"RobustSoliton",
"RunlengthCapacity",
"SelfDualCodeCheck"
]
},
{
"page": "Boxpierce",
"title": "Box-Pierce Q test for autocorrelation",
"topics": [
"Boxpierce"
]
},
{
"page": "BrayCurt",
"title": "Bray & Curtis (1957) dissimilarity between two non-negative vectors",
"topics": [
"BrayCurt"
]
},
{
"page": "Brenier1d",
"title": "Brenier optimal transport map in one dimension",
"topics": [
"Brenier1d"
]
},
{
"page": "Bridgs",
"title": "Bridge sampling ratio of normalizing constants (Meng-Wong)",
"topics": [
"Bridgs"
]
},
{
"page": "Brier",
"title": "(IPCW) Brier score for survival models",
"topics": [
"Brier"
]
},
{
"page": "Brierscore",
"title": "Brier score for a categorical predictive distribution",
"topics": [
"Brierscore"
]
},
{
"page": "Brrhyper",
"title": "Default hyperparameters of the Bayesian ridge regression prior",
"topics": [
"Brrhyper"
]
},
{
"page": "Bsmed",
"title": "Bootstrap percentile CI for the indirect effect in simple mediation",
"topics": [
"Bsmed"
]
},
{
"page": "Btwcent",
"title": "Betweenness centrality by Brandes' algorithm",
"topics": [
"Btwcent"
]
},
{
"page": "Bucherind",
"title": "Bucher adjusted indirect comparison",
"topics": [
"Bucherind"
]
},
{
"page": "BudykoOlr",
"title": "Climate and agreement statistics",
"topics": [
"BudykoOlr",
"EmpiricalBreakdownPoint",
"FleissKappa",
"PrewhitenedMannKendall"
]
},
{
"page": "BufferStripWidth",
"title": "Agricultural design: buffer width, wetland sizing, range condition, rotations, variable-rate zones, drain spacing",
"topics": [
"BufferStripWidth",
"DrainWaterTable",
"HooghoudtSpacing",
"RangeCondition",
"RotationScore",
"VariableRateZones",
"WetlandAreaPkc"
]
},
{
"page": "build_chain",
"title": "Build a complete chain from a list of entries",
"topics": [
"build_chain"
]
},
{
"page": "BurrDist",
"title": "Burr (type XII) distribution",
"topics": [
"BurrDist"
]
},
{
"page": "buttbp",
"title": "Butterworth bandpass filter",
"topics": [
"buttbp"
]
},
{
"page": "buttbs",
"title": "Butterworth bandstop (notch) filter",
"topics": [
"buttbs"
]
},
{
"page": "butthp",
"title": "Butterworth highpass filter",
"topics": [
"butthp"
]
},
{
"page": "buttlp",
"title": "Butterworth lowpass filter",
"topics": [
"buttlp"
]
},
{
"page": "bvcchy",
"title": "Bivariate Cauchy density",
"topics": [
"bvcchy"
]
},
{
"page": "bvncnd",
"title": "Conditional distribution in a bivariate normal",
"topics": [
"bvncnd"
]
},
{
"page": "BvnModel",
"title": "Joint density of X and Y = mX + Z",
"topics": [
"BvnModel"
]
},
{
"page": "BvNormDist",
"title": "Bivariate normal density and distribution function",
"topics": [
"BvNormDist"
]
},
{
"page": "BvPois",
"title": "Bivariate Poisson distribution",
"topics": [
"BvPois"
]
},
{
"page": "Bymfit",
"title": "Besag-York-Mollie disease-mapping log-posterior kernel",
"topics": [
"Bymfit"
]
},
{
"page": "CallDemandModel",
"title": "Calls-for-service demand and queueing-based staffing",
"topics": [
"CallDemandModel",
"ErlangC",
"PatrolCarRequirement",
"StaffingRequirement"
]
},
{
"page": "car_rho_bounds",
"title": "Valid interval for the CAR dependence parameter rho",
"topics": [
"car_rho_bounds"
]
},
{
"page": "car_rho_ols",
"title": "Haining's least-squares estimator of the CAR dependence parameter",
"topics": [
"car_rho_ols"
]
},
{
"page": "Cardioid",
"title": "Cardioid distribution on the circle",
"topics": [
"Cardioid"
]
},
{
"page": "CarPoissonTarget",
"title": "Spatial MCMC: Poisson-CAR target, Metropolis-Hastings, HMC, NUTS and parallel tempering",
"topics": [
"CarPoissonTarget",
"HmcSpatial",
"MhSpatial",
"NutsSpatial",
"TemperedSpatial"
]
},
{
"page": "CarPrecision",
"title": "Latent Gaussian fields and spatial GLMM simulation",
"topics": [
"CarPrecision",
"GmrfSimulate",
"SarCovariance",
"SpatialGlmmSimulate"
]
},
{
"page": "carvar",
"title": "Gaussian conditional autoregressive (CAR) models",
"topics": [
"carbym",
"cargmm",
"caricar",
"carjac",
"carres",
"carsim",
"carvar"
]
},
{
"page": "CategoricalDist",
"title": "Categorical distribution",
"topics": [
"CategoricalDist"
]
},
{
"page": "Catnext",
"title": "Pick the next item: maximum Fisher information at the current theta",
"topics": [
"Catnext"
]
},
{
"page": "catrnd",
"title": "Cochran-Armitage test for a linear trend in proportions",
"topics": [
"catrnd"
]
},
{
"page": "Cauchw",
"title": "Cauchy weight function for iteratively reweighted least squares",
"topics": [
"Cauchw"
]
},
{
"page": "cauchy_combination",
"title": "Cauchy combination test (Liu and Xie 2020)",
"topics": [
"cauchy_combination"
]
},
{
"page": "causal_e_value",
"title": "Causal formulas: E-value, mediation and the front-door",
"topics": [
"causal_e_value",
"causal_mediation_baron_kenny",
"frontdoor_adjustment",
"frontdoor_criterion",
"front_door",
"mediation_analysis",
"mediation_formula",
"unmeasured_conf_bias"
]
},
{
"page": "Causbckd",
"title": "Backdoor-adjusted average treatment effect by stratification",
"topics": [
"Causbckd"
]
},
{
"page": "Causdr2",
"title": "Double/debiased ML for the partially linear model (DML2)",
"topics": [
"Causdr2"
]
},
{
"page": "Causgsw",
"title": "Generalizability diagnostics from trial and target sampling scores",
"topics": [
"Causgsw"
]
},
{
"page": "Causipw",
"title": "IPW average treatment effect on the Crump-trimmed overlap sample",
"topics": [
"Causipw"
]
},
{
"page": "Causmnde",
"title": "Natural direct and indirect effects, then split each again",
"topics": [
"Causmnde"
]
},
{
"page": "Causrdd",
"title": "Sharp regression-discontinuity estimate by local linear fits",
"topics": [
"Causrdd"
]
},
{
"page": "Causrddf",
"title": "Fuzzy regression-discontinuity estimate (Wald ratio of jumps)",
"topics": [
"Causrddf"
]
},
{
"page": "Causrddh",
"title": "Imbens-Kalyanaraman plug-in bandwidth for RDD (NBER w14726)",
"topics": [
"Causrddh"
]
},
{
"page": "Caustmle",
"title": "Targeted maximum likelihood estimator of the ATE",
"topics": [
"Caustmle"
]
},
{
"page": "Caustrnsp",
"title": "Generalize or transport a randomized-trial effect by weighting",
"topics": [
"Caustrnsp"
]
},
{
"page": "Ccdsgn",
"title": "Unmatched case-control OR",
"topics": [
"Ccdsgn"
]
},
{
"page": "Ccelo",
"title": "Categorical cross-entropy loss",
"topics": [
"Ccelo"
]
},
{
"page": "Ccmem",
"title": "Cross-classified membership weight matrix",
"topics": [
"Ccmem"
]
},
{
"page": "CcmeWqi",
"title": "Water quality indices",
"topics": [
"CarlsonTsi",
"CcmeWqi",
"DoSaturation",
"WeightedArithmeticWqi"
]
},
{
"page": "Ccngg",
"title": "Nakagawa-Schielzeth R-squared for a mixed model",
"topics": [
"Ccngg"
]
},
{
"page": "CCRSO_TABLE1_RELEASES",
"title": "CCRSO Table 1 - 5-year average annual conditional releases",
"topics": [
"CCRSO_TABLE1_RELEASES"
]
},
{
"page": "CCRSO_TABLE2_FLOW",
"title": "CCRSO Table 2 - prisoner flow 2013/14-2017/18",
"topics": [
"CCRSO_TABLE2_FLOW"
]
},
{
"page": "CCRSO_TABLE3_AGE",
"title": "CCRSO/StatsCan Table 3 - 2018 age distribution",
"topics": [
"CCRSO_TABLE3_AGE"
]
},
{
"page": "Cde",
"title": "Controlled direct effect at a fixed mediator value",
"topics": [
"Cde"
]
},
{
"page": "Cdyll",
"title": "Years of life lost and the cost-effectiveness plane",
"topics": [
"Cdyll",
"Hecea"
]
},
{
"page": "CentralizationIndex",
"title": "Centralization indices",
"topics": [
"CentralizationIndex"
]
},
{
"page": "cepst",
"title": "Real cepstrum",
"topics": [
"cepst"
]
},
{
"page": "Cfafm2",
"title": "CFA multi-factor with cross-loadings allowed",
"topics": [
"Cfafm2"
]
},
{
"page": "Cfaftr",
"title": "CFA one factor, ML estimation",
"topics": [
"Cfaftr"
]
},
{
"page": "cgnonl_beta_fletcher_reeves",
"title": "cgnonl_beta_fletcher_reeves",
"topics": [
"cgnonl_beta_fletcher_reeves"
]
},
{
"page": "cgnonl_beta_polak_ribiere",
"title": "cgnonl_beta_polak_ribiere",
"topics": [
"cgnonl_beta_polak_ribiere"
]
},
{
"page": "cgnonl_cubic_interpolate",
"title": "cgnonl_cubic_interpolate",
"topics": [
"cgnonl_cubic_interpolate"
]
},
{
"page": "cgnonl_line_search_fr",
"title": "cgnonl_line_search_fr",
"topics": [
"cgnonl_line_search_fr"
]
},
{
"page": "cgnonl_nonlinear_cg",
"title": "cgnonl_nonlinear_cg",
"topics": [
"cgnonl_nonlinear_cg"
]
},
{
"page": "chain_entry",
"title": "One step of the hash chain",
"topics": [
"chain_entry"
]
},
{
"page": "Chancap",
"title": "Channel capacity by the Blahut-Arimoto algorithm",
"topics": [
"Chancap"
]
},
{
"page": "ChangeVector",
"title": "Change vector analysis and thematic map accuracy",
"topics": [
"AccuracyAssessment",
"ChangeVector"
]
},
{
"page": "Chasym",
"title": "Asymptotic-variance check for an IPW marginal structural model",
"topics": [
"Chasym"
]
},
{
"page": "cheatsheet",
"title": "Method cheat sheet",
"topics": [
"cheatsheet"
]
},
{
"page": "Chebbasis",
"title": "Chebyshev polynomial basis of the first kind",
"topics": [
"Chebbasis"
]
},
{
"page": "check_referential_integrity",
"title": "Check referential integrity (child FK -> parent PK)",
"topics": [
"check_referential_integrity"
]
},
{
"page": "Chernbnd",
"title": "Chernoff bound on an upper tail",
"topics": [
"Chernbnd"
]
},
{
"page": "Chgseg",
"title": "Penalised mean-change segmentation via PELT",
"topics": [
"Chgseg"
]
},
{
"page": "chi2_goodness_of_fit",
"title": "Chi-squared goodness-of-fit test",
"topics": [
"chi2_goodness_of_fit"
]
},
{
"page": "chi2_independence",
"title": "Chi-squared test of independence",
"topics": [
"chi2_independence"
]
},
{
"page": "ChiDep",
"title": "Tail-dependence chi",
"topics": [
"ChiDep"
]
},
{
"page": "chin1",
"title": "The (N - 1) chi-square test for a fourfold table",
"topics": [
"chin1"
]
},
{
"page": "ChinesePostman",
"title": "Arc and vehicle routing",
"topics": [
"ChinesePostman",
"MultiDepotVrp",
"PeriodicVrp",
"PickupDeliveryInsertion",
"SolomonVrptw",
"StochasticRouteCost"
]
},
{
"page": "Chipsq",
"title": "ChIP-seq candidate-peak significance (MACS dynamic lambda)",
"topics": [
"Chipsq"
]
},
{
"page": "chisqmc",
"title": "Chi-square test with a Monte Carlo p-value",
"topics": [
"chisqmc"
]
},
{
"page": "CholeskySim",
"title": "Cholesky (LU) simulation of a Gaussian random field",
"topics": [
"CholeskySim"
]
},
{
"page": "Chowfc",
"title": "Chow forecast (predictive-failure) test (Chow 1960)",
"topics": [
"Chowfc"
]
},
{
"page": "Chrbnd",
"title": "Chernozhukov-Lee-Rosen intersection bounds with independent cells",
"topics": [
"Chrbnd"
]
},
{
"page": "chronos_dequantize",
"title": "chronos_dequantize",
"topics": [
"chronos_dequantize"
]
},
{
"page": "chronos_detokenize",
"title": "chronos_detokenize",
"topics": [
"chronos_detokenize"
]
},
{
"page": "chronos_forecast_summary",
"title": "chronos_forecast_summary",
"topics": [
"chronos_forecast_summary"
]
},
{
"page": "chronos_mean_scale",
"title": "chronos_mean_scale",
"topics": [
"chronos_mean_scale"
]
},
{
"page": "chronos_quantile_bins",
"title": "chronos_quantile_bins",
"topics": [
"chronos_quantile_bins"
]
},
{
"page": "chronos_quantize",
"title": "chronos_quantize",
"topics": [
"chronos_quantize"
]
},
{
"page": "chronos_tokenize",
"title": "chronos_tokenize",
"topics": [
"chronos_tokenize"
]
},
{
"page": "chronos_uniform_bins",
"title": "chronos_uniform_bins",
"topics": [
"chronos_uniform_bins"
]
},
{
"page": "Chzlt",
"title": "Cinelli-Hazlett sensitivity to an unobserved confounder",
"topics": [
"Chzlt"
]
},
{
"page": "Cif",
"title": "Cumulative incidence function under competing risks",
"topics": [
"Cif",
"morie_survival_cif"
]
},
{
"page": "CiOvlap",
"title": "Error-bar overlap rule",
"topics": [
"CiOvlap"
]
},
{
"page": "CircularSummary",
"title": "Circular summaries and von Mises maximum likelihood (circular package conventions)",
"topics": [
"CircularSummary",
"VonmisesMle"
]
},
{
"page": "CircUnif",
"title": "Circular uniform distribution",
"topics": [
"CircUnif"
]
},
{
"page": "ckan_metadata",
"title": "CKAN Metadata for Open Data APIs",
"topics": [
"ckan_metadata"
]
},
{
"page": "ClarkeWrightVrp",
"title": "Vehicle routing, trip distribution, mode choice, assignment and signals",
"topics": [
"ClarkeWrightVrp",
"GravityDistribution",
"LogitModeShares",
"TrafficAssignment",
"WebsterSignal"
]
},
{
"page": "ClassicalMds",
"title": "Classical (Torgerson) multidimensional scaling",
"topics": [
"ClassicalMds"
]
},
{
"page": "Clbuvc",
"title": "CLUB upper bound on mutual information",
"topics": [
"Clbuvc"
]
},
{
"page": "Clcrp",
"title": "Distance-dependent CRP",
"topics": [
"Clcrp"
]
},
{
"page": "cles",
"title": "Common Language Effect Size (probability of superiority)",
"topics": [
"cles"
]
},
{
"page": "cli_main",
"title": "Command-line analysis entry point",
"topics": [
"cli_main"
]
},
{
"page": "cliffs_delta",
"title": "Cliff's delta",
"topics": [
"cliffs_delta"
]
},
{
"page": "Clipsi",
"title": "CLIP image-text similarity",
"topics": [
"Clipsi"
]
},
{
"page": "Clipxi",
"title": "CLIP image encoder",
"topics": [
"Clipxi"
]
},
{
"page": "Clocent",
"title": "Closeness centrality",
"topics": [
"Clocent"
]
},
{
"page": "Clr",
"title": "Centred log-ratio transform of a composition",
"topics": [
"Clr"
]
},
{
"page": "Clrinv",
"title": "Inverse centred log-ratio transform",
"topics": [
"Clrinv"
]
},
{
"page": "Clrmedian",
"title": "Geometric median of a composition in clr coordinates",
"topics": [
"Clrmedian"
]
},
{
"page": "Clrpca",
"title": "Principal component analysis in centred log-ratio coordinates",
"topics": [
"Clrpca"
]
},
{
"page": "Clstcoef",
"title": "Fraction of a node neighbours that are themselves connected",
"topics": [
"Clstcoef"
]
},
{
"page": "Clus1",
"title": "One-stage cluster sample: every element of each sampled cluster",
"topics": [
"Clus1"
]
},
{
"page": "Clusdes",
"title": "Choose the cluster size that buys the most precision per unit cost",
"topics": [
"Clusdes"
]
},
{
"page": "Cluseq",
"title": "SNP-distance sequence clustering",
"topics": [
"Cluseq"
]
},
{
"page": "ClusteringIndex",
"title": "Spatial clustering indices",
"topics": [
"ClusteringIndex"
]
},
{
"page": "Cncpat",
"title": "ControlNet attachment",
"topics": [
"Cncpat"
]
},
{
"page": "cndchk",
"title": "Conditional negative definiteness of a semivariogram matrix",
"topics": [
"cndchk"
]
},
{
"page": "Cndent",
"title": "Conditional entropy of a 2-D joint pmf",
"topics": [
"Cndent"
]
},
{
"page": "Cndmi",
"title": "Conditional mutual information of a 3-D joint pmf",
"topics": [
"Cndmi"
]
},
{
"page": "Cnffvw",
"title": "Cinelli-Hazlett sensitivity under a hypothesised confounder",
"topics": [
"Cnffvw"
]
},
{
"page": "Cnsint",
"title": "Concurrent calibration with anchor items",
"topics": [
"Cnsint"
]
},
{
"page": "CnsRos",
"title": "Rosenbaum sensitivity bounds, Wilcoxon signed-rank",
"topics": [
"CnsRos"
]
},
{
"page": "cochrans_q",
"title": "Cochran's Q test",
"topics": [
"cochrans_q"
]
},
{
"page": "coef.morie_lm",
"title": "Extract coefficients from method for 'morie_lm' objects",
"topics": [
"coef.morie_lm"
]
},
{
"page": "coef.morie_lmm",
"title": "Fixed-effect estimates of a morie mixed model",
"topics": [
"coef.morie_lmm"
]
},
{
"page": "coefficient_of_variation",
"title": "Coefficient of variation",
"topics": [
"coefficient_of_variation"
]
},
{
"page": "cohens_d",
"title": "Cohen's d for independent samples",
"topics": [
"cohens_d"
]
},
{
"page": "cohens_f",
"title": "Cohen's f from eta-squared",
"topics": [
"cohens_f"
]
},
{
"page": "cohens_kappa",
"title": "Cohen's kappa for two raters",
"topics": [
"cohens_kappa"
]
},
{
"page": "cohens_w",
"title": "Cohen's w for chi-squared",
"topics": [
"cohens_w"
]
},
{
"page": "CoherentFields",
"title": "Two Gaussian fields with a constant spectral coherence",
"topics": [
"CoherentFields"
]
},
{
"page": "CoinPmf",
"title": "Fair-coin Heads-count pmf",
"topics": [
"CoinPmf"
]
},
{
"page": "CoinVar",
"title": "Variance of one fair coin flip",
"topics": [
"CoinVar"
]
},
{
"page": "Colbrt",
"title": "ColBERT late interaction",
"topics": [
"Colbrt"
]
},
{
"page": "ColE",
"title": "Cold-start user fallback",
"topics": [
"ColE"
]
},
{
"page": "collinearity_diagnostics",
"title": "Comprehensive multicollinearity diagnostics",
"topics": [
"collinearity_diagnostics"
]
},
{
"page": "ColMet",
"title": "Recall at k",
"topics": [
"ColMet"
]
},
{
"page": "column_rule",
"title": "Construct a column rule",
"topics": [
"column_rule"
]
},
{
"page": "Comemb",
"title": "node2vec embeddings",
"topics": [
"Comemb"
]
},
{
"page": "Comlou",
"title": "Louvain community detection",
"topics": [
"Comlou"
]
},
{
"page": "commands_by_category",
"title": "Commands grouped by category",
"topics": [
"commands_by_category"
]
},
{
"page": "CompareNeighbours",
"title": "Neighbours present in one weights matrix but not the other",
"topics": [
"CompareNeighbours"
]
},
{
"page": "Compcen",
"title": "Centre of a compositional data set",
"topics": [
"Compcen"
]
},
{
"page": "Compclos",
"title": "Closure operator C(x) onto the simplex of constant sum 'total'",
"topics": [
"Compclos"
]
},
{
"page": "Compdist",
"title": "Aitchison distance between two compositions",
"topics": [
"Compdist"
]
},
{
"page": "Compgeo",
"title": "Geometric mean of the parts of a single composition",
"topics": [
"Compgeo"
]
},
{
"page": "Compip",
"title": "Aitchison inner product on the simplex",
"topics": [
"Compip"
]
},
{
"page": "Compjsd",
"title": "Jensen-Shannon divergence for compositional data",
"topics": [
"Compjsd"
]
},
{
"page": "Compkm",
"title": "Lloyd k-means on compositions, run in clr coordinates",
"topics": [
"Compkm"
]
},
{
"page": "Comple",
"title": "Score knowledge-graph triples in a complex embedding space",
"topics": [
"Comple"
]
},
{
"page": "Complrm",
"title": "Mean of a compositional sample taken in log-ratio coordinates",
"topics": [
"Complrm"
]
},
{
"page": "Compnorm",
"title": "Aitchison norm of a composition",
"topics": [
"Compnorm"
]
},
{
"page": "Compshan",
"title": "Shannon entropy of a composition",
"topics": [
"Compshan"
]
},
{
"page": "Comptml",
"title": "Treatment effect on a composition, estimated in clr coordinates",
"topics": [
"Comptml"
]
},
{
"page": "Comptotvar",
"title": "Total variance of a compositional data set",
"topics": [
"Comptotvar"
]
},
{
"page": "compute_goodness_of_fit",
"title": "Comprehensive goodness-of-fit statistics",
"topics": [
"compute_goodness_of_fit"
]
},
{
"page": "compute_influence",
"title": "Compute leverage and influence diagnostics",
"topics": [
"compute_influence"
]
},
{
"page": "compute_residuals",
"title": "Compute residual diagnostics",
"topics": [
"compute_residuals"
]
},
{
"page": "compute_vif",
"title": "Variance Inflation Factors",
"topics": [
"compute_vif"
]
},
{
"page": "Compvar",
"title": "Variation matrix: the pairwise log-ratio variances",
"topics": [
"Compvar"
]
},
{
"page": "ComVar2",
"title": "Modified percentile bootstrap for two variances",
"topics": [
"ComVar2"
]
},
{
"page": "ConditionalTurningBands",
"title": "Turning-bands conditioning, ensembles and diagnostics of simulations",
"topics": [
"BoundaryProbability",
"ClassProportions",
"ConditionalTurningBands",
"Connectivity",
"DirectionalVariogram",
"IndicatorVariogram",
"StandardiseRealisations",
"TbBandConvergence",
"TbEnsemble"
]
},
{
"page": "Configmodel",
"title": "Configuration model with a prescribed degree sequence",
"topics": [
"Configmodel"
]
},
{
"page": "confint.morie_lm",
"title": "Confidence intervals for method for 'morie_lm' objects",
"topics": [
"confint.morie_lm"
]
},
{
"page": "ConsensusClustering",
"title": "Resampling-based clustering: consensus, evidence accumulation, stability",
"topics": [
"ClusterStability",
"ConsensusClustering",
"EvidenceAccumulation"
]
},
{
"page": "ConstrainedHierarchical",
"title": "Spatially constrained hierarchical clustering",
"topics": [
"ConstrainedHierarchical"
]
},
{
"page": "Contam",
"title": "Huber's epsilon-contamination neighbourhood",
"topics": [
"Contam"
]
},
{
"page": "ContFr",
"title": "Continued fraction expansion of a real number",
"topics": [
"ContFr"
]
},
{
"page": "Conti",
"title": "Continued fraction convergents of pi",
"topics": [
"Conti"
]
},
{
"page": "ContourFill",
"title": "Contours: isobands, clipping, band quantities, labels, smoothing",
"topics": [
"ContourBands",
"ContourClip",
"ContourFill",
"ContourLabels",
"ContourQuantity",
"ContourSmooth"
]
},
{
"page": "ContRC",
"title": "Content-based recommendation",
"topics": [
"ContRC"
]
},
{
"page": "Contse",
"title": "SimCSE contrastive sentence embedding",
"topics": [
"Contse"
]
},
{
"page": "ConvexHull",
"title": "Convex-hull shape metrics and alpha shapes",
"topics": [
"AlphaShape",
"ConvexHull",
"Delaunay",
"HullMetrics",
"TriangleQuality"
]
},
{
"page": "Convgs",
"title": "Convergent validity of a reflective construct",
"topics": [
"Convgs"
]
},
{
"page": "Convnx",
"title": "ConvNeXt block",
"topics": [
"Convnx"
]
},
{
"page": "CooksD",
"title": "Cook (1977) distance for a linear model",
"topics": [
"CooksD"
]
},
{
"page": "CopulaDens",
"title": "Bivariate copula densities",
"topics": [
"CopulaDens"
]
},
{
"page": "corcin",
"title": "Pairs needed for a correlation confidence interval of given limits",
"topics": [
"corcin"
]
},
{
"page": "corcmp",
"title": "Comparison and pooling of independent correlation coefficients",
"topics": [
"corcmp"
]
},
{
"page": "corrci",
"title": "Confidence interval for a correlation via Fisher's z",
"topics": [
"corrci"
]
},
{
"page": "correlation_dist",
"title": "Correlation distance between two objects",
"topics": [
"correlation_dist"
]
},
{
"page": "correlation_matrix",
"title": "Pairwise correlation matrix with p-values",
"topics": [
"correlation_matrix"
]
},
{
"page": "correlation_table",
"title": "Pairwise correlation matrix with significance stars",
"topics": [
"correlation_table"
]
},
{
"page": "corrho",
"title": "Test of a Pearson correlation against zero or a hypothesised value",
"topics": [
"corrho"
]
},
{
"page": "corrng",
"title": "Admissible range of a correlation given two others",
"topics": [
"corrng"
]
},
{
"page": "corss",
"title": "Sample size or power for the test of a correlation",
"topics": [
"corss"
]
},
{
"page": "corwil",
"title": "Williams' test for two dependent correlations",
"topics": [
"corwil"
]
},
{
"page": "corwsn",
"title": "Sample size for a correlation confidence interval of given width",
"topics": [
"corwsn"
]
},
{
"page": "CovIndep",
"title": "Covariance vanishing test for independence",
"topics": [
"CovIndep"
]
},
{
"page": "Covpop",
"title": "Coverage correction of sampling weights to known population totals",
"topics": [
"Covpop"
]
},
{
"page": "Covrat",
"title": "COVRATIO effect of deleting obs i on the precision of beta-hat",
"topics": [
"Covrat"
]
},
{
"page": "CovShort",
"title": "Covariance by the shortcut formula",
"topics": [
"CovShort"
]
},
{
"page": "CovZMean",
"title": "Covariance of zero-mean data",
"topics": [
"CovZMean"
]
},
{
"page": "CoxProcess",
"title": "Cox process given its realised intensity field",
"topics": [
"CoxProcess"
]
},
{
"page": "Coxsnell",
"title": "Cox-Snell residuals from a fitted Cox model",
"topics": [
"Coxsnell",
"morie_survival_coxsnell"
]
},
{
"page": "CpAls",
"title": "Space-time decompositions: CP and Tucker tensors, matrix factorisation, Haar wavelets, harmonic trends",
"topics": [
"CpAls",
"HaarDwt",
"HaarDwt2d",
"HaarMra",
"HaarMra2d",
"HarmonicRegression",
"MatrixFactorizationAls",
"TuckerHooi",
"WaveletDetrend"
]
},
{
"page": "cramers_v",
"title": "Cramer's V for a contingency table",
"topics": [
"cramers_v"
]
},
{
"page": "create_reproducibility_manifest",
"title": "Create a manifest of the environment for reproducibility",
"topics": [
"create_reproducibility_manifest"
]
},
{
"page": "cross_validate",
"title": "Cross-validate a model using a user-supplied fit/predict pair",
"topics": [
"cross_validate"
]
},
{
"page": "Crp",
"title": "Chinese restaurant process seating",
"topics": [
"Crp"
]
},
{
"page": "Crpcol",
"title": "Collapsed Gibbs sampler for a CRP mixture",
"topics": [
"Crpcol"
]
},
{
"page": "Crpgib",
"title": "CRP Gibbs sampler with auxiliary parameters",
"topics": [
"Crpgib"
]
},
{
"page": "Crrcim",
"title": "Cumulative incidence function under competing risks",
"topics": [
"Crrcim"
]
},
{
"page": "Crsent",
"title": "Cross entropy of p relative to q",
"topics": [
"Crsent"
]
},
{
"page": "CrtnRoadNoise",
"title": "Road, construction, vibration and underwater noise; dose-response",
"topics": [
"ConstructionNoise",
"CrtnRoadNoise",
"NoiseAnnoyance",
"NoiseHealthRisk",
"UnderwaterPropagation",
"VibrationPropagation"
]
},
{
"page": "crypto_shred",
"title": "Crypto-shred: destroy a KEK and report what that covers",
"topics": [
"crypto_shred"
]
},
{
"page": "csinv",
"title": "Inverse of a compound-symmetric matrix",
"topics": [
"csinv"
]
},
{
"page": "CsiWeights",
"title": "Crime Severity Index, U-learner, repeated k-fold indices, FastICA and rStress MDS",
"topics": [
"CrimeSeverityIndex",
"CsiWeights",
"FastIca",
"RepeatedKfoldIndices",
"RstressMds",
"ULearnerCate"
]
},
{
"page": "CSRGlobalTest",
"title": "DCLF and MAD Monte Carlo tests of complete spatial randomness",
"topics": [
"CSRGlobalTest"
]
},
{
"page": "Cstat",
"title": "Concordance statistic (C-index) for survival models",
"topics": [
"Cstat"
]
},
{
"page": "Ctomeg",
"title": "McDonald's omega total",
"topics": [
"Ctomeg"
]
},
{
"page": "ctresid",
"title": "Pearson and adjusted residuals of a contingency table",
"topics": [
"ctresid"
]
},
{
"page": "CttAlpha",
"title": "Cronbach (1951) coefficient alpha",
"topics": [
"CttAlpha"
]
},
{
"page": "CttAlphaMax",
"title": "Cronbach (1951) alpha with each item deleted in turn",
"topics": [
"CttAlphaMax"
]
},
{
"page": "CuckooSearch",
"title": "Cuckoo search via Levy flights",
"topics": [
"CuckooSearch"
]
},
{
"page": "Cumcif",
"title": "Cumulative incidence function (alias of Crrcim)",
"topics": [
"Cumcif"
]
},
{
"page": "Curvature",
"title": "Curvature and viewshed",
"topics": [
"Curvature",
"Viewshed"
]
},
{
"page": "CurveFpca",
"title": "Functional PCA, graph convolution and attention, persistent homology, possibilistic clustering, REML components",
"topics": [
"CrossedRandomEffects",
"CurveFpca",
"GatLayer",
"GcnLayer",
"NestedRandomEffects",
"PersistenceLandscape",
"PossibilisticCmeans",
"RemlComponents",
"RipsPersistence",
"SgcRidge"
]
},
{
"page": "Cuttip",
"title": "Gomory cutting-plane method for integer linear programs",
"topics": [
"Cuttip"
]
},
{
"page": "cvbnd",
"title": "Hoeffding confidence bound for a cross-validated risk",
"topics": [
"cvbnd"
]
},
{
"page": "d_to_nnt",
"title": "Convert Cohen's d to NNT given a control event rate",
"topics": [
"d_to_nnt"
]
},
{
"page": "d_to_or",
"title": "Convert Cohen's d to odds ratio",
"topics": [
"d_to_or"
]
},
{
"page": "d_to_r",
"title": "Convert Cohen's d to Pearson r",
"topics": [
"d_to_r"
]
},
{
"page": "D8FlowDirection",
"title": "D8 flow direction, accumulation and watershed (terra conventions)",
"topics": [
"D8FlowDirection",
"FlowAccumulation",
"Watershed"
]
},
{
"page": "dagostino_pearson",
"title": "D'Agostino-Pearson omnibus normality test",
"topics": [
"dagostino_pearson"
]
},
{
"page": "DagumDist",
"title": "Dagum distribution",
"topics": [
"DagumDist"
]
},
{
"page": "dataset_catalog",
"title": "MORIE Dataset Catalog",
"topics": [
"dataset_catalog"
]
},
{
"page": "DbscanClusters",
"title": "Density-based clustering: DBSCAN and OPTICS (dbscan conventions)",
"topics": [
"DbscanClusters",
"OpticsOrdering"
]
},
{
"page": "Dccgrch",
"title": "DCC(1,1) GARCH front end",
"topics": [
"Dccgrch"
]
},
{
"page": "Dcsub",
"title": "Basic biomedical signal measures",
"topics": [
"Dcsub",
"Prdur",
"Sactv"
]
},
{
"page": "Ddimst",
"title": "One DDIM reverse step",
"topics": [
"Ddimst"
]
},
{
"page": "Ddqn",
"title": "Tabular Double Q-learning on an explicit finite MDP",
"topics": [
"Ddqn"
]
},
{
"page": "decision_curve_analysis",
"title": "Decision curve analysis",
"topics": [
"decision_curve_analysis"
]
},
{
"page": "decoupling_test",
"title": "Doob decoupling test",
"topics": [
"decoupling_test"
]
},
{
"page": "DeepF",
"title": "DeepFM",
"topics": [
"DeepF"
]
},
{
"page": "Deepw",
"title": "DeepWalk node embeddings",
"topics": [
"Deepw"
]
},
{
"page": "Deepwk",
"title": "DeepWalk node embeddings (alias of Deepw)",
"topics": [
"Deepwk"
]
},
{
"page": "Defdtr",
"title": "Deformable DETR attention",
"topics": [
"Defdtr"
]
},
{
"page": "definite_integral",
"title": "Applied methods: FTC integral, Fredholm inverse problem, scale-test ARE, custody credit, posterior variances, utility shocks, PMM, TD3 and CV2",
"topics": [
"custody_days_credit",
"cv2_genomic",
"definite_integral",
"gibbons_are_scale_tests",
"horowitz_fredholm_eq",
"irt_variance_legislator",
"mi_pmm",
"simulate_utility_shocks",
"td3"
]
},
{
"page": "Deftef",
"title": "Design effect DEFF from the two variances",
"topics": [
"Deftef"
]
},
{
"page": "Degmat",
"title": "Degree matrix and volume of a graph",
"topics": [
"Degmat"
]
},
{
"page": "Delaunay3D",
"title": "Meshes: 3-D Delaunay tetrahedralisation, 3-D Voronoi cells, Ruppert refinement",
"topics": [
"Delaunay3D",
"RuppertRefine",
"Voronoi3D"
]
},
{
"page": "DelaunayTriangulation",
"title": "Delaunay triangulation by Bowyer-Watson insertion",
"topics": [
"DelaunayTriangulation"
]
},
{
"page": "delete_d_jackknife",
"title": "Delete-d (generalised) jackknife",
"topics": [
"delete_d_jackknife"
]
},
{
"page": "Denclue",
"title": "Density and prototype clustering: DENCLUE, FLAME, PFCM, growing neural gas",
"topics": [
"Denclue",
"FlameClustering",
"GrowingNeuralGas",
"PossibilisticFcm"
]
},
{
"page": "denoise",
"title": "Reverse chain",
"topics": [
"denoise",
"diffusionrec",
"diffusionrecommender",
"diffusion_rec"
]
},
{
"page": "DensProb",
"title": "Interval probability from a density on a grid",
"topics": [
"DensProb"
]
},
{
"page": "DensProbC",
"title": "Centered-interval probability from a density",
"topics": [
"DensProbC"
]
},
{
"page": "DeprivationIndex",
"title": "Area deprivation indices (Townsend, Carstairs)",
"topics": [
"DeprivationIndex"
]
},
{
"page": "DepthH",
"title": "Depth of a point as the thinnest halfspace through it",
"topics": [
"DepthH"
]
},
{
"page": "DepthP",
"title": "Stahel-Donoho projection depth",
"topics": [
"DepthP"
]
},
{
"page": "DepthS",
"title": "Liu simplicial depth",
"topics": [
"DepthS"
]
},
{
"page": "derive_context_keys",
"title": "Derive one key per context, all independent",
"topics": [
"derive_context_keys"
]
},
{
"page": "Desigeff",
"title": "Design effect of unequal weighting and clustering",
"topics": [
"Desigeff"
]
},
{
"page": "design_from_prior",
"title": "Sobol design pushed through the prior's inverse CDF",
"topics": [
"design_from_prior"
]
},
{
"page": "detect_overfitting",
"title": "Bootstrap optimism-corrected performance",
"topics": [
"detect_overfitting"
]
},
{
"page": "Detrbb",
"title": "DETR set prediction",
"topics": [
"Detrbb"
]
},
{
"page": "Detrnd",
"title": "Linear detrending of a series",
"topics": [
"Detrnd"
]
},
{
"page": "Devresid",
"title": "Deviance residuals from a fitted Cox model",
"topics": [
"Devresid",
"morie_survival_deviance"
]
},
{
"page": "dfa",
"title": "Detrended fluctuation analysis (DFA)",
"topics": [
"dfa"
]
},
{
"page": "Dfbetas",
"title": "DFBETAS, the scaled per-coefficient deletion influence",
"topics": [
"Dfbetas"
]
},
{
"page": "Dfbetb",
"title": "DFBETAS scaled change in coefficient j when obs i deleted",
"topics": [
"Dfbetb"
]
},
{
"page": "Dffit",
"title": "DFFITS scaled change in fitted value when obs i deleted",
"topics": [
"Dffit"
]
},
{
"page": "Dffitsols",
"title": "DFFITS, the scaled deletion influence on the fitted value",
"topics": [
"Dffitsols"
]
},
{
"page": "Dgi",
"title": "Deep Graph Infomax",
"topics": [
"Dgi"
]
},
{
"page": "did_estimate",
"title": "DiD contrast of eq. (13.7) under arbitrary unit weights",
"topics": [
"did_estimate"
]
},
{
"page": "did_forest",
"title": "Heterogeneous ATT: DiD taken locally under forest weights",
"topics": [
"didforest",
"did_forest"
]
},
{
"page": "DieVar",
"title": "Variance of a fair die roll",
"topics": [
"DieVar"
]
},
{
"page": "Difent",
"title": "Differential entropy of a density given on a grid",
"topics": [
"Difent"
]
},
{
"page": "DiffP",
"title": "DiffPool differentiable graph pooling",
"topics": [
"DiffP"
]
},
{
"page": "DiffQuot2",
"title": "Difference quotient of x squared",
"topics": [
"DiffQuot2"
]
},
{
"page": "DiffQuotN",
"title": "Difference quotient of x to the n",
"topics": [
"DiffQuotN"
]
},
{
"page": "Difmh",
"title": "Mantel-Haenszel differential item functioning statistic",
"topics": [
"Difmh"
]
},
{
"page": "Difpst",
"title": "DIF p-difference per item",
"topics": [
"Difpst"
]
},
{
"page": "Difsib",
"title": "SIBTEST differential item functioning (Shealy and Stout 1993)",
"topics": [
"Difsib"
]
},
{
"page": "Dimensionality",
"title": "Dimensionality of spatial data",
"topics": [
"Dimensionality"
]
},
{
"page": "DinfFlowDirection",
"title": "D-infinity and multiple flow direction routing",
"topics": [
"DinfFlowDirection",
"MfdFlowAccumulation"
]
},
{
"page": "Dinmlt",
"title": "DINO multi-crop consistency",
"topics": [
"Dinmlt"
]
},
{
"page": "Dinocenter",
"title": "DINO teacher centring and sharpening",
"topics": [
"Dinocenter"
]
},
{
"page": "Dinoloss",
"title": "DINO self-distillation cross-entropy",
"topics": [
"Dinoloss"
]
},
{
"page": "Dinov2",
"title": "DINOv2 representation objective",
"topics": [
"Dinov2"
]
},
{
"page": "Diophs",
"title": "Solve a linear Diophantine equation ax + by = c",
"topics": [
"Diophs"
]
},
{
"page": "DiopT",
"title": "Farey sequence of order n",
"topics": [
"DiopT"
]
},
{
"page": "DirectSampling",
"title": "Multiple-point simulation from a training image",
"topics": [
"DirectSampling"
]
},
{
"page": "Diripr",
"title": "Dirichlet-multinomial conjugate model",
"topics": [
"Diripr"
]
},
{
"page": "DissectionIndex",
"title": "Landscape shape and fragmentation indices",
"topics": [
"DissectionIndex",
"ForestFragmentation"
]
},
{
"page": "DissimilarityIndex",
"title": "Dissimilarity and segregation indices",
"topics": [
"DissimilarityIndex"
]
},
{
"page": "Distilkl",
"title": "Temperature-scaled knowledge-distillation loss",
"topics": [
"Distilkl"
]
},
{
"page": "DistM",
"title": "Bilinear triple score with a diagonal relation matrix",
"topics": [
"DistM"
]
},
{
"page": "DistrictCompactness",
"title": "District compactness",
"topics": [
"DistrictCompactness"
]
},
{
"page": "Divgvs",
"title": "Fornell-Larcker discriminant validity",
"topics": [
"Divgvs"
]
},
{
"page": "Divrate",
"title": "Divergent transition count and rate",
"topics": [
"Divrate"
]
},
{
"page": "DixonQ",
"title": "Dixon (1953) ratio for the most extreme observation",
"topics": [
"DixonQ"
]
},
{
"page": "Doctide",
"title": "de Chaisemartin-D'Haultfoeuille DID_M estimator",
"topics": [
"Doctide"
]
},
{
"page": "doob_trends",
"title": "Doob Federal Court Affidavit - national-aggregate trend analyses",
"topics": [
"doob_trends"
]
},
{
"page": "Dpacct",
"title": "Moments accountant for DP-SGD",
"topics": [
"Dpacct"
]
},
{
"page": "Dpedt",
"title": "Blackwell-MacQueen predictive rule",
"topics": [
"Dpedt"
]
},
{
"page": "Dpgem",
"title": "GEM stick-breaking weights",
"topics": [
"Dpgem"
]
},
{
"page": "Dpineq",
"title": "Data-processing inequality for a Markov chain X -> Y -> Z",
"topics": [
"Dpineq"
]
},
{
"page": "Dpld",
"title": "l-diversity of a released table, in all three senses",
"topics": [
"Dpld"
]
},
{
"page": "Dpmem",
"title": "Dirichlet process mixture model",
"topics": [
"Dpmem"
]
},
{
"page": "Dpparit",
"title": "Pitman-Yor two-parameter process",
"topics": [
"Dpparit"
]
},
{
"page": "Dprime",
"title": "Lewontin's D' for two biallelic loci from unphased genotypes",
"topics": [
"Dprime"
]
},
{
"page": "Dpsbm",
"title": "Dirichlet-process stochastic block model",
"topics": [
"Dpsbm"
]
},
{
"page": "Dpsing",
"title": "Test a Dirichlet-process partition cluster count against its prior",
"topics": [
"Dpsing"
]
},
{
"page": "Dpvar",
"title": "Differentially private variance of bounded data",
"topics": [
"Dpvar"
]
},
{
"page": "Drbnk",
"title": "Adaptively-weighted AIPW for data collected by a bandit",
"topics": [
"Drbnk"
]
},
{
"page": "Drbsze",
"title": "Finite-sample size correction for the doubly robust DiD estimator",
"topics": [
"Drbsze"
]
},
{
"page": "Drcef",
"title": "Callaway-Sant'Anna event-study aggregation of doubly robust ATT(g,t)",
"topics": [
"Drcef"
]
},
{
"page": "Drclt",
"title": "Cluster-robust inference for the doubly robust DiD estimator",
"topics": [
"Drclt"
]
},
{
"page": "Drctf",
"title": "DiD with a continuous treatment: ATT(d|d) levels and ACRT slopes",
"topics": [
"Drctf"
]
},
{
"page": "Drhtg",
"title": "Heterogeneous conditional ATT: the DR-DiD moment inside each stratum",
"topics": [
"Drhtg"
]
},
{
"page": "Drlp1",
"title": "LP-DiD: local-projection difference-in-differences with clean controls",
"topics": [
"Drlp1"
]
},
{
"page": "Drnpc",
"title": "Negative-control falsification for the doubly robust DiD estimator",
"topics": [
"Drnpc"
]
},
{
"page": "Dropmask",
"title": "Apply a dropout mask with inverted scaling",
"topics": [
"Dropmask"
]
},
{
"page": "Drrct",
"title": "Experimental selection correction: an RCT-validated control function",
"topics": [
"Drrct"
]
},
{
"page": "Drspa",
"title": "Doubly robust DiD with a spatial lag of treatment",
"topics": [
"Drspa"
]
},
{
"page": "Drspr",
"title": "Direct and spillover ATT under an exposure mapping",
"topics": [
"Drspr"
]
},
{
"page": "Drsta",
"title": "Doubly robust DiD over a staggered adoption design: the ATT(g,t) table",
"topics": [
"Drsta"
]
},
{
"page": "Drvst",
"title": "Variance-stabilised DR-DiD: trim the DR weights, report the ESS",
"topics": [
"Drvst"
]
},
{
"page": "Dssoot",
"title": "Percentile bootstrap confidence interval for the indirect effect ab",
"topics": [
"Dssoot"
]
},
{
"page": "DsspAssign",
"title": "DSSP secondary-structure assignment from backbone coordinates",
"topics": [
"DsspAssign",
"DsspHbondEnergy"
]
},
{
"page": "Dyntmt",
"title": "Value of a dynamic treatment regime under a marginal structural model",
"topics": [
"Dyntmt"
]
},
{
"page": "e_value",
"title": "E-value for unmeasured confounding (continuous-ATE scale)",
"topics": [
"e_value"
]
},
{
"page": "e_value_d",
"title": "E-value for a standardised mean difference (Cohen's d)",
"topics": [
"e_value_d"
]
},
{
"page": "e_value_hr",
"title": "E-value for a hazard ratio",
"topics": [
"e_value_hr"
]
},
{
"page": "e_value_or",
"title": "E-value for an odds ratio",
"topics": [
"e_value_or"
]
},
{
"page": "e_value_rr",
"title": "E-value for a risk ratio",
"topics": [
"e_value_rr"
]
},
{
"page": "Eaiprl",
"title": "Augmented IPW estimator of the ATE",
"topics": [
"Eaiprl"
]
},
{
"page": "Eapinfo",
"title": "EAP ability estimate with its posterior sd and Fisher information",
"topics": [
"Eapinfo"
]
},
{
"page": "Ebfmi",
"title": "Energy Bayesian fraction of missing information",
"topics": [
"Ebfmi"
]
},
{
"page": "EbGlobal",
"title": "Empirical Bayes rate smoothing and probability maps",
"topics": [
"EbGlobal",
"EbLocal",
"ProbabilityMap"
]
},
{
"page": "Ec25Correction",
"title": "Soil process indices",
"topics": [
"CesiumRedistribution",
"Ec25Correction",
"IpccSoilCarbon",
"RweqWindErosion"
]
},
{
"page": "Ecfp4",
"title": "Extended-connectivity fingerprint, radius 2 (ECFP4)",
"topics": [
"Ecfp4"
]
},
{
"page": "Ecfp6",
"title": "Extended-connectivity fingerprint, radius 3 (ECFP6)",
"topics": [
"Ecfp6"
]
},
{
"page": "ecgdet",
"title": "Pan-Tompkins QRS / R-peak detector",
"topics": [
"ecgdet"
]
},
{
"page": "Editdist",
"title": "Levenshtein distance between two sequences",
"topics": [
"Editdist"
]
},
{
"page": "EDivisive",
"title": "E-divisive multiple changepoint estimation (energy distance)",
"topics": [
"EDivisive"
]
},
{
"page": "EfaNfactors",
"title": "Number of factors to retain",
"topics": [
"EfaNfactors"
]
},
{
"page": "effect_size_result",
"title": "Build an effect-size result",
"topics": [
"effect_size_result"
]
},
{
"page": "effect_sizes",
"title": "Comprehensive effect-size calculations",
"topics": [
"effect_sizes"
]
},
{
"page": "effective_sample_size",
"title": "Effective sample size",
"topics": [
"effective_sample_size"
]
},
{
"page": "effects",
"title": "Treatment effect estimators and marginal-effects extenders",
"topics": [
"effects"
]
},
{
"page": "efficacy_test_and_release",
"title": "Test-and-release efficacy (eq. 2 of Ashcroft et al. 2021)",
"topics": [
"efficacy_test_and_release",
"testandrelease"
]
},
{
"page": "Effmod",
"title": "Effect modification on the additive and the multiplicative scale",
"topics": [
"Effmod"
]
},
{
"page": "Eigcent",
"title": "Eigenvector centrality",
"topics": [
"Eigcent"
]
},
{
"page": "Eigengap",
"title": "Choose k from the spectrum: the gap after the small eigenvalues",
"topics": [
"Eigengap"
]
},
{
"page": "EkfF",
"title": "Extended Kalman filter",
"topics": [
"EkfF"
]
},
{
"page": "EllipticScan",
"title": "Cluster detection beyond the circular scan",
"topics": [
"CuzickEdwards",
"EllipticScan",
"FixedCircleScan",
"FlexScan",
"FlexZones",
"LawsonWaller",
"NormalScan"
]
},
{
"page": "EllisonGlaeser",
"title": "Ellison-Glaeser agglomeration and Duranton-Overman K-density",
"topics": [
"CoagglomerationIndex",
"DurantonOverman",
"EllisonGlaeser"
]
},
{
"page": "Elnetr",
"title": "Lasso, elastic net and principal component front ends",
"topics": [
"Elnetr",
"Lasr",
"Pcaprx"
]
},
{
"page": "em_imputation",
"title": "EM imputation under a multivariate normal model",
"topics": [
"em_imputation"
]
},
{
"page": "Emaxr",
"title": "EM step (single iteration) for random-effects variance",
"topics": [
"Emaxr"
]
},
{
"page": "EmbeddingQuality",
"title": "Embedding quality, alienation coefficient and dissimilarity checks",
"topics": [
"AlienationCoefficient",
"DissimilarityCheck",
"EmbeddingQuality"
]
},
{
"page": "Emdtsm",
"title": "Empirical Mode Decomposition",
"topics": [
"Emdtsm"
]
},
{
"page": "Emisinv",
"title": "Activity-data times emission-factor inventory",
"topics": [
"Emisinv"
]
},
{
"page": "Emkfst",
"title": "EM for state-space parameters",
"topics": [
"Emkfst"
]
},
{
"page": "Empbnp",
"title": "Robbins' nonparametric empirical Bayes",
"topics": [
"Empbnp"
]
},
{
"page": "empirical_bayes_moran",
"title": "Empirical-Bayes standardized Moran I for area rates",
"topics": [
"empirical_bayes_moran"
]
},
{
"page": "encode_bpe",
"title": "Encode text with a trained BPE model",
"topics": [
"encode_bpe"
]
},
{
"page": "Enetobj",
"title": "Elastic net penalized residual sum of squares",
"topics": [
"Enetobj"
]
},
{
"page": "Epbias",
"title": "Bias correction for exposure misclassification",
"topics": [
"Epbias"
]
},
{
"page": "Epeols",
"title": "Expected prediction error of least squares at a test point (ESL eq. 2.27)",
"topics": [
"Epeols"
]
},
{
"page": "Epetheor",
"title": "Population least-squares coefficient from second moments (ESL eq. 2.16)",
"topics": [
"Epetheor"
]
},
{
"page": "Epicur",
"title": "Epidemic curve smoothing",
"topics": [
"Epicur"
]
},
{
"page": "epsilon_squared",
"title": "Epsilon-squared (Kelley, 1935)",
"topics": [
"epsilon_squared"
]
},
{
"page": "Eqmm",
"title": "Mean-mean equating coefficients",
"topics": [
"Eqmm"
]
},
{
"page": "Eqms",
"title": "Mean-sigma equating coefficients",
"topics": [
"Eqms"
]
},
{
"page": "EquivalentLevel",
"title": "Environmental noise level statistics and rating indicators",
"topics": [
"AircraftDnl",
"CombineLevels",
"DayEveningNight",
"EquivalentLevel",
"LevelStatistics",
"SoundExposureLevel"
]
},
{
"page": "Erdosg",
"title": "Erdos-Renyi G(n,p)",
"topics": [
"Erdosg"
]
},
{
"page": "Ergmod",
"title": "Exponential random graph model",
"topics": [
"Ergmod"
]
},
{
"page": "ErlangRenewal",
"title": "Closed-form results from Grimmett and Stirzaker",
"topics": [
"AcfSpectralDensity",
"ArSpectralDensity",
"BerksonCorrelation",
"BetaBinomialPmf",
"BivNormDependence",
"ErlangRenewal",
"MaAutocorrelation",
"MaximalCorrelation",
"MillsConditionalMean",
"SimpleEpidemicDuration",
"StirlingGammaRatio",
"TelegraphProcess",
"WienerConditionalCorrelation"
]
},
{
"page": "ErrorOperator",
"title": "Spatial multiplier of an autoregressive error",
"topics": [
"ErrorOperator"
]
},
{
"page": "Erstst",
"title": "Elliott-Rothenberg-Stock GLS-detrended ADF (DF-GLS)",
"topics": [
"Erstst"
]
},
{
"page": "escape_whitespace",
"title": "Escape whitespace as U+2581, optionally prefixing the marker",
"topics": [
"escape_whitespace"
]
},
{
"page": "Esllso",
"title": "Lasso by cyclic coordinate descent",
"topics": [
"Esllso"
]
},
{
"page": "Esmoeg",
"title": "Exploratory SEM with target rotation",
"topics": [
"Esmoeg"
]
},
{
"page": "Esnnts",
"title": "Echo state network (reservoir computing)",
"topics": [
"Esnnts"
]
},
{
"page": "estimate_ate",
"title": "IPW-weighted OLS ATE",
"topics": [
"estimate_ate"
]
},
{
"page": "estimate_ate_gcomputation",
"title": "G-computation ATE with its sandwich SE",
"topics": [
"estimate_ate_gcomputation"
]
},
{
"page": "estimate_pi0",
"title": "Estimate the proportion of true null hypotheses (pi0)",
"topics": [
"estimate_pi0"
]
},
{
"page": "estimate_pliv",
"title": "Partially Linear IV (PLIV) / Local Average Treatment Effect",
"topics": [
"estimate_pliv"
]
},
{
"page": "estimate_plr",
"title": "Partially Linear Regression (PLR) ATE",
"topics": [
"estimate_plr"
]
},
{
"page": "EstradaIndex",
"title": "Spectral graph indices and label-propagation communities",
"topics": [
"Communicability",
"EstradaIndex",
"LabelPropagation",
"ModularityMatrix",
"PerronFrobenius",
"RandicIndex"
]
},
{
"page": "EsumConv",
"title": "Expectation of a sum from the convolved pmf",
"topics": [
"EsumConv"
]
},
{
"page": "EsumIid",
"title": "Expectation of a sum of n i.i.d. variables",
"topics": [
"EsumIid"
]
},
{
"page": "Esumtv",
"title": "Effective resistance between nodes",
"topics": [
"Esumtv"
]
},
{
"page": "eta_squared",
"title": "Eta-squared from ANOVA sums of squares",
"topics": [
"eta_squared"
]
},
{
"page": "Etsmod",
"title": "ETS state-space (error/trend/seasonal)",
"topics": [
"Etsmod"
]
},
{
"page": "EuclP",
"title": "Polynomial GCD via Euclid",
"topics": [
"EuclP"
]
},
{
"page": "Evalu",
"title": "E-values and the joint-significance mediation test",
"topics": [
"Evaltw",
"Evalu",
"Jntmed"
]
},
{
"page": "Evangia",
"title": "Empirical angular measure of the k largest radii",
"topics": [
"Evangia"
]
},
{
"page": "Evdec",
"title": "Runs declustering of threshold exceedances",
"topics": [
"Evdec"
]
},
{
"page": "Evespot",
"title": "Expected shortfall of a GPD tail above a threshold",
"topics": [
"Evespot"
]
},
{
"page": "Evhpvr",
"title": "Heffernan-Tawn conditional extremes model",
"topics": [
"Evhpvr"
]
},
{
"page": "Evhrid",
"title": "Husler-Reiss bivariate dependence",
"topics": [
"Evhrid"
]
},
{
"page": "Evmrlp",
"title": "Mean residual life plot",
"topics": [
"Evmrlp"
]
},
{
"page": "Evmsexp",
"title": "Bivariate logistic max-stable distribution",
"topics": [
"Evmsexp"
]
},
{
"page": "Evpdfn",
"title": "Empirical Pickands dependence function",
"topics": [
"Evpdfn"
]
},
{
"page": "Evpot",
"title": "Peaks-over-threshold GPD fit + scale-invariance check",
"topics": [
"Evpot"
]
},
{
"page": "Evstud",
"title": "Event-study leads + lags coefficients",
"topics": [
"Evstud"
]
},
{
"page": "Evtlmom",
"title": "Trimmed L-moments TL(s,t) of a sample",
"topics": [
"Evtlmom"
]
},
{
"page": "Evtrecords",
"title": "Upper record times and counts in a sequence",
"topics": [
"Evtrecords"
]
},
{
"page": "Evtsthr",
"title": "Threshold selection by local variance of the GPD estimates",
"topics": [
"Evtsthr"
]
},
{
"page": "Evvarpot",
"title": "Value-at-risk from a fitted GPD tail",
"topics": [
"Evvarpot"
]
},
{
"page": "Exp3",
"title": "Exp3 exponential-weight adversarial bandit",
"topics": [
"Exp3"
]
},
{
"page": "expand",
"title": "HKDF Expand: counter-mode OKM",
"topics": [
"expand"
]
},
{
"page": "ExpectedCounts",
"title": "Indirect standardisation and standardised ratios",
"topics": [
"ExpectedCounts",
"StandardizedRatio"
]
},
{
"page": "ExpIntP",
"title": "Exponential waiting-time interval probability",
"topics": [
"ExpIntP"
]
},
{
"page": "Expkern",
"title": "Exponential (Laplace) kernel matrix",
"topics": [
"Expkern"
]
},
{
"page": "explain_file",
"title": "Human-readable description of a morie output CSV",
"topics": [
"explain_file"
]
},
{
"page": "explain_known_files",
"title": "Names of all morie output CSVs with registered explanations",
"topics": [
"explain_known_files"
]
},
{
"page": "ExpLinApx",
"title": "Linear approximation to the exponential",
"topics": [
"ExpLinApx"
]
},
{
"page": "ExplPow",
"title": "Explanatory power",
"topics": [
"ExplPow"
]
},
{
"page": "ExpMean",
"title": "Mean of an exponential waiting time",
"topics": [
"ExpMean"
]
},
{
"page": "ExpMom2",
"title": "Second moment and variance of an exponential waiting time",
"topics": [
"ExpMom2"
]
},
{
"page": "ExposureIndex",
"title": "Exposure (interaction) index",
"topics": [
"ExposureIndex"
]
},
{
"page": "ExpTaylor",
"title": "Taylor series for the exponential",
"topics": [
"ExpTaylor"
]
},
{
"page": "Exptest",
"title": "Certify that a test sequence has exponentially small error probabilities",
"topics": [
"Exptest"
]
},
{
"page": "extenders_nonparam",
"title": "FDR / nonparametric / quantile / latent-class extenders (Phase 1.n)",
"topics": [
"extenders_nonparam"
]
},
{
"page": "extenders_rdd",
"title": "RDD / IRT extenders (Phase 1.l)",
"topics": [
"extenders_rdd"
]
},
{
"page": "extenders_spatial",
"title": "Spatial / multivariate / meta-analysis extenders (Phase 1.m)",
"topics": [
"extenders_spatial"
]
},
{
"page": "extenders_stats",
"title": "Stats extenders (Phase 1.k)",
"topics": [
"extenders_stats"
]
},
{
"page": "external_validate",
"title": "External validation on new data",
"topics": [
"external_validate"
]
},
{
"page": "extract",
"title": "HKDF Extract: PRK = HMAC(salt, IKM)",
"topics": [
"extract"
]
},
{
"page": "FacilityLocation",
"title": "Uncapacitated facility location",
"topics": [
"FacilityLocation"
]
},
{
"page": "factor_analysis_ml",
"title": "Maximum-likelihood factor analysis by EM",
"topics": [
"factor_analysis_ml"
]
},
{
"page": "FactorialKrige",
"title": "Factorial kriging of one nested component",
"topics": [
"FactorialKrige"
]
},
{
"page": "fairness_cityprofile",
"title": "City-agnostic data profiles for the predictive-policing audit",
"topics": [
"fairness_cityprofile"
]
},
{
"page": "fairness_metrics",
"title": "Group-disparity metrics for auditing classification systems",
"topics": [
"fairness_metrics"
]
},
{
"page": "fairness_temporal",
"title": "Multi-city temporal disparity audit",
"topics": [
"fairness_temporal"
]
},
{
"page": "fallback_procedure",
"title": "Fallback (fixed-sequence with alpha spending)",
"topics": [
"fallback_procedure"
]
},
{
"page": "Fano",
"title": "Fano lower bound on the error probability of an M-ary test",
"topics": [
"Fano"
]
},
{
"page": "Farsig",
"title": "Farrington flexible algorithm",
"topics": [
"Farsig"
]
},
{
"page": "FayHerriot",
"title": "Fay-Herriot and Battese-Harter-Fuller small-area EBLUPs",
"topics": [
"BhfEblup",
"FayHerriot"
]
},
{
"page": "FcaAccessibility",
"title": "Spatial accessibility: FCA family, gravity access and nearest facility",
"topics": [
"FcaAccessibility",
"GravityAccessibility",
"NearestFacility",
"RadiationFlows"
]
},
{
"page": "Fcfp4",
"title": "Functional-class fingerprint, radius 2 (FCFP4)",
"topics": [
"Fcfp4"
]
},
{
"page": "Fdaclust",
"title": "Functional clustering by k-means on B-spline coefficients",
"topics": [
"Fdaclust"
]
},
{
"page": "fdiffm",
"title": "First-difference model for elongated field layouts",
"topics": [
"fdiffm"
]
},
{
"page": "FDiverg",
"title": "Csiszar (1967) f-divergence D_f(p || q) = sum q f(p/q)",
"topics": [
"FDiverg"
]
},
{
"page": "Ferror",
"title": "Renewal-equation forecast",
"topics": [
"Ferror"
]
},
{
"page": "Fevdc",
"title": "Forecast error variance decomposition from a VAR(1)",
"topics": [
"Fevdc"
]
},
{
"page": "FillSinks",
"title": "Priority-flood depression filling",
"topics": [
"FillSinks"
]
},
{
"page": "FilteredKrige",
"title": "Kriging filters, indicator ccdf, kriging efficiency and collocated co-kriging",
"topics": [
"CollocatedCokriging",
"FilteredKrige",
"IndicatorCcdf",
"KrigingEfficiency"
]
},
{
"page": "FilterSim",
"title": "FILTERSIM multiple-point simulation",
"topics": [
"FilterSim"
]
},
{
"page": "Finalsz",
"title": "Final epidemic size (Kermack-McKendrick)",
"topics": [
"Finalsz"
]
},
{
"page": "Finegray",
"title": "Fine-Gray subdistribution hazard model",
"topics": [
"Finegray",
"morie_survival_finegray"
]
},
{
"page": "FireflyAlgorithm",
"title": "Firefly algorithm",
"topics": [
"FireflyAlgorithm"
]
},
{
"page": "fisher_combined",
"title": "Fisher's method for combining independent p-values",
"topics": [
"fisher_combined"
]
},
{
"page": "fisher_exact_test",
"title": "Fisher's exact test for a 2x2 table",
"topics": [
"fisher_exact_test"
]
},
{
"page": "FiskDist",
"title": "Fisk (log-logistic) distribution",
"topics": [
"FiskDist"
]
},
{
"page": "Fistalasso",
"title": "FISTA for the LASSO",
"topics": [
"Fistalasso"
]
},
{
"page": "fitted.morie_bayes_fit",
"title": "Posterior fitted values (on the response scale)",
"topics": [
"fitted.morie_bayes_fit"
]
},
{
"page": "fitted.morie_lm",
"title": "Extract fitted values from method for 'morie_lm' objects",
"topics": [
"fitted.morie_lm"
]
},
{
"page": "fitted.morie_lmm",
"title": "Fitted values of a morie mixed model",
"topics": [
"fitted.morie_lmm"
]
},
{
"page": "FitVariogram",
"title": "Weighted least squares variogram fitting (gstat fit.variogram weights)",
"topics": [
"FitVariogram"
]
},
{
"page": "fixed_effects_meta",
"title": "Fixed-effects (inverse-variance) meta-analytic pooling",
"topics": [
"fixed_effects_meta"
]
},
{
"page": "fixed_sequence",
"title": "Fixed-sequence (predetermined order) testing",
"topics": [
"fixed_sequence"
]
},
{
"page": "fleiss_kappa",
"title": "Fleiss' kappa for multiple raters",
"topics": [
"fleiss_kappa"
]
},
{
"page": "Flmgcr",
"title": "Flamingo gated cross-attention",
"topics": [
"Flmgcr"
]
},
{
"page": "FlowCapturingLocation",
"title": "Flow-capturing location model",
"topics": [
"FlowCapturingLocation"
]
},
{
"page": "Flowmm",
"title": "Maximum flow / minimum cut",
"topics": [
"Flowmm"
]
},
{
"page": "Flsh2",
"title": "FlashAttention tiled exact attention",
"topics": [
"Flsh2"
]
},
{
"page": "Fnested",
"title": "F-test for a nested pair of linear models (ESL eq. 3.13)",
"topics": [
"Fnested"
]
},
{
"page": "FocalStatistics",
"title": "Focal statistics, filters and grey morphology",
"topics": [
"CannyEdges",
"FilterKernel",
"FocalFilter",
"FocalStatistics",
"GreyMorphology"
]
},
{
"page": "FoodDesert",
"title": "USDA low-income, low-access (LILA) food deserts",
"topics": [
"FoodDesert"
]
},
{
"page": "format_dataframe",
"title": "Apply uniform formatting to numeric columns",
"topics": [
"format_dataframe"
]
},
{
"page": "format_number",
"title": "Format a single number according to style conventions",
"topics": [
"format_number"
]
},
{
"page": "format.morie_otis_result",
"title": "Format method for 'morie_otis_result' objects",
"topics": [
"format.morie_otis_result"
]
},
{
"page": "forward_corrupt",
"title": "Forward corruption (closed form)",
"topics": [
"forward_corrupt"
]
},
{
"page": "fourier_terms",
"title": "Cosine and sine Fourier pairs, exactly periodic with 'period'",
"topics": [
"fourier_terms"
]
},
{
"page": "Fpc",
"title": "Finite population correction factor for sampling without replacement",
"topics": [
"Fpc"
]
},
{
"page": "fraction_missing_information",
"title": "Fraction of missing information of multiply imputed estimates",
"topics": [
"fraction_missing_information"
]
},
{
"page": "Frdbnd",
"title": "Frechet-Hoeffding bounds on a joint distribution",
"topics": [
"Frdbnd"
]
},
{
"page": "FrechetDist",
"title": "Frechet distribution",
"topics": [
"FrechetDist"
]
},
{
"page": "FriedF",
"title": "Friedman's test in F form",
"topics": [
"FriedF"
]
},
{
"page": "friedman_test",
"title": "Friedman test (repeated-measures rank ANOVA)",
"topics": [
"friedman_test"
]
},
{
"page": "FrmlD",
"title": "Formal derivative of a polynomial",
"topics": [
"FrmlD"
]
},
{
"page": "frns_metrics",
"title": "Group-disparity metrics for auditing classification and risk systems",
"topics": [
"frns_metrics"
]
},
{
"page": "frns_predpol",
"title": "Generalised predictive-policing disparity audit",
"topics": [
"frns_predpol"
]
},
{
"page": "frns_temporal",
"title": "Multi-city temporal disparity audit",
"topics": [
"frns_temporal"
]
},
{
"page": "Frtaxd",
"title": "Spatial Shannon-Wiener taxon diversity on a rectangular grid",
"topics": [
"forest_taxon_diversity",
"Frtaxd"
]
},
{
"page": "Frwol2",
"title": "Frank-Wolfe conditional gradient",
"topics": [
"Frwol2"
]
},
{
"page": "full_diagnostics",
"title": "Full diagnostic report",
"topics": [
"full_diagnostics"
]
},
{
"page": "FuzzyCmeans",
"title": "Fuzzy c-means and Gaussian mean shift",
"topics": [
"FuzzyCmeans",
"MeanShift"
]
},
{
"page": "Fwlfwd",
"title": "Fully corrective Frank-Wolfe",
"topics": [
"Fwlfwd"
]
},
{
"page": "Fxidf",
"title": "Test whether the effect of X differs across levels of a modifier",
"topics": [
"Fxidf"
]
},
{
"page": "Ga_opt",
"title": "Genetic algorithm with elitist truncation selection",
"topics": [
"Ga_opt"
]
},
{
"page": "gabc_log_likelihood",
"title": "Wilkinson's GABC log-likelihood",
"topics": [
"gabc_log_likelihood"
]
},
{
"page": "gam_smoother",
"title": "Thin-plate spline smoother",
"topics": [
"gam_smoother"
]
},
{
"page": "gamfit",
"title": "Fitting the gamma distribution",
"topics": [
"gamfit"
]
},
{
"page": "gamma_generation_time",
"title": "Build a gamma generation-time density on a grid, normalised",
"topics": [
"gamma_generation_time"
]
},
{
"page": "GammaFromLambdaDelta",
"title": "Observational-study sensitivity: amplification, U-statistic bounds, crosscut test and design sensitivity",
"topics": [
"AmplifyGamma",
"CrosscutDesignSensitivity",
"CrosscutTest",
"GammaFromLambdaDelta",
"UStatisticSensitivity"
]
},
{
"page": "Ganomscore",
"title": "GANomaly anomaly score from encoder-decoder-encoder latents",
"topics": [
"Ganomscore"
]
},
{
"page": "Gat",
"title": "Let each node decide how much to weight each neighbour",
"topics": [
"Gat"
]
},
{
"page": "Gatemd",
"title": "Multi-head graph attention layer",
"topics": [
"Gatemd"
]
},
{
"page": "GatV2",
"title": "GATv2 attention layer",
"topics": [
"GatV2"
]
},
{
"page": "GaussApx",
"title": "Gaussian approximation to the centred fair-coin binomial",
"topics": [
"GaussApx"
]
},
{
"page": "GaussCount",
"title": "Expected Gaussian count over N repetitions",
"topics": [
"GaussCount"
]
},
{
"page": "GaussDom",
"title": "Domain check for the Gaussian approximation",
"topics": [
"GaussDom"
]
},
{
"page": "Gausslik",
"title": "Gaussian conditional log-likelihood (ESL eq. 2.35)",
"topics": [
"Gausslik"
]
},
{
"page": "Gaussm",
"title": "Gaussian mechanism for differential privacy",
"topics": [
"Gaussm"
]
},
{
"page": "GaussTail",
"title": "Gaussian ordinate many standard deviations out",
"topics": [
"GaussTail"
]
},
{
"page": "Gblupeq",
"title": "Cholesky re-parameterization that makes GBLUP an ordinary mixed model",
"topics": [
"Gblupeq"
]
},
{
"page": "Gblupr",
"title": "Genomic BLUP",
"topics": [
"Gblupr"
]
},
{
"page": "Gbtcom",
"title": "Goodman-Bacon three-way composition of the TWFE DiD coefficient",
"topics": [
"Gbtcom"
]
},
{
"page": "Gcnchb",
"title": "ChebNet spectral filter",
"topics": [
"Gcnchb"
]
},
{
"page": "Gcnemd",
"title": "Graph convolution layer before the renormalisation trick",
"topics": [
"Gcnemd"
]
},
{
"page": "GcnII",
"title": "GCNII: initial residual plus identity mapping",
"topics": [
"GcnII"
]
},
{
"page": "GcnL",
"title": "Graph convolution with the renormalisation trick",
"topics": [
"GcnL"
]
},
{
"page": "Gdpf",
"title": "Gaussian differential privacy: the trade-off function",
"topics": [
"Gdpf"
]
},
{
"page": "GelmanKingIncumbency",
"title": "Electoral systems and legislative polarization",
"topics": [
"GelmanKingIncumbency",
"NetworkPolarization",
"ProportionalSeats",
"RunoffWinner"
]
},
{
"page": "GenLambda",
"title": "Generalized lambda distribution (Ramberg-Schmeiser)",
"topics": [
"GenLambda"
]
},
{
"page": "Genvdm",
"title": "D study: project a G study onto a proposed facet size",
"topics": [
"Genvdm"
]
},
{
"page": "Genvxt",
"title": "Generalizability coefficient for the crossed p x i design",
"topics": [
"Genvxt"
]
},
{
"page": "GeomExp",
"title": "Expectation of the geometric distribution",
"topics": [
"GeomExp"
]
},
{
"page": "Gesd",
"title": "Generalized ESD test for outliers (Rosner 1983)",
"topics": [
"Gesd"
]
},
{
"page": "Getisordg",
"title": "Getis-Ord global G, with its exact randomisation moments",
"topics": [
"Getisordg"
]
},
{
"page": "Gevcdf",
"title": "GEV distribution function (Coles 2001 eq. 3.2)",
"topics": [
"Gevcdf"
]
},
{
"page": "Ghosalabsolutecontinuity",
"title": "Posterior via Radon-Nikodym under domination",
"topics": [
"Ghosalabsolutecontinuity"
]
},
{
"page": "Ghosaladaptthm",
"title": "Adaptive hierarchical model prior",
"topics": [
"Ghosaladaptthm"
]
},
{
"page": "Ghosalbayesruleinfinite",
"title": "Bayes's rule with a dominated likelihood",
"topics": [
"Ghosalbayesruleinfinite"
]
},
{
"page": "Ghosalbbcensored",
"title": "Bayesian bootstrap for censored data",
"topics": [
"Ghosalbbcensored"
]
},
{
"page": "Ghosalbernsteinfeller",
"title": "Bernstein-Feller approximation of a CDF",
"topics": [
"Ghosalbernsteinfeller"
]
},
{
"page": "Ghosalbetaprocdef",
"title": "Beta-process definition",
"topics": [
"Ghosalbetaprocdef"
]
},
{
"page": "Ghosalbmprior",
"title": "Brownian-motion prior",
"topics": [
"Ghosalbmprior"
]
},
{
"page": "Ghosalbpcont",
"title": "Beta-process Levy measure",
"topics": [
"Ghosalbpcont"
]
},
{
"page": "Ghosalbpdiscrete",
"title": "Discrete-time beta process",
"topics": [
"Ghosalbpdiscrete"
]
},
{
"page": "Ghosalbppathgen",
"title": "Beta-process path by Poisson jumps",
"topics": [
"Ghosalbppathgen"
]
},
{
"page": "Ghosalcoxbvm",
"title": "Semiparametric Bernstein-von Mises for the Cox model",
"topics": [
"Ghosalcoxbvm"
]
},
{
"page": "Ghosalcoxbvmsp",
"title": "Cox-model Bernstein-von Mises",
"topics": [
"Ghosalcoxbvmsp"
]
},
{
"page": "Ghosalcoxmodel",
"title": "Cox model with a beta-process baseline",
"topics": [
"Ghosalcoxmodel"
]
},
{
"page": "Ghosalcoxpost",
"title": "Cox posterior via the partial likelihood",
"topics": [
"Ghosalcoxpost"
]
},
{
"page": "Ghosalcredsetcov",
"title": "Credible-set frequentist coverage",
"topics": [
"Ghosalcredsetcov"
]
},
{
"page": "Ghosaldpbvm",
"title": "Dirichlet-process Bernstein-von Mises (Brownian bridge)",
"topics": [
"Ghosaldpbvm"
]
},
{
"page": "Ghosaleffinflfn",
"title": "Efficient influence function for F(t)",
"topics": [
"Ghosaleffinflfn"
]
},
{
"page": "Ghosalepgp",
"title": "Expectation propagation for Gaussian process classification",
"topics": [
"Ghosalepgp"
]
},
{
"page": "Ghosalexplink",
"title": "Exponential-link density prior",
"topics": [
"Ghosalexplink"
]
},
{
"page": "Ghosalfbmprior",
"title": "Fractional Brownian motion prior",
"topics": [
"Ghosalfbmprior"
]
},
{
"page": "Ghosalfrsbinreg",
"title": "Random-series binary regression",
"topics": [
"Ghosalfrsbinreg"
]
},
{
"page": "Ghosalfrsdensity",
"title": "Density estimation via a finite random series prior",
"topics": [
"Ghosalfrsdensity"
]
},
{
"page": "Ghosalfrspoireg",
"title": "Random-series Poisson regression",
"topics": [
"Ghosalfrspoireg"
]
},
{
"page": "Ghosalfrsreg",
"title": "Finite-random-series regression",
"topics": [
"Ghosalfrsreg"
]
},
{
"page": "Ghosalfuncreg",
"title": "Functional linear regression",
"topics": [
"Ghosalfuncreg"
]
},
{
"page": "Ghosalgpadaptthm",
"title": "GP length-scale adaptation",
"topics": [
"Ghosalgpadaptthm"
]
},
{
"page": "Ghosalgpbinregcrt",
"title": "GP binary-regression contraction",
"topics": [
"Ghosalgpbinregcrt"
]
},
{
"page": "Ghosalgpcrtthm",
"title": "Gaussian contraction-rate equation",
"topics": [
"Ghosalgpcrtthm"
]
},
{
"page": "Ghosalgpdefrkhs",
"title": "Gaussian process RKHS, Euclidean case",
"topics": [
"Ghosalgpdefrkhs"
]
},
{
"page": "Ghosalgpdenscrt",
"title": "GP density estimation contraction rate via the concentration function",
"topics": [
"Ghosalgpdenscrt"
]
},
{
"page": "Ghosalgpincreasingprior",
"title": "Increasing-process prior via an exponentiated Gaussian process",
"topics": [
"Ghosalgpincreasingprior"
]
},
{
"page": "Ghosalgplaplace",
"title": "Laplace approximation for GP posteriors",
"topics": [
"Ghosalgplaplace"
]
},
{
"page": "Ghosalgppriordef",
"title": "Gaussian-process prior draw",
"topics": [
"Ghosalgppriordef"
]
},
{
"page": "Ghosalhistogramprior",
"title": "Histogram (binning) density prior",
"topics": [
"Ghosalhistogramprior"
]
},
{
"page": "Ghosalinfdimbvm",
"title": "Parametric Bernstein-von Mises",
"topics": [
"Ghosalinfdimbvm"
]
},
{
"page": "Ghosalmixbp",
"title": "Mixtures of beta processes",
"topics": [
"Ghosalmixbp"
]
},
{
"page": "Ghosalmixturebasisprior",
"title": "Mixture-of-kernels density prior",
"topics": [
"Ghosalmixturebasisprior"
]
},
{
"page": "Ghosalmodselbic",
"title": "Bayes-factor model selection consistency",
"topics": [
"Ghosalmodselbic"
]
},
{
"page": "Ghosalnpbinaryreg",
"title": "Nonparametric binary regression with a Gaussian process prior",
"topics": [
"Ghosalnpbinaryreg"
]
},
{
"page": "Ghosalnpnormalreg",
"title": "Nonparametric normal regression with a Gaussian process prior",
"topics": [
"Ghosalnpnormalreg"
]
},
{
"page": "Ghosalnppoissonreg",
"title": "Nonparametric Poisson regression with a Gaussian process prior",
"topics": [
"Ghosalnppoissonreg"
]
},
{
"page": "Ghosalntrbvm",
"title": "Neutral-to-the-right functional Bernstein-von Mises",
"topics": [
"Ghosalntrbvm"
]
},
{
"page": "Ghosalntrconsist",
"title": "Neutral-to-the-right posterior consistency",
"topics": [
"Ghosalntrconsist"
]
},
{
"page": "Ghosalntrdef",
"title": "Neutral-to-the-right processes",
"topics": [
"Ghosalntrdef"
]
},
{
"page": "Ghosalntrlevy",
"title": "Neutral-to-the-right Levy (Laplace) functional",
"topics": [
"Ghosalntrlevy"
]
},
{
"page": "Ghosalparamnpbf",
"title": "Parametric versus nonparametric Bayes factor",
"topics": [
"Ghosalparamnpbf"
]
},
{
"page": "Ghosalparamrate",
"title": "Adaptation to a parametric truth",
"topics": [
"Ghosalparamrate"
]
},
{
"page": "Ghosalpriorposteriorupdate",
"title": "Sequential prior-to-posterior updating",
"topics": [
"Ghosalpriorposteriorupdate"
]
},
{
"page": "Ghosalrandombasisexpansion",
"title": "Random basis expansion prior",
"topics": [
"Ghosalrandombasisexpansion"
]
},
{
"page": "Ghosalrescalgp",
"title": "Rescaled Gaussian process",
"topics": [
"Ghosalrescalgp"
]
},
{
"page": "Ghosalrkhsnorm",
"title": "Concentration-function terms",
"topics": [
"Ghosalrkhsnorm"
]
},
{
"page": "Ghosalrlprocess",
"title": "Riemann-Liouville process",
"topics": [
"Ghosalrlprocess"
]
},
{
"page": "Ghosalrndseriespr",
"title": "Random-series prior with random dimension",
"topics": [
"Ghosalrndseriespr"
]
},
{
"page": "Ghosalselfsimgp",
"title": "Self-similarity of fractional Brownian motion",
"topics": [
"Ghosalselfsimgp"
]
},
{
"page": "Ghosalsemiparabvm",
"title": "Semiparametric Bernstein-von Mises for a smooth functional",
"topics": [
"Ghosalsemiparabvm"
]
},
{
"page": "Ghosalsemiparaeff",
"title": "Semiparametric efficiency bound",
"topics": [
"Ghosalsemiparaeff"
]
},
{
"page": "Ghosalseriesgp",
"title": "Series (eigenexpansion) Gaussian process",
"topics": [
"Ghosalseriesgp"
]
},
{
"page": "Ghosalsmhazgp",
"title": "Smooth hazard via a Gaussian process",
"topics": [
"Ghosalsmhazgp"
]
},
{
"page": "Ghosalstatgpspec",
"title": "Stationary GP via Bochner's theorem",
"topics": [
"Ghosalstatgpspec"
]
},
{
"page": "Ghosalstrictsbvm",
"title": "Strict semiparametric Bernstein-von Mises conditions",
"topics": [
"Ghosalstrictsbvm"
]
},
{
"page": "Ghosalstrongapxdp",
"title": "Strong approximation for the Dirichlet process",
"topics": [
"Ghosalstrongapxdp"
]
},
{
"page": "Ghosalsurvdpkm",
"title": "DP posterior survival and its Kaplan-Meier limit",
"topics": [
"Ghosalsurvdpkm"
]
},
{
"page": "Ghosalsurvdppost",
"title": "Dirichlet-process posterior with right-censored data",
"topics": [
"Ghosalsurvdppost"
]
},
{
"page": "Ghosaltwomodeladp",
"title": "Two-model adaptation",
"topics": [
"Ghosaltwomodeladp"
]
},
{
"page": "Ghosalunivweights",
"title": "Universal model weights",
"topics": [
"Ghosalunivweights"
]
},
{
"page": "Ghosalwnadapt",
"title": "Spike-and-slab white-noise adaptation",
"topics": [
"Ghosalwnadapt"
]
},
{
"page": "Ghosalwnfullbvm",
"title": "White-noise full Bernstein-von Mises",
"topics": [
"Ghosalwnfullbvm"
]
},
{
"page": "Ghosalwnlinbvm",
"title": "White-noise linear-functional Bernstein-von Mises",
"topics": [
"Ghosalwnlinbvm"
]
},
{
"page": "GHSecant",
"title": "Generalized secant hyperbolic distribution",
"topics": [
"GHSecant"
]
},
{
"page": "Gibbsm",
"title": "Gibbs sampler",
"topics": [
"Gibbsm"
]
},
{
"page": "GibbsPseudolikelihood",
"title": "Maximum pseudolikelihood for Gibbs point processes",
"topics": [
"GibbsPseudolikelihood"
]
},
{
"page": "GirvanNewman",
"title": "Girvan-Newman communities",
"topics": [
"GirvanNewman"
]
},
{
"page": "gktau",
"title": "Goodman and Kruskal's tau",
"topics": [
"gktau"
]
},
{
"page": "glass_delta",
"title": "Glass's delta - control-group SD denominator",
"topics": [
"glass_delta"
]
},
{
"page": "GlmProfCI",
"title": "Profile likelihood interval for a GLM coefficient",
"topics": [
"GlmProfCI"
]
},
{
"page": "GlmStdRes",
"title": "Pearson and standardized Pearson residuals for binomial and Poisson GLMs",
"topics": [
"GlmStdRes"
]
},
{
"page": "Glpopt",
"title": "Linear programme by the two-phase primal simplex method",
"topics": [
"Glpopt"
]
},
{
"page": "gmconj",
"title": "Gamma-conjugate posterior for an exponential or Poisson rate",
"topics": [
"gmconj"
]
},
{
"page": "GMErrorSAR",
"title": "Spatial error model by generalised moments",
"topics": [
"GMErrorSAR"
]
},
{
"page": "Gmsbound",
"title": "Generalised moment selection test statistic",
"topics": [
"Gmsbound"
]
},
{
"page": "Gnsetenr",
"title": "Gene-set enrichment analysis enrichment score (GSEA)",
"topics": [
"Gnsetenr"
]
},
{
"page": "Goenr",
"title": "GO term enrichment (hypergeometric upper tail)",
"topics": [
"Goenr"
]
},
{
"page": "gp_fit",
"title": "Fit a GP emulator to a log-likelihood ensemble",
"topics": [
"gp_fit"
]
},
{
"page": "gp_predict",
"title": "Posterior mean and sd at theta",
"topics": [
"gp_predict"
]
},
{
"page": "Gpcgs",
"title": "Sparse variational GP classification",
"topics": [
"Gpcgs"
]
},
{
"page": "Gpcla",
"title": "Gaussian process classification by the Laplace approximation",
"topics": [
"Gpcla"
]
},
{
"page": "GpClassify",
"title": "GP classification, sparse GPs, warping and deep-GP samples",
"topics": [
"DeepGpSample",
"GpClassify",
"GpSparse",
"KumaraswamyWarp"
]
},
{
"page": "Gpcm",
"title": "Generalized partial credit model",
"topics": [
"Gpcm"
]
},
{
"page": "GpCovariance",
"title": "Gaussian process regression, sampling and fitting",
"topics": [
"GpCovariance",
"GpFit",
"GpLoo",
"GpPredict",
"GpSample"
]
},
{
"page": "Gpdcdf",
"title": "GPD distribution function of threshold excesses (Coles 2001 eq. 4.2)",
"topics": [
"Gpdcdf"
]
},
{
"page": "GpdD",
"title": "Generalized Pareto distribution",
"topics": [
"GpdD"
]
},
{
"page": "GpdDistribution",
"title": "Generalized Pareto distribution and its threshold-exceedance fit",
"topics": [
"GpdDistribution",
"Gpfit"
]
},
{
"page": "Gpdkl",
"title": "Deep kernel learning",
"topics": [
"Gpdkl"
]
},
{
"page": "Gpdsh",
"title": "Density-shift detection on a stream by Kullback-Leibler divergence",
"topics": [
"Gpdsh"
]
},
{
"page": "Gphtr",
"title": "Heteroscedastic Gaussian process regression",
"topics": [
"Gphtr"
]
},
{
"page": "Gpkern",
"title": "Composing Gaussian process kernels",
"topics": [
"Gpkern"
]
},
{
"page": "Gpmlt",
"title": "Multi-task Gaussian process",
"topics": [
"Gpmlt"
]
},
{
"page": "Gpmoe",
"title": "Predict from K Gaussian-process experts under a softmax gate",
"topics": [
"Gpmoe"
]
},
{
"page": "Gppost",
"title": "Posterior mean of a Gaussian process at new inputs",
"topics": [
"Gppost"
]
},
{
"page": "Gpreg",
"title": "Gaussian-process regression with a squared-exponential kernel",
"topics": [
"Gpreg"
]
},
{
"page": "Gpregb",
"title": "Gaussian process regression with the hyperparameters integrated out",
"topics": [
"Gpregb"
]
},
{
"page": "Gpresid",
"title": "Gaussian process fitted to the residuals of a parametric fit",
"topics": [
"Gpresid"
]
},
{
"page": "gps_abc",
"title": "GPS-ABC adaptive sampler, Meeds & Welling Algorithm 2",
"topics": [
"gps_abc"
]
},
{
"page": "Gpsps",
"title": "Spectral mixture kernel GP",
"topics": [
"Gpsps"
]
},
{
"page": "Gpsvi",
"title": "Sparse variational GP regression",
"topics": [
"Gpsvi"
]
},
{
"page": "Gpt2",
"title": "GPT-2 decoder-only language model",
"topics": [
"Gpt2"
]
},
{
"page": "Gpvar",
"title": "Predictive variance of a Gaussian process",
"topics": [
"Gpvar"
]
},
{
"page": "Gpvbo",
"title": "Bayesian optimisation: expected improvement under a GP posterior",
"topics": [
"Gpvbo"
]
},
{
"page": "Gradds",
"title": "Gradient descent",
"topics": [
"Gradds"
]
},
{
"page": "GraphDegreeCentrality",
"title": "Graphs, SAT and CRFs: degree centrality, diameter, betweenness, CDCL, linear-chain CRF",
"topics": [
"CdclSolve",
"CrfFit",
"CrfMarginals",
"CrfViterbi",
"GraphBetweenness",
"GraphDegreeCentrality",
"GraphDiameter"
]
},
{
"page": "Graphlap",
"title": "Combinatorial (unnormalised) Laplacian of a weighted graph",
"topics": [
"Graphlap"
]
},
{
"page": "GraphModularity",
"title": "Community detection: fast greedy modularity and walktrap",
"topics": [
"FastGreedyModularity",
"GraphModularity",
"WalktrapCommunities"
]
},
{
"page": "GravityPPML",
"title": "Gravity model of flows by Poisson pseudo-maximum likelihood",
"topics": [
"GravityPPML"
]
},
{
"page": "GreyWolfOptimizer",
"title": "Grey wolf optimizer",
"topics": [
"GreyWolfOptimizer"
]
},
{
"page": "GridContiguity",
"title": "Spatial weights construction",
"topics": [
"DistanceBandWeights",
"GridContiguity",
"InverseDistanceWeights",
"KernelWeights",
"KnnWeights",
"SymmetrizeWeights",
"WeightsComponents"
]
},
{
"page": "Grmsam",
"title": "Samejima graded response model",
"topics": [
"Grmsam"
]
},
{
"page": "group_delay",
"title": "Signal measures: group delay, noise level, LMS bound, QRS duration, ZCR, spectrogram",
"topics": [
"group_delay",
"max_step_size",
"noise_power",
"noise_psd",
"qrs_duration",
"spcgm",
"zero_crossing_rate"
]
},
{
"page": "group_time_att",
"title": "Group-time ATT(g,t) under staggered adoption",
"topics": [
"group_time_att"
]
},
{
"page": "GroupAvg",
"title": "Group-average relation",
"topics": [
"GroupAvg"
]
},
{
"page": "GroverS",
"title": "Grover quantum search",
"topics": [
"GroverS"
]
},
{
"page": "GrowingDegreeDays",
"title": "Agroclimatic indicators",
"topics": [
"ChillAccumulation",
"ColdSpellDuration",
"GrowingDegreeDays",
"GrowingSeasonLength",
"LaiFromSavi",
"RainfallAdequacy"
]
},
{
"page": "GrubbsT",
"title": "Grubbs (1969) test that the most extreme observation is an outlier",
"topics": [
"GrubbsT"
]
},
{
"page": "GS2SLSSAC",
"title": "SAC model by generalised spatial two-stage least squares",
"topics": [
"GS2SLSSAC"
]
},
{
"page": "Gstabwt",
"title": "Stabilized inverse-probability-of-treatment weights",
"topics": [
"Gstabwt"
]
},
{
"page": "GtDorfman",
"title": "Dorfman group testing operating characteristics",
"topics": [
"GtDorfman"
]
},
{
"page": "GtPrev",
"title": "Prevalence from group testing",
"topics": [
"GtPrev"
]
},
{
"page": "GtRegEM",
"title": "Group testing regression by Xie's EM algorithm",
"topics": [
"GtRegEM"
]
},
{
"page": "GtwrFit",
"title": "Space-time models: GTWR, multiscale GWR by backfitting, Gaussian hidden Markov models",
"topics": [
"GaussianHmm",
"GtwrFit",
"MgwrBackfit"
]
},
{
"page": "GumbelDist",
"title": "Gumbel (type I extreme value) distribution",
"topics": [
"GumbelDist"
]
},
{
"page": "GWRBasic",
"title": "Geographically weighted regression with its diagnostics",
"topics": [
"GWRBasic"
]
},
{
"page": "gwrcoef",
"title": "Geographically weighted regression: coefficients, diagnostics and GLMs",
"topics": [
"gwrcoef",
"gwrcv",
"gwrdlt",
"gwrfwl",
"gwrhat",
"gwrlgt",
"gwrpois",
"gwrres",
"gwrstd",
"gwrsur",
"gwrtst"
]
},
{
"page": "GwrCollinearity",
"title": "GWR collinearity diagnostics, prediction, F tests and GW summary statistics",
"topics": [
"GwrCollinearity",
"GwrFTests",
"GwrPredict",
"GwSummary"
]
},
{
"page": "GWRKernelWeights",
"title": "Geographical kernel weights of one regression location",
"topics": [
"GWRKernelWeights"
]
},
{
"page": "Gxeblup",
"title": "Multi-environment genomic model with a genotype-by-environment term",
"topics": [
"Gxeblup"
]
},
{
"page": "HalfHeads",
"title": "Stirling approximation to the half-heads probability",
"topics": [
"HalfHeads"
]
},
{
"page": "harmonic_mean_p",
"title": "Harmonic mean p-value",
"topics": [
"harmonic_mean_p"
]
},
{
"page": "HarrisHawksOptimizer",
"title": "Harris hawks optimization",
"topics": [
"HarrisHawksOptimizer"
]
},
{
"page": "hawkes_spatial",
"title": "Spatiotemporal (self-exciting) Hawkes point process",
"topics": [
"hawkes_spatial"
]
},
{
"page": "hazard_ratio_table",
"title": "Hazard-ratio table from Cox model components",
"topics": [
"hazard_ratio_table"
]
},
{
"page": "Hazratio",
"title": "Hazard ratios with confidence intervals",
"topics": [
"Hazratio",
"morie_survival_hr"
]
},
{
"page": "hcepst",
"title": "Complex cepstrum with phase unwrapping",
"topics": [
"hcepst"
]
},
{
"page": "hdecon",
"title": "Homomorphic deconvolution via cepstral liftering",
"topics": [
"hdecon"
]
},
{
"page": "HealthConcentrationIndex",
"title": "Health inequality indices",
"topics": [
"HealthConcentrationIndex",
"SpatialGini",
"TheilDecomposition"
]
},
{
"page": "hedges_g",
"title": "Hedges' g - bias-corrected Cohen's d",
"topics": [
"hedges_g"
]
},
{
"page": "Hendmme",
"title": "Henderson's mixed model equations for the BLUE and the BLUP",
"topics": [
"Hendmme"
]
},
{
"page": "Hertbg",
"title": "Genomic heritability from a relationship matrix",
"topics": [
"Hertbg"
]
},
{
"page": "Hetero",
"title": "Heterotrait-monotrait ratio of correlations (HTMT)",
"topics": [
"Hetero"
]
},
{
"page": "Hetlc",
"title": "Marker heterozygosity and min-max normalisation",
"topics": [
"Hetlc",
"Mmaxn"
]
},
{
"page": "Hetmix",
"title": "Basic reproduction number under heterogeneous mixing",
"topics": [
"Hetmix"
]
},
{
"page": "Hexgrd",
"title": "Hexagonal grid binning (Carr hbin)",
"topics": [
"hexagonal_grid",
"Hexgrd"
]
},
{
"page": "hfd",
"title": "Higuchi fractal dimension",
"topics": [
"hfd"
]
},
{
"page": "Hffdsg",
"title": "Hoeffding's inequality",
"topics": [
"Hffdsg"
]
},
{
"page": "hierarchical_bonferroni",
"title": "Hierarchical (serial gatekeeping) Bonferroni procedure",
"topics": [
"hierarchical_bonferroni"
]
},
{
"page": "HierarchicalClustering",
"title": "Agglomerative hierarchical clustering and tree cutting (hclust conventions)",
"topics": [
"CutTree",
"HierarchicalClustering"
]
},
{
"page": "Hiermodel",
"title": "Hierarchical normal model: shrinkage of group means towards mu",
"topics": [
"Hiermodel"
]
},
{
"page": "Hillq",
"title": "Hill numbers of order q",
"topics": [
"Hillq"
]
},
{
"page": "Hillshade",
"title": "Hillshade (terra shade)",
"topics": [
"Hillshade"
]
},
{
"page": "HistogramTransform",
"title": "Map values onto a target marginal distribution by ranks",
"topics": [
"HistogramTransform"
]
},
{
"page": "history_match",
"title": "Sequential history matching",
"topics": [
"history_match"
]
},
{
"page": "Hits",
"title": "HITS hubs and authorities",
"topics": [
"Hits"
]
},
{
"page": "HitsR",
"title": "Hit rate at k",
"topics": [
"HitsR"
]
},
{
"page": "Hittime",
"title": "Expected steps for a simple random walk to reach a target vertex",
"topics": [
"Hittime"
]
},
{
"page": "hkdf",
"title": "HKDF: extract-then-expand, or expand-only on uniform input",
"topics": [
"hkdf"
]
},
{
"page": "Hmcsam",
"title": "Hamiltonian Monte Carlo",
"topics": [
"Hmcsam"
]
},
{
"page": "Hmicl",
"title": "In-context learning by prompt-conditioned scoring",
"topics": [
"Hmicl"
]
},
{
"page": "Hmmprf",
"title": "Profile hidden Markov model: forward likelihood and Viterbi alignment",
"topics": [
"Hmmprf"
]
},
{
"page": "HmmTag",
"title": "Hidden Markov part-of-speech tagging by the Viterbi algorithm",
"topics": [
"HmmTag"
]
},
{
"page": "Hmstrn",
"title": "Effect of a treatment rule read off from each time point onwards",
"topics": [
"Hmstrn"
]
},
{
"page": "hochberg",
"title": "Hochberg step-up FWER procedure",
"topics": [
"hochberg"
]
},
{
"page": "HockStick",
"title": "Hockey-stick identity",
"topics": [
"HockStick"
]
},
{
"page": "Hodprc",
"title": "Hodrick-Prescott filter",
"topics": [
"Hodprc"
]
},
{
"page": "holiday_matrix",
"title": "One indicator per holiday, optionally widened by a window",
"topics": [
"holiday_matrix"
]
},
{
"page": "holm",
"title": "Holm step-down FWER procedure",
"topics": [
"holm"
]
},
{
"page": "holm_sidak",
"title": "Holm-Sidak step-down procedure",
"topics": [
"holm_sidak"
]
},
{
"page": "Holmadj",
"title": "Holm step-down multiple-testing procedure (Holm 1979)",
"topics": [
"Holmadj"
]
},
{
"page": "hommel",
"title": "Hommel FWER procedure",
"topics": [
"hommel"
]
},
{
"page": "hosmer_lemeshow_test",
"title": "Hosmer-Lemeshow goodness-of-fit test for logistic regression",
"topics": [
"hosmer_lemeshow_test"
]
},
{
"page": "Hotcld",
"title": "Getis-Ord Gi-star hot and cold spot map with FDR screening",
"topics": [
"Hotcld",
"hot_cold_spots"
]
},
{
"page": "HotellingModel",
"title": "Hotelling-Downs competition of n candidates over uniform voters",
"topics": [
"HotellingModel"
]
},
{
"page": "hrvfd",
"title": "HRV frequency-domain metrics (VLF, LF, HF, LF/HF)",
"topics": [
"hrvfd"
]
},
{
"page": "hrvnl",
"title": "HRV nonlinear metrics (Poincare SD1, SD2)",
"topics": [
"hrvnl"
]
},
{
"page": "hrvtd",
"title": "HRV time-domain metrics (SDNN, RMSSD, pNN50)",
"topics": [
"hrvtd"
]
},
{
"page": "hsbkrg",
"title": "Bayesian kriging over the Matern class (Handcock and Stein 1993)",
"topics": [
"hsbkrg"
]
},
{
"page": "Hsirt",
"title": "Item response diagnostics: unpredictable voters, Lord's DIF test, SE of theta and average variance extracted",
"topics": [
"Ave",
"Hsirt",
"Lordzs",
"Semthe"
]
},
{
"page": "Htebias",
"title": "Best-linear-predictor calibration test for heterogeneous treatment effects",
"topics": [
"Htebias"
]
},
{
"page": "Htvar1",
"title": "Horvitz-Thompson variance of the estimated total",
"topics": [
"Htvar1"
]
},
{
"page": "Huberw",
"title": "Huber weight function",
"topics": [
"Huberw"
]
},
{
"page": "Hubrho",
"title": "Huber loss function",
"topics": [
"Hubrho"
]
},
{
"page": "Hubsa",
"title": "HITS run to convergence",
"topics": [
"Hubsa"
]
},
{
"page": "HypercubeQueue",
"title": "Police operations research models",
"topics": [
"HypercubeQueue",
"ShortCrimeLattice",
"ShortCrimePde",
"SquareRootLaw"
]
},
{
"page": "HyperLim",
"title": "Hypergeometric pmf against its binomial limit",
"topics": [
"HyperLim"
]
},
{
"page": "hyperplane_side",
"title": "Evaluate a hyperplane: value, side and distance",
"topics": [
"hyperplane_side"
]
},
{
"page": "i_squared",
"title": "I^2 heterogeneity statistic (Higgins)",
"topics": [
"i_squared"
]
},
{
"page": "Ibdmtx",
"title": "Pairwise identity-by-descent matrix (PLINK method of moments)",
"topics": [
"Ibdmtx"
]
},
{
"page": "Icarbm",
"title": "Intrinsic conditional autoregressive prior",
"topics": [
"Icarbm"
]
},
{
"page": "Icc12c",
"title": "The six Shrout-Fleiss intraclass correlations from one rating table",
"topics": [
"Icc12c"
]
},
{
"page": "IccA",
"title": "Agreement that counts a systematic rater offset as disagreement",
"topics": [
"IccA"
]
},
{
"page": "IccC",
"title": "Agreement up to a constant offset per rater",
"topics": [
"IccC"
]
},
{
"page": "Idealf",
"title": "Ideal fourths",
"topics": [
"Idealf"
]
},
{
"page": "IdfFit",
"title": "Rainfall-runoff: IDF curves, unit hydrographs, baseflow and hydrograph summaries",
"topics": [
"BaseflowFilter",
"ConvolveRunoff",
"HydrographSummary",
"IdfFit",
"UnitHydrograph"
]
},
{
"page": "IdwPredict",
"title": "Inverse distance weighting, cross-validation and modified Shepard weights",
"topics": [
"IdwCv",
"IdwPowerSearch",
"IdwPredict",
"ShepardPredict"
]
},
{
"page": "Igrav",
"title": "Gravity and spatial interaction models",
"topics": [
"Igrav",
"Igravbl",
"Igravcl",
"Igraver",
"Igravfe",
"Igravlm",
"Igravnb",
"Igravrt",
"Igravsq",
"Igravvf",
"Igravwl"
]
},
{
"page": "Ihstst",
"title": "Integrated haplotype score (iHS) for one core SNP",
"topics": [
"Ihstst"
]
},
{
"page": "Imai",
"title": "Average causal mediation effects under sequential ignorability",
"topics": [
"Imai"
]
},
{
"page": "ImperviousIndices",
"title": "Impervious-surface indices",
"topics": [
"ImperviousIndices"
]
},
{
"page": "implausible",
"title": "Wilkinson implausibility rule, eq. (3)",
"topics": [
"implausible"
]
},
{
"page": "importance_weights",
"title": "Importance weights over diffusion steps",
"topics": [
"importance_weights"
]
},
{
"page": "incidence_rate_difference",
"title": "Incidence rate difference (IRD)",
"topics": [
"incidence_rate_difference"
]
},
{
"page": "inclusion_proof",
"title": "Build an inclusion proof (audit path) for the given leaf",
"topics": [
"inclusion_proof"
]
},
{
"page": "Incrtio",
"title": "Incidence rate ratio",
"topics": [
"Incrtio"
]
},
{
"page": "Infcrt",
"title": "WAIC and PSIS-LOO computed from the same draws, so they can disagree",
"topics": [
"Infcrt"
]
},
{
"page": "Infgnt",
"title": "Fisher information metric of a parametric family",
"topics": [
"Infgnt"
]
},
{
"page": "Infiltration",
"title": "Infiltration, runoff and erosion models",
"topics": [
"Infiltration",
"Rusle",
"ScsRunoff"
]
},
{
"page": "install_cli",
"title": "Install the rmorie command-line launcher",
"topics": [
"install_cli"
]
},
{
"page": "Intanl",
"title": "Treatment by covariate interaction in a marginal structural model",
"topics": [
"Intanl"
]
},
{
"page": "Intlpa",
"title": "Interior-point linear programming by the logarithmic barrier",
"topics": [
"Intlpa"
]
},
{
"page": "Intmd4",
"title": "Split a total effect into mediation, interaction, both, neither",
"topics": [
"Intmd4"
]
},
{
"page": "intraclass_correlation",
"title": "Intraclass correlation coefficient (Shrout & Fleiss 1979)",
"topics": [
"intraclass_correlation"
]
},
{
"page": "Intvse",
"title": "Effects defined by shifting the mediator distribution, not the value",
"topics": [
"Intvse"
]
},
{
"page": "invbpr",
"title": "Prevalence from inverse binomial sampling",
"topics": [
"invbpr"
]
},
{
"page": "InvPredCI",
"title": "Inverse-prediction interval in a logistic model",
"topics": [
"InvPredCI"
]
},
{
"page": "Ipferd",
"title": "IPW estimate with a replicate-weight (jackknife) variance",
"topics": [
"Ipferd"
]
},
{
"page": "Ipfsfa",
"title": "Barrier interior-point solver for smooth inequality-constrained problems",
"topics": [
"Ipfsfa"
]
},
{
"page": "IpwSn",
"title": "IPW sensitivity analysis for a non-ignorable selection mechanism",
"topics": [
"IpwSn"
]
},
{
"page": "Ipwsrv",
"title": "Inverse probability weighting combined with survey design weights",
"topics": [
"Ipwsrv"
]
},
{
"page": "IqrA",
"title": "Flag points outside the Tukey fences",
"topics": [
"IqrA"
]
},
{
"page": "Irfun",
"title": "Orthogonalised impulse response function of a VAR(p)",
"topics": [
"Irfun"
]
},
{
"page": "Irlsfn",
"title": "One outer iteration of iteratively reweighted least squares",
"topics": [
"Irlsfn"
]
},
{
"page": "IrrigationWaterQuality",
"title": "Irrigation suitability, solids, loads and treatment removal",
"topics": [
"ConstituentLoad",
"IrrigationWaterQuality",
"RemovalEfficiency",
"SuspendedSolids",
"TotalDissolvedSolids"
]
},
{
"page": "Irt1pl",
"title": "Rasch one-parameter logistic model",
"topics": [
"Irt1pl"
]
},
{
"page": "Irt4pl",
"title": "Four-parameter logistic item response model",
"topics": [
"Irt4pl"
]
},
{
"page": "Irtgpc",
"title": "Generalized partial credit model (alias of Gpcm)",
"topics": [
"Irtgpc"
]
},
{
"page": "Irtgrm",
"title": "Samejima graded response model (alias of Grmsam)",
"topics": [
"Irtgrm"
]
},
{
"page": "Irtmh1",
"title": "Mantel-Haenszel differential item functioning",
"topics": [
"Irtmh1"
]
},
{
"page": "Irtprc",
"title": "Masters' partial credit model",
"topics": [
"Irtprc"
]
},
{
"page": "IsolationIndex",
"title": "Isolation index",
"topics": [
"IsolationIndex"
]
},
{
"page": "IsoLines",
"title": "Contour lines by marching squares",
"topics": [
"IsoLines"
]
},
{
"page": "Itinft",
"title": "Item information function",
"topics": [
"Itinft"
]
},
{
"page": "Itnnrs",
"title": "Item nonresponse adjustment by weighting classes",
"topics": [
"Itnnrs"
]
},
{
"page": "Itr2dd",
"title": "Optimal individualized treatment regime chosen by DR-DiD value",
"topics": [
"Itr2dd"
]
},
{
"page": "Itrct1",
"title": "Doubly robust DiD with a treatment by covariate interaction",
"topics": [
"Itrct1"
]
},
{
"page": "Itrlrn",
"title": "Iterative Q-learning (backward induction) for a dynamic regime",
"topics": [
"Itrlrn"
]
},
{
"page": "Ivartest",
"title": "Anderson-Rubin weak-instrument-robust test",
"topics": [
"Ivartest"
]
},
{
"page": "Ivclr",
"title": "Moreira conditional likelihood-ratio weak-IV-robust test",
"topics": [
"Ivclr"
]
},
{
"page": "Ivfstage",
"title": "First-stage F statistic for weak instruments",
"topics": [
"Ivfstage"
]
},
{
"page": "Iwdid",
"title": "Sun-Abraham interaction-weighted difference in differences",
"topics": [
"Iwdid"
]
},
{
"page": "jackknife",
"title": "Delete-one (leave-one-out) jackknife",
"topics": [
"jackknife"
]
},
{
"page": "Jackvar",
"title": "Jackknife variance for survey estimates",
"topics": [
"Jackvar"
]
},
{
"page": "james_stein",
"title": "James-Stein shrinkage of a vector of normal means",
"topics": [
"james_stein"
]
},
{
"page": "Jarow",
"title": "String similarity that rewards a shared prefix",
"topics": [
"Jarow"
]
},
{
"page": "jarque_bera",
"title": "Jarque-Bera test for normality",
"topics": [
"jarque_bera"
]
},
{
"page": "JayaAlgorithm",
"title": "Jaya algorithm",
"topics": [
"JayaAlgorithm"
]
},
{
"page": "Jntent",
"title": "Joint entropy of a 2-D joint pmf",
"topics": [
"Jntent"
]
},
{
"page": "Jntfr",
"title": "Joint shared-frailty model for recurrent events and a terminal event",
"topics": [
"Jntfr"
]
},
{
"page": "Jntlmm",
"title": "Two-stage joint model for a longitudinal outcome and survival",
"topics": [
"Jntlmm"
]
},
{
"page": "JohanQ",
"title": "Johansen test of a linear hypothesis about trimmed means",
"topics": [
"JohanQ"
]
},
{
"page": "JohnsonSB",
"title": "Johnson SB distribution",
"topics": [
"JohnsonSB"
]
},
{
"page": "JohnsonSU",
"title": "Johnson SU distribution",
"topics": [
"JohnsonSU"
]
},
{
"page": "Joincnt",
"title": "Join-count statistics for binary spatial data (Cliff and Ord 1981)",
"topics": [
"Joincnt"
]
},
{
"page": "JoinCountMulti",
"title": "Join counts for a map of k colours",
"topics": [
"JoinCountMulti"
]
},
{
"page": "JordanCanonical",
"title": "Exact algebra: Jordan form, Galois groups, shunting yard",
"topics": [
"GaloisGroup",
"JordanCanonical",
"ShuntingYard"
]
},
{
"page": "Jsdiv",
"title": "Jensen-Shannon divergence between two discrete distributions",
"topics": [
"Jsdiv"
]
},
{
"page": "JtcDecay",
"title": "Crime geography: journey to crime, circle hypothesis, hit score and risk terrain",
"topics": [
"CircleHypothesis",
"JtcCalibrate",
"JtcDecay",
"JtcSurface",
"RiskLayers",
"RiskTerrain",
"SearchCost"
]
},
{
"page": "Jzdiff",
"title": "Jensen-Shannon divergence",
"topics": [
"Jzdiff"
]
},
{
"page": "Kabsch",
"title": "Kabsch optimal superposition",
"topics": [
"Kabsch"
]
},
{
"page": "kalman_filter_1d",
"title": "Kalman filter for the scalar linear-Gaussian state-space model",
"topics": [
"kalman_filter_1d"
]
},
{
"page": "Kalmf",
"title": "Kalman filter for the linear Gaussian state space model",
"topics": [
"Kalmf"
]
},
{
"page": "KalmS",
"title": "Rauch-Tung-Striebel fixed-interval smoother",
"topics": [
"KalmS"
]
},
{
"page": "Kanon",
"title": "k-anonymity check on a set of quasi-identifiers",
"topics": [
"Kanon"
]
},
{
"page": "Kappacoef",
"title": "Cohen's Kappa coefficient of agreement for a two-class confusion table",
"topics": [
"Kappacoef"
]
},
{
"page": "KappaCoh",
"title": "Cohen (1960) kappa for two raters",
"topics": [
"KappaCoh"
]
},
{
"page": "KappaWt",
"title": "Cohen (1968) weighted kappa",
"topics": [
"KappaWt"
]
},
{
"page": "Katz",
"title": "Katz centrality",
"topics": [
"Katz"
]
},
{
"page": "Katzc",
"title": "Katz centrality excluding the length-zero walk",
"topics": [
"Katzc"
]
},
{
"page": "Katzcn",
"title": "Katz centrality with an explicit baseline weight",
"topics": [
"Katzcn"
]
},
{
"page": "kbwrt",
"title": "Rule-of-thumb kernel bandwidths",
"topics": [
"bwrot",
"kbwrt"
]
},
{
"page": "Kcompo",
"title": "Sequential composition of k differentially private mechanisms",
"topics": [
"Kcompo"
]
},
{
"page": "kde",
"title": "Kernel density estimation",
"topics": [
"kde"
]
},
{
"page": "Keggp",
"title": "KEGG pathway over-representation analysis",
"topics": [
"Keggp"
]
},
{
"page": "kendall_correlation",
"title": "Kendall's tau-b correlation",
"topics": [
"kendall_correlation"
]
},
{
"page": "KentDens",
"title": "Kent (FB5) density on the sphere",
"topics": [
"KentDens"
]
},
{
"page": "Kernblup",
"title": "Kernel covariance of a replicated-line predictor",
"topics": [
"Kernblup"
]
},
{
"page": "kernel_cond_moments",
"title": "Kernel-weighted conditional mean and variance",
"topics": [
"kernel_cond_moments"
]
},
{
"page": "kernel_eval",
"title": "Evaluate a kernel function at point u",
"topics": [
"kernel_eval"
]
},
{
"page": "kernel_ridge_regression",
"title": "Kernel ridge regression",
"topics": [
"kernel_ridge_regression"
]
},
{
"page": "kernel-codes",
"title": "Kernel type integer codes",
"topics": [
"kernel-codes",
"KERNEL_BIWEIGHT",
"KERNEL_EPANECHNIKOV",
"KERNEL_GAUSSIAN",
"KERNEL_TRIANGULAR",
"KERNEL_UNIFORM"
]
},
{
"page": "KernelCusum",
"title": "Kernel change-point analysis (KFDR running-maximum scan)",
"topics": [
"KernelCusum"
]
},
{
"page": "KernelIntensity",
"title": "Gaussian kernel intensity of a point pattern with edge corrections",
"topics": [
"KernelIntensity"
]
},
{
"page": "KernelPoint",
"title": "A kernel point of a transferable-utility game by Stearns' transfers",
"topics": [
"KernelPoint"
]
},
{
"page": "kfd",
"title": "Katz fractal dimension",
"topics": [
"kfd"
]
},
{
"page": "Kgnn",
"title": "Relational graph convolution (R-GCN)",
"topics": [
"Kgnn"
]
},
{
"page": "Khatd",
"title": "Shape of the importance-weight tail, per observation",
"topics": [
"Khatd"
]
},
{
"page": "KimFordingMedian",
"title": "Kim and Fording (1998) median voter from party positions and votes",
"topics": [
"KimFordingMedian"
]
},
{
"page": "Klchain",
"title": "Chain rule for relative entropy",
"topics": [
"Klchain"
]
},
{
"page": "Kldsupp",
"title": "Check the extended Schwartz conditions and return the resulting bound",
"topics": [
"Kldsupp"
]
},
{
"page": "Klmcmc",
"title": "Kullback-Leibler diagnostic between an MCMC chain and its target",
"topics": [
"Klmcmc"
]
},
{
"page": "Klmflt",
"title": "Kalman forward recursion driven by a model specification",
"topics": [
"Klmflt"
]
},
{
"page": "Klmsm1",
"title": "Kullback-Leibler divergence with additive smoothing for sparse counts",
"topics": [
"Klmsm1"
]
},
{
"page": "Klmsmh",
"title": "RTS backward recursion applied to an existing forward pass",
"topics": [
"Klmsmh"
]
},
{
"page": "KmeansLloyd",
"title": "Partitioning clustering: Lloyd k-means and PAM",
"topics": [
"KmeansLloyd",
"PamMedoids"
]
},
{
"page": "KnnDistances",
"title": "Point-pattern extras: nearest neighbours, quadrats, Morisita, cross-K, segregation",
"topics": [
"CrossK",
"KnnDistances",
"MorisitaIndex",
"QuadratTest",
"SegregationTest"
]
},
{
"page": "KnoxTest",
"title": "Space-time interaction, Mantel, geographic profiling and related tools",
"topics": [
"AoristicWeights",
"CrossCorrelation",
"EofAnalysis",
"GeographicProfile",
"Kde2d",
"KnoxTest",
"MantelTest",
"NearRepeatTable"
]
},
{
"page": "Konfound",
"title": "Robustness of an inference: percent bias to invalidate, RIR and ITCV",
"topics": [
"Konfound"
]
},
{
"page": "Kpmnci",
"title": "Pointwise confidence interval for a Kaplan-Meier curve (Greenwood)",
"topics": [
"Kpmnci"
]
},
{
"page": "KPMoments",
"title": "Kelejian-Prucha moment system for a spatial autoregressive error",
"topics": [
"KPMoments"
]
},
{
"page": "Kpmsmp",
"title": "Hall-Wellner simultaneous confidence band for a Kaplan-Meier curve",
"topics": [
"Kpmsmp"
]
},
{
"page": "Krfgrp",
"title": "Stochastic Kronecker graph edge-probability model",
"topics": [
"Krfgrp"
]
},
{
"page": "krftst",
"title": "Kenward-Roger adjusted F test for fixed effects",
"topics": [
"krftst"
]
},
{
"page": "Krig",
"title": "Ordinary kriging with a spherical variogram",
"topics": [
"Krig"
]
},
{
"page": "KrigCl",
"title": "Ordinary kriging (alias of Krig)",
"topics": [
"KrigCl"
]
},
{
"page": "Krige",
"title": "Kriging prediction and variance (gstat conventions)",
"topics": [
"Krige"
]
},
{
"page": "KrigeCV",
"title": "Cross-validation of kriging (gstat krige.cv)",
"topics": [
"KrigeCV"
]
},
{
"page": "KrigeLognormal",
"title": "Lognormal kriging with the unbiased back-transform",
"topics": [
"KrigeLognormal"
]
},
{
"page": "KrigingCovariance",
"title": "Covariance of a gstat vgm model",
"topics": [
"KrigingCovariance"
]
},
{
"page": "KrigingQuantile",
"title": "Gaussian quantile and exceedance maps of kriging predictions",
"topics": [
"KrigingExceedance",
"KrigingQuantile"
]
},
{
"page": "KrigingSystem",
"title": "Augmented universal kriging system",
"topics": [
"KrigingSystem"
]
},
{
"page": "Krigsm",
"title": "Ordinary kriging interpolation (alias of Krig)",
"topics": [
"Krigsm"
]
},
{
"page": "Krpalp",
"title": "Krippendorff's alpha reliability",
"topics": [
"Krpalp"
]
},
{
"page": "Krreg",
"title": "Kernel ridge regression in its dual form",
"topics": [
"Krreg"
]
},
{
"page": "KrrFDA",
"title": "Ridge regression carried out on inner products instead of features",
"topics": [
"KrrFDA"
]
},
{
"page": "kruskal_wallis",
"title": "Kruskal-Wallis H-test",
"topics": [
"kruskal_wallis"
]
},
{
"page": "ks_test_one_sample",
"title": "One-sample Kolmogorov-Smirnov test",
"topics": [
"ks_test_one_sample"
]
},
{
"page": "ks_test_two_sample",
"title": "Two-sample Kolmogorov-Smirnov test",
"topics": [
"ks_test_two_sample"
]
},
{
"page": "Ksamp",
"title": "K-sample Anderson-Darling test",
"topics": [
"Ksamp"
]
},
{
"page": "Kstest1",
"title": "Two-sample Kolmogorov-Smirnov test of H0: F1 = F2",
"topics": [
"Kstest1"
]
},
{
"page": "Ktrend",
"title": "Kendall rank-correlation test for monotone trend (Kendall 1938)",
"topics": [
"Ktrend"
]
},
{
"page": "Kvquant",
"title": "Quantize one vector: rotate, scalar-quantize, dequantize, rotate back",
"topics": [
"Kvquant"
]
},
{
"page": "L1med",
"title": "Multivariate median that minimises total distance, not squared distance",
"topics": [
"L1med"
]
},
{
"page": "L2pen",
"title": "Ridge (weight-decay) regularized loss",
"topics": [
"L2pen"
]
},
{
"page": "LabelPatches",
"title": "Patches of a categorical raster",
"topics": [
"LabelPatches"
]
},
{
"page": "Lacgear",
"title": "Lattice spatial autocorrelation statistics",
"topics": [
"Lacbivl",
"Lacgear",
"Lacgetg",
"Lacgmc",
"Laclihh",
"Laclihl",
"Laclimc",
"Laclisa",
"Lacscat",
"Lacscor",
"Lactest",
"Lacvgm"
]
},
{
"page": "LagOperator",
"title": "Spatial lag operator",
"topics": [
"LagOperator"
]
},
{
"page": "LanchesterSquareOutcome",
"title": "Lanchester attrition models",
"topics": [
"LanchesterFit",
"LanchesterSimulate",
"LanchesterSquareOutcome"
]
},
{
"page": "Landmark",
"title": "Landmark analysis of survival",
"topics": [
"Landmark",
"morie_survival_landmark"
]
},
{
"page": "LandscapeFragmentation",
"title": "Landscape fragmentation",
"topics": [
"LandscapeFragmentation"
]
},
{
"page": "Lapeig-laplmo",
"title": "Eigenvectors of the normalised Laplacian for the k smallest values",
"topics": [
"Lapeig"
]
},
{
"page": "LapEig",
"title": "Laplacian eigenmap embedding",
"topics": [
"LapEig",
"laplacian_eigenmaps"
]
},
{
"page": "LaplaceDist",
"title": "Laplace distribution",
"topics": [
"LaplaceDist"
]
},
{
"page": "LaplaceTransformNum",
"title": "Analytic tools: Laplace transform and inversion, Laurent coefficients, Walsh-Hadamard, polynomials, heat equation",
"topics": [
"FunctionalNorm",
"HadamardInverse",
"HadamardTransform",
"HeatEquationSeries",
"LaplaceTransformNum",
"LaurentCoefficients",
"PolyExpand",
"PolyFactorModP",
"QuadraticRoots",
"RationalCancel",
"StehfestInverse",
"TalbotInverse"
]
},
{
"page": "Lapspec",
"title": "Eigenvalues of the normalised Laplacian, in increasing order",
"topics": [
"Lapspec"
]
},
{
"page": "lasso_fista",
"title": "Lasso via ISTA / FISTA",
"topics": [
"lasso_fista"
]
},
{
"page": "Lassoobj",
"title": "Penalized residual sum of squares of the lasso, with its soft-threshold step",
"topics": [
"Lassoobj"
]
},
{
"page": "Lassrg",
"title": "Least squares with an L1 penalty, which selects while it shrinks",
"topics": [
"Lassrg"
]
},
{
"page": "LateralRange",
"title": "Search theory for search and rescue planning",
"topics": [
"DatumProbabilityMap",
"LateralRange",
"OptimalCircularSearch",
"OptimalEffortAllocation",
"ParallelSweepPod",
"SearchPod",
"SearchSuccess",
"SweepWidth"
]
},
{
"page": "LatticeRandomWalk",
"title": "Random walks, Brownian motion, Brownian bridges and percolation",
"topics": [
"BrownianBridgeUd",
"BrownianMotion",
"CorrelatedRandomWalk",
"CrwMsd",
"LatticeRandomWalk",
"SitePercolation"
]
},
{
"page": "Layrnm",
"title": "LayerNorm per-token normalization",
"topics": [
"Layrnm"
]
},
{
"page": "LbfgsbMinimize",
"title": "L-BFGS-B bound-constrained minimisation",
"topics": [
"LbfgsbMinimize"
]
},
{
"page": "Lctest",
"title": "Nyblom-Hansen test that every OLS parameter is stable",
"topics": [
"Lctest"
]
},
{
"page": "Ldcmpr",
"title": "Linkage disequilibrium r-squared between two biallelic loci",
"topics": [
"Ldcmpr"
]
},
{
"page": "Ldphr",
"title": "Hadamard response estimator for local differential privacy",
"topics": [
"Ldphr"
]
},
{
"page": "Ldprun",
"title": "LD-based variant pruning (PLINK indep-pairwise scheme)",
"topics": [
"Ldprun"
]
},
{
"page": "leave_one_out_cv",
"title": "Leave-one-out cross-validation",
"topics": [
"leave_one_out_cv"
]
},
{
"page": "Lecam",
"title": "Le Cam's bound on the posterior mass of an alternative set",
"topics": [
"Lecam"
]
},
{
"page": "Leid",
"title": "Partition a graph into well-connected communities",
"topics": [
"Leid"
]
},
{
"page": "LemR",
"title": "Leiden refined community detection",
"topics": [
"leiden_grph",
"LemR"
]
},
{
"page": "levene_test",
"title": "Levene's test for equality of variances",
"topics": [
"levene_test"
]
},
{
"page": "Lftrt",
"title": "Kaplan-Meier estimator with left truncation (delayed entry)",
"topics": [
"Lftrt"
]
},
{
"page": "LgcpSimulate",
"title": "Cox point processes",
"topics": [
"LgcpMoments",
"LgcpSimulate",
"ThomasPcf",
"ThomasSimulate",
"VoronoiResiduals"
]
},
{
"page": "Lgtnfit",
"title": "Fit the logistic-normal by transforming, then fitting a normal",
"topics": [
"Lgtnfit"
]
},
{
"page": "Lgtnpdf",
"title": "Additive logistic-normal density at a composition",
"topics": [
"Lgtnpdf"
]
},
{
"page": "Lgtnsim",
"title": "Draw compositions from a logistic-normal via the alr inverse",
"topics": [
"Lgtnsim"
]
},
{
"page": "license_check",
"title": "Runtime license-compatibility guard for morie",
"topics": [
"license_check"
]
},
{
"page": "likelihood_ratio_test",
"title": "Likelihood ratio test for nested models",
"topics": [
"likelihood_ratio_test"
]
},
{
"page": "lilef",
"title": "Lilliefors test for normality with the Dallal-Wilkinson p-value",
"topics": [
"lilef"
]
},
{
"page": "lilliefors_test",
"title": "Lilliefors goodness-of-fit test",
"topics": [
"lilliefors_test"
]
},
{
"page": "Linatt",
"title": "Linformer linear-complexity attention via low-rank projection",
"topics": [
"Linatt"
]
},
{
"page": "LinconBT",
"title": "Bootstrap-t all-pairs comparison of trimmed means",
"topics": [
"LinconBT"
]
},
{
"page": "LinearAssignment",
"title": "Linear sum assignment (Hungarian method)",
"topics": [
"LinearAssignment"
]
},
{
"page": "link_test",
"title": "Pregibon link test",
"topics": [
"link_test"
]
},
{
"page": "Linkpr",
"title": "Link prediction scores: common neighbours, Adamic-Adar, resource allocation",
"topics": [
"Linkpr",
"link_prediction"
]
},
{
"page": "LinModel",
"title": "The Y = mX + Z correlation model",
"topics": [
"LinModel"
]
},
{
"page": "LinModelInv",
"title": "Y = mX + Z model from sigma_x, sigma_y and r",
"topics": [
"LinModelInv"
]
},
{
"page": "linsys",
"title": "Classify and solve a linear equation system",
"topics": [
"linsys"
]
},
{
"page": "Lints",
"title": "Linear Thompson sampling for contextual bandits",
"topics": [
"Lints"
]
},
{
"page": "LipinskiRuleOfFive",
"title": "Oral drug-likeness rule filters",
"topics": [
"EganEgg",
"LipinskiRuleOfFive",
"OralBioavailabilityRules",
"VeberRules"
]
},
{
"page": "Lisamoran",
"title": "Local Moran's I per location (re-export)",
"topics": [
"Lisamoran"
]
},
{
"page": "littles_mcar_test",
"title": "Little's test of missing completely at random",
"topics": [
"littles_mcar_test"
]
},
{
"page": "Ljungbox",
"title": "Ljung-Box Q test for autocorrelation",
"topics": [
"Ljungbox"
]
},
{
"page": "LkjCorr",
"title": "LKJ density on correlation matrices",
"topics": [
"LkjCorr"
]
},
{
"page": "ll3way",
"title": "Hierarchical log-linear model for a three-way table",
"topics": [
"ll3way"
]
},
{
"page": "Llgsq",
"title": "Goodness of fit of a loglinear model: Pearson chi-squared and G^2",
"topics": [
"Llgsq"
]
},
{
"page": "LmcCovariance",
"title": "Linear model of coregionalization and cokriging",
"topics": [
"LmcCokriging",
"LmcCovariance"
]
},
{
"page": "lmdiag",
"title": "Lagrange multiplier (Rao score) tests for spatial dependence after OLS",
"topics": [
"lmdiag",
"lmerr",
"lmjoint",
"lmkp",
"lmlag",
"lmrerr",
"lmrerr2",
"lmrlag",
"lmsarma",
"lmsdm",
"lmslx",
"sphet"
]
},
{
"page": "Lmmform",
"title": "Linear mixed model in the form Y = X beta + Z u + e, with its two means and marginal variance",
"topics": [
"Lmmform"
]
},
{
"page": "Lmpi",
"title": "Prediction and mean-response intervals for the linear regression model",
"topics": [
"Lmpi"
]
},
{
"page": "LMSpatialTests",
"title": "Lagrange multiplier tests for spatial dependence after OLS",
"topics": [
"LMSpatialTests"
]
},
{
"page": "Lnfrm",
"title": "Log-normal frailty Cox model (penalized partial likelihood + REML)",
"topics": [
"Lnfrm"
]
},
{
"page": "local_fdr",
"title": "Local false discovery rate via empirical-Bayes two-component mixture",
"topics": [
"local_fdr"
]
},
{
"page": "local_linear",
"title": "Local linear kernel regression",
"topics": [
"local_linear"
]
},
{
"page": "Localgeary",
"title": "Local Geary's c per location",
"topics": [
"Localgeary"
]
},
{
"page": "LocalGetisOrd",
"title": "Local Getis-Ord Gi and Gi* statistics",
"topics": [
"LocalGetisOrd"
]
},
{
"page": "Localmoran",
"title": "Local Moran's I (LISA)",
"topics": [
"Localmoran"
]
},
{
"page": "LocalMoranBivariate",
"title": "Local bivariate Moran statistic with conditional permutation",
"topics": [
"LocalMoranBivariate"
]
},
{
"page": "LocationQuotient",
"title": "Regional specialization and concentration indices",
"topics": [
"HerfindahlIndex",
"KrugmanIndex",
"LocationQuotient",
"LqGini"
]
},
{
"page": "log_likelihood",
"title": "Log-likelihood of a PLSA model",
"topics": [
"log_likelihood"
]
},
{
"page": "LogitCompetition",
"title": "Multiparty competition under logit probabilistic voting",
"topics": [
"LogitCompetition"
]
},
{
"page": "LogitVote",
"title": "Spatial voting models for one voter",
"topics": [
"BoltzmannVote",
"CauchyVote",
"DiscountUtility",
"LogitVote",
"MedianVoter2d",
"MixedLogitVote",
"MixedUtility",
"NormalVote",
"ProbitVote",
"ProximityVote",
"QuadUtility",
"UtilityMax",
"VoteTrade2d",
"VoteTrading",
"WeightedVote"
]
},
{
"page": "logLik.morie_lmm",
"title": "Log-likelihood of a morie mixed model",
"topics": [
"logLik.morie_lmm"
]
},
{
"page": "Logor",
"title": "Odds ratio over a fixed interval of an interval-scaled predictor",
"topics": [
"Logor"
]
},
{
"page": "Logrank",
"title": "Mantel log-rank test comparing two survival curves",
"topics": [
"Logrank"
]
},
{
"page": "LogRSM",
"title": "Kernel smoother for a binary outcome",
"topics": [
"LogRSM"
]
},
{
"page": "longitudinal_sim",
"title": "Synchronised longitudinal-panel simulation (R parity)",
"topics": [
"longitudinal_sim"
]
},
{
"page": "loocv_bandwidth",
"title": "Leave-one-out cross-validation bandwidth for NW regression",
"topics": [
"loocv_bandwidth"
]
},
{
"page": "Loopr",
"title": "Replace the largest importance weights by fitted Pareto quantiles",
"topics": [
"Loopr"
]
},
{
"page": "Lpdual",
"title": "Form the dual of a linear program and certify a pair of solutions",
"topics": [
"Lpdual"
]
},
{
"page": "Lppd",
"title": "lppd, effective parameters and WAIC from a matrix of log densities",
"topics": [
"Lppd"
]
},
{
"page": "lrgtst",
"title": "Likelihood-ratio (G) test for frequencies",
"topics": [
"lrgtst"
]
},
{
"page": "Lrresid",
"title": "Residual analysis for the logistic regression model",
"topics": [
"Lrresid"
]
},
{
"page": "Lrtest",
"title": "Likelihood ratio test from two maximised log-likelihoods",
"topics": [
"Lrtest"
]
},
{
"page": "Lrwald",
"title": "Wald statistic and confidence interval for logistic-regression coefficients",
"topics": [
"Lrwald"
]
},
{
"page": "LsqFit",
"title": "Least-squares line",
"topics": [
"LsqFit"
]
},
{
"page": "LsqResid",
"title": "Residual sum at the least-squares optimum",
"topics": [
"LsqResid"
]
},
{
"page": "LsqRev",
"title": "Reverse least-squares line",
"topics": [
"LsqRev"
]
},
{
"page": "Ltkm",
"title": "Kaplan-Meier estimator under left truncation",
"topics": [
"Ltkm",
"morie_survival_left_truncated_km"
]
},
{
"page": "LuvR",
"title": "Louvain community detection with a resolution parameter",
"topics": [
"louvain",
"LuvR"
]
},
{
"page": "Lzcomp",
"title": "Count the distinct phrases a sequence needs to build itself",
"topics": [
"Lzcomp"
]
},
{
"page": "MaccsFingerprint",
"title": "Cheminformatics filters on SMILES: MACCS keys, REOS alerts and synthetic accessibility",
"topics": [
"MaccsFingerprint",
"MolecularProperties",
"MorganEnvironments",
"ReosFilter",
"SaScore"
]
},
{
"page": "Maetst",
"title": "Mean absolute error of a set of predictions",
"topics": [
"Maetst"
]
},
{
"page": "maf_calculation",
"title": "Minor allele frequency of each SNP in a marker matrix",
"topics": [
"maf_calculation"
]
},
{
"page": "mafft_anchors_from",
"title": "mafft_anchors_from",
"topics": [
"mafft_anchors_from"
]
},
{
"page": "mafft_clean",
"title": "mafft_clean",
"topics": [
"mafft_clean"
]
},
{
"page": "mafft_default_raw",
"title": "mafft_default_raw",
"topics": [
"mafft_default_raw"
]
},
{
"page": "mafft_degap",
"title": "mafft_degap",
"topics": [
"mafft_degap"
]
},
{
"page": "mafft_gap_profiles",
"title": "mafft_gap_profiles",
"topics": [
"mafft_gap_profiles"
]
},
{
"page": "mafft_jtt_exchangeability",
"title": "mafft_jtt_exchangeability",
"topics": [
"mafft_jtt_exchangeability"
]
},
{
"page": "mafft_lookup",
"title": "mafft_lookup",
"topics": [
"mafft_lookup"
]
},
{
"page": "mafft_nw",
"title": "mafft_nw",
"topics": [
"mafft_nw"
]
},
{
"page": "mafft_peaks",
"title": "mafft_peaks",
"topics": [
"mafft_peaks"
]
},
{
"page": "mafft_site_score",
"title": "mafft_site_score",
"topics": [
"mafft_site_score"
]
},
{
"page": "mafft_weights",
"title": "mafft_weights",
"topics": [
"mafft_weights"
]
},
{
"page": "mafft_xcorr_direct",
"title": "mafft_xcorr_direct",
"topics": [
"mafft_xcorr_direct"
]
},
{
"page": "mafft_xcorr_fft",
"title": "mafft_xcorr_fft",
"topics": [
"mafft_xcorr_fft"
]
},
{
"page": "Mafnpr",
"title": "The coordinates of a funnel plot, and the funnel itself",
"topics": [
"Mafnpr"
]
},
{
"page": "Mafrt",
"title": "Variance-stabilising double arcsine transform of a proportion",
"topics": [
"Mafrt"
]
},
{
"page": "Mafrti",
"title": "Get a proportion back out of the double-arcsine scale",
"topics": [
"Mafrti"
]
},
{
"page": "Mafsn",
"title": "How many missing null studies would sink a significant result",
"topics": [
"Mafsn"
]
},
{
"page": "Magal",
"title": "Put every study on the same vertical scale before comparing them",
"topics": [
"Magal"
]
},
{
"page": "Magsd",
"title": "Standardise by the control group's spread, not the pooled spread",
"topics": [
"Magsd"
]
},
{
"page": "Mahaldep",
"title": "Mahalanobis depth of each row of x",
"topics": [
"Mahaldep"
]
},
{
"page": "MajorityCompare",
"title": "Spatial voting: majority rule, pivots, Pareto sets and win sets",
"topics": [
"MajorityCompare",
"ParetoSet",
"PivotPoints",
"WinSet"
]
},
{
"page": "MajorityTournament",
"title": "Majority tournament solutions",
"topics": [
"MajorityTournament"
]
},
{
"page": "Mamh",
"title": "Mantel-Haenszel pooled odds ratio, given four parallel cell vectors",
"topics": [
"Mamh"
]
},
{
"page": "Manh2h",
"title": "Ask whether the direct evidence on one comparison agrees with the rest",
"topics": [
"Manh2h"
]
},
{
"page": "ManifestoScales",
"title": "Left-right scales from Manifesto Project category percentages",
"topics": [
"ManifestoScales"
]
},
{
"page": "Manlmm",
"title": "Borrow strength across a whole network of pairwise comparisons",
"topics": [
"Manlmm"
]
},
{
"page": "mann_whitney_u",
"title": "Mann-Whitney U / Wilcoxon rank-sum test",
"topics": [
"mann_whitney_u"
]
},
{
"page": "MannKendall",
"title": "Mann-Kendall trend test",
"topics": [
"MannKendall"
]
},
{
"page": "Manski",
"title": "Manski no-assumption bounds on the ATE",
"topics": [
"Manski"
]
},
{
"page": "manski_bounds",
"title": "Manski worst-case bounds for the ATE",
"topics": [
"manski_bounds"
]
},
{
"page": "Manskif",
"title": "Manski worst-case bounds on a partially observed mean",
"topics": [
"Manskif"
]
},
{
"page": "Maorw",
"title": "How many missing studies would drag the effect down to trivial",
"topics": [
"Maorw"
]
},
{
"page": "MapEquation",
"title": "Community detection by the map equation",
"topics": [
"InfomapPartition",
"MapEquation"
]
},
{
"page": "Mapeto",
"title": "Pool 2x2 tables when the events are rare enough to break the others",
"topics": [
"Mapeto"
]
},
{
"page": "MapMet",
"title": "Mean average precision at k",
"topics": [
"MapMet"
]
},
{
"page": "MapProject",
"title": "Map projections, UTM zones, local tangent planes and geodesics",
"topics": [
"GeodeticToEnu",
"GreatCircleDistance",
"MapProject",
"UtmZone",
"VincentyInverse"
]
},
{
"page": "Marba",
"title": "The variance of a standardised effect when the two scores are paired",
"topics": [
"Marba"
]
},
{
"page": "Mareg",
"title": "Explain between-study heterogeneity with study-level covariates",
"topics": [
"Mareg"
]
},
{
"page": "marginal_structural_model",
"title": "Marginal structural model by IPTW, and DBSCAN on point locations",
"topics": [
"marginal_structural_model",
"spatial_dbscan"
]
},
{
"page": "MarkCorrelation",
"title": "Mark correlation function",
"topics": [
"MarkCorrelation"
]
},
{
"page": "MarkDependenceTest",
"title": "Random-labelling test of mark dependence",
"topics": [
"MarkDependenceTest"
]
},
{
"page": "MarkVariogram",
"title": "Mark variogram",
"topics": [
"MarkVariogram"
]
},
{
"page": "MarshallEb",
"title": "Empirical-Bayes disease rates and the Potthoff-Whittinghill test",
"topics": [
"MarshallEb",
"PotthoffWhittinghill"
]
},
{
"page": "Martcons",
"title": "Cesaro-average Hellinger discrepancy of the predictive densities",
"topics": [
"Martcons"
]
},
{
"page": "Martingale",
"title": "Martingale residuals from a fitted Cox model",
"topics": [
"Martingale",
"morie_survival_martingale"
]
},
{
"page": "Matccd",
"title": "Estimate the odds ratio without ever estimating the matching sets",
"topics": [
"Matccd"
]
},
{
"page": "Matgame",
"title": "Value and optimal strategies of a zero-sum matrix game",
"topics": [
"Matgame"
]
},
{
"page": "Math.morie_gr_dual",
"title": "Elementary functions on dual numbers",
"topics": [
"Math.morie_gr_dual"
]
},
{
"page": "Math.morie_gvar",
"title": "Elementary functions on reverse-mode tape nodes",
"topics": [
"Math.morie_gvar"
]
},
{
"page": "Matrim",
"title": "Adjust a pooled effect for suppressed small studies",
"topics": [
"Matrim"
]
},
{
"page": "MaxentFeatures",
"title": "Species distribution and home range: MaxEnt features, fit and prediction; k-LoCoH isopleths",
"topics": [
"LocohHomeRange",
"MaxentFeatures",
"MaxentFit",
"MaxentPredict"
]
},
{
"page": "MaximalCovering",
"title": "Maximal covering location",
"topics": [
"MaximalCovering"
]
},
{
"page": "MaxPRegions",
"title": "Max-p regionalization",
"topics": [
"MaxPRegions"
]
},
{
"page": "McIntegrate",
"title": "Spatial Monte Carlo estimation",
"topics": [
"McControlVariate",
"McConvergence",
"McIntegrate",
"McStratified",
"McZonal"
]
},
{
"page": "mcnemar_test",
"title": "McNemar's test (paired nominal data)",
"topics": [
"mcnemar_test"
]
},
{
"page": "McpHomeRange",
"title": "Wildlife ecology: home range, distance sampling, occupancy, abundance and connectivity",
"topics": [
"CircuitResistance",
"DistanceSampling",
"GeneFlowNm",
"HabitatSuitabilityIndex",
"HanskiConnectivity",
"LeastCostPath",
"McpHomeRange",
"NmixtureModel",
"OccupancyModel",
"PartialMantel",
"ResistanceFromSuitability"
]
},
{
"page": "Mdppol",
"title": "Policy iteration for a finite MDP",
"topics": [
"Mdppol"
]
},
{
"page": "Mdpval",
"title": "Value iteration for a finite MDP",
"topics": [
"Mdpval"
]
},
{
"page": "MdsJackknife",
"title": "Jackknife and bootstrap stability of a SMACOF solution",
"topics": [
"MdsBootstrap",
"MdsJackknife"
]
},
{
"page": "MeanExc",
"title": "Davison & Smith (1990) empirical mean excess over a threshold grid",
"topics": [
"MeanExc"
]
},
{
"page": "MedCI",
"title": "Confidence limits for a product, without pretending it is normal",
"topics": [
"MedCI"
]
},
{
"page": "MedianLines",
"title": "Coalition games in the spatial model",
"topics": [
"MedianLines",
"MinimalRangeCoalition",
"QuotaSolution",
"RoemerPune",
"SpatialHeart",
"WeightedGameCore"
]
},
{
"page": "Medint",
"title": "The part of the effect that needs mediation and interaction at once",
"topics": [
"Medint"
]
},
{
"page": "Medmsm",
"title": "Direct and indirect effects from weighted marginal models",
"topics": [
"Medmsm"
]
},
{
"page": "Mercerchk",
"title": "Mercer check: is a Gram matrix a valid kernel matrix?",
"topics": [
"Mercerchk"
]
},
{
"page": "merkle_root",
"title": "RFC 6962 Merkle Tree Hash",
"topics": [
"merkle_root"
]
},
{
"page": "Meta1l",
"title": "S-, T-, X- and R-metalearners of the CATE with OLS base learners",
"topics": [
"Meta1l"
]
},
{
"page": "Meteor",
"title": "METEOR machine-translation metric (exact-match stage)",
"topics": [
"Meteor"
]
},
{
"page": "mgamrf",
"title": "Multivariate gamma random field from a shared component",
"topics": [
"mgamrf"
]
},
{
"page": "mgwrfit",
"title": "Multiscale geographically weighted regression (MGWR)",
"topics": [
"mgwraic",
"mgwrbk",
"mgwrbw",
"mgwrcof",
"mgwrcv",
"mgwrdg",
"mgwrfit",
"mgwrhat",
"mgwrres",
"mgwrsig",
"mgwrstd",
"mgwrtst"
]
},
{
"page": "MgwrLocalT",
"title": "Inference for (multiscale) geographically weighted regression",
"topics": [
"BandwidthConfidenceInterval",
"MgwrLocalT"
]
},
{
"page": "Mhors",
"title": "Mantel-Haenszel pooled odds ratio across 2x2 strata",
"topics": [
"Mhors"
]
},
{
"page": "Mhrate",
"title": "Mantel-Haenszel summary incidence rate ratio",
"topics": [
"Mhrate"
]
},
{
"page": "Middleout",
"title": "Middle-out reconciliation of a hierarchical forecast",
"topics": [
"Middleout"
]
},
{
"page": "midranks",
"title": "Midrank vector with tie summary (Gibbons Ch 5.6.2)",
"topics": [
"midranks"
]
},
{
"page": "miexp",
"title": "Moran's I tests: moments, z-score and the residual variants",
"topics": [
"miexp",
"miiv",
"mimc",
"miml",
"minorm",
"miols",
"miorig",
"mirand",
"mivar",
"mizval"
]
},
{
"page": "miprgr_branch_and_bound",
"title": "miprgr_branch_and_bound",
"topics": [
"miprgr_branch_and_bound"
]
},
{
"page": "miprgr_cheatsheet",
"title": "miprgr_cheatsheet",
"topics": [
"miprgr_cheatsheet"
]
},
{
"page": "miprgr_enumerate_integer",
"title": "miprgr_enumerate_integer",
"topics": [
"miprgr_enumerate_integer"
]
},
{
"page": "miprgr_fractional_variable",
"title": "miprgr_fractional_variable",
"topics": [
"miprgr_fractional_variable"
]
},
{
"page": "miprgr_round_relaxation",
"title": "miprgr_round_relaxation",
"topics": [
"miprgr_round_relaxation"
]
},
{
"page": "miprgr_simplex",
"title": "Two-phase simplex with Bland\\'s rule, maximising c\\'x",
"topics": [
"miprgr_simplex"
]
},
{
"page": "miprgr_solve_relaxation",
"title": "miprgr_solve_relaxation",
"topics": [
"miprgr_solve_relaxation"
]
},
{
"page": "Mirt2",
"title": "Compensatory multidimensional item response probabilities",
"topics": [
"Mirt2"
]
},
{
"page": "Mirt3",
"title": "Three-dimensional compensatory MIRT, no guessing parameter",
"topics": [
"Mirt3"
]
},
{
"page": "MissinM",
"title": "Pattern-mixture sensitivity analysis over a shift parameter delta",
"topics": [
"MissinM"
]
},
{
"page": "Misspecbd",
"title": "Worst-case bias bound under local misspecification",
"topics": [
"Misspecbd"
]
},
{
"page": "mistr_apply_rope",
"title": "mistr_apply_rope",
"topics": [
"mistr_apply_rope"
]
},
{
"page": "mistr_attention_span",
"title": "mistr_attention_span",
"topics": [
"mistr_attention_span"
]
},
{
"page": "mistr_cheatsheet",
"title": "mistr_cheatsheet",
"topics": [
"mistr_cheatsheet"
]
},
{
"page": "mistr_grouped_query_attention",
"title": "mistr_grouped_query_attention",
"topics": [
"mistr_grouped_query_attention"
]
},
{
"page": "mistr_mistral_block",
"title": "mistr_mistral_block",
"topics": [
"mistr_mistral_block"
]
},
{
"page": "mistr_rms_norm",
"title": "mistr_rms_norm",
"topics": [
"mistr_rms_norm"
]
},
{
"page": "mistr_rope_angles",
"title": "mistr_rope_angles",
"topics": [
"mistr_rope_angles"
]
},
{
"page": "mistr_sliding_window_mask",
"title": "mistr_sliding_window_mask",
"topics": [
"mistr_sliding_window_mask"
]
},
{
"page": "mistr_swiglu",
"title": "mistr_swiglu",
"topics": [
"mistr_swiglu"
]
},
{
"page": "Mivbound",
"title": "Monotone instrumental variable bounds",
"topics": [
"Mivbound"
]
},
{
"page": "Mktest",
"title": "Mann-Kendall rank test for monotone trend",
"topics": [
"Mktest"
]
},
{
"page": "MlFac",
"title": "Maximum likelihood factor analysis",
"topics": [
"MlFac"
]
},
{
"page": "Mlfit",
"title": "Gaussian ML log-likelihood of a linear mixed model at a given V",
"topics": [
"Mlfit"
]
},
{
"page": "Mlloglik",
"title": "Maximum likelihood log-likelihood of the linear regression model",
"topics": [
"Mlloglik"
]
},
{
"page": "Mlpv",
"title": "Pseudo-R^2 at level 1: residual variance removed by the predictors",
"topics": [
"Mlpv"
]
},
{
"page": "Mlwz",
"title": "Standardise a level-1 variable inside each cluster",
"topics": [
"Mlwz"
]
},
{
"page": "Mnpenlik",
"title": "Penalized log-likelihood of the multinomial logistic model",
"topics": [
"Mnpenlik"
]
},
{
"page": "model_comparison_table",
"title": "Compare multiple model fits on AIC, BIC, log-likelihood and (optional) LR tests",
"topics": [
"model_comparison_table"
]
},
{
"page": "Modlar",
"title": "Modularity Q of a community partition (alias of Sgtmodq)",
"topics": [
"Modlar"
]
},
{
"page": "Modulq",
"title": "Newman-Girvan modularity Q (alias of Sgtmodq)",
"topics": [
"Modulq"
]
},
{
"page": "Moetop",
"title": "Mixture-of-experts top-k routing with auxiliary load-balance loss",
"topics": [
"Moetop"
]
},
{
"page": "momento_cheatsheet",
"title": "momento_cheatsheet",
"topics": [
"momento_cheatsheet"
]
},
{
"page": "momento_harmonise",
"title": "momento_harmonise",
"topics": [
"momento_harmonise"
]
},
{
"page": "momento_mask_patches",
"title": "momento_mask_patches",
"topics": [
"momento_mask_patches"
]
},
{
"page": "momento_masked_loss",
"title": "momento_masked_loss",
"topics": [
"momento_masked_loss"
]
},
{
"page": "momento_reconstruction_curve",
"title": "momento_reconstruction_curve",
"topics": [
"momento_reconstruction_curve"
]
},
{
"page": "momento_task_mask",
"title": "momento_task_mask",
"topics": [
"momento_task_mask"
]
},
{
"page": "Momprior",
"title": "Check that a moment sequence is realisable by a measure on [0, 1]",
"topics": [
"Momprior"
]
},
{
"page": "monte_carlo_spatial_test",
"title": "Monte Carlo spatial test by random relabelling",
"topics": [
"monte_carlo_spatial_test"
]
},
{
"page": "MoranEigenvectorFilter",
"title": "Stepwise Moran eigenvector spatial filter",
"topics": [
"MoranEigenvectorFilter"
]
},
{
"page": "MoranEigenvectors",
"title": "Eigenvector spatial filtering",
"topics": [
"EigenvectorFiltering",
"GetisFilter",
"MoranEigenvectors"
]
},
{
"page": "MoranPermutationTest",
"title": "Spatial regression extras: Moran permutation test, spautolm, S2SLS lag and Durbin, KP-HET, J-test, Satorra-Bentler",
"topics": [
"GmErrorHet",
"MoranPermutationTest",
"S2slsLag",
"SatorraBentler",
"SpatialJTest",
"SpautolmFit"
]
},
{
"page": "MoranScatter",
"title": "Moran scatterplot coordinates and influence measures",
"topics": [
"MoranScatter"
]
},
{
"page": "morie_aalen_johansen",
"title": "Aalen-Johansen multistate transition matrix",
"topics": [
"morie_aalen_johansen"
]
},
{
"page": "morie_abcrej",
"title": "ABC rejection sampler",
"topics": [
"morie_abcrej"
]
},
{
"page": "morie_abod",
"title": "Angle-based outlier detection (ABOD)",
"topics": [
"morie_abod"
]
},
{
"page": "morie_acceptance_rate_diagnostic",
"title": "MCMC acceptance-rate diagnostic",
"topics": [
"morie_acceptance_rate_diagnostic"
]
},
{
"page": "morie_acf_estimate",
"title": "Autocorrelation estimate with both divisors",
"topics": [
"morie_acf_estimate"
]
},
{
"page": "morie_aci",
"title": "Adaptive conformal inference (ch. 17 p. 519)",
"topics": [
"morie_aci"
]
},
{
"page": "morie_actuarial_lifetable",
"title": "Actuarial (grouped) life table",
"topics": [
"morie_actuarial_lifetable"
]
},
{
"page": "morie_adagrad",
"title": "AdaGrad optimiser step",
"topics": [
"morie_adagrad"
]
},
{
"page": "morie_adam",
"title": "Adam optimiser step",
"topics": [
"morie_adam"
]
},
{
"page": "morie_adamw_step",
"title": "AdamW step with decoupled weight decay",
"topics": [
"morie_adamw_step"
]
},
{
"page": "morie_adf_test",
"title": "Augmented Dickey-Fuller test",
"topics": [
"morie_adf_test"
]
},
{
"page": "morie_adfur",
"title": "Augmented Dickey-Fuller unit-root test (ch. 6 p. 149)",
"topics": [
"morie_adfur"
]
},
{
"page": "morie_adjacency_matrix",
"title": "Adjacency matrix from an edge list",
"topics": [
"morie_adjacency_matrix"
]
},
{
"page": "morie_adjustment_variances",
"title": "Covariate adjustment can cost precision under unequal allocation",
"topics": [
"morie_adjustment_variances"
]
},
{
"page": "morie_aes",
"title": "AES block cipher object (digest's AES, natively)",
"topics": [
"morie_aes"
]
},
{
"page": "morie_aft",
"title": "Parametric accelerated failure time models",
"topics": [
"morie_aft"
]
},
{
"page": "morie_aft_generalized_gamma",
"title": "Log-normal accelerated-failure-time model",
"topics": [
"morie_aft_generalized_gamma"
]
},
{
"page": "morie_aft_log_logistic",
"title": "Log-logistic accelerated-failure-time model",
"topics": [
"morie_aft_log_logistic"
]
},
{
"page": "morie_aft_residuals",
"title": "Residuals from an AFT fit",
"topics": [
"morie_aft_residuals"
]
},
{
"page": "morie_aft_weibull",
"title": "Weibull accelerated-failure-time model",
"topics": [
"morie_aft_weibull"
]
},
{
"page": "morie_age_crime_aggregate",
"title": "Aggregate age-crime curve of a mixture of latent types",
"topics": [
"morie_age_crime_aggregate"
]
},
{
"page": "morie_age_crime_type_cov",
"title": "Cross-age covariance of a type mixture, and its test on panel counts",
"topics": [
"morie_age_crime_panel_cov",
"morie_age_crime_type_cov"
]
},
{
"page": "morie_aggregateatt",
"title": "Aggregate ATT(g, t) cells",
"topics": [
"morie_aggregateatt"
]
},
{
"page": "morie_aitchison_balance",
"title": "Aitchison balance coordinate",
"topics": [
"morie_aitchison_balance"
]
},
{
"page": "morie_akp_effect_size",
"title": "Robust effect size, median error and Winsorized regression",
"topics": [
"morie_akp_effect_size",
"morie_median_se",
"morie_winsorized_regression"
]
},
{
"page": "morie_alammar_sdp_attention",
"title": "Scaled dot-product attention (Vaswani et al. 2017)",
"topics": [
"morie_alammar_sdp_attention"
]
},
{
"page": "morie_alloc_optimal",
"title": "Cost-constrained optimal allocation across strata",
"topics": [
"morie_alloc_optimal"
]
},
{
"page": "morie_alpha_from_gamma",
"title": "Invert gamma = 1 / (alpha (1 - alpha)) for alpha",
"topics": [
"morie_alpha_from_gamma"
]
},
{
"page": "morie_alteration_ramsey",
"title": "The alteration method",
"topics": [
"morie_alteration_ramsey"
]
},
{
"page": "morie_anamorphosis",
"title": "Anamorphosis transform",
"topics": [
"morie_anamorphosis"
]
},
{
"page": "morie_anm_direction",
"title": "Causal direction from an additive noise model",
"topics": [
"morie_anm_direction"
]
},
{
"page": "morie_anominate_ideal_points",
"title": "Alpha-NOMINATE ideal-point estimation via 'anominate'",
"topics": [
"morie_anominate_ideal_points"
]
},
{
"page": "morie_anova_one_way",
"title": "One-way ANOVA",
"topics": [
"morie_anova_one_way"
]
},
{
"page": "morie_anova_twoway",
"title": "Two-way ANOVA with sequential (Type I) sums of squares",
"topics": [
"morie_anova_twoway"
]
},
{
"page": "morie_approx_dp",
"title": "Empirical delta at a fixed epsilon",
"topics": [
"morie_approx_dp"
]
},
{
"page": "morie_approx_ladder",
"title": "The (1 + a)^n approximation ladder",
"topics": [
"morie_approx_ladder"
]
},
{
"page": "morie_ar_spectrum",
"title": "Autoregressive power spectrum",
"topics": [
"morie_ar_spectrum"
]
},
{
"page": "morie_arch_in_mean",
"title": "ARCH(1)-in-mean model",
"topics": [
"morie_arch_in_mean"
]
},
{
"page": "morie_arch_lm_test",
"title": "Engle's ARCH-LM test",
"topics": [
"morie_arch_lm_test"
]
},
{
"page": "morie_are_nonparametric",
"title": "Asymptotic relative efficiencies of the classical location tests",
"topics": [
"morie_are_nonparametric"
]
},
{
"page": "morie_are_orthogonal",
"title": "Are two Latin squares orthogonal?",
"topics": [
"morie_are_orthogonal"
]
},
{
"page": "morie_arsau_analyze_aggregate_summary",
"title": "Analysis of the ARSAU aggregate-summary-by-year file (2020-2022)",
"topics": [
"morie_arsau_analyze_aggregate_summary"
]
},
{
"page": "morie_arsau_analyze_detailed_dataset",
"title": "Wide-format analysis of the 2020-2022 detailed-incident dataset",
"topics": [
"morie_arsau_analyze_detailed_dataset"
]
},
{
"page": "morie_arsau_analyze_individual_records",
"title": "End-to-end analysis of the ARSAU individual_records CSV for one year",
"topics": [
"morie_arsau_analyze_individual_records"
]
},
{
"page": "morie_arsau_analyze_main_records",
"title": "End-to-end analysis of the ARSAU main_records CSV for one year",
"topics": [
"morie_arsau_analyze_main_records"
]
},
{
"page": "morie_arsau_analyze_probe_cycle_records",
"title": "Analysis of ARSAU probe_cycle_records (CEW telemetry)",
"topics": [
"morie_arsau_analyze_probe_cycle_records"
]
},
{
"page": "morie_arsau_analyze_weapon_records",
"title": "Analysis of ARSAU weapon_records",
"topics": [
"morie_arsau_analyze_weapon_records"
]
},
{
"page": "morie_arsau_available_datasets",
"title": "List ARSAU dataset kinds, optionally restricted to one year",
"topics": [
"morie_arsau_available_datasets"
]
},
{
"page": "morie_arsau_available_years",
"title": "List ARSAU year / year-range buckets",
"topics": [
"morie_arsau_available_years"
]
},
{
"page": "morie_arsau_ckan_url",
"title": "Build the upstream CKAN 'datastore_search' URL for a registry entry",
"topics": [
"morie_arsau_ckan_url"
]
},
{
"page": "morie_arsau_describe",
"title": "Describe a single ARSAU dataset entry",
"topics": [
"morie_arsau_describe"
]
},
{
"page": "morie_arsau_download",
"title": "Bulk-download ARSAU CSVs from the Ontario Data Catalogue",
"topics": [
"morie_arsau_download"
]
},
{
"page": "morie_arsau_fetch_sidecar",
"title": "Fetch the CKAN sidecar JSON for a registry entry",
"topics": [
"morie_arsau_fetch_sidecar"
]
},
{
"page": "morie_arsau_load_aggregate_summary",
"title": "Load ARSAU aggregate-summary-by-year CSV (2020-2022 only)",
"topics": [
"morie_arsau_load_aggregate_summary"
]
},
{
"page": "morie_arsau_load_detailed_dataset",
"title": "Load ARSAU detailed-incident-level CSV (2020-2022 only)",
"topics": [
"morie_arsau_load_detailed_dataset"
]
},
{
"page": "morie_arsau_load_individual_records",
"title": "Load ARSAU individual_records CSV",
"topics": [
"morie_arsau_load_individual_records"
]
},
{
"page": "morie_arsau_load_main_records",
"title": "Load ARSAU main_records CSV for the given year",
"topics": [
"morie_arsau_load_main_records"
]
},
{
"page": "morie_arsau_load_probe_cycle_records",
"title": "Load ARSAU probe_cycle_records CSV (CEW telemetry)",
"topics": [
"morie_arsau_load_probe_cycle_records"
]
},
{
"page": "morie_arsau_load_weapon_records",
"title": "Load ARSAU weapon_records CSV",
"topics": [
"morie_arsau_load_weapon_records"
]
},
{
"page": "morie_arsau_read_markdown_dictionary",
"title": "Read an Ontario-Catalogue Markdown data-dictionary sidecar",
"topics": [
"morie_arsau_read_markdown_dictionary"
]
},
{
"page": "morie_arsau_read_sidecar",
"title": "Read a CKAN datastore_search JSON sidecar",
"topics": [
"morie_arsau_read_sidecar"
]
},
{
"page": "morie_arsau_read_xlsx_dictionary",
"title": "Read an Ontario-Catalogue XLSX data-dictionary sidecar",
"topics": [
"morie_arsau_read_xlsx_dictionary"
]
},
{
"page": "morie_arsau_registry_df",
"title": "ARSAU registry rendered as a tidy 'data.frame'",
"topics": [
"morie_arsau_registry_df"
]
},
{
"page": "morie_arsau_sidecar_schema",
"title": "Extract a simplified '[name, type, notes]' schema from a parsed CKAN sidecar",
"topics": [
"morie_arsau_sidecar_schema"
]
},
{
"page": "morie_arsau_sidecar_to_frame",
"title": "Convert a CKAN sidecar's 'records' array-of-arrays into a 'data.frame'",
"topics": [
"morie_arsau_sidecar_to_frame"
]
},
{
"page": "morie_astle_balding_grm",
"title": "Astle-Balding genomic relationship matrix",
"topics": [
"morie_astle_balding_grm"
]
},
{
"page": "morie_ate_potential_outcomes",
"title": "Average treatment effect from potential outcomes",
"topics": [
"morie_ate_potential_outcomes"
]
},
{
"page": "morie_ate_sensitivity",
"title": "l1 sensitivity of a TMLE of the ATE",
"topics": [
"morie_ate_sensitivity"
]
},
{
"page": "morie_att_weights",
"title": "ATT inverse-probability-of-treatment weights",
"topics": [
"morie_att_weights"
]
},
{
"page": "morie_attenuation_two_regressor",
"title": "Measurement error attenuates only in the one-regressor case",
"topics": [
"morie_attenuation_two_regressor"
]
},
{
"page": "morie_attractiveness_contraction",
"title": "The discrete attractiveness field contracts absent crime",
"topics": [
"morie_attractiveness_contraction"
]
},
{
"page": "morie_audit_all_variables",
"title": "Audit both OTIS and ARSAU",
"topics": [
"morie_audit_all_variables"
]
},
{
"page": "morie_audit_arsau_variables",
"title": "Audit every ARSAU variable",
"topics": [
"morie_audit_arsau_variables"
]
},
{
"page": "morie_audit_categories",
"title": "Audit the categorical columns of a data frame for coding hazards",
"topics": [
"morie_audit_categories"
]
},
{
"page": "morie_audit_otis_variables",
"title": "Audit every OTIS variable",
"topics": [
"morie_audit_otis_variables"
]
},
{
"page": "morie_audit_public_outputs",
"title": "Audit declared outputs against files on disk",
"topics": [
"morie_audit_public_outputs"
]
},
{
"page": "morie_autocorf",
"title": "Sample autocorrelation function (ch. 3)",
"topics": [
"morie_autocorf"
]
},
{
"page": "morie_autocorrelation",
"title": "Sample autocorrelation function with Ljung-Box",
"topics": [
"morie_autocorrelation"
]
},
{
"page": "morie_autoencoder_anomaly",
"title": "Linear-autoencoder reconstruction-error anomaly score",
"topics": [
"morie_autoencoder_anomaly"
]
},
{
"page": "morie_autoform",
"title": "Autoformer decomposition plus Auto-Correlation, eqs. (1), (5), (6)",
"topics": [
"morie_autoform"
]
},
{
"page": "morie_average_derivative",
"title": "Density-weighted average derivative",
"topics": [
"morie_average_derivative"
]
},
{
"page": "morie_average_derivative_hat",
"title": "Sample average derivative with an undersmoothed bandwidth",
"topics": [
"morie_average_derivative_hat"
]
},
{
"page": "morie_azuma_bound",
"title": "Azuma-Hoeffding for a martingale with bounded differences",
"topics": [
"morie_azuma_bound"
]
},
{
"page": "morie_backdoor_adjustment",
"title": "Back-door adjustment formula",
"topics": [
"morie_backdoor_adjustment"
]
},
{
"page": "morie_backdoor_criterion",
"title": "Back-door criterion for an adjustment set",
"topics": [
"morie_backdoor_criterion"
]
},
{
"page": "morie_backdoor_sets",
"title": "Admissible back-door adjustment sets, smallest first",
"topics": [
"morie_backdoor_sets"
]
},
{
"page": "morie_backfitting",
"title": "Backfitting for additive models",
"topics": [
"morie_backfitting"
]
},
{
"page": "morie_backlog_censoring",
"title": "The disposed-cases mean as a bound: what the pending cases imply",
"topics": [
"morie_backlog_censoring"
]
},
{
"page": "morie_backlog_published",
"title": "Published court statistics read through Little's law",
"topics": [
"morie_backlog_published"
]
},
{
"page": "morie_balanced_twophase",
"title": "Balanced/spread-sample and two-phase variance estimators",
"topics": [
"morie_balanced_twophase"
]
},
{
"page": "morie_bandwidth_mise",
"title": "MISE-optimal bandwidth",
"topics": [
"morie_bandwidth_mise"
]
},
{
"page": "morie_baron_kenny",
"title": "Baron-Kenny stepwise mediation",
"topics": [
"morie_baron_kenny"
]
},
{
"page": "morie_bartels_rank_test",
"title": "Bartels rank test of randomness (native)",
"topics": [
"morie_bartels_rank_test"
]
},
{
"page": "morie_baseline_hazard",
"title": "Kernel-smoothed baseline hazard",
"topics": [
"morie_baseline_hazard"
]
},
{
"page": "morie_basEvap",
"title": "FAO-56 Penman-Monteith reference evapotranspiration",
"topics": [
"morie_basEvap"
]
},
{
"page": "morie_basic_composition",
"title": "Basic and advanced privacy composition",
"topics": [
"morie_basic_composition"
]
},
{
"page": "morie_bayes",
"title": "Bayes' theorem over a complete hypothesis set",
"topics": [
"morie_bayes"
]
},
{
"page": "morie_bayes_binomial",
"title": "Bayesian inference for a binomial probability",
"topics": [
"morie_bayes_binomial"
]
},
{
"page": "morie_bayes_compare",
"title": "Compare posterior means against an external (lm) reference",
"topics": [
"morie_bayes_compare"
]
},
{
"page": "morie_bayes_continue",
"title": "Continue an MCMC run from its final state",
"topics": [
"morie_bayes_continue"
]
},
{
"page": "morie_bayes_cpi_genomic",
"title": "BayesC-pi spike-and-slab variable selection (short Gibbs)",
"topics": [
"morie_bayes_cpi_genomic"
]
},
{
"page": "morie_bayes_density",
"title": "Posterior density plot for one parameter (BS6.3)",
"topics": [
"morie_bayes_density"
]
},
{
"page": "morie_bayes_diagnostics",
"title": "All convergence diagnostics for a fit (details of each checker used)",
"topics": [
"morie_bayes_diagnostics"
]
},
{
"page": "morie_bayes_ess",
"title": "Effective sample size (per parameter, autocorrelation-based)",
"topics": [
"morie_bayes_ess"
]
},
{
"page": "morie_bayes_factor_savage_dickey",
"title": "Savage-Dickey Bayes factor (Verdinelli & Wasserman 1995, bfsd)",
"topics": [
"morie_bayes_factor_savage_dickey"
]
},
{
"page": "morie_bayes_geweke",
"title": "Geweke convergence diagnostic (z-score comparing chain segments)",
"topics": [
"morie_bayes_geweke"
]
},
{
"page": "morie_bayes_lm",
"title": "Fit a Bayesian linear regression by random-walk Metropolis",
"topics": [
"morie_bayes_lm"
]
},
{
"page": "morie_bayes_plot",
"title": "Default MCMC plot (trace, density, or both)",
"topics": [
"morie_bayes_plot"
]
},
{
"page": "morie_bayes_rhat",
"title": "Gelman-Rubin R-hat for a set of MCMC chains",
"topics": [
"morie_bayes_rhat"
]
},
{
"page": "morie_bayes_ridge_gibbs",
"title": "BayesA via short Gibbs sampler (Meuwissen-Hayes-Goddard 2001)",
"topics": [
"morie_bayes_ridge_gibbs"
]
},
{
"page": "morie_bayes_risk_dpi",
"title": "Post-processing cannot lower the best squared-error risk",
"topics": [
"morie_bayes_risk_dpi"
]
},
{
"page": "morie_bayesian_horseshoe",
"title": "Horseshoe linear regression (Makalic & Schmidt 2016 Gibbs sampler)",
"topics": [
"morie_bayesian_horseshoe"
]
},
{
"page": "morie_bayesian_lasso_full",
"title": "Bayesian LASSO (Park & Casella 2008 short Gibbs)",
"topics": [
"morie_bayesian_lasso_full"
]
},
{
"page": "morie_bayesian_regression",
"title": "Bayesian regression samplers",
"topics": [
"morie_bayesian_regression",
"morie_brr_gibbs"
]
},
{
"page": "morie_bayesian_ridge_regression",
"title": "Bayesian ridge regression (RR-BLUP closed form)",
"topics": [
"morie_bayesian_ridge_regression"
]
},
{
"page": "morie_bayisr",
"title": "Importance resampling (SIR)",
"topics": [
"morie_bayisr"
]
},
{
"page": "morie_bcoinbf",
"title": "Free-bias against fixed-bias coin (eq. 3.12, 3.20, 3.22, pp. 52-53)",
"topics": [
"morie_bcoinbf"
]
},
{
"page": "morie_bcoinlik",
"title": "Bent-coin likelihood (eq. 3.8, p. 51)",
"topics": [
"morie_bcoinlik"
]
},
{
"page": "morie_bcoinpri",
"title": "Uniform prior density on the bent-coin bias (eq. 3.9, p. 51)",
"topics": [
"morie_bcoinpri"
]
},
{
"page": "morie_bdtrns",
"title": "Manski no-assumptions identification bound for a mean",
"topics": [
"morie_bdtrns"
]
},
{
"page": "morie_bekk_garch",
"title": "Scalar BEKK(1,1) multivariate GARCH",
"topics": [
"morie_bekk_garch"
]
},
{
"page": "morie_bell_number",
"title": "Bell numbers by the Bell triangle",
"topics": [
"morie_bell_number"
]
},
{
"page": "morie_bellman_value",
"title": "Value iteration with the Bellman contraction audited",
"topics": [
"morie_bellman_value"
]
},
{
"page": "morie_beta_schedule_values",
"title": "Beta schedule values for T diffusion steps (hmdfw)",
"topics": [
"morie_beta_schedule_values"
]
},
{
"page": "morie_betti_numbers_gf2",
"title": "Simplicial Betti numbers over F_2",
"topics": [
"morie_betti_numbers_gf2"
]
},
{
"page": "morie_bibd_parameters",
"title": "Necessary conditions for a balanced incomplete block design",
"topics": [
"morie_bibd_parameters"
]
},
{
"page": "morie_bic_model_average",
"title": "BIC model averaging",
"topics": [
"morie_bic_model_average"
]
},
{
"page": "morie_bicdag",
"title": "Gaussian BIC score of a DAG",
"topics": [
"morie_bicdag"
]
},
{
"page": "morie_big_add",
"title": "Add two arbitrary-precision integers",
"topics": [
"morie_big_add"
]
},
{
"page": "morie_big_binom",
"title": "Exact binomial coefficient",
"topics": [
"morie_big_binom"
]
},
{
"page": "morie_big_cmp",
"title": "Compare two arbitrary-precision integers",
"topics": [
"morie_big_cmp"
]
},
{
"page": "morie_big_divmod_small",
"title": "Divide an arbitrary-precision integer by a small whole number",
"topics": [
"morie_big_divmod_small"
]
},
{
"page": "morie_big_factorial",
"title": "Exact factorial",
"topics": [
"morie_big_factorial"
]
},
{
"page": "morie_big_fits_double",
"title": "Is this value exactly representable as an R double?",
"topics": [
"morie_big_fits_double"
]
},
{
"page": "morie_big_mul",
"title": "Multiply two arbitrary-precision integers",
"topics": [
"morie_big_mul"
]
},
{
"page": "morie_big_ndigits",
"title": "Number of decimal digits",
"topics": [
"morie_big_ndigits"
]
},
{
"page": "morie_big_pow",
"title": "Raise an arbitrary-precision integer to a whole power",
"topics": [
"morie_big_pow"
]
},
{
"page": "morie_big_sub",
"title": "Subtract arbitrary-precision integers",
"topics": [
"morie_big_sub"
]
},
{
"page": "morie_bigint",
"title": "Arbitrary-precision integer from a string or a double",
"topics": [
"morie_bigint"
]
},
{
"page": "morie_binary_mediation",
"title": "Binary-outcome causal mediation by inverse odds-ratio weighting",
"topics": [
"morie_binary_mediation"
]
},
{
"page": "morie_binomial_dist",
"title": "Binomial distribution surface",
"topics": [
"morie_binomial_dist"
]
},
{
"page": "morie_binomial_inference",
"title": "Binomial inference: PMF, MLE variance, Wilson interval, true confidence",
"topics": [
"morie_binomial_inference"
]
},
{
"page": "morie_binseg",
"title": "Binary segmentation for changepoints",
"topics": [
"morie_binseg"
]
},
{
"page": "morie_binsumga",
"title": "Gaussian sum behind the central binomial approximation (eq. 1.41, p. 17)",
"topics": [
"morie_binsumga"
]
},
{
"page": "morie_bipartite_matching",
"title": "Maximum bipartite matching by augmenting paths",
"topics": [
"morie_bipartite_matching"
]
},
{
"page": "morie_bl_distance",
"title": "Bounded-Lipschitz distance",
"topics": [
"morie_bl_distance"
]
},
{
"page": "morie_blastp",
"title": "BLAST maximal segment pair with Karlin-Altschul E-value",
"topics": [
"morie_blastp"
]
},
{
"page": "morie_blocking_crossover",
"title": "Blocking does not always increase power: the within-pair correlation crossover",
"topics": [
"morie_blocking_crossover"
]
},
{
"page": "morie_blomqvist_beta",
"title": "Blomqvist's beta from a copula",
"topics": [
"morie_blomqvist_beta"
]
},
{
"page": "morie_bnd_imbens_manski",
"title": "Imbens-Manski confidence interval for a partially identified parameter",
"topics": [
"morie_bnd_imbens_manski"
]
},
{
"page": "morie_bnd_lp",
"title": "Sharp linear-programming bounds on a linear target",
"topics": [
"morie_bnd_lp"
]
},
{
"page": "morie_bnd_manski",
"title": "Manski worst-case bounds on a partially observed mean",
"topics": [
"morie_bnd_manski"
]
},
{
"page": "morie_bnd_moment_inequality",
"title": "Moment-inequality criterion and confidence set",
"topics": [
"morie_bnd_moment_inequality"
]
},
{
"page": "morie_bnd_polya_tree",
"title": "Polya-tree posterior mean density",
"topics": [
"morie_bnd_polya_tree"
]
},
{
"page": "morie_bndapp",
"title": "Manski-Pepper MTR-MTS bounds",
"topics": [
"morie_bndapp"
]
},
{
"page": "morie_bndest",
"title": "Manski worst-case bounds",
"topics": [
"morie_bndest"
]
},
{
"page": "morie_bns_jump_test",
"title": "Barndorff-Nielsen & Shephard jump test",
"topics": [
"morie_bns_jump_test"
]
},
{
"page": "morie_boot",
"title": "Native nonparametric bootstrap (ordinary, stratified)",
"topics": [
"morie_boot"
]
},
{
"page": "morie_boot_basic_ci",
"title": "Direct bridge to 'boot::boot.ci'",
"topics": [
"morie_boot_basic_ci"
]
},
{
"page": "morie_boot_ci",
"title": "Native bootstrap confidence intervals",
"topics": [
"morie_boot_ci"
]
},
{
"page": "morie_boot_nonoverlap_block",
"title": "Non-overlapping block bootstrap",
"topics": [
"morie_boot_nonoverlap_block"
]
},
{
"page": "morie_boot_run",
"title": "Direct bridge to 'boot::boot'",
"topics": [
"morie_boot_run"
]
},
{
"page": "morie_bootss",
"title": "Rao-Wu-Yue rescaled bootstrap for stratified cluster samples",
"topics": [
"morie_bootss"
]
},
{
"page": "morie_bootstrap_sample",
"title": "Bootstrap resampling for any statistic",
"topics": [
"morie_bootstrap_sample"
]
},
{
"page": "morie_boryis",
"title": "Borusyak-Jaravel-Spiess event-study imputation estimator",
"topics": [
"morie_boryis"
]
},
{
"page": "morie_bounds_confidence",
"title": "Imbens-Manski confidence interval for a partially identified parameter",
"topics": [
"morie_bounds_confidence"
]
},
{
"page": "morie_bowarc",
"title": "Whether a pair forms a bow",
"topics": [
"morie_bowarc"
]
},
{
"page": "morie_boxcox",
"title": "Box-Cox transformation (ch. 6 p. 164)",
"topics": [
"morie_boxcox"
]
},
{
"page": "morie_boxplot_outliers",
"title": "Boxplot outlier rules on the ideal fourths",
"topics": [
"morie_boxplot_outliers"
]
},
{
"page": "morie_boyd_backtracking",
"title": "Backtracking line search",
"topics": [
"morie_boyd_backtracking"
]
},
{
"page": "morie_boyd_newton",
"title": "Newton step for unconstrained minimisation",
"topics": [
"morie_boyd_newton"
]
},
{
"page": "morie_boyd_newton_decrement",
"title": "Newton decrement",
"topics": [
"morie_boyd_newton_decrement"
]
},
{
"page": "morie_boyd_proximal",
"title": "Proximal operator",
"topics": [
"morie_boyd_proximal"
]
},
{
"page": "morie_bracken_abundance",
"title": "Bracken Bayesian abundance re-estimation",
"topics": [
"morie_bracken_abundance"
]
},
{
"page": "morie_breslow_tie_correction",
"title": "Cox model with the Breslow tie correction",
"topics": [
"morie_breslow_tie_correction"
]
},
{
"page": "morie_breusch_pagan",
"title": "Regression diagnostics",
"topics": [
"morie_breusch_pagan",
"morie_durbin_watson",
"morie_vif"
]
},
{
"page": "morie_bricklayer",
"title": "Assemble the rest of the morie family from R",
"topics": [
"morie_bricklayer"
]
},
{
"page": "morie_bridge_covariance",
"title": "Brownian bridge covariance",
"topics": [
"morie_bridge_covariance"
]
},
{
"page": "morie_bridgs",
"title": "Bridge sampling estimate of a ratio of normalising constants",
"topics": [
"morie_bridgs"
]
},
{
"page": "morie_brr_balanced",
"title": "Balanced repeated replication half-samples",
"topics": [
"morie_brr_balanced"
]
},
{
"page": "morie_brr_variance",
"title": "Balanced repeated replication variance",
"topics": [
"morie_brr_variance"
]
},
{
"page": "morie_brute_force_max_weight",
"title": "Maximum-weight independent set by exhaustive search",
"topics": [
"morie_brute_force_max_weight"
]
},
{
"page": "morie_bspline_basis",
"title": "B-spline basis by the Cox-de Boor recursion",
"topics": [
"morie_bspline_basis"
]
},
{
"page": "morie_bt_632",
"title": ".632 error estimator - alias entry point",
"topics": [
"morie_bt_632"
]
},
{
"page": "morie_bt_bias",
"title": "Bootstrap bias estimate and correction",
"topics": [
"morie_bt_bias"
]
},
{
"page": "morie_bt_ci_ratio",
"title": "Percentile bootstrap CI for a ratio",
"topics": [
"morie_bt_ci_ratio"
]
},
{
"page": "morie_bt_iid",
"title": "Nonparametric IID bootstrap of a statistic",
"topics": [
"morie_bt_iid"
]
},
{
"page": "morie_bt_jackknife",
"title": "Leave-one-out jackknife",
"topics": [
"morie_bt_jackknife"
]
},
{
"page": "morie_bt_oob",
"title": "Out-of-bag error for a bootstrap-aggregated predictor",
"topics": [
"morie_bt_oob"
]
},
{
"page": "morie_bt_var",
"title": "Bootstrap variance from replicates",
"topics": [
"morie_bt_var"
]
},
{
"page": "morie_btarsv",
"title": "Buhlmann AR-sieve bootstrap",
"topics": [
"morie_btarsv"
]
},
{
"page": "morie_btcalib",
"title": "Loh exact bootstrap calibration of the t interval",
"topics": [
"morie_btcalib"
]
},
{
"page": "morie_btdbl",
"title": "Beran prepivoted double-bootstrap confidence interval",
"topics": [
"morie_btdbl"
]
},
{
"page": "morie_btiseq",
"title": "Beran prepivoted and twice-prepivoted bootstrap mean test",
"topics": [
"morie_btiseq"
]
},
{
"page": "morie_budget_optimum",
"title": "Stick versus carrot under a fixed budget: the interior optimum",
"topics": [
"morie_budget_optimum"
]
},
{
"page": "morie_budget_rule",
"title": "The budget-constrained optimal rule is a threshold on the conditional effect",
"topics": [
"morie_budget_rule"
]
},
{
"page": "morie_build_outputs_manifest",
"title": "Build an outputs manifest from a directory of artifacts",
"topics": [
"morie_build_outputs_manifest"
]
},
{
"page": "morie_builtin_db",
"title": "Get path to the built-in MORIE datasets database",
"topics": [
"morie_builtin_db"
]
},
{
"page": "morie_burg_method",
"title": "Burg lattice AR estimation",
"topics": [
"morie_burg_method"
]
},
{
"page": "morie_burkov_linear_function",
"title": "Linear model f(x) = wx + b (Burkov Eq 1.1)",
"topics": [
"morie_burkov_linear_function"
]
},
{
"page": "morie_burkov_mse_cost",
"title": "MSE cost of the linear model (Burkov Eq 1.3)",
"topics": [
"morie_burkov_mse_cost"
]
},
{
"page": "morie_burkov_squared_error",
"title": "Squared error (Burkov Eq 1.2)",
"topics": [
"morie_burkov_squared_error"
]
},
{
"page": "morie_burnside_orbit_count",
"title": "Burnside's lemma",
"topics": [
"morie_burnside_orbit_count"
]
},
{
"page": "morie_bvdecomp",
"title": "Squared-error decomposition into bias, variance and noise (p. 158)",
"topics": [
"morie_bvdecomp"
]
},
{
"page": "morie_cache_clear",
"title": "Clear morie's persistent cache directory",
"topics": [
"morie_cache_clear"
]
},
{
"page": "morie_cache_dir",
"title": "morie cache contract",
"topics": [
"morie_cache_dir"
]
},
{
"page": "morie_cache_file",
"title": "Cache local RDS/CSV data into the SQLite database",
"topics": [
"morie_cache_file"
]
},
{
"page": "morie_cache_list",
"title": "List all tables in the MORIE cache",
"topics": [
"morie_cache_list"
]
},
{
"page": "morie_cache_load",
"title": "Load a table from the MORIE cache",
"topics": [
"morie_cache_load"
]
},
{
"page": "morie_cache_store",
"title": "Store a data frame in the MORIE cache",
"topics": [
"morie_cache_store"
]
},
{
"page": "morie_calculate_ebac",
"title": "Calculate estimated Blood Alcohol Concentration (eBAC)",
"topics": [
"morie_calculate_ebac"
]
},
{
"page": "morie_calculate_ipw_weights",
"title": "Calculate inverse probability of treatment weights (IPTW)",
"topics": [
"morie_calculate_ipw_weights"
]
},
{
"page": "morie_calfeat",
"title": "Calendar and time features (ch. 6 p. 168)",
"topics": [
"morie_calfeat"
]
},
{
"page": "morie_calibration",
"title": "Mixed-model calibration estimators",
"topics": [
"morie_calibration"
]
},
{
"page": "morie_calibration_chi2",
"title": "Chi-square calibration to known margins",
"topics": [
"morie_calibration_chi2"
]
},
{
"page": "morie_calibration_flip",
"title": "Calibration to the recorded label and to the true label cannot both hold across groups",
"topics": [
"morie_calibration_flip"
]
},
{
"page": "morie_calibration_weights",
"title": "Calibration weights via iterative proportional fitting (raking)",
"topics": [
"morie_calibration_weights"
]
},
{
"page": "morie_canonicalize_cpads_data",
"title": "Canonicalize raw CPADS PUMF columns",
"topics": [
"morie_canonicalize_cpads_data"
]
},
{
"page": "morie_capsule_bundle",
"title": "Bundle a capsule into one signed file",
"concept": [
"capsule"
],
"topics": [
"morie_capsule_bundle"
]
},
{
"page": "morie_capsule_compare",
"title": "Recompute a manifest's recorded results against the data",
"concept": [
"capsule"
],
"topics": [
"morie_capsule_compare"
]
},
{
"page": "morie_capsule_drift",
"title": "Compare a fresh copy of a capsule's data with the pinned one",
"concept": [
"capsule"
],
"topics": [
"morie_capsule_drift"
]
},
{
"page": "morie_capsule_manifest",
"title": "Build, and optionally write, a capsule's results manifest",
"concept": [
"capsule"
],
"topics": [
"morie_capsule_manifest"
]
},
{
"page": "morie_capsule_sign",
"title": "Sign a capsule digest or manifest",
"concept": [
"capsule"
],
"topics": [
"morie_capsule_sign"
]
},
{
"page": "morie_capsule_verify",
"title": "Verify a data capsule",
"concept": [
"capsule"
],
"topics": [
"morie_capsule_verify"
]
},
{
"page": "morie_capsule_write",
"title": "Write the provenance record of a data capsule",
"concept": [
"capsule"
],
"topics": [
"morie_capsule_write"
]
},
{
"page": "morie_catalan_number",
"title": "Catalan numbers",
"topics": [
"morie_catalan_number"
]
},
{
"page": "morie_categorical_models",
"title": "Ordinal and multinomial models",
"topics": [
"morie_categorical_models",
"morie_multinomial_loglik",
"morie_multinomial_probs",
"morie_ordinal_probit_gibbs",
"morie_ordinal_probs",
"morie_penalized_multinomial",
"morie_penalized_poisson"
]
},
{
"page": "morie_catstop",
"title": "CAT precision stopping rule",
"topics": [
"morie_catstop"
]
},
{
"page": "morie_caus_aipw",
"title": "Augmented inverse-probability-weighted (doubly robust) ATE",
"topics": [
"morie_caus_aipw"
]
},
{
"page": "morie_caus_did_sun_abraham",
"title": "Sun-Abraham interaction-weighted event study",
"topics": [
"morie_caus_did_sun_abraham"
]
},
{
"page": "morie_caus_dml_partial_lin",
"title": "Double machine learning for the partially linear model",
"topics": [
"morie_caus_dml_partial_lin"
]
},
{
"page": "morie_caus_iv_2sls",
"title": "Two-stage least squares",
"topics": [
"morie_caus_iv_2sls"
]
},
{
"page": "morie_caus_iv_dag",
"title": "The instrumental-variable estimand under a causal graph",
"topics": [
"morie_caus_iv_dag"
]
},
{
"page": "morie_caus_iv_late",
"title": "Local average treatment effect",
"topics": [
"morie_caus_iv_late"
]
},
{
"page": "morie_caus_iv_liml",
"title": "Limited-information maximum likelihood",
"topics": [
"morie_caus_iv_liml"
]
},
{
"page": "morie_causal_caliper_matching",
"title": "Caliper matching on the propensity score",
"topics": [
"morie_causal_caliper_matching"
]
},
{
"page": "morie_causal_did_three_way",
"title": "Triple difference (DDD)",
"topics": [
"morie_causal_did_three_way"
]
},
{
"page": "morie_causal_falsification_test",
"title": "Pre-treatment falsification (placebo) regression",
"topics": [
"morie_causal_falsification_test"
]
},
{
"page": "morie_causal_forest",
"title": "Honest causal forest for heterogeneous treatment effects",
"topics": [
"morie_causal_forest"
]
},
{
"page": "morie_causal_forest_bootstrap",
"title": "Bootstrap confidence intervals for the causal-forest CATE",
"topics": [
"morie_causal_forest_bootstrap"
]
},
{
"page": "morie_causal_forest_predict",
"title": "Predict CATEs at new covariate values from a fitted causal forest",
"topics": [
"morie_causal_forest_predict"
]
},
{
"page": "morie_causal_impact",
"title": "Bayesian structural time-series intervention analysis",
"topics": [
"morie_causal_impact"
]
},
{
"page": "morie_causal_iptw_atoweights",
"title": "Propensity weights: ATO overlap, ATE, or ATT",
"topics": [
"morie_causal_iptw_atoweights"
]
},
{
"page": "morie_causal_mahalanobis_match",
"title": "Mahalanobis-distance matching",
"topics": [
"morie_causal_mahalanobis_match"
]
},
{
"page": "morie_causal_mediation",
"title": "Causal mediation by inverse probability weighting",
"topics": [
"morie_causal_mediation"
]
},
{
"page": "morie_causal_overlap_diagnostic",
"title": "Common-support and positivity diagnostic",
"topics": [
"morie_causal_overlap_diagnostic"
]
},
{
"page": "morie_causal_robust_se",
"title": "Robust / heteroskedasticity-consistent variance for a fitted model",
"topics": [
"morie_causal_robust_se"
]
},
{
"page": "morie_causal_rosenbaum_bound",
"title": "Rosenbaum sensitivity bound for matched pairs",
"topics": [
"morie_causal_rosenbaum_bound"
]
},
{
"page": "morie_causal_survival_blp",
"title": "Best linear predictor for the causal survival forest CATE",
"topics": [
"morie_causal_survival_blp"
]
},
{
"page": "morie_causal_survival_forest",
"title": "Causal survival forest on IPCW restricted-mean-survival pseudo outcomes",
"topics": [
"morie_causal_survival_forest"
]
},
{
"page": "morie_causal_weighting",
"title": "Estimate balancing weights",
"topics": [
"morie_causal_weighting"
]
},
{
"page": "morie_causdidcs",
"title": "Callaway-Sant'Anna staggered DiD (module-name entry point)",
"topics": [
"morie_causdidcs"
]
},
{
"page": "morie_causdidev",
"title": "Event-study imputation estimator (module-name entry point)",
"topics": [
"morie_causdidev"
]
},
{
"page": "morie_cause_specific_hazard",
"title": "Cause-specific hazard model",
"topics": [
"morie_cause_specific_hazard"
]
},
{
"page": "morie_causrdd",
"title": "Sharp regression discontinuity, local linear",
"topics": [
"morie_causrdd"
]
},
{
"page": "morie_causrddf",
"title": "Fuzzy regression discontinuity (Wald ratio)",
"topics": [
"morie_causrddf"
]
},
{
"page": "morie_causrddh",
"title": "Imbens-Kalyanaraman optimal RDD bandwidth",
"topics": [
"morie_causrddh"
]
},
{
"page": "morie_causrung",
"title": "One rung of the Ladder of Causation (Table 2.1, ch. 2 p. 15)",
"topics": [
"morie_causrung"
]
},
{
"page": "morie_cbcapx",
"title": "Gaussian approximation to the central binomial (eq. 1.40, p. 17)",
"topics": [
"morie_cbcapx"
]
},
{
"page": "morie_ccc_multivariate_garch",
"title": "CCC multivariate GARCH (Bollerslev 1990)",
"topics": [
"morie_ccc_multivariate_garch"
]
},
{
"page": "morie_cdaeRC_decode",
"title": "morie_cdaeRC_decode",
"topics": [
"morie_cdaeRC_decode"
]
},
{
"page": "morie_cdp_subgaussian_amplification",
"title": "Zero-concentrated DP composition",
"topics": [
"morie_cdp_subgaussian_amplification"
]
},
{
"page": "morie_censoring_at_risk_weight",
"title": "Inverse-probability-of-censoring weights",
"topics": [
"morie_censoring_at_risk_weight"
]
},
{
"page": "morie_cepstrum_pitch",
"title": "Cepstral pitch estimate",
"topics": [
"morie_cepstrum_pitch"
]
},
{
"page": "morie_check_buffer",
"title": "Validate a DQN replay buffer of (s, a, r, s2[, done]) rows (hmdqn)",
"topics": [
"morie_check_buffer"
]
},
{
"page": "morie_check_plugin_license",
"title": "Check whether a downstream package's SPDX is GPL-compatible",
"topics": [
"morie_check_plugin_license"
]
},
{
"page": "morie_cheeger_bound",
"title": "Conductance of a hot-spot set and the Cheeger bound on the spectral gap",
"topics": [
"morie_cheeger_bound"
]
},
{
"page": "morie_chen_transform",
"title": "Chen's (2002) rank estimator of T",
"topics": [
"morie_chen_transform"
]
},
{
"page": "morie_chernoff_bound",
"title": "Chernoff bounds on a binomial tail",
"topics": [
"morie_chernoff_bound"
]
},
{
"page": "morie_chgseg",
"title": "Mean-change segmentation (PELT)",
"topics": [
"morie_chgseg"
]
},
{
"page": "morie_chi_square_test",
"title": "Chi-square test of independence or goodness-of-fit",
"topics": [
"morie_chi_square_test"
]
},
{
"page": "morie_choice_based_max_score",
"title": "Maximum-score estimator for a choice-based sample",
"topics": [
"morie_choice_based_max_score"
]
},
{
"page": "morie_choice_based_shares",
"title": "Variance-minimising shares for a choice-based sample",
"topics": [
"morie_choice_based_shares"
]
},
{
"page": "morie_christoffersen_cc",
"title": "Christoffersen conditional-coverage test for VaR exceedances",
"topics": [
"morie_christoffersen_cc"
]
},
{
"page": "morie_ckan_search",
"title": "Search any CKAN open-data portal for datasets",
"topics": [
"morie_ckan_search"
]
},
{
"page": "morie_classify_variable",
"title": "Classify one variable",
"topics": [
"morie_classify_variable"
]
},
{
"page": "morie_cli",
"title": "Run the rmorie command line",
"topics": [
"morie_cli"
]
},
{
"page": "morie_cli_dump_catalog",
"title": "Emit a unified catalog CSV for the rmorie command line",
"topics": [
"morie_cli_dump_catalog"
]
},
{
"page": "morie_cliff_delta",
"title": "Rank-based comparisons of two groups",
"topics": [
"morie_brunner_munzel",
"morie_cliff_delta"
]
},
{
"page": "morie_cluster",
"title": "k-means clustering with a full model-object contract",
"topics": [
"morie_cluster"
]
},
{
"page": "morie_cluster_batch",
"title": "Batch-cluster several data sets",
"topics": [
"morie_cluster_batch"
]
},
{
"page": "morie_cluster_robust_effect",
"title": "Cluster-robust (CR0) treatment effect",
"topics": [
"morie_cluster_robust_effect"
]
},
{
"page": "morie_cluster_sample",
"title": "Two-stage cluster sampling",
"topics": [
"morie_cluster_sample"
]
},
{
"page": "morie_cluster_twostage",
"title": "Cluster and two-stage sampling estimators",
"topics": [
"morie_cluster_twostage"
]
},
{
"page": "morie_cluster_variance",
"title": "Variance of a mean under cluster sampling",
"topics": [
"morie_cluster_variance"
]
},
{
"page": "morie_cmuti",
"title": "Mutual information via the Gaussian copula",
"topics": [
"morie_cmuti"
]
},
{
"page": "morie_cnn_genomic",
"title": "CNN genomic predictor (Conv1D + GAP + dense, base R)",
"topics": [
"morie_cnn_genomic"
]
},
{
"page": "morie_cohens_d",
"title": "Cohen's d effect size",
"topics": [
"morie_cohens_d"
]
},
{
"page": "morie_coherence",
"title": "Magnitude-squared coherence between two time series",
"topics": [
"morie_coherence"
]
},
{
"page": "morie_coin_independence",
"title": "General independence test via 'coin'",
"topics": [
"morie_coin_independence"
]
},
{
"page": "morie_coin_oneway",
"title": "Permutation one-way ANOVA via 'coin'",
"topics": [
"morie_coin_oneway"
]
},
{
"page": "morie_coin_wilcoxon",
"title": "Permutation Wilcoxon test via 'coin'",
"topics": [
"morie_coin_wilcoxon"
]
},
{
"page": "morie_cokriging",
"title": "Co-kriging front door",
"topics": [
"morie_cokriging"
]
},
{
"page": "morie_collider",
"title": "Collider structures (immoralities, v-structures), ch. 5 p. 82",
"topics": [
"morie_collider"
]
},
{
"page": "morie_collider_arrest",
"title": "Collider bias from conditioning on arrest: the odds ratio among arrestees",
"topics": [
"morie_collider_arrest"
]
},
{
"page": "morie_compare_nested_logistic_models",
"title": "Compare nested logistic-regression models via likelihood-ratio test",
"topics": [
"morie_compare_nested_logistic_models"
]
},
{
"page": "morie_competing_risks_fg",
"title": "Fine-Gray subdistribution hazard model",
"topics": [
"morie_competing_risks_fg"
]
},
{
"page": "morie_composition_budget",
"title": "Basic composition of privacy budgets",
"topics": [
"morie_composition_budget"
]
},
{
"page": "morie_compute_design_weights",
"title": "Compute inverse-probability design weights",
"topics": [
"morie_compute_design_weights"
]
},
{
"page": "morie_concentration_decompose",
"title": "Decompose crime concentration into crime-free places and the rest",
"topics": [
"morie_concentration_decompose"
]
},
{
"page": "morie_concentration_dispersion",
"title": "Dispersion of place counts against the Poisson null and its mixtures",
"topics": [
"morie_concentration_dispersion"
]
},
{
"page": "morie_concentration_distinct_growth",
"title": "Growth of the number of distinct places under preferential allocation",
"topics": [
"morie_concentration_distinct_growth"
]
},
{
"page": "morie_concentration_gini",
"title": "Gini coefficient of a count vector",
"topics": [
"morie_concentration_gini"
]
},
{
"page": "morie_concentration_null_bound",
"title": "How much Gini concentration chance alone can show",
"topics": [
"morie_concentration_null_bound"
]
},
{
"page": "morie_concordance_corr",
"title": "Lin's concordance correlation coefficient",
"topics": [
"morie_concordance_corr"
]
},
{
"page": "morie_concordance_incomplete",
"title": "Kendall's coefficient of concordance W (Gibbons Ch 12.5)",
"topics": [
"morie_concordance_incomplete"
]
},
{
"page": "morie_concordance_index",
"title": "Harrell's concordance index",
"topics": [
"morie_concordance_index",
"morie_survival_concordance"
]
},
{
"page": "morie_confusion_matrix_metrics",
"title": "Confusion matrix with precision / recall / F1 (R parity)",
"topics": [
"morie_confusion_matrix_metrics"
]
},
{
"page": "morie_contagion_branching",
"title": "Branching-ratio arithmetic of a self-exciting (Hawkes) crime process",
"topics": [
"morie_contagion_branching"
]
},
{
"page": "morie_contagion_extinction",
"title": "Extinction probability of a near-repeat chain",
"topics": [
"morie_contagion_extinction"
]
},
{
"page": "morie_contagion_moment_witness",
"title": "Two count laws with the same mean and variance: what a K-function cannot tell apart",
"topics": [
"morie_contagion_moment_witness"
]
},
{
"page": "morie_contaminated_bounds",
"title": "Contaminated-sample bounds for a recorded proportion",
"topics": [
"morie_contaminated_bounds"
]
},
{
"page": "morie_contingency_coefficient",
"title": "Pearson contingency coefficient C (Gibbons Ch 14.2.1)",
"topics": [
"morie_contingency_coefficient"
]
},
{
"page": "morie_contraction_conditions",
"title": "i.i.d. posterior contraction theorem",
"topics": [
"morie_contraction_conditions"
]
},
{
"page": "morie_control_comparison",
"title": "Nonparametric many-to-one comparisons to a control (Gibbons Ch 10.7)",
"topics": [
"morie_control_comparison"
]
},
{
"page": "morie_control_median_test",
"title": "Mood's median (control-median) test (Gibbons Ch 6.5)",
"topics": [
"morie_control_median_test"
]
},
{
"page": "morie_convex_hull",
"title": "Convex hull of planar points",
"topics": [
"morie_convex_hull"
]
},
{
"page": "morie_convlayer",
"title": "Output of a convolutional layer, Equation 12-1 (p. 423)",
"topics": [
"morie_convlayer"
]
},
{
"page": "morie_convolution",
"title": "Convolutional feature maps",
"topics": [
"morie_conv2d",
"morie_convolution"
]
},
{
"page": "morie_copod",
"title": "COPOD: copula-based outlier detection",
"topics": [
"morie_copod"
]
},
{
"page": "morie_copula_cdf",
"title": "Bivariate copula CDF",
"topics": [
"morie_copula_cdf"
]
},
{
"page": "morie_copula_fit",
"title": "Fit a copula by maximum-likelihood via 'copula'",
"topics": [
"morie_copula_fit"
]
},
{
"page": "morie_copula_sample",
"title": "Draw a random sample from a copula via 'copula'",
"topics": [
"morie_copula_sample"
]
},
{
"page": "morie_copula_spearman",
"title": "Spearman's rho implied by a copula",
"topics": [
"morie_copula_spearman"
]
},
{
"page": "morie_copula_survival",
"title": "Survival copula for paired event times",
"topics": [
"morie_copula_survival"
]
},
{
"page": "morie_copula_tau",
"title": "Kendall's tau implied by a copula parameter",
"topics": [
"morie_copula_tau"
]
},
{
"page": "morie_cor_pearson_cpp",
"title": "morie_cor_pearson_cpp",
"topics": [
"morie_cor_pearson_cpp"
]
},
{
"page": "morie_corrections_uof_resource_ids",
"title": "Canonical CKAN resource id table for the Corrections UoF dataset",
"topics": [
"morie_corrections_uof_resource_ids"
]
},
{
"page": "morie_count_edges",
"title": "Number of edges in a simple undirected graph",
"topics": [
"morie_count_edges"
]
},
{
"page": "morie_count_glm",
"title": "Count-model (Poisson family) utilities",
"topics": [
"morie_count_glm"
]
},
{
"page": "morie_court_backlog",
"title": "Court backlog arithmetic: Little's law on a docket",
"topics": [
"morie_court_backlog"
]
},
{
"page": "morie_covariate_balance",
"title": "Standardised mean difference for covariate balance",
"topics": [
"morie_covariate_balance"
]
},
{
"page": "morie_covariate_balance_check",
"title": "Covariate balance after weighting or matching",
"topics": [
"morie_covariate_balance_check"
]
},
{
"page": "morie_cox_breslow_step",
"title": "Breslow estimator of the baseline cumulative hazard",
"topics": [
"morie_cox_breslow_step"
]
},
{
"page": "morie_cox_dfbeta_influence",
"title": "Cox DFBETA influence diagnostics",
"topics": [
"morie_cox_dfbeta_influence"
]
},
{
"page": "morie_cox_frailty",
"title": "Cox model with a shared gamma frailty",
"topics": [
"morie_cox_frailty"
]
},
{
"page": "morie_cox_martingale_residuals",
"title": "Cox martingale residuals",
"topics": [
"morie_cox_martingale_residuals"
]
},
{
"page": "morie_cox_overdispersion",
"title": "Any intensity heterogeneity forces overdispersion",
"topics": [
"morie_cox_overdispersion"
]
},
{
"page": "morie_cox_ph",
"title": "Cox proportional-hazards model",
"topics": [
"morie_cox_partial_loglik",
"morie_cox_ph",
"morie_survival_cox"
]
},
{
"page": "morie_cox_schoenfeld_residuals",
"title": "Schoenfeld residuals and the proportional-hazards test",
"topics": [
"morie_cox_schoenfeld_residuals"
]
},
{
"page": "morie_cox_score_process",
"title": "Cox partial-likelihood score process",
"topics": [
"morie_cox_score_process"
]
},
{
"page": "morie_cox_stratified",
"title": "Stratified Cox model",
"topics": [
"morie_cox_stratified"
]
},
{
"page": "morie_cox_time_varying",
"title": "Cox model with time-varying COEFFICIENTS",
"topics": [
"morie_cox_time_varying"
]
},
{
"page": "morie_cpads_canonicalize_frame",
"title": "Canonicalize raw CPADS PUMF columns into morie's analysis schema",
"topics": [
"morie_cpads_canonicalize_frame"
]
},
{
"page": "morie_cpads_contract",
"title": "Return the CPADS analysis-frame contract",
"topics": [
"morie_cpads_contract"
]
},
{
"page": "morie_cpads_has_raw_columns",
"title": "Detect whether a data frame looks like raw CPADS PUMF data",
"topics": [
"morie_cpads_has_raw_columns"
]
},
{
"page": "morie_cpads_infer_file_format",
"title": "Infer the on-disk file format for a CPADS file path",
"topics": [
"morie_cpads_infer_file_format"
]
},
{
"page": "morie_cpads_missing_variables",
"title": "Identify missing canonical CPADS variables in a column set",
"topics": [
"morie_cpads_missing_variables"
]
},
{
"page": "morie_cpads_validate_frame",
"title": "Validate a data frame against the canonical CPADS analysis contract",
"topics": [
"morie_cpads_validate_frame"
]
},
{
"page": "morie_cpd_all_analyses",
"title": "Run the full Chicago Police Department analysis suite",
"topics": [
"morie_cpd_all_analyses"
]
},
{
"page": "morie_cqr",
"title": "Conformalized quantile regression (ch. 17 pp. 514-515)",
"topics": [
"morie_cqr"
]
},
{
"page": "morie_cramers_v",
"title": "Cramer's V for categorical association",
"topics": [
"morie_cramers_v"
]
},
{
"page": "morie_cressie_hawkins",
"title": "Cressie-Hawkins robust semivariogram estimator",
"topics": [
"morie_cressie_hawkins"
]
},
{
"page": "morie_crim_etas",
"title": "Temporal ETAS model (Epidemic-Type Aftershock Sequence)",
"topics": [
"morie_crim_etas"
]
},
{
"page": "morie_crim_hawkes_multivariate",
"title": "Multivariate Hawkes process (exponential kernels)",
"topics": [
"morie_crim_hawkes_multivariate"
]
},
{
"page": "morie_crim_near_repeat",
"title": "Knox near-repeat test (space-time interaction)",
"topics": [
"morie_crim_near_repeat"
]
},
{
"page": "morie_crim_risk_terrain",
"title": "Risk terrain model",
"topics": [
"morie_crim_risk_terrain"
]
},
{
"page": "morie_crosstab_verify",
"title": "Prove a recode with a before/after cross-tabulation",
"topics": [
"morie_crosstab_verify"
]
},
{
"page": "morie_croston",
"title": "Croston's method for intermittent demand",
"topics": [
"morie_croston"
]
},
{
"page": "morie_crypto_chacha20_poly1305_decrypt",
"title": "ChaCha20-Poly1305 IETF authenticated decryption",
"topics": [
"morie_crypto_chacha20_poly1305_decrypt"
]
},
{
"page": "morie_crypto_chacha20_poly1305_encrypt",
"title": "ChaCha20-Poly1305 IETF authenticated encryption",
"topics": [
"morie_crypto_chacha20_poly1305_encrypt"
]
},
{
"page": "morie_crypto_hkdf_sha256",
"title": "HKDF-SHA256 (RFC 5869) key derivation",
"topics": [
"morie_crypto_hkdf_sha256"
]
},
{
"page": "morie_crypto_hqc_decaps",
"title": "HQC-128 decapsulation",
"topics": [
"morie_crypto_hqc_decaps"
]
},
{
"page": "morie_crypto_hqc_encaps",
"title": "HQC-128 encapsulation",
"topics": [
"morie_crypto_hqc_encaps"
]
},
{
"page": "morie_crypto_hqc_keygen",
"title": "HQC-128 keypair (code-based KEM)",
"topics": [
"morie_crypto_hqc_keygen"
]
},
{
"page": "morie_crypto_hybrid_container_version",
"title": "Which hybrid container a ciphertext is",
"topics": [
"morie_crypto_hybrid_container_version"
]
},
{
"page": "morie_crypto_hybrid_decrypt",
"title": "Hybrid decrypt: ML-KEM-768 + ChaCha20-Poly1305",
"topics": [
"morie_crypto_hybrid_decrypt"
]
},
{
"page": "morie_crypto_hybrid_encrypt",
"title": "Hybrid encrypt: ML-KEM-768 + ChaCha20-Poly1305",
"topics": [
"morie_crypto_hybrid_encrypt"
]
},
{
"page": "morie_crypto_hybrid_keygen",
"title": "Generate an ML-KEM-768 key pair for hybrid encryption",
"topics": [
"morie_crypto_hybrid_keygen"
]
},
{
"page": "morie_crypto_keystore_create",
"title": "Create a new empty morie keystore",
"topics": [
"morie_crypto_keystore_create"
]
},
{
"page": "morie_crypto_keystore_list",
"title": "List key names in the morie keystore",
"topics": [
"morie_crypto_keystore_list"
]
},
{
"page": "morie_crypto_keystore_load",
"title": "Load a key pair from the morie keystore",
"topics": [
"morie_crypto_keystore_load"
]
},
{
"page": "morie_crypto_keystore_public_key",
"title": "Public key of a key pair in the morie keystore",
"topics": [
"morie_crypto_keystore_public_key"
]
},
{
"page": "morie_crypto_keystore_store",
"title": "Store a key pair in the morie keystore",
"topics": [
"morie_crypto_keystore_store"
]
},
{
"page": "morie_crypto_lamport_keygen",
"title": "Lamport one-time signature keypair (native, SHA-256)",
"topics": [
"morie_crypto_lamport_keygen"
]
},
{
"page": "morie_crypto_lamport_sign",
"title": "Sign one message with a Lamport keypair (native)",
"topics": [
"morie_crypto_lamport_sign"
]
},
{
"page": "morie_crypto_lamport_verify",
"title": "Verify a Lamport one-time signature (native)",
"topics": [
"morie_crypto_lamport_verify"
]
},
{
"page": "morie_crypto_liboqs_available",
"title": "Is liboqs available in this morie build?",
"topics": [
"morie_crypto_liboqs_available"
]
},
{
"page": "morie_crypto_liboqs_version",
"title": "liboqs runtime version string",
"topics": [
"morie_crypto_liboqs_version"
]
},
{
"page": "morie_crypto_mldsa65_keygen",
"title": "ML-DSA-65 keypair generation (NIST FIPS 204)",
"topics": [
"morie_crypto_mldsa65_keygen"
]
},
{
"page": "morie_crypto_mldsa65_sign",
"title": "ML-DSA-65 signature",
"topics": [
"morie_crypto_mldsa65_sign"
]
},
{
"page": "morie_crypto_mldsa65_verify",
"title": "ML-DSA-65 signature verification",
"topics": [
"morie_crypto_mldsa65_verify"
]
},
{
"page": "morie_crypto_mlkem768_decaps",
"title": "ML-KEM-768 decapsulation",
"topics": [
"morie_crypto_mlkem768_decaps"
]
},
{
"page": "morie_crypto_mlkem768_encaps",
"title": "ML-KEM-768 encapsulation",
"topics": [
"morie_crypto_mlkem768_encaps"
]
},
{
"page": "morie_crypto_mlkem768_keygen",
"title": "ML-KEM-768 keypair generation (NIST FIPS 203)",
"topics": [
"morie_crypto_mlkem768_keygen"
]
},
{
"page": "morie_crypto_pqc_inventory",
"title": "Inventory of the post-quantum families available in this build",
"topics": [
"morie_crypto_pqc_inventory"
]
},
{
"page": "morie_crypto_random_bytes",
"title": "Cryptographically secure random bytes",
"topics": [
"morie_crypto_random_bytes"
]
},
{
"page": "morie_crypto_slhdsa_keygen",
"title": "SLH-DSA-SHA2-128s keypair (hash-based signatures, FIPS 205)",
"topics": [
"morie_crypto_slhdsa_keygen"
]
},
{
"page": "morie_crypto_slhdsa_sign",
"title": "SLH-DSA-SHA2-128s signature",
"topics": [
"morie_crypto_slhdsa_sign"
]
},
{
"page": "morie_crypto_slhdsa_verify",
"title": "SLH-DSA-SHA2-128s verification",
"topics": [
"morie_crypto_slhdsa_verify"
]
},
{
"page": "morie_crypto_sodium_available",
"title": "Is libsodium available in this morie build?",
"topics": [
"morie_crypto_sodium_available"
]
},
{
"page": "morie_crypto_sodium_version",
"title": "libsodium runtime version string",
"topics": [
"morie_crypto_sodium_version"
]
},
{
"page": "morie_csr_nn_test",
"title": "Test a point pattern against complete spatial randomness",
"topics": [
"morie_csr_nn_test"
]
},
{
"page": "morie_cssant",
"title": "Callaway-Sant'Anna staggered difference-in-differences",
"topics": [
"morie_cssant"
]
},
{
"page": "morie_cycle_index_necklaces",
"title": "Necklaces: colourings up to rotation",
"topics": [
"morie_cycle_index_necklaces"
]
},
{
"page": "morie_dag",
"title": "Build a causal DAG",
"topics": [
"morie_dag"
]
},
{
"page": "morie_dag_estimate",
"title": "Estimate the identified effect from data",
"topics": [
"morie_dag_estimate"
]
},
{
"page": "morie_dag_identify",
"title": "Identify the causal effect in a DAG (backdoor criterion)",
"topics": [
"morie_dag_identify"
]
},
{
"page": "morie_dag_refute",
"title": "Refute an estimated causal effect",
"topics": [
"morie_dag_refute"
]
},
{
"page": "morie_dark_figure_bounds",
"title": "Sharp bounds on a true victimisation rate and its dark figure",
"topics": [
"morie_dark_figure_bounds"
]
},
{
"page": "morie_dark_figure_breakdown",
"title": "Breakdown analysis for a dark-figure conclusion",
"topics": [
"morie_dark_figure_breakdown"
]
},
{
"page": "morie_dark_figure_comparable",
"title": "The dark figure from a survey that covers only some offences",
"topics": [
"morie_dark_figure_comparable"
]
},
{
"page": "morie_dark_figure_hierarchy",
"title": "Incident versus offence counting: the hierarchy-rule arithmetic",
"topics": [
"morie_dark_figure_hierarchy"
]
},
{
"page": "morie_dark_figure_no_interaction",
"title": "The never-listed cell of a three-list capture under no three-way interaction",
"topics": [
"morie_dark_figure_no_interaction"
]
},
{
"page": "morie_dark_figure_three_list",
"title": "Three-list capture-recapture: what is and is not identified",
"topics": [
"morie_dark_figure_three_list"
]
},
{
"page": "morie_dark_figure_two_source",
"title": "Two-source (capture-recapture) count with a dependence box",
"topics": [
"morie_dark_figure_two_source"
]
},
{
"page": "morie_data_variance",
"title": "Population and sample variance of a data vector",
"topics": [
"morie_data_variance"
]
},
{
"page": "morie_dataset_catalog",
"title": "List all datasets in the MORIE catalog",
"topics": [
"morie_dataset_catalog"
]
},
{
"page": "morie_dataset_column_profile",
"title": "Build a single-column profile record",
"topics": [
"morie_dataset_column_profile"
]
},
{
"page": "morie_dataset_detect_role",
"title": "Detect the suggested epidemiological role of a column",
"topics": [
"morie_dataset_detect_role"
]
},
{
"page": "morie_dataset_infer_level",
"title": "Infer the Stevens NOIR measurement level for a single vector",
"topics": [
"morie_dataset_infer_level"
]
},
{
"page": "morie_dataset_info",
"title": "Get metadata for a single dataset",
"topics": [
"morie_dataset_info"
]
},
{
"page": "morie_dataset_load",
"title": "Load a dataset from a CSV / TSV / Excel / Parquet / JSON file",
"topics": [
"morie_dataset_load"
]
},
{
"page": "morie_dataset_portal_catalog",
"title": "Build the cross-portal dataset catalog",
"topics": [
"morie_dataset_portal_catalog"
]
},
{
"page": "morie_dataset_portal_catalog_clear_cache",
"title": "Clear the session-scoped portal-catalog cache",
"topics": [
"morie_dataset_portal_catalog_clear_cache"
]
},
{
"page": "morie_dataset_profile",
"title": "Fully profile a data frame without prior schema knowledge",
"topics": [
"morie_dataset_profile"
]
},
{
"page": "morie_dataset_profile_summary_table",
"title": "Render a human-readable dataset profile summary table",
"topics": [
"morie_dataset_profile_summary_table"
]
},
{
"page": "morie_dataset_profile_to_list",
"title": "Serialize a dataset profile to a plain nested list",
"topics": [
"morie_dataset_profile_to_list"
]
},
{
"page": "morie_dataset_suggest_plan",
"title": "Suggest an ordered analysis plan based on a dataset profile",
"topics": [
"morie_dataset_suggest_plan"
]
},
{
"page": "morie_dataset_summarize_column",
"title": "Compute level-appropriate summary statistics for one column",
"topics": [
"morie_dataset_summarize_column"
]
},
{
"page": "morie_datasets_arcgis_item_by_id",
"title": "Generic by-id loader for any ArcGIS Online Feature Service item",
"topics": [
"morie_datasets_arcgis_item_by_id"
]
},
{
"page": "morie_datasets_arcgis_item_metadata",
"title": "Resolve any ArcGIS Online item id to its FeatureServer URL + canonical metadata",
"topics": [
"morie_datasets_arcgis_item_metadata"
]
},
{
"page": "morie_datasets_arsau_aggregate_summary",
"title": "Ontario Use-of-Force aggregate summary (5-year 2020-2022, pre-RBDS rollup)",
"topics": [
"morie_datasets_arsau_aggregate_summary"
]
},
{
"page": "morie_datasets_arsau_detailed_dataset",
"title": "Ontario Use-of-Force detailed dataset (5-year 2020-2022, pre-RBDS)",
"topics": [
"morie_datasets_arsau_detailed_dataset"
]
},
{
"page": "morie_datasets_arsau_uof_individual_records",
"title": "Ontario Use-of-Force individual records (one row per individual-in-incident)",
"topics": [
"morie_datasets_arsau_uof_individual_records"
]
},
{
"page": "morie_datasets_arsau_uof_main_records",
"title": "Ontario Use-of-Force main records (one row per incident)",
"topics": [
"morie_datasets_arsau_uof_main_records"
]
},
{
"page": "morie_datasets_arsau_uof_probe_cycle_records",
"title": "Ontario Use-of-Force probe-cycle records (one row per CEW cartridge probe per individual-in-incident)",
"topics": [
"morie_datasets_arsau_uof_probe_cycle_records"
]
},
{
"page": "morie_datasets_arsau_uof_weapon_records",
"title": "Ontario Use-of-Force weapon records (one row per weapon per individual-in-incident)",
"topics": [
"morie_datasets_arsau_uof_weapon_records"
]
},
{
"page": "morie_datasets_bigquery",
"title": "Pull a BigQuery table (or filtered slice) as a 'data.frame'",
"topics": [
"morie_datasets_bigquery"
]
},
{
"page": "morie_datasets_browse",
"title": "Browse + filter the morie cross-portal dataset catalog",
"topics": [
"morie_datasets_browse"
]
},
{
"page": "morie_datasets_calgary_community_crime_stats",
"title": "Calgary Community Crime Statistics (sample)",
"topics": [
"morie_datasets_calgary_community_crime_stats",
"morie_datasets_calgary_fire_response_calls",
"morie_datasets_calgary_fire_stations"
]
},
{
"page": "morie_datasets_calgary_open_crime_adjacent_layers",
"title": "Calgary Open Data crime-adjacent catalog",
"topics": [
"morie_datasets_calgary_open_crime_adjacent_layers",
"morie_datasets_edmonton_open_crime_adjacent_layers",
"morie_datasets_ottawa_open_crime_adjacent_layers"
]
},
{
"page": "morie_datasets_calgary_socrata_by_id",
"title": "Fetch a Calgary Open Data Socrata dataset by ID",
"topics": [
"morie_datasets_calgary_socrata_by_id",
"morie_datasets_edmonton_socrata_by_id"
]
},
{
"page": "morie_datasets_chicago_arrests",
"title": "City of Chicago Open Data - Arrests feed ('dpt3-jri9')",
"topics": [
"morie_datasets_chicago_arrests"
]
},
{
"page": "morie_datasets_chicago_community_areas",
"title": "Chicago Community Area boundaries (cauq-8yn6)",
"topics": [
"morie_datasets_chicago_community_areas"
]
},
{
"page": "morie_datasets_chicago_crime",
"title": "City of Chicago \"Crimes - 2001 to Present\" feed ('ijzp-q8t2')",
"topics": [
"morie_datasets_chicago_crime"
]
},
{
"page": "morie_datasets_chicago_crime_map",
"title": "City of Chicago \"Crimes - 2001 to Present - Map\" view ('ahwe-kpsy')",
"topics": [
"morie_datasets_chicago_crime_map"
]
},
{
"page": "morie_datasets_chicago_crime_odata",
"title": "City of Chicago Crimes feed via OData v4 ('ijzp-q8t2')",
"topics": [
"morie_datasets_chicago_crime_odata"
]
},
{
"page": "morie_datasets_chicago_crime_resolved",
"title": "One-call Chicago crime + boundary + dictionary join",
"topics": [
"morie_datasets_chicago_crime_resolved"
]
},
{
"page": "morie_datasets_chicago_crime_soql",
"title": "City of Chicago Crimes feed - arbitrary-SoQL escape hatch",
"topics": [
"morie_datasets_chicago_crime_soql"
]
},
{
"page": "morie_datasets_chicago_iucr_codes",
"title": "Chicago Police Department - Illinois Uniform Crime Reporting (IUCR) code dictionary (c7ck-438e)",
"topics": [
"morie_datasets_chicago_iucr_codes"
]
},
{
"page": "morie_datasets_chicago_neighborhoods",
"title": "Chicago Neighborhoods boundary (Office of Tourism)",
"topics": [
"morie_datasets_chicago_neighborhoods"
]
},
{
"page": "morie_datasets_chicago_police_beats",
"title": "Chicago Police Beats (current) boundaries ('n9it-hstw')",
"topics": [
"morie_datasets_chicago_police_beats"
]
},
{
"page": "morie_datasets_chicago_police_districts",
"title": "Chicago Police Districts (current) boundaries (24zt-jpfn)",
"topics": [
"morie_datasets_chicago_police_districts"
]
},
{
"page": "morie_datasets_chicago_wards",
"title": "Chicago City Council Ward boundaries (sp34-6z76)",
"topics": [
"morie_datasets_chicago_wards"
]
},
{
"page": "morie_datasets_cihi_data_tables",
"title": "Catalogue of CIHI open data-table workbooks",
"topics": [
"morie_datasets_cihi_data_tables"
]
},
{
"page": "morie_datasets_ckan_package",
"title": "Pull every CSV resource of a CKAN package as a list of data frames",
"topics": [
"morie_datasets_ckan_package"
]
},
{
"page": "morie_datasets_ckan_search",
"title": "Search a CKAN open-data portal by free-text query",
"topics": [
"morie_datasets_ckan_search"
]
},
{
"page": "morie_datasets_corrections_uof_ethnic_origin",
"title": "Inmate-participant ethnic origin",
"topics": [
"morie_datasets_corrections_uof_ethnic_origin"
]
},
{
"page": "morie_datasets_corrections_uof_incident_type",
"title": "Incident-type lookup",
"topics": [
"morie_datasets_corrections_uof_incident_type"
]
},
{
"page": "morie_datasets_corrections_uof_incidents",
"title": "Use-of-force incidents (head dataset)",
"topics": [
"morie_datasets_corrections_uof_incidents"
]
},
{
"page": "morie_datasets_corrections_uof_indigenous",
"title": "Inmate-participant Indigenous identity",
"topics": [
"morie_datasets_corrections_uof_indigenous"
]
},
{
"page": "morie_datasets_corrections_uof_inmate_incident",
"title": "Inmate-to-incidents bridging table",
"topics": [
"morie_datasets_corrections_uof_inmate_incident"
]
},
{
"page": "morie_datasets_corrections_uof_inmate_participant",
"title": "Inmate-participant demographics (head)",
"topics": [
"morie_datasets_corrections_uof_inmate_participant"
]
},
{
"page": "morie_datasets_corrections_uof_institution_summary",
"title": "Institution-level annual incident summary",
"topics": [
"morie_datasets_corrections_uof_institution_summary"
]
},
{
"page": "morie_datasets_corrections_uof_location_summary",
"title": "Location-of-incident annual summary",
"topics": [
"morie_datasets_corrections_uof_location_summary"
]
},
{
"page": "morie_datasets_corrections_uof_race",
"title": "Inmate-participant race",
"topics": [
"morie_datasets_corrections_uof_race"
]
},
{
"page": "morie_datasets_corrections_uof_religion",
"title": "Inmate-participant religion",
"topics": [
"morie_datasets_corrections_uof_religion"
]
},
{
"page": "morie_datasets_corrections_uof_select_incident_summary",
"title": "Select-incident-type annual summary",
"topics": [
"morie_datasets_corrections_uof_select_incident_summary"
]
},
{
"page": "morie_datasets_corrections_uof_staff_incident",
"title": "Staff-to-incidents bridging table",
"topics": [
"morie_datasets_corrections_uof_staff_incident"
]
},
{
"page": "morie_datasets_cpads",
"title": "Load the Canadian Postsecondary Alcohol and Drug-use Survey (CPADS)",
"topics": [
"morie_datasets_cpads"
]
},
{
"page": "morie_datasets_cpd_public_arrests",
"title": "Chicago Police Department - Public Arrest Data (2014-2017)",
"topics": [
"morie_datasets_cpd_public_arrests"
]
},
{
"page": "morie_datasets_edmonton_police_stations",
"title": "Edmonton Police Station locations",
"topics": [
"morie_datasets_edmonton_fire_stations",
"morie_datasets_edmonton_police_stations"
]
},
{
"page": "morie_datasets_external_socrata_layers",
"title": "List the external Socrata datasets wrapped by morie",
"topics": [
"morie_datasets_external_socrata_layers"
]
},
{
"page": "morie_datasets_load_by_key",
"title": "Load a dataset by its cross-portal catalog 'dataset_key'",
"topics": [
"morie_datasets_load_by_key"
]
},
{
"page": "morie_datasets_montreal_ckan_resource",
"title": "Fetch records from any donnees.montreal.ca CKAN datastore resource",
"topics": [
"morie_datasets_montreal_ckan_resource"
]
},
{
"page": "morie_datasets_montreal_justice_safety_layers",
"title": "Donnees Montreal \"Loi, justice et securite publique\" catalog",
"topics": [
"morie_datasets_montreal_justice_safety_layers"
]
},
{
"page": "morie_datasets_montreal_sim_intervention_types",
"title": "SIM intervention TYPE -> French description dictionary",
"topics": [
"morie_datasets_montreal_sim_intervention_types"
]
},
{
"page": "morie_datasets_montreal_sim_interventions",
"title": "SIM Montreal Fire Service intervention records (sample)",
"topics": [
"morie_datasets_montreal_sim_interventions"
]
},
{
"page": "morie_datasets_namus_missing_persons",
"title": "NamUs missing-persons case metadata",
"topics": [
"morie_datasets_namus_missing_persons"
]
},
{
"page": "morie_datasets_nibrs",
"title": "FBI NIBRS offence-event records via the Crime Data Explorer API",
"topics": [
"morie_datasets_nibrs"
]
},
{
"page": "morie_datasets_nist_rds",
"title": "NIST Reference Datasets (RDS) catalog metadata",
"topics": [
"morie_datasets_nist_rds"
]
},
{
"page": "morie_datasets_nyc_boroughs",
"title": "NYC Borough Boundaries ('gthc-hcne')",
"topics": [
"morie_datasets_nyc_boroughs"
]
},
{
"page": "morie_datasets_nyc_boundaries_catalog",
"title": "Unified catalog of NYC OpenData boundary loaders",
"topics": [
"morie_datasets_nyc_boundaries_catalog"
]
},
{
"page": "morie_datasets_nyc_community_districts",
"title": "NYC community district boundaries",
"topics": [
"morie_datasets_nyc_community_districts"
]
},
{
"page": "morie_datasets_nyc_council_districts",
"title": "NYC City Council district boundaries",
"topics": [
"morie_datasets_nyc_council_districts"
]
},
{
"page": "morie_datasets_nyc_ntas_2020",
"title": "NYC Neighborhood Tabulation Areas (2020)",
"topics": [
"morie_datasets_nyc_ntas_2020"
]
},
{
"page": "morie_datasets_nyc_nypd_arrests_historic",
"title": "NYPD Arrests Data (Historic)",
"topics": [
"morie_datasets_nyc_nypd_arrests_historic"
]
},
{
"page": "morie_datasets_nyc_nypd_arrests_ytd",
"title": "NYPD Arrest Data (Year to Date)",
"topics": [
"morie_datasets_nyc_nypd_arrests_ytd"
]
},
{
"page": "morie_datasets_nyc_nypd_boro_crosswalk",
"title": "NYPD borough-code cross-reference (1-letter / UPPER / numeric)",
"topics": [
"morie_datasets_nyc_nypd_boro_crosswalk"
]
},
{
"page": "morie_datasets_nyc_nypd_by_key",
"title": "Generic NYC NYPD dataset loader by registry key",
"topics": [
"morie_datasets_nyc_nypd_by_key"
]
},
{
"page": "morie_datasets_nyc_nypd_complaint_historic",
"title": "NYPD Complaint Data Historic",
"topics": [
"morie_datasets_nyc_nypd_complaint_historic"
]
},
{
"page": "morie_datasets_nyc_nypd_complaint_ytd",
"title": "NYPD Complaint Data Current (Year To Date)",
"topics": [
"morie_datasets_nyc_nypd_complaint_ytd"
]
},
{
"page": "morie_datasets_nyc_nypd_hate_crimes",
"title": "NYPD Hate Crimes",
"topics": [
"morie_datasets_nyc_nypd_hate_crimes"
]
},
{
"page": "morie_datasets_nyc_nypd_law_books",
"title": "NYS / NYC statute book code dictionary",
"topics": [
"morie_datasets_nyc_nypd_law_books"
]
},
{
"page": "morie_datasets_nyc_nypd_layers",
"title": "List the NYPD criminal-justice Socrata datasets wrapped by morie",
"topics": [
"morie_datasets_nyc_nypd_layers"
]
},
{
"page": "morie_datasets_nyc_nypd_offense_codes",
"title": "NYPD offense-code dictionary ('ky_cd' + 'pd_cd' + descriptions)",
"topics": [
"morie_datasets_nyc_nypd_offense_codes"
]
},
{
"page": "morie_datasets_nyc_nypd_resolved",
"title": "One-call NYPD data + borough + precinct join",
"topics": [
"morie_datasets_nyc_nypd_resolved"
]
},
{
"page": "morie_datasets_nyc_nypd_uof_incidents",
"title": "NYPD Use of Force Incidents",
"topics": [
"morie_datasets_nyc_nypd_uof_incidents"
]
},
{
"page": "morie_datasets_nyc_nypd_uof_subjects",
"title": "NYPD Use of Force: Subjects",
"topics": [
"morie_datasets_nyc_nypd_uof_subjects"
]
},
{
"page": "morie_datasets_nyc_nypd_vehicle_stops",
"title": "NYPD Vehicle Stop Reports",
"topics": [
"morie_datasets_nyc_nypd_vehicle_stops"
]
},
{
"page": "morie_datasets_nyc_opendata_bulk_layers",
"title": "NYC OpenData bulk catalog (2851 entities)",
"topics": [
"morie_datasets_calgary_opendata_bulk_layers",
"morie_datasets_chicago_opendata_bulk_layers",
"morie_datasets_edmonton_opendata_bulk_layers",
"morie_datasets_montreal_opendata_bulk_layers",
"morie_datasets_nyc_opendata_bulk_layers",
"morie_datasets_ottawa_opendata_bulk_layers",
"morie_datasets_toronto_opendata_bulk_layers",
"morie_datasets_vancouver_opendata_bulk_layers"
]
},
{
"page": "morie_datasets_nyc_police_precincts",
"title": "NYC Police Precincts boundary layer ('y76i-bdw7')",
"topics": [
"morie_datasets_nyc_police_precincts"
]
},
{
"page": "morie_datasets_nyc_school_districts",
"title": "NYC public school district boundaries (NYS K-12)",
"topics": [
"morie_datasets_nyc_school_districts"
]
},
{
"page": "morie_datasets_nyc_socrata_by_id",
"title": "Fetch a NYC OpenData Socrata dataset by ID",
"topics": [
"morie_datasets_chicago_socrata_by_id",
"morie_datasets_nyc_socrata_by_id"
]
},
{
"page": "morie_datasets_nyc_stop_and_frisk",
"title": "NYPD Stop, Question and Frisk (SQF) microdata via NYC OpenData",
"topics": [
"morie_datasets_nyc_stop_and_frisk"
]
},
{
"page": "morie_datasets_nyc_zctas",
"title": "NYC ZIP Code Tabulation Areas (ZCTAs)",
"topics": [
"morie_datasets_nyc_zctas"
]
},
{
"page": "morie_datasets_ontario_ckan_by_key",
"title": "Generic Ontario CKAN dataset loader (by registry key)",
"topics": [
"morie_datasets_ontario_ckan_by_key"
]
},
{
"page": "morie_datasets_ontario_ckan_layers",
"title": "List the Ontario CKAN datasets wrapped by morie",
"topics": [
"morie_datasets_ontario_ckan_layers"
]
},
{
"page": "morie_datasets_otis_a01",
"title": "Load the OTIS A01 Restrictive-Confinement Detailed Dataset",
"topics": [
"morie_datasets_otis_a01"
]
},
{
"page": "morie_datasets_otis_a01_restrictive_confinement",
"title": "OTIS a01 - Restrictive Confinement (detailed per-individual)",
"topics": [
"morie_datasets_otis_a01_restrictive_confinement"
]
},
{
"page": "morie_datasets_otis_b01_segregation_detailed",
"title": "OTIS b01 - Segregation detailed (per-individual episodes)",
"topics": [
"morie_datasets_otis_b01_segregation_detailed"
]
},
{
"page": "morie_datasets_otis_b02_segregation_total_days",
"title": "OTIS b02 - Segregation total days per individual",
"topics": [
"morie_datasets_otis_b02_segregation_total_days"
]
},
{
"page": "morie_datasets_otis_b03_seg_alerts_by_institution",
"title": "OTIS b03 - Segregation placements: alerts + hold by institution",
"topics": [
"morie_datasets_otis_b03_seg_alerts_by_institution"
]
},
{
"page": "morie_datasets_otis_b04_seg_consecutive_by_region",
"title": "OTIS b04 - Segregation consecutive durations by region",
"topics": [
"morie_datasets_otis_b04_seg_consecutive_by_region"
]
},
{
"page": "morie_datasets_otis_b05_seg_consecutive_lengths",
"title": "OTIS b05 - Segregation placements by consecutive-length bucket",
"topics": [
"morie_datasets_otis_b05_seg_consecutive_lengths"
]
},
{
"page": "morie_datasets_otis_b06_seg_reason_by_institution",
"title": "OTIS b06 - Segregation placements: reason for placement by institution",
"topics": [
"morie_datasets_otis_b06_seg_reason_by_institution"
]
},
{
"page": "morie_datasets_otis_b07_seg_alerts_by_gender",
"title": "OTIS b07 - Segregation placements: alerts + hold by gender",
"topics": [
"morie_datasets_otis_b07_seg_alerts_by_gender"
]
},
{
"page": "morie_datasets_otis_b08_seg_consecutive_by_institution",
"title": "OTIS b08 - Segregation consecutive durations by institution",
"topics": [
"morie_datasets_otis_b08_seg_consecutive_by_institution"
]
},
{
"page": "morie_datasets_otis_b09_seg_n_times",
"title": "OTIS b09 - Individuals in segregation by number of times placed",
"topics": [
"morie_datasets_otis_b09_seg_n_times"
]
},
{
"page": "morie_datasets_otis_c01_individuals_total",
"title": "OTIS c01 - Total individuals (in custody / restrictive confinement / segregation)",
"topics": [
"morie_datasets_otis_c01_individuals_total"
]
},
{
"page": "morie_datasets_otis_c02_individuals_by_institution",
"title": "OTIS c02 - Individuals by institution",
"topics": [
"morie_datasets_otis_c02_individuals_by_institution"
]
},
{
"page": "morie_datasets_otis_c03_individuals_race_by_gender",
"title": "OTIS c03 - Individuals by race x gender",
"topics": [
"morie_datasets_otis_c03_individuals_race_by_gender"
]
},
{
"page": "morie_datasets_otis_c04_individuals_race_by_region",
"title": "OTIS c04 - Individuals by race x region",
"topics": [
"morie_datasets_otis_c04_individuals_race_by_region"
]
},
{
"page": "morie_datasets_otis_c05_individuals_religion_by_region",
"title": "OTIS c05 - Individuals by religion x region",
"topics": [
"morie_datasets_otis_c05_individuals_religion_by_region"
]
},
{
"page": "morie_datasets_otis_c06_individuals_age_by_region",
"title": "OTIS c06 - Individuals by age category x region",
"topics": [
"morie_datasets_otis_c06_individuals_age_by_region"
]
},
{
"page": "morie_datasets_otis_c07_individuals_alerts",
"title": "OTIS c07 - Individuals: alerts + hold flags",
"topics": [
"morie_datasets_otis_c07_individuals_alerts"
]
},
{
"page": "morie_datasets_otis_c08_individuals_religion_by_gender",
"title": "OTIS c08 - Individuals by religion x gender",
"topics": [
"morie_datasets_otis_c08_individuals_religion_by_gender"
]
},
{
"page": "morie_datasets_otis_c09_individuals_age_by_gender",
"title": "OTIS c09 - Individuals by age category x gender",
"topics": [
"morie_datasets_otis_c09_individuals_age_by_gender"
]
},
{
"page": "morie_datasets_otis_c10_aggregate_durations_by_institution",
"title": "OTIS c10 - Aggregate durations by institution",
"topics": [
"morie_datasets_otis_c10_aggregate_durations_by_institution"
]
},
{
"page": "morie_datasets_otis_c11_aggregate_lengths",
"title": "OTIS c11 - Aggregate lengths",
"topics": [
"morie_datasets_otis_c11_aggregate_lengths"
]
},
{
"page": "morie_datasets_otis_c12_aggregate_durations_by_region",
"title": "OTIS c12 - Aggregate durations by region",
"topics": [
"morie_datasets_otis_c12_aggregate_durations_by_region"
]
},
{
"page": "morie_datasets_otis_d01_deaths_in_custody",
"title": "OTIS Deaths-in-Custody detailed dataset (d01)",
"topics": [
"morie_datasets_otis_d01_deaths_in_custody"
]
},
{
"page": "morie_datasets_otis_d02_deaths_by_gender",
"title": "OTIS d02 - Deaths in custody by gender",
"topics": [
"morie_datasets_otis_d02_deaths_by_gender"
]
},
{
"page": "morie_datasets_otis_d03_deaths_by_race",
"title": "OTIS d03 - Deaths in custody by race",
"topics": [
"morie_datasets_otis_d03_deaths_by_race"
]
},
{
"page": "morie_datasets_otis_d04_deaths_by_religion",
"title": "OTIS d04 - Deaths in custody by religion",
"topics": [
"morie_datasets_otis_d04_deaths_by_religion"
]
},
{
"page": "morie_datasets_otis_d05_deaths_by_age_category",
"title": "OTIS d05 - Deaths in custody by age category",
"topics": [
"morie_datasets_otis_d05_deaths_by_age_category"
]
},
{
"page": "morie_datasets_otis_d06_cause_by_alert",
"title": "OTIS d06 - Deaths in custody by alert type x institution",
"topics": [
"morie_datasets_otis_d06_cause_by_alert"
]
},
{
"page": "morie_datasets_otis_d07_alerts_by_housing_unit",
"title": "OTIS d07 - Deaths in custody alerts x housing unit",
"topics": [
"morie_datasets_otis_d07_alerts_by_housing_unit"
]
},
{
"page": "morie_datasets_siu_director_reports",
"title": "SIU director's-reports index (legacy PDF anchors)",
"topics": [
"morie_datasets_siu_director_reports"
]
},
{
"page": "morie_datasets_siu_report_fields",
"title": "Extract structured fields from an SIU director's-report text or URL",
"topics": [
"morie_datasets_siu_report_fields"
]
},
{
"page": "morie_datasets_siu_report_text",
"title": "Download an SIU director's-report PDF and return its plain text",
"topics": [
"morie_datasets_siu_report_text"
]
},
{
"page": "morie_datasets_statcan_ccjs_cubes",
"title": "Statistics Canada CCJS cube registry (curated subset)",
"topics": [
"morie_datasets_statcan_ccjs_cubes"
]
},
{
"page": "morie_datasets_statcan_cube_metadata",
"title": "Fetch a StatCan cube's metadata (dimensions + members) via WDS",
"topics": [
"morie_datasets_statcan_cube_metadata"
]
},
{
"page": "morie_datasets_statcan_full_csv_url",
"title": "Get the bulk-CSV download URL for a StatCan cube",
"topics": [
"morie_datasets_statcan_full_csv_url"
]
},
{
"page": "morie_datasets_statcan_vectors",
"title": "Fetch the latest N periods for a set of StatCan vectors via WDS",
"topics": [
"morie_datasets_statcan_vectors"
]
},
{
"page": "morie_datasets_summary",
"title": "Tally the cross-portal catalog by source + api_mode",
"topics": [
"morie_datasets_summary"
]
},
{
"page": "morie_datasets_toronto_ambulance_stations",
"title": "Toronto Ambulance station locations",
"topics": [
"morie_datasets_toronto_ambulance_stations"
]
},
{
"page": "morie_datasets_toronto_asr_miscellaneous",
"title": "TPS Annual Statistical Report - Miscellaneous data (aggregated)",
"topics": [
"morie_datasets_toronto_asr_miscellaneous"
]
},
{
"page": "morie_datasets_toronto_open_ckan_resource",
"title": "Fetch records from any open.toronto.ca CKAN resource",
"topics": [
"morie_datasets_toronto_open_ckan_resource"
]
},
{
"page": "morie_datasets_toronto_open_crime_adjacent_layers",
"title": "Toronto Open Data crime-adjacent CKAN catalog",
"topics": [
"morie_datasets_toronto_open_crime_adjacent_layers"
]
},
{
"page": "morie_datasets_toronto_zoning_per_neighbourhood",
"title": "Toronto Zoning per Neighbourhood (EsriCanadaEducation)",
"topics": [
"morie_datasets_toronto_zoning_per_neighbourhood"
]
},
{
"page": "morie_datasets_tps_2008_firs",
"title": "2008 FIRS",
"topics": [
"morie_datasets_tps_2008_firs"
]
},
{
"page": "morie_datasets_tps_2009_firs",
"title": "2009 FIRS",
"topics": [
"morie_datasets_tps_2009_firs"
]
},
{
"page": "morie_datasets_tps_2010_firs",
"title": "2010 FIRS",
"topics": [
"morie_datasets_tps_2010_firs"
]
},
{
"page": "morie_datasets_tps_2011_firs",
"title": "2011 FIRS",
"topics": [
"morie_datasets_tps_2011_firs"
]
},
{
"page": "morie_datasets_tps_2012_firs",
"title": "2012 FIRS",
"topics": [
"morie_datasets_tps_2012_firs"
]
},
{
"page": "morie_datasets_tps_2013_firs",
"title": "2013 FIRS",
"topics": [
"morie_datasets_tps_2013_firs"
]
},
{
"page": "morie_datasets_tps_administrative",
"title": "Administrative (ASR-AD-TBL-001)",
"topics": [
"morie_datasets_tps_administrative"
]
},
{
"page": "morie_datasets_tps_arcgis_hub_by_id",
"title": "Generic TPS ArcGIS Hub dataset loader by hub_id",
"topics": [
"morie_datasets_tps_arcgis_hub_by_id"
]
},
{
"page": "morie_datasets_tps_arcgis_hub_download",
"title": "Direct multi-format downloader (binary or text) for a TPS Hub item",
"topics": [
"morie_datasets_tps_arcgis_hub_download"
]
},
{
"page": "morie_datasets_tps_arcgis_hub_layers",
"title": "List the Toronto Police Service ArcGIS Hub datasets wrapped by morie",
"topics": [
"morie_datasets_tps_arcgis_hub_layers"
]
},
{
"page": "morie_datasets_tps_arrested_and_charged_persons",
"title": "Arrested and Charged Persons (ASR-ENF-TBL-001)",
"topics": [
"morie_datasets_tps_arrested_and_charged_persons"
]
},
{
"page": "morie_datasets_tps_arrests_and_strip_searches",
"title": "Arrests and Strip Searches (RBDC-ARR-TBL-001)",
"topics": [
"morie_datasets_tps_arrests_and_strip_searches"
]
},
{
"page": "morie_datasets_tps_assault",
"title": "TPS PSDP - Assault",
"topics": [
"morie_datasets_tps_assault"
]
},
{
"page": "morie_datasets_tps_automobile_ksi",
"title": "Automobile KSI",
"topics": [
"morie_datasets_tps_automobile_ksi"
]
},
{
"page": "morie_datasets_tps_autotheft",
"title": "TPS PSDP - Auto Theft",
"topics": [
"morie_datasets_tps_autotheft"
]
},
{
"page": "morie_datasets_tps_bicycle_thefts",
"title": "Bicycle Thefts Open Data",
"topics": [
"morie_datasets_tps_bicycle_thefts"
]
},
{
"page": "morie_datasets_tps_bicycletheft",
"title": "TPS PSDP - Bicycle Theft",
"topics": [
"morie_datasets_tps_bicycletheft"
]
},
{
"page": "morie_datasets_tps_breakandenter",
"title": "TPS PSDP - Break and Enter",
"topics": [
"morie_datasets_tps_breakandenter"
]
},
{
"page": "morie_datasets_tps_budget_2020",
"title": "Budget_2020",
"topics": [
"morie_datasets_tps_budget_2020"
]
},
{
"page": "morie_datasets_tps_budget_2021",
"title": "Budget_2021",
"topics": [
"morie_datasets_tps_budget_2021"
]
},
{
"page": "morie_datasets_tps_budget_2022",
"title": "Budget_2022",
"topics": [
"morie_datasets_tps_budget_2022"
]
},
{
"page": "morie_datasets_tps_budget_2023",
"title": "Budget_2023",
"topics": [
"morie_datasets_tps_budget_2023"
]
},
{
"page": "morie_datasets_tps_budget_2024",
"title": "Budget_2024",
"topics": [
"morie_datasets_tps_budget_2024"
]
},
{
"page": "morie_datasets_tps_budget_2025",
"title": "Budget_2025",
"topics": [
"morie_datasets_tps_budget_2025"
]
},
{
"page": "morie_datasets_tps_budget_2026",
"title": "Budget_2026",
"topics": [
"morie_datasets_tps_budget_2026"
]
},
{
"page": "morie_datasets_tps_budget_by_command",
"title": "Budget_by_Command",
"topics": [
"morie_datasets_tps_budget_by_command"
]
},
{
"page": "morie_datasets_tps_calls_for_service_attended",
"title": "Calls for Service Attended (ASR-CS-TBL-003)",
"topics": [
"morie_datasets_tps_calls_for_service_attended"
]
},
{
"page": "morie_datasets_tps_community_safety_indicators",
"title": "Community Safety Indicators Open Data",
"topics": [
"morie_datasets_tps_community_safety_indicators"
]
},
{
"page": "morie_datasets_tps_complaint_dispositions",
"title": "Complaint Dispositions (ASR-PCF-TBL-003)",
"topics": [
"morie_datasets_tps_complaint_dispositions"
]
},
{
"page": "morie_datasets_tps_cyclist_ksi",
"title": "Cyclist KSI",
"topics": [
"morie_datasets_tps_cyclist_ksi"
]
},
{
"page": "morie_datasets_tps_dispatched_calls_by_division",
"title": "Dispatched Calls by Division (ASR-CS-TBL-001)",
"topics": [
"morie_datasets_tps_dispatched_calls_by_division"
]
},
{
"page": "morie_datasets_tps_facilities",
"title": "Facilities",
"topics": [
"morie_datasets_tps_facilities"
]
},
{
"page": "morie_datasets_tps_fatals_ksi",
"title": "Fatals KSI",
"topics": [
"morie_datasets_tps_fatals_ksi"
]
},
{
"page": "morie_datasets_tps_firearms_top_calibres",
"title": "Firearms Top Calibres (ASR-F-TBL-001)",
"topics": [
"morie_datasets_tps_firearms_top_calibres"
]
},
{
"page": "morie_datasets_tps_gross_expenditures_by_division",
"title": "Gross Expenditures by Division (ASR-PB-TBL-001)",
"topics": [
"morie_datasets_tps_gross_expenditures_by_division"
]
},
{
"page": "morie_datasets_tps_gross_operating_budget",
"title": "Gross Operating Budget (ASR-PB-TBL-005)",
"topics": [
"morie_datasets_tps_gross_operating_budget"
]
},
{
"page": "morie_datasets_tps_hatecrimes",
"title": "TPS PSDP - Hate Crimes",
"topics": [
"morie_datasets_tps_hatecrimes"
]
},
{
"page": "morie_datasets_tps_homicide",
"title": "TPS Homicides feed",
"topics": [
"morie_datasets_tps_homicide"
]
},
{
"page": "morie_datasets_tps_homicides",
"title": "TPS PSDP - Homicides",
"topics": [
"morie_datasets_tps_homicides"
]
},
{
"page": "morie_datasets_tps_intimate_partner_family_violence",
"title": "TPS PSDP - Intimate Partner and Family Violence",
"topics": [
"morie_datasets_tps_intimate_partner_family_violence"
]
},
{
"page": "morie_datasets_tps_investigated_alleged_misconduct",
"title": "Investigated Alleged Misconduct (ASR-PCF-TBL-002)",
"topics": [
"morie_datasets_tps_investigated_alleged_misconduct"
]
},
{
"page": "morie_datasets_tps_killed_and_seriously_injured",
"title": "Killed and Seriously Injured",
"topics": [
"morie_datasets_tps_killed_and_seriously_injured"
]
},
{
"page": "morie_datasets_tps_layers",
"title": "List the TPS open-data layers included with morie",
"topics": [
"morie_datasets_tps_layers"
]
},
{
"page": "morie_datasets_tps_major_crime",
"title": "TPS Major Crime Indicators feed",
"topics": [
"morie_datasets_tps_major_crime"
]
},
{
"page": "morie_datasets_tps_mha_apprehensions",
"title": "TPS Mental Health Act Apprehensions (PSDP)",
"topics": [
"morie_datasets_tps_mha_apprehensions"
]
},
{
"page": "morie_datasets_tps_miscellaneous_calls_for_service",
"title": "Miscellaneous Calls for Service (ASR-CS-TBL-002)",
"topics": [
"morie_datasets_tps_miscellaneous_calls_for_service"
]
},
{
"page": "morie_datasets_tps_miscellaneous_data",
"title": "Miscellaneous Data (ASR-MISC-TBL-001)",
"topics": [
"morie_datasets_tps_miscellaneous_data"
]
},
{
"page": "morie_datasets_tps_miscellaneous_firearms",
"title": "Miscellaneous Firearms (ASR-F-TBL-003)",
"topics": [
"morie_datasets_tps_miscellaneous_firearms"
]
},
{
"page": "morie_datasets_tps_motorcylist_ksi",
"title": "Motorcylist KSI",
"topics": [
"morie_datasets_tps_motorcylist_ksi"
]
},
{
"page": "morie_datasets_tps_neighbourhood_crime_rates",
"title": "Neighbourhood Crime Rates Open Data",
"topics": [
"morie_datasets_tps_neighbourhood_crime_rates"
]
},
{
"page": "morie_datasets_tps_passenger_ksi",
"title": "Passenger KSI",
"topics": [
"morie_datasets_tps_passenger_ksi"
]
},
{
"page": "morie_datasets_tps_patrol_zone",
"title": "Patrol Zone",
"topics": [
"morie_datasets_tps_patrol_zone"
]
},
{
"page": "morie_datasets_tps_pedestrian_ksi",
"title": "Pedestrian KSI",
"topics": [
"morie_datasets_tps_pedestrian_ksi"
]
},
{
"page": "morie_datasets_tps_personnel_by_command",
"title": "Personnel by Command (ASR-PB-TBL-004)",
"topics": [
"morie_datasets_tps_personnel_by_command"
]
},
{
"page": "morie_datasets_tps_personnel_by_rank",
"title": "Personnel by Rank (ASR-PB-TBL-002)",
"topics": [
"morie_datasets_tps_personnel_by_rank"
]
},
{
"page": "morie_datasets_tps_personnel_by_rank_by_division",
"title": "Personnel by Rank by Division (ASR-PB-TBL-003)",
"topics": [
"morie_datasets_tps_personnel_by_rank_by_division"
]
},
{
"page": "morie_datasets_tps_persons_in_crisis_calls_for_service_attended",
"title": "Persons in Crisis Calls for Service Attended Open Data",
"topics": [
"morie_datasets_tps_persons_in_crisis_calls_for_service_attended"
]
},
{
"page": "morie_datasets_tps_police_divisions",
"title": "Toronto Police Service police division boundaries",
"topics": [
"morie_datasets_tps_police_divisions"
]
},
{
"page": "morie_datasets_tps_psdp_resolved",
"title": "One-call TPS PSDP data + boundary metadata join",
"topics": [
"morie_datasets_tps_psdp_resolved"
]
},
{
"page": "morie_datasets_tps_regulated_interactions",
"title": "Regulated Interactions (ASR-RI-TBL-001)",
"topics": [
"morie_datasets_tps_regulated_interactions"
]
},
{
"page": "morie_datasets_tps_reported_crimes",
"title": "Reported Crimes (ASR-RC-TBL-001)",
"topics": [
"morie_datasets_tps_reported_crimes"
]
},
{
"page": "morie_datasets_tps_robbery",
"title": "TPS PSDP - Robbery",
"topics": [
"morie_datasets_tps_robbery"
]
},
{
"page": "morie_datasets_tps_search_of_persons",
"title": "Search of Persons (ASR-SP-TBL-001)",
"topics": [
"morie_datasets_tps_search_of_persons"
]
},
{
"page": "morie_datasets_tps_shooting_firearm_discharges",
"title": "TPS PSDP - Shooting and Firearm Discharges",
"topics": [
"morie_datasets_tps_shooting_firearm_discharges"
]
},
{
"page": "morie_datasets_tps_shootings",
"title": "TPS Shootings and Firearm Discharges feed",
"topics": [
"morie_datasets_tps_shootings"
]
},
{
"page": "morie_datasets_tps_staffing_by_command",
"title": "Staffing_by_Command",
"topics": [
"morie_datasets_tps_staffing_by_command"
]
},
{
"page": "morie_datasets_tps_theft_from_motor_vehicle",
"title": "TPS PSDP - Theft From Motor Vehicle",
"topics": [
"morie_datasets_tps_theft_from_motor_vehicle"
]
},
{
"page": "morie_datasets_tps_theft_over",
"title": "TPS PSDP - Theft Over",
"topics": [
"morie_datasets_tps_theft_over"
]
},
{
"page": "morie_datasets_tps_tickets_issued",
"title": "Tickets Issued (ASR-ENF-TBL-002)",
"topics": [
"morie_datasets_tps_tickets_issued"
]
},
{
"page": "morie_datasets_tps_top_20_offences_of_firearm_seizures",
"title": "Top 20 Offences of Firearm Seizures (ASR-F-TBL-002)",
"topics": [
"morie_datasets_tps_top_20_offences_of_firearm_seizures"
]
},
{
"page": "morie_datasets_tps_total_public_complaints",
"title": "Total Public Complaints (ASR-PCF-TBL-001)",
"topics": [
"morie_datasets_tps_total_public_complaints"
]
},
{
"page": "morie_datasets_tps_traffic_collisions",
"title": "Traffic Collisions Open Data (ASR-T-TBL-001)",
"topics": [
"morie_datasets_tps_traffic_collisions"
]
},
{
"page": "morie_datasets_tps_use_of_force_call_for_service_types",
"title": "Use of Force: Call for Service Types (RBDC-UOF-TBL-004)",
"topics": [
"morie_datasets_tps_use_of_force_call_for_service_types"
]
},
{
"page": "morie_datasets_tps_use_of_force_call_sources_by_month",
"title": "Use of Force: Call Sources by Month (RBDC-UOF-TBL-001)",
"topics": [
"morie_datasets_tps_use_of_force_call_sources_by_month"
]
},
{
"page": "morie_datasets_tps_use_of_force_gender_composition",
"title": "Use of Force: Gender Composition (RBDC-UOF-TBL-006)",
"topics": [
"morie_datasets_tps_use_of_force_gender_composition"
]
},
{
"page": "morie_datasets_tps_use_of_force_location_of_occurrences",
"title": "Use of Force: Location of Occurrences (RBDC-UOF-TBL-003)",
"topics": [
"morie_datasets_tps_use_of_force_location_of_occurrences"
]
},
{
"page": "morie_datasets_tps_use_of_force_occurrence_category",
"title": "Use of Force: Occurrence Category (RBDC-UOF-TBL-005)",
"topics": [
"morie_datasets_tps_use_of_force_occurrence_category"
]
},
{
"page": "morie_datasets_tps_use_of_force_time_of_day_trends",
"title": "Use of Force: Time of Day Trends (RBDC-UOF-TBL-002)",
"topics": [
"morie_datasets_tps_use_of_force_time_of_day_trends"
]
},
{
"page": "morie_datasets_tps_use_of_force_use_of_force_types_and_perceived_weapons",
"title": "Use of Force: Use of Force Types and Perceived Weapons (RBDC-UOF-TBL-007)",
"topics": [
"morie_datasets_tps_use_of_force_use_of_force_types_and_perceived_weapons"
]
},
{
"page": "morie_datasets_tps_victims_of_crime",
"title": "Victims of Crime (ASR-VC-TBL-001)",
"topics": [
"morie_datasets_tps_victims_of_crime"
]
},
{
"page": "morie_datasets_vancouver_opendata_by_id",
"title": "Fetch records from a Vancouver Open Data dataset by ID",
"topics": [
"morie_datasets_vancouver_opendata_by_id"
]
},
{
"page": "morie_datasets_vancouver_opendata_layers",
"title": "Vancouver Open Data full dataset catalog (Opendatasoft v2.1)",
"topics": [
"morie_datasets_vancouver_opendata_layers"
]
},
{
"page": "morie_datasets_vic_cache_dir",
"title": "Local cache directory for the Victorian workbooks",
"topics": [
"morie_datasets_vic_cache_dir"
]
},
{
"page": "morie_datasets_vic_catalog",
"title": "Catalog of the Victorian Crime Statistics Agency data tables",
"topics": [
"morie_datasets_vic_catalog"
]
},
{
"page": "morie_datasets_vic_sheets",
"title": "Sheet names in a cached Victorian workbook",
"topics": [
"morie_datasets_vic_sheets"
]
},
{
"page": "morie_datasets_vic_table",
"title": "Read one Victorian crime data table",
"topics": [
"morie_datasets_vic_table"
]
},
{
"page": "morie_datasets_vpd_crime",
"title": "Load Vancouver Police Department crime incident data",
"topics": [
"morie_datasets_vpd_crime"
]
},
{
"page": "morie_datasets_vpd_legal_disclaimer",
"title": "Read VPD's legal disclaimer (included verbatim from the zip)",
"topics": [
"morie_datasets_vpd_legal_disclaimer"
]
},
{
"page": "morie_datasette_databases",
"title": "List the databases served by a Datasette instance",
"topics": [
"morie_datasette_databases"
]
},
{
"page": "morie_datasette_read",
"title": "Read rows from a Datasette table (or run read-only SQL)",
"topics": [
"morie_datasette_read"
]
},
{
"page": "morie_db_connect",
"title": "Connect to the MORIE cache database",
"topics": [
"morie_db_connect"
]
},
{
"page": "morie_db_create_indexes",
"title": "Create the recommended B-tree indexes for a morie cache table",
"topics": [
"morie_db_create_indexes"
]
},
{
"page": "morie_dcc_garch",
"title": "DCC(1,1) GARCH front-end",
"topics": [
"morie_dcc_garch"
]
},
{
"page": "morie_dcc_multivariate_garch",
"title": "DCC multivariate GARCH (Engle 2002)",
"topics": [
"morie_dcc_multivariate_garch"
]
},
{
"page": "morie_ddqn",
"title": "Tabular Double Q-learning",
"topics": [
"morie_ddqn"
]
},
{
"page": "morie_decision_tree_split",
"title": "Decision tree split (R parity)",
"topics": [
"morie_decision_tree_split"
]
},
{
"page": "morie_decode_labelled",
"title": "Decode a labelled import by its value labels, by code",
"topics": [
"morie_decode_labelled"
]
},
{
"page": "morie_deconv_normality",
"title": "Asymptotic normality of the deconvolution estimator",
"topics": [
"morie_deconv_normality"
]
},
{
"page": "morie_deconv_rate",
"title": "Convergence rates for deconvolution",
"topics": [
"morie_deconv_rate"
]
},
{
"page": "morie_deconvolution",
"title": "Deconvolution kernel density estimate",
"topics": [
"morie_deconvolution"
]
},
{
"page": "morie_deep_learning_genomic",
"title": "Single-hidden-layer MLP genomic predictor (base R)",
"topics": [
"morie_deep_learning_genomic"
]
},
{
"page": "morie_default_synthetic_name_map",
"title": "Default synthetic-data variable name map",
"topics": [
"morie_default_synthetic_name_map"
]
},
{
"page": "morie_default_workflow_map",
"title": "Default workflow step map",
"topics": [
"morie_default_workflow_map"
]
},
{
"page": "morie_deff_cluster",
"title": "Design effect from clustering",
"topics": [
"morie_deff_cluster"
]
},
{
"page": "morie_density",
"title": "Density of an undirected simple graph (Wasserman & Faust Ch 4, densty)",
"topics": [
"morie_density"
]
},
{
"page": "morie_dependent_censoring_hazard",
"title": "Dependent-censoring diagnostic",
"topics": [
"morie_dependent_censoring_hazard"
]
},
{
"page": "morie_derangement_rounding",
"title": "The rounding identity for derangements",
"topics": [
"morie_derangement_rounding"
]
},
{
"page": "morie_derangements",
"title": "Derangements: permutations with no fixed point",
"topics": [
"morie_derangements"
]
},
{
"page": "morie_desc_atkinson",
"title": "Atkinson inequality index via 'DescTools'",
"topics": [
"morie_desc_atkinson"
]
},
{
"page": "morie_desc_cramers_v",
"title": "Cramer's V via 'DescTools'",
"topics": [
"morie_desc_cramers_v"
]
},
{
"page": "morie_desc_gini",
"title": "Gini coefficient via 'DescTools'",
"topics": [
"morie_desc_gini"
]
},
{
"page": "morie_desc_kappa",
"title": "Cohen / Fleiss kappa via 'DescTools'",
"topics": [
"morie_desc_kappa"
]
},
{
"page": "morie_desc_winsorize",
"title": "Winsorize a numeric vector via 'DescTools'",
"topics": [
"morie_desc_winsorize"
]
},
{
"page": "morie_describe",
"title": "Print the pedagogical narrative for a morie callable",
"topics": [
"morie_describe"
]
},
{
"page": "morie_describe_by_name",
"title": "String-only variant of 'morie_describe'",
"topics": [
"morie_describe_by_name"
]
},
{
"page": "morie_design_effect",
"title": "Design effect (DEFF)",
"topics": [
"morie_design_effect"
]
},
{
"page": "morie_design_effect_direction",
"title": "The design effect of clustering is direction-specific",
"topics": [
"morie_design_effect_direction"
]
},
{
"page": "morie_design_sensitivity",
"title": "Design sensitivity: the Gamma at which a sensitivity analysis's limiting power turns",
"topics": [
"morie_design_sensitivity"
]
},
{
"page": "morie_det_rng",
"title": "SHA-keyed deterministic RNG for Py<->R parity",
"topics": [
"morie_det_rng"
]
},
{
"page": "morie_det_rng_sha_hex",
"title": "SHA-256 hex digest of \"name:seed\" (for Py<->R cross-check)",
"topics": [
"morie_det_rng_sha_hex"
]
},
{
"page": "morie_detection_rate_shift",
"title": "Detection-rate arithmetic of downgrade-and-caution",
"topics": [
"morie_detection_rate_shift"
]
},
{
"page": "morie_deterrence_design_check",
"title": "Which deterrence partial effects a design can identify",
"topics": [
"morie_deterrence_design_check"
]
},
{
"page": "morie_deterrence_logcost",
"title": "The expected-cost deterrence response on the log scale, and the discount it implies",
"topics": [
"morie_deterrence_logcost"
]
},
{
"page": "morie_deterrence_response",
"title": "Direction of deterrence without convexity: optimal offending under certainty and severity",
"topics": [
"morie_deterrence_response"
]
},
{
"page": "morie_deviance_residual_cox",
"title": "Cox deviance residuals",
"topics": [
"morie_deviance_residual_cox"
]
},
{
"page": "morie_dfbeta_cox",
"title": "Fit a Cox model and return its DFBETA diagnostics",
"topics": [
"morie_dfbeta_cox"
]
},
{
"page": "morie_dfl_reweight",
"title": "DiNardo-Fortin-Lemieux reweighting: composition and structure without a linear model",
"topics": [
"morie_dfl_reweight"
]
},
{
"page": "morie_diagnostic_prevalence",
"title": "Diagnostic-test prevalence and Dorfman group testing",
"topics": [
"morie_diagnostic_prevalence"
]
},
{
"page": "morie_did",
"title": "Difference-in-differences with automatic staggered-design handling",
"topics": [
"morie_did"
]
},
{
"page": "morie_did_2x2",
"title": "Classic 2x2 Difference-in-Differences estimator",
"topics": [
"morie_did_2x2"
]
},
{
"page": "morie_did_aggregate_gt_att",
"title": "Aggregate group-time ATTs into summary parameters",
"topics": [
"morie_did_aggregate_gt_att"
]
},
{
"page": "morie_did_bacon_decomposition",
"title": "Goodman-Bacon decomposition of the TWFE DiD estimator",
"topics": [
"morie_did_bacon_decomposition"
]
},
{
"page": "morie_did_borusyak",
"title": "Borusyak, Jaravel & Spiess (2024) imputation estimator",
"topics": [
"morie_did_borusyak"
]
},
{
"page": "morie_did_chaisemartin_dhaultfoeuille",
"title": "Heterogeneity-robust DiD (de Chaisemartin & D'Haultfoeuille, 2020)",
"topics": [
"morie_did_chaisemartin_dhaultfoeuille"
]
},
{
"page": "morie_did_continuous_treatment",
"title": "DiD with a continuous (dose) treatment",
"topics": [
"morie_did_continuous_treatment"
]
},
{
"page": "morie_did_diagnostics",
"title": "Comprehensive diagnostics for a 2x2 DiD setting",
"topics": [
"morie_did_diagnostics"
]
},
{
"page": "morie_did_did2s",
"title": "Gardner (2022) two-stage difference-in-differences",
"topics": [
"morie_did_did2s"
]
},
{
"page": "morie_did_doubly_robust",
"title": "Doubly-robust DiD (Sant'Anna & Zhao, 2020)",
"topics": [
"morie_did_doubly_robust"
]
},
{
"page": "morie_did_event_study",
"title": "Event-study DiD specification",
"topics": [
"morie_did_event_study"
]
},
{
"page": "morie_did_fuzzy",
"title": "Fuzzy DiD (LATE) via 2SLS",
"topics": [
"morie_did_fuzzy"
]
},
{
"page": "morie_did_group_time_att",
"title": "Callaway-Sant'Anna group-time average treatment effects",
"topics": [
"morie_did_group_time_att"
]
},
{
"page": "morie_did_heterogeneous",
"title": "Heterogeneity-robust DiD by sub-group / moderator quantile",
"topics": [
"morie_did_heterogeneous"
]
},
{
"page": "morie_did_honest_sensitivity",
"title": "Honest (relative-magnitudes) sensitivity for event-study estimates",
"topics": [
"morie_did_honest_sensitivity"
]
},
{
"page": "morie_did_panel_fe",
"title": "Two-way fixed-effects DiD (panel)",
"topics": [
"morie_did_panel_fe"
]
},
{
"page": "morie_did_parallel_trends_data",
"title": "Group-by-time outcome means for parallel-trends visualisation",
"topics": [
"morie_did_parallel_trends_data"
]
},
{
"page": "morie_did_placebo_test_group",
"title": "Placebo DiD on sub-groups expected to be unaffected",
"topics": [
"morie_did_placebo_test_group"
]
},
{
"page": "morie_did_placebo_test_outcome",
"title": "Placebo DiD on outcomes that should be unaffected",
"topics": [
"morie_did_placebo_test_outcome"
]
},
{
"page": "morie_did_placebo_test_time",
"title": "Placebo DiD at fake treatment times",
"topics": [
"morie_did_placebo_test_time"
]
},
{
"page": "morie_did_repeated_cross_section",
"title": "Repeated cross-section DiD (optionally weighted)",
"topics": [
"morie_did_repeated_cross_section"
]
},
{
"page": "morie_did_sensitivity_analysis",
"title": "Sensitivity of DiD estimate to parallel-trends violations",
"topics": [
"morie_did_sensitivity_analysis"
]
},
{
"page": "morie_did_staggered",
"title": "Staggered DiD via group-time ATTs with aggregation",
"topics": [
"morie_did_staggered"
]
},
{
"page": "morie_did_sun_abraham",
"title": "Sun & Abraham (2021) interaction-weighted event study",
"topics": [
"morie_did_sun_abraham"
]
},
{
"page": "morie_did_synthdid_estimate",
"title": "Synthetic DiD, explicit-name API (native)",
"topics": [
"morie_did_synthdid_estimate"
]
},
{
"page": "morie_did_synthetic",
"title": "Synthetic Difference-in-Differences (Arkhangelsky et al., 2021)",
"topics": [
"morie_did_synthetic"
]
},
{
"page": "morie_did_test_parallel_trends",
"title": "Pre-trend test for parallel trends",
"topics": [
"morie_did_test_parallel_trends"
]
},
{
"page": "morie_did_triple_difference",
"title": "Triple-difference (DDD) estimator",
"topics": [
"morie_did_triple_difference"
]
},
{
"page": "morie_did_twoway_fe_weights",
"title": "Diagnose TWFE-DiD weights (de Chaisemartin & D'Haultfoeuille, 2020)",
"topics": [
"morie_did_twoway_fe_weights"
]
},
{
"page": "morie_did_wild_cluster_bootstrap",
"title": "DiD with wild cluster bootstrap p-values (Cameron-Gelbach-Miller, 2008)",
"topics": [
"morie_did_wild_cluster_bootstrap"
]
},
{
"page": "morie_dif_delta_mh",
"title": "Mantel-Haenszel DIF effect on the ETS delta scale",
"topics": [
"morie_dif_delta_mh"
]
},
{
"page": "morie_dif_sample_size",
"title": "Focal-group sample size for detecting differential item functioning",
"topics": [
"morie_dif_sample_size"
]
},
{
"page": "morie_difference_sign_test",
"title": "Difference-sign test (native)",
"topics": [
"morie_difference_sign_test"
]
},
{
"page": "morie_diffser",
"title": "Differencing, ordinary and seasonal (ch. 6 pp. 155-158)",
"topics": [
"morie_diffser"
]
},
{
"page": "morie_diffu_diffusion_forward",
"title": "DDPM forward (noising) process",
"topics": [
"morie_diffu_diffusion_forward"
]
},
{
"page": "morie_diffusion_score_matching",
"title": "Denoising score-matching objective",
"topics": [
"morie_diffusion_score_matching"
]
},
{
"page": "morie_digest",
"title": "Hash an R object, string, raw vector or file (digest's digest, natively)",
"topics": [
"morie_digest"
]
},
{
"page": "morie_digest2int",
"title": "Bob Jenkins' one-at-a-time hash of strings to integers (digest's digest2int)",
"topics": [
"morie_digest2int"
]
},
{
"page": "morie_dilworth_decomposition",
"title": "Dilworth's theorem, with both sides computed",
"topics": [
"morie_dilworth_decomposition"
]
},
{
"page": "morie_direct_discrete",
"title": "Direct estimation with discrete covariates",
"topics": [
"morie_direct_discrete"
]
},
{
"page": "morie_dirmulti",
"title": "Direct multi-step forecasting (ch. 18 p. 548)",
"topics": [
"morie_dirmulti"
]
},
{
"page": "morie_dirrec",
"title": "DirRec strategy (ch. 18 p. 551)",
"topics": [
"morie_dirrec"
]
},
{
"page": "morie_discrete_wavelet_anomaly",
"title": "Haar wavelet anomaly detection",
"topics": [
"morie_discrete_wavelet_anomaly"
]
},
{
"page": "morie_disparity_benchmark",
"title": "Disparity benchmarks: the product identity and the exposure-offset shift",
"topics": [
"morie_disparity_benchmark"
]
},
{
"page": "morie_disparity_decomposition",
"title": "Oaxaca-Blinder decomposition of a sentencing gap, with its two references and the interaction",
"topics": [
"morie_disparity_decomposition"
]
},
{
"page": "morie_disparity_exposure_bounds",
"title": "Bounds on a police-outcome disparity when exposure is measured by a proxy",
"topics": [
"morie_disparity_exposure_bounds"
]
},
{
"page": "morie_dist_cdf",
"title": "Cumulative distribution function at points",
"topics": [
"morie_dist_cdf"
]
},
{
"page": "morie_dist_integrate",
"title": "Integrate a function against a distribution's support, with checks",
"topics": [
"morie_dist_integrate"
]
},
{
"page": "morie_dist_moment",
"title": "A moment of a distribution, analytically or by integration",
"topics": [
"morie_dist_moment"
]
},
{
"page": "morie_dist_pdf",
"title": "Density / mass of a distribution at points",
"topics": [
"morie_dist_pdf"
]
},
{
"page": "morie_dist_quantile",
"title": "Quantile function",
"topics": [
"morie_dist_quantile"
]
},
{
"page": "morie_dist_sum",
"title": "Sum a discrete distribution's mass (or a moment) with convergence check",
"topics": [
"morie_dist_sum"
]
},
{
"page": "morie_distribution",
"title": "Construct a general probability-distribution object",
"topics": [
"morie_distribution"
]
},
{
"page": "morie_dml_clustered",
"title": "Cluster-robust double machine learning for the ATE",
"topics": [
"morie_dml_clustered"
]
},
{
"page": "morie_docalc",
"title": "Which of the three rules of do-calculus applies (p. 119)",
"topics": [
"morie_docalc"
]
},
{
"page": "morie_dointerv",
"title": "The do-operator as graph surgery (modularity, ch. 7 p. 154)",
"topics": [
"morie_dointerv"
]
},
{
"page": "morie_dominant_singularity_growth",
"title": "Exponential growth rate from the dominant singularity",
"topics": [
"morie_dominant_singularity_growth"
]
},
{
"page": "morie_download",
"title": "Download a file with an automatic Wayback fallback (via rmoriebricklayer)",
"topics": [
"morie_download"
]
},
{
"page": "morie_download_bootstrap",
"title": "Download bootstrap weight files from CKAN API",
"topics": [
"morie_download_bootstrap"
]
},
{
"page": "morie_dp_adam",
"title": "One DP-Adam step",
"topics": [
"morie_dp_adam"
]
},
{
"page": "morie_dp_changepoint",
"title": "Differentially private changepoint location",
"topics": [
"morie_dp_changepoint"
]
},
{
"page": "morie_dp_count",
"title": "Differentially private count",
"topics": [
"morie_dp_count"
]
},
{
"page": "morie_dp_covariance",
"title": "Differentially private covariance matrix",
"topics": [
"morie_dp_covariance"
]
},
{
"page": "morie_dp_exponential_mechanism",
"title": "Exponential mechanism for private selection",
"topics": [
"morie_dp_exponential_mechanism"
]
},
{
"page": "morie_dp_fedavg",
"title": "Differentially private federated averaging",
"topics": [
"morie_dp_fedavg"
]
},
{
"page": "morie_dp_gan",
"title": "Differentially private GAN discriminator step",
"topics": [
"morie_dp_gan"
]
},
{
"page": "morie_dp_gaussian_mechanism",
"title": "Gaussian mechanism for (epsilon, delta)-differential privacy",
"topics": [
"morie_dp_gaussian_mechanism"
]
},
{
"page": "morie_dp_gaussian_mixture",
"title": "Bayesian nonparametric DP Gaussian mixture via 'dirichletprocess'",
"topics": [
"morie_dp_gaussian_mixture"
]
},
{
"page": "morie_dp_histogram",
"title": "Differentially private histogram",
"topics": [
"morie_dp_histogram"
]
},
{
"page": "morie_dp_kmeans",
"title": "Differentially private k-means",
"topics": [
"morie_dp_kmeans"
]
},
{
"page": "morie_dp_laplace_mechanism",
"title": "Laplace mechanism for pure epsilon-differential privacy",
"topics": [
"morie_dp_laplace_mechanism"
]
},
{
"page": "morie_dp_logistic",
"title": "Differentially private logistic regression",
"topics": [
"morie_dp_logistic"
]
},
{
"page": "morie_dp_mean",
"title": "Differentially private mean of a bounded sample",
"topics": [
"morie_dp_mean"
]
},
{
"page": "morie_dp_median",
"title": "Differentially private median",
"topics": [
"morie_dp_median"
]
},
{
"page": "morie_dp_minmax",
"title": "Private substitute for a range",
"topics": [
"morie_dp_minmax"
]
},
{
"page": "morie_dp_pca",
"title": "Differentially private PCA",
"topics": [
"morie_dp_pca"
]
},
{
"page": "morie_dp_predictive",
"title": "Exact Dirichlet-process posterior predictive",
"topics": [
"morie_dp_predictive"
]
},
{
"page": "morie_dp_quantile",
"title": "Differentially private quantile",
"topics": [
"morie_dp_quantile"
]
},
{
"page": "morie_dp_release_calibration",
"title": "Calibrate a privacy budget to a tolerable error, or the reverse",
"topics": [
"morie_dp_release_calibration"
]
},
{
"page": "morie_dp_sgd",
"title": "One DP-SGD step",
"topics": [
"morie_dp_sgd"
]
},
{
"page": "morie_dp_sum",
"title": "Differentially private bounded sum",
"topics": [
"morie_dp_sum"
]
},
{
"page": "morie_dp_survival",
"title": "Dirichlet-process survival and the Kaplan-Meier limit",
"topics": [
"morie_dp_survival"
]
},
{
"page": "morie_dp_synthetic_data",
"title": "Differentially private synthetic data",
"topics": [
"morie_dp_synthetic_data"
]
},
{
"page": "morie_dp_unit_definition",
"title": "Unit of privacy: how many records one entity contributes",
"topics": [
"morie_dp_unit_definition"
]
},
{
"page": "morie_dpss",
"title": "Discrete prolate spheroidal (Slepian) sequences",
"topics": [
"morie_dpss"
]
},
{
"page": "morie_dqm_check",
"title": "Differentiability in quadratic mean",
"topics": [
"morie_dqm_check"
]
},
{
"page": "morie_dr_did",
"title": "Doubly robust difference-in-differences",
"topics": [
"morie_dr_did"
]
},
{
"page": "morie_dr_learner",
"title": "DR-learner: doubly robust meta-learner for the CATE",
"topics": [
"morie_dr_learner"
]
},
{
"page": "morie_dr_overlap_weighted",
"title": "Overlap-weighted doubly-robust ATE with cross-fitting",
"topics": [
"morie_dr_overlap_weighted"
]
},
{
"page": "morie_drift_forecast",
"title": "Random-walk-with-drift forecast",
"topics": [
"morie_drift_forecast"
]
},
{
"page": "morie_dseptest",
"title": "d-separation of x and y given z (ch. 6)",
"topics": [
"morie_dseptest"
]
},
{
"page": "morie_dsp_acf_from_psd",
"title": "Autocorrelation from PSD (Wiener-Khinchin)",
"topics": [
"morie_dsp_acf_from_psd"
]
},
{
"page": "morie_dsp_alpha_trimmed_mean",
"title": "Alpha-trimmed mean filter",
"topics": [
"morie_dsp_alpha_trimmed_mean"
]
},
{
"page": "morie_dsp_amplitude_histogram",
"title": "Amplitude histogram features",
"topics": [
"morie_dsp_amplitude_histogram"
]
},
{
"page": "morie_dsp_arc_length",
"title": "Signal arc length",
"topics": [
"morie_dsp_arc_length"
]
},
{
"page": "morie_dsp_band_power",
"title": "Bandpower over [f_low, f_high]",
"topics": [
"morie_dsp_band_power"
]
},
{
"page": "morie_dsp_baseline_correlation",
"title": "Baseline-corrected Pearson correlation",
"topics": [
"morie_dsp_baseline_correlation"
]
},
{
"page": "morie_dsp_centroidal_time",
"title": "Centroidal time",
"topics": [
"morie_dsp_centroidal_time"
]
},
{
"page": "morie_dsp_coherence",
"title": "Coherence-squared spectrum",
"topics": [
"morie_dsp_coherence"
]
},
{
"page": "morie_dsp_coherence_spectrum",
"title": "Coherence spectrum (alias to morie_dsp_coherence)",
"topics": [
"morie_dsp_coherence_spectrum"
]
},
{
"page": "morie_dsp_comb",
"title": "Comb filter built from cascaded notches",
"topics": [
"morie_dsp_comb"
]
},
{
"page": "morie_dsp_complex_cepstrum",
"title": "Complex cepstrum",
"topics": [
"morie_dsp_complex_cepstrum"
]
},
{
"page": "morie_dsp_complex_demodulation",
"title": "Complex demodulation around a carrier",
"topics": [
"morie_dsp_complex_demodulation"
]
},
{
"page": "morie_dsp_crest_factor",
"title": "Crest factor (peak / RMS)",
"topics": [
"morie_dsp_crest_factor"
]
},
{
"page": "morie_dsp_cross_correlation",
"title": "Normalised cross-correlation up to a maximum lag",
"topics": [
"morie_dsp_cross_correlation"
]
},
{
"page": "morie_dsp_cross_correlation_cpp",
"title": "morie_dsp_cross_correlation_cpp",
"topics": [
"morie_dsp_cross_correlation_cpp"
]
},
{
"page": "morie_dsp_csd",
"title": "Cross-spectral density (Welch)",
"topics": [
"morie_dsp_csd"
]
},
{
"page": "morie_dsp_cv",
"title": "Coefficient of variation",
"topics": [
"morie_dsp_cv"
]
},
{
"page": "morie_dsp_derivative_detect",
"title": "Derivative-based peak detection",
"topics": [
"morie_dsp_derivative_detect"
]
},
{
"page": "morie_dsp_dicrotic_notch",
"title": "Dicrotic-notch detection in pulse waves",
"topics": [
"morie_dsp_dicrotic_notch"
]
},
{
"page": "morie_dsp_ensemble_average",
"title": "Ensemble average over fixed-length segments",
"topics": [
"morie_dsp_ensemble_average"
]
},
{
"page": "morie_dsp_entropy_histogram",
"title": "Shannon entropy from amplitude histogram",
"topics": [
"morie_dsp_entropy_histogram"
]
},
{
"page": "morie_dsp_even_odd",
"title": "Even-odd decomposition of a finite signal",
"topics": [
"morie_dsp_even_odd"
]
},
{
"page": "morie_dsp_fbm_synthesis",
"title": "Fractional Brownian motion synthesis (1/f^beta)",
"topics": [
"morie_dsp_fbm_synthesis"
]
},
{
"page": "morie_dsp_form_factor",
"title": "Form factor (RMS / mean-absolute)",
"topics": [
"morie_dsp_form_factor"
]
},
{
"page": "morie_dsp_fractal_dim_psd",
"title": "Fractal dimension from log-log PSD slope",
"topics": [
"morie_dsp_fractal_dim_psd"
]
},
{
"page": "morie_dsp_hann_filter",
"title": "Hann-windowed smoothing filter",
"topics": [
"morie_dsp_hann_filter"
]
},
{
"page": "morie_dsp_higuchi_fd",
"title": "Higuchi fractal dimension",
"topics": [
"morie_dsp_higuchi_fd"
]
},
{
"page": "morie_dsp_hilbert_envelope",
"title": "Hilbert envelope",
"topics": [
"morie_dsp_hilbert_envelope"
]
},
{
"page": "morie_dsp_hjorth",
"title": "All three Hjorth parameters",
"topics": [
"morie_dsp_hjorth"
]
},
{
"page": "morie_dsp_hjorth_activity",
"title": "Hjorth activity (variance)",
"topics": [
"morie_dsp_hjorth_activity"
]
},
{
"page": "morie_dsp_hjorth_complexity",
"title": "Hjorth complexity",
"topics": [
"morie_dsp_hjorth_complexity"
]
},
{
"page": "morie_dsp_hjorth_mobility",
"title": "Hjorth mobility",
"topics": [
"morie_dsp_hjorth_mobility"
]
},
{
"page": "morie_dsp_homomorphic",
"title": "Homomorphic high-pass filter",
"topics": [
"morie_dsp_homomorphic"
]
},
{
"page": "morie_dsp_hr_from_rr",
"title": "Heart rate from RR intervals (BPM)",
"topics": [
"morie_dsp_hr_from_rr"
]
},
{
"page": "morie_dsp_integrated_emg",
"title": "Integrated EMG (sum of absolute values)",
"topics": [
"morie_dsp_integrated_emg"
]
},
{
"page": "morie_dsp_katz_fd",
"title": "Katz / box-counting fractal dimension",
"topics": [
"morie_dsp_katz_fd"
]
},
{
"page": "morie_dsp_lms",
"title": "LMS adaptive filter (Widrow-Hoff)",
"topics": [
"morie_dsp_lms"
]
},
{
"page": "morie_dsp_lms_cpp",
"title": "morie_dsp_lms_cpp",
"topics": [
"morie_dsp_lms_cpp"
]
},
{
"page": "morie_dsp_matched",
"title": "Matched filter (time-reversed template correlator)",
"topics": [
"morie_dsp_matched"
]
},
{
"page": "morie_dsp_mean_abs",
"title": "Mean absolute value",
"topics": [
"morie_dsp_mean_abs"
]
},
{
"page": "morie_dsp_mean_frequency",
"title": "Mean frequency from PSD",
"topics": [
"morie_dsp_mean_frequency"
]
},
{
"page": "morie_dsp_median_filter",
"title": "Median filter",
"topics": [
"morie_dsp_median_filter"
]
},
{
"page": "morie_dsp_median_filter_cpp",
"title": "morie_dsp_median_filter_cpp",
"topics": [
"morie_dsp_median_filter_cpp"
]
},
{
"page": "morie_dsp_median_frequency",
"title": "Median frequency from PSD",
"topics": [
"morie_dsp_median_frequency"
]
},
{
"page": "morie_dsp_min_phase",
"title": "Minimum-phase correspondent via cepstral folding",
"topics": [
"morie_dsp_min_phase"
]
},
{
"page": "morie_dsp_moving_average",
"title": "Moving-average filter (boxcar)",
"topics": [
"morie_dsp_moving_average"
]
},
{
"page": "morie_dsp_myopulse_rate",
"title": "Myopulse percentage rate",
"topics": [
"morie_dsp_myopulse_rate"
]
},
{
"page": "morie_dsp_nlms",
"title": "NLMS adaptive filter",
"topics": [
"morie_dsp_nlms"
]
},
{
"page": "morie_dsp_nlms_cpp",
"title": "morie_dsp_nlms_cpp",
"topics": [
"morie_dsp_nlms_cpp"
]
},
{
"page": "morie_dsp_notch",
"title": "IIR notch filter (single frequency)",
"topics": [
"morie_dsp_notch"
]
},
{
"page": "morie_dsp_onset_detect",
"title": "Energy-onset detection",
"topics": [
"morie_dsp_onset_detect"
]
},
{
"page": "morie_dsp_pan_tompkins",
"title": "Pan-Tompkins QRS detector",
"topics": [
"morie_dsp_pan_tompkins"
]
},
{
"page": "morie_dsp_parzen_pdf",
"title": "Parzen kernel density estimate",
"topics": [
"morie_dsp_parzen_pdf"
]
},
{
"page": "morie_dsp_psd_bartlett",
"title": "Bartlett PSD estimate",
"topics": [
"morie_dsp_psd_bartlett"
]
},
{
"page": "morie_dsp_psd_periodogram",
"title": "Periodogram PSD estimate",
"topics": [
"morie_dsp_psd_periodogram"
]
},
{
"page": "morie_dsp_psd_to_db",
"title": "Convert PSD to decibels",
"topics": [
"morie_dsp_psd_to_db"
]
},
{
"page": "morie_dsp_psd_welch",
"title": "Welch PSD estimate (rmorie native)",
"topics": [
"morie_dsp_psd_welch"
]
},
{
"page": "morie_dsp_qrs_features",
"title": "QRS-style waveform descriptors",
"topics": [
"morie_dsp_qrs_features"
]
},
{
"page": "morie_dsp_rls",
"title": "RLS adaptive filter",
"topics": [
"morie_dsp_rls"
]
},
{
"page": "morie_dsp_rls_cpp",
"title": "morie_dsp_rls_cpp",
"topics": [
"morie_dsp_rls_cpp"
]
},
{
"page": "morie_dsp_rms",
"title": "Root mean square",
"topics": [
"morie_dsp_rms"
]
},
{
"page": "morie_dsp_ruler_fd",
"title": "Ruler / divider fractal dimension",
"topics": [
"morie_dsp_ruler_fd"
]
},
{
"page": "morie_dsp_shannon_energy",
"title": "Shannon-energy envelope",
"topics": [
"morie_dsp_shannon_energy"
]
},
{
"page": "morie_dsp_shape_factor",
"title": "Shape factor (mean-absolute / mean-sqrt-absolute-squared)",
"topics": [
"morie_dsp_shape_factor"
]
},
{
"page": "morie_dsp_slope_sign_changes",
"title": "Slope sign changes",
"topics": [
"morie_dsp_slope_sign_changes"
]
},
{
"page": "morie_dsp_snr",
"title": "Signal-to-noise ratio estimate (dB)",
"topics": [
"morie_dsp_snr"
]
},
{
"page": "morie_dsp_snr_improvement",
"title": "SNR improvement attributable to a filter (dB)",
"topics": [
"morie_dsp_snr_improvement"
]
},
{
"page": "morie_dsp_spectral_edge",
"title": "Spectral edge frequency (e.g. SEF95)",
"topics": [
"morie_dsp_spectral_edge"
]
},
{
"page": "morie_dsp_spectral_entropy",
"title": "Spectral entropy (Shannon, base 2)",
"topics": [
"morie_dsp_spectral_entropy"
]
},
{
"page": "morie_dsp_spectral_flatness",
"title": "Spectral flatness (Wiener entropy)",
"topics": [
"morie_dsp_spectral_flatness"
]
},
{
"page": "morie_dsp_spectral_kurtosis",
"title": "Spectral kurtosis from PSD",
"topics": [
"morie_dsp_spectral_kurtosis"
]
},
{
"page": "morie_dsp_spectral_moment",
"title": "k-th spectral moment",
"topics": [
"morie_dsp_spectral_moment"
]
},
{
"page": "morie_dsp_spectral_ratio",
"title": "Spectral power ratio between two bands",
"topics": [
"morie_dsp_spectral_ratio"
]
},
{
"page": "morie_dsp_synchronized_average",
"title": "Synchronized average around trigger indices",
"topics": [
"morie_dsp_synchronized_average"
]
},
{
"page": "morie_dsp_t_wave",
"title": "T-wave detection by post-QRS argmax search",
"topics": [
"morie_dsp_t_wave"
]
},
{
"page": "morie_dsp_teager_energy",
"title": "Teager-Kaiser energy operator",
"topics": [
"morie_dsp_teager_energy"
]
},
{
"page": "morie_dsp_template_match",
"title": "Normalised template matching",
"topics": [
"morie_dsp_template_match"
]
},
{
"page": "morie_dsp_threshold_detect",
"title": "Threshold-based event detection",
"topics": [
"morie_dsp_threshold_detect"
]
},
{
"page": "morie_dsp_turning_points",
"title": "Turning-points stationarity test",
"topics": [
"morie_dsp_turning_points"
]
},
{
"page": "morie_dsp_turns_count",
"title": "Willison turns count",
"topics": [
"morie_dsp_turns_count"
]
},
{
"page": "morie_dsp_variance_ratio",
"title": "Variance ratio (x vs. y)",
"topics": [
"morie_dsp_variance_ratio"
]
},
{
"page": "morie_dsp_waveform_length",
"title": "Waveform length (total variation)",
"topics": [
"morie_dsp_waveform_length"
]
},
{
"page": "morie_dsp_waveform_length_norm",
"title": "Per-sample (normalised) waveform length",
"topics": [
"morie_dsp_waveform_length_norm"
]
},
{
"page": "morie_dsp_wiener_filter",
"title": "Wiener filter (frequency domain)",
"topics": [
"morie_dsp_wiener_filter"
]
},
{
"page": "morie_dsp_wiener_hopf",
"title": "Solve the Wiener-Hopf normal equations",
"topics": [
"morie_dsp_wiener_hopf"
]
},
{
"page": "morie_dsp_willison_amplitude",
"title": "Willison amplitude",
"topics": [
"morie_dsp_willison_amplitude"
]
},
{
"page": "morie_dsp_window",
"title": "Window function generator",
"topics": [
"morie_dsp_window"
]
},
{
"page": "morie_dsp_zero_crossing",
"title": "Zero-crossing rate",
"topics": [
"morie_dsp_zero_crossing"
]
},
{
"page": "morie_dual_frame_total",
"title": "Dual-frame total with sample overlap (Hartley 1962)",
"topics": [
"morie_dual_frame_total"
]
},
{
"page": "morie_dueling_q",
"title": "Combine value + advantage streams: Q = V + A - mean_a A (hmdldqn)",
"topics": [
"morie_dueling_q"
]
},
{
"page": "morie_dunnett_test",
"title": "Dunnett's many-to-one comparisons with exact multivariate-t p-values",
"topics": [
"morie_dunnett_test"
]
},
{
"page": "morie_e_div",
"title": "E-divisive multiple change-point analysis",
"topics": [
"morie_e_div"
]
},
{
"page": "morie_e_value",
"title": "Compute E-value for unmeasured confounding",
"topics": [
"morie_e_value"
]
},
{
"page": "morie_ecfp4",
"title": "ECFP4 extended-connectivity fingerprint",
"topics": [
"morie_ecfp4"
]
},
{
"page": "morie_ecfp6",
"title": "ECFP6 extended-connectivity fingerprint",
"topics": [
"morie_ecfp6"
]
},
{
"page": "morie_ecod",
"title": "ECOD - empirical-CDF outlier detection",
"topics": [
"morie_ecod"
]
},
{
"page": "morie_ecological_bounds",
"title": "Duncan-Davis bounds: what neighbourhood marginals say about an individual rate",
"topics": [
"morie_ecological_bounds"
]
},
{
"page": "morie_ecological_decompose",
"title": "Between/within decomposition of a correlation across groups",
"topics": [
"morie_ecological_decompose"
]
},
{
"page": "morie_ecsTCR_co2_forcing",
"title": "Radiative forcing from a CO2 concentration ratio",
"topics": [
"morie_ecsTCR_co2_forcing"
]
},
{
"page": "morie_eda_as_vector",
"title": "Strip attributes from a vector for attribute-agnostic processing",
"topics": [
"morie_eda_as_vector"
]
},
{
"page": "morie_eda_compare",
"title": "Standardised comparison of several tables' summaries",
"topics": [
"morie_eda_compare"
]
},
{
"page": "morie_eda_dataset",
"title": "Bundle several EDA tables into an explicit multi-table dataset",
"topics": [
"morie_eda_dataset"
]
},
{
"page": "morie_eda_index",
"title": "The index column name of an EDA table",
"topics": [
"morie_eda_index"
]
},
{
"page": "morie_eda_join",
"title": "Join two EDA tables on an explicitly named column",
"topics": [
"morie_eda_join"
]
},
{
"page": "morie_eda_plot",
"title": "Accessible exploratory plot of one variable against another",
"topics": [
"morie_eda_plot"
]
},
{
"page": "morie_eda_summary",
"title": "Automatic per-variable summary of a table (meta-level extraction)",
"topics": [
"morie_eda_summary"
]
},
{
"page": "morie_eda_table",
"title": "Construct an EDA table with an explicit index column",
"topics": [
"morie_eda_table"
]
},
{
"page": "morie_effective_dose",
"title": "Effective dose with Fieller's interval",
"topics": [
"morie_effective_dose"
]
},
{
"page": "morie_effective_sample_size",
"title": "Kish effective sample size",
"topics": [
"morie_effective_sample_size"
]
},
{
"page": "morie_effective_sample_size_bayes",
"title": "Effective sample size of an MCMC chain",
"topics": [
"morie_effective_sample_size_bayes"
]
},
{
"page": "morie_effective_sample_size_bulk",
"title": "Bulk effective sample size",
"topics": [
"morie_effective_sample_size_bulk"
]
},
{
"page": "morie_effective_sample_size_tail",
"title": "Tail effective sample size",
"topics": [
"morie_effective_sample_size_tail"
]
},
{
"page": "morie_effects_comparisons",
"title": "Contrasts / comparisons (native)",
"topics": [
"morie_effects_comparisons"
]
},
{
"page": "morie_effects_emmeans",
"title": "Estimated marginal means (native)",
"topics": [
"morie_effects_emmeans"
]
},
{
"page": "morie_effects_predictions",
"title": "Adjusted predictions (native)",
"topics": [
"morie_effects_predictions"
]
},
{
"page": "morie_effects_slopes",
"title": "Marginal slopes (partial derivatives, native)",
"topics": [
"morie_effects_slopes"
]
},
{
"page": "morie_effects_tidy",
"title": "Tidy coefficient table for a fitted model",
"topics": [
"morie_effects_tidy"
]
},
{
"page": "morie_efficiency_bound_ate",
"title": "Semiparametric efficiency and minimax bounds for the ATE",
"topics": [
"morie_efficiency_bound_ate"
]
},
{
"page": "morie_efron_tie_correction",
"title": "Cox model with the Efron tie correction",
"topics": [
"morie_efron_tie_correction"
]
},
{
"page": "morie_eg_coint",
"title": "Engle-Granger two-step cointegration test",
"topics": [
"morie_eg_coint"
]
},
{
"page": "morie_egarch_model",
"title": "EGARCH(1,1) asymmetric volatility model",
"topics": [
"morie_egarch_model"
]
},
{
"page": "morie_egregious_loss_forest",
"title": "Loss-balanced (imbalance-regularized) causal forest",
"topics": [
"morie_egregious_loss_forest"
]
},
{
"page": "morie_ehhdec",
"title": "EHH decay around a core SNP",
"topics": [
"morie_ehhdec"
]
},
{
"page": "morie_emd",
"title": "Earth Mover's Distance between two distributions over one domain",
"topics": [
"morie_emd"
]
},
{
"page": "morie_emissions_carbon_intensity",
"title": "Carbon intensity of a country's grid",
"topics": [
"morie_emissions_carbon_intensity"
]
},
{
"page": "morie_emissions_start",
"title": "Track the energy and carbon cost of a computation",
"topics": [
"morie_emissions_start",
"morie_emissions_stop",
"morie_emissions_track"
]
},
{
"page": "morie_emissions_verify",
"title": "Verify an emissions capsule",
"topics": [
"morie_emissions_verify"
]
},
{
"page": "morie_empirical_bayes",
"title": "Parametric empirical Bayes shrinkage (Morris 1983)",
"topics": [
"morie_empirical_bayes"
]
},
{
"page": "morie_empirical_bayes_dp",
"title": "Empirical Bayes for the Dirichlet concentration",
"topics": [
"morie_empirical_bayes_dp"
]
},
{
"page": "morie_empirical_sup_norm",
"title": "Exact uniform norm of the empirical process",
"topics": [
"morie_empirical_sup_norm"
]
},
{
"page": "morie_ensemble_average",
"title": "Ensemble average across realisations",
"topics": [
"morie_ensemble_average"
]
},
{
"page": "morie_ensure_extras",
"title": "Ensure optional packages are installed (interactive helper)",
"topics": [
"morie_ensure_extras"
]
},
{
"page": "morie_entheo_analyze_subject",
"title": "Per-subject DMT vs PCB BOLD pipeline",
"topics": [
"morie_entheo_analyze_subject"
]
},
{
"page": "morie_entheo_available_subjects",
"title": "DMT_Imaging: list motion-survived subject IDs",
"topics": [
"morie_entheo_available_subjects"
]
},
{
"page": "morie_entheo_clone_dmt_imaging",
"title": "Clone the DMT_Imaging dataset (Timmerman et al.) into a local cache",
"topics": [
"morie_entheo_clone_dmt_imaging"
]
},
{
"page": "morie_entheo_dataset_overview",
"title": "DMT_Imaging: dataset overview",
"topics": [
"morie_entheo_dataset_overview"
]
},
{
"page": "morie_entheo_dynamic_functional_connectivity",
"title": "Sliding-window dynamic functional connectivity (dRSFC)",
"topics": [
"morie_entheo_dynamic_functional_connectivity"
]
},
{
"page": "morie_entheo_load_eeg_region",
"title": "DMT_Imaging: load IRASA EEG regressors for one cortical region",
"topics": [
"morie_entheo_load_eeg_region"
]
},
{
"page": "morie_entheo_load_fmri_subject",
"title": "DMT_Imaging: load one subject's BOLD AAL parcellation",
"topics": [
"morie_entheo_load_fmri_subject"
]
},
{
"page": "morie_entheo_lz_complexity",
"title": "Lempel-Ziv (LZ76) complexity of a binarised signal",
"topics": [
"morie_entheo_lz_complexity"
]
},
{
"page": "morie_entheo_spectral_band_power",
"title": "EEG band-power decomposition via Welch PSD",
"topics": [
"morie_entheo_spectral_band_power"
]
},
{
"page": "morie_entropy_balancing",
"title": "Entropy balancing",
"topics": [
"morie_entropy_balancing"
]
},
{
"page": "morie_entropy_integral",
"title": "Bracketing entropy integral and the Donsker conditions",
"topics": [
"morie_entropy_integral"
]
},
{
"page": "morie_envhealth_attributable_fraction",
"title": "Population attributable fraction",
"topics": [
"morie_envhealth_attributable_fraction"
]
},
{
"page": "morie_envhealth_burden",
"title": "End-to-end pollution burden",
"topics": [
"morie_envhealth_burden"
]
},
{
"page": "morie_envhealth_burden_by_fsa",
"title": "Per-area pollution burden",
"topics": [
"morie_envhealth_burden_by_fsa"
]
},
{
"page": "morie_envhealth_crf_pm25",
"title": "Concentration-response functions for PM2.5 and NO2",
"topics": [
"morie_envhealth_cheatsheet",
"morie_envhealth_crf_no2",
"morie_envhealth_crf_pm25"
]
},
{
"page": "morie_envhealth_equity",
"title": "Concentration index of exposure by income",
"topics": [
"morie_envhealth_equity"
]
},
{
"page": "morie_envhealth_mortality_displaced",
"title": "Deaths avoided under a counterfactual exposure reduction",
"topics": [
"morie_envhealth_mortality_displaced"
]
},
{
"page": "morie_envhealth_sensitivity",
"title": "Exposure-response double machine learning with bootstrap sensitivity",
"topics": [
"morie_envhealth_sensitivity"
]
},
{
"page": "morie_epsilon_dp",
"title": "Empirical epsilon of a mechanism",
"topics": [
"morie_epsilon_dp"
]
},
{
"page": "morie_equating_haebara",
"title": "Haebara IRT linking",
"topics": [
"morie_equating_haebara"
]
},
{
"page": "morie_equating_stocking_lord",
"title": "Stocking-Lord IRT linking",
"topics": [
"morie_equating_stocking_lord"
]
},
{
"page": "morie_erdos_ko_rado",
"title": "Erdos-Ko-Rado: the largest intersecting family of k-subsets",
"topics": [
"morie_erdos_ko_rado"
]
},
{
"page": "morie_erdos_szekeres_check",
"title": "The Erdos-Szekeres monotone subsequence theorem",
"topics": [
"morie_erdos_szekeres_check"
]
},
{
"page": "morie_esl_adaboost",
"title": "AdaBoost.M1 with decision stumps",
"topics": [
"morie_esl_adaboost"
]
},
{
"page": "morie_esl_adaboost_predict",
"title": "Classify with an AdaBoost committee",
"topics": [
"morie_esl_adaboost_predict"
]
},
{
"page": "morie_esl_aic_score",
"title": "Akaike information criterion",
"topics": [
"morie_esl_aic_score"
]
},
{
"page": "morie_esl_archetypes",
"title": "Archetypal analysis",
"topics": [
"morie_esl_archetypes"
]
},
{
"page": "morie_esl_backprop",
"title": "Backpropagation sweep",
"topics": [
"morie_esl_backprop"
]
},
{
"page": "morie_esl_basis_fit_se",
"title": "Basis-expansion fit with pointwise standard errors",
"topics": [
"morie_esl_basis_fit_se"
]
},
{
"page": "morie_esl_bayes_basis_posterior",
"title": "Posterior of basis coefficients under a Gaussian prior",
"topics": [
"morie_esl_bayes_basis_posterior"
]
},
{
"page": "morie_esl_bic_score",
"title": "Bayesian information criterion",
"topics": [
"morie_esl_bic_score"
]
},
{
"page": "morie_esl_boltzmann",
"title": "Restricted Boltzmann machine (contrastive divergence)",
"topics": [
"morie_esl_boltzmann"
]
},
{
"page": "morie_esl_bootstrap_err",
"title": "Bootstrap estimates of prediction error",
"topics": [
"morie_esl_bootstrap_err"
]
},
{
"page": "morie_esl_cross_entropy",
"title": "Cross-entropy (multinomial deviance) loss",
"topics": [
"morie_esl_cross_entropy"
]
},
{
"page": "morie_esl_curds_whey",
"title": "Curds and whey multiple-response shrinkage",
"topics": [
"morie_esl_curds_whey"
]
},
{
"page": "morie_esl_cv_score",
"title": "K-fold cross-validation",
"topics": [
"morie_esl_cv_score"
]
},
{
"page": "morie_esl_decision_tree",
"title": "CART regression tree",
"topics": [
"morie_esl_decision_tree"
]
},
{
"page": "morie_esl_dirichlet_posterior",
"title": "Dirichlet posterior for category probabilities",
"topics": [
"morie_esl_dirichlet_posterior"
]
},
{
"page": "morie_esl_dirichlet_proc",
"title": "Dirichlet process (stick-breaking)",
"topics": [
"morie_esl_dirichlet_proc"
]
},
{
"page": "morie_esl_dropout",
"title": "Inverted dropout",
"topics": [
"morie_esl_dropout"
]
},
{
"page": "morie_esl_effective_dof",
"title": "Effective degrees of freedom of a linear smoother",
"topics": [
"morie_esl_effective_dof"
]
},
{
"page": "morie_esl_em_gmm",
"title": "EM for a Gaussian mixture",
"topics": [
"morie_esl_em_gmm"
]
},
{
"page": "morie_esl_f_test",
"title": "F test of nested linear models",
"topics": [
"morie_esl_f_test"
]
},
{
"page": "morie_esl_fda",
"title": "Flexible discriminant analysis by optimal scoring",
"topics": [
"morie_esl_fda"
]
},
{
"page": "morie_esl_forward_stagewise",
"title": "Incremental forward stagewise regression",
"topics": [
"morie_esl_forward_stagewise"
]
},
{
"page": "morie_esl_fwer",
"title": "Family-wise error rate of M tests",
"topics": [
"morie_esl_fwer"
]
},
{
"page": "morie_esl_gam",
"title": "Generalized additive model by backfitting and local scoring",
"topics": [
"morie_esl_gam"
]
},
{
"page": "morie_esl_gaussian_mixture",
"title": "Gaussian mixture density",
"topics": [
"morie_esl_gaussian_mixture"
]
},
{
"page": "morie_esl_gbm",
"title": "Gradient boosting with squared-error loss",
"topics": [
"morie_esl_gbm"
]
},
{
"page": "morie_esl_gbm_multiclass",
"title": "K-class gradient tree boosting",
"topics": [
"morie_esl_gbm_multiclass"
]
},
{
"page": "morie_esl_gbm_predict",
"title": "Predict with a gradient-boosting model",
"topics": [
"morie_esl_gbm_predict"
]
},
{
"page": "morie_esl_gcv",
"title": "Generalized cross-validation",
"topics": [
"morie_esl_gcv"
]
},
{
"page": "morie_esl_ggm_fit",
"title": "Gaussian graphical model with known structure",
"topics": [
"morie_esl_ggm_fit"
]
},
{
"page": "morie_esl_graphical_lasso",
"title": "Graphical lasso",
"topics": [
"morie_esl_graphical_lasso"
]
},
{
"page": "morie_esl_hme",
"title": "Hierarchical mixture of experts fitted by EM",
"topics": [
"morie_esl_hme"
]
},
{
"page": "morie_esl_ica",
"title": "Independent component analysis (FastICA)",
"topics": [
"morie_esl_ica"
]
},
{
"page": "morie_esl_indicator_regression",
"title": "Classification by indicator-matrix regression",
"topics": [
"morie_esl_indicator_regression"
]
},
{
"page": "morie_esl_ising_fit",
"title": "Ising model: exact maximum likelihood",
"topics": [
"morie_esl_ising_fit"
]
},
{
"page": "morie_esl_isomap",
"title": "Isomap",
"topics": [
"morie_esl_isomap"
]
},
{
"page": "morie_esl_iwls",
"title": "Iteratively reweighted least squares for a GLM",
"topics": [
"morie_esl_iwls"
]
},
{
"page": "morie_esl_kernel_density",
"title": "Parzen kernel density estimate",
"topics": [
"morie_esl_kernel_density"
]
},
{
"page": "morie_esl_l1_logistic",
"title": "L1-penalised logistic regression",
"topics": [
"morie_esl_l1_logistic"
]
},
{
"page": "morie_esl_l1_margin",
"title": "L1-normalised margin of an additive classifier",
"topics": [
"morie_esl_l1_margin"
]
},
{
"page": "morie_esl_lasso",
"title": "Lasso by cyclic coordinate descent",
"topics": [
"morie_esl_lasso"
]
},
{
"page": "morie_esl_lda_disc",
"title": "Linear discriminant analysis discriminants",
"topics": [
"morie_esl_lda_disc"
]
},
{
"page": "morie_esl_least_angle_reg",
"title": "Least angle regression",
"topics": [
"morie_esl_least_angle_reg"
]
},
{
"page": "morie_esl_linear_prediction_error",
"title": "Prediction error of a least-squares fit",
"topics": [
"morie_esl_linear_prediction_error"
]
},
{
"page": "morie_esl_lle",
"title": "Locally linear embedding",
"topics": [
"morie_esl_lle"
]
},
{
"page": "morie_esl_local_linear",
"title": "Local linear regression",
"topics": [
"morie_esl_local_linear"
]
},
{
"page": "morie_esl_local_logistic",
"title": "Local multinomial logistic regression",
"topics": [
"morie_esl_local_logistic"
]
},
{
"page": "morie_esl_logistic_reg",
"title": "Logistic regression",
"topics": [
"morie_esl_logistic_reg"
]
},
{
"page": "morie_esl_mallows_cp",
"title": "Mallows' Cp in ESL's scaling",
"topics": [
"morie_esl_mallows_cp"
]
},
{
"page": "morie_esl_markov_rf",
"title": "Pairwise Markov random field",
"topics": [
"morie_esl_markov_rf"
]
},
{
"page": "morie_esl_mars",
"title": "Multivariate adaptive regression splines",
"topics": [
"morie_esl_mars"
]
},
{
"page": "morie_esl_mda",
"title": "Mixture discriminant analysis by EM",
"topics": [
"morie_esl_mda"
]
},
{
"page": "morie_esl_mdl",
"title": "Minimum description length",
"topics": [
"morie_esl_mdl"
]
},
{
"page": "morie_esl_median_nn_radius",
"title": "Median nearest-neighbour radius in the unit ball",
"topics": [
"morie_esl_median_nn_radius"
]
},
{
"page": "morie_esl_multi_output_ls",
"title": "Multiple-output least squares",
"topics": [
"morie_esl_multi_output_ls"
]
},
{
"page": "morie_esl_multinomial_l1",
"title": "L1-penalised multinomial logistic regression",
"topics": [
"morie_esl_multinomial_l1"
]
},
{
"page": "morie_esl_multinomial_logit",
"title": "Multinomial logistic regression",
"topics": [
"morie_esl_multinomial_logit"
]
},
{
"page": "morie_esl_mvn_em_missing",
"title": "EM for a multivariate normal with missing values",
"topics": [
"morie_esl_mvn_em_missing"
]
},
{
"page": "morie_esl_nadaraya_watson",
"title": "Nadaraya-Watson kernel smoother",
"topics": [
"morie_esl_nadaraya_watson"
]
},
{
"page": "morie_esl_naive_bayes",
"title": "Gaussian naive Bayes",
"topics": [
"morie_esl_naive_bayes"
]
},
{
"page": "morie_esl_natural_spline",
"title": "Natural cubic spline basis",
"topics": [
"morie_esl_natural_spline"
]
},
{
"page": "morie_esl_neural_net",
"title": "Single-hidden-layer neural network",
"topics": [
"morie_esl_neural_net"
]
},
{
"page": "morie_esl_nmf",
"title": "Non-negative matrix factorisation",
"topics": [
"morie_esl_nmf"
]
},
{
"page": "morie_esl_ols_normal_equations",
"title": "Least squares by QR (ESL eq 3.6)",
"topics": [
"morie_esl_ols_normal_equations"
]
},
{
"page": "morie_esl_one_se_rule",
"title": "One-standard-error rule",
"topics": [
"morie_esl_one_se_rule"
]
},
{
"page": "morie_esl_oob_632",
"title": "The .632 and .632+ prediction-error estimators",
"topics": [
"morie_esl_oob_632"
]
},
{
"page": "morie_esl_parametric_bootstrap",
"title": "Parametric bootstrap bands for a basis-expansion fit",
"topics": [
"morie_esl_parametric_bootstrap"
]
},
{
"page": "morie_esl_partial_dependence",
"title": "Partial dependence",
"topics": [
"morie_esl_partial_dependence"
]
},
{
"page": "morie_esl_pca_svd",
"title": "Principal components by the singular value decomposition",
"topics": [
"morie_esl_pca_svd"
]
},
{
"page": "morie_esl_pca_transform",
"title": "Project rows onto fitted principal components",
"topics": [
"morie_esl_pca_transform"
]
},
{
"page": "morie_esl_pcr",
"title": "Principal components regression",
"topics": [
"morie_esl_pcr"
]
},
{
"page": "morie_esl_penalized_logistic",
"title": "Penalised logistic regression on a basis",
"topics": [
"morie_esl_penalized_logistic"
]
},
{
"page": "morie_esl_precision_regression",
"title": "Regressions and partial correlations implied by a precision matrix",
"topics": [
"morie_esl_precision_regression"
]
},
{
"page": "morie_esl_principal_curve",
"title": "Principal curves",
"topics": [
"morie_esl_principal_curve"
]
},
{
"page": "morie_esl_prodenica",
"title": "Product density ICA (ProDenICA)",
"topics": [
"morie_esl_prodenica"
]
},
{
"page": "morie_esl_projection_pursuit",
"title": "Projection pursuit regression",
"topics": [
"morie_esl_projection_pursuit"
]
},
{
"page": "morie_esl_prototype_lvq",
"title": "Learning vector quantization (LVQ1)",
"topics": [
"morie_esl_prototype_lvq"
]
},
{
"page": "morie_esl_qda",
"title": "Quadratic discriminant analysis discriminants",
"topics": [
"morie_esl_qda"
]
},
{
"page": "morie_esl_random_forest",
"title": "Random forest for regression",
"topics": [
"morie_esl_random_forest"
]
},
{
"page": "morie_esl_rbf_network",
"title": "Radial basis function network with fixed prototypes",
"topics": [
"morie_esl_rbf_network"
]
},
{
"page": "morie_esl_residual_sum_squares",
"title": "Residual sum of squares at given coefficients",
"topics": [
"morie_esl_residual_sum_squares"
]
},
{
"page": "morie_esl_residual_variance",
"title": "Unbiased residual variance for a linear model",
"topics": [
"morie_esl_residual_variance"
]
},
{
"page": "morie_esl_ridge",
"title": "Ridge regression with an unpenalised intercept",
"topics": [
"morie_esl_ridge"
]
},
{
"page": "morie_esl_score_match",
"title": "Score-matching objective",
"topics": [
"morie_esl_score_match"
]
},
{
"page": "morie_esl_se_beta",
"title": "Standard errors of given coefficients",
"topics": [
"morie_esl_se_beta"
]
},
{
"page": "morie_esl_self_organize",
"title": "Self-organizing map",
"topics": [
"morie_esl_self_organize"
]
},
{
"page": "morie_esl_sis_screening",
"title": "Sure independence screening",
"topics": [
"morie_esl_sis_screening"
]
},
{
"page": "morie_esl_smoothing_spline",
"title": "Smoothing spline via the Reinsch penalty",
"topics": [
"morie_esl_smoothing_spline"
]
},
{
"page": "morie_esl_sparse_pca",
"title": "Sparse principal components",
"topics": [
"morie_esl_sparse_pca"
]
},
{
"page": "morie_esl_supervised_pc",
"title": "Supervised principal components",
"topics": [
"morie_esl_supervised_pc"
]
},
{
"page": "morie_esl_svc",
"title": "Linear support vector classifier",
"topics": [
"morie_esl_svc"
]
},
{
"page": "morie_esl_svm_kernel",
"title": "Kernel support vector machine",
"topics": [
"morie_esl_svm_kernel"
]
},
{
"page": "morie_esl_svr",
"title": "Support-vector regression with the epsilon-insensitive loss",
"topics": [
"morie_esl_svr"
]
},
{
"page": "morie_esl_test_r2",
"title": "Test-set R-squared relative to the constant model",
"topics": [
"morie_esl_test_r2"
]
},
{
"page": "morie_esl_thin_plate_spline",
"title": "Two-dimensional thin-plate smoothing spline",
"topics": [
"morie_esl_thin_plate_spline"
]
},
{
"page": "morie_esl_tree_predict",
"title": "Predict with a regression tree",
"topics": [
"morie_esl_tree_predict"
]
},
{
"page": "morie_esl_var_beta_hat",
"title": "Covariance of the least-squares coefficients",
"topics": [
"morie_esl_var_beta_hat"
]
},
{
"page": "morie_esl_varying_coef",
"title": "Varying-coefficient model",
"topics": [
"morie_esl_varying_coef"
]
},
{
"page": "morie_esl_vc_bound",
"title": "Vapnik-Chervonenkis bounds on the test error",
"topics": [
"morie_esl_vc_bound"
]
},
{
"page": "morie_esl_wavelet_smooth",
"title": "Haar wavelet smoothing",
"topics": [
"morie_esl_wavelet_smooth"
]
},
{
"page": "morie_esl_weight_decay",
"title": "Weight-decay penalty and its gradient",
"topics": [
"morie_esl_weight_decay"
]
},
{
"page": "morie_esl_z_score",
"title": "Z-scores of regression coefficients",
"topics": [
"morie_esl_z_score"
]
},
{
"page": "morie_ess_autocorrelation",
"title": "Effective sample size of an autocorrelated sequence",
"topics": [
"morie_ess_autocorrelation"
]
},
{
"page": "morie_estimate_atc",
"title": "Estimate the Average Treatment Effect on the Controls (ATC)",
"topics": [
"morie_estimate_atc"
]
},
{
"page": "morie_estimate_ate",
"title": "Estimate the Average Treatment Effect (ATE) via Hajek IPW",
"topics": [
"morie_estimate_ate"
]
},
{
"page": "morie_estimate_att",
"title": "Estimate the Average Treatment Effect on the Treated (ATT)",
"topics": [
"morie_estimate_att"
]
},
{
"page": "morie_estimate_cate",
"title": "Estimate per-unit Conditional Average Treatment Effects (CATE)",
"topics": [
"morie_estimate_cate"
]
},
{
"page": "morie_estimate_double_ml",
"title": "Estimate ATE via Double Machine Learning (Partially Linear Regression)",
"topics": [
"morie_estimate_double_ml"
]
},
{
"page": "morie_estimate_dr_forest",
"title": "Doubly-robust ATE via causal forest (grf)",
"topics": [
"morie_estimate_dr_forest"
]
},
{
"page": "morie_estimate_g_computation",
"title": "G-computation (outcome regression) ATE estimator",
"topics": [
"morie_estimate_g_computation"
]
},
{
"page": "morie_estimate_gate",
"title": "Estimate Group Average Treatment Effects (GATE)",
"topics": [
"morie_estimate_gate"
]
},
{
"page": "morie_estimate_irm",
"title": "Estimate the ATE via the Interactive Regression Model (IRM)",
"topics": [
"morie_estimate_irm"
]
},
{
"page": "morie_estimate_late",
"title": "Estimate the Local Average Treatment Effect (LATE) via 2SLS / Wald",
"topics": [
"morie_estimate_late"
]
},
{
"page": "morie_eta_squared",
"title": "Eta-squared from F-statistic",
"topics": [
"morie_eta_squared"
]
},
{
"page": "morie_euler_characteristic",
"title": "The Euler characteristic",
"topics": [
"morie_euler_characteristic"
]
},
{
"page": "morie_evalue",
"title": "E-value for a bias-scale effect estimate (native)",
"topics": [
"morie_evalue"
]
},
{
"page": "morie_evidmix",
"title": "Total evidence over a set of models (eq. 3.19, p. 53)",
"topics": [
"morie_evidmix"
]
},
{
"page": "morie_evratio",
"title": "Posterior model ratio as a product of penalties (eq. 28.13-28.14, p. 351)",
"topics": [
"morie_evratio"
]
},
{
"page": "morie_evt_dedh",
"title": "Dekkers-Einmahl-de Haan moment estimator",
"topics": [
"morie_evt_dedh"
]
},
{
"page": "morie_evt_extremal_intervals",
"title": "Ferro-Segers intervals estimator of the extremal index",
"topics": [
"morie_evt_extremal_intervals"
]
},
{
"page": "morie_evt_extremal_runs",
"title": "Runs estimator of the extremal index",
"topics": [
"morie_evt_extremal_runs"
]
},
{
"page": "morie_evt_extremal_sliding",
"title": "Sliding-blocks estimator of the extremal index",
"topics": [
"morie_evt_extremal_sliding"
]
},
{
"page": "morie_evt_gev_cdf",
"title": "GEV distribution function (Coles 2001 eq. 3.2)",
"topics": [
"morie_evt_gev_cdf"
]
},
{
"page": "morie_evt_gev_lmoments",
"title": "L-moment estimator of the GEV parameters",
"topics": [
"morie_evt_gev_lmoments"
]
},
{
"page": "morie_evt_gev_loglik",
"title": "GEV log-likelihood (Coles 2001 eq. 3.7-3.9)",
"topics": [
"morie_evt_gev_loglik"
]
},
{
"page": "morie_evt_gev_logpdf",
"title": "GEV log-density (Coles 2001 eq. 3.7 integrand; Gumbel eq. 3.9)",
"topics": [
"morie_evt_gev_logpdf"
]
},
{
"page": "morie_evt_gev_mle",
"title": "GEV maximum-likelihood fit (Coles 2001 sec. 3.3.2)",
"topics": [
"morie_evt_gev_mle"
]
},
{
"page": "morie_evt_gev_pwm",
"title": "PWM estimator for the GEV - alias",
"topics": [
"morie_evt_gev_pwm"
]
},
{
"page": "morie_evt_gpd_pwm",
"title": "PWM estimator of the generalised Pareto distribution",
"topics": [
"morie_evt_gpd_pwm"
]
},
{
"page": "morie_evt_hill",
"title": "Hill estimator of the tail index",
"topics": [
"morie_evt_hill"
]
},
{
"page": "morie_evt_hill_alias",
"title": "Hill tail-index estimator - alias entry point",
"topics": [
"morie_evt_hill_alias"
]
},
{
"page": "morie_evt_madogram",
"title": "Madogram estimator of the Pickands dependence function",
"topics": [
"morie_evt_madogram"
]
},
{
"page": "morie_evt_pickands",
"title": "Pickands estimator of the extreme-value index",
"topics": [
"morie_evt_pickands"
]
},
{
"page": "morie_ewma_volatility",
"title": "EWMA volatility (RiskMetrics 1996)",
"topics": [
"morie_ewma_volatility"
]
},
{
"page": "morie_exact_conditional",
"title": "Exact conditional logistic inference",
"topics": [
"morie_exact_conditional"
]
},
{
"page": "morie_expandcv",
"title": "Expanding-window cross-validation (ch. 5 p. 130)",
"topics": [
"morie_expandcv"
]
},
{
"page": "morie_experiment_anova",
"title": "One-way and block-randomized ANOVA (crim shelf)",
"topics": [
"morie_experiment_anova"
]
},
{
"page": "morie_expmc",
"title": "Exponential mechanism (module-name entry point)",
"topics": [
"morie_expmc"
]
},
{
"page": "morie_exponential_dist",
"title": "Exponential waiting-time surface",
"topics": [
"morie_exponential_dist"
]
},
{
"page": "morie_exposure_positivity",
"title": "Exposure positivity under Bernoulli assignment",
"topics": [
"morie_exposure_positivity"
]
},
{
"page": "morie_extremal_index_clusters",
"title": "The extremal index of a block-cluster process is the reciprocal cluster size",
"topics": [
"morie_extremal_index_clusters"
]
},
{
"page": "morie_extreme_value_copula",
"title": "Bivariate extreme-value copula from a Pickands dependence function",
"topics": [
"morie_extreme_value_copula"
]
},
{
"page": "morie_f_change",
"title": "Nested-model F change tests",
"topics": [
"morie_f_change"
]
},
{
"page": "morie_face_smooth",
"title": "FACE sandwich-smoothed covariance with FPCA",
"topics": [
"morie_face_smooth"
]
},
{
"page": "morie_fairness_apply_profile",
"title": "Rename a city data.frame onto the canonical audit schema",
"topics": [
"morie_fairness_apply_profile"
]
},
{
"page": "morie_fairness_average_odds_difference",
"title": "Average Odds Difference (mean of TPR and FPR gaps)",
"topics": [
"morie_fairness_average_odds_difference"
]
},
{
"page": "morie_fairness_base_rate_bounds",
"title": "Sharp bounds on a true base rate from a noisy recorded one",
"topics": [
"morie_fairness_base_rate_bounds"
]
},
{
"page": "morie_fairness_bias_amplification",
"title": "Bias Amplification Score (parity gap x Gini of group rates)",
"topics": [
"morie_fairness_bias_amplification"
]
},
{
"page": "MORIE_FAIRNESS_CANONICAL_FIELDS",
"title": "The five canonical per-area fields the audit consumes",
"topics": [
"MORIE_FAIRNESS_CANONICAL_FIELDS"
]
},
{
"page": "morie_fairness_city_profile",
"title": "Construct a city profile (column map onto the canonical audit schema)",
"topics": [
"morie_fairness_city_profile"
]
},
{
"page": "morie_fairness_column_map",
"title": "Column map for a city profile",
"topics": [
"morie_fairness_column_map"
]
},
{
"page": "morie_fairness_compare_groups",
"title": "Can two groups' true base rates be ordered under label noise?",
"topics": [
"morie_fairness_compare_groups"
]
},
{
"page": "morie_fairness_ctgan_debiaser",
"title": "Rebalance a biased tabular dataset by group",
"topics": [
"morie_fairness_ctgan_debiaser"
]
},
{
"page": "morie_fairness_demographic_parity",
"title": "Demographic Parity Gap (difference in favourable-outcome rates)",
"topics": [
"morie_fairness_demographic_parity"
]
},
{
"page": "morie_fairness_disparate_impact",
"title": "Disparate Impact Ratio (EEOC four-fifths / 80% rule)",
"topics": [
"morie_fairness_disparate_impact"
]
},
{
"page": "morie_fairness_equalized_odds",
"title": "Equalized Odds (TPR / FPR gaps across groups)",
"topics": [
"morie_fairness_equalized_odds"
]
},
{
"page": "morie_fairness_gan",
"title": "Counterfactual fairness via GAN-style generative models",
"topics": [
"morie_fairness_gan"
]
},
{
"page": "morie_fairness_get_city",
"title": "Look up a registered city profile by case-insensitive name",
"topics": [
"morie_fairness_get_city"
]
},
{
"page": "morie_fairness_gini",
"title": "Gini coefficient (concentration of a non-negative distribution)",
"topics": [
"morie_fairness_gini"
]
},
{
"page": "morie_fairness_implied_fpr",
"title": "The false positive rate implied by base rate, PPV and FNR",
"topics": [
"morie_fairness_implied_fpr"
]
},
{
"page": "morie_fairness_list_cities",
"title": "List registered city profile names",
"topics": [
"morie_fairness_list_cities"
]
},
{
"page": "morie_fairness_noisy_or_detection",
"title": "Noisy-OR patrol-detection probabilities",
"topics": [
"morie_fairness_noisy_or_detection"
]
},
{
"page": "morie_fairness_predpol",
"title": "Predictive-policing disparity audit (port of morie.fairness.predpol)",
"topics": [
"morie_fairness_predpol"
]
},
{
"page": "morie_fairness_predpol_aggregate_areas",
"title": "Aggregate per-record predictive-policing data to per-area",
"topics": [
"morie_fairness_predpol_aggregate_areas"
]
},
{
"page": "morie_fairness_predpol_calibration_audit",
"title": "Predicted-vs-realised rank audit by demographic group",
"topics": [
"morie_fairness_predpol_calibration_audit"
]
},
{
"page": "morie_fairness_predpol_score_disparity",
"title": "Descriptive score-by-group disparity",
"topics": [
"morie_fairness_predpol_score_disparity"
]
},
{
"page": "morie_fairness_predpol_temporal_audit",
"title": "Audit how disparity metrics move over time and across cities",
"topics": [
"morie_fairness_predpol_temporal_audit"
]
},
{
"page": "morie_fairness_rates",
"title": "Group-wise rates from a confusion table",
"topics": [
"morie_fairness_rates"
]
},
{
"page": "morie_fairness_register_city",
"title": "Register a city profile in the process-local registry",
"topics": [
"morie_fairness_register_city"
]
},
{
"page": "morie_fairness_simulate_biased_crime_data",
"title": "Synthetic predictive-policing dataset with a known disparity",
"topics": [
"morie_fairness_simulate_biased_crime_data"
]
},
{
"page": "morie_fairness_simulation",
"title": "Simulation primitives for the predictive-policing audit subsystem",
"topics": [
"morie_fairness_simulation"
]
},
{
"page": "morie_fairness_spatial_gan",
"title": "Learn a 2-D crime/patrol location distribution",
"topics": [
"morie_fairness_spatial_gan"
]
},
{
"page": "morie_fairness_true_rate",
"title": "Recover a true base rate when the noise rates are known",
"topics": [
"morie_fairness_true_rate"
]
},
{
"page": "morie_fairness_xai",
"title": "Model-agnostic explainability (XAI) for bias discovery",
"topics": [
"morie_fairness_xai"
]
},
{
"page": "morie_fairness_xai_ale",
"title": "First-order Accumulated Local Effects (Apley & Zhu)",
"topics": [
"morie_fairness_xai_ale"
]
},
{
"page": "morie_fairness_xai_ceteris_paribus",
"title": "Ceteris-paribus profile for one instance",
"topics": [
"morie_fairness_xai_ceteris_paribus"
]
},
{
"page": "morie_fairness_xai_partial_dependence",
"title": "Partial dependence on one feature (Friedman)",
"topics": [
"morie_fairness_xai_partial_dependence"
]
},
{
"page": "morie_fairness_xai_permutation_importance",
"title": "Permutation feature importance (model-agnostic)",
"topics": [
"morie_fairness_xai_permutation_importance"
]
},
{
"page": "morie_fairness_xai_shap_values",
"title": "Shapley feature attributions for one instance (sampling estimator)",
"topics": [
"morie_fairness_xai_shap_values"
]
},
{
"page": "morie_faithchk",
"title": "Faithfulness for one triple (ch. 5 p. 77)",
"topics": [
"morie_faithchk"
]
},
{
"page": "morie_fast_available",
"title": "Is the R-side JIT acceleration active?",
"topics": [
"morie_fast_available"
]
},
{
"page": "morie_fauzi_b1_coefficient",
"title": "Bias coefficient b_1 (4.14)",
"topics": [
"morie_fauzi_b1_coefficient"
]
},
{
"page": "morie_fauzi_b2_coefficient",
"title": "Bias coefficient b_2 (4.15)",
"topics": [
"morie_fauzi_b2_coefficient"
]
},
{
"page": "morie_fauzi_b3_coefficient",
"title": "Bias coefficient b_3 (4.21)",
"topics": [
"morie_fauzi_b3_coefficient"
]
},
{
"page": "morie_fauzi_boundary_free_kde",
"title": "Boundary-free kernel density estimate by bijection",
"topics": [
"morie_fauzi_boundary_free_kde"
]
},
{
"page": "morie_fauzi_conditions_c1_c6",
"title": "Conditions C1-C6 of Chapter 4",
"topics": [
"morie_fauzi_conditions_c1_c6"
]
},
{
"page": "morie_fauzi_cumulative_survival_1",
"title": "First cumulative survival estimator (4.8)",
"topics": [
"morie_fauzi_cumulative_survival_1"
]
},
{
"page": "morie_fauzi_cumulative_survival_2",
"title": "Second cumulative survival estimator (4.17)",
"topics": [
"morie_fauzi_cumulative_survival_2"
]
},
{
"page": "morie_fauzi_gamma_kde",
"title": "Chen gamma-kernel density estimate",
"topics": [
"morie_fauzi_gamma_kde"
]
},
{
"page": "morie_fauzi_kde",
"title": "Rosenblatt-Parzen kernel density estimate",
"topics": [
"morie_fauzi_kde"
]
},
{
"page": "morie_fauzi_kdfe",
"title": "Nadaraya kernel distribution-function estimator",
"topics": [
"morie_fauzi_kdfe"
]
},
{
"page": "morie_fauzi_kernel_quantile",
"title": "Kernel quantile estimator (3.1)",
"topics": [
"morie_fauzi_kernel_quantile"
]
},
{
"page": "morie_fauzi_lemma_3_1",
"title": "Lemma 3.1: asymptotic representation of the kernel quantile estimator",
"topics": [
"morie_fauzi_lemma_3_1"
]
},
{
"page": "morie_fauzi_mise",
"title": "MISE of a second-order kernel density estimate",
"topics": [
"morie_fauzi_mise"
]
},
{
"page": "morie_fauzi_mrl_boundary_free_2",
"title": "Boundary-free mean-residual-life estimator (4.24)",
"topics": [
"morie_fauzi_mrl_boundary_free_2"
]
},
{
"page": "morie_fauzi_mrl_naive",
"title": "Naive kernel mean-residual-life estimator (4.2)",
"topics": [
"morie_fauzi_mrl_naive"
]
},
{
"page": "morie_fauzi_muller_kernel",
"title": "Fourth-order Mueller kernel",
"topics": [
"morie_fauzi_muller_kernel"
]
},
{
"page": "morie_fauzi_order_m_kernel",
"title": "Order-m kernel and its moments",
"topics": [
"morie_fauzi_order_m_kernel"
]
},
{
"page": "morie_fauzi_quantile_amse",
"title": "AMSE of the sample quantile (3.3)",
"topics": [
"morie_fauzi_quantile_amse"
]
},
{
"page": "morie_fauzi_theorem_4_3",
"title": "Theorem 4.3: biases and variances of the boundary-free MRL estimators",
"topics": [
"morie_fauzi_theorem_4_3"
]
},
{
"page": "morie_fauzi_theorem_4_4",
"title": "Theorem 4.4: asymptotic normality of the boundary-free MRL estimators",
"topics": [
"morie_fauzi_theorem_4_4"
]
},
{
"page": "morie_fauzi_theorem_4_5",
"title": "Theorem 4.5: strong uniform consistency on a bounded interval",
"topics": [
"morie_fauzi_theorem_4_5"
]
},
{
"page": "morie_fauzi_theorem_4_6",
"title": "Theorem 4.6: the mean-residual-life identity at the start of support",
"topics": [
"morie_fauzi_theorem_4_6"
]
},
{
"page": "MORIE_FCFP_FEATURE_CLASSES",
"title": "Pharmacophoric feature classes used by FCFP, in RDKit bit order",
"topics": [
"MORIE_FCFP_FEATURE_CLASSES"
]
},
{
"page": "morie_fcfp4",
"title": "FCFP4 functional-class fingerprint",
"topics": [
"morie_fcfp4"
]
},
{
"page": "morie_fdr_qvalues",
"title": "FDR / q-values / tail-area p-values via 'fdrtool'",
"topics": [
"morie_fdr_qvalues"
]
},
{
"page": "morie_feedback_loop_bound",
"title": "Proved upper bound on how far the naive loop can move in N steps",
"topics": [
"morie_feedback_loop_bound"
]
},
{
"page": "morie_feedback_loop_envelope",
"title": "Drift envelope of the discovery urn at any rates",
"topics": [
"morie_feedback_loop_envelope"
]
},
{
"page": "morie_feedback_loop_limit",
"title": "Proved limit of the two-region feedback loop",
"topics": [
"morie_feedback_loop_limit"
]
},
{
"page": "morie_feedback_loop_meanfield",
"title": "Mean-field feedback-loop recursion for two regions",
"topics": [
"morie_feedback_loop_meanfield"
]
},
{
"page": "morie_feedback_loop_sim",
"title": "Stochastic urn simulation of the two-region feedback loop",
"topics": [
"morie_feedback_loop_sim"
]
},
{
"page": "morie_feedback_loop_urn_law",
"title": "Exact law of the stochastic two-region urn with equal rates",
"topics": [
"morie_feedback_loop_urn_law"
]
},
{
"page": "morie_felsen",
"title": "Felsenstein (1981) pruning likelihood under the F81 model",
"topics": [
"morie_felsen"
]
},
{
"page": "morie_fetch",
"title": "Fetch a dataset from any URL, with automatic format detection",
"topics": [
"morie_fetch"
]
},
{
"page": "morie_fetch_arcgis",
"title": "Query an ArcGIS FeatureServer / MapServer layer",
"topics": [
"morie_fetch_arcgis"
]
},
{
"page": "morie_fetch_ckan",
"title": "Fetch data from the CKAN API and cache it",
"topics": [
"morie_fetch_ckan"
]
},
{
"page": "morie_fetch_csv",
"title": "Read CSV/TSV natively",
"topics": [
"morie_fetch_csv"
]
},
{
"page": "morie_fetch_html",
"title": "Parse HTML natively (tolerant tag scanner, pure R)",
"topics": [
"morie_fetch_html"
]
},
{
"page": "morie_fetch_json",
"title": "Parse JSON text natively (pure R)",
"topics": [
"morie_fetch_json"
]
},
{
"page": "morie_fetch_naps",
"title": "Fetch one NAPS hourly air-quality file",
"topics": [
"morie_fetch_naps"
]
},
{
"page": "morie_fetch_parquet",
"title": "Minimal native Parquet reader (pure R)",
"topics": [
"morie_fetch_parquet"
]
},
{
"page": "morie_fetch_siu",
"title": "Fetch the Ontario SIU corpus into a 64-column SIU.csv",
"topics": [
"morie_fetch_siu"
]
},
{
"page": "morie_fetch_tps",
"title": "Fetch a TPS category from the Toronto Police ArcGIS REST endpoint",
"topics": [
"morie_fetch_tps"
]
},
{
"page": "morie_fetch_unified",
"title": "Unified fetch dispatcher",
"topics": [
"morie_fetch_unified"
]
},
{
"page": "morie_fetch_xml",
"title": "Parse XML natively into a list tree (pure R)",
"topics": [
"morie_fetch_xml"
]
},
{
"page": "morie_find_project_root",
"title": "Find a project root directory",
"topics": [
"morie_find_project_root"
]
},
{
"page": "morie_fine_gray_subdistribution_hazard",
"title": "Fine-Gray cumulative incidence",
"topics": [
"morie_fine_gray_subdistribution_hazard"
]
},
{
"page": "morie_fir_filter",
"title": "General FIR filter",
"topics": [
"morie_fir_filter"
]
},
{
"page": "morie_first_moment_ramsey",
"title": "Erdos's first-moment lower bound on the diagonal Ramsey number",
"topics": [
"morie_first_moment_ramsey"
]
},
{
"page": "morie_first_passage_time",
"title": "First-passage-time probability in a panel model",
"topics": [
"morie_first_passage_time"
]
},
{
"page": "morie_fisher_exact_test",
"title": "Fisher's exact test for 2x2 tables",
"topics": [
"morie_fisher_exact_test"
]
},
{
"page": "morie_fit_indices",
"title": "OLS fit indices: variance partition, R squared, adjusted R squared, F",
"topics": [
"morie_fit_indices"
]
},
{
"page": "morie_forsnp",
"title": "Forensic DNA random-match probability and likelihood ratio",
"topics": [
"morie_forsnp"
]
},
{
"page": "morie_fourfeat",
"title": "Fourier seasonality terms (ch. 4 p. 61, ch. 5 p. 95)",
"topics": [
"morie_fourfeat"
]
},
{
"page": "morie_frechet_check",
"title": "Frechet differentiability check",
"topics": [
"morie_frechet_check"
]
},
{
"page": "morie_friedman_urn",
"title": "The Friedman urn: cross-colour reinforcement pulls the share to one half",
"topics": [
"morie_friedman_urn"
]
},
{
"page": "morie_functional_delta",
"title": "Functional delta method with its remainder",
"topics": [
"morie_functional_delta"
]
},
{
"page": "morie_functional_regression",
"title": "Functional data analysis",
"topics": [
"morie_fda_beta_function",
"morie_fda_bic",
"morie_fda_design",
"morie_fda_fit",
"morie_fda_loocv",
"morie_functional_regression"
]
},
{
"page": "morie_g_formula",
"title": "Parametric g-formula standardised means",
"topics": [
"morie_g_formula"
]
},
{
"page": "morie_gam",
"title": "Generalized additive model fitted natively (mgcv-compatible)",
"topics": [
"morie_gam"
]
},
{
"page": "morie_gamma_bound",
"title": "A Rosenbaum Gamma bound is not a test of ignorability",
"topics": [
"morie_gamma_bound"
]
},
{
"page": "morie_gamma_frailty_cox",
"title": "Gamma-frailty Cox model",
"topics": [
"morie_gamma_frailty_cox"
]
},
{
"page": "morie_garch_fit",
"title": "Fit a GARCH(1,1) model to a return series",
"topics": [
"morie_garch_fit"
]
},
{
"page": "morie_garch_recursion",
"title": "Conditional-variance recursion",
"topics": [
"morie_garch_recursion"
]
},
{
"page": "morie_garch_spec_fit",
"title": "Fit a conditional-variance model by Gaussian quasi-ML",
"topics": [
"morie_garch_spec_fit"
]
},
{
"page": "morie_garch_var_es",
"title": "Value-at-Risk and expected shortfall of a one-step forecast",
"topics": [
"morie_garch_var_es"
]
},
{
"page": "morie_gate",
"title": "Group average treatment effects (GATE) by stratified AIPW",
"topics": [
"morie_gate"
]
},
{
"page": "morie_gaussian_approx",
"title": "Gaussian approximations of binomial and Poisson peaks",
"topics": [
"morie_gaussian_approx"
]
},
{
"page": "morie_gblup_full",
"title": "GBLUP - full mixed-model implementation",
"topics": [
"morie_gblup_full"
]
},
{
"page": "morie_gchpost",
"title": "Posterior over the input of a Gaussian channel (eq. 11.27-11.29, p. 182)",
"topics": [
"morie_gchpost"
]
},
{
"page": "morie_gee_regression",
"title": "Generalized estimating equations with a robust covariance",
"topics": [
"morie_gee_regression"
]
},
{
"page": "morie_generalized_gamma_aft",
"title": "Generalized gamma AFT model",
"topics": [
"morie_generalized_gamma_aft"
]
},
{
"page": "morie_generate_ar_coefficients",
"title": "Generate a stationarity-preserving AR coefficient matrix",
"topics": [
"morie_generate_ar_coefficients"
]
},
{
"page": "morie_generate_synthetic_data",
"title": "Generate synthetic epidemiology-style tabular data",
"topics": [
"morie_generate_synthetic_data"
]
},
{
"page": "morie_generate_var_coefficients",
"title": "Generate a VAR(L) coefficient array as a 3-d list",
"topics": [
"morie_generate_var_coefficients"
]
},
{
"page": "morie_genomic_cross_validation",
"title": "K-fold cross-validation for genomic-prediction accuracy",
"topics": [
"morie_genomic_cross_validation"
]
},
{
"page": "morie_genomic_prediction",
"title": "Genomic prediction models",
"topics": [
"morie_bayes_gblup",
"morie_gblup",
"morie_gblup_model",
"morie_genomic_prediction",
"morie_grm",
"morie_snp_blup"
]
},
{
"page": "morie_geods",
"title": "Geodesics of a four-dimensional metric",
"topics": [
"morie_geods"
]
},
{
"page": "morie_geostat_krige",
"title": "Kriging interpolation via 'gstat'",
"topics": [
"morie_geostat_krige"
]
},
{
"page": "morie_geostat_variogram",
"title": "Empirical variogram via 'gstat'",
"topics": [
"morie_geostat_variogram"
]
},
{
"page": "morie_geron_1cycle_schedule",
"title": "1cycle learning-rate and momentum schedule (Geron Ch 11, morie.fn gr1cy)",
"topics": [
"morie_geron_1cycle_schedule"
]
},
{
"page": "morie_geron_a2c",
"title": "Advantage actor-critic, A2C (Geron Ch 19, morie.fn hma2c)",
"topics": [
"morie_geron_a2c"
]
},
{
"page": "morie_geron_a3c",
"title": "Asynchronous advantage actor-critic, A3C (Geron Ch 19, morie.fn hma3c)",
"topics": [
"morie_geron_a3c"
]
},
{
"page": "morie_geron_action_value_function",
"title": "Action-value function Q^pi (Geron Ch 18, morie.fn grqpi)",
"topics": [
"morie_geron_action_value_function"
]
},
{
"page": "morie_geron_actor_critic_advantage",
"title": "One-step actor-critic advantage (Geron Ch 19, morie.fn grac)",
"topics": [
"morie_geron_actor_critic_advantage"
]
},
{
"page": "morie_geron_adaboost",
"title": "Discrete AdaBoost (Geron Ch 6, morie.fn hmadab)",
"topics": [
"morie_geron_adaboost"
]
},
{
"page": "morie_geron_adaboost_weight_update",
"title": "AdaBoost sample-weight update (Geron Ch 6, morie.fn gradaw)",
"topics": [
"morie_geron_adaboost_weight_update"
]
},
{
"page": "morie_geron_adagrad",
"title": "AdaGrad step, negative-step convention (Geron Ch 11, morie.fn hmadgr)",
"topics": [
"morie_geron_adagrad"
]
},
{
"page": "morie_geron_adagrad_update",
"title": "AdaGrad update, theta-first signature (Geron Ch 11, morie.fn grada2)",
"topics": [
"morie_geron_adagrad_update"
]
},
{
"page": "morie_geron_adam",
"title": "Adam step (Geron Ch 11, morie.fn hmadam)",
"topics": [
"morie_geron_adam"
]
},
{
"page": "morie_geron_adam_update",
"title": "Adam update, theta-first signature (Geron Ch 11, morie.fn gradmo)",
"topics": [
"morie_geron_adam_update"
]
},
{
"page": "morie_geron_adamax",
"title": "AdaMax step (Geron Ch 11, morie.fn hmadmx)",
"topics": [
"morie_geron_adamax"
]
},
{
"page": "morie_geron_adamw",
"title": "AdamW step with decoupled weight decay (Geron Ch 11, morie.fn hmadmw)",
"topics": [
"morie_geron_adamw"
]
},
{
"page": "morie_geron_adamw_decoupled_weight_decay",
"title": "AdamW with decoupled weight decay (Geron Ch 11, morie.fn grwdc)",
"topics": [
"morie_geron_adamw_decoupled_weight_decay"
]
},
{
"page": "morie_geron_agglomerative",
"title": "Agglomerative hierarchical clustering (Geron Ch 8, morie.fn hmagc)",
"topics": [
"morie_geron_agglomerative"
]
},
{
"page": "morie_geron_aic",
"title": "Akaike information criterion, vectorised (Geron Ch 8, morie.fn hmaic)",
"topics": [
"morie_geron_aic"
]
},
{
"page": "morie_geron_aic_gmm",
"title": "AIC for GMM model selection (Geron Ch 8, morie.fn graic)",
"topics": [
"morie_geron_aic_gmm"
]
},
{
"page": "morie_geron_albert",
"title": "ALBERT encoder: factorised embedding + cross-layer sharing (Geron Ch 15, hmalbt)",
"topics": [
"morie_geron_albert"
]
},
{
"page": "morie_geron_alexnet",
"title": "AlexNet architecture resolved to shapes and parameter counts (Geron Ch 12, hmalex)",
"topics": [
"morie_geron_alexnet"
]
},
{
"page": "morie_geron_anomaly_autoencoder",
"title": "Autoencoder anomaly detection (Geron Ch 8, morie.fn hmanae)",
"topics": [
"morie_geron_anomaly_autoencoder"
]
},
{
"page": "morie_geron_anomaly_gmm",
"title": "GMM-based anomaly detection: log-density thresholding (hmgand)",
"topics": [
"morie_geron_anomaly_gmm"
]
},
{
"page": "morie_geron_arima",
"title": "ARIMA(p, d, q) by Hannan-Rissanen (Geron Ch 13, morie.fn hmarim)",
"topics": [
"morie_geron_arima"
]
},
{
"page": "morie_geron_arima_forecast",
"title": "One-step ARIMA forecast with known coefficients (Geron Ch 13, grarma)",
"topics": [
"morie_geron_arima_forecast"
]
},
{
"page": "morie_geron_auc_roc",
"title": "ROC AUC via the Mann-Whitney identity (Geron Ch 3, morie.fn hmauc)",
"topics": [
"morie_geron_auc_roc"
]
},
{
"page": "morie_geron_auc_roc_trapezoid",
"title": "ROC curve and AUC by the trapezoid rule (Geron Ch 3, morie.fn grauc)",
"topics": [
"morie_geron_auc_roc_trapezoid"
]
},
{
"page": "morie_geron_autoencoder",
"title": "Linear undercomplete autoencoder solved by SVD (Geron Ch 18, hmaen)",
"topics": [
"morie_geron_autoencoder"
]
},
{
"page": "morie_geron_autoencoder_reconstruction_loss",
"title": "Autoencoder reconstruction loss and compression (Geron Ch 18, grael)",
"topics": [
"morie_geron_autoencoder_reconstruction_loss"
]
},
{
"page": "morie_geron_autograd",
"title": "Reverse-mode autograd over a scalar tape (Geron Ch 10, morie.fn hmagrd)",
"topics": [
"morie_geron_autograd"
]
},
{
"page": "morie_geron_autograd_chain_rule",
"title": "Reverse-mode autodiff over a chain of Jacobians (Geron Ch 10, graut)",
"topics": [
"morie_geron_autograd_chain_rule"
]
},
{
"page": "morie_geron_auxiliary_task_pretraining",
"title": "Auxiliary-task pretraining then fine-tuning (Geron Ch 11, hmauxpt)",
"topics": [
"morie_geron_auxiliary_task_pretraining"
]
},
{
"page": "morie_geron_backprop_through_time",
"title": "Backpropagation through time (Geron Ch 13, morie.fn grbptt)",
"topics": [
"morie_geron_backprop_through_time"
]
},
{
"page": "morie_geron_backpropagation",
"title": "Backpropagation through a feed-forward net (Geron Ch 9, morie.fn hmbp)",
"topics": [
"morie_geron_backpropagation"
]
},
{
"page": "morie_geron_backpropagation_gradient",
"title": "Backpropagation from stored activations (Geron Ch 9/10, morie.fn grbp)",
"topics": [
"morie_geron_backpropagation_gradient"
]
},
{
"page": "morie_geron_bagging",
"title": "Bagging over LCG-seeded bootstrap replicates (Geron Ch 6, morie.fn hmbag)",
"topics": [
"morie_geron_bagging"
]
},
{
"page": "morie_geron_bagging_predictor",
"title": "Bagging aggregator over member predictions (Geron Ch 6, morie.fn grbag)",
"topics": [
"morie_geron_bagging_predictor"
]
},
{
"page": "morie_geron_bahdanau_attention",
"title": "Bahdanau (additive) attention (Geron Ch 14, morie.fn hmbdn and grbah)",
"topics": [
"morie_geron_bahdanau_attention"
]
},
{
"page": "morie_geron_bart",
"title": "BART text-infilling corruption and reconstruction score (Geron Ch 15, hmbart)",
"topics": [
"morie_geron_bart"
]
},
{
"page": "morie_geron_batch_gd_grad",
"title": "MSE gradient of linear regression (Geron Ch 4, morie.fn hmbgdg)",
"topics": [
"morie_geron_batch_gd_grad"
]
},
{
"page": "morie_geron_batch_gradient_descent",
"title": "Batch gradient descent with full loss curve (Geron Ch 4 Eq 4-6, grbgd)",
"topics": [
"morie_geron_batch_gradient_descent"
]
},
{
"page": "morie_geron_batch_learning",
"title": "Batch (offline) learning by closed-form least squares (Geron Ch 1, hmbat)",
"topics": [
"morie_geron_batch_learning"
]
},
{
"page": "morie_geron_batch_normalization",
"title": "Batch normalization with running statistics (Geron Ch 11, morie.fn hmbntr)",
"topics": [
"morie_geron_batch_normalization"
]
},
{
"page": "morie_geron_batch_normalization_paper",
"title": "Batch normalization, Ioffe-Szegedy running form (Geron Ch 11, morie.fn grbn)",
"topics": [
"morie_geron_batch_normalization_paper"
]
},
{
"page": "morie_geron_batch_size_heuristic",
"title": "Batch-size heuristic (Geron Ch 9, morie.fn hmbsz)",
"topics": [
"morie_geron_batch_size_heuristic"
]
},
{
"page": "morie_geron_beam_search",
"title": "Beam search over a next-token scorer (Geron Ch 14, morie.fn hmbms)",
"topics": [
"morie_geron_beam_search"
]
},
{
"page": "morie_geron_beam_search_decoder",
"title": "Beam search over a fixed per-step score table (Geron Ch 14, grbeam)",
"topics": [
"morie_geron_beam_search_decoder"
]
},
{
"page": "morie_geron_bert",
"title": "BERT encoder forward pass with MLM + NSP heads (Geron Ch 15, hmbert)",
"topics": [
"morie_geron_bert"
]
},
{
"page": "morie_geron_bert_finetune",
"title": "Fine-tune a frozen encoder with a softmax head (Geron Ch 15, hmbftn)",
"topics": [
"morie_geron_bert_finetune"
]
},
{
"page": "morie_geron_bert_mlm_loss",
"title": "BERT masked-language-model loss (Geron Ch 16, morie.fn grmlm)",
"topics": [
"morie_geron_bert_mlm_loss"
]
},
{
"page": "morie_geron_bert_nsp_loss",
"title": "BERT next-sentence-prediction loss (morie.fn grnsp)",
"topics": [
"morie_geron_bert_nsp_loss"
]
},
{
"page": "morie_geron_bf16",
"title": "BF16 quantisation of float32 values (Geron Appendix B, morie.fn hmbf16)",
"topics": [
"morie_geron_bf16"
]
},
{
"page": "morie_geron_bf16_range",
"title": "bfloat16 round-trip and range report (Geron Appendix B, morie.fn grbf16)",
"topics": [
"morie_geron_bf16_range"
]
},
{
"page": "morie_geron_bias_variance_tradeoff",
"title": "Bias-variance decomposition (Geron Ch 1, morie.fn hmbv)",
"topics": [
"morie_geron_bias_variance_tradeoff"
]
},
{
"page": "morie_geron_bic",
"title": "Bayesian information criterion, vectorised (Geron Ch 8, morie.fn hmbic)",
"topics": [
"morie_geron_bic"
]
},
{
"page": "morie_geron_bic_gmm",
"title": "BIC for GMM model selection (Geron Ch 8, morie.fn grbic)",
"topics": [
"morie_geron_bic_gmm"
]
},
{
"page": "morie_geron_bidirectional_combine",
"title": "Combine the two directions of a bidirectional layer (Geron Ch 14, grbrnn)",
"topics": [
"morie_geron_bidirectional_combine"
]
},
{
"page": "morie_geron_bidirectional_rnn",
"title": "Bidirectional RNN forward pass (Geron Ch 14, morie.fn hmbrnn)",
"topics": [
"morie_geron_bidirectional_rnn"
]
},
{
"page": "morie_geron_binary_classification",
"title": "Binary classification by thresholding a logistic score (Geron Ch 3, hmbin)",
"topics": [
"morie_geron_binary_classification"
]
},
{
"page": "morie_geron_biological_neuron",
"title": "McCulloch-Pitts neuron (Geron Ch 9, morie.fn hmbnm)",
"topics": [
"morie_geron_biological_neuron"
]
},
{
"page": "morie_geron_birch",
"title": "BIRCH clustering (Geron Ch 8, morie.fn hmbrch)",
"topics": [
"morie_geron_birch"
]
},
{
"page": "morie_geron_blip",
"title": "BLIP pretraining objectives on paired embeddings (Geron Ch 16, hmblip)",
"topics": [
"morie_geron_blip"
]
},
{
"page": "morie_geron_blip_itm_itc",
"title": "BLIP multi-task loss with captioning head (Geron Ch 16, morie.fn grblip)",
"topics": [
"morie_geron_blip_itm_itc"
]
},
{
"page": "morie_geron_blip2",
"title": "BLIP-2 Q-Former bridge (Geron Ch 16, morie.fn hmblp2)",
"topics": [
"morie_geron_blip2"
]
},
{
"page": "morie_geron_block_feed_forward",
"title": "Position-wise feed-forward used by the transformer blocks",
"topics": [
"morie_geron_block_feed_forward"
]
},
{
"page": "morie_geron_block_layer_norm",
"title": "Layer norm used by the transformer blocks (morie.fn grteb)",
"topics": [
"morie_geron_block_layer_norm"
]
},
{
"page": "morie_geron_block_multi_head_attention",
"title": "Per-head multi-head attention used by the transformer blocks",
"topics": [
"morie_geron_block_multi_head_attention"
]
},
{
"page": "morie_geron_block_params",
"title": "Exact transformer block parameter count, itemised (hmdctr)",
"topics": [
"morie_geron_block_params"
]
},
{
"page": "morie_geron_box_iou",
"title": "Intersection over union of two (x1,y1,x2,y2) boxes",
"topics": [
"morie_geron_box_iou"
]
},
{
"page": "morie_geron_bpe_merge",
"title": "Learn a fixed number of BPE merges (Geron Ch 14, morie.fn grbpe)",
"topics": [
"morie_geron_bpe_merge"
]
},
{
"page": "morie_geron_bpe_tokenizer",
"title": "Byte-pair encoding to a target vocabulary size (Geron Ch 14, hmbpet)",
"topics": [
"morie_geron_bpe_tokenizer"
]
},
{
"page": "morie_geron_cart_algorithm",
"title": "CART algorithm (Geron Ch 5, morie.fn hmcart)",
"topics": [
"morie_geron_cart_algorithm"
]
},
{
"page": "morie_geron_cart_split_cost",
"title": "CART split cost at a node (Geron Ch 5 Eq 5-2, morie.fn grcart)",
"topics": [
"morie_geron_cart_split_cost"
]
},
{
"page": "morie_geron_causal_mask",
"title": "Causal mask: TRUE where attention is forbidden (strictly future) (hmdctr)",
"topics": [
"morie_geron_causal_mask"
]
},
{
"page": "morie_geron_ch4_cross_entropy_gradient_vector",
"title": "Cross-entropy gradient for one class (morie.fn grn024)",
"topics": [
"morie_geron_ch4_cross_entropy_gradient_vector"
]
},
{
"page": "morie_geron_ch4_elastic_net_cost_function",
"title": "Elastic net cost, Eq 4-13 convention (morie.fn grn013)",
"topics": [
"morie_geron_ch4_elastic_net_cost_function"
]
},
{
"page": "morie_geron_ch4_lasso_regression_cost_function",
"title": "Lasso cost, 2*alpha convention (Geron Eq 4-11, morie.fn grn011)",
"topics": [
"morie_geron_ch4_lasso_regression_cost_function"
]
},
{
"page": "morie_geron_ch4_linear_regression_prediction",
"title": "Linear regression prediction (Geron Eq 4-2, morie.fn grn002)",
"topics": [
"morie_geron_ch4_linear_regression_prediction"
]
},
{
"page": "morie_geron_ch4_logistic_regression_prediction",
"title": "Logistic thresholded prediction (Geron Ch 4, morie.fn grn016)",
"topics": [
"morie_geron_ch4_logistic_regression_prediction"
]
},
{
"page": "morie_geron_ch4_mse_gradient_vector",
"title": "MSE gradient vector (Geron Eq 4-7, morie.fn grn007)",
"topics": [
"morie_geron_ch4_mse_gradient_vector"
]
},
{
"page": "morie_geron_ch4_normal_equation",
"title": "Normal equation (Geron Eq 4-5, morie.fn grn005)",
"topics": [
"morie_geron_ch4_normal_equation"
]
},
{
"page": "morie_geron_ch4_simple_linear_life_satisfaction",
"title": "Life satisfaction ~ GDP per capita (Geron Eq 4-1, morie.fn grn001)",
"topics": [
"morie_geron_ch4_simple_linear_life_satisfaction"
]
},
{
"page": "morie_geron_ch4_softmax_function",
"title": "Softmax function (morie.fn grn021)",
"topics": [
"morie_geron_ch4_softmax_function"
]
},
{
"page": "morie_geron_char_rnn",
"title": "Character-level RNN language model (Geron Ch 16, morie.fn hmchrn)",
"topics": [
"morie_geron_char_rnn"
]
},
{
"page": "morie_geron_classification_localization",
"title": "Classification + localization head (Geron Ch 14, morie.fn hmclc)",
"topics": [
"morie_geron_classification_localization"
]
},
{
"page": "morie_geron_classification_mlp",
"title": "Classification MLP (Geron Ch 10, morie.fn hmclsn)",
"topics": [
"morie_geron_classification_mlp"
]
},
{
"page": "morie_geron_classification_mlp_output",
"title": "Classification MLP softmax head (Geron Ch 10, morie.fn grmlc)",
"topics": [
"morie_geron_classification_mlp_output"
]
},
{
"page": "morie_geron_classification_tree",
"title": "Classification tree with leaf-frequency probabilities (Geron Ch 6, morie.fn hmcdt)",
"topics": [
"morie_geron_classification_tree"
]
},
{
"page": "morie_geron_clip",
"title": "CLIP pretraining with zero-shot prompt matching (Geron Ch 16, morie.fn hmclip)",
"topics": [
"morie_geron_clip"
]
},
{
"page": "morie_geron_clip_contrastive_loss",
"title": "CLIP symmetric contrastive loss (Geron Ch 16, morie.fn grclp)",
"topics": [
"morie_geron_clip_contrastive_loss"
]
},
{
"page": "morie_geron_computational_graph",
"title": "Computational graph with reverse-mode differentiation (Geron Ch 12, morie.fn hmcgrf)",
"topics": [
"morie_geron_computational_graph"
]
},
{
"page": "morie_geron_conditional_p",
"title": "Row-wise Gaussian affinities with sigma solved to hit a target perplexity",
"topics": [
"morie_geron_conditional_p"
]
},
{
"page": "morie_geron_confusion_matrix",
"title": "Confusion matrix with per-class precision/recall/F1 (Geron Ch 3, grcfm)",
"topics": [
"morie_geron_confusion_matrix"
]
},
{
"page": "morie_geron_confusion_matrix_labeled",
"title": "Confusion matrix over arbitrary labels (Geron Ch 3, morie.fn hmcfm)",
"topics": [
"morie_geron_confusion_matrix_labeled"
]
},
{
"page": "morie_geron_contrastive_infonce",
"title": "InfoNCE contrastive loss (Geron Ch 16, morie.fn grctr)",
"topics": [
"morie_geron_contrastive_infonce"
]
},
{
"page": "morie_geron_contrastive_learning",
"title": "Contrastive learning with in-batch negatives (hmcst)",
"topics": [
"morie_geron_contrastive_learning"
]
},
{
"page": "morie_geron_conv_autoencoder_trained",
"title": "Trained patch convolutional autoencoder (Geron Ch 18, morie.fn hmcae)",
"topics": [
"morie_geron_conv_autoencoder_trained"
]
},
{
"page": "morie_geron_conv_output_size",
"title": "Convolution output size per axis (Geron Ch 12, morie.fn grcos)",
"topics": [
"morie_geron_conv_output_size"
]
},
{
"page": "morie_geron_conv2d_forward",
"title": "2-D convolution forward pass, single filter (Geron Ch 12, morie.fn grcvf)",
"topics": [
"morie_geron_conv2d_forward"
]
},
{
"page": "morie_geron_convolutional_autoencoder",
"title": "Convolutional autoencoder forward pass (Geron Ch 18, morie.fn grcae)",
"topics": [
"morie_geron_convolutional_autoencoder"
]
},
{
"page": "morie_geron_cosine_annealing",
"title": "Cosine annealing schedule (Geron Ch 11, morie.fn hmlcos)",
"topics": [
"morie_geron_cosine_annealing"
]
},
{
"page": "morie_geron_credit_assignment",
"title": "Credit assignment over a trajectory (Geron Ch 18, morie.fn hmcrd)",
"topics": [
"morie_geron_credit_assignment"
]
},
{
"page": "morie_geron_cross_attention",
"title": "Scaled dot-product cross-attention (Geron Ch 15, morie.fn grca)",
"topics": [
"morie_geron_cross_attention"
]
},
{
"page": "morie_geron_cross_attention_report",
"title": "Cross-attention with per-query entropy (Geron Ch 15, morie.fn hmcatt)",
"topics": [
"morie_geron_cross_attention_report"
]
},
{
"page": "morie_geron_cross_entropy_cost",
"title": "Softmax cross-entropy cost (Geron Ch 4, morie.fn hmcec)",
"topics": [
"morie_geron_cross_entropy_cost"
]
},
{
"page": "morie_geron_cross_entropy_gradient",
"title": "Softmax cross-entropy gradient (Geron Ch 4, morie.fn hmceg)",
"topics": [
"morie_geron_cross_entropy_gradient"
]
},
{
"page": "morie_geron_cross_validation_score",
"title": "K-fold cross-validation score (Geron Ch 1/2, morie.fn grcvs)",
"topics": [
"morie_geron_cross_validation_score"
]
},
{
"page": "morie_geron_curse_dimensionality",
"title": "Curse of dimensionality (Geron Ch 8, morie.fn hmcod)",
"topics": [
"morie_geron_curse_dimensionality"
]
},
{
"page": "morie_geron_dalle",
"title": "DALL-E autoregressive image-token generation (hmdale)",
"topics": [
"morie_geron_dalle"
]
},
{
"page": "morie_geron_dalle_autoregressive_token",
"title": "DALL-E autoregressive image-token scoring (Geron Ch 16, morie.fn grdal)",
"topics": [
"morie_geron_dalle_autoregressive_token"
]
},
{
"page": "morie_geron_dataloader",
"title": "Mini-batch index plan with deterministic Fisher-Yates shuffling (hmdld)",
"topics": [
"morie_geron_dataloader"
]
},
{
"page": "morie_geron_dataloader_minibatch",
"title": "Mini-batch DataLoader indices (Geron Ch 10, morie.fn grdlm)",
"topics": [
"morie_geron_dataloader_minibatch"
]
},
{
"page": "morie_geron_dbscan",
"title": "DBSCAN clustering, BFS over density-connected cores (hmdbs)",
"topics": [
"morie_geron_dbscan"
]
},
{
"page": "morie_geron_dbscan_core_point",
"title": "DBSCAN core / border / noise points (Geron Ch 8, morie.fn grdbs)",
"topics": [
"morie_geron_dbscan_core_point"
]
},
{
"page": "morie_geron_dcgan",
"title": "DCGAN generator/discriminator resolved to concrete shapes (hmdcg)",
"topics": [
"morie_geron_dcgan"
]
},
{
"page": "morie_geron_dcgan_generator",
"title": "DCGAN generator forward pass (Geron Ch 18, morie.fn grdcgan)",
"topics": [
"morie_geron_dcgan_generator"
]
},
{
"page": "morie_geron_ddim",
"title": "DDIM deterministic sub-sequence sampling (hmddim)",
"topics": [
"morie_geron_ddim"
]
},
{
"page": "morie_geron_ddim_sampling_step",
"title": "Deterministic DDIM sampling step (Geron Ch 18, morie.fn grddim)",
"topics": [
"morie_geron_ddim_sampling_step"
]
},
{
"page": "morie_geron_ddpg",
"title": "Linear DDPG: TD critic, deterministic-policy-gradient actor, Polyak targets (hmddpg)",
"topics": [
"morie_geron_ddpg"
]
},
{
"page": "morie_geron_ddpm",
"title": "DDPM: per-timestep affine eps-model trained on the noise-prediction objective (hmddpm)",
"topics": [
"morie_geron_ddpm"
]
},
{
"page": "morie_geron_ddpm_forward_process",
"title": "DDPM forward diffusion q(x_t | x_0) (Geron Ch 18, morie.fn grdpmf)",
"topics": [
"morie_geron_ddpm_forward_process"
]
},
{
"page": "morie_geron_ddpm_reverse_step",
"title": "DDPM reverse denoising step (Geron Ch 18, morie.fn grdpmr)",
"topics": [
"morie_geron_ddpm_reverse_step"
]
},
{
"page": "morie_geron_ddpm_simple_loss",
"title": "DDPM simplified noise-prediction loss (Geron Ch 18, morie.fn grdpml)",
"topics": [
"morie_geron_ddpm_simple_loss"
]
},
{
"page": "morie_geron_decision_boundary",
"title": "Decision boundary for logistic regression theta^T x = 0 (hmdbd)",
"topics": [
"morie_geron_decision_boundary"
]
},
{
"page": "morie_geron_decoder_only",
"title": "Decoder-only transformer (GPT family), resolved to exact param counts (hmdctr)",
"topics": [
"morie_geron_decoder_only"
]
},
{
"page": "morie_geron_deep_rnn",
"title": "Deep (stacked) RNN forward pass, bottom-up per time step (hmdrnn)",
"topics": [
"morie_geron_deep_rnn"
]
},
{
"page": "morie_geron_deit",
"title": "DeiT: ViT architecture + distillation token, resolved concretely (hmdeit)",
"topics": [
"morie_geron_deit"
]
},
{
"page": "morie_geron_deit_distillation_loss",
"title": "DeiT hard-label distillation loss (Geron Ch 16, morie.fn grdeit)",
"topics": [
"morie_geron_deit_distillation_loss"
]
},
{
"page": "morie_geron_denoising_autoencoder",
"title": "Denoising autoencoder loss against the clean input (Geron Ch 18, grdae)",
"topics": [
"morie_geron_denoising_autoencoder"
]
},
{
"page": "morie_geron_denoising_autoencoder_train",
"title": "Denoising autoencoder, trained (hmdae)",
"topics": [
"morie_geron_denoising_autoencoder_train"
]
},
{
"page": "morie_geron_detr",
"title": "DETR: CNN-transformer pipeline resolved concretely; matching via Hungarian (hmdetr)",
"topics": [
"morie_geron_detr"
]
},
{
"page": "morie_geron_detr_hungarian_matching",
"title": "DETR Hungarian matching and set-prediction loss (Geron Ch 16, grdetr)",
"topics": [
"morie_geron_detr_hungarian_matching"
]
},
{
"page": "morie_geron_diffusion_forward",
"title": "Diffusion forward process: chain and closed form, cross-checked (hmdfw)",
"topics": [
"morie_geron_diffusion_forward"
]
},
{
"page": "morie_geron_diffusion_reverse",
"title": "Ancestral DDPM reverse sampling from x_T to x_0 (hmdrv)",
"topics": [
"morie_geron_diffusion_reverse"
]
},
{
"page": "morie_geron_digamma",
"title": "Digamma psi(x) for x > 0 (recurrence + asymptotic series; no scipy dep)",
"topics": [
"morie_geron_digamma"
]
},
{
"page": "morie_geron_dino",
"title": "DINO cross-view self-distillation with centering, sharpening, momentum teacher (hmdino)",
"topics": [
"morie_geron_dino"
]
},
{
"page": "morie_geron_dino_self_distillation",
"title": "DINO self-distillation loss (Geron Ch 16, morie.fn grdino)",
"topics": [
"morie_geron_dino_self_distillation"
]
},
{
"page": "morie_geron_discounted_return",
"title": "Discounted return (Geron Ch 18, morie.fn grret)",
"topics": [
"morie_geron_discounted_return"
]
},
{
"page": "morie_geron_distilbert",
"title": "DistilBERT triple-loss distillation (hmdbrt)",
"topics": [
"morie_geron_distilbert"
]
},
{
"page": "morie_geron_double_dqn",
"title": "Double DQN training loop; target computed by the online/target argmax split (hmddqn)",
"topics": [
"morie_geron_double_dqn"
]
},
{
"page": "morie_geron_double_dqn_target",
"title": "Double DQN targets (Geron Ch 19, morie.fn grddqn)",
"topics": [
"morie_geron_double_dqn_target"
]
},
{
"page": "morie_geron_dpo",
"title": "Direct preference optimization (DPO) loss (hmdpo)",
"topics": [
"morie_geron_dpo"
]
},
{
"page": "morie_geron_dpo_loss",
"title": "Direct Preference Optimization loss (Geron Ch 15, morie.fn grdpo)",
"topics": [
"morie_geron_dpo_loss"
]
},
{
"page": "morie_geron_dqn",
"title": "Tabular DQN: replay mini-batches + periodically synced target net (hmdqn)",
"topics": [
"morie_geron_dqn"
]
},
{
"page": "morie_geron_dqn_loss",
"title": "DQN bootstrap MSE loss (Geron Ch 19, morie.fn grdqnl)",
"topics": [
"morie_geron_dqn_loss"
]
},
{
"page": "morie_geron_dropout",
"title": "Inverted dropout (Geron Ch 11, morie.fn grdro)",
"topics": [
"morie_geron_dropout"
]
},
{
"page": "morie_geron_dropout_alt",
"title": "Inverted dropout: y = mask * x / (1 - p) (hmdrp)",
"topics": [
"morie_geron_dropout_alt"
]
},
{
"page": "morie_geron_dueling_dqn",
"title": "Dueling DQN decomposition (Geron Ch 19, morie.fn grduel)",
"topics": [
"morie_geron_dueling_dqn"
]
},
{
"page": "morie_geron_dueling_dqn_alt",
"title": "Dueling DQN: exact gradients through Q = V + A - mean(A) (hmdldqn)",
"topics": [
"morie_geron_dueling_dqn_alt"
]
},
{
"page": "morie_geron_dynamic_quantization",
"title": "Dynamic INT8 quantization (Geron Ch 19, morie.fn grdyq)",
"topics": [
"morie_geron_dynamic_quantization"
]
},
{
"page": "morie_geron_dynamic_quantization_alt",
"title": "Dynamic quantization: static per-tensor weights, dynamic per-batch activations (hmdqnt)",
"topics": [
"morie_geron_dynamic_quantization_alt"
]
},
{
"page": "morie_geron_early_stopping",
"title": "Early stopping on validation RMSE (Geron Ch 4, morie.fn greast)",
"topics": [
"morie_geron_early_stopping"
]
},
{
"page": "morie_geron_early_stopping_alt",
"title": "Early stopping: batch GD keeping the best validation snapshot (hmearl)",
"topics": [
"morie_geron_early_stopping_alt"
]
},
{
"page": "morie_geron_elastic_net",
"title": "Elastic net cost: MSE + r\\*alpha\\*L1 + (1-r)/2\\*alpha\\*L2 (hmenet)",
"topics": [
"morie_geron_elastic_net"
]
},
{
"page": "morie_geron_elastic_net_cost",
"title": "Elastic net cost (Geron Eq 4-12, morie.fn grelas)",
"topics": [
"morie_geron_elastic_net_cost"
]
},
{
"page": "morie_geron_elbo",
"title": "VAE evidence lower bound with closed-form Gaussian KL (hmelb)",
"topics": [
"morie_geron_elbo"
]
},
{
"page": "morie_geron_elu",
"title": "ELU activation (Geron Ch 11, morie.fn hmelu)",
"topics": [
"morie_geron_elu"
]
},
{
"page": "morie_geron_embedding_lookup",
"title": "Embedding table lookup (Geron Ch 16, morie.fn gremb)",
"topics": [
"morie_geron_embedding_lookup"
]
},
{
"page": "morie_geron_encoder_decoder_seq2seq",
"title": "Encoder-decoder seq2seq decoding loop (Geron Ch 16, morie.fn gredsq)",
"topics": [
"morie_geron_encoder_decoder_seq2seq"
]
},
{
"page": "morie_geron_encoder_decoder_transformer",
"title": "Original encoder-decoder transformer, resolved to exact params + 3 masks (hmencd)",
"topics": [
"morie_geron_encoder_decoder_transformer"
]
},
{
"page": "morie_geron_encoder_only",
"title": "Encoder-only transformer (BERT-family), resolved to exact param counts (hmencox)",
"topics": [
"morie_geron_encoder_only"
]
},
{
"page": "morie_geron_encoder_params",
"title": "Parameter count of a transformer encoder stack",
"topics": [
"morie_geron_encoder_params"
]
},
{
"page": "morie_geron_entropy_impurity",
"title": "Shannon entropy impurity (Geron Ch 6, morie.fn hment)",
"topics": [
"morie_geron_entropy_impurity"
]
},
{
"page": "morie_geron_epsilon_greedy",
"title": "Epsilon-greedy action selection (Geron Ch 18, morie.fn grepl)",
"topics": [
"morie_geron_epsilon_greedy"
]
},
{
"page": "morie_geron_epsilon_greedy_alt",
"title": "Epsilon-greedy action distribution, deterministic LCG draw (hmeg)",
"topics": [
"morie_geron_epsilon_greedy_alt"
]
},
{
"page": "morie_geron_error_analysis",
"title": "Error analysis via row-normalised confusion matrix, diagonal removed (hmeaf)",
"topics": [
"morie_geron_error_analysis"
]
},
{
"page": "morie_geron_explained_variance_ratio",
"title": "Explained variance ratio (Geron Ch 8, morie.fn grevr)",
"topics": [
"morie_geron_explained_variance_ratio"
]
},
{
"page": "morie_geron_explained_variance_ratio_alt",
"title": "Explained variance ratio from the SVD of the centred data matrix (hmevr)",
"topics": [
"morie_geron_explained_variance_ratio_alt"
]
},
{
"page": "morie_geron_exploding_gradients",
"title": "Exploding gradients: gradients grow through layers",
"topics": [
"morie_geron_exploding_gradients"
]
},
{
"page": "morie_geron_extra_trees",
"title": "Extra-trees: uniformly random per-feature split thresholds (hmext)",
"topics": [
"morie_geron_extra_trees"
]
},
{
"page": "morie_geron_f1_score",
"title": "F1 score (Geron Eq 3-3, morie.fn grf1)",
"topics": [
"morie_geron_f1_score"
]
},
{
"page": "morie_geron_f1_score_alt",
"title": "F1 score from a confusion matrix delegated to hmcfm (hmf1)",
"topics": [
"morie_geron_f1_score_alt"
]
},
{
"page": "morie_geron_fashion_mnist",
"title": "FashionMNIST CNN architecture resolved to concrete shapes (hmfmn)",
"topics": [
"morie_geron_fashion_mnist"
]
},
{
"page": "morie_geron_fcn",
"title": "Fully convolutional network forward pass, dense per-pixel prediction (hmfcn)",
"topics": [
"morie_geron_fcn"
]
},
{
"page": "morie_geron_fcn_upsample",
"title": "Transposed convolution / FCN upsampling (Geron Ch 14, morie.fn grfcn)",
"topics": [
"morie_geron_fcn_upsample"
]
},
{
"page": "morie_geron_feature_importance_mdi",
"title": "Forest feature importance, MDI (Geron Ch 7, morie.fn grfim)",
"topics": [
"morie_geron_feature_importance_mdi"
]
},
{
"page": "morie_geron_feature_map",
"title": "Feature map: activation(conv(x, K) + b), convolution delegated to hmfmap's core (hmfmap)",
"topics": [
"morie_geron_feature_map"
]
},
{
"page": "morie_geron_feature_map_dim",
"title": "Convolutional feature-map size (Geron Ch 14, morie.fn grfmp)",
"topics": [
"morie_geron_feature_map_dim"
]
},
{
"page": "morie_geron_few_shot",
"title": "Few-shot in-context prompt construction with a zero-shot control (hmfsf)",
"topics": [
"morie_geron_few_shot"
]
},
{
"page": "morie_geron_filter_kernel",
"title": "Convolutional filter tensor, He-initialised (Geron Ch 12, morie.fn hmkrn)",
"topics": [
"morie_geron_filter_kernel"
]
},
{
"page": "morie_geron_finetune_lm",
"title": "Native SGD fine-tuning with frozen params, warmup and weight decay (hmfth)",
"topics": [
"morie_geron_finetune_lm"
]
},
{
"page": "morie_geron_fit_ab",
"title": "Fit (a, b) so 1/(1 + a d^(2b)) matches UMAP's target curve",
"topics": [
"morie_geron_fit_ab"
]
},
{
"page": "morie_geron_flamingo",
"title": "Flamingo: perceiver resampler + tanh-gated cross-attention (hmflmg)",
"topics": [
"morie_geron_flamingo"
]
},
{
"page": "morie_geron_flamingo_cross_modal_attn",
"title": "Flamingo tanh-gated cross-attention (Geron Ch 17, morie.fn grflam)",
"topics": [
"morie_geron_flamingo_cross_modal_attn"
]
},
{
"page": "morie_geron_flash_attention",
"title": "FlashAttention: tiled online-softmax, exact (hmfa)",
"topics": [
"morie_geron_flash_attention"
]
},
{
"page": "morie_geron_flash_attention_tile",
"title": "FlashAttention tiled online softmax (Geron Ch 16, morie.fn grflash)",
"topics": [
"morie_geron_flash_attention_tile"
]
},
{
"page": "morie_geron_forward_autodiff",
"title": "Forward-mode automatic differentiation via dual numbers (hmfad)",
"topics": [
"morie_geron_forward_autodiff"
]
},
{
"page": "morie_geron_forward_mode_autodiff",
"title": "Forward-mode autodiff (Geron Ch 12, morie.fn grfad)",
"topics": [
"morie_geron_forward_mode_autodiff"
]
},
{
"page": "morie_geron_fp16_mixed_precision",
"title": "FP16 mixed precision with loss scaling (Geron Ch 19, morie.fn grfp6)",
"topics": [
"morie_geron_fp16_mixed_precision"
]
},
{
"page": "morie_geron_fp16_quant",
"title": "IEEE-754 binary16 round-trip with field decomposition (hmfp16)",
"topics": [
"morie_geron_fp16_quant"
]
},
{
"page": "morie_geron_fp32",
"title": "IEEE-754 binary32 field decomposition, reconstructed exactly (hmfp32)",
"topics": [
"morie_geron_fp32"
]
},
{
"page": "morie_geron_gan",
"title": "GAN minimax training: linear generator vs logistic discriminator (hmgan)",
"topics": [
"morie_geron_gan"
]
},
{
"page": "morie_geron_gan_minimax",
"title": "GAN minimax objective (Geron Ch 17, morie.fn grgan)",
"topics": [
"morie_geron_gan_minimax"
]
},
{
"page": "morie_geron_gan_mode_collapse_metric",
"title": "GAN mode coverage / collapse (Geron Ch 17, morie.fn grmcol)",
"topics": [
"morie_geron_gan_mode_collapse_metric"
]
},
{
"page": "morie_geron_gaussian_mixture",
"title": "Gaussian mixture model fit via EM, log-space responsibilities (hmgmm)",
"topics": [
"morie_geron_gaussian_mixture"
]
},
{
"page": "morie_geron_gaussian_rand_projection",
"title": "Gaussian random projection X' = X R, R_ij ~ N(0, 1/d') (hmgrp)",
"topics": [
"morie_geron_gaussian_rand_projection"
]
},
{
"page": "morie_geron_gaussian_random_projection",
"title": "Gaussian random projection (Geron Ch 8, morie.fn grgrp)",
"topics": [
"morie_geron_gaussian_random_projection"
]
},
{
"page": "morie_geron_gelu",
"title": "GELU activation (Geron Ch 11, morie.fn hmgelu)",
"topics": [
"morie_geron_gelu"
]
},
{
"page": "morie_geron_gini_impurity",
"title": "Gini impurity (Geron Ch 6, morie.fn hmgini)",
"topics": [
"morie_geron_gini_impurity"
]
},
{
"page": "morie_geron_gini_impurity_grgin",
"title": "Gini impurity (Geron Eq 5-1, morie.fn grgin)",
"topics": [
"morie_geron_gini_impurity_grgin"
]
},
{
"page": "morie_geron_glorot_init",
"title": "Glorot (Xavier) initialization (Geron Ch 11, morie.fn hmxav)",
"topics": [
"morie_geron_glorot_init"
]
},
{
"page": "morie_geron_glorot_xavier_init",
"title": "Glorot (Xavier) initialization (Geron Ch 11, morie.fn grxvi)",
"topics": [
"morie_geron_glorot_xavier_init"
]
},
{
"page": "morie_geron_gmm_em_step",
"title": "Gaussian mixture EM step (Geron Ch 9, morie.fn grgmem)",
"topics": [
"morie_geron_gmm_em_step"
]
},
{
"page": "morie_geron_gmm_log_likelihood",
"title": "Gaussian mixture log-likelihood (Geron Ch 9, morie.fn grgmll)",
"topics": [
"morie_geron_gmm_log_likelihood"
]
},
{
"page": "morie_geron_gmm_n_params",
"title": "Free-parameter count of a GMM (Geron Ch 8, morie.fn graic)",
"topics": [
"morie_geron_gmm_n_params"
]
},
{
"page": "morie_geron_googlenet",
"title": "GoogLeNet architecture resolved to concrete shapes and exact params (hmgoog)",
"topics": [
"morie_geron_googlenet"
]
},
{
"page": "morie_geron_gpt_autoregressive_loss",
"title": "Autoregressive next-token cross-entropy (Geron Ch 16, morie.fn grgptl)",
"topics": [
"morie_geron_gpt_autoregressive_loss"
]
},
{
"page": "morie_geron_gpt1",
"title": "GPT-1 architecture (delegated to hmdctr) plus the causal LM objective (hmgpt1)",
"topics": [
"morie_geron_gpt1"
]
},
{
"page": "morie_geron_gpt2",
"title": "GPT-2 released configurations resolved through hmdctr (hmgpt2)",
"topics": [
"morie_geron_gpt2"
]
},
{
"page": "morie_geron_gpt3",
"title": "GPT-3 175B architecture accounting: exact params, shape trace, KV-cache (hmgpt3)",
"topics": [
"morie_geron_gpt3"
]
},
{
"page": "morie_geron_gradient_boosting",
"title": "Gradient boosted regression trees: fit residuals sequentially (hmgbrt)",
"topics": [
"morie_geron_gradient_boosting"
]
},
{
"page": "morie_geron_gradient_boosting_residual",
"title": "Gradient boosting residual stage (Geron Ch 7, morie.fn grgbm)",
"topics": [
"morie_geron_gradient_boosting_residual"
]
},
{
"page": "morie_geron_gradient_clipping",
"title": "Gradient clipping by global norm (Geron Ch 11, morie.fn hmgcl)",
"topics": [
"morie_geron_gradient_clipping"
]
},
{
"page": "morie_geron_gradient_clipping_grgcl",
"title": "Global-norm gradient clipping (Geron Ch 11, morie.fn grgcl)",
"topics": [
"morie_geron_gradient_clipping_grgcl"
]
},
{
"page": "morie_geron_grid_search",
"title": "Exhaustive grid search scored by K-fold CV, delegated to 'morie_geron_cross_validation_score' (hmgrs)",
"topics": [
"morie_geron_grid_search"
]
},
{
"page": "morie_geron_grid_search_cv",
"title": "Exhaustive grid search with K-fold CV (Geron Ch 2, morie.fn grgs)",
"topics": [
"morie_geron_grid_search_cv"
]
},
{
"page": "morie_geron_gru",
"title": "GRU cell forward step (Geron Ch 13, morie.fn hmgru)",
"topics": [
"morie_geron_gru"
]
},
{
"page": "morie_geron_gru_cell",
"title": "GRU cell forward pass (Geron Ch 15, morie.fn grgruc)",
"topics": [
"morie_geron_gru_cell"
]
},
{
"page": "morie_geron_hard_voting",
"title": "Hard voting ensemble (Geron Ch 7, morie.fn grvoth)",
"topics": [
"morie_geron_hard_voting"
]
},
{
"page": "morie_geron_he_init",
"title": "He (Kaiming) normal initialization (Geron Ch 11, morie.fn grhei)",
"topics": [
"morie_geron_he_init"
]
},
{
"page": "morie_geron_he_init_hmhei",
"title": "He (Kaiming) initialization (Geron Ch 11, morie.fn hmhei)",
"topics": [
"morie_geron_he_init_hmhei"
]
},
{
"page": "morie_geron_heaviside",
"title": "Heaviside step activation (Geron Ch 10, morie.fn hmhev)",
"topics": [
"morie_geron_heaviside"
]
},
{
"page": "morie_geron_heaviside_step",
"title": "Heaviside step activation (Geron Ch 10, morie.fn grhev)",
"topics": [
"morie_geron_heaviside_step"
]
},
{
"page": "morie_geron_hebb_rule",
"title": "Hebbian update (Geron Ch 10, morie.fn hmhebb)",
"topics": [
"morie_geron_hebb_rule"
]
},
{
"page": "morie_geron_hebb_rule_grhbb",
"title": "Perceptron (Hebb) learning rule (Geron Ch 10, morie.fn grhbb)",
"topics": [
"morie_geron_hebb_rule_grhbb"
]
},
{
"page": "morie_geron_hf_pipelines",
"title": "Task pipeline: preprocess -> model -> postprocess (Geron Ch 14, morie.fn hmhfpi)",
"topics": [
"morie_geron_hf_pipelines"
]
},
{
"page": "morie_geron_hf_trainer",
"title": "Trainer loop: mini-batch SGD with per-epoch evaluation (Geron Ch 14, morie.fn hmhftn)",
"topics": [
"morie_geron_hf_trainer"
]
},
{
"page": "morie_geron_hidden_layers_heuristic",
"title": "Depth selection by validation-error plateau (Geron Ch 9, morie.fn hmhplm)",
"topics": [
"morie_geron_hidden_layers_heuristic"
]
},
{
"page": "morie_geron_histogram_gradient_boosting",
"title": "Histogram-based gradient boosting, squared loss (Geron Ch 6, morie.fn hmhgb)",
"topics": [
"morie_geron_histogram_gradient_boosting"
]
},
{
"page": "morie_geron_hyperparameter_tuning",
"title": "Hyperparameter tuning by grid or random search (Geron Ch 1, morie.fn hmhpt)",
"topics": [
"morie_geron_hyperparameter_tuning"
]
},
{
"page": "morie_geron_image_segmentation",
"title": "Colour segmentation by k-means on pixels (Geron Ch 8, morie.fn hmiseg)",
"topics": [
"morie_geron_image_segmentation"
]
},
{
"page": "morie_geron_imputation_median",
"title": "Median imputation with a missingness indicator (Geron Ch 2, morie.fn hmimp)",
"topics": [
"morie_geron_imputation_median"
]
},
{
"page": "morie_geron_in_context_learning",
"title": "In-context / few-shot prompt assembly (Geron Ch 16, morie.fn grinc)",
"topics": [
"morie_geron_in_context_learning"
]
},
{
"page": "morie_geron_in_context_learning_hmicl",
"title": "In-context learning by prompt-conditioned scoring (Geron Ch 15, morie.fn hmicl)",
"topics": [
"morie_geron_in_context_learning_hmicl"
]
},
{
"page": "morie_geron_incremental_pca",
"title": "Incremental PCA via streaming co-moment updates (Geron Ch 7, morie.fn hmipca)",
"topics": [
"morie_geron_incremental_pca"
]
},
{
"page": "morie_geron_information_gain",
"title": "Information gain of a split (Geron Ch 6, morie.fn grig)",
"topics": [
"morie_geron_information_gain"
]
},
{
"page": "morie_geron_information_gain_hmigr",
"title": "Information gain from a split, entropy criterion (Geron Ch 5, morie.fn hmigr)",
"topics": [
"morie_geron_information_gain_hmigr"
]
},
{
"page": "morie_geron_instance_based",
"title": "k-nearest-neighbour (instance-based) prediction (Geron Ch 1, morie.fn hmins)",
"topics": [
"morie_geron_instance_based"
]
},
{
"page": "morie_geron_int8_quant",
"title": "Post-training integer quantization (Geron Ch 17, morie.fn hmint8)",
"topics": [
"morie_geron_int8_quant"
]
},
{
"page": "morie_geron_int8_quantization",
"title": "Symmetric INT8 quantization (Geron Ch 19, morie.fn grq8)",
"topics": [
"morie_geron_int8_quantization"
]
},
{
"page": "morie_geron_isomap",
"title": "Isomap: geodesic MDS via kNN graph + Dijkstra (Geron Ch 7, morie.fn hmiso)",
"topics": [
"morie_geron_isomap"
]
},
{
"page": "morie_geron_johnson_lindenstrauss",
"title": "Johnson-Lindenstrauss minimum dimension (Geron Ch 8, morie.fn hmjl)",
"topics": [
"morie_geron_johnson_lindenstrauss"
]
},
{
"page": "morie_geron_johnson_lindenstrauss_bound",
"title": "Johnson-Lindenstrauss minimum dimension (Geron Ch 8, morie.fn grjll)",
"topics": [
"morie_geron_johnson_lindenstrauss_bound"
]
},
{
"page": "morie_geron_kernel_pca_from_gram",
"title": "Eigen-decomposition step shared by every kernel-PCA variant (Geron Ch 7, morie.fn hmkprbf helper)",
"topics": [
"morie_geron_kernel_pca_from_gram"
]
},
{
"page": "morie_geron_kernel_pca_poly",
"title": "Kernel PCA with polynomial kernel (Geron Ch 7, morie.fn hmkppl)",
"topics": [
"morie_geron_kernel_pca_poly"
]
},
{
"page": "morie_geron_kernel_pca_rbf",
"title": "Kernel PCA with an RBF kernel (Geron Ch 8, morie.fn grkpc)",
"topics": [
"morie_geron_kernel_pca_rbf"
]
},
{
"page": "morie_geron_kernel_pca_rbf_hmkprbf",
"title": "Kernel PCA with RBF kernel (Geron Ch 7, morie.fn hmkprbf)",
"topics": [
"morie_geron_kernel_pca_rbf_hmkprbf"
]
},
{
"page": "morie_geron_kernel_pca_sigmoid",
"title": "Kernel PCA with sigmoid kernel (Geron Ch 7, morie.fn hmkpsg)",
"topics": [
"morie_geron_kernel_pca_sigmoid"
]
},
{
"page": "morie_geron_kfold",
"title": "K-fold partition plus CV score (Geron Ch 2, morie.fn hmkfd)",
"topics": [
"morie_geron_kfold"
]
},
{
"page": "morie_geron_kfold_cv",
"title": "K-fold cross-validation splits (Geron Ch 2, morie.fn grkfd)",
"topics": [
"morie_geron_kfold_cv"
]
},
{
"page": "morie_geron_kl_divergence_gaussian",
"title": "Gaussian KL to N(0, I) (Geron Ch 17, morie.fn grkldg)",
"topics": [
"morie_geron_kl_divergence_gaussian"
]
},
{
"page": "morie_geron_kmeans",
"title": "k-means clustering via Lloyd's algorithm, k-means++ seeded (Geron Ch 8, morie.fn hmkmn)",
"topics": [
"morie_geron_kmeans"
]
},
{
"page": "morie_geron_kmeans_limits",
"title": "k-means assumption diagnostics (Geron Ch 8, morie.fn hmkmlim)",
"topics": [
"morie_geron_kmeans_limits"
]
},
{
"page": "morie_geron_kmeans_objective",
"title": "k-means inertia (Geron Ch 9, morie.fn grkmo)",
"topics": [
"morie_geron_kmeans_objective"
]
},
{
"page": "morie_geron_kmeans_plus_plus",
"title": "k-means++ seeding, D^2 sampling (Geron Ch 8, morie.fn hmkmpp)",
"topics": [
"morie_geron_kmeans_plus_plus"
]
},
{
"page": "morie_geron_kmeans_pp_seeding",
"title": "k-means++ seeding (Geron Ch 9, morie.fn grkmpp)",
"topics": [
"morie_geron_kmeans_pp_seeding"
]
},
{
"page": "morie_geron_knowledge_distillation",
"title": "Knowledge distillation loss (Geron Ch 17, morie.fn hmkd)",
"topics": [
"morie_geron_knowledge_distillation"
]
},
{
"page": "morie_geron_knowledge_distillation_loss",
"title": "Knowledge distillation loss (Geron Ch 19, morie.fn grkdl)",
"topics": [
"morie_geron_knowledge_distillation_loss"
]
},
{
"page": "morie_geron_kv_cache_compress",
"title": "KV-cache quantization (Geron Ch 17, morie.fn hmkvc)",
"topics": [
"morie_geron_kv_cache_compress"
]
},
{
"page": "morie_geron_kv_cache_compression",
"title": "KV-cache memory footprint (Geron Ch 16, morie.fn grkvc)",
"topics": [
"morie_geron_kv_cache_compression"
]
},
{
"page": "morie_geron_l1_regularization",
"title": "L1 (Lasso) penalty, subgradient and prox (Geron Ch 4, morie.fn hml1r)",
"topics": [
"morie_geron_l1_regularization"
]
},
{
"page": "morie_geron_l2_regularization",
"title": "L2 (ridge) regularization penalty (Geron Ch 11, morie.fn hml2r)",
"topics": [
"morie_geron_l2_regularization"
]
},
{
"page": "morie_geron_lasso_cost",
"title": "Lasso cost (Geron Eq 4-10, morie.fn grlaso)",
"topics": [
"morie_geron_lasso_cost"
]
},
{
"page": "morie_geron_lasso_cost_hmlaso",
"title": "Lasso cost = MSE + alpha * L1 penalty (Geron Ch 4, morie.fn hmlaso)",
"topics": [
"morie_geron_lasso_cost_hmlaso"
]
},
{
"page": "morie_geron_layer_norm_rnn",
"title": "Layer normalization inside an RNN cell (Geron Ch 13, morie.fn hmlnr)",
"topics": [
"morie_geron_layer_norm_rnn"
]
},
{
"page": "morie_geron_layer_normalization",
"title": "Layer normalization (Geron Ch 15, morie.fn grln)",
"topics": [
"morie_geron_layer_normalization"
]
},
{
"page": "morie_geron_layer_normalization_hmlntr",
"title": "Layer normalization: per-sample, across features (Geron Ch 11, morie.fn hmlntr)",
"topics": [
"morie_geron_layer_normalization_hmlntr"
]
},
{
"page": "morie_geron_layer_norms",
"title": "Per-layer gradient L2 norms, ordered input side first",
"topics": [
"morie_geron_layer_norms"
]
},
{
"page": "morie_geron_leaky_relu",
"title": "Leaky ReLU (Geron Ch 11, morie.fn hmlrel)",
"topics": [
"morie_geron_leaky_relu"
]
},
{
"page": "morie_geron_learning_curves",
"title": "Learning curves (Geron Ch 4, morie.fn grlrnc)",
"topics": [
"morie_geron_learning_curves"
]
},
{
"page": "morie_geron_learning_curves_hmlcv",
"title": "Learning curves: train/validation RMSE vs training-set size (Geron Ch 4, morie.fn hmlcv)",
"topics": [
"morie_geron_learning_curves_hmlcv"
]
},
{
"page": "morie_geron_learning_rate_heuristic",
"title": "Learning-rate finder heuristic: divergence point / safety (Geron Ch 9, morie.fn hmlrh)",
"topics": [
"morie_geron_learning_rate_heuristic"
]
},
{
"page": "morie_geron_learning_rate_schedule",
"title": "Power (1/t) learning-rate schedule (Geron Ch 4, morie.fn hmlrs)",
"topics": [
"morie_geron_learning_rate_schedule"
]
},
{
"page": "morie_geron_lenet5",
"title": "LeNet-5 architecture resolution (Geron Ch 12, morie.fn hmlnet)",
"topics": [
"morie_geron_lenet5"
]
},
{
"page": "morie_geron_linear_layer_forward",
"title": "Affine layer forward pass (Geron Ch 10, morie.fn grlinf)",
"topics": [
"morie_geron_linear_layer_forward"
]
},
{
"page": "morie_geron_linear_regression_life",
"title": "Life satisfaction = theta0 + theta1 * GDP per capita (Geron Ch 1, morie.fn hmlrl)",
"topics": [
"morie_geron_linear_regression_life"
]
},
{
"page": "morie_geron_linreg_mse_cost",
"title": "Linear-regression MSE cost and gradient (Geron Ch 4, morie.fn hmmsec)",
"topics": [
"morie_geron_linreg_mse_cost"
]
},
{
"page": "morie_geron_linreg_mse_cost_grmse",
"title": "Linear-regression MSE cost (Geron Eq 4-3, morie.fn grmse)",
"topics": [
"morie_geron_linreg_mse_cost_grmse"
]
},
{
"page": "morie_geron_linreg_pytorch",
"title": "Linear regression by SGD, cross-checked against normal equations (Geron Ch 10, morie.fn hmlrpt)",
"topics": [
"morie_geron_linreg_pytorch"
]
},
{
"page": "morie_geron_local_outlier_factor",
"title": "Local outlier factor (Geron Ch 9, morie.fn grlof)",
"topics": [
"morie_geron_local_outlier_factor"
]
},
{
"page": "morie_geron_local_outlier_factor_hmlof",
"title": "Local outlier factor (Geron Ch 8, morie.fn hmlof)",
"topics": [
"morie_geron_local_outlier_factor_hmlof"
]
},
{
"page": "morie_geron_locally_linear_embedding",
"title": "Locally linear embedding (Geron Ch 7, morie.fn hmlle)",
"topics": [
"morie_geron_locally_linear_embedding"
]
},
{
"page": "morie_geron_logistic_cost",
"title": "Binary log loss (Geron Ch 4, morie.fn hmlogcl)",
"topics": [
"morie_geron_logistic_cost"
]
},
{
"page": "morie_geron_logistic_cost_gradient",
"title": "Logistic cost gradient (Geron Eq 4-18, morie.fn grlogg)",
"topics": [
"morie_geron_logistic_cost_gradient"
]
},
{
"page": "morie_geron_logistic_cross_entropy_cost",
"title": "Binary logistic log-loss (Geron Eq 4-17, morie.fn grlogc)",
"topics": [
"morie_geron_logistic_cross_entropy_cost"
]
},
{
"page": "morie_geron_logistic_gradient",
"title": "Logistic gradient and Hessian (Geron Ch 4, morie.fn hmlogg)",
"topics": [
"morie_geron_logistic_gradient"
]
},
{
"page": "morie_geron_logistic_probability",
"title": "Logistic probability (Geron Ch 4, morie.fn hmlogp)",
"topics": [
"morie_geron_logistic_probability"
]
},
{
"page": "morie_geron_logistic_regression_probability",
"title": "Logistic regression probability (Geron Eq 4-15, morie.fn grlogp)",
"topics": [
"morie_geron_logistic_regression_probability"
]
},
{
"page": "morie_geron_lr_cosine_annealing",
"title": "Cosine annealing LR schedule (Geron Ch 11, morie.fn grlrco)",
"topics": [
"morie_geron_lr_cosine_annealing"
]
},
{
"page": "morie_geron_lr_exponential",
"title": "Exponential learning-rate decay (Geron Ch 11, morie.fn hmlrex)",
"topics": [
"morie_geron_lr_exponential"
]
},
{
"page": "morie_geron_lr_exponential_schedule",
"title": "Exponential LR decay (Geron Ch 11, morie.fn grlrex)",
"topics": [
"morie_geron_lr_exponential_schedule"
]
},
{
"page": "morie_geron_lstm",
"title": "LSTM cell forward step (Geron Ch 13, morie.fn hmlstm)",
"topics": [
"morie_geron_lstm"
]
},
{
"page": "morie_geron_lstm_cell",
"title": "LSTM cell forward pass (Geron Ch 15, morie.fn grlstc)",
"topics": [
"morie_geron_lstm_cell"
]
},
{
"page": "morie_geron_mae",
"title": "Mean absolute error (Geron Ch 2, morie.fn hmmae)",
"topics": [
"morie_geron_mae"
]
},
{
"page": "morie_geron_mae_grmae",
"title": "Mean absolute error (Geron Eq 2-2, morie.fn grmae)",
"topics": [
"morie_geron_mae_grmae"
]
},
{
"page": "morie_geron_masked_lm",
"title": "Masked language modelling objective (Geron Ch 15, morie.fn hmmlm)",
"topics": [
"morie_geron_masked_lm"
]
},
{
"page": "morie_geron_matmul_order",
"title": "Optimal parenthesisation of a matrix chain (matrix-chain DP)",
"topics": [
"morie_geron_matmul_order"
]
},
{
"page": "morie_geron_max_norm",
"title": "Max-norm projection (Geron Ch 11, morie.fn hmmnr)",
"topics": [
"morie_geron_max_norm"
]
},
{
"page": "morie_geron_max_norm_regularization",
"title": "Max-norm weight projection (Geron Ch 11, morie.fn grmnr)",
"topics": [
"morie_geron_max_norm_regularization"
]
},
{
"page": "morie_geron_max_pool",
"title": "Max pooling over a sliding window, per channel (Geron Ch 12, morie.fn hmmxp)",
"topics": [
"morie_geron_max_pool"
]
},
{
"page": "morie_geron_max_pooling",
"title": "2D max pooling (Geron Ch 14, morie.fn grmpl)",
"topics": [
"morie_geron_max_pooling"
]
},
{
"page": "morie_geron_mc_dropout",
"title": "Monte Carlo dropout uncertainty (Geron Ch 11, morie.fn hmmcd)",
"topics": [
"morie_geron_mc_dropout"
]
},
{
"page": "morie_geron_mdp",
"title": "MDP validation plus value iteration (Geron Ch 19, morie.fn hmmdp)",
"topics": [
"morie_geron_mdp"
]
},
{
"page": "morie_geron_mds",
"title": "Classical (Torgerson) multidimensional scaling (Geron Ch 7, morie.fn hmmds)",
"topics": [
"morie_geron_mds"
]
},
{
"page": "morie_geron_mean_shift",
"title": "Mean shift: mode-seeking via kernel density gradient ascent (Geron Ch 8, morie.fn hmmnsh)",
"topics": [
"morie_geron_mean_shift"
]
},
{
"page": "morie_geron_memory_cell",
"title": "Memory-cell recurrence c_t = f(c_prev, x_t) (Geron Ch 13, morie.fn hmmcel)",
"topics": [
"morie_geron_memory_cell"
]
},
{
"page": "morie_geron_min_max_scaling",
"title": "Min-max scaling to a target range (Geron Ch 2, morie.fn hmmms)",
"topics": [
"morie_geron_min_max_scaling"
]
},
{
"page": "morie_geron_minibatch_gd",
"title": "Mini-batch gradient descent (Geron Ch 4, morie.fn hmmbgd)",
"topics": [
"morie_geron_minibatch_gd"
]
},
{
"page": "morie_geron_minibatch_gradient_descent",
"title": "Mini-batch gradient descent (Geron Ch 4, morie.fn grmgd)",
"topics": [
"morie_geron_minibatch_gradient_descent"
]
},
{
"page": "morie_geron_minibatch_kmeans",
"title": "Mini-batch k-means, Sculley's variant (Geron Ch 8, morie.fn hmmbkm)",
"topics": [
"morie_geron_minibatch_kmeans"
]
},
{
"page": "morie_geron_minmax_scaler",
"title": "Min-max feature scaling (Geron Ch 2, morie.fn grmms)",
"topics": [
"morie_geron_minmax_scaler"
]
},
{
"page": "morie_geron_mistral7b",
"title": "Mistral-7B architecture accounting (Geron Ch 15, morie.fn hmmis7)",
"topics": [
"morie_geron_mistral7b"
]
},
{
"page": "morie_geron_mlp",
"title": "MLP forward pass (Geron Ch 9, morie.fn hmmlpf)",
"topics": [
"morie_geron_mlp"
]
},
{
"page": "morie_geron_mlp_forward",
"title": "MLP forward pass (Geron Ch 10, morie.fn grmlpf)",
"topics": [
"morie_geron_mlp_forward"
]
},
{
"page": "morie_geron_mode_collapse",
"title": "Mode-collapse diagnostics (Geron Ch 18, morie.fn hmmdc)",
"topics": [
"morie_geron_mode_collapse"
]
},
{
"page": "morie_geron_model_based",
"title": "Model-based least squares, closed form cross-checked against descent (Geron Ch 1, morie.fn hmmod)",
"topics": [
"morie_geron_model_based"
]
},
{
"page": "morie_geron_model_context_protocol",
"title": "MCP/JSON-RPC 2.0 exchange with envelope validation (Geron Ch 15, morie.fn hmmcp)",
"topics": [
"morie_geron_model_context_protocol"
]
},
{
"page": "morie_geron_model_parallelism",
"title": "Optimal contiguous model-parallel layer partition (Geron Ch 17, morie.fn hmmpp)",
"topics": [
"morie_geron_model_parallelism"
]
},
{
"page": "morie_geron_momentum",
"title": "Momentum step (Geron Ch 11, morie.fn hmmom)",
"topics": [
"morie_geron_momentum"
]
},
{
"page": "morie_geron_momentum_update",
"title": "Momentum optimizer step (Geron Ch 11, morie.fn grmom)",
"topics": [
"morie_geron_momentum_update"
]
},
{
"page": "morie_geron_multi_head_attention",
"title": "Multi-head attention (Geron Ch 16, morie.fn grmha)",
"topics": [
"morie_geron_multi_head_attention"
]
},
{
"page": "morie_geron_multihead_attention",
"title": "Multi-head attention: split, attend, concat, project (Geron Ch 15, morie.fn hmmha)",
"topics": [
"morie_geron_multihead_attention"
]
},
{
"page": "morie_geron_multilabel",
"title": "Multilabel classification metrics, leave-one-out kNN default (Geron Ch 3, morie.fn hmmlb)",
"topics": [
"morie_geron_multilabel"
]
},
{
"page": "morie_geron_multilabel_classification",
"title": "Multilabel thresholded prediction (Geron Ch 3, morie.fn grmlb)",
"topics": [
"morie_geron_multilabel_classification"
]
},
{
"page": "morie_geron_multinomial_logistic",
"title": "Softmax regression fitted by batch gradient descent (Geron Ch 4, morie.fn hmmnl)",
"topics": [
"morie_geron_multinomial_logistic"
]
},
{
"page": "morie_geron_multioutput",
"title": "Multioutput classification by k-NN vote (Geron Ch 3, morie.fn hmmto)",
"topics": [
"morie_geron_multioutput"
]
},
{
"page": "morie_geron_nadam",
"title": "NAdam step (Geron Ch 11, morie.fn hmnadm)",
"topics": [
"morie_geron_nadam"
]
},
{
"page": "morie_geron_nesterov",
"title": "Nesterov accelerated gradient (Geron Ch 11, morie.fn hmnag)",
"topics": [
"morie_geron_nesterov"
]
},
{
"page": "morie_geron_nesterov_accelerated_gradient",
"title": "Nesterov accelerated gradient (Geron Ch 11, morie.fn grnag)",
"topics": [
"morie_geron_nesterov_accelerated_gradient"
]
},
{
"page": "morie_geron_neurons_per_layer",
"title": "Neurons-per-layer heuristic (Geron Ch 10, morie.fn hmnpl)",
"topics": [
"morie_geron_neurons_per_layer"
]
},
{
"page": "morie_geron_nmf_objective",
"title": "NMF Frobenius objective (Geron Ch 8, morie.fn grnmfo)",
"topics": [
"morie_geron_nmf_objective"
]
},
{
"page": "morie_geron_normal_equation",
"title": "OLS by the normal equation (Geron Ch 4, morie.fn hmneq)",
"topics": [
"morie_geron_normal_equation"
]
},
{
"page": "morie_geron_normal_equation_grnorm",
"title": "Normal equation, closed-form OLS (morie.fn grnorm)",
"topics": [
"morie_geron_normal_equation_grnorm"
]
},
{
"page": "morie_geron_numerical_differentiation",
"title": "Central-difference numerical derivative (morie.fn grnud)",
"topics": [
"morie_geron_numerical_differentiation"
]
},
{
"page": "morie_geron_one_cycle",
"title": "1cycle learning-rate policy (Geron Ch 11, morie.fn hml1c)",
"topics": [
"morie_geron_one_cycle"
]
},
{
"page": "morie_geron_one_hot_encoding",
"title": "One-hot encoding (Geron Ch 2, morie.fn hmohe)",
"topics": [
"morie_geron_one_hot_encoding"
]
},
{
"page": "morie_geron_one_hot_encoding_grohe",
"title": "One-hot encoding (Geron Ch 2, morie.fn grohe)",
"topics": [
"morie_geron_one_hot_encoding_grohe"
]
},
{
"page": "morie_geron_one_vs_one",
"title": "One-vs-One multiclass reduction (Geron Ch 3, morie.fn grovo)",
"topics": [
"morie_geron_one_vs_one"
]
},
{
"page": "morie_geron_one_vs_rest",
"title": "One-vs-Rest multiclass reduction (Geron Ch 3, morie.fn grovr)",
"topics": [
"morie_geron_one_vs_rest"
]
},
{
"page": "morie_geron_ordinal_encoding",
"title": "Ordinal encoding (Geron Ch 2, morie.fn hmord)",
"topics": [
"morie_geron_ordinal_encoding"
]
},
{
"page": "morie_geron_ordinal_encoding_grord",
"title": "Ordinal encoding (Geron Ch 2, morie.fn grord)",
"topics": [
"morie_geron_ordinal_encoding_grord"
]
},
{
"page": "morie_geron_overfitting",
"title": "Overfitting diagnostic (Geron Ch 4, morie.fn hmovf)",
"topics": [
"morie_geron_overfitting"
]
},
{
"page": "morie_geron_overfitting_gap",
"title": "Train-validation generalisation gap (morie.fn groft)",
"topics": [
"morie_geron_overfitting_gap"
]
},
{
"page": "morie_geron_pca_projection",
"title": "PCA projection (Geron Ch 8, morie.fn grpcap)",
"topics": [
"morie_geron_pca_projection"
]
},
{
"page": "morie_geron_peephole_lstm_cell",
"title": "Peephole LSTM cell (Geron Ch 15, morie.fn grpels)",
"topics": [
"morie_geron_peephole_lstm_cell"
]
},
{
"page": "morie_geron_perceiver_io",
"title": "Perceiver IO cross-attention bottleneck (morie.fn grpio)",
"topics": [
"morie_geron_perceiver_io"
]
},
{
"page": "morie_geron_perceptron",
"title": "Perceptron learning rule (Geron Ch 10, morie.fn hmpcpt)",
"topics": [
"morie_geron_perceptron"
]
},
{
"page": "morie_geron_permutation_masks",
"title": "Two-stream attention masks for a factorisation order",
"topics": [
"morie_geron_permutation_masks"
]
},
{
"page": "morie_geron_polynomial_features",
"title": "Polynomial feature expansion (Geron Ch 4, morie.fn grpoly)",
"topics": [
"morie_geron_polynomial_features"
]
},
{
"page": "morie_geron_positional_encoding",
"title": "Sinusoidal positional encoding (Geron Ch 16, morie.fn hmpe)",
"topics": [
"morie_geron_positional_encoding"
]
},
{
"page": "morie_geron_ppo_clipped_objective",
"title": "PPO clipped surrogate objective (Geron Ch 19, morie.fn grppo)",
"topics": [
"morie_geron_ppo_clipped_objective"
]
},
{
"page": "morie_geron_precision",
"title": "Precision TP/(TP+FP) (Geron Ch 3, morie.fn grpre)",
"topics": [
"morie_geron_precision"
]
},
{
"page": "morie_geron_precision_recall_curve",
"title": "Precision-recall curve (Geron Ch 3, morie.fn grprc)",
"topics": [
"morie_geron_precision_recall_curve"
]
},
{
"page": "morie_geron_predict_tree",
"title": "Route rows down a fitted CART tree (morie.fn hmcart::predict_tree)",
"topics": [
"morie_geron_predict_tree"
]
},
{
"page": "morie_geron_prioritized_experience_weight",
"title": "Prioritized replay importance weights (Geron Ch 19, morie.fn grpex)",
"topics": [
"morie_geron_prioritized_experience_weight"
]
},
{
"page": "morie_geron_pyramid_vit_stage",
"title": "Pyramid ViT spatial-reduction attention (morie.fn grpvt)",
"topics": [
"morie_geron_pyramid_vit_stage"
]
},
{
"page": "morie_geron_q_learning",
"title": "Tabular Q-learning update (Geron Ch 18, morie.fn hmql)",
"topics": [
"morie_geron_q_learning"
]
},
{
"page": "morie_geron_q_learning_update",
"title": "Q-learning update (Geron Ch 18, morie.fn grql)",
"topics": [
"morie_geron_q_learning_update"
]
},
{
"page": "morie_geron_q_value_iteration",
"title": "Q-value iteration (Geron Ch 19, morie.fn grbo)",
"topics": [
"morie_geron_q_value_iteration"
]
},
{
"page": "morie_geron_quantization_aware_training",
"title": "Quantization-aware training (Geron Ch 19, morie.fn grqat)",
"topics": [
"morie_geron_quantization_aware_training"
]
},
{
"page": "morie_geron_quantize",
"title": "Nearest-codebook-entry assignment",
"topics": [
"morie_geron_quantize"
]
},
{
"page": "morie_geron_quantize_symmetric",
"title": "Symmetric quantization kernel (morie.fn grq8 'quantize_symmetric')",
"topics": [
"morie_geron_quantize_symmetric"
]
},
{
"page": "morie_geron_randomized_search_cv",
"title": "Randomized search with K-fold CV (Geron Ch 2, morie.fn grrnd)",
"topics": [
"morie_geron_randomized_search_cv"
]
},
{
"page": "morie_geron_recall",
"title": "Recall TP/(TP+FN) (Geron Ch 3, morie.fn grrec)",
"topics": [
"morie_geron_recall"
]
},
{
"page": "morie_geron_regression_mlp_output",
"title": "Regression MLP output head (Geron Ch 10, morie.fn grmlr)",
"topics": [
"morie_geron_regression_mlp_output"
]
},
{
"page": "morie_geron_reinforce_policy_gradient",
"title": "REINFORCE Monte Carlo policy gradient (Geron Ch 18, morie.fn grrein)",
"topics": [
"morie_geron_reinforce_policy_gradient"
]
},
{
"page": "morie_geron_reparameterization_trick",
"title": "Reparameterization trick (Geron Ch 17, morie.fn grrep)",
"topics": [
"morie_geron_reparameterization_trick"
]
},
{
"page": "morie_geron_resnet",
"title": "Residual block (Geron Ch 14, morie.fn hmresn)",
"topics": [
"morie_geron_resnet"
]
},
{
"page": "morie_geron_resnet_skip",
"title": "Residual (skip) connection (Geron Ch 14, morie.fn grrsk)",
"topics": [
"morie_geron_resnet_skip"
]
},
{
"page": "morie_geron_reverse_mode_autodiff",
"title": "Reverse-mode automatic differentiation (Geron Ch 12, morie.fn grrad)",
"topics": [
"morie_geron_reverse_mode_autodiff"
]
},
{
"page": "morie_geron_ridge_cost",
"title": "Ridge cost (Geron Ch 4, morie.fn hmridg)",
"topics": [
"morie_geron_ridge_cost"
]
},
{
"page": "morie_geron_ridge_cost_grridg",
"title": "Ridge cost (Geron Ch 4, morie.fn grridg)",
"topics": [
"morie_geron_ridge_cost_grridg"
]
},
{
"page": "morie_geron_ridge_normal",
"title": "Ridge by the augmented normal equation (Geron Ch 4, morie.fn hmridn)",
"topics": [
"morie_geron_ridge_normal"
]
},
{
"page": "morie_geron_ridge_normal_equation",
"title": "Ridge closed form (Geron Ch 4, morie.fn grridn)",
"topics": [
"morie_geron_ridge_normal_equation"
]
},
{
"page": "morie_geron_rlhf_reward_kl_objective",
"title": "RLHF reward minus KL objective (Geron Ch 15, morie.fn grrlhf)",
"topics": [
"morie_geron_rlhf_reward_kl_objective"
]
},
{
"page": "morie_geron_rmse",
"title": "Root mean squared error (Geron Ch 2, morie.fn hmrms)",
"topics": [
"morie_geron_rmse"
]
},
{
"page": "morie_geron_rmse_grrmse",
"title": "Root mean squared error (Geron Ch 2, morie.fn grrmse)",
"topics": [
"morie_geron_rmse_grrmse"
]
},
{
"page": "morie_geron_roberta",
"title": "RoBERTa pretraining with dynamic masking (Geron Ch 15, morie.fn hmbrob)",
"topics": [
"morie_geron_roberta"
]
},
{
"page": "morie_geron_roc_curve",
"title": "ROC curve (Geron Ch 3, morie.fn grroc)",
"topics": [
"morie_geron_roc_curve"
]
},
{
"page": "morie_geron_run_graph",
"title": "Re-execute a traced graph, enforcing recorded shapes (ncol must match)",
"topics": [
"morie_geron_run_graph"
]
},
{
"page": "morie_geron_sac",
"title": "Soft actor-critic (SAC): entropy-regularized max reward",
"topics": [
"morie_geron_sac"
]
},
{
"page": "morie_geron_save_load_pytorch",
"title": "Save and load PyTorch model state_dict (RDS-native stand-in)",
"topics": [
"morie_geron_save_load_pytorch"
]
},
{
"page": "morie_geron_scaled_dot_product",
"title": "Scaled dot-product attention",
"topics": [
"morie_geron_scaled_dot_product"
]
},
{
"page": "morie_geron_scaled_dot_product_attention",
"title": "Scaled dot-product attention (Geron Ch 16, morie.fn grsdpa)",
"topics": [
"morie_geron_scaled_dot_product_attention"
]
},
{
"page": "morie_geron_score_matching_loss",
"title": "Denoising score matching loss (Geron Ch 18, morie.fn grscm)",
"topics": [
"morie_geron_score_matching_loss"
]
},
{
"page": "morie_geron_self_attention",
"title": "Self-attention, single head (Geron Ch 16, morie.fn grsa)",
"topics": [
"morie_geron_self_attention"
]
},
{
"page": "morie_geron_self_attention_modules",
"title": "Self-attention: Q=K=V come from same input",
"topics": [
"morie_geron_self_attention_modules"
]
},
{
"page": "morie_geron_self_supervised",
"title": "Self-supervised learning: generate labels from the data itself via pretext task",
"topics": [
"morie_geron_self_supervised"
]
},
{
"page": "morie_geron_selu",
"title": "SELU activation (Geron Ch 11, morie.fn hmselu)",
"topics": [
"morie_geron_selu"
]
},
{
"page": "morie_geron_semantic_segmentation",
"title": "Semantic segmentation: per-pixel class labels",
"topics": [
"morie_geron_semantic_segmentation"
]
},
{
"page": "morie_geron_semisupervised",
"title": "Semi-supervised learning: small labeled set plus large unlabeled pool",
"topics": [
"morie_geron_semisupervised"
]
},
{
"page": "morie_geron_semisupervised_cluster",
"title": "Semi-supervised learning via k-means representative labeling",
"topics": [
"morie_geron_semisupervised_cluster"
]
},
{
"page": "morie_geron_senet",
"title": "Squeeze-and-Excitation (SENet) block for channel recalibration",
"topics": [
"morie_geron_senet"
]
},
{
"page": "morie_geron_senet_squeeze_excite",
"title": "Squeeze-and-Excitation channel attention (Geron Ch 14, morie.fn grsen)",
"topics": [
"morie_geron_senet_squeeze_excite"
]
},
{
"page": "morie_geron_sentiment_analysis",
"title": "Sentiment analysis with RNN or transformer on tokens",
"topics": [
"morie_geron_sentiment_analysis"
]
},
{
"page": "morie_geron_sentiment_binary",
"title": "Binary sentiment head (Geron Ch 16, morie.fn grsnt)",
"topics": [
"morie_geron_sentiment_binary"
]
},
{
"page": "morie_geron_separable_params",
"title": "Depthwise separable convolution weight count",
"topics": [
"morie_geron_separable_params"
]
},
{
"page": "morie_geron_seq2seq",
"title": "Sequence-to-sequence encoder-decoder architecture",
"topics": [
"morie_geron_seq2seq"
]
},
{
"page": "morie_geron_sft",
"title": "Supervised fine-tuning (SFT) on instruction-response pairs",
"topics": [
"morie_geron_sft"
]
},
{
"page": "morie_geron_sft_objective",
"title": "SFT masked cross-entropy objective (Geron Ch 15, morie.fn grsft)",
"topics": [
"morie_geron_sft_objective"
]
},
{
"page": "morie_geron_sgd_classifier",
"title": "SGD classifier with hinge loss (linear SVM) trained by stochastic gradient descent",
"topics": [
"morie_geron_sgd_classifier"
]
},
{
"page": "morie_geron_sgd_update",
"title": "Single-sample SGD step (Geron Ch 4, morie.fn hmsgdu)",
"topics": [
"morie_geron_sgd_update"
]
},
{
"page": "morie_geron_shannon_entropy",
"title": "Shannon entropy (Geron Eq 5-3, morie.fn grent)",
"topics": [
"morie_geron_shannon_entropy"
]
},
{
"page": "morie_geron_sigmoid",
"title": "Logistic sigmoid (Geron Ch 4, morie.fn hmsigm)",
"topics": [
"morie_geron_sigmoid"
]
},
{
"page": "morie_geron_sigmoid_grsig",
"title": "Logistic (sigmoid) activation (morie.fn grsig)",
"topics": [
"morie_geron_sigmoid_grsig"
]
},
{
"page": "morie_geron_silhouette",
"title": "Silhouette score for cluster quality",
"topics": [
"morie_geron_silhouette"
]
},
{
"page": "morie_geron_silhouette_score",
"title": "Silhouette score (Geron Ch 9, morie.fn grsil)",
"topics": [
"morie_geron_silhouette_score"
]
},
{
"page": "morie_geron_simple_imputer",
"title": "Simple imputation (Geron Ch 2, morie.fn grimp)",
"topics": [
"morie_geron_simple_imputer"
]
},
{
"page": "morie_geron_simple_rnn",
"title": "Simple RNN forward pass over a sequence",
"topics": [
"morie_geron_simple_rnn"
]
},
{
"page": "morie_geron_simple_rnn_cell",
"title": "Simple (Elman) RNN cell (Geron Ch 15, morie.fn grrnnc)",
"topics": [
"morie_geron_simple_rnn_cell"
]
},
{
"page": "morie_geron_sinusoidal_positional_encoding",
"title": "Sinusoidal positional encoding (Geron Ch 16, morie.fn grpe)",
"topics": [
"morie_geron_sinusoidal_positional_encoding"
]
},
{
"page": "morie_geron_soft_voting",
"title": "Soft voting ensemble (Geron Ch 7, morie.fn grvots)",
"topics": [
"morie_geron_soft_voting"
]
},
{
"page": "morie_geron_softmax_cost_gradient",
"title": "Softmax cross-entropy gradient (morie.fn grxeng)",
"topics": [
"morie_geron_softmax_cost_gradient"
]
},
{
"page": "morie_geron_softmax_cross_entropy_cost",
"title": "Softmax cross-entropy cost (morie.fn grxent)",
"topics": [
"morie_geron_softmax_cross_entropy_cost"
]
},
{
"page": "morie_geron_softmax_function",
"title": "Softmax (Geron Ch 4, morie.fn hmsftm)",
"topics": [
"morie_geron_softmax_function"
]
},
{
"page": "morie_geron_softmax_probability",
"title": "Softmax regression probabilities (morie.fn grsmxp)",
"topics": [
"morie_geron_softmax_probability"
]
},
{
"page": "morie_geron_softmax_score",
"title": "Linear class scores for softmax regression (Geron Ch 4, morie.fn hmsfts)",
"topics": [
"morie_geron_softmax_score"
]
},
{
"page": "morie_geron_softmax_score_grsmxs",
"title": "Softmax class scores (morie.fn grsmxs)",
"topics": [
"morie_geron_softmax_score_grsmxs"
]
},
{
"page": "morie_geron_span_corrupt",
"title": "T5 span-corruption masking (encoder input / decoder target / spans)",
"topics": [
"morie_geron_span_corrupt"
]
},
{
"page": "morie_geron_sparse_autoencoder",
"title": "Sparse autoencoder objective (Geron Ch 17, morie.fn grsae)",
"topics": [
"morie_geron_sparse_autoencoder"
]
},
{
"page": "morie_geron_sparse_rand_projection",
"title": "Sparse random projection matrix with {-1,0,+1} entries",
"topics": [
"morie_geron_sparse_rand_projection"
]
},
{
"page": "morie_geron_spectral_clustering",
"title": "Spectral clustering: eigenvectors of graph Laplacian",
"topics": [
"morie_geron_spectral_clustering"
]
},
{
"page": "morie_geron_stacked_autoencoder",
"title": "Stacked (deep) autoencoder forward pass (morie.fn grstae)",
"topics": [
"morie_geron_stacked_autoencoder"
]
},
{
"page": "morie_geron_stacked_autoencoder_modules",
"title": "Stacked (deep) autoencoder with multiple encoding/decoding layers",
"topics": [
"morie_geron_stacked_autoencoder_modules"
]
},
{
"page": "morie_geron_stacking",
"title": "Stacking (blending): meta-learner combines outputs of base learners",
"topics": [
"morie_geron_stacking"
]
},
{
"page": "morie_geron_stacking_predictor",
"title": "Stacking blender (Geron Ch 7, morie.fn grstk)",
"topics": [
"morie_geron_stacking_predictor"
]
},
{
"page": "morie_geron_standardization",
"title": "Standardization / z-score (Geron Ch 2, morie.fn hmstz)",
"topics": [
"morie_geron_standardization"
]
},
{
"page": "morie_geron_standardization_grstd",
"title": "Standardization (Geron Ch 2, morie.fn grstd)",
"topics": [
"morie_geron_standardization_grstd"
]
},
{
"page": "morie_geron_state_value_function",
"title": "State value function V^pi (Geron Ch 18, morie.fn grvpi)",
"topics": [
"morie_geron_state_value_function"
]
},
{
"page": "morie_geron_static_ptq",
"title": "Static post-training quantization (Geron Ch 19, morie.fn grptq)",
"topics": [
"morie_geron_static_ptq"
]
},
{
"page": "morie_geron_stochastic_gradient_descent",
"title": "Stochastic gradient descent (Geron Ch 4, morie.fn grsgd)",
"topics": [
"morie_geron_stochastic_gradient_descent"
]
},
{
"page": "morie_geron_stratified_sampling",
"title": "Stratified sampling preserves class/strata proportions in each split",
"topics": [
"morie_geron_stratified_sampling"
]
},
{
"page": "morie_geron_stride",
"title": "Stride: step size of kernel sliding over input",
"topics": [
"morie_geron_stride"
]
},
{
"page": "morie_geron_supervised_learning",
"title": "Supervised learning paradigm: learn mapping f(x)->y from labeled examples",
"topics": [
"morie_geron_supervised_learning"
]
},
{
"page": "morie_geron_svd_pseudoinverse",
"title": "OLS via SVD pseudoinverse (robust to singular X^T X)",
"topics": [
"morie_geron_svd_pseudoinverse"
]
},
{
"page": "morie_geron_swin",
"title": "Swin Transformer: shifted-window attention",
"topics": [
"morie_geron_swin"
]
},
{
"page": "morie_geron_swin_window_attention",
"title": "Swin windowed self-attention (morie.fn grswin)",
"topics": [
"morie_geron_swin_window_attention"
]
},
{
"page": "morie_geron_swish",
"title": "Swish / SiLU activation (Geron Ch 11, morie.fn hmswi)",
"topics": [
"morie_geron_swish"
]
},
{
"page": "morie_geron_symbolic_diff",
"title": "Symbolic differentiation: manipulate algebraic expressions analytically",
"topics": [
"morie_geron_symbolic_diff"
]
},
{
"page": "morie_geron_symbolic_differentiation",
"title": "Symbolic differentiation (Geron Ch 12, morie.fn grsmd)",
"topics": [
"morie_geron_symbolic_differentiation"
]
},
{
"page": "morie_geron_symd_evaluate",
"title": "Evaluate a parsed tree against an environment (no eval/parse of R code)",
"topics": [
"morie_geron_symd_evaluate"
]
},
{
"page": "morie_geron_symd_parse",
"title": "Parse an arithmetic expression string into an R tree",
"topics": [
"morie_geron_symd_parse"
]
},
{
"page": "morie_geron_symd_to_string",
"title": "Render a parsed tree back to infix source",
"topics": [
"morie_geron_symd_to_string"
]
},
{
"page": "morie_geron_t5",
"title": "T5: text-to-text transfer transformer (encoder-decoder)",
"topics": [
"morie_geron_t5"
]
},
{
"page": "morie_geron_t5_restore",
"title": "Rebuild the original sequence from a T5-corrupted input and its target",
"topics": [
"morie_geron_t5_restore"
]
},
{
"page": "morie_geron_tanh",
"title": "Hyperbolic tangent activation (Geron Ch 10, morie.fn hmtanh)",
"topics": [
"morie_geron_tanh"
]
},
{
"page": "morie_geron_tanh_activation",
"title": "Hyperbolic tangent activation (morie.fn grtnh)",
"topics": [
"morie_geron_tanh_activation"
]
},
{
"page": "morie_geron_td_learning",
"title": "TD(0) value update (Geron Ch 18, morie.fn hmtd)",
"topics": [
"morie_geron_td_learning"
]
},
{
"page": "morie_geron_td_zero_update",
"title": "TD(0) value update (Geron Ch 18, morie.fn grtd0)",
"topics": [
"morie_geron_td_zero_update"
]
},
{
"page": "morie_geron_td3",
"title": "Twin delayed DDPG (TD3): two critics + delayed policy updates",
"topics": [
"morie_geron_td3"
]
},
{
"page": "morie_geron_temperature_sampling",
"title": "Temperature-scaled softmax (Geron Ch 16, morie.fn grtmp)",
"topics": [
"morie_geron_temperature_sampling"
]
},
{
"page": "morie_geron_tensor_parallelism",
"title": "Tensor parallelism: split individual tensors across devices",
"topics": [
"morie_geron_tensor_parallelism"
]
},
{
"page": "morie_geron_threshold_logic_unit",
"title": "Threshold logic unit (Geron Ch 10, morie.fn grtlu)",
"topics": [
"morie_geron_threshold_logic_unit"
]
},
{
"page": "morie_geron_time_series_forecast",
"title": "Time series forecasting with a fixed-width lag window",
"topics": [
"morie_geron_time_series_forecast"
]
},
{
"page": "morie_geron_tlu",
"title": "Threshold logic unit (Geron Ch 10, morie.fn hmtlu)",
"topics": [
"morie_geron_tlu"
]
},
{
"page": "morie_geron_topk_sampling",
"title": "Top-k truncated sampling distribution (morie.fn grtop)",
"topics": [
"morie_geron_topk_sampling"
]
},
{
"page": "morie_geron_torch_compile",
"title": "torch.compile: graph-capturing JIT for forward and backward passes",
"topics": [
"morie_geron_torch_compile"
]
},
{
"page": "morie_geron_torchscript",
"title": "TorchScript: statically-typed graph representation of PyTorch models",
"topics": [
"morie_geron_torchscript"
]
},
{
"page": "morie_geron_trace",
"title": "Trace a typed graph by running model ops on example_inputs once",
"topics": [
"morie_geron_trace"
]
},
{
"page": "morie_geron_train_logreg",
"title": "Batch-GD logistic fit used by the OvR / OvO reductions (morie.fn grovr)",
"topics": [
"morie_geron_train_logreg"
]
},
{
"page": "morie_geron_transfer_learning",
"title": "Transfer learning: reuse pretrained model, fine-tune on new task",
"topics": [
"morie_geron_transfer_learning"
]
},
{
"page": "morie_geron_transformer",
"title": "Transformer architecture (Vaswani et al. 2017)",
"topics": [
"morie_geron_transformer"
]
},
{
"page": "morie_geron_transformer_decoder_block",
"title": "Transformer decoder block, post-norm (Geron Ch 16, morie.fn grtdb)",
"topics": [
"morie_geron_transformer_decoder_block"
]
},
{
"page": "morie_geron_transformer_encoder_block",
"title": "Transformer encoder block, post-norm (Geron Ch 16, morie.fn grteb)",
"topics": [
"morie_geron_transformer_encoder_block"
]
},
{
"page": "morie_geron_transformer_feedforward",
"title": "Transformer position-wise feed-forward (Geron Ch 16, morie.fn grffn)",
"topics": [
"morie_geron_transformer_feedforward"
]
},
{
"page": "morie_geron_tree_classification_leaf",
"title": "Classification tree leaf (Geron Ch 6, morie.fn grtrc)",
"topics": [
"morie_geron_tree_classification_leaf"
]
},
{
"page": "morie_geron_tree_high_variance",
"title": "Decision-tree variance via bootstrap resampling of CART trees (hmdthv)",
"topics": [
"morie_geron_tree_high_variance"
]
},
{
"page": "morie_geron_tree_regression_leaf",
"title": "Regression tree leaf (Geron Ch 6, morie.fn grtrv)",
"topics": [
"morie_geron_tree_regression_leaf"
]
},
{
"page": "morie_geron_tree_regularization",
"title": "Tree regularization: constrained vs unconstrained CART (hmdtr)",
"topics": [
"morie_geron_tree_regularization"
]
},
{
"page": "morie_geron_tree_sensitivity_scale",
"title": "Decision-tree scale invariance: CART thresholds under x' = a*x + b (hmdtst)",
"topics": [
"morie_geron_tree_sensitivity_scale"
]
},
{
"page": "morie_geron_trl_finetune",
"title": "Fine-tuning via TRL (Transformer Reinforcement Learning) library",
"topics": [
"morie_geron_trl_finetune"
]
},
{
"page": "morie_geron_tsne",
"title": "t-SNE: KL divergence between joint probabilities in high- and low-dim",
"topics": [
"morie_geron_tsne"
]
},
{
"page": "morie_geron_umap",
"title": "UMAP: uniform manifold approximation, preserves local and some global structure",
"topics": [
"morie_geron_umap"
]
},
{
"page": "morie_geron_underfitting",
"title": "Underfitting diagnosis (Geron Ch 4, morie.fn hmuf)",
"topics": [
"morie_geron_underfitting"
]
},
{
"page": "morie_geron_unsupervised_learning",
"title": "Unsupervised learning: discover structure from unlabeled data",
"topics": [
"morie_geron_unsupervised_learning"
]
},
{
"page": "morie_geron_unsupervised_pretraining",
"title": "Unsupervised pretraining: learn representation via reconstruction before labels",
"topics": [
"morie_geron_unsupervised_pretraining"
]
},
{
"page": "morie_geron_vae",
"title": "Variational autoencoder with latent Gaussian prior",
"topics": [
"morie_geron_vae"
]
},
{
"page": "morie_geron_vae_elbo",
"title": "VAE evidence lower bound (Geron Ch 17, morie.fn grvae)",
"topics": [
"morie_geron_vae_elbo"
]
},
{
"page": "morie_geron_vae_loss_and_grads",
"title": "ELBO loss and exact gradients for fixed reparameterisation noise",
"topics": [
"morie_geron_vae_loss_and_grads"
]
},
{
"page": "morie_geron_value_function",
"title": "State value function by exact policy evaluation (Geron Ch 18, morie.fn hmvf)",
"topics": [
"morie_geron_value_function"
]
},
{
"page": "morie_geron_value_iteration",
"title": "Value iteration on the Bellman optimality operator (Geron Ch 19, hmbel)",
"topics": [
"morie_geron_value_iteration"
]
},
{
"page": "morie_geron_vanishing_gradients",
"title": "Vanishing gradients: small gradients shrink through many layers",
"topics": [
"morie_geron_vanishing_gradients"
]
},
{
"page": "morie_geron_variational_bayes_gmm",
"title": "Bayesian Gaussian mixture with variational inference (VBGMM)",
"topics": [
"morie_geron_variational_bayes_gmm"
]
},
{
"page": "morie_geron_videobert",
"title": "VideoBERT: transformer on discretized video tokens + text",
"topics": [
"morie_geron_videobert"
]
},
{
"page": "morie_geron_vilbert",
"title": "ViLBERT: dual-stream vision-language transformer",
"topics": [
"morie_geron_vilbert"
]
},
{
"page": "morie_geron_vision_transformer",
"title": "Vision Transformer (ViT): transformer on image patches",
"topics": [
"morie_geron_vision_transformer"
]
},
{
"page": "morie_geron_vit_patch_embedding",
"title": "ViT patch embedding (Geron Ch 17, morie.fn grvit)",
"topics": [
"morie_geron_vit_patch_embedding"
]
},
{
"page": "morie_geron_voting_hard",
"title": "Hard voting ensemble (Geron Ch 7, morie.fn hmvth)",
"topics": [
"morie_geron_voting_hard"
]
},
{
"page": "morie_geron_vq_vae",
"title": "Discrete VAE (VQ-VAE): vector-quantized latents with codebook",
"topics": [
"morie_geron_vq_vae"
]
},
{
"page": "morie_geron_warm_restarts",
"title": "Warm restarts: cosine decay with periodic restarts (SGDR)",
"topics": [
"morie_geron_warm_restarts"
]
},
{
"page": "morie_geron_weight_pruning",
"title": "Magnitude-based unstructured pruning (Geron Ch 19, morie.fn grprn)",
"topics": [
"morie_geron_weight_pruning"
]
},
{
"page": "morie_geron_word_embeddings",
"title": "Word embeddings: dense vector representations learned per token",
"topics": [
"morie_geron_word_embeddings"
]
},
{
"page": "morie_geron_wordpiece_tokenizer",
"title": "WordPiece tokenizer: maximum likelihood subword segmentation",
"topics": [
"morie_geron_wordpiece_tokenizer"
]
},
{
"page": "morie_geron_wordpiece_tokenizer_score",
"title": "WordPiece merge score (Geron Ch 16, morie.fn grwpc)",
"topics": [
"morie_geron_wordpiece_tokenizer_score"
]
},
{
"page": "morie_geron_xception",
"title": "Xception: extreme inception using depthwise separable convolutions",
"topics": [
"morie_geron_xception"
]
},
{
"page": "morie_geron_xgboost",
"title": "XGBoost: regularized gradient boosting with second-order Taylor approximation",
"topics": [
"morie_geron_xgboost"
]
},
{
"page": "morie_geron_xgboost_gain",
"title": "XGBoost regularized split gain (Geron Ch 7, morie.fn grxgbg)",
"topics": [
"morie_geron_xgboost_gain"
]
},
{
"page": "morie_geron_xlnet",
"title": "XLNet: permutation-based autoregressive pretraining",
"topics": [
"morie_geron_xlnet"
]
},
{
"page": "morie_geron_yolo",
"title": "YOLO: single-shot object detection via grid regression",
"topics": [
"morie_geron_yolo"
]
},
{
"page": "morie_geron_yolo_grid_loss",
"title": "YOLO grid loss (Geron Ch 14, morie.fn gryol)",
"topics": [
"morie_geron_yolo_grid_loss"
]
},
{
"page": "morie_geron_zero_shot",
"title": "Zero-shot learning: LLM generalizes to unseen tasks from prompt only",
"topics": [
"morie_geron_zero_shot"
]
},
{
"page": "morie_gh_crm_laplace_gamma",
"title": "Laplace exponent of a gamma completely random measure",
"topics": [
"morie_gh_crm_laplace_gamma"
]
},
{
"page": "morie_gh_dirichlet_moments",
"title": "First two moments of a Dirichlet distribution for a pair of cells",
"topics": [
"morie_gh_dirichlet_moments"
]
},
{
"page": "morie_gh_discrete_hazard",
"title": "Discrete hazard rates of a probability vector",
"topics": [
"morie_gh_discrete_hazard"
]
},
{
"page": "morie_gh_dp_median_cdf",
"title": "Distribution function of the median of a Dirichlet process",
"topics": [
"morie_gh_dp_median_cdf"
]
},
{
"page": "morie_gh_dp_ndistinct",
"title": "Mean and variance of the number of distinct values in a DP sample",
"topics": [
"morie_gh_dp_ndistinct"
]
},
{
"page": "morie_gh_dp_posterior",
"title": "Dirichlet-process posterior for the mass of a single set",
"topics": [
"morie_gh_dp_posterior"
]
},
{
"page": "morie_gh_dp_predictive",
"title": "Polya-urn predictive weights of a Dirichlet process",
"topics": [
"morie_gh_dp_predictive"
]
},
{
"page": "morie_gh_eps_dp_size",
"title": "Stick-breaking truncation level for a given residual tolerance",
"topics": [
"morie_gh_eps_dp_size"
]
},
{
"page": "morie_gh_ewens_log",
"title": "Log Ewens sampling formula for a partition given by multiplicities",
"topics": [
"morie_gh_ewens_log"
]
},
{
"page": "morie_gh_hellinger2",
"title": "Half the squared Hellinger distance between two discrete distributions",
"topics": [
"morie_gh_hellinger2"
]
},
{
"page": "morie_gh_ibp_expected_dishes",
"title": "Expected number of features in an Indian buffet process",
"topics": [
"morie_gh_ibp_expected_dishes"
]
},
{
"page": "morie_gh_kl",
"title": "Kullback-Leibler divergence between two discrete distributions",
"topics": [
"morie_gh_kl"
]
},
{
"page": "morie_gh_ncrm_laplace",
"title": "Laplace transform of a discretised completely random measure",
"topics": [
"morie_gh_ncrm_laplace"
]
},
{
"page": "morie_gh_pt_bits",
"title": "Leading binary-expansion bits of a point in the unit interval",
"topics": [
"morie_gh_pt_bits"
]
},
{
"page": "morie_gh_pt_posterior_density",
"title": "Polya-tree posterior density at a point",
"topics": [
"morie_gh_pt_posterior_density"
]
},
{
"page": "morie_gh_py_eppf_log",
"title": "Log exchangeable partition probability function of a Pitman-Yor process",
"topics": [
"morie_gh_py_eppf_log"
]
},
{
"page": "morie_gh_renyi",
"title": "Renyi divergence of order alpha between two discrete distributions",
"topics": [
"morie_gh_renyi"
]
},
{
"page": "morie_gh_stick_breaking",
"title": "Stick-breaking weights from a vector of beta sticks",
"topics": [
"morie_gh_stick_breaking"
]
},
{
"page": "morie_gh_wn_posterior",
"title": "Conjugate posterior for one white-noise (normal mean) coordinate",
"topics": [
"morie_gh_wn_posterior"
]
},
{
"page": "morie_ghosal_adaptation",
"title": "Adaptive contraction rates over a smoothness grid",
"topics": [
"morie_ghosal_adaptation"
]
},
{
"page": "morie_ghosal_bernstein_von_mises",
"title": "BvM diagnostic for the mean functional under a DP prior",
"topics": [
"morie_ghosal_bernstein_von_mises"
]
},
{
"page": "morie_ghosal_contraction_rate",
"title": "Minimax posterior-contraction rate",
"topics": [
"morie_ghosal_contraction_rate"
]
},
{
"page": "morie_ghosal_dirichlet_posterior",
"title": "Dirichlet-process posterior (conjugate update)",
"topics": [
"morie_ghosal_dirichlet_posterior"
]
},
{
"page": "morie_ghosal_dpmixture_density",
"title": "DP mixture density estimate (Neal 2000 algorithm 3)",
"topics": [
"morie_ghosal_dpmixture_density"
]
},
{
"page": "morie_ghosal_empirical_bayes",
"title": "Empirical-Bayes alpha MLE for a DP, given the observed K_n",
"topics": [
"morie_ghosal_empirical_bayes"
]
},
{
"page": "morie_ghosal_gp_matern",
"title": "GP posterior mean with Matern kernel",
"topics": [
"morie_ghosal_gp_matern"
]
},
{
"page": "morie_ghosal_gp_squared_exponential",
"title": "GP posterior mean with squared-exponential kernel",
"topics": [
"morie_ghosal_gp_squared_exponential"
]
},
{
"page": "morie_ghosal_hierarchical_bayes",
"title": "Escobar-West augmentation for alpha given K_n with a Gamma(a, b) hyperprior",
"topics": [
"morie_ghosal_hierarchical_bayes"
]
},
{
"page": "morie_ghosal_log_density",
"title": "Log-spline density estimator (Stone 1990, Ghosal Ch 8)",
"topics": [
"morie_ghosal_log_density"
]
},
{
"page": "morie_ghosal_moment_matching",
"title": "Posterior mean / variance of G(A) for DP(alpha, G0) and A = (A_lower, A_upper]",
"topics": [
"morie_ghosal_moment_matching"
]
},
{
"page": "morie_ghosal_neutral_right",
"title": "Neutral-to-the-right posterior survival (Doksum 1974)",
"topics": [
"morie_ghosal_neutral_right"
]
},
{
"page": "morie_ghosal_np_classification",
"title": "Probit-GP classifier (Laplace approximation)",
"topics": [
"morie_ghosal_np_classification"
]
},
{
"page": "morie_ghosal_np_regression",
"title": "GP nonparametric regression",
"topics": [
"morie_ghosal_np_regression"
]
},
{
"page": "morie_ghosal_np_testing",
"title": "Polya-tree Bayes factor for H0: F = N(loc, scale^2)",
"topics": [
"morie_ghosal_np_testing"
]
},
{
"page": "morie_ghosal_posterior_consistency",
"title": "Schwartz posterior-consistency diagnostic (Bayesian bootstrap)",
"topics": [
"morie_ghosal_posterior_consistency"
]
},
{
"page": "morie_ghosal_sieve_prior",
"title": "Bernstein-polynomial sieve density estimator (Petrone 1999)",
"topics": [
"morie_ghosal_sieve_prior"
]
},
{
"page": "morie_ghosal_stick_breaking_trunc",
"title": "Truncated stick-breaking representation of DP(alpha, G0)",
"topics": [
"morie_ghosal_stick_breaking_trunc"
]
},
{
"page": "morie_ghosal_survival_beta_process",
"title": "Beta-process posterior survival (Hjort 1990)",
"topics": [
"morie_ghosal_survival_beta_process"
]
},
{
"page": "morie_ghosal_wavelet_prior",
"title": "Haar-wavelet spike-and-slab BayesThresh estimator (Abramovich 1998)",
"topics": [
"morie_ghosal_wavelet_prior"
]
},
{
"page": "morie_gini_gamma",
"title": "Gini's gamma from a copula",
"topics": [
"morie_gini_gamma"
]
},
{
"page": "morie_gllsuff",
"title": "Gaussian log likelihood in sufficient statistics (eq. 24.5-24.6, p. 319)",
"topics": [
"morie_gllsuff"
]
},
{
"page": "morie_glm_nb",
"title": "Native negative-binomial GLM (reproduces MASS::glm.nb)",
"topics": [
"morie_glm_nb"
]
},
{
"page": "morie_glmm",
"title": "Generalized linear mixed model by the Laplace approximation (native)",
"topics": [
"morie_glmm"
]
},
{
"page": "morie_glrtest",
"title": "Page likelihood-ratio CUSUM (Lai 1995, eq. 2.3)",
"topics": [
"morie_glrtest"
]
},
{
"page": "morie_gm_error_sar",
"title": "Generalised-moments estimator for the spatial error model",
"topics": [
"morie_gm_error_sar"
]
},
{
"page": "morie_gmm_log_pdf",
"title": "Log density of a multivariate normal at every row of X (hmgmm)",
"topics": [
"morie_gmm_log_pdf"
]
},
{
"page": "morie_goodman_minimum",
"title": "Least monochromatic triangles in any two-colouring of K_n",
"topics": [
"morie_goodman_minimum"
]
},
{
"page": "morie_goodman_triangles",
"title": "Monochromatic triangles by Goodman's identity",
"topics": [
"morie_goodman_triangles"
]
},
{
"page": "morie_gp_density_rate",
"title": "Gaussian-process density contraction rate",
"topics": [
"morie_gp_density_rate"
]
},
{
"page": "morie_gpl_compatible_licenses",
"title": "Vector of SPDX identifiers recognised as GPL-compatible",
"topics": [
"morie_gpl_compatible_licenses"
]
},
{
"page": "morie_gr_dual",
"title": "Forward-mode autodiff dual number (morie.fn grfad 'Dual')",
"topics": [
"morie_gr_dual"
]
},
{
"page": "morie_gradient_boosting_ensemble",
"title": "Gradient boosting ensemble (R parity)",
"topics": [
"morie_gradient_boosting_ensemble"
]
},
{
"page": "morie_gradient_boosting_genomic",
"title": "Gradient-boosting genomic predictor (Friedman 2001)",
"topics": [
"morie_gradient_boosting_genomic"
]
},
{
"page": "morie_gradient_descent_update",
"title": "Gradient-descent parameter update",
"topics": [
"morie_gradient_descent_update"
]
},
{
"page": "morie_gradient_descent_vanilla",
"title": "Vanilla batch gradient descent for OLS (R parity)",
"topics": [
"morie_gradient_descent_vanilla"
]
},
{
"page": "morie_granger_test",
"title": "Granger causality F-test",
"topics": [
"morie_granger_test"
]
},
{
"page": "morie_graphic_matroid",
"title": "The cycle matroid of a graph",
"topics": [
"morie_graphic_matroid"
]
},
{
"page": "morie_greedy_independent_set",
"title": "The greedy algorithm on an independence system",
"topics": [
"morie_greedy_independent_set"
]
},
{
"page": "morie_greg",
"title": "Generalised regression (GREG) estimator",
"topics": [
"morie_greg"
]
},
{
"page": "morie_greg_variance",
"title": "GREG variance estimators and g-weights",
"topics": [
"morie_greg_variance"
]
},
{
"page": "morie_grid_search_cv",
"title": "Grid search with cross-validation (R parity)",
"topics": [
"morie_grid_search_cv"
]
},
{
"page": "morie_grm_vanraden",
"title": "VanRaden Genomic Relationship Matrix",
"topics": [
"morie_grm_vanraden"
]
},
{
"page": "morie_grouptimeatt",
"title": "Group-time average treatment effects ATT(g, t)",
"topics": [
"morie_grouptimeatt"
]
},
{
"page": "morie_grphmr_centrality",
"title": "Graphormer centrality encoding",
"topics": [
"morie_grphmr_centrality"
]
},
{
"page": "morie_grphmr_edge",
"title": "Edge-feature bias averaged along the shortest path",
"topics": [
"morie_grphmr_edge"
]
},
{
"page": "morie_grphmr_sp",
"title": "All-pairs shortest path distances",
"topics": [
"morie_grphmr_sp"
]
},
{
"page": "morie_grphmr_spatial",
"title": "Spatial bias from the shortest-path distance matrix",
"topics": [
"morie_grphmr_spatial"
]
},
{
"page": "morie_gru4r_bpr",
"title": "BPR ranking loss",
"topics": [
"morie_gru4r_bpr"
]
},
{
"page": "morie_gru4r_gru",
"title": "One GRU update (single layer)",
"topics": [
"morie_gru4r_gru"
]
},
{
"page": "morie_gru4r_mrr",
"title": "Mean reciprocal rank at k",
"topics": [
"morie_gru4r_mrr"
]
},
{
"page": "morie_gru4r_recall",
"title": "Recall at k",
"topics": [
"morie_gru4r_recall"
]
},
{
"page": "morie_gru4r_top1",
"title": "TOP1 ranking loss",
"topics": [
"morie_gru4r_top1"
]
},
{
"page": "morie_gsageemd_layer",
"title": "One GraphSAGE layer",
"topics": [
"morie_gsageemd_layer"
]
},
{
"page": "morie_gsageemd_loss",
"title": "Unsupervised graph-based loss",
"topics": [
"morie_gsageemd_loss"
]
},
{
"page": "morie_gsageemd_sample",
"title": "Fixed-size neighbour sample",
"topics": [
"morie_gsageemd_sample"
]
},
{
"page": "morie_gsplat_composite",
"title": "Front-to-back alpha compositing",
"topics": [
"morie_gsplat_composite"
]
},
{
"page": "morie_gsplat_covariance",
"title": "Build the 3D covariance as Sigma = R S S' R'",
"topics": [
"morie_gsplat_covariance"
]
},
{
"page": "morie_gsplat_density",
"title": "Adaptive density control",
"topics": [
"morie_gsplat_density"
]
},
{
"page": "morie_gsplat_project",
"title": "EWA projection of the 3D covariance",
"topics": [
"morie_gsplat_project"
]
},
{
"page": "morie_gsplat_psd",
"title": "Positive semi-definiteness check",
"topics": [
"morie_gsplat_psd"
]
},
{
"page": "morie_gtrf_attention",
"title": "Neighbourhood-restricted attention",
"topics": [
"morie_gtrf_attention"
]
},
{
"page": "morie_gtrf_lap_pe",
"title": "Laplacian positional encoding",
"topics": [
"morie_gtrf_lap_pe"
]
},
{
"page": "morie_gtrf_laplacian",
"title": "Graph Laplacian",
"topics": [
"morie_gtrf_laplacian"
]
},
{
"page": "morie_gtrf_layer",
"title": "Graph transformer layer",
"topics": [
"morie_gtrf_layer"
]
},
{
"page": "morie_gtrf_sign_flip",
"title": "Random sign flip",
"topics": [
"morie_gtrf_sign_flip"
]
},
{
"page": "morie_gwasem_kinship_ibs",
"title": "IBS relatedness matrix",
"topics": [
"morie_gwasem_kinship_ibs"
]
},
{
"page": "morie_gxe_interaction_model",
"title": "Two-way GxE ANOVA with EMS variance components",
"topics": [
"morie_gxe_interaction_model"
]
},
{
"page": "morie_hajek_mean",
"title": "Hajek ratio estimator of a population mean",
"topics": [
"morie_hajek_mean"
]
},
{
"page": "morie_hall_condition",
"title": "Hall's marriage theorem",
"topics": [
"morie_hall_condition"
]
},
{
"page": "morie_hamming_bound",
"title": "The Hamming sphere-packing bound",
"topics": [
"morie_hamming_bound"
]
},
{
"page": "morie_hapblk",
"title": "Gabriel et al. (2002) haplotype blocks",
"topics": [
"morie_hapblk"
]
},
{
"page": "morie_happy_ending_quadrilateral",
"title": "The happy ending problem",
"topics": [
"morie_happy_ending_quadrilateral"
]
},
{
"page": "morie_hardy_ramanujan_partitions",
"title": "Hardy-Ramanujan asymptotics for the partition function",
"topics": [
"morie_hardy_ramanujan_partitions"
]
},
{
"page": "morie_harrell_davis",
"title": "Robust measures of location",
"topics": [
"morie_harrell_davis",
"morie_mom_estimator",
"morie_one_step_m",
"morie_pbos"
]
},
{
"page": "morie_has",
"title": "Are an example's prerequisites available?",
"topics": [
"morie_has"
]
},
{
"page": "morie_has_clique",
"title": "Does the graph contain a clique on k vertices?",
"topics": [
"morie_has_clique"
]
},
{
"page": "morie_hawkes_baseline_integral_cpp",
"title": "morie_hawkes_baseline_integral_cpp",
"topics": [
"morie_hawkes_baseline_integral_cpp"
]
},
{
"page": "morie_hawkes_fit",
"title": "Fit a Hawkes (self-exciting point process) model by maximum likelihood",
"topics": [
"morie_hawkes_fit"
]
},
{
"page": "morie_hawkes_kernel_cdf_cpp",
"title": "morie_hawkes_kernel_cdf_cpp",
"topics": [
"morie_hawkes_kernel_cdf_cpp"
]
},
{
"page": "morie_hawkes_kernel_density_cpp",
"title": "morie_hawkes_kernel_density_cpp",
"topics": [
"morie_hawkes_kernel_density_cpp"
]
},
{
"page": "morie_hawkes_ll_exp_const_cpp",
"title": "morie_hawkes_ll_exp_const_cpp",
"topics": [
"morie_hawkes_ll_exp_const_cpp"
]
},
{
"page": "morie_hawkes_ll_gamma_const_cpp",
"title": "morie_hawkes_ll_gamma_const_cpp",
"topics": [
"morie_hawkes_ll_gamma_const_cpp"
]
},
{
"page": "morie_hawkes_ll_lomax_const_cpp",
"title": "morie_hawkes_ll_lomax_const_cpp",
"topics": [
"morie_hawkes_ll_lomax_const_cpp"
]
},
{
"page": "morie_hawkes_ll_weibull_const_cpp",
"title": "morie_hawkes_ll_weibull_const_cpp",
"topics": [
"morie_hawkes_ll_weibull_const_cpp"
]
},
{
"page": "morie_hawkes_pair_excitation_sum_cpp",
"title": "morie_hawkes_pair_excitation_sum_cpp",
"topics": [
"morie_hawkes_pair_excitation_sum_cpp"
]
},
{
"page": "morie_hawkes_st_fit",
"title": "Fit a spatiotemporal Hawkes process by maximum likelihood",
"topics": [
"morie_hawkes_st_fit"
]
},
{
"page": "morie_hawkes_st_intensity",
"title": "Conditional intensity of a spatiotemporal Hawkes process at a query point",
"topics": [
"morie_hawkes_st_intensity"
]
},
{
"page": "morie_hawkes_st_loglik",
"title": "Log-likelihood of a spatiotemporal Hawkes process",
"topics": [
"morie_hawkes_st_loglik"
]
},
{
"page": "morie_hawkes_st_simulate",
"title": "Simulate a spatiotemporal Hawkes process (immigrant-offspring branching)",
"topics": [
"morie_hawkes_st_simulate"
]
},
{
"page": "morie_hazard_selection",
"title": "Built-in selection in period-by-period hazard ratios",
"topics": [
"morie_hazard_selection"
]
},
{
"page": "morie_hbos",
"title": "HBOS - histogram-based outlier score",
"topics": [
"morie_hbos"
]
},
{
"page": "morie_hdbsc",
"title": "HDBSCAN* flat clustering by excess-of-mass stability extraction",
"topics": [
"morie_hdbsc"
]
},
{
"page": "morie_hedges_g",
"title": "Hedges' g (bias-corrected Cohen's d)",
"topics": [
"morie_hedges_g"
]
},
{
"page": "morie_helly_intervals",
"title": "Helly's theorem on the line",
"topics": [
"morie_helly_intervals"
]
},
{
"page": "morie_heteroskedastic_scales",
"title": "Heteroskedastic IRT scales given ideal points (Lauderdale 2010)",
"topics": [
"morie_heteroskedastic_scales"
]
},
{
"page": "morie_hits",
"title": "HITS hubs and authorities",
"topics": [
"morie_hits"
]
},
{
"page": "morie_hlm_components",
"title": "Multilevel variance components, ICC and likelihood-ratio test",
"topics": [
"morie_hlm_components"
]
},
{
"page": "morie_hlmgr",
"title": "HLM T (tau) matrix by method of moments, with EB shrinkage",
"topics": [
"morie_hlmgr"
]
},
{
"page": "morie_hmac",
"title": "HMAC (digest's hmac, natively)",
"topics": [
"morie_hmac"
]
},
{
"page": "morie_hockey_stick",
"title": "Hockey-stick identity",
"topics": [
"morie_hockey_stick"
]
},
{
"page": "morie_holt_linear",
"title": "Holt's linear trend exponential smoothing",
"topics": [
"morie_holt_linear"
]
},
{
"page": "morie_holt_winters",
"title": "Holt-Winters seasonal exponential smoothing",
"topics": [
"morie_holt_winters"
]
},
{
"page": "morie_holt_winters_additive",
"title": "Holt-Winters with additive seasonality",
"topics": [
"morie_holt_winters_additive"
]
},
{
"page": "morie_holt_winters_mult",
"title": "Holt-Winters with multiplicative seasonality",
"topics": [
"morie_holt_winters_mult"
]
},
{
"page": "morie_hook_lengths",
"title": "Hook length of every cell of a Young diagram",
"topics": [
"morie_hook_lengths"
]
},
{
"page": "morie_horvitz_thompson",
"title": "Horvitz-Thompson estimator of a population total",
"topics": [
"morie_horvitz_thompson"
]
},
{
"page": "morie_hosted_manifest",
"title": "Curated datasets at data.rmorie.com",
"topics": [
"morie_hosted_datasets",
"morie_hosted_manifest",
"morie_load_hosted_dataset"
]
},
{
"page": "morie_hot",
"title": "HOT SAX discord discovery",
"topics": [
"morie_hot"
]
},
{
"page": "morie_hotspot_shrinkage",
"title": "Empirical-Bayes shrinkage of hot-spot counts: the expected size of the fall",
"topics": [
"morie_hotspot_shrinkage"
]
},
{
"page": "morie_hotspot_threshold",
"title": "Hot-spot threshold of the Short-Brantingham reaction-diffusion model",
"topics": [
"morie_hotspot_threshold"
]
},
{
"page": "morie_hsic",
"title": "Hilbert-Schmidt Independence Criterion",
"topics": [
"morie_hsic"
]
},
{
"page": "morie_hsicstat",
"title": "Hilbert-Schmidt independence criterion for an ANM residual test",
"topics": [
"morie_hsicstat"
]
},
{
"page": "morie_ht_elephant",
"title": "Basu's elephant: the Horvitz-Thompson total is unbiased with exploding variance",
"topics": [
"morie_ht_elephant"
]
},
{
"page": "morie_ht_estimators",
"title": "Horvitz-Thompson estimators of the total and mean",
"topics": [
"morie_ht_estimators"
]
},
{
"page": "morie_hte_blp_test",
"title": "Best-linear-predictor test for treatment-effect heterogeneity",
"topics": [
"morie_hte_blp_test"
]
},
{
"page": "morie_hurst_r",
"title": "Hurst exponent via rescaled-range (R/S) analysis",
"topics": [
"morie_hurst_r"
]
},
{
"page": "morie_hypergeometric_dist",
"title": "Hypergeometric pmf and its binomial limit",
"topics": [
"morie_hypergeometric_dist"
]
},
{
"page": "morie_ibdmtx",
"title": "Pairwise IBD matrix (PLINK method of moments)",
"topics": [
"morie_ibdmtx"
]
},
{
"page": "morie_icc_rho",
"title": "Intracluster correlation and Kish's design effect",
"topics": [
"morie_icc_rho"
]
},
{
"page": "morie_ichimura",
"title": "Ichimura semiparametric least squares",
"topics": [
"morie_ichimura"
]
},
{
"page": "morie_ideal_fourths",
"title": "Ideal fourths and the interquartile range",
"topics": [
"morie_idealf_iqr",
"morie_ideal_fourths"
]
},
{
"page": "morie_identify_estimate_refute",
"title": "Model, identify, estimate, refute",
"topics": [
"morie_identify_estimate_refute"
]
},
{
"page": "morie_ihstst",
"title": "Integrated haplotype score (iHS)",
"topics": [
"morie_ihstst"
]
},
{
"page": "morie_impala_vtrace",
"title": "IMPALA V-trace off-policy targets",
"topics": [
"morie_impala_vtrace"
]
},
{
"page": "morie_impute_column",
"title": "Impute missing values in a column",
"topics": [
"morie_impute_column"
]
},
{
"page": "morie_impute_untreated",
"title": "Impute untreated potential outcomes",
"topics": [
"morie_impute_untreated"
]
},
{
"page": "morie_incapacitation",
"title": "Incapacitation arithmetic: crime rate, prevented share and the marginal year",
"topics": [
"morie_incapacitation"
]
},
{
"page": "morie_incapacitation_career",
"title": "Incapacitation under desistance and replacement",
"topics": [
"morie_incapacitation_career"
]
},
{
"page": "morie_inception_module",
"title": "GoogLeNet/Inception parameter count, module by module (hmgoog)",
"topics": [
"morie_inception_module"
]
},
{
"page": "morie_incidence_check",
"title": "Verify a block design directly from its blocks",
"topics": [
"morie_incidence_check"
]
},
{
"page": "morie_indep_test",
"title": "Native general independence permutation test",
"topics": [
"morie_indep_test"
]
},
{
"page": "morie_index_regression",
"title": "Single-index kernel regression",
"topics": [
"morie_index_regression"
]
},
{
"page": "morie_indirect_effect_sobel",
"title": "Product-of-coefficients indirect effect with the Sobel standard error",
"topics": [
"morie_indirect_effect_sobel"
]
},
{
"page": "morie_infer_measurement_level",
"title": "Infer the measurement level of a vector",
"topics": [
"morie_infer_measurement_level"
]
},
{
"page": "morie_ingest_a2aj_citation_edges",
"title": "Citation network edges from A2AJ case records",
"topics": [
"morie_ingest_a2aj_citation_edges"
]
},
{
"page": "morie_ingest_a2aj_coverage",
"title": "A2AJ corpus coverage",
"topics": [
"morie_ingest_a2aj_coverage"
]
},
{
"page": "morie_ingest_a2aj_download",
"title": "Download one A2AJ dataset's Parquet file",
"topics": [
"morie_ingest_a2aj_download"
]
},
{
"page": "morie_ingest_a2aj_fetch",
"title": "Fetch one A2AJ document by citation",
"topics": [
"morie_ingest_a2aj_fetch"
]
},
{
"page": "morie_ingest_a2aj_gaps",
"title": "Courts and tribunals the A2AJ corpus does not carry",
"topics": [
"morie_ingest_a2aj_gaps"
]
},
{
"page": "morie_ingest_a2aj_load",
"title": "Load one A2AJ dataset as a data.frame",
"topics": [
"morie_ingest_a2aj_load"
]
},
{
"page": "morie_ingest_a2aj_search",
"title": "Search A2AJ case law or legislation",
"topics": [
"morie_ingest_a2aj_search"
]
},
{
"page": "morie_ingest_a2aj_url",
"title": "Parquet URL of one A2AJ dataset",
"topics": [
"morie_ingest_a2aj_url"
]
},
{
"page": "morie_ingest_bigquery_build_sql",
"title": "Build a parameter-safe BigQuery SELECT",
"topics": [
"morie_ingest_bigquery_build_sql"
]
},
{
"page": "morie_ingest_bigquery_query",
"title": "Execute a BigQuery SQL query and return a data.frame",
"topics": [
"morie_ingest_bigquery_query"
]
},
{
"page": "morie_ingest_bigquery_table",
"title": "Pull a BigQuery table (or filtered slice) into a data.frame",
"topics": [
"morie_ingest_bigquery_table"
]
},
{
"page": "morie_ingest_canlii_case",
"title": "Metadata of one CanLII decision",
"topics": [
"morie_ingest_canlii_case"
]
},
{
"page": "morie_ingest_canlii_case_id",
"title": "CanLII identifiers from a neutral citation",
"topics": [
"morie_ingest_canlii_case_id"
]
},
{
"page": "morie_ingest_canlii_cases",
"title": "Decisions of one CanLII database",
"topics": [
"morie_ingest_canlii_cases"
]
},
{
"page": "morie_ingest_canlii_citator",
"title": "Citation network of one CanLII decision",
"topics": [
"morie_ingest_canlii_citator"
]
},
{
"page": "morie_ingest_canlii_databases",
"title": "CanLII case-law databases",
"topics": [
"morie_ingest_canlii_databases"
]
},
{
"page": "morie_ingest_canlii_legislation",
"title": "Metadata of one CanLII statute or regulation",
"topics": [
"morie_ingest_canlii_legislation"
]
},
{
"page": "morie_ingest_canlii_legislation_databases",
"title": "CanLII legislation databases",
"topics": [
"morie_ingest_canlii_legislation_databases"
]
},
{
"page": "morie_ingest_canlii_legislations",
"title": "Statutes or regulations of one CanLII legislation database",
"topics": [
"morie_ingest_canlii_legislations"
]
},
{
"page": "morie_ingest_chicago_crime",
"title": "Pull the City of Chicago \"Crimes - 2001 to Present\" feed",
"topics": [
"morie_ingest_chicago_crime"
]
},
{
"page": "morie_ingest_chicago_crime_bigquery",
"title": "BigQuery mirror of the Chicago crime feed",
"topics": [
"morie_ingest_chicago_crime_bigquery"
]
},
{
"page": "morie_ingest_chicago_resources",
"title": "Built-in Chicago / Socrata resource registry",
"topics": [
"morie_ingest_chicago_resources"
]
},
{
"page": "morie_ingest_chicago_socrata",
"title": "Fetch every row from a Socrata SoDA JSON endpoint",
"topics": [
"morie_ingest_chicago_socrata"
]
},
{
"page": "morie_ingest_cihi_xlsx",
"title": "Download a CIHI indicator .xlsx data table",
"topics": [
"morie_ingest_cihi_xlsx"
]
},
{
"page": "morie_ingest_ckan_fetch_package_csvs",
"title": "Fetch every CSV / TSV resource in a CKAN package",
"topics": [
"morie_ingest_ckan_fetch_package_csvs"
]
},
{
"page": "morie_ingest_ckan_package_search",
"title": "Search a CKAN portal for packages (raw)",
"topics": [
"morie_ingest_ckan_package_search"
]
},
{
"page": "morie_ingest_ckan_package_show",
"title": "Fetch one CKAN package's metadata",
"topics": [
"morie_ingest_ckan_package_show"
]
},
{
"page": "morie_ingest_ckan_read_resource",
"title": "Download a CKAN resource as a data.frame",
"topics": [
"morie_ingest_ckan_read_resource"
]
},
{
"page": "morie_ingest_ckan_resource_show",
"title": "Fetch one CKAN resource's metadata",
"topics": [
"morie_ingest_ckan_resource_show"
]
},
{
"page": "morie_ingest_ckan_search_packages",
"title": "Search a CKAN portal and return a flat metadata data.frame",
"topics": [
"morie_ingest_ckan_search_packages"
]
},
{
"page": "morie_ingest_forensics_namus_missing",
"title": "Pull NamUs missing-persons case metadata",
"topics": [
"morie_ingest_forensics_namus_missing"
]
},
{
"page": "morie_ingest_forensics_nibrs",
"title": "Pull FBI NIBRS offence-event records via Crime Data Explorer",
"topics": [
"morie_ingest_forensics_nibrs"
]
},
{
"page": "morie_ingest_forensics_nist_rds",
"title": "Pull NIST Reference Datasets (RDS) catalog metadata",
"topics": [
"morie_ingest_forensics_nist_rds"
]
},
{
"page": "morie_ingest_statcan_cansim",
"title": "Fetch a Statistics Canada NDM / cansim table",
"topics": [
"morie_ingest_statcan_cansim"
]
},
{
"page": "morie_ingest_statcan_csv",
"title": "Download a StatCan PUMF / CSV product",
"topics": [
"morie_ingest_statcan_csv"
]
},
{
"page": "morie_ingest_statcan_vectors",
"title": "Fetch StatCan series by vector ID (keyless WDS, latest-N periods)",
"topics": [
"morie_ingest_statcan_vectors"
]
},
{
"page": "morie_ingest_tps_feature_layer",
"title": "Fetch every feature from a TPS ArcGIS FeatureServer layer",
"topics": [
"morie_ingest_tps_feature_layer"
]
},
{
"page": "morie_ingest_tps_fetch",
"title": "Fetch a TPS open-data layer by short name",
"topics": [
"morie_ingest_tps_fetch"
]
},
{
"page": "morie_ingest_tps_layers",
"title": "Built-in TPS open-data layer registry",
"topics": [
"morie_ingest_tps_layers"
]
},
{
"page": "morie_inspect_output",
"title": "Inspect a serialised analysis output (JSON, CSV, or RDS)",
"topics": [
"morie_inspect_output"
]
},
{
"page": "morie_inspection_game",
"title": "The criminal-citizen-inspector game cycles",
"topics": [
"morie_inspection_game"
]
},
{
"page": "morie_install_extras",
"title": "Install morie's optional dependencies (interactive helper)",
"topics": [
"morie_install_extras"
]
},
{
"page": "morie_instrument_check",
"title": "Instrument relevance and exogeneity diagnostics",
"topics": [
"morie_instrument_check"
]
},
{
"page": "morie_instrumental_inequality",
"title": "Pearl's instrumental inequality on a finite table",
"topics": [
"morie_instrumental_inequality"
]
},
{
"page": "morie_interracial_rates",
"title": "Interracial offending rates against the random-mixing null",
"topics": [
"morie_interracial_rates"
]
},
{
"page": "morie_interventional_effects",
"title": "Interventional (randomised-mediator) direct and indirect effects",
"topics": [
"morie_interventional_effects"
]
},
{
"page": "morie_invariance_compare",
"title": "Compare nested measurement-invariance models",
"topics": [
"morie_invariance_compare"
]
},
{
"page": "morie_irt_2pl",
"title": "Fit a two-parameter logistic (2PL) IRT model",
"topics": [
"morie_irt_2pl"
]
},
{
"page": "morie_irt_eap",
"title": "EAP ability scores for new respondents under a fitted 2PL model",
"topics": [
"morie_irt_eap"
]
},
{
"page": "morie_irt_grm",
"title": "Fit Samejima's graded response model (GRM)",
"topics": [
"morie_irt_grm"
]
},
{
"page": "morie_irt_marginal_reliability",
"title": "Marginal reliability of an item response theory scale",
"topics": [
"morie_irt_marginal_reliability"
]
},
{
"page": "morie_irt_theta_se",
"title": "Standard error of an item response theory score",
"topics": [
"morie_irt_theta_se"
]
},
{
"page": "morie_is_backdoor_admissible",
"title": "Does Z satisfy Pearl's back-door criterion?",
"topics": [
"morie_is_backdoor_admissible"
]
},
{
"page": "morie_is_latin_square",
"title": "Is every symbol used once in each row and each column?",
"topics": [
"morie_is_latin_square"
]
},
{
"page": "morie_is_matroid",
"title": "Check the two matroid axioms directly",
"topics": [
"morie_is_matroid"
]
},
{
"page": "morie_is_over_legal_limit",
"title": "Test whether an eBAC exceeds a legal driving limit",
"topics": [
"morie_is_over_legal_limit"
]
},
{
"page": "morie_isolation_forest",
"title": "Isolation forest",
"topics": [
"morie_isolation_forest"
]
},
{
"page": "morie_itrans",
"title": "iTransformer: variates as tokens, attention across variates",
"topics": [
"morie_itrans"
]
},
{
"page": "morie_its",
"title": "Interrupted time-series analysis (segmented regression)",
"topics": [
"morie_its"
]
},
{
"page": "morie_iv_2sls",
"title": "Two-stage least squares with a weak-instrument refusal gate",
"topics": [
"morie_iv_2sls"
]
},
{
"page": "morie_iv_anderson_rubin",
"title": "Anderson-Rubin (AR) weak-IV-robust test",
"topics": [
"morie_iv_anderson_rubin"
]
},
{
"page": "morie_iv_anderson_rubin_ci",
"title": "Grid-based Anderson-Rubin confidence interval for a single endogenous variable",
"topics": [
"morie_iv_anderson_rubin_ci"
]
},
{
"page": "morie_iv_conditional_lr",
"title": "Conditional likelihood-ratio (CLR) test of Moreira (2003)",
"topics": [
"morie_iv_conditional_lr"
]
},
{
"page": "morie_iv_cragg_donald",
"title": "Cragg-Donald weak-instrument F statistic",
"topics": [
"morie_iv_cragg_donald"
]
},
{
"page": "morie_iv_cue_gmm",
"title": "Continuously-Updated GMM (CUE-GMM)",
"topics": [
"morie_iv_cue_gmm"
]
},
{
"page": "morie_iv_diagnostics",
"title": "Composite IV diagnostics",
"topics": [
"morie_iv_diagnostics"
]
},
{
"page": "morie_iv_durbin_wu_hausman",
"title": "Durbin-Wu-Hausman test of endogeneity",
"topics": [
"morie_iv_durbin_wu_hausman"
]
},
{
"page": "morie_iv_first_stage_diagnostics",
"title": "First-stage F-statistics and partial R^2",
"topics": [
"morie_iv_first_stage_diagnostics"
]
},
{
"page": "morie_iv_gmm",
"title": "Generalised Method of Moments (GMM) IV",
"topics": [
"morie_iv_gmm"
]
},
{
"page": "morie_iv_hansen_j",
"title": "Hansen J test of overidentifying restrictions (robust)",
"topics": [
"morie_iv_hansen_j"
]
},
{
"page": "morie_iv_hausman",
"title": "Hausman test: OLS vs 2SLS",
"topics": [
"morie_iv_hausman"
]
},
{
"page": "morie_iv_jive",
"title": "Jackknife IV (JIVE; Angrist, Imbens & Krueger 1999)",
"topics": [
"morie_iv_jive"
]
},
{
"page": "morie_iv_kleibergen_paap",
"title": "Kleibergen-Paap rk statistic and rk Wald F for weak identification",
"topics": [
"morie_iv_kleibergen_paap"
]
},
{
"page": "morie_iv_liml",
"title": "Limited-Information Maximum Likelihood (LIML)",
"topics": [
"morie_iv_liml"
]
},
{
"page": "morie_iv_montiel_olea_pflueger",
"title": "Montiel Olea-Pflueger effective F test for weak instruments",
"topics": [
"morie_iv_montiel_olea_pflueger"
]
},
{
"page": "morie_iv_natural_bounds",
"title": "Instrumental-variable natural bounds",
"topics": [
"morie_iv_natural_bounds"
]
},
{
"page": "morie_iv_panel",
"title": "Panel IV with unit (and optional time) fixed effects via within-transform",
"topics": [
"morie_iv_panel"
]
},
{
"page": "morie_iv_probit",
"title": "IV Probit (Rivers-Vuong control function)",
"topics": [
"morie_iv_probit"
]
},
{
"page": "morie_iv_residual_analysis",
"title": "IV residual analysis",
"topics": [
"morie_iv_residual_analysis"
]
},
{
"page": "morie_iv_sargan",
"title": "Sargan test of overidentifying restrictions (homoskedastic)",
"topics": [
"morie_iv_sargan"
]
},
{
"page": "morie_iv_split_sample",
"title": "Split-sample IV",
"topics": [
"morie_iv_split_sample"
]
},
{
"page": "morie_iv_stock_yogo",
"title": "Stock-Yogo critical values (TSLS maximal size, one endogenous regressor)",
"topics": [
"morie_iv_stock_yogo"
]
},
{
"page": "morie_iv_tsls",
"title": "Two-Stage Least Squares (2SLS)",
"topics": [
"morie_iv_tsls"
]
},
{
"page": "morie_iv_wald",
"title": "Wald (single-instrument) estimator",
"topics": [
"morie_iv_wald"
]
},
{
"page": "morie_jackknife_estimate",
"title": "Delete-1 jackknife variance estimate",
"topics": [
"morie_jackknife_estimate"
]
},
{
"page": "morie_jacquez_knn",
"title": "Jacquez k-nearest-neighbour test for space-time interaction",
"topics": [
"morie_jacquez_knn"
]
},
{
"page": "morie_jnpnt",
"title": "Joinpoint regression for survival hazard trends",
"topics": [
"morie_jnpnt"
]
},
{
"page": "morie_johansen_cointegration",
"title": "Johansen trace test for cointegration",
"topics": [
"morie_johansen_cointegration"
]
},
{
"page": "morie_joint_convergence",
"title": "Joint convergence of parameter and nuisance",
"topics": [
"morie_joint_convergence"
]
},
{
"page": "morie_joseph_croston_intermittent",
"title": "Croston with demand classification",
"topics": [
"morie_joseph_croston_intermittent"
]
},
{
"page": "morie_joseph_mint_reconciliation",
"title": "MinT / OLS / WLS forecast reconciliation",
"topics": [
"morie_joseph_mint_reconciliation"
]
},
{
"page": "morie_joseph_naive_forecast",
"title": "Naive and seasonal-naive forecast",
"topics": [
"morie_joseph_naive_forecast"
]
},
{
"page": "morie_joseph_simple_exponential_smoothing",
"title": "Simple exponential smoothing",
"topics": [
"morie_joseph_simple_exponential_smoothing"
]
},
{
"page": "morie_joseph_ts_outlier_detection",
"title": "Rolling-median outlier detection for a time series",
"topics": [
"morie_joseph_ts_outlier_detection"
]
},
{
"page": "morie_json_stringify",
"title": "Serialize an R object to JSON natively (pure R)",
"topics": [
"morie_json_stringify"
]
},
{
"page": "morie_jsonlt_base64_dec",
"title": "Base64 decode to a raw vector",
"topics": [
"morie_jsonlt_base64url_dec",
"morie_jsonlt_base64_dec"
]
},
{
"page": "morie_jsonlt_base64_enc",
"title": "Base64 encode a raw vector or character string",
"topics": [
"morie_jsonlt_base64url_enc",
"morie_jsonlt_base64_enc"
]
},
{
"page": "morie_jsonlt_rbind_pages",
"title": "Row-bind a list of data.frames with differing columns (jsonlite's rbind_pages)",
"topics": [
"morie_jsonlt_rbind_pages"
]
},
{
"page": "morie_jsonlt_read_json",
"title": "Read and write JSON files",
"topics": [
"morie_jsonlt_read_json",
"morie_jsonlt_write_json"
]
},
{
"page": "morie_jsonlt_stream_in",
"title": "Stream newline-delimited JSON (jsonlite's stream_in / stream_out)",
"topics": [
"morie_jsonlt_stream_in",
"morie_jsonlt_stream_out"
]
},
{
"page": "morie_jsonlt_validate",
"title": "Validate JSON text",
"topics": [
"morie_jsonlt_validate"
]
},
{
"page": "morie_judge_iv",
"title": "Judge-leniency instrument on observed data: Wald ratio, compliers and the defier sensitivity",
"topics": [
"morie_judge_iv"
]
},
{
"page": "morie_judge_iv_population",
"title": "Judge-leniency instrument on a known population: the exact decomposition",
"topics": [
"morie_judge_iv_population"
]
},
{
"page": "morie_judge_monotonicity",
"title": "Monotonicity of judges without outcomes: do the leniency rankings hold in every subgroup?",
"topics": [
"morie_judge_monotonicity"
]
},
{
"page": "morie_judge_slope_test",
"title": "The many-judge slope test of monotonicity",
"topics": [
"morie_judge_slope_test"
]
},
{
"page": "morie_kalman_filter",
"title": "Kalman filter predict-update for a linear-Gaussian state-space model",
"topics": [
"morie_kalman_filter"
]
},
{
"page": "morie_kamath_3h_alignment",
"title": "3H alignment score (Kamath Ch 5)",
"topics": [
"morie_kamath_3h_alignment"
]
},
{
"page": "morie_kamath_adalora_rank_allocation",
"title": "AdaLoRA SVD update with importance-based pruning (Kamath Ch 4)",
"topics": [
"morie_kamath_adalora_rank_allocation"
]
},
{
"page": "morie_kamath_alibi_bias",
"title": "ALiBi biased attention (Kamath Ch 2)",
"topics": [
"morie_kamath_alibi_bias"
]
},
{
"page": "morie_kamath_autoprompt_gradient_search",
"title": "AutoPrompt trigger search (Kamath Ch 3)",
"topics": [
"morie_kamath_autoprompt_gradient_search"
]
},
{
"page": "morie_kamath_bertscore",
"title": "BERTScore end to end (Kamath Ch 8)",
"topics": [
"morie_kamath_bertscore"
]
},
{
"page": "morie_kamath_best_of_n_sampling",
"title": "Best-of-N sampling (Kamath Ch 5)",
"topics": [
"morie_kamath_best_of_n_sampling"
]
},
{
"page": "morie_kamath_bleu_score",
"title": "Corpus-level BLEU (Kamath Ch 8)",
"topics": [
"morie_kamath_bleu_score"
]
},
{
"page": "morie_kamath_bm25_score",
"title": "BM25 relevance score (Kamath Ch 7)",
"topics": [
"morie_kamath_bm25_score"
]
},
{
"page": "morie_kamath_bradley_terry_preference",
"title": "Bradley-Terry preference probability, vectorised (Kamath Ch 5)",
"topics": [
"morie_kamath_bradley_terry_preference"
]
},
{
"page": "morie_kamath_ch3_back_translation_prob",
"title": "Round-trip back-translation prompt score (Kamath Eq 3.9)",
"topics": [
"morie_kamath_ch3_back_translation_prob"
]
},
{
"page": "morie_kamath_ch3_dante_cloze",
"title": "Cloze knowledge probe (Kamath Eq 3.4)",
"topics": [
"morie_kamath_ch3_dante_cloze"
]
},
{
"page": "morie_kamath_ch3_prefix_prompt_template",
"title": "Ch 3 prompt templates (Kamath Eq 3.5-3.7)",
"topics": [
"morie_kamath_ch3_cloze_prompt_template",
"morie_kamath_ch3_prefix_prompt_template",
"morie_kamath_ch3_translate_prefix_prompt"
]
},
{
"page": "morie_kamath_ch3_prefix_tuning_obj",
"title": "Prefix-tuning objective (Kamath Eq 3.12)",
"topics": [
"morie_kamath_ch3_prefix_tuning_obj"
]
},
{
"page": "morie_kamath_ch3_prompt_label_mapping",
"title": "Prompt-based classification through a label word map (Kamath Eq 3.1)",
"topics": [
"morie_kamath_ch3_prompt_label_mapping"
]
},
{
"page": "morie_kamath_ch3_prompt_search_argmax",
"title": "Answer search over filled prompts (Kamath Eq 3.3)",
"topics": [
"morie_kamath_ch3_prompt_search_argmax"
]
},
{
"page": "morie_kamath_ch3_prompt_softmax_label",
"title": "Label-word softmax (Kamath Eq 3.2)",
"topics": [
"morie_kamath_ch3_prompt_softmax_label"
]
},
{
"page": "morie_kamath_ch3_qa_trigger_template",
"title": "Adversarial-trigger QA prompt (Kamath Eq 3.10)",
"topics": [
"morie_kamath_ch3_qa_trigger_template"
]
},
{
"page": "morie_kamath_ch3_t5_template_obj",
"title": "T5 template generation objective (Kamath Eq 3.11)",
"topics": [
"morie_kamath_ch3_t5_template_obj"
]
},
{
"page": "morie_kamath_ch3_top1_prompt_metric",
"title": "Top-1 prompt selection accuracy (Kamath Eq 3.8)",
"topics": [
"morie_kamath_ch3_top1_prompt_metric"
]
},
{
"page": "morie_kamath_ch4_full_finetune_obj",
"title": "Fine-tuning objectives (Kamath Eq 4.3-4.4)",
"topics": [
"morie_kamath_ch4_full_finetune_obj",
"morie_kamath_ch4_lora_obj"
]
},
{
"page": "morie_kamath_ch4_krona_efficient",
"title": "KronA matrix-free product (Kamath Eq 4.7)",
"topics": [
"morie_kamath_ch4_krona_efficient"
]
},
{
"page": "morie_kamath_ch4_krona_tuned_weights",
"title": "KronA layer output and merged weights (Kamath Eq 4.8-4.9)",
"topics": [
"morie_kamath_ch4_krona_output",
"morie_kamath_ch4_krona_tuned_weights"
]
},
{
"page": "morie_kamath_ch4_kronecker_product",
"title": "Kronecker product (Kamath Eq 4.6)",
"topics": [
"morie_kamath_ch4_kronecker_product"
]
},
{
"page": "morie_kamath_ch4_loftq_objective",
"title": "LoftQ Frobenius objective (Kamath Eq 4.11)",
"topics": [
"morie_kamath_ch4_loftq_objective"
]
},
{
"page": "morie_kamath_ch4_lora_forward",
"title": "LoRA forward pass (Kamath Eq 4.5)",
"topics": [
"morie_kamath_ch4_lora_forward"
]
},
{
"page": "morie_kamath_ch4_series_adapter",
"title": "Bottleneck adapters (Kamath Eq 4.1-4.2)",
"topics": [
"morie_kamath_ch4_parallel_adapter",
"morie_kamath_ch4_series_adapter"
]
},
{
"page": "morie_kamath_ch4_vera_forward",
"title": "VeRA forward pass (Kamath Eq 4.10)",
"topics": [
"morie_kamath_ch4_vera_forward"
]
},
{
"page": "morie_kamath_ch5_bradley_terry_pref",
"title": "Bradley-Terry preference probability (Kamath Eq 5.8-5.9)",
"topics": [
"morie_kamath_ch5_bradley_terry_pref",
"morie_kamath_ch5_pref_sigmoid_form"
]
},
{
"page": "morie_kamath_ch5_dpo_loss",
"title": "DPO loss (Kamath Eq 5.12)",
"topics": [
"morie_kamath_ch5_dpo_loss"
]
},
{
"page": "morie_kamath_ch5_dpo_pref_simplified",
"title": "DPO preference probability (Kamath Eq 5.10-5.11)",
"topics": [
"morie_kamath_ch5_dpo_pref_simplified",
"morie_kamath_ch5_dpo_pref_substituted"
]
},
{
"page": "morie_kamath_ch5_dpo_reward_optimal",
"title": "Reward implied by an optimal policy (Kamath Eq 5.7)",
"topics": [
"morie_kamath_ch5_dpo_reward_optimal"
]
},
{
"page": "morie_kamath_ch5_reward_kl_penalty",
"title": "KL-penalised RLHF reward (Kamath Eq 5.2)",
"topics": [
"morie_kamath_ch5_reward_kl_penalty"
]
},
{
"page": "morie_kamath_ch5_reward_loss_pairwise",
"title": "Pairwise reward-model losses (Kamath Eq 5.1, 5.3)",
"topics": [
"morie_kamath_ch5_reward_loss_pairwise",
"morie_kamath_ch5_rm_bradley_terry"
]
},
{
"page": "morie_kamath_ch5_rlhf_objective",
"title": "RLHF objective and PPO loss (Kamath Eq 5.4-5.5)",
"topics": [
"morie_kamath_ch5_ppo_loss",
"morie_kamath_ch5_rlhf_objective"
]
},
{
"page": "morie_kamath_ch5_rlhf_optimal_policy",
"title": "Optimal KL-regularised policy (Kamath Eq 5.6)",
"topics": [
"morie_kamath_ch5_rlhf_optimal_policy"
]
},
{
"page": "morie_kamath_ch6_alignment_function",
"title": "AlignScore alignment function (Kamath Eq 6.2)",
"topics": [
"morie_kamath_ch6_alignment_function"
]
},
{
"page": "morie_kamath_ch6_alignscore_total_loss",
"title": "AlignScore joint loss (Kamath Eq 6.3)",
"topics": [
"morie_kamath_ch6_alignscore_total_loss"
]
},
{
"page": "morie_kamath_ch6_cat_metric",
"title": "Context Association Test score (Kamath Eq 6.12)",
"topics": [
"morie_kamath_ch6_cat_metric"
]
},
{
"page": "morie_kamath_ch6_cbs_variance",
"title": "Categorical Bias Score (Kamath Eq 6.9)",
"topics": [
"morie_kamath_ch6_cbs_variance"
]
},
{
"page": "morie_kamath_ch6_ceat_random_effects",
"title": "CEAT random-effects pooled WEAT (Kamath Eq 6.7)",
"topics": [
"morie_kamath_ch6_ceat_random_effects"
]
},
{
"page": "morie_kamath_ch6_co_occurrence_bias",
"title": "Co-Occurrence Bias Score (Kamath Eq 6.14)",
"topics": [
"morie_kamath_ch6_co_occurrence_bias"
]
},
{
"page": "morie_kamath_ch6_debias_regularizer",
"title": "Counterfactual-pair debiasing and the gender direction (Eq 6.18-6.19)",
"topics": [
"morie_kamath_ch6_debias_regularizer",
"morie_kamath_ch6_gender_direction"
]
},
{
"page": "morie_kamath_ch6_demographic_representation",
"title": "Demographic representation and stereotypical association (Eq 6.15-6.16)",
"topics": [
"morie_kamath_ch6_demographic_representation",
"morie_kamath_ch6_stereotypical_assoc"
]
},
{
"page": "morie_kamath_ch6_differential_privacy",
"title": "Differential privacy check (Kamath Eq 6.32)",
"topics": [
"morie_kamath_ch6_differential_privacy"
]
},
{
"page": "morie_kamath_ch6_ear_entropy_reg",
"title": "Entropy-based attention regularisation (Kamath Eq 6.21)",
"topics": [
"morie_kamath_ch6_ear_entropy_reg"
]
},
{
"page": "morie_kamath_ch6_emt_metric",
"title": "Toxicity metrics (Kamath Eq 6.23-6.25)",
"topics": [
"morie_kamath_ch6_emt_metric",
"morie_kamath_ch6_toxicity_probability",
"morie_kamath_ch6_toxic_fraction"
]
},
{
"page": "morie_kamath_ch6_factscore",
"title": "FActScore (Kamath Eq 6.1)",
"topics": [
"morie_kamath_ch6_factscore"
]
},
{
"page": "morie_kamath_ch6_gedi_combined_loss",
"title": "GeDi combined loss (Kamath Eq 6.29)",
"topics": [
"morie_kamath_ch6_gedi_combined_loss"
]
},
{
"page": "morie_kamath_ch6_gender_projection_reg",
"title": "Gender-projection regulariser (Kamath Eq 6.20)",
"topics": [
"morie_kamath_ch6_gender_projection_reg"
]
},
{
"page": "morie_kamath_ch6_honest_score",
"title": "HONEST score (Kamath Eq 6.17)",
"topics": [
"morie_kamath_ch6_honest_score"
]
},
{
"page": "morie_kamath_ch6_log_prob_ratio_attr",
"title": "Equalising log-probability-ratio regulariser (Kamath Eq 6.22)",
"topics": [
"morie_kamath_ch6_log_prob_ratio_attr"
]
},
{
"page": "morie_kamath_ch6_lpbs_bias",
"title": "Log-Probability Bias Score (Kamath Eq 6.8)",
"topics": [
"morie_kamath_ch6_lpbs_bias"
]
},
{
"page": "morie_kamath_ch6_lstm_chain_rule",
"title": "Chain-rule sequence probability (Kamath Eq 6.26)",
"topics": [
"morie_kamath_ch6_lstm_chain_rule"
]
},
{
"page": "morie_kamath_ch6_lstm_softmax_word",
"title": "LSTM vocabulary softmax and Affect-LM (Kamath Eq 6.27-6.28)",
"topics": [
"morie_kamath_ch6_affect_lm",
"morie_kamath_ch6_lstm_softmax_word"
]
},
{
"page": "morie_kamath_ch6_perplexity_leakage",
"title": "Worst-case perplexity leakage (Kamath Eq 6.33)",
"topics": [
"morie_kamath_ch6_perplexity_leakage"
]
},
{
"page": "morie_kamath_ch6_pii_likelihood",
"title": "ProPILE PII likelihood (Kamath Eq 6.31)",
"topics": [
"morie_kamath_ch6_pii_likelihood"
]
},
{
"page": "morie_kamath_ch6_pll",
"title": "Pseudo-log-likelihood and CrowS-Pairs (Kamath Eq 6.10-6.11)",
"topics": [
"morie_kamath_ch6_cps_metric",
"morie_kamath_ch6_pll"
]
},
{
"page": "morie_kamath_ch6_self_diagnosis_prob",
"title": "Self-diagnosis probability (Kamath Eq 6.30)",
"topics": [
"morie_kamath_ch6_self_diagnosis_prob"
]
},
{
"page": "morie_kamath_ch6_sgs_invariance",
"title": "Social Group Substitution invariance (Kamath Eq 6.13)",
"topics": [
"morie_kamath_ch6_sgs_invariance"
]
},
{
"page": "morie_kamath_ch6_weat_effect_size",
"title": "WEAT effect size (Kamath Eq 6.6)",
"topics": [
"morie_kamath_ch6_weat_effect_size"
]
},
{
"page": "morie_kamath_ch6_weat_function",
"title": "WEAT test statistic (Kamath Eq 6.4)",
"topics": [
"morie_kamath_ch6_weat_function"
]
},
{
"page": "morie_kamath_ch6_weat_similarity",
"title": "WEAT differential association (Kamath Eq 6.5)",
"topics": [
"morie_kamath_ch6_weat_similarity"
]
},
{
"page": "morie_kamath_ch7_answer_relevance",
"title": "RAGAS answer relevance (Kamath Eq 7.3)",
"topics": [
"morie_kamath_ch7_answer_relevance"
]
},
{
"page": "morie_kamath_ch7_faithfulness_metric",
"title": "RAG faithfulness (Kamath Eq 7.2)",
"topics": [
"morie_kamath_ch7_faithfulness_metric"
]
},
{
"page": "morie_kamath_ch8_bertscore_recall",
"title": "BERTScore recall, precision and F1 (Kamath Eq 8.7-8.9)",
"topics": [
"morie_kamath_ch8_bertscore_f1",
"morie_kamath_ch8_bertscore_precision",
"morie_kamath_ch8_bertscore_recall"
]
},
{
"page": "morie_kamath_ch8_bleu_n_geom_mean",
"title": "BLEU geometric mean and final score (Kamath Eq 8.3, 8.5)",
"topics": [
"morie_kamath_ch8_bleu_final",
"morie_kamath_ch8_bleu_n_geom_mean"
]
},
{
"page": "morie_kamath_ch8_bleu_precision",
"title": "BLEU clipped n-gram precision (Kamath Eq 8.2)",
"topics": [
"morie_kamath_ch8_bleu_precision"
]
},
{
"page": "morie_kamath_ch8_brevity_penalty",
"title": "BLEU brevity penalty (Kamath Eq 8.4)",
"topics": [
"morie_kamath_ch8_brevity_penalty"
]
},
{
"page": "morie_kamath_ch8_geval_score",
"title": "G-Eval probability-weighted score (Kamath Eq 8.15)",
"topics": [
"morie_kamath_ch8_geval_score"
]
},
{
"page": "morie_kamath_ch8_moverscore_distance",
"title": "MoverScore n-gram distance and Sentence Mover's Distance (Eq 8.11, 8.14)",
"topics": [
"morie_kamath_ch8_moverscore_distance",
"morie_kamath_ch8_smd"
]
},
{
"page": "morie_kamath_ch8_ngram_embedding",
"title": "MoverScore n-gram embedding (Kamath Eq 8.12)",
"topics": [
"morie_kamath_ch8_ngram_embedding"
]
},
{
"page": "morie_kamath_ch8_ngram_weight",
"title": "MoverScore n-gram weight (Kamath Eq 8.13)",
"topics": [
"morie_kamath_ch8_ngram_weight"
]
},
{
"page": "morie_kamath_ch8_pass_at_k",
"title": "Unbiased pass@k (Kamath Eq 8.16)",
"topics": [
"morie_kamath_ch8_pass_at_k",
"morie_kamath_pass_at_k"
]
},
{
"page": "morie_kamath_ch8_perplexity",
"title": "Perplexity from token probabilities (Kamath Eq 8.1)",
"topics": [
"morie_kamath_ch8_perplexity"
]
},
{
"page": "morie_kamath_ch8_rouge_n",
"title": "ROUGE-N recall over multiple references (Kamath Eq 8.6)",
"topics": [
"morie_kamath_ch8_rouge_n"
]
},
{
"page": "morie_kamath_ch8_wmd",
"title": "Word Mover's Distance as an exact transport LP (Kamath Eq 8.10)",
"topics": [
"morie_kamath_ch8_wmd"
]
},
{
"page": "morie_kamath_ch9_clip_image_to_text",
"title": "CLIP contrastive losses (Kamath Eq 9.5-9.7)",
"topics": [
"morie_kamath_ch9_clip_contrastive_total",
"morie_kamath_ch9_clip_image_to_text",
"morie_kamath_ch9_clip_text_to_image"
]
},
{
"page": "morie_kamath_ch9_flamingo_factorized",
"title": "Flamingo factorized likelihood and dataset mix (Kamath Eq 9.21-9.22)",
"topics": [
"morie_kamath_ch9_flamingo_dataset_mix",
"morie_kamath_ch9_flamingo_factorized"
]
},
{
"page": "morie_kamath_ch9_fom_loss",
"title": "Frame order modelling loss (Kamath Eq 9.15)",
"topics": [
"morie_kamath_ch9_fom_loss"
]
},
{
"page": "morie_kamath_ch9_input_alignment_loss",
"title": "Input-alignment text-generation objective (Kamath Eq 9.2)",
"topics": [
"morie_kamath_ch9_input_alignment_loss"
]
},
{
"page": "morie_kamath_ch9_input_projector",
"title": "Input and output projectors (Kamath Eq 9.3, 9.19)",
"topics": [
"morie_kamath_ch9_input_projector",
"morie_kamath_ch9_output_alignment"
]
},
{
"page": "morie_kamath_ch9_itm_loss",
"title": "Image-text matching binary loss (Kamath Eq 9.13)",
"topics": [
"morie_kamath_ch9_itm_loss"
]
},
{
"page": "morie_kamath_ch9_ldm_loss",
"title": "Conditional LDM noise-prediction loss (Kamath Eq 9.20)",
"topics": [
"morie_kamath_ch9_ldm_loss"
]
},
{
"page": "morie_kamath_ch9_llm_signal_tokens",
"title": "LLM text and signal tokens (Kamath Eq 9.4)",
"topics": [
"morie_kamath_ch9_llm_signal_tokens"
]
},
{
"page": "morie_kamath_ch9_mm_instr_predict",
"title": "Multimodal instruction-tuned prediction (Kamath Eq 9.16)",
"topics": [
"morie_kamath_ch9_mm_instr_predict"
]
},
{
"page": "morie_kamath_ch9_mml_vlm_loss",
"title": "Visual-linguistic matching losses (Kamath Eq 9.8-9.9)",
"topics": [
"morie_kamath_ch9_itm_hard_negative",
"morie_kamath_ch9_mml_vlm_loss"
]
},
{
"page": "morie_kamath_ch9_mmllm_autoregressive",
"title": "Autoregressive response losses (Kamath Eq 9.14, 9.17)",
"topics": [
"morie_kamath_ch9_itg_loss",
"morie_kamath_ch9_mmllm_autoregressive"
]
},
{
"page": "morie_kamath_ch9_moc_loss",
"title": "Masked object classification loss (Kamath Eq 9.12)",
"topics": [
"morie_kamath_ch9_moc_loss"
]
},
{
"page": "morie_kamath_ch9_modality_encoder",
"title": "Modality encoder (Kamath Eq 9.1)",
"topics": [
"morie_kamath_ch9_modality_encoder"
]
},
{
"page": "morie_kamath_ch9_output_projector_mse",
"title": "Output-projector MSE objective (Kamath Eq 9.18)",
"topics": [
"morie_kamath_ch9_output_projector_mse"
]
},
{
"page": "morie_kamath_ch9_simvlm_mlm",
"title": "SimVLM masked-token loss with visual conditioning (Kamath Eq 9.10)",
"topics": [
"morie_kamath_ch9_simvlm_mlm"
]
},
{
"page": "morie_kamath_ch9_simvlm_prefixlm",
"title": "PrefixLM suffix negative log-likelihood (Kamath Eq 9.11)",
"topics": [
"morie_kamath_ch9_simvlm_prefixlm"
]
},
{
"page": "morie_kamath_chain_of_thought",
"title": "Zero-shot chain-of-thought prompting (Kamath Ch 4)",
"topics": [
"morie_kamath_chain_of_thought"
]
},
{
"page": "morie_kamath_chinchilla_compute_optimal",
"title": "Chinchilla compute-optimal split (Kamath Ch 1)",
"topics": [
"morie_kamath_chinchilla_compute_optimal"
]
},
{
"page": "morie_kamath_christiano_deep_rl_feedback",
"title": "Preference-based reward learning (Kamath Ch 5)",
"topics": [
"morie_kamath_christiano_deep_rl_feedback"
]
},
{
"page": "morie_kamath_constitutional_ai_loop",
"title": "Constitutional AI critique-revise loop (Kamath Ch 5)",
"topics": [
"morie_kamath_constitutional_ai_loop"
]
},
{
"page": "morie_kamath_corrective_rag",
"title": "Corrective RAG retrieval router (Kamath Ch 7)",
"topics": [
"morie_kamath_corrective_rag"
]
},
{
"page": "morie_kamath_cross_encoder_rerank",
"title": "Cross-encoder re-ranking (Kamath Ch 7)",
"topics": [
"morie_kamath_cross_encoder_rerank"
]
},
{
"page": "morie_kamath_crowspairs_bias",
"title": "CrowS-Pairs stereotype preference rate (Kamath Ch 6)",
"topics": [
"morie_kamath_crowspairs_bias"
]
},
{
"page": "morie_kamath_dense_passage_retrieval",
"title": "Dense passage retrieval top-k (Kamath Ch 7)",
"topics": [
"morie_kamath_dense_passage_retrieval"
]
},
{
"page": "morie_kamath_differential_privacy",
"title": "(eps, delta)-DP definition check (Kamath Ch 6)",
"topics": [
"morie_kamath_differential_privacy"
]
},
{
"page": "morie_kamath_double_quantization",
"title": "Double quantization of the scale constants (Kamath Ch 4)",
"topics": [
"morie_kamath_double_quantization"
]
},
{
"page": "morie_kamath_dpo_loss",
"title": "DPO loss from log-probabilities (Kamath Ch 5)",
"topics": [
"morie_kamath_dpo_loss"
]
},
{
"page": "morie_kamath_emergent_abilities",
"title": "Emergent-ability step metric (Kamath Ch 1)",
"topics": [
"morie_kamath_emergent_abilities"
]
},
{
"page": "morie_kamath_expert_capacity_factor",
"title": "MoE per-expert capacity (Kamath Ch 2)",
"topics": [
"morie_kamath_expert_capacity_factor"
]
},
{
"page": "morie_kamath_factscore",
"title": "FactScore over atomic claims (Kamath Ch 6)",
"topics": [
"morie_kamath_factscore"
]
},
{
"page": "morie_kamath_fasttext_subword",
"title": "FastText subword sum (Kamath Ch 2)",
"topics": [
"morie_kamath_fasttext_subword",
"morie_kamath_word_ngrams"
]
},
{
"page": "morie_kamath_few_shot_exemplar_selection",
"title": "Top-K few-shot exemplar selection (Kamath Ch 3)",
"topics": [
"morie_kamath_few_shot_exemplar_selection"
]
},
{
"page": "morie_kamath_g_eval",
"title": "G-Eval rubric score from judge logits (Kamath Ch 8)",
"topics": [
"morie_kamath_g_eval"
]
},
{
"page": "morie_kamath_glove_cost",
"title": "GloVe weighted least-squares cost (Kamath Ch 2)",
"topics": [
"morie_kamath_glove_cost",
"morie_kamath_glove_weight"
]
},
{
"page": "morie_kamath_groundedness_reward",
"title": "Groundedness reward (Kamath Ch 7)",
"topics": [
"morie_kamath_groundedness_reward"
]
},
{
"page": "morie_kamath_houlsby_adapter",
"title": "Houlsby bottleneck adapter (Kamath Ch 4)",
"topics": [
"morie_kamath_houlsby_adapter"
]
},
{
"page": "morie_kamath_hybrid_retrieval_fusion",
"title": "Hybrid dense/sparse score fusion (Kamath Ch 7)",
"topics": [
"morie_kamath_hybrid_retrieval_fusion"
]
},
{
"page": "morie_kamath_hyde_hypothetical_doc",
"title": "HyDE retrieval via a hypothetical document (Kamath Ch 7)",
"topics": [
"morie_kamath_hyde_hypothetical_doc"
]
},
{
"page": "morie_kamath_image_text_contrastive",
"title": "Image-text contrastive loss (Kamath Ch 9)",
"topics": [
"morie_kamath_image_text_contrastive"
]
},
{
"page": "morie_kamath_image_text_matching",
"title": "Image-text matching head (Kamath Ch 9)",
"topics": [
"morie_kamath_image_text_matching"
]
},
{
"page": "morie_kamath_in_context_learning_prob",
"title": "In-context learning conditional probability (Kamath Ch 3)",
"topics": [
"morie_kamath_in_context_learning_prob"
]
},
{
"page": "morie_kamath_instruction_tuning_loss",
"title": "Instruction-tuning cross entropy (Kamath Ch 4)",
"topics": [
"morie_kamath_instruction_tuning_loss"
]
},
{
"page": "morie_kamath_kl_reward_shaping",
"title": "KL-shaped RLHF reward (Kamath Ch 5)",
"topics": [
"morie_kamath_kl_reward_shaping"
]
},
{
"page": "morie_kamath_llava_visual_instruction",
"title": "LLaVA visual instruction assembly (Kamath Ch 9)",
"topics": [
"morie_kamath_llava_visual_instruction"
]
},
{
"page": "morie_kamath_lora_weight_update",
"title": "LoRA forward with alpha/r scaling (Kamath Ch 4)",
"topics": [
"morie_kamath_lora_weight_update"
]
},
{
"page": "morie_kamath_mamba_ssm",
"title": "Mamba selective SSM scan (Kamath Ch 2)",
"topics": [
"morie_kamath_mamba_ssm"
]
},
{
"page": "morie_kamath_medusa_heads",
"title": "Medusa multi-head speculative prediction (Kamath Ch 8)",
"topics": [
"morie_kamath_medusa_heads"
]
},
{
"page": "morie_kamath_membership_inference",
"title": "Membership inference by loss threshold (Kamath Ch 6)",
"topics": [
"morie_kamath_membership_inference"
]
},
{
"page": "morie_kamath_memorization_exposure",
"title": "Canary exposure (Kamath Ch 6)",
"topics": [
"morie_kamath_memorization_exposure"
]
},
{
"page": "morie_kamath_moe_load_balance_loss",
"title": "MoE auxiliary load-balancing loss (Kamath Ch 2)",
"topics": [
"morie_kamath_moe_load_balance_loss"
]
},
{
"page": "morie_kamath_moe_router_softmax",
"title": "Softmax top-k MoE router (Kamath Ch 2)",
"topics": [
"morie_kamath_moe_router_softmax"
]
},
{
"page": "morie_kamath_moe_top_k_gating",
"title": "Top-k gate renormalisation (Kamath Ch 2)",
"topics": [
"morie_kamath_moe_top_k_gating"
]
},
{
"page": "morie_kamath_moverscore",
"title": "MoverScore from exact Word Mover's Distance (Kamath Ch 8)",
"topics": [
"morie_kamath_moverscore",
"morie_kamath_word_movers_distance"
]
},
{
"page": "morie_kamath_multimodal_mae",
"title": "Multimodal MAE squared reconstruction loss (Kamath Ch 9)",
"topics": [
"morie_kamath_multimodal_mae"
]
},
{
"page": "morie_kamath_nextgpt_any2any",
"title": "NExT-GPT any-to-any pipeline (Kamath Ch 9)",
"topics": [
"morie_kamath_nextgpt_any2any"
]
},
{
"page": "morie_kamath_nf4_datatype",
"title": "NF4 equal-mass normal quantile grid (Kamath Ch 4)",
"topics": [
"morie_kamath_nf4_datatype",
"morie_kamath_normal_quantile"
]
},
{
"page": "morie_kamath_ngram_language_model",
"title": "N-gram MLE conditional probability (Kamath Ch 2)",
"topics": [
"morie_kamath_ngram_language_model"
]
},
{
"page": "morie_kamath_nucleus_sampling",
"title": "Nucleus (top-p) truncation (Kamath Ch 8)",
"topics": [
"morie_kamath_nucleus_sampling"
]
},
{
"page": "morie_kamath_p_tuning_v2",
"title": "P-tuning v2 deep prefix concatenation (Kamath Ch 4)",
"topics": [
"morie_kamath_p_tuning_v2"
]
},
{
"page": "morie_kamath_perplexity",
"title": "Perplexity from log-probabilities (Kamath Ch 8)",
"topics": [
"morie_kamath_perplexity"
]
},
{
"page": "morie_kamath_pet_loss",
"title": "PET loss (Kamath Ch 3)",
"topics": [
"morie_kamath_pet_loss"
]
},
{
"page": "morie_kamath_post_ln_transformer",
"title": "Post-LN and pre-LN transformer blocks (Kamath Ch 2)",
"topics": [
"morie_kamath_layer_norm_rows",
"morie_kamath_post_ln_transformer",
"morie_kamath_pre_ln_transformer"
]
},
{
"page": "morie_kamath_ppo_rlhf_objective",
"title": "PPO-RLHF objective (Kamath Ch 5)",
"topics": [
"morie_kamath_ppo_rlhf_objective"
]
},
{
"page": "morie_kamath_prefix_tuning",
"title": "Prefix tuning key/value concatenation (Kamath Ch 4)",
"topics": [
"morie_kamath_prefix_tuning"
]
},
{
"page": "morie_kamath_prompt_tuning",
"title": "Prompt tuning soft-prompt prepend (Kamath Ch 4)",
"topics": [
"morie_kamath_prompt_tuning"
]
},
{
"page": "morie_kamath_q_former",
"title": "Q-Former cross-attention (Kamath Ch 9)",
"topics": [
"morie_kamath_q_former"
]
},
{
"page": "morie_kamath_qlora_4bit",
"title": "QLoRA 4-bit forward (Kamath Ch 4)",
"topics": [
"morie_kamath_dequantize_nf4",
"morie_kamath_qlora_4bit"
]
},
{
"page": "morie_kamath_ragas_answer_relevance",
"title": "RAGAS answer relevance from a reverse-question model (Kamath Ch 7)",
"topics": [
"morie_kamath_ragas_answer_relevance"
]
},
{
"page": "morie_kamath_ragas_context_relevance",
"title": "RAGAS context relevance (Kamath Ch 7)",
"topics": [
"morie_kamath_ragas_context_relevance"
]
},
{
"page": "morie_kamath_reciprocal_rank_fusion",
"title": "Reciprocal rank fusion (Kamath Ch 7)",
"topics": [
"morie_kamath_reciprocal_rank_fusion"
]
},
{
"page": "morie_kamath_rejection_sampling_finetune",
"title": "Rejection-sampling fine-tuning (Kamath Ch 5)",
"topics": [
"morie_kamath_rejection_sampling_finetune"
]
},
{
"page": "morie_kamath_retnet_retention",
"title": "RetNet retention (Kamath Ch 2)",
"topics": [
"morie_kamath_retnet_retention"
]
},
{
"page": "morie_kamath_reward_model_training_loss",
"title": "Bradley-Terry reward-model NLL over preference pairs (Kamath Ch 5)",
"topics": [
"morie_kamath_reward_model_training_loss"
]
},
{
"page": "morie_kamath_rlaif_objective",
"title": "RLAIF Bradley-Terry reward fit (Kamath Ch 5)",
"topics": [
"morie_kamath_rlaif_objective"
]
},
{
"page": "morie_kamath_rlhf_pipeline",
"title": "RLHF pipeline SFT -> RM -> PPO (Kamath Ch 5)",
"topics": [
"morie_kamath_rlhf_pipeline"
]
},
{
"page": "morie_kamath_rms_norm",
"title": "RMSNorm (Kamath Ch 2)",
"topics": [
"morie_kamath_rms_norm"
]
},
{
"page": "morie_kamath_rotary_positional_embedding",
"title": "Rotary positional embedding (Kamath Ch 2)",
"topics": [
"morie_kamath_rotary_positional_embedding"
]
},
{
"page": "morie_kamath_rouge_n",
"title": "ROUGE-N against a single reference (Kamath Ch 8)",
"topics": [
"morie_kamath_rouge_n"
]
},
{
"page": "morie_kamath_rwkv_time_mix",
"title": "RWKV time-mixing (Kamath Ch 2)",
"topics": [
"morie_kamath_rwkv_time_mix"
]
},
{
"page": "morie_kamath_scaling_laws",
"title": "Power-law scaling (Kamath Ch 1)",
"topics": [
"morie_kamath_scaling_laws"
]
},
{
"page": "morie_kamath_self_consistency",
"title": "Self-consistency majority vote (Kamath Ch 4)",
"topics": [
"morie_kamath_self_consistency"
]
},
{
"page": "morie_kamath_self_rag",
"title": "Self-RAG reflection-token decisions (Kamath Ch 7)",
"topics": [
"morie_kamath_self_rag"
]
},
{
"page": "morie_kamath_sentencepiece_tokenizer",
"title": "SentencePiece unigram trainer (Kamath Ch 2)",
"topics": [
"morie_kamath_sentencepiece_tokenizer"
]
},
{
"page": "morie_kamath_speculative_decoding",
"title": "Speculative decoding accept/reject (Kamath Ch 8)",
"topics": [
"morie_kamath_speculative_decoding"
]
},
{
"page": "morie_kamath_step_back_prompting",
"title": "Step-back prompting (Kamath Ch 4)",
"topics": [
"morie_kamath_step_back_prompting"
]
},
{
"page": "morie_kamath_stereoset_bias",
"title": "StereoSet stereotype-preference fraction (Kamath Ch 6)",
"topics": [
"morie_kamath_stereoset_bias"
]
},
{
"page": "morie_kamath_summarize_from_feedback",
"title": "Summarisation from human feedback (Kamath Ch 5)",
"topics": [
"morie_kamath_summarize_from_feedback"
]
},
{
"page": "morie_kamath_swiglu_activation",
"title": "SwiGLU activation (Kamath Ch 2)",
"topics": [
"morie_kamath_swiglu_activation",
"morie_kamath_swish"
]
},
{
"page": "morie_kamath_t5_span_corruption",
"title": "T5 span corruption (Kamath Ch 2)",
"topics": [
"morie_kamath_t5_span_corruption"
]
},
{
"page": "morie_kamath_temperature_sampling",
"title": "Temperature-scaled softmax (Kamath Ch 8)",
"topics": [
"morie_kamath_temperature_sampling"
]
},
{
"page": "morie_kamath_toxigen_score",
"title": "ToxiGen classifier toxicity probability (Kamath Ch 6)",
"topics": [
"morie_kamath_toxigen_score"
]
},
{
"page": "morie_kamath_tree_of_thoughts",
"title": "Tree-of-thoughts beam search (Kamath Ch 4)",
"topics": [
"morie_kamath_tree_of_thoughts"
]
},
{
"page": "morie_kamath_unigram_lm_tokenizer",
"title": "Unigram LM tokenizer by EM (Kamath Ch 2)",
"topics": [
"morie_kamath_unigram_lm_tokenizer",
"morie_kamath_unigram_loglik",
"morie_kamath_viterbi_segment"
]
},
{
"page": "morie_kamath_vera_adapter",
"title": "VeRA adapter (Kamath Ch 4)",
"topics": [
"morie_kamath_vera_adapter"
]
},
{
"page": "morie_kamath_verbalizer_mapping",
"title": "Verbalizer class probabilities (Kamath Ch 3)",
"topics": [
"morie_kamath_verbalizer_mapping"
]
},
{
"page": "morie_kamath_word2vec_skipgram",
"title": "Skip-gram log-likelihood with the full softmax (Kamath Ch 2)",
"topics": [
"morie_kamath_word2vec_skipgram"
]
},
{
"page": "morie_kamath_yarn_context_extrapolation",
"title": "YaRN NTK-aware RoPE frequency rescaling (Kamath Ch 2)",
"topics": [
"morie_kamath_yarn_context_extrapolation"
]
},
{
"page": "morie_kaplan_meier",
"title": "Kaplan-Meier product-limit estimator",
"topics": [
"morie_kaplan_meier",
"morie_survival_km"
]
},
{
"page": "morie_kcusum",
"title": "Kernel change-point analysis (KFDR scan)",
"topics": [
"morie_kcusum"
]
},
{
"page": "morie_kde_h",
"title": "Kernel density estimate with the Silverman bandwidth",
"topics": [
"morie_kde_h"
]
},
{
"page": "morie_kde2d",
"title": "Native 2-D kernel density estimate (reproduces MASS::kde2d)",
"topics": [
"morie_kde2d"
]
},
{
"page": "morie_kendall_tau",
"title": "Kendall's tau-b",
"topics": [
"morie_kendall_tau"
]
},
{
"page": "morie_kendall_tau_partial",
"title": "Kendall partial-tau correlation (Gibbons Ch 12.6)",
"topics": [
"morie_kendall_tau_partial"
]
},
{
"page": "morie_kernel_methods",
"title": "Kernel and RKHS methods",
"topics": [
"morie_arccos_kernel",
"morie_bayesian_kernel_blup",
"morie_kernel_blup_gxe",
"morie_kernel_blup_replicated",
"morie_kernel_eigen_design",
"morie_kernel_matrix",
"morie_kernel_methods",
"morie_nystrom",
"morie_rkhs_fit",
"morie_rkhs_mixed_equations",
"morie_rkhs_predict",
"morie_sparse_kernel_design"
]
},
{
"page": "morie_kernel_pca",
"title": "Kernel principal components analysis via 'kernlab'",
"topics": [
"morie_kernel_pca"
]
},
{
"page": "morie_kernel_quantile",
"title": "Conditional quantile by inverting a kernel CDF",
"topics": [
"morie_kernel_quantile"
]
},
{
"page": "morie_km_hadamard",
"title": "Kaplan-Meier Hadamard derivative",
"topics": [
"morie_km_hadamard"
]
},
{
"page": "morie_km_native",
"title": "Kaplan-Meier product-limit estimator, computed natively",
"topics": [
"morie_km_native"
]
},
{
"page": "morie_kmeans_clustering",
"title": "K-means clustering (R parity)",
"topics": [
"morie_kmeans_clustering"
]
},
{
"page": "morie_kmt_bound",
"title": "KMT strong-approximation bound",
"topics": [
"morie_kmt_bound"
]
},
{
"page": "morie_knn_entropy",
"title": "Kozachenko-Leonenko k-nearest-neighbour differential entropy",
"topics": [
"morie_knn_entropy"
]
},
{
"page": "morie_konig_theorem",
"title": "Konig's theorem: maximum matching equals minimum vertex cover",
"topics": [
"morie_konig_theorem"
]
},
{
"page": "morie_kriging",
"title": "Ordinary kriging system, variances, and the exponential semivariogram",
"topics": [
"morie_kriging"
]
},
{
"page": "morie_kriging_pred_error",
"title": "Ordinary kriging with a Prasad-Rao corrected prediction error",
"topics": [
"morie_kriging_pred_error"
]
},
{
"page": "morie_kruskal_wallis_test",
"title": "Kruskal-Wallis non-parametric ANOVA",
"topics": [
"morie_kruskal_wallis_test"
]
},
{
"page": "morie_ks_limit",
"title": "Kolmogorov-Smirnov limits and exact one-sided tail",
"topics": [
"morie_ks_limit"
]
},
{
"page": "morie_kupiec_var_test",
"title": "Kupiec unconditional-coverage LR test for VaR exceedances",
"topics": [
"morie_kupiec_var_test"
]
},
{
"page": "morie_lagfeat",
"title": "Lag features (ch. 6 p. 170)",
"topics": [
"morie_lagfeat"
]
},
{
"page": "morie_lamb",
"title": "LAMB layer-wise adaptive moments",
"topics": [
"morie_lamb"
]
},
{
"page": "morie_laniyonu_actuarial_risk_disparity",
"title": "Replication of O'Connell & Laniyonu (2025) - CSC actuarial-risk disparity",
"topics": [
"morie_laniyonu_actuarial_risk_disparity"
]
},
{
"page": "morie_laniyonu_gentrification_policing",
"title": "Replication of Laniyonu (2018) - Coffee Shops and Street Stops",
"topics": [
"morie_laniyonu_gentrification_policing"
]
},
{
"page": "morie_laniyonu_smi_force_disparity",
"title": "Replication of Laniyonu & Goff (2021) - Police force vs SMI disparity",
"topics": [
"morie_laniyonu_smi_force_disparity"
]
},
{
"page": "morie_laplace_noise",
"title": "One draw from Lap(0, b) by inverse transform",
"topics": [
"morie_laplace_noise"
]
},
{
"page": "morie_laplc",
"title": "Laplace mechanism (primary entry point)",
"topics": [
"morie_laplc"
]
},
{
"page": "morie_lars",
"title": "LARS layer-wise adaptive rate scaling",
"topics": [
"morie_lars"
]
},
{
"page": "morie_lasso_path",
"title": "Coordinate-descent lasso path",
"topics": [
"morie_lasso_path"
]
},
{
"page": "morie_latin_square",
"title": "A Latin square of order n",
"topics": [
"morie_latin_square"
]
},
{
"page": "morie_lattice_convex_hull",
"title": "Convex hull of lattice points by Andrew's monotone chain",
"topics": [
"morie_lattice_convex_hull"
]
},
{
"page": "morie_lcg_normal",
"title": "Deterministic standard normals from the shared LCG + Box-Muller (hmdfw)",
"topics": [
"morie_lcg_normal"
]
},
{
"page": "morie_lcmm_latent_class",
"title": "Latent-class mixed models via 'lcmm'",
"topics": [
"morie_lcmm_latent_class"
]
},
{
"page": "morie_ld",
"title": "Linkage disequilibrium (r-squared) between two loci",
"topics": [
"morie_ld"
]
},
{
"page": "morie_ld50",
"title": "Median lethal dose from a quantal assay",
"topics": [
"morie_ld50"
]
},
{
"page": "morie_ldiff",
"title": "l-diversity of an anonymised release",
"topics": [
"morie_ldiff"
]
},
{
"page": "morie_learning_curve",
"title": "Learning curve - train / val MSE vs training-set size (R parity)",
"topics": [
"morie_learning_curve"
]
},
{
"page": "morie_least_squares",
"title": "Least-squares line and sample correlation",
"topics": [
"morie_least_squares"
]
},
{
"page": "morie_levene_test",
"title": "Levene test for equality of variances",
"topics": [
"morie_levene_test"
]
},
{
"page": "morie_lewbel_binary",
"title": "Lewbel's special-regressor estimator for binary response",
"topics": [
"morie_lewbel_binary"
]
},
{
"page": "morie_license_metadata",
"title": "morie's SPDX-style licence metadata",
"topics": [
"morie_license_metadata"
]
},
{
"page": "morie_lil_ratio",
"title": "Law of the iterated logarithm ratio and the Chung liminf constant",
"topics": [
"morie_lil_ratio"
]
},
{
"page": "morie_linear_corr_model",
"title": "The Y = mX + Z correlation model",
"topics": [
"morie_linear_corr_model"
]
},
{
"page": "morie_linear_regression_ols",
"title": "Ordinary least squares closed-form solution (R parity)",
"topics": [
"morie_linear_regression_ols"
]
},
{
"page": "morie_linevid",
"title": "Marginal likelihood of a straight-line model (eq. 28.22, Ex. 28.2, p. 352)",
"topics": [
"morie_linevid"
]
},
{
"page": "morie_linkhae",
"title": "Haebara characteristic-curve IRT linking",
"topics": [
"morie_linkhae"
]
},
{
"page": "morie_linkmm",
"title": "IRT moment linking (mean/mean, mean/sigma)",
"topics": [
"morie_linkmm"
]
},
{
"page": "morie_linkqp",
"title": "Stocking-Lord characteristic-curve IRT linking",
"topics": [
"morie_linkqp"
]
},
{
"page": "morie_list_datasets",
"title": "List all datasets with cache status",
"topics": [
"morie_list_datasets"
]
},
{
"page": "morie_list_morie_modules",
"title": "List implemented MORIE CPADS modules",
"topics": [
"morie_list_morie_modules"
]
},
{
"page": "morie_llm_agent_available",
"title": "Return TRUE when a language model is set up",
"topics": [
"morie_llm_agent_available"
]
},
{
"page": "morie_llm_ask",
"title": "Send a prompt to a language model",
"topics": [
"morie_llm_ask"
]
},
{
"page": "morie_llm_ask_multi",
"title": "Ask a language model with a whole conversation",
"topics": [
"morie_llm_ask_multi"
]
},
{
"page": "morie_llm_backend",
"title": "Bring-your-own language model: backend, model list and chat",
"topics": [
"morie_llm_backend",
"morie_llm_chat",
"morie_llm_models"
]
},
{
"page": "morie_llm_config",
"title": "Show or save the language-model settings",
"topics": [
"morie_llm_config"
]
},
{
"page": "morie_llm_detect_provider",
"title": "The language-model route that would answer",
"topics": [
"morie_llm_detect_provider"
]
},
{
"page": "morie_llm_login",
"title": "Sign in for the curated tables at data.rmorie.com",
"topics": [
"morie_llm_login"
]
},
{
"page": "morie_llm_logout",
"title": "Forget the stored data key",
"topics": [
"morie_llm_logout"
]
},
{
"page": "morie_llm_ollama_models",
"title": "List the models an Ollama server is serving",
"topics": [
"morie_llm_ollama_models"
]
},
{
"page": "morie_llm_probe_ollama",
"title": "Probe a local Ollama instance",
"topics": [
"morie_llm_probe_ollama"
]
},
{
"page": "morie_llm_provider_set",
"title": "Attach your own model endpoint",
"topics": [
"morie_llm_provider_set",
"morie_llm_provider_show",
"morie_llm_provider_unset"
]
},
{
"page": "morie_llm_request_completion",
"title": "POST a chat-completion request to an OpenAI-compatible endpoint",
"topics": [
"morie_llm_request_completion"
]
},
{
"page": "morie_lm",
"title": "Fit a regression model with a full model-object contract",
"topics": [
"morie_lm"
]
},
{
"page": "morie_lm_scaling",
"title": "Scaling of a morie_lm fit with the number of observations",
"topics": [
"morie_lm_scaling"
]
},
{
"page": "morie_lmm",
"title": "Linear mixed-effects model (native, lme4/nlme compatible)",
"topics": [
"morie_lmm"
]
},
{
"page": "morie_lms_error",
"title": "LMS instantaneous error and gradient",
"topics": [
"morie_lms_error"
]
},
{
"page": "morie_load_cpads",
"title": "Load CPADS data: local files -> cache -> CKAN API",
"topics": [
"morie_load_cpads"
]
},
{
"page": "morie_load_cpads_data",
"title": "Load the real CPADS CSV from this repository",
"topics": [
"morie_load_cpads_data"
]
},
{
"page": "morie_load_dataset",
"title": "Load a dataset by catalog key",
"topics": [
"morie_load_dataset"
]
},
{
"page": "morie_local_dp_randomised_response",
"title": "k-ary randomised response (local differential privacy)",
"topics": [
"morie_local_dp_randomised_response"
]
},
{
"page": "morie_local_linear",
"title": "Local linear regression",
"topics": [
"morie_local_linear"
]
},
{
"page": "morie_local_outlier_factor",
"title": "Local outlier factor",
"topics": [
"morie_local_outlier_factor"
]
},
{
"page": "morie_locfdr_estimate",
"title": "Local FDR estimation via 'locfdr'",
"topics": [
"morie_locfdr_estimate"
]
},
{
"page": "morie_locp",
"title": "Local polynomial regression smoother",
"topics": [
"morie_locp"
]
},
{
"page": "morie_logistic_effects",
"title": "Logistic regression effect and fit statistics",
"topics": [
"morie_logistic_effects"
]
},
{
"page": "morie_logistic_fit",
"title": "Logistic regression model, likelihood, and MLE",
"topics": [
"morie_logistic_fit"
]
},
{
"page": "morie_logistic_wald",
"title": "Logistic-scale Wald intervals for odds ratios and probabilities",
"topics": [
"morie_logistic_wald"
]
},
{
"page": "morie_logit_link",
"title": "Logit link utilities",
"topics": [
"morie_logit_link"
]
},
{
"page": "morie_logit_rescale",
"title": "Rescaling of logit coefficients across nested models",
"topics": [
"morie_logit_rescale"
]
},
{
"page": "morie_logit_separation",
"title": "Detect separation in a logistic regression and show why the fit cannot converge",
"topics": [
"morie_logit_separation"
]
},
{
"page": "morie_logrank_test",
"title": "Log-rank test",
"topics": [
"morie_logrank_test",
"morie_survival_logrank"
]
},
{
"page": "morie_logtrans",
"title": "Log transformation with an optional offset (ch. 6 p. 163)",
"topics": [
"morie_logtrans"
]
},
{
"page": "morie_lord_chisq",
"title": "Lord's chi-square test for differential item functioning",
"topics": [
"morie_lord_chisq"
]
},
{
"page": "morie_loss_stream_variance",
"title": "Variance of a training-loss stream, keeping the correlation",
"topics": [
"morie_loss_stream_variance"
]
},
{
"page": "morie_lovasz_local_lemma",
"title": "The symmetric Lovasz Local Lemma",
"topics": [
"morie_lovasz_local_lemma"
]
},
{
"page": "morie_lrprod",
"title": "Posterior odds as a running product of ratios (eq. 3.31, p. 63)",
"topics": [
"morie_lrprod"
]
},
{
"page": "morie_m_normality",
"title": "M-estimator asymptotic normality",
"topics": [
"morie_m_normality"
]
},
{
"page": "morie_mad",
"title": "Median absolute deviation and the MAD-median outlier rule",
"topics": [
"morie_mad",
"morie_madn",
"morie_mad_median_rule",
"morie_mad_rescaled"
]
},
{
"page": "morie_make_raw",
"title": "Coerce to raw the way digest::makeRaw does",
"topics": [
"morie_make_raw"
]
},
{
"page": "morie_mann_whitney_test",
"title": "Mann-Whitney U test (Wilcoxon rank-sum)",
"topics": [
"morie_mann_whitney_test"
]
},
{
"page": "morie_manski",
"title": "Manski no-assumption bounds on the average treatment effect",
"topics": [
"morie_manski"
]
},
{
"page": "morie_manskif",
"title": "Manski worst-case bounds (full interface)",
"topics": [
"morie_manskif"
]
},
{
"page": "morie_mantel_number",
"title": "Mantel's theorem: the triangle-free case",
"topics": [
"morie_mantel_number"
]
},
{
"page": "morie_mapets",
"title": "Mean absolute percentage error (ch. 19 p. 568)",
"topics": [
"morie_mapets"
]
},
{
"page": "morie_marginal_integration",
"title": "Marginal integration for a nonparametric additive model",
"topics": [
"morie_marginal_integration"
]
},
{
"page": "morie_marginal_structural_models",
"title": "Marginal structural models",
"topics": [
"morie_marginal_structural_models",
"morie_msm_accelerated_failure",
"morie_msm_cox_marginal",
"morie_msm_gmm_estimator",
"morie_msm_linear",
"morie_msm_logistic",
"morie_msm_negative_binomial",
"morie_msm_poisson",
"morie_msm_time_varying_exposure"
]
},
{
"page": "morie_marginals_verify",
"title": "Verify recoded category counts against the counts a release published",
"topics": [
"morie_marginals_verify"
]
},
{
"page": "morie_marker_variance",
"title": "Marker variance-component estimation",
"topics": [
"morie_marker_variance"
]
},
{
"page": "morie_matching_abadie_imbens_kernel_cpp",
"title": "morie_matching_abadie_imbens_kernel_cpp",
"topics": [
"morie_matching_abadie_imbens_kernel_cpp"
]
},
{
"page": "morie_matching_abadie_imbens_se",
"title": "Abadie-Imbens standard error for matching estimators",
"topics": [
"morie_matching_abadie_imbens_se"
]
},
{
"page": "morie_matching_atc_matched",
"title": "ATC from a matched sample",
"topics": [
"morie_matching_atc_matched"
]
},
{
"page": "morie_matching_ate_matched",
"title": "ATE from a matched / weighted sample",
"topics": [
"morie_matching_ate_matched"
]
},
{
"page": "morie_matching_att_matched",
"title": "ATT from a matched sample",
"topics": [
"morie_matching_att_matched"
]
},
{
"page": "morie_matching_balance",
"title": "Balance diagnostics for matched / weighted samples",
"topics": [
"morie_matching_balance"
]
},
{
"page": "morie_matching_balance_table",
"title": "Publication-ready balance table",
"topics": [
"morie_matching_balance_table"
]
},
{
"page": "morie_matching_cardinality",
"title": "Cardinality matching",
"topics": [
"morie_matching_cardinality"
]
},
{
"page": "morie_matching_cem",
"title": "Coarsened Exact Matching (CEM)",
"topics": [
"morie_matching_cem"
]
},
{
"page": "morie_matching_cem_strata_cpp",
"title": "morie_matching_cem_strata_cpp",
"topics": [
"morie_matching_cem_strata_cpp"
]
},
{
"page": "morie_matching_common_support",
"title": "Restrict a sample to the region of common support",
"topics": [
"morie_matching_common_support"
]
},
{
"page": "morie_matching_doubly_robust",
"title": "Doubly-robust ATT combining matching and regression",
"topics": [
"morie_matching_doubly_robust"
]
},
{
"page": "morie_matching_entropy_balance",
"title": "Entropy balancing weights (Hainmueller, 2012)",
"topics": [
"morie_matching_entropy_balance"
]
},
{
"page": "morie_matching_estimate_propensity",
"title": "Estimate propensity scores",
"topics": [
"morie_matching_estimate_propensity"
]
},
{
"page": "morie_matching_euclidean_pairs_cpp",
"title": "morie_matching_euclidean_pairs_cpp",
"topics": [
"morie_matching_euclidean_pairs_cpp"
]
},
{
"page": "morie_matching_exact",
"title": "Exact matching on discrete covariates",
"topics": [
"morie_matching_exact"
]
},
{
"page": "morie_matching_full",
"title": "Optimal full matching",
"topics": [
"morie_matching_full"
]
},
{
"page": "morie_matching_genetic",
"title": "Genetic matching (Diamond & Sekhon, 2013)",
"topics": [
"morie_matching_genetic"
]
},
{
"page": "morie_matching_longitudinal",
"title": "Longitudinal matching for panel data",
"topics": [
"morie_matching_longitudinal"
]
},
{
"page": "morie_matching_love_plot_data",
"title": "Love-plot data: pre- vs post-matching balance",
"topics": [
"morie_matching_love_plot_data"
]
},
{
"page": "morie_matching_mahalanobis",
"title": "Mahalanobis distance matching",
"topics": [
"morie_matching_mahalanobis"
]
},
{
"page": "morie_matching_mahalanobis_pairs_cpp",
"title": "morie_matching_mahalanobis_pairs_cpp",
"topics": [
"morie_matching_mahalanobis_pairs_cpp"
]
},
{
"page": "morie_matching_multi_treatment",
"title": "Matching with multiple (> 2) treatment groups",
"topics": [
"morie_matching_multi_treatment"
]
},
{
"page": "morie_matching_nearest_neighbor",
"title": "Nearest-neighbour propensity-score matching",
"topics": [
"morie_matching_nearest_neighbor"
]
},
{
"page": "morie_matching_nn_select_cpp",
"title": "morie_matching_nn_select_cpp",
"topics": [
"morie_matching_nn_select_cpp"
]
},
{
"page": "morie_matching_optimal_pair",
"title": "Optimal pair matching",
"topics": [
"morie_matching_optimal_pair"
]
},
{
"page": "morie_matching_overlap",
"title": "Propensity-score overlap diagnostics",
"topics": [
"morie_matching_overlap"
]
},
{
"page": "morie_matching_quality",
"title": "Comprehensive matching-quality assessment",
"topics": [
"morie_matching_quality"
]
},
{
"page": "morie_matching_rosenbaum_bounds",
"title": "Rosenbaum bounds for hidden bias",
"topics": [
"morie_matching_rosenbaum_bounds"
]
},
{
"page": "morie_matching_subclassify",
"title": "Subclassification (stratification) on the propensity score",
"topics": [
"morie_matching_subclassify"
]
},
{
"page": "morie_matching_trim_propensity",
"title": "Trim propensity scores to a fixed range",
"topics": [
"morie_matching_trim_propensity"
]
},
{
"page": "morie_matching_variable_ratio",
"title": "Variable-ratio matching on propensity score",
"topics": [
"morie_matching_variable_ratio"
]
},
{
"page": "morie_matheron_estimator",
"title": "Matheron classical semivariogram estimator with its (4.25) variance",
"topics": [
"morie_matheron_estimator"
]
},
{
"page": "morie_matroid_bases",
"title": "The maximal independent sets",
"topics": [
"morie_matroid_bases"
]
},
{
"page": "morie_matroid_circuits",
"title": "The minimal dependent sets",
"topics": [
"morie_matroid_circuits"
]
},
{
"page": "morie_matroid_dual",
"title": "The dual matroid",
"topics": [
"morie_matroid_dual"
]
},
{
"page": "morie_matroid_rank",
"title": "Rank of a subset: the size of its largest independent subset",
"topics": [
"morie_matroid_rank"
]
},
{
"page": "morie_matrxP",
"title": "Matrix profile of a time series",
"topics": [
"morie_matrxP"
]
},
{
"page": "morie_matthews_corrcoef",
"title": "Matthews correlation coefficient from label vectors",
"topics": [
"morie_matthews_corrcoef"
]
},
{
"page": "morie_max_flow_min_cut",
"title": "Max-flow min-cut by Ford-Fulkerson with breadth-first augmentation",
"topics": [
"morie_max_flow_min_cut"
]
},
{
"page": "morie_maximum_score",
"title": "Manski maximum score and Horowitz's smoothed version",
"topics": [
"morie_maximum_score"
]
},
{
"page": "morie_mcc_counts",
"title": "Matthews correlation coefficient from confusion-matrix counts",
"topics": [
"morie_mcc_counts"
]
},
{
"page": "morie_mcd_outlier",
"title": "MCD-based robust Mahalanobis outlier detection",
"topics": [
"morie_mcd_outlier"
]
},
{
"page": "morie_mcdrop",
"title": "Average the softmax over repeated stochastic forward passes (p. 410)",
"topics": [
"morie_mcdrop"
]
},
{
"page": "morie_mctsr",
"title": "Monte Carlo tree search with UCT and random rollouts",
"topics": [
"morie_mctsr"
]
},
{
"page": "morie_mdlpost",
"title": "Two-part minimum-description-length message length (eq. 28.16-28.17, p. 352)",
"topics": [
"morie_mdlpost"
]
},
{
"page": "morie_mdpval",
"title": "Value iteration for a finite Markov decision process",
"topics": [
"morie_mdpval"
]
},
{
"page": "morie_mean_cpp",
"title": "morie_mean_cpp",
"topics": [
"morie_mean_cpp"
]
},
{
"page": "morie_mectest",
"title": "Markov equivalence, ch. 5 p. 85 (Verma and Pearl, 1991)",
"topics": [
"morie_mectest"
]
},
{
"page": "morie_median_voter_ci",
"title": "Median voter with a standard error that does not assume normality",
"topics": [
"morie_median_voter_ci"
]
},
{
"page": "morie_mediation_difference",
"title": "Difference-in-coefficients mediation estimator",
"topics": [
"morie_mediation_difference"
]
},
{
"page": "morie_mediation_product",
"title": "Product-of-coefficients mediation estimator",
"topics": [
"morie_mediation_product"
]
},
{
"page": "morie_mediation_sensitivity",
"title": "Mediation sensitivity to an unobserved confounder",
"topics": [
"morie_mediation_sensitivity"
]
},
{
"page": "morie_meta_convert",
"title": "Meta-analysis effect-size conversions",
"topics": [
"morie_meta_convert"
]
},
{
"page": "morie_meta_dl_bias",
"title": "Size of the DerSimonian-Laird truncation bias under homogeneity",
"topics": [
"morie_meta_dl_bias"
]
},
{
"page": "morie_meta_effect_sizes",
"title": "Meta-analysis effect sizes: d, Hedges g, RR, OR, Fisher Z",
"topics": [
"morie_meta_effect_sizes"
]
},
{
"page": "morie_meta_hksj",
"title": "Hartung-Knapp-Sidik-Jonkman interval with DerSimonian-Laird or REML heterogeneity",
"topics": [
"morie_meta_hksj"
]
},
{
"page": "morie_meta_pool",
"title": "Meta-analysis pooling: weights, mean effect, Q, I squared, tau squared",
"topics": [
"morie_meta_pool"
]
},
{
"page": "morie_meta_random_effects",
"title": "Fixed-effect and DerSimonian-Laird random-effects pooling, with what the truncation does",
"topics": [
"morie_meta_random_effects"
]
},
{
"page": "morie_meta_rma",
"title": "Random- / fixed-effects meta-analysis via 'metafor'",
"topics": [
"morie_meta_rma"
]
},
{
"page": "morie_mfird",
"title": "MIRT a to factor-loading conversion (and inverse)",
"topics": [
"morie_mfird"
]
},
{
"page": "morie_midas_regression",
"title": "MIDAS regression with Beta-polynomial weights",
"topics": [
"morie_midas_regression"
]
},
{
"page": "morie_midegf",
"title": "Barnard-Rubin adjusted degrees of freedom for MI inference",
"topics": [
"morie_midegf"
]
},
{
"page": "morie_miefcl",
"title": "Pool a scalar estimate across multiply imputed data sets",
"topics": [
"morie_miefcl"
]
},
{
"page": "morie_miest1",
"title": "Mutual information by k-nearest-neighbour statistics (KSG)",
"topics": [
"morie_miest1"
]
},
{
"page": "morie_mini_batch_gradient",
"title": "Mini-batch stochastic gradient descent for OLS (R parity)",
"topics": [
"morie_mini_batch_gradient"
]
},
{
"page": "morie_minimax_regret_constant",
"title": "The constant in the Hirano-Porter treatment-choice bound",
"topics": [
"morie_minimax_regret_constant"
]
},
{
"page": "morie_minimum_spanning_tree",
"title": "Kruskal's algorithm: greedy on the cycle matroid",
"topics": [
"morie_minimum_spanning_tree"
]
},
{
"page": "morie_miord2",
"title": "MICE: multivariate imputation by chained equations (normal model)",
"topics": [
"morie_miord2"
]
},
{
"page": "morie_misclass_correct",
"title": "Correct a prevalence for identification error",
"topics": [
"morie_misclass_correct"
]
},
{
"page": "morie_misclass_count",
"title": "Adjust an observed count for identification error",
"topics": [
"morie_misclass_count"
]
},
{
"page": "morie_misclassified_contrast",
"title": "Misclassification of an ordinal exposure can reverse a contrast",
"topics": [
"morie_misclassified_contrast"
]
},
{
"page": "morie_misclassified_rd",
"title": "Nondifferential misclassification of a binary exposure",
"topics": [
"morie_misclassified_rd"
]
},
{
"page": "morie_mixed_models",
"title": "Linear mixed-model estimation",
"topics": [
"morie_blue_blup_v",
"morie_em_lmm",
"morie_lmm_loglik",
"morie_mixed_models",
"morie_mme",
"morie_one_way",
"morie_reml_loglik"
]
},
{
"page": "morie_ml_apply_smote",
"title": "Apply SMOTE oversampling to balance a binary outcome",
"topics": [
"morie_ml_apply_smote"
]
},
{
"page": "morie_ml_assess",
"title": "Assess model performance",
"topics": [
"morie_ml_assess"
]
},
{
"page": "morie_ml_bake",
"title": "Apply (or reverse) a prepped recipe to data",
"topics": [
"morie_ml_bake"
]
},
{
"page": "morie_ml_center",
"title": "Center a numeric vector to a target mean",
"topics": [
"morie_ml_center"
]
},
{
"page": "morie_ml_eval_robustness",
"title": "Evaluate Random Forest robustness",
"topics": [
"morie_ml_eval_robustness"
]
},
{
"page": "morie_ml_impute",
"title": "Mean/median/constant imputation of a numeric vector",
"topics": [
"morie_ml_impute"
]
},
{
"page": "morie_ml_load",
"title": "Load a trained model from disk",
"topics": [
"morie_ml_load"
]
},
{
"page": "morie_ml_model",
"title": "Specify a supervised model without fitting it",
"topics": [
"morie_ml_model"
]
},
{
"page": "morie_ml_prep",
"title": "Estimate recipe parameters from training data",
"topics": [
"morie_ml_prep"
]
},
{
"page": "morie_ml_recipe",
"title": "Specify a preprocessing recipe",
"topics": [
"morie_ml_recipe"
]
},
{
"page": "morie_ml_resample",
"title": "Train + assess across resampling folds and combine the results",
"topics": [
"morie_ml_resample"
]
},
{
"page": "morie_ml_save",
"title": "Save a trained model to disk",
"topics": [
"morie_ml_save"
]
},
{
"page": "morie_ml_scale",
"title": "Scale a numeric vector by a target standard deviation",
"topics": [
"morie_ml_scale"
]
},
{
"page": "morie_ml_split",
"title": "Partition data into train / test / validation sets",
"topics": [
"morie_ml_split"
]
},
{
"page": "morie_ml_train",
"title": "Train a specified model",
"topics": [
"morie_ml_train"
]
},
{
"page": "morie_ml_train_time",
"title": "Estimate time to train a model",
"topics": [
"morie_ml_train_time"
]
},
{
"page": "morie_ml_tune",
"title": "Tune hyperparameters by cross-validated grid search",
"topics": [
"morie_ml_tune"
]
},
{
"page": "morie_mlogit_probs",
"title": "Multinomial probabilities and conditional odds ratios",
"topics": [
"morie_mlogit_probs"
]
},
{
"page": "morie_mmd_test",
"title": "Kernel two-sample test using the maximum mean discrepancy",
"topics": [
"morie_mmd_test"
]
},
{
"page": "morie_mobius_inversion",
"title": "Mobius inversion over the divisor lattice",
"topics": [
"morie_mobius_inversion"
]
},
{
"page": "morie_model_assisted",
"title": "Model-assisted (GREG) estimators",
"topics": [
"morie_model_assisted"
]
},
{
"page": "morie_model_metrics",
"title": "Model evaluation metrics",
"topics": [
"morie_binary_metrics",
"morie_brier",
"morie_class_metrics",
"morie_mcc",
"morie_model_metrics",
"morie_pca",
"morie_ridge"
]
},
{
"page": "morie_module_names",
"title": "Names of the CPADS analysis modules",
"topics": [
"morie_module_names"
]
},
{
"page": "morie_monotone_causal_forest",
"title": "Causal forest with an isotonic monotonicity constraint on the CATE",
"topics": [
"morie_monotone_causal_forest"
]
},
{
"page": "morie_moran_trend",
"title": "Moran's I under a deterministic trend, and on regression residuals",
"topics": [
"morie_moran_trend"
]
},
{
"page": "morie_morans_i",
"title": "Moran's I and its asymptotic test",
"topics": [
"morie_morans_i",
"morie_morans_i_test"
]
},
{
"page": "morie_motiff",
"title": "Milo et al. (2002) network-motif significance",
"topics": [
"morie_motiff"
]
},
{
"page": "morie_moving_average",
"title": "Causal moving-average (boxcar) filter",
"topics": [
"morie_moving_average"
]
},
{
"page": "morie_mrcv_glmm",
"title": "MRCV loglinear means and GLMM linear predictors",
"topics": [
"morie_mrcv_glmm"
]
},
{
"page": "morie_mrm_dags",
"title": "Canonical MRM causal DAGs",
"topics": [
"morie_mrm_dags"
]
},
{
"page": "morie_mrm_estimate_causal_effect",
"title": "Estimate a causal effect through the full MRM pipeline",
"topics": [
"morie_mrm_estimate_causal_effect"
]
},
{
"page": "morie_mrm_load_si_dataset",
"title": "Load a special-investigations sample with provenance metadata",
"topics": [
"morie_mrm_load_si_dataset"
]
},
{
"page": "morie_mrm_reconcile",
"title": "Reconcile two sources under an explicit matching schema",
"topics": [
"morie_mrm_reconcile"
]
},
{
"page": "morie_mrm_report",
"title": "Render a publication-ready table from an MRM effect object",
"topics": [
"morie_mrm_report"
]
},
{
"page": "morie_msglen",
"title": "Message length and its probability, in bits (eq. 28.15, p. 352)",
"topics": [
"morie_msglen"
]
},
{
"page": "morie_mtdrl_TabularHistoryAgent-class",
"title": "A reference inner learner",
"topics": [
"morie_mtdrl_TabularHistoryAgent",
"morie_mtdrl_TabularHistoryAgent-class"
]
},
{
"page": "morie_multi_horizon_ks",
"title": "Multi-horizon distributional accuracy test",
"topics": [
"morie_multi_horizon_ks"
]
},
{
"page": "morie_multi_trait_gblup",
"title": "Multi-trait GBLUP via vec-stacked mixed-model equations",
"topics": [
"morie_multi_trait_gblup"
]
},
{
"page": "morie_multicategory_logit",
"title": "Multicategory logit models",
"topics": [
"morie_multicategory_logit"
]
},
{
"page": "morie_multinomial_coef",
"title": "Multinomial coefficient with an implicit leftover committee",
"topics": [
"morie_multinomial_coef"
]
},
{
"page": "morie_multinomial_pmf",
"title": "Multinomial and contingency-table PMFs",
"topics": [
"morie_multinomial_pmf"
]
},
{
"page": "morie_multiple_testing",
"title": "Multiple testing correction and multiplicity-adjusted inference",
"topics": [
"morie_multiple_testing"
]
},
{
"page": "morie_multitrait",
"title": "Multi-trait and genotype-by-environment models",
"topics": [
"morie_bmtme_conditionals",
"morie_gxe_blup",
"morie_gxe_multitrait",
"morie_multitrait",
"morie_multitrait_ridge"
]
},
{
"page": "morie_mupostsg",
"title": "Posterior over the mean at known noise level (eq. 24.9-24.11, p. 320)",
"topics": [
"morie_mupostsg"
]
},
{
"page": "morie_mvn_with_covariance",
"title": "Draw multivariate normal samples under a structured covariance",
"topics": [
"morie_mvn_with_covariance"
]
},
{
"page": "morie_mvnorm_pmv",
"title": "Multivariate normal rectangle probability via 'mvtnorm'",
"topics": [
"morie_mvnorm_pmv"
]
},
{
"page": "morie_mvnorm_sample",
"title": "Sample from the multivariate normal via 'mvtnorm'",
"topics": [
"morie_mvnorm_sample"
]
},
{
"page": "morie_mvrnorm",
"title": "Native multivariate normal sampling (reproduces MASS::mvrnorm)",
"topics": [
"morie_mvrnorm"
]
},
{
"page": "morie_myopic_example",
"title": "The greedy allocation loses to the patient one when the discount is high",
"topics": [
"morie_myopic_example"
]
},
{
"page": "morie_nb_matches_zip",
"title": "A negative binomial with the zero share and mean of any zero-inflated Poisson",
"topics": [
"morie_nb_matches_zip"
]
},
{
"page": "morie_nbeats_basis",
"title": "N-BEATS-style polynomial + Fourier basis-expansion forecasting",
"topics": [
"morie_nbeats_basis"
]
},
{
"page": "morie_neff_cluster",
"title": "Effective sample size under clustering",
"topics": [
"morie_neff_cluster"
]
},
{
"page": "morie_nelson_aalen",
"title": "Nelson-Aalen cumulative hazard",
"topics": [
"morie_nelson_aalen",
"morie_survival_nelsonaalen"
]
},
{
"page": "morie_nesterov",
"title": "Nesterov accelerated gradient step",
"topics": [
"morie_nesterov"
]
},
{
"page": "morie_netcms",
"title": "Gaussian graphical model by the graphical lasso",
"topics": [
"morie_netcms"
]
},
{
"page": "morie_neural_kantorovich_map",
"title": "Monotone 1-D transport map fitted to the quantile coupling",
"topics": [
"morie_neural_kantorovich_map"
]
},
{
"page": "morie_neural_network",
"title": "Feed-forward neural networks",
"topics": [
"morie_ann_forward",
"morie_ann_gradients",
"morie_ann_train",
"morie_neural_network"
]
},
{
"page": "morie_newey_west_vcov",
"title": "Newey-West HAC covariance",
"topics": [
"morie_newey_west_vcov"
]
},
{
"page": "morie_nhitsnet",
"title": "N-HiTS multi-rate sampling with hierarchical interpolation",
"topics": [
"morie_nhitsnet"
]
},
{
"page": "morie_nls",
"title": "Nonlinear least squares from a model formula (native)",
"topics": [
"coef.morie_nls",
"deviance.morie_nls",
"df.residual.morie_nls",
"fitted.morie_nls",
"logLik.morie_nls",
"morie_nls",
"predict.morie_nls",
"print.morie_nls",
"print.summary.morie_nls",
"residuals.morie_nls",
"summary.morie_nls",
"vcov.morie_nls"
]
},
{
"page": "morie_nls_weight_function",
"title": "Efficient weight function for semiparametric weighted NLS",
"topics": [
"morie_nls_weight_function"
]
},
{
"page": "morie_nonbacktracking_matrix",
"title": "Non-backtracking (Hashimoto) matrix",
"topics": [
"morie_nonbacktracking_matrix"
]
},
{
"page": "morie_nonresponse_region",
"title": "The identification region of a proportion under nonresponse",
"topics": [
"morie_nonresponse_region"
]
},
{
"page": "morie_normal_pdf_cpp",
"title": "morie_normal_pdf_cpp",
"topics": [
"morie_normal_pdf_cpp"
]
},
{
"page": "morie_normal_scores",
"title": "Normal-scores transform",
"topics": [
"morie_normal_scores"
]
},
{
"page": "morie_np_kernel_reg",
"title": "Nonparametric kernel regression via 'np'",
"topics": [
"morie_np_kernel_reg"
]
},
{
"page": "morie_npiv_operator",
"title": "Sieve estimate of the NPIV operator",
"topics": [
"morie_npiv_operator"
]
},
{
"page": "morie_npiv_quantile",
"title": "Nonparametric quantile IV",
"topics": [
"morie_npiv_quantile"
]
},
{
"page": "morie_nw_regression",
"title": "Nadaraya-Watson kernel regression",
"topics": [
"morie_nw_regression"
]
},
{
"page": "morie_nyc_nypd_socrata_cap_note",
"title": "Socrata default-API-cap note + pagination wiring",
"topics": [
"morie_nyc_nypd_socrata_cap_note"
]
},
{
"page": "morie_nypd_all_analyses",
"title": "Run the full New York Police Department analysis suite",
"topics": [
"morie_nypd_all_analyses"
]
},
{
"page": "morie_odds_ratio_check",
"title": "Check reported odds ratios against a labelled table, under every relabelling",
"topics": [
"morie_odds_ratio_check"
]
},
{
"page": "morie_odds_ratio_ci",
"title": "Odds ratio and confidence interval from a 2x2 contingency table",
"topics": [
"morie_odds_ratio_ci"
]
},
{
"page": "morie_ols",
"title": "Ordinary least squares with inference",
"topics": [
"morie_ols"
]
},
{
"page": "morie_ols_simple",
"title": "Simple OLS regression by the textbook equations",
"topics": [
"morie_ols_simple"
]
},
{
"page": "morie_ols_two_iv",
"title": "Two-predictor OLS slopes from correlations",
"topics": [
"morie_ols_two_iv"
]
},
{
"page": "morie_omega_squared",
"title": "Omega-squared (less biased than eta-squared)",
"topics": [
"morie_omega_squared"
]
},
{
"page": "morie_one_sample_coverage",
"title": "One-sample coverage probability (Gibbons Ch 2.11.1)",
"topics": [
"morie_one_sample_coverage"
]
},
{
"page": "morie_one_sample_t_test",
"title": "One-sample t-test",
"topics": [
"morie_one_sample_t_test"
]
},
{
"page": "morie_one_step_efficient",
"title": "One Newton step from any root-n start to asymptotic efficiency",
"topics": [
"morie_one_step_efficient"
]
},
{
"page": "morie_oneway_test",
"title": "Native one-way permutation test (Fisher-Pitman)",
"topics": [
"morie_oneway_test"
]
},
{
"page": "morie_optimal_alpha",
"title": "Theorem 5.2: the variance-minimising trimming threshold",
"topics": [
"morie_optimal_alpha"
]
},
{
"page": "morie_optimal_alpha_att",
"title": "Theorem 5.3: the one-sided threshold for the effect on the treated",
"topics": [
"morie_optimal_alpha_att"
]
},
{
"page": "morie_optimal_overlap_weight",
"title": "Variance-minimising overlap weight for a dual-frame estimator",
"topics": [
"morie_optimal_overlap_weight"
]
},
{
"page": "morie_order_statistic_laws",
"title": "Order-statistic laws: median, coverage, binomial-beta identity",
"topics": [
"morie_order_statistic_laws"
]
},
{
"page": "morie_ordered_alternatives_test",
"title": "Jonckheere-Terpstra ordered-alternatives test (Gibbons Ch 10.6)",
"topics": [
"morie_ordered_alternatives_test"
]
},
{
"page": "morie_ordered_categories",
"title": "Linear-by-linear association test for ordered categories (Gibbons Ch 14.6.1)",
"topics": [
"morie_ordered_categories"
]
},
{
"page": "morie_ordered_max_score",
"title": "Maximum-score estimator for an ordered-response model",
"topics": [
"morie_ordered_max_score"
]
},
{
"page": "morie_ordinal_logit_ca",
"title": "Ordinal (cumulative) logit utilities",
"topics": [
"morie_ordinal_logit_ca"
]
},
{
"page": "morie_ospats",
"title": "Model-based prediction of design variances (Ospats family)",
"topics": [
"morie_ospats"
]
},
{
"page": "morie_otis_aipw_ate",
"title": "Doubly-robust (AIPW) ATE on OTIS data via cross-fitted nuisances",
"topics": [
"morie_otis_aipw_ate"
]
},
{
"page": "morie_otis_aipw_superlearner",
"title": "SuperLearner-stacked AIPW on OTIS data (native)",
"topics": [
"morie_otis_aipw_superlearner"
]
},
{
"page": "morie_otis_all_analyses",
"title": "Run the full OTIS analysis capsule",
"topics": [
"morie_otis_all_analyses"
]
},
{
"page": "morie_otis_all_analyze",
"title": "Comprehensive per-dataset analyses for ALL 28 OTIS public-release files",
"topics": [
"morie_otis_all_analyze"
]
},
{
"page": "morie_otis_analyze_a01",
"title": "OTIS a01 high-level causal analysis (MatchIt + IRM-DML)",
"topics": [
"morie_otis_analyze_a01"
]
},
{
"page": "morie_otis_analyze_a01_ruhela_alt_age",
"title": "a01 alt-T Ruhela: Age 50+ -> vm count",
"topics": [
"morie_otis_analyze_a01_mrm_alt_age",
"morie_otis_analyze_a01_ruhela_alt_age"
]
},
{
"page": "morie_otis_analyze_a01_ruhela_alt_gender",
"title": "a01 alt-T Ruhela: Female -> vm count",
"topics": [
"morie_otis_analyze_a01_mrm_alt_gender",
"morie_otis_analyze_a01_ruhela_alt_gender"
]
},
{
"page": "morie_otis_analyze_a01_ruhela_alt_toronto",
"title": "a01 alt-T Ruhela: Toronto region -> vm count",
"topics": [
"morie_otis_analyze_a01_mrm_alt_toronto",
"morie_otis_analyze_a01_ruhela_alt_toronto"
]
},
{
"page": "morie_otis_analyze_a01_ruhela_formulations",
"title": "OTIS a01 Ruhela formulations (full DLRM)",
"topics": [
"morie_otis_analyze_a01_dlrm",
"morie_otis_analyze_a01_mrm",
"morie_otis_analyze_a01_ruhela_formulations"
]
},
{
"page": "morie_otis_analyze_a01_ruhela_per_year",
"title": "Per-year full-DLRM on the a01 canonical formulation",
"topics": [
"morie_otis_analyze_a01_mrm_per_year",
"morie_otis_analyze_a01_ruhela_per_year"
]
},
{
"page": "morie_otis_analyze_a01_ruhela_subgroup_female",
"title": "a01 subgroup Ruhela: Female-only cell frame",
"topics": [
"morie_otis_analyze_a01_mrm_subgroup_female",
"morie_otis_analyze_a01_ruhela_subgroup_female"
]
},
{
"page": "morie_otis_analyze_a01_ruhela_subgroup_male",
"title": "a01 subgroup Ruhela: Male-only cell frame",
"topics": [
"morie_otis_analyze_a01_mrm_subgroup_male",
"morie_otis_analyze_a01_ruhela_subgroup_male"
]
},
{
"page": "morie_otis_analyze_a01_with_csi_context",
"title": "OTIS a01 causal pipeline + Toronto Crime Severity Index context",
"topics": [
"morie_otis_analyze_a01_with_csi_context"
]
},
{
"page": "morie_otis_analyze_all",
"title": "Run every OTIS analyzer against a named list of datasets",
"topics": [
"morie_otis_analyze_all"
]
},
{
"page": "morie_otis_analyze_b01",
"title": "Person-level segregation-placement analysis (b01)",
"topics": [
"morie_otis_analyze_b01"
]
},
{
"page": "morie_otis_analyze_b01_ruhela_alt_age",
"title": "b01 alt-T Ruhela: Age 50+ -> vm count",
"topics": [
"morie_otis_analyze_b01_mrm_alt_age",
"morie_otis_analyze_b01_ruhela_alt_age"
]
},
{
"page": "morie_otis_analyze_b01_ruhela_alt_gender",
"title": "b01 alt-T Ruhela: Female -> vm count",
"topics": [
"morie_otis_analyze_b01_mrm_alt_gender",
"morie_otis_analyze_b01_ruhela_alt_gender"
]
},
{
"page": "morie_otis_analyze_b01_ruhela_alt_toronto",
"title": "b01 alt-T Ruhela: Toronto region -> vm count",
"topics": [
"morie_otis_analyze_b01_mrm_alt_toronto",
"morie_otis_analyze_b01_ruhela_alt_toronto"
]
},
{
"page": "morie_otis_analyze_b01_ruhela_formulations",
"title": "OTIS b01 Ruhela formulations (full DLRM)",
"topics": [
"morie_otis_analyze_b01_dlrm",
"morie_otis_analyze_b01_mrm",
"morie_otis_analyze_b01_ruhela_formulations"
]
},
{
"page": "morie_otis_analyze_b01_ruhela_per_year",
"title": "Per-year full-DLRM on the b01 canonical formulation",
"topics": [
"morie_otis_analyze_b01_mrm_per_year",
"morie_otis_analyze_b01_ruhela_per_year"
]
},
{
"page": "morie_otis_analyze_b01_ruhela_subgroup_female",
"title": "b01 subgroup Ruhela: Female-only cell frame",
"topics": [
"morie_otis_analyze_b01_mrm_subgroup_female",
"morie_otis_analyze_b01_ruhela_subgroup_female"
]
},
{
"page": "morie_otis_analyze_b01_ruhela_subgroup_male",
"title": "b01 subgroup Ruhela: Male-only cell frame",
"topics": [
"morie_otis_analyze_b01_mrm_subgroup_male",
"morie_otis_analyze_b01_ruhela_subgroup_male"
]
},
{
"page": "morie_otis_analyze_b02",
"title": "Aggregate segregation days per person per year (b02)",
"topics": [
"morie_otis_analyze_b02"
]
},
{
"page": "morie_otis_analyze_b02_ruhela_alt_age",
"title": "b02 alt-T Ruhela: Age 50+ -> seg-day count",
"topics": [
"morie_otis_analyze_b02_mrm_alt_age",
"morie_otis_analyze_b02_ruhela_alt_age"
]
},
{
"page": "morie_otis_analyze_b02_ruhela_alt_region",
"title": "b02 alt-T Ruhela: Toronto region -> seg-day count",
"topics": [
"morie_otis_analyze_b02_mrm_alt_region",
"morie_otis_analyze_b02_ruhela_alt_region"
]
},
{
"page": "morie_otis_analyze_b02_ruhela_formulations",
"title": "OTIS b02 Ruhela formulations: T=Female -> seg-day count",
"topics": [
"morie_otis_analyze_b02_dlrm",
"morie_otis_analyze_b02_mrm",
"morie_otis_analyze_b02_ruhela_formulations"
]
},
{
"page": "morie_otis_analyze_b03",
"title": "Segregation placements by alert x institution (b03)",
"topics": [
"morie_otis_analyze_b03"
]
},
{
"page": "morie_otis_analyze_b03_ruhela_aggregate",
"title": "b03 aggregate Ruhela: Alert presence -> seg placements",
"topics": [
"morie_otis_analyze_b03_mrm_aggregate",
"morie_otis_analyze_b03_ruhela_aggregate"
]
},
{
"page": "morie_otis_analyze_b04",
"title": "Placement durations by region & gender (b04)",
"topics": [
"morie_otis_analyze_b04"
]
},
{
"page": "morie_otis_analyze_b04_ruhela_aggregate",
"title": "b04 aggregate Ruhela: Female -> median seg duration",
"topics": [
"morie_otis_analyze_b04_mrm_aggregate",
"morie_otis_analyze_b04_ruhela_aggregate"
]
},
{
"page": "morie_otis_analyze_b05",
"title": "Distribution of placements by binned duration (b05)",
"topics": [
"morie_otis_analyze_b05"
]
},
{
"page": "morie_otis_analyze_b05_mandela_classification",
"title": "Mandela-RF on b05 - per-placement Mandela classification by year",
"topics": [
"morie_otis_analyze_b05_mandela_classification"
]
},
{
"page": "morie_otis_analyze_b05_ruhela_aggregate",
"title": "b05 aggregate Ruhela: schema-no-demographic guard",
"topics": [
"morie_otis_analyze_b05_ruhela_aggregate"
]
},
{
"page": "morie_otis_analyze_b06",
"title": "Reasons for placement x institution x gender (b06)",
"topics": [
"morie_otis_analyze_b06"
]
},
{
"page": "morie_otis_analyze_b06_ruhela_aggregate",
"title": "b06 aggregate Ruhela: Disciplinary reason -> seg placements",
"topics": [
"morie_otis_analyze_b06_mrm_aggregate",
"morie_otis_analyze_b06_ruhela_aggregate"
]
},
{
"page": "morie_otis_analyze_b07",
"title": "Alerts x gender (b07)",
"topics": [
"morie_otis_analyze_b07"
]
},
{
"page": "morie_otis_analyze_b07_ruhela_aggregate",
"title": "b07 aggregate Ruhela (pivot to long): With-alert -> seg placements",
"topics": [
"morie_otis_analyze_b07_mrm_aggregate",
"morie_otis_analyze_b07_ruhela_aggregate"
]
},
{
"page": "morie_otis_analyze_b08",
"title": "Durations by institution & gender (b08)",
"topics": [
"morie_otis_analyze_b08"
]
},
{
"page": "morie_otis_analyze_b08_ruhela_aggregate",
"title": "b08 aggregate Ruhela: Female -> median seg duration (institution-clustered)",
"topics": [
"morie_otis_analyze_b08_mrm_aggregate",
"morie_otis_analyze_b08_ruhela_aggregate"
]
},
{
"page": "morie_otis_analyze_b09",
"title": "Individuals by number of placements x gender (b09)",
"topics": [
"morie_otis_analyze_b09"
]
},
{
"page": "morie_otis_analyze_b09_ruhela_aggregate",
"title": "b09 aggregate Ruhela: Female -> individuals in segregation",
"topics": [
"morie_otis_analyze_b09_mrm_aggregate",
"morie_otis_analyze_b09_ruhela_aggregate"
]
},
{
"page": "morie_otis_analyze_c_chi2",
"title": "MRM chi-square family on c-series",
"topics": [
"morie_otis_analyze_c_chi2"
]
},
{
"page": "morie_otis_analyze_c01",
"title": "Total individuals x custody/RC/seg x gender (c01)",
"topics": [
"morie_otis_analyze_c01"
]
},
{
"page": "morie_otis_analyze_c01_ruhela_aggregate",
"title": "c01 aggregate Ruhela: Female -> RC count",
"topics": [
"morie_otis_analyze_c01_mrm_aggregate",
"morie_otis_analyze_c01_ruhela_aggregate"
]
},
{
"page": "morie_otis_analyze_c01_ruhela_aggregate_region_cluster",
"title": "c01 region-cluster variant (year-clustered GEE)",
"topics": [
"morie_otis_analyze_c01_mrm_aggregate_region_cluster",
"morie_otis_analyze_c01_ruhela_aggregate_region_cluster"
]
},
{
"page": "morie_otis_analyze_c02",
"title": "Individuals in RC/seg by institution (c02)",
"topics": [
"morie_otis_analyze_c02"
]
},
{
"page": "morie_otis_analyze_c02_ruhela_aggregate",
"title": "c02 aggregate Ruhela: Female -> RC (institution GEE)",
"topics": [
"morie_otis_analyze_c02_mrm_aggregate",
"morie_otis_analyze_c02_ruhela_aggregate"
]
},
{
"page": "morie_otis_analyze_c03",
"title": "Individuals x race x gender (c03)",
"topics": [
"morie_otis_analyze_c03"
]
},
{
"page": "morie_otis_analyze_c03_ruhela_aggregate",
"title": "c03 aggregate Ruhela: Indigenous -> RC",
"topics": [
"morie_otis_analyze_c03_mrm_aggregate",
"morie_otis_analyze_c03_ruhela_aggregate"
]
},
{
"page": "morie_otis_analyze_c04",
"title": "Individuals in RC/seg by race x region (c04)",
"topics": [
"morie_otis_analyze_c04"
]
},
{
"page": "morie_otis_analyze_c04_ruhela_aggregate",
"title": "c04 aggregate Ruhela: Indigenous -> RC (by region)",
"topics": [
"morie_otis_analyze_c04_mrm_aggregate",
"morie_otis_analyze_c04_ruhela_aggregate"
]
},
{
"page": "morie_otis_analyze_c04_ruhela_aggregate_region_cluster",
"title": "c04 region-cluster variant",
"topics": [
"morie_otis_analyze_c04_mrm_aggregate_region_cluster",
"morie_otis_analyze_c04_ruhela_aggregate_region_cluster"
]
},
{
"page": "morie_otis_analyze_c05",
"title": "Individuals in RC/seg by religion x region (c05)",
"topics": [
"morie_otis_analyze_c05"
]
},
{
"page": "morie_otis_analyze_c05_ruhela_aggregate",
"title": "c05 aggregate Ruhela: non-majority religion -> RC",
"topics": [
"morie_otis_analyze_c05_mrm_aggregate",
"morie_otis_analyze_c05_ruhela_aggregate"
]
},
{
"page": "morie_otis_analyze_c06",
"title": "Individuals in RC/seg by age category x region (c06)",
"topics": [
"morie_otis_analyze_c06"
]
},
{
"page": "morie_otis_analyze_c06_ruhela_aggregate",
"title": "c06 aggregate Ruhela: Age 50+ -> RC",
"topics": [
"morie_otis_analyze_c06_mrm_aggregate",
"morie_otis_analyze_c06_ruhela_aggregate"
]
},
{
"page": "morie_otis_analyze_c07",
"title": "Individuals x alerts x gender (c07)",
"topics": [
"morie_otis_analyze_c07"
]
},
{
"page": "morie_otis_analyze_c07_ruhela_aggregate",
"title": "c07 aggregate Ruhela: Alert presence x Gender -> RC",
"topics": [
"morie_otis_analyze_c07_mrm_aggregate",
"morie_otis_analyze_c07_ruhela_aggregate"
]
},
{
"page": "morie_otis_analyze_c08",
"title": "Individuals by religion x gender (c08)",
"topics": [
"morie_otis_analyze_c08"
]
},
{
"page": "morie_otis_analyze_c08_ruhela_aggregate",
"title": "c08 aggregate Ruhela: non-majority religion x gender -> RC",
"topics": [
"morie_otis_analyze_c08_mrm_aggregate",
"morie_otis_analyze_c08_ruhela_aggregate"
]
},
{
"page": "morie_otis_analyze_c09",
"title": "Individuals by age category x gender (c09)",
"topics": [
"morie_otis_analyze_c09"
]
},
{
"page": "morie_otis_analyze_c09_ruhela_aggregate",
"title": "c09 aggregate Ruhela: Age 50+ x gender -> RC",
"topics": [
"morie_otis_analyze_c09_mrm_aggregate",
"morie_otis_analyze_c09_ruhela_aggregate"
]
},
{
"page": "morie_otis_analyze_c10",
"title": "RC/seg aggregate durations by institution (c10)",
"topics": [
"morie_otis_analyze_c10"
]
},
{
"page": "morie_otis_analyze_c10_ruhela_aggregate",
"title": "c10 aggregate Ruhela: Female -> median RC days (institution GEE)",
"topics": [
"morie_otis_analyze_c10_mrm_aggregate",
"morie_otis_analyze_c10_ruhela_aggregate"
]
},
{
"page": "morie_otis_analyze_c11",
"title": "Individuals by aggregate-duration bin (c11)",
"topics": [
"morie_otis_analyze_c11"
]
},
{
"page": "morie_otis_analyze_c11_mandela_classification",
"title": "Mandela-RF on c11 - per-individual Mandela classification by year",
"topics": [
"morie_otis_analyze_c11_mandela_classification"
]
},
{
"page": "morie_otis_analyze_c11_ruhela_aggregate",
"title": "c11 aggregate Ruhela: long-duration bin (>=16 days) -> RC",
"topics": [
"morie_otis_analyze_c11_mrm_aggregate",
"morie_otis_analyze_c11_ruhela_aggregate"
]
},
{
"page": "morie_otis_analyze_c12",
"title": "RC/seg aggregate durations by region & gender (c12)",
"topics": [
"morie_otis_analyze_c12"
]
},
{
"page": "morie_otis_analyze_c12_ruhela_aggregate",
"title": "c12 aggregate Ruhela: Female -> median RC days (by region)",
"topics": [
"morie_otis_analyze_c12_mrm_aggregate",
"morie_otis_analyze_c12_ruhela_aggregate"
]
},
{
"page": "morie_otis_analyze_d_chi2",
"title": "MRM chi-square family on d-series",
"topics": [
"morie_otis_analyze_d_chi2"
]
},
{
"page": "morie_otis_analyze_d01",
"title": "Person-level custodial deaths (d01)",
"topics": [
"morie_otis_analyze_d01"
]
},
{
"page": "morie_otis_analyze_d02",
"title": "Custodial deaths by gender (d02)",
"topics": [
"morie_otis_analyze_d02"
]
},
{
"page": "morie_otis_analyze_d02_ruhela_aggregate",
"title": "d02 aggregate Ruhela: Female -> custodial deaths",
"topics": [
"morie_otis_analyze_d02_mrm_aggregate",
"morie_otis_analyze_d02_ruhela_aggregate"
]
},
{
"page": "morie_otis_analyze_d03",
"title": "Custodial deaths by race (d03)",
"topics": [
"morie_otis_analyze_d03"
]
},
{
"page": "morie_otis_analyze_d03_ruhela_aggregate",
"title": "d03 aggregate Ruhela: Indigenous -> custodial deaths",
"topics": [
"morie_otis_analyze_d03_mrm_aggregate",
"morie_otis_analyze_d03_ruhela_aggregate"
]
},
{
"page": "morie_otis_analyze_d04",
"title": "Custodial deaths by religion (d04)",
"topics": [
"morie_otis_analyze_d04"
]
},
{
"page": "morie_otis_analyze_d04_ruhela_aggregate",
"title": "d04 aggregate Ruhela: non-majority religion -> custodial deaths",
"topics": [
"morie_otis_analyze_d04_mrm_aggregate",
"morie_otis_analyze_d04_ruhela_aggregate"
]
},
{
"page": "morie_otis_analyze_d05",
"title": "Custodial deaths by age category (d05)",
"topics": [
"morie_otis_analyze_d05"
]
},
{
"page": "morie_otis_analyze_d05_ruhela_aggregate",
"title": "d05 aggregate Ruhela: Age 50+ -> custodial deaths",
"topics": [
"morie_otis_analyze_d05_mrm_aggregate",
"morie_otis_analyze_d05_ruhela_aggregate"
]
},
{
"page": "morie_otis_analyze_d06",
"title": "Custodial deaths by alert x medical cause (d06)",
"topics": [
"morie_otis_analyze_d06"
]
},
{
"page": "morie_otis_analyze_d07",
"title": "Custodial deaths by alert x housing unit (d07)",
"topics": [
"morie_otis_analyze_d07"
]
},
{
"page": "morie_otis_analyze_otis_mandela_provincial_vs_federal",
"title": "Mandela-RF cross-comparison: Ontario provincial vs federal SIU",
"topics": [
"morie_otis_analyze_otis_mandela_provincial_vs_federal"
]
},
{
"page": "morie_otis_analyze_ruhela_grid",
"title": "Aggregate Ruhela grid: one-page IRR comparison across analyzers",
"topics": [
"morie_otis_analyze_ruhela_grid"
]
},
{
"page": "morie_otis_analyze_ruhela_master",
"title": "Paper-ready master report - every Ruhela formulation in one result",
"topics": [
"morie_otis_analyze_ruhela_master"
]
},
{
"page": "morie_otis_analyze_ruhela_per_year",
"title": "Per-fiscal-year full-DLRM Ruhela formulation driver",
"topics": [
"morie_otis_analyze_ruhela_per_year"
]
},
{
"page": "morie_otis_analyzers",
"title": "Registry of dataset-id -> analyzer",
"topics": [
"morie_otis_analyzers"
]
},
{
"page": "morie_otis_astcmb",
"title": "Alert-state combination encoding (8 combos -> complexity index)",
"topics": [
"morie_otis_astcmb"
]
},
{
"page": "morie_otis_atc",
"title": "Doubly robust average treatment effect on the controls (ATC)",
"topics": [
"morie_otis_atc"
]
},
{
"page": "morie_otis_balance",
"title": "Covariate balance before and after weighting or matching",
"topics": [
"morie_otis_balance"
]
},
{
"page": "morie_otis_causal_grid",
"title": "Run IPW / AIPW / IRM-DML on the three canonical (T, Y) pairs",
"topics": [
"morie_otis_causal_grid"
]
},
{
"page": "morie_otis_churn",
"title": "Goffmanian institutional-churn analyses on OTIS",
"topics": [
"morie_otis_churn"
]
},
{
"page": "morie_otis_churn_analyze_all",
"title": "Run all 11 OTIS-churn analyses",
"topics": [
"morie_otis_churn_analyze_all"
]
},
{
"page": "morie_otis_classify_mandela_combo",
"title": "Mandela alert-state classifier for an OTIS placement row",
"topics": [
"morie_otis_classify_mandela_combo"
]
},
{
"page": "morie_otis_disciplinary_medical_overlap",
"title": "Disciplinary x medical-protection overlap",
"topics": [
"morie_otis_disciplinary_medical_overlap"
]
},
{
"page": "morie_otis_embedding_distribution",
"title": "Total-days embedding distribution (lognormal vs Pareto vs exp)",
"topics": [
"morie_otis_embedding_distribution"
]
},
{
"page": "morie_otis_figures",
"title": "Export the OTIS restrictive-confinement figures",
"topics": [
"morie_otis_figures"
]
},
{
"page": "morie_otis_gcomputation",
"title": "Parametric g-computation of the ATE on OTIS data",
"topics": [
"morie_otis_gcomputation"
]
},
{
"page": "morie_otis_intra_year_transition_matrix",
"title": "Intra-year region-to-region transition matrix",
"topics": [
"morie_otis_intra_year_transition_matrix"
]
},
{
"page": "morie_otis_ipw_ate",
"title": "Hajek-stabilised IPW estimator of the ATE on OTIS data",
"topics": [
"morie_otis_ipw_ate"
]
},
{
"page": "morie_otis_irm_dml",
"title": "Interactive Regression Model DML on OTIS data (ATE, ATTE, ATC)",
"topics": [
"morie_otis_irm_dml"
]
},
{
"page": "morie_otis_irr_glmm_vm",
"title": "Poisson + Negative-Binomial IRR for volatility ~ alert complexity",
"topics": [
"morie_otis_irr_glmm_vm"
]
},
{
"page": "morie_otis_load",
"title": "Load the canonical OTIS CSV",
"topics": [
"morie_otis_load"
]
},
{
"page": "morie_otis_make_pair_a",
"title": "Pair (a): MentalHealth_Alert -> SuicideRisk_Alert (binary -> binary)",
"topics": [
"morie_otis_make_pair_a"
]
},
{
"page": "morie_otis_make_pair_alert_to_volatility_a01",
"title": "a01-aware wrapper for the Ruhela alert -> volatility builder",
"topics": [
"morie_otis_make_pair_alert_to_volatility_a01"
]
},
{
"page": "morie_otis_make_pair_alert_to_volatility_all",
"title": "Run both Ruhela and Naive alert -> volatility builders",
"topics": [
"morie_otis_make_pair_alert_to_volatility_all"
]
},
{
"page": "morie_otis_make_pair_alert_to_volatility_naive",
"title": "Naive (max-simultaneous-flags + binary vm) alert -> volatility builder",
"topics": [
"morie_otis_make_pair_alert_to_volatility_naive"
]
},
{
"page": "morie_otis_make_pair_alert_to_volatility_ruhela",
"title": "Ruhela's primary alert-complexity -> regional-volatility builder",
"topics": [
"morie_otis_make_pair_alert_to_volatility_ruhela"
]
},
{
"page": "morie_otis_make_pair_b",
"title": "Pair (b): HighAlertComplexity -> AnyReadmission",
"topics": [
"morie_otis_make_pair_b"
]
},
{
"page": "morie_otis_make_pair_c",
"title": "Pair (c): RegionalVolatility -> SegregationDays",
"topics": [
"morie_otis_make_pair_c"
]
},
{
"page": "morie_otis_mortification_cooccurrence",
"title": "Mortification co-occurrence (concurrent alerts)",
"topics": [
"morie_otis_mortification_cooccurrence"
]
},
{
"page": "morie_otis_otdesc",
"title": "Full OTIS descriptive statistics suite",
"topics": [
"morie_otis_otdesc"
]
},
{
"page": "morie_otis_otdml",
"title": "Cross-fitted partially linear DML (ATE/ATT) on OTIS",
"topics": [
"morie_otis_otdml"
]
},
{
"page": "morie_otis_path_complexity_gini",
"title": "Path complexity Gini by (year, region)",
"topics": [
"morie_otis_path_complexity_gini"
]
},
{
"page": "morie_otis_per_year_irm_dml",
"title": "IRM-DML (or the whole estimator battery) fitted separately by year",
"topics": [
"morie_otis_per_year_irm_dml"
]
},
{
"page": "morie_otis_pipeline",
"title": "OTIS analysis pipeline (RichResult-style driver)",
"topics": [
"morie_otis_pipeline"
]
},
{
"page": "morie_otis_plr",
"title": "Partially Linear Regression DML on OTIS data (native)",
"topics": [
"morie_otis_plr"
]
},
{
"page": "morie_otis_primitives",
"title": "Ontario Restrictive Confinement (OTIS) primitive analyses",
"topics": [
"morie_otis_alert_state_combo",
"morie_otis_descriptives",
"morie_otis_dml",
"morie_otis_primitives",
"morie_otis_rc_trends",
"morie_otis_regional_placement",
"morie_otis_volatility"
]
},
{
"page": "morie_otis_psm",
"title": "Propensity-score 1:k NN matching with caliper on OTIS data (native)",
"topics": [
"morie_otis_psm"
]
},
{
"page": "morie_otis_psm_subclass",
"title": "Propensity-score subclassification ATE on OTIS data (native)",
"topics": [
"morie_otis_psm_subclass"
]
},
{
"page": "morie_otis_rctrnd",
"title": "Restrictive-confinement trends over time by region",
"topics": [
"morie_otis_rctrnd"
]
},
{
"page": "morie_otis_regC_demog_contingency",
"title": "Multi-region path x Gender / Age contingency",
"topics": [
"morie_otis_regC_demog_contingency"
]
},
{
"page": "morie_otis_region_alert_state_richness",
"title": "Region x alert state richness",
"topics": [
"morie_otis_region_alert_state_richness"
]
},
{
"page": "morie_otis_repeat_placement_concentration",
"title": "Repeat-placement concentration (Goffmanian cyclical-inmate)",
"topics": [
"morie_otis_repeat_placement_concentration"
]
},
{
"page": "morie_otis_rplace",
"title": "Regional placement matrix by age group",
"topics": [
"morie_otis_rplace"
]
},
{
"page": "morie_otis_tps_analyze_all",
"title": "Run all OTIS-TPS overlay analyses",
"topics": [
"morie_otis_tps_analyze_all"
]
},
{
"page": "morie_otis_tps_composite_overlay",
"title": "Composite overlay (alias for the YoY correlation)",
"topics": [
"morie_otis_tps_composite_overlay"
]
},
{
"page": "morie_otis_tps_overlay",
"title": "Cross-link OTIS (Ontario corrections) with TPS (Toronto police) data",
"topics": [
"morie_otis_tps_overlay"
]
},
{
"page": "morie_otis_tps_per_region_rollup",
"title": "OTIS seg/RC totals per region x year (Toronto row flagged for TPS-overlay use)",
"topics": [
"morie_otis_tps_per_region_rollup"
]
},
{
"page": "morie_otis_tps_yoy_correlation",
"title": "Year-over-year correlation between OTIS Toronto-region segregation placements and TPS incident counts (per category)",
"topics": [
"morie_otis_tps_yoy_correlation"
]
},
{
"page": "morie_otis_volat",
"title": "Regional volatility / placement movement",
"topics": [
"morie_otis_volat"
]
},
{
"page": "morie_otis_within_year_placement_count",
"title": "Within-year placement-count distribution",
"topics": [
"morie_otis_within_year_placement_count"
]
},
{
"page": "morie_otis_within_year_region_diversity",
"title": "Within-year region diversity",
"topics": [
"morie_otis_within_year_region_diversity"
]
},
{
"page": "morie_outcome_test",
"title": "The outcome test: equal hit rates do not certify equal thresholds",
"topics": [
"morie_outcome_test"
]
},
{
"page": "morie_over",
"title": "Propensity-score overlap diagnostics",
"topics": [
"morie_over"
]
},
{
"page": "morie_oversample_factor",
"title": "Oversampling factor to reach a target subgroup composition",
"topics": [
"morie_oversample_factor"
]
},
{
"page": "morie_owate_weights",
"title": "Theorem 5.4 / Corollary 5.2 OWATE weights",
"topics": [
"morie_owate_weights"
]
},
{
"page": "morie_p_ibs_given_ibd",
"title": "IBS-given-IBD probabilities for one SNP (PLINK Table 1)",
"topics": [
"morie_p_ibs_given_ibd"
]
},
{
"page": "morie_pacfts",
"title": "Partial autocorrelation by the Durbin-Levinson recursion (ch. 3)",
"topics": [
"morie_pacfts"
]
},
{
"page": "morie_paired_t_test",
"title": "Paired t-test",
"topics": [
"morie_paired_t_test"
]
},
{
"page": "morie_pan_tompkins_update",
"title": "Pan-Tompkins adaptive threshold update",
"topics": [
"morie_pan_tompkins_update"
]
},
{
"page": "morie_panel_cointegration",
"title": "Pedroni panel cointegration statistics",
"topics": [
"morie_panel_cointegration"
]
},
{
"page": "morie_panel_deconvolution",
"title": "Panel-data deconvolution of the individual and idiosyncratic densities",
"topics": [
"morie_panel_deconvolution"
]
},
{
"page": "morie_panel_densities",
"title": "Both panel-deconvolution density estimators",
"topics": [
"morie_panel_densities"
]
},
{
"page": "morie_panel_max_score",
"title": "Maximum-score estimator for panel binary response",
"topics": [
"morie_panel_max_score"
]
},
{
"page": "morie_parse_nypd_law_code",
"title": "Parse an NYPD 'law_code' string into its structural fields",
"topics": [
"morie_parse_nypd_law_code"
]
},
{
"page": "morie_partial_cor",
"title": "Partial correlation matrix (native)",
"topics": [
"morie_partial_cor"
]
},
{
"page": "morie_partial_cor_test",
"title": "Partial correlation test for one pair given controls (native)",
"topics": [
"morie_partial_cor_test"
]
},
{
"page": "morie_partial_permutations",
"title": "Partial permutations N_P_n",
"topics": [
"morie_partial_permutations"
]
},
{
"page": "morie_partial_tau",
"title": "Kendall partial tau controlling for z",
"topics": [
"morie_partial_tau"
]
},
{
"page": "morie_partially_linear",
"title": "Robinson's partially linear model",
"topics": [
"morie_partially_linear"
]
},
{
"page": "morie_partition_count",
"title": "Integer partition counts by the pentagonal number theorem",
"topics": [
"morie_partition_count"
]
},
{
"page": "morie_partitions_into_parts",
"title": "Partitions of n into exactly k positive parts",
"topics": [
"morie_partitions_into_parts"
]
},
{
"page": "morie_partitions_of",
"title": "Every partition of an integer",
"topics": [
"morie_partitions_of"
]
},
{
"page": "morie_party_problem",
"title": "The party problem: R(3,3) = 6, proved rather than quoted",
"topics": [
"morie_party_problem"
]
},
{
"page": "morie_party_unity",
"title": "Party unity score per legislator",
"topics": [
"morie_party_unity"
]
},
{
"page": "morie_patchts",
"title": "PatchTST patching with reversible instance normalization",
"topics": [
"morie_patchts"
]
},
{
"page": "morie_paths",
"title": "Resolve standard project paths",
"topics": [
"morie_paths"
]
},
{
"page": "morie_pca_dimension_reduction",
"title": "PCA via SVD for dimension reduction (R parity)",
"topics": [
"morie_pca_dimension_reduction"
]
},
{
"page": "morie_pcg_filter",
"title": "Phonocardiogram (PCG) bandpass filter",
"topics": [
"morie_pcg_filter"
]
},
{
"page": "morie_pcm",
"title": "Partial credit model category probabilities",
"topics": [
"morie_pcm"
]
},
{
"page": "morie_pelt",
"title": "Exact penalised changepoint detection (PELT)",
"topics": [
"morie_pelt"
]
},
{
"page": "morie_penalised_tmle",
"title": "Penalised doubly robust TMLE",
"topics": [
"morie_penalised_tmle"
]
},
{
"page": "morie_penalized_regression",
"title": "Elastic-net regression via coordinate descent (base R)",
"topics": [
"morie_penalized_regression"
]
},
{
"page": "morie_percentage_bend_correlation",
"title": "Robust correlations",
"topics": [
"morie_percentage_bend_correlation",
"morie_winsorized_correlation"
]
},
{
"page": "morie_percentile_modified_rank",
"title": "Percentile-modified rank (Gastwirth) two-sample test (Gibbons Ch 8.3.3)",
"topics": [
"morie_percentile_modified_rank"
]
},
{
"page": "morie_performance_check_collinearity",
"title": "Collinearity / VIF check via 'performance'",
"topics": [
"morie_performance_check_collinearity"
]
},
{
"page": "morie_performance_check_model",
"title": "Regression-diagnostic plots via 'performance'",
"topics": [
"morie_performance_check_model"
]
},
{
"page": "morie_performance_check_outliers",
"title": "Outlier check via 'performance'",
"topics": [
"morie_performance_check_outliers"
]
},
{
"page": "morie_performance_r2",
"title": "R-squared family via 'performance'",
"topics": [
"morie_performance_r2"
]
},
{
"page": "morie_permutation_attention_masks",
"title": "Two-stream attention masks induced by a factorization order",
"topics": [
"morie_permutation_attention_masks"
]
},
{
"page": "morie_permutation_lm_loss",
"title": "Permutation language-model loss under one factorization order",
"topics": [
"morie_permutation_lm_loss"
]
},
{
"page": "morie_permutation_valid",
"title": "Exactness of the permutation p-value over a finite design",
"topics": [
"morie_permutation_valid"
]
},
{
"page": "morie_pgdsdg",
"title": "Projected gradient descent",
"topics": [
"morie_pgdsdg"
]
},
{
"page": "morie_pick_theorem",
"title": "Pick's theorem, checked by counting rather than assumed",
"topics": [
"morie_pick_theorem"
]
},
{
"page": "morie_pinball",
"title": "Pinball (quantile) loss (ch. 17 p. 494)",
"topics": [
"morie_pinball"
]
},
{
"page": "morie_placebo_dr_did",
"title": "Placebo doubly-robust difference-in-differences",
"topics": [
"morie_placebo_dr_did"
]
},
{
"page": "morie_plncF",
"title": "Planck spectral radiance B(lambda, T)",
"topics": [
"morie_plncF"
]
},
{
"page": "morie_pmf_convolve",
"title": "pmf of the sum of two independent discrete variables",
"topics": [
"morie_pmf_convolve"
]
},
{
"page": "morie_pmf_moments",
"title": "Moments of a discrete pmf",
"topics": [
"morie_pmf_moments"
]
},
{
"page": "morie_point_biserial_r",
"title": "Point-biserial correlation",
"topics": [
"morie_point_biserial_r"
]
},
{
"page": "morie_poisson_chernoff",
"title": "Chernoff tail bound for a Poisson count",
"topics": [
"morie_poisson_chernoff"
]
},
{
"page": "morie_poisson_dist",
"title": "Poisson distribution surface",
"topics": [
"morie_poisson_dist"
]
},
{
"page": "morie_poisson_loglinear",
"title": "Poisson regression and loglinear models",
"topics": [
"morie_poisson_loglinear"
]
},
{
"page": "morie_polqnt",
"title": "PolarQuant polar-transformation quantization",
"topics": [
"morie_polqnt"
]
},
{
"page": "morie_polr",
"title": "Native ordered logistic/probit regression (reproduces MASS::polr)",
"topics": [
"morie_polr"
]
},
{
"page": "morie_polya_tree_density",
"title": "Polya tree posterior mean density",
"topics": [
"morie_polya_tree_density"
]
},
{
"page": "morie_polya_tree_mixture",
"title": "Mixture of Polya trees",
"topics": [
"morie_polya_tree_mixture"
]
},
{
"page": "morie_polya_tree_rate",
"title": "Adaptive Polya tree rate",
"topics": [
"morie_polya_tree_rate"
]
},
{
"page": "morie_polynomial_regression",
"title": "Polynomial regression (R parity)",
"topics": [
"morie_polynomial_regression"
]
},
{
"page": "morie_portmanteau_hosking",
"title": "Hosking's multivariate portmanteau test for white noise",
"topics": [
"morie_portmanteau_hosking"
]
},
{
"page": "morie_poschk",
"title": "Positivity: every treatment value has positive probability in every covariate stratum (ch. 7 p. 157)",
"topics": [
"morie_poschk"
]
},
{
"page": "morie_post_lasso",
"title": "Refit by OLS on the lasso's selected support",
"topics": [
"morie_post_lasso"
]
},
{
"page": "morie_postgapx",
"title": "Quadratic approximation to a posterior (eq. 28.5, p. 344)",
"topics": [
"morie_postgapx"
]
},
{
"page": "morie_postodds",
"title": "Posterior odds from a likelihood ratio and prior odds (eq. 3.21, p. 53)",
"topics": [
"morie_postodds"
]
},
{
"page": "morie_power_prop_test",
"title": "Power for a two-proportion z-test",
"topics": [
"morie_power_prop_test"
]
},
{
"page": "morie_power_t_test",
"title": "Power for a two-sample t-test",
"topics": [
"morie_power_t_test"
]
},
{
"page": "morie_power_ttest_crim",
"title": "Statistical power for t-based tests (crim shelf)",
"topics": [
"morie_power_ttest_crim"
]
},
{
"page": "morie_ppcor_partial",
"title": "Partial correlation via 'ppcor'",
"topics": [
"morie_ppcor_partial"
]
},
{
"page": "morie_ppcor_semipartial",
"title": "Semi-partial correlation via 'ppcor'",
"topics": [
"morie_ppcor_semipartial"
]
},
{
"page": "morie_pps_sample",
"title": "Probability proportional to size (PPS) sampling",
"topics": [
"morie_pps_sample"
]
},
{
"page": "morie_pps_variance",
"title": "pps with-replacement variance estimator of a total",
"topics": [
"morie_pps_variance"
]
},
{
"page": "morie_prediction_accuracy",
"title": "Genomic-prediction accuracy metrics",
"topics": [
"morie_prediction_accuracy"
]
},
{
"page": "morie_predictive_recursion",
"title": "Newton's predictive recursion",
"topics": [
"morie_predictive_recursion"
]
},
{
"page": "morie_predpol_aggregate_areas",
"title": "Aggregate per-record predictive-policing data to one row per area",
"topics": [
"morie_predpol_aggregate_areas"
]
},
{
"page": "morie_predpol_calibration_audit",
"title": "Audit whether an algorithm's area risk ranking matches realised outcomes",
"topics": [
"morie_predpol_calibration_audit"
]
},
{
"page": "morie_predpol_score_disparity",
"title": "Descriptive disparity in a risk score across groups",
"topics": [
"morie_predpol_score_disparity"
]
},
{
"page": "morie_predpol_temporal_audit",
"title": "Audit how disparity metrics move over time and across cities",
"topics": [
"morie_predpol_temporal_audit"
]
},
{
"page": "morie_pretprep",
"title": "Preprocess an image for a pretrained model (pp. 458-459)",
"topics": [
"morie_pretprep"
]
},
{
"page": "morie_privacy_amplification",
"title": "Privacy amplification by subsampling",
"topics": [
"morie_privacy_amplification"
]
},
{
"page": "morie_private_accuracy_tradeoff",
"title": "Privacy-accuracy trade-off",
"topics": [
"morie_private_accuracy_tradeoff"
]
},
{
"page": "morie_private_ci",
"title": "An interval that accounts for the mechanism's own variance",
"topics": [
"morie_private_ci"
]
},
{
"page": "morie_private_release",
"title": "Laplace mechanism release",
"topics": [
"morie_private_release"
]
},
{
"page": "morie_prob_rules",
"title": "Elementary probability rules for an event pair",
"topics": [
"morie_prob_rules"
]
},
{
"page": "morie_probability_of_necessity",
"title": "Probability of necessity: bounds for attributing an outcome to a sanction",
"topics": [
"morie_probability_of_necessity"
]
},
{
"page": "morie_profile_dataset",
"title": "Profile a data.frame: per-column types, missingness, summary statistics",
"topics": [
"morie_profile_dataset"
]
},
{
"page": "morie_prophet_components",
"title": "Prophet-style additive decomposition (linear trend + Fourier seasonality)",
"topics": [
"morie_prophet_components"
]
},
{
"page": "morie_proportion_ci",
"title": "Wilson score confidence interval for a proportion",
"topics": [
"morie_proportion_ci"
]
},
{
"page": "morie_psdmt",
"title": "Multitaper power spectral density",
"topics": [
"morie_psdmt"
]
},
{
"page": "morie_psm_balance",
"title": "Propensity-score matching standardized absolute bias",
"topics": [
"morie_psm_balance"
]
},
{
"page": "morie_psy_eap_theta",
"title": "Expected a posteriori ability estimate",
"topics": [
"morie_psy_eap_theta"
]
},
{
"page": "morie_psy_icc1k",
"title": "ICC(1,k): one-way random, average of k ratings",
"topics": [
"morie_psy_icc1k"
]
},
{
"page": "morie_psy_icc2k",
"title": "ICC(2,k): two-way random, average measure",
"topics": [
"morie_psy_icc2k"
]
},
{
"page": "morie_psy_icc3k",
"title": "ICC(3,k): two-way mixed, average measure",
"topics": [
"morie_psy_icc3k"
]
},
{
"page": "morie_psy_ma_loo",
"title": "Leave-one-out influence for meta-analysis",
"topics": [
"morie_psy_ma_loo"
]
},
{
"page": "morie_psy_ma_paule_mandel",
"title": "Paule-Mandel between-study variance",
"topics": [
"morie_psy_ma_paule_mandel"
]
},
{
"page": "morie_psy_ma_reml",
"title": "REML between-study variance",
"topics": [
"morie_psy_ma_reml"
]
},
{
"page": "morie_psy_ma_rve",
"title": "Robust variance estimation for dependent effect sizes",
"topics": [
"morie_psy_ma_rve"
]
},
{
"page": "morie_psy_map_theta",
"title": "Maximum a posteriori ability estimate",
"topics": [
"morie_psy_map_theta"
]
},
{
"page": "morie_psy_mle_theta",
"title": "Maximum-likelihood ability estimate",
"topics": [
"morie_psy_mle_theta"
]
},
{
"page": "morie_psy_theta_eap",
"title": "EAP theta over a response matrix - alias",
"topics": [
"morie_psy_theta_eap"
]
},
{
"page": "morie_psy_theta_map",
"title": "MAP theta over a response matrix - alias",
"topics": [
"morie_psy_theta_map"
]
},
{
"page": "morie_psy_wle_theta",
"title": "Warm's weighted likelihood ability estimate",
"topics": [
"morie_psy_wle_theta"
]
},
{
"page": "morie_psymet_alpha",
"title": "Cronbach's coefficient alpha with Feldt CI",
"topics": [
"morie_psymet_alpha"
]
},
{
"page": "morie_psymet_alphadel",
"title": "Alpha if item deleted",
"topics": [
"morie_psymet_alphadel"
]
},
{
"page": "morie_psymet_ave",
"title": "Average variance extracted (AVE) from factor loadings. Mean(lambda^2)",
"topics": [
"morie_psymet_ave"
]
},
{
"page": "morie_psymet_bartlett",
"title": "Bartlett's test of sphericity",
"topics": [
"morie_psymet_bartlett"
]
},
{
"page": "morie_psymet_cr",
"title": "Composite reliability from standardized factor loadings. CR = (sum lambda)^2 / ((sum lambda)^2 + sum(1 - lambda^2))",
"topics": [
"morie_psymet_cr"
]
},
{
"page": "morie_psymet_discrimination",
"title": "Item discrimination (D-statistic)",
"topics": [
"morie_psymet_discrimination"
]
},
{
"page": "morie_psymet_itemtotal",
"title": "Corrected item-total correlations",
"topics": [
"morie_psymet_itemtotal"
]
},
{
"page": "morie_psymet_kmo",
"title": "Kaiser-Meyer-Olkin sampling adequacy",
"topics": [
"morie_psymet_kmo"
]
},
{
"page": "morie_psymet_omega",
"title": "McDonald's omega total and hierarchical",
"topics": [
"morie_psymet_omega"
]
},
{
"page": "morie_psymet_parallel",
"title": "Horn's parallel analysis - suggested number of factors",
"topics": [
"morie_psymet_parallel"
]
},
{
"page": "morie_psymet_splithalf",
"title": "Spearman-Brown split-half reliability",
"topics": [
"morie_psymet_splithalf"
]
},
{
"page": "morie_ptmcmc",
"title": "Parallel tempering (replica exchange)",
"topics": [
"morie_ptmcmc"
]
},
{
"page": "morie_qlearn",
"title": "Tabular Q-learning",
"topics": [
"morie_qlearn"
]
},
{
"page": "morie_qlrtst",
"title": "Quandt/Andrews sup-Wald structural-break test",
"topics": [
"morie_qlrtst"
]
},
{
"page": "morie_qmDS",
"title": "Empirical quantile mapping",
"topics": [
"morie_qmDS"
]
},
{
"page": "morie_qte_firpo",
"title": "Quantile treatment effect via Firpo IPW",
"topics": [
"morie_qte_firpo"
]
},
{
"page": "morie_quanrg",
"title": "Exact regression quantile (Koenker-Bassett)",
"topics": [
"morie_quanrg"
]
},
{
"page": "morie_quantile_causal_forest",
"title": "Quantile-balanced causal forest for distributional treatment effects",
"topics": [
"morie_quantile_causal_forest"
]
},
{
"page": "morie_quantile_hadamard",
"title": "Quantile Hadamard sandwich inequality",
"topics": [
"morie_quantile_hadamard"
]
},
{
"page": "morie_quantile_reg",
"title": "Quantile regression via 'quantreg'",
"topics": [
"morie_quantile_reg"
]
},
{
"page": "morie_quantreg",
"title": "Linear quantile regression (ch. 17 p. 500)",
"topics": [
"morie_quantreg"
]
},
{
"page": "morie_r3post",
"title": "R3 repetition-code bit posterior (MacKay 2003 eq. 1.18, p. 9)",
"topics": [
"morie_r3post"
]
},
{
"page": "morie_rake",
"title": "Rake a weight vector to known population margins",
"topics": [
"morie_rake"
]
},
{
"page": "morie_ramsey_lower_bound_probabilistic",
"title": "Erdos's probabilistic lower bound on the diagonal R(k, k)",
"topics": [
"morie_ramsey_lower_bound_probabilistic"
]
},
{
"page": "morie_ramsey_number",
"title": "Ramsey number R(k, l), exactly or as an interval",
"topics": [
"morie_ramsey_number"
]
},
{
"page": "morie_ramsey_upper_bound",
"title": "Upper bounds on R(k, l) from the classical arguments",
"topics": [
"morie_ramsey_upper_bound"
]
},
{
"page": "morie_ramsey_witness",
"title": "Verify a Ramsey lower-bound witness",
"topics": [
"morie_ramsey_witness"
]
},
{
"page": "morie_random_forest_ensemble",
"title": "Random Forest ensemble (R parity)",
"topics": [
"morie_random_forest_ensemble"
]
},
{
"page": "morie_random_forest_genomic",
"title": "Random-forest genomic predictor",
"topics": [
"morie_random_forest_genomic"
]
},
{
"page": "morie_random_search_cv",
"title": "Random search hyperparameter optimisation (R parity)",
"topics": [
"morie_random_search_cv"
]
},
{
"page": "morie_random_walk_exposure",
"title": "Random-walk exposure on a graph is proportional to degree",
"topics": [
"morie_random_walk_exposure"
]
},
{
"page": "morie_randomized_response_dp",
"title": "Warner randomized response",
"topics": [
"morie_randomized_response_dp"
]
},
{
"page": "morie_randtests_bartels",
"title": "Bartels rank test via 'randtests'",
"topics": [
"morie_randtests_bartels"
]
},
{
"page": "morie_randtests_runs",
"title": "Wald-Wolfowitz runs test via 'randtests'",
"topics": [
"morie_randtests_runs"
]
},
{
"page": "morie_randtests_turning_point",
"title": "Turning-point test via 'randtests'",
"topics": [
"morie_randtests_turning_point"
]
},
{
"page": "morie_rank_based_test",
"title": "Mann's rank test for randomness (Gibbons Ch 3.5)",
"topics": [
"morie_rank_based_test"
]
},
{
"page": "morie_rank_exact_null",
"title": "Exact null distributions of Kendall's tau and Spearman's rho",
"topics": [
"morie_rank_exact_null"
]
},
{
"page": "morie_rank_index",
"title": "Semiparametric rank estimators of a single-index model",
"topics": [
"morie_rank_index"
]
},
{
"page": "morie_rank_order_statistics",
"title": "Signed ranks of paired differences (Gibbons Ch 5.5)",
"topics": [
"morie_rank_order_statistics"
]
},
{
"page": "morie_rank_persistence_null",
"title": "Rank-persistence null: the overlap of two top-k sets by chance",
"topics": [
"morie_rank_persistence_null"
]
},
{
"page": "morie_rank_placements",
"title": "Rank placements of Y among X order statistics (Gibbons Ch 2.11.3)",
"topics": [
"morie_rank_placements"
]
},
{
"page": "morie_rank_tie_variance",
"title": "Tie-corrected null variances for the rank-sum family",
"topics": [
"morie_rank_tie_variance"
]
},
{
"page": "morie_ranking_resolution",
"title": "Resolution of a ranking built from noisy risk scores",
"topics": [
"morie_ranking_resolution"
]
},
{
"page": "morie_ranova",
"title": "ANOVA (method-of-moments) estimation of variance components",
"topics": [
"morie_ranova"
]
},
{
"page": "morie_raoscot",
"title": "Rao-Scott first-order corrected goodness-of-fit chi-square",
"topics": [
"morie_raoscot"
]
},
{
"page": "morie_rappor",
"title": "RAPPOR encode (Sec. 2, steps 1-4)",
"topics": [
"morie_rappor"
]
},
{
"page": "morie_rappor_decode",
"title": "RAPPOR decode (Sec. 4)",
"topics": [
"morie_rappor_decode"
]
},
{
"page": "morie_rappor_epsilon",
"title": "RAPPOR privacy parameters (Theorems 1 and 2)",
"topics": [
"morie_rappor_epsilon"
]
},
{
"page": "morie_rappor_star",
"title": "RAPPOR effective report probabilities (Lemma 1)",
"topics": [
"morie_rappor_star"
]
},
{
"page": "morie_rate_check",
"title": "Measure a convergence exponent",
"topics": [
"morie_rate_check"
]
},
{
"page": "morie_ratio_estimator",
"title": "Ratio estimator with an auxiliary variable",
"topics": [
"morie_ratio_estimator"
]
},
{
"page": "morie_rational_gf_coefficients",
"title": "Power-series coefficients of a rational generating function",
"topics": [
"morie_rational_gf_coefficients"
]
},
{
"page": "morie_rct_tests",
"title": "Randomized-experiment test statistics",
"topics": [
"morie_rct_tests"
]
},
{
"page": "morie_rd_mserd_bandwidth",
"title": "MSE-optimal RD bandwidths (rdrobust's mserd)",
"topics": [
"morie_rd_mserd_bandwidth"
]
},
{
"page": "morie_rdd",
"title": "Sharp / fuzzy regression discontinuity with bundled diagnostics",
"topics": [
"morie_rdd"
]
},
{
"page": "morie_rdd_bandwidth_cct",
"title": "MSE-optimal RD bandwidth (Calonico, Cattaneo and Farrell 2020)",
"topics": [
"morie_rdd_bandwidth_cct"
]
},
{
"page": "morie_rdd_bandwidth_ik",
"title": "Imbens-Kalyanaraman (IK) MSE-optimal bandwidth",
"topics": [
"morie_rdd_bandwidth_ik"
]
},
{
"page": "morie_rdd_bandwidth_rot",
"title": "Rule-of-thumb (ROT) bandwidth - Silverman-style on running variable",
"topics": [
"morie_rdd_bandwidth_rot"
]
},
{
"page": "morie_rdd_bandwidth_sensitivity",
"title": "Bandwidth sensitivity sweep",
"topics": [
"morie_rdd_bandwidth_sensitivity"
]
},
{
"page": "morie_rdd_bias_corrected",
"title": "CCT bias-corrected, robust-SE RDD inference",
"topics": [
"morie_rdd_bias_corrected"
]
},
{
"page": "morie_rdd_cattaneo_density",
"title": "Cattaneo-Jansson-Ma (2020) local-polynomial density test",
"topics": [
"morie_rdd_cattaneo_density"
]
},
{
"page": "morie_rdd_covariate_balance",
"title": "Covariate balance at the cutoff",
"topics": [
"morie_rdd_covariate_balance"
]
},
{
"page": "morie_rdd_density_test",
"title": "McCrary-style RD density test via 'rddensity'",
"topics": [
"morie_rdd_density_test"
]
},
{
"page": "morie_rdd_discrete",
"title": "RDD with discrete running variable",
"topics": [
"morie_rdd_discrete"
]
},
{
"page": "morie_rdd_donut",
"title": "Donut-hole RDD",
"topics": [
"morie_rdd_donut"
]
},
{
"page": "morie_rdd_fuzzy",
"title": "Fuzzy RDD treatment effect via instrumented Wald ratio",
"topics": [
"morie_rdd_fuzzy"
]
},
{
"page": "morie_rdd_geographic",
"title": "Geographic / boundary RDD on a signed distance",
"topics": [
"morie_rdd_geographic"
]
},
{
"page": "morie_rdd_kernels",
"title": "RDD kernel functions",
"topics": [
"morie_rdd_kernels",
"morie_rdd_kernel_epanechnikov",
"morie_rdd_kernel_gaussian",
"morie_rdd_kernel_triangular",
"morie_rdd_kernel_uniform"
]
},
{
"page": "morie_rdd_kink",
"title": "Regression kink design - slope discontinuity at the cutoff",
"topics": [
"morie_rdd_kink"
]
},
{
"page": "morie_rdd_local_polynomial",
"title": "Local polynomial regression at user-supplied evaluation points",
"topics": [
"morie_rdd_local_polynomial"
]
},
{
"page": "morie_rdd_local_randinf",
"title": "Local-randomisation RDD inference via 'rdlocrand'",
"topics": [
"morie_rdd_local_randinf"
]
},
{
"page": "morie_rdd_local_randomisation",
"title": "Local-randomisation RDD via permutation in a fixed window",
"topics": [
"morie_rdd_local_randomisation"
]
},
{
"page": "morie_rdd_mccrary",
"title": "McCrary (2008) density manipulation test",
"topics": [
"morie_rdd_mccrary"
]
},
{
"page": "morie_rdd_placebo_cutoff",
"title": "Placebo cutoff falsification test",
"topics": [
"morie_rdd_placebo_cutoff"
]
},
{
"page": "morie_rdd_plot_data",
"title": "Binned scatter + global-polynomial data for an RD plot",
"topics": [
"morie_rdd_plot_data"
]
},
{
"page": "morie_rdd_power",
"title": "RDD power calculation",
"topics": [
"morie_rdd_power"
]
},
{
"page": "morie_rdd_power_calc",
"title": "Power calculations for RDD designs via 'rdpower'",
"topics": [
"morie_rdd_power_calc"
]
},
{
"page": "morie_rdd_sample_size",
"title": "RDD sample-size determination",
"topics": [
"morie_rdd_sample_size"
]
},
{
"page": "morie_rdd_sharp",
"title": "Sharp RDD treatment effect at the cutoff",
"topics": [
"morie_rdd_sharp"
]
},
{
"page": "morie_rdkfp",
"title": "RDKit path-based topological fingerprint",
"topics": [
"morie_rdkfp"
]
},
{
"page": "morie_rdp_compose",
"title": "Compose identical sampled Gaussians (Mironov 2017, Proposition 1)",
"topics": [
"morie_rdp_compose"
]
},
{
"page": "morie_rdp_gaussian",
"title": "Gaussian RDP curve (Mironov 2017, Corollary 3)",
"topics": [
"morie_rdp_gaussian"
]
},
{
"page": "morie_rdp_laplace",
"title": "Laplace RDP curve (Mironov 2017, Corollary 2)",
"topics": [
"morie_rdp_laplace"
]
},
{
"page": "morie_rdp_sampled_gaussian",
"title": "Exact RDP of the sampled Gaussian mechanism at an integer order",
"topics": [
"morie_rdp_sampled_gaussian"
]
},
{
"page": "morie_rdpc",
"title": "Renyi-DP budget of the Gaussian mechanism",
"topics": [
"morie_rdpc"
]
},
{
"page": "morie_rdpcomp",
"title": "RDP curve of composed sampled Gaussians, optionally converted to DP",
"topics": [
"morie_rdpcomp"
]
},
{
"page": "morie_read_outputs_manifest",
"title": "Read outputs manifest from a project",
"topics": [
"morie_read_outputs_manifest"
]
},
{
"page": "morie_read_parquet",
"title": "Read a Parquet file",
"topics": [
"morie_read_parquet"
]
},
{
"page": "morie_recmulti",
"title": "Recursive multi-step forecasting (ch. 18 p. 546)",
"topics": [
"morie_recmulti"
]
},
{
"page": "morie_recommended_pair_test",
"title": "Recommended bivariate test for a pair of variables",
"topics": [
"morie_recommended_pair_test"
]
},
{
"page": "morie_recommended_summary",
"title": "Recommended summary statistic for a single variable",
"topics": [
"morie_recommended_summary"
]
},
{
"page": "morie_reconcile_hierarchy",
"title": "Reconcile hierarchical forecasts so they add up",
"topics": [
"morie_reconcile_hierarchy"
]
},
{
"page": "morie_recording_map",
"title": "Recorded counts from true counts through a recording matrix",
"topics": [
"morie_recording_map"
]
},
{
"page": "morie_recording_regimes",
"title": "Two recording regimes from one recorded series",
"topics": [
"morie_recording_regimes"
]
},
{
"page": "morie_regime_switching",
"title": "Markov-switching regression (Hamilton 1989)",
"topics": [
"morie_regime_switching"
]
},
{
"page": "morie_regime_value",
"title": "Value of a treatment regime (AIPW / IPW / regression)",
"topics": [
"morie_regime_value"
]
},
{
"page": "morie_regression",
"title": "Linear regression with inference, robust errors and diagnostics",
"topics": [
"morie_regression"
]
},
{
"page": "morie_regression_estimator",
"title": "Regression (difference) estimator",
"topics": [
"morie_regression_estimator"
]
},
{
"page": "morie_regression_to_mean",
"title": "Regression to the mean at selected hot spots",
"topics": [
"morie_regression_to_mean"
]
},
{
"page": "morie_regularization_path",
"title": "Ridge / LASSO / ElasticNet regularization path (R parity)",
"topics": [
"morie_regularization_path"
]
},
{
"page": "morie_relabel_forensics",
"title": "Name the mechanical step that reproduces a label permutation",
"topics": [
"morie_relabel_forensics"
]
},
{
"page": "morie_relative_risk_from_or",
"title": "Relative risk from an odds ratio: the interval that arrest-only data allow",
"topics": [
"morie_relative_risk_from_or"
]
},
{
"page": "morie_relmae",
"title": "Relative mean absolute error against a benchmark (ch. 19 p. 571)",
"topics": [
"morie_relmae"
]
},
{
"page": "morie_renyi_dp_composition",
"title": "Renyi differential privacy composition",
"topics": [
"morie_renyi_dp_composition"
]
},
{
"page": "morie_repcn",
"title": "Blocklength reaching a target error rate (eq. 1.44-1.45, p. 17)",
"topics": [
"morie_repcn"
]
},
{
"page": "morie_repcpb",
"title": "Block error probability of the RN repetition code (eq. 1.42-1.43, p. 17)",
"topics": [
"morie_repcpb"
]
},
{
"page": "morie_reportm",
"title": "Report Noisy Max",
"topics": [
"morie_reportm"
]
},
{
"page": "morie_residual_edf",
"title": "Empirical distribution of regression residuals",
"topics": [
"morie_residual_edf"
]
},
{
"page": "morie_respwt",
"title": "Weighting-class adjustment for survey nonresponse",
"topics": [
"morie_respwt"
]
},
{
"page": "morie_ridge_estimator",
"title": "Ridge regression closed form, the grid-search default estimator (hmgrs)",
"topics": [
"morie_ridge_estimator"
]
},
{
"page": "morie_rif_quantile",
"title": "Recentered influence function of a quantile, and the gap it carries",
"topics": [
"morie_rif_gap",
"morie_rif_quantile"
]
},
{
"page": "morie_ripley_csr_test",
"title": "CSR test via Monte Carlo envelopes on Ripley's K",
"topics": [
"morie_ripley_csr_test"
]
},
{
"page": "morie_ripley_k",
"title": "Ripley's K function",
"topics": [
"morie_ripley_k"
]
},
{
"page": "morie_risk_difference_ci",
"title": "Risk difference (ARD) with Newcombe CI",
"topics": [
"morie_risk_difference_ci"
]
},
{
"page": "morie_risk_ratio_ci",
"title": "Risk ratio (relative risk) with log-normal CI",
"topics": [
"morie_risk_ratio_ci"
]
},
{
"page": "morie_rkhs_full",
"title": "RKHS regression with Gaussian kernel",
"topics": [
"morie_rkhs_full"
]
},
{
"page": "morie_rlearn",
"title": "Residualized (Robinson-style) CATE estimator",
"topics": [
"morie_rlearn"
]
},
{
"page": "morie_rlm",
"title": "Native robust (Huber M-estimator) regression (reproduces MASS::rlm)",
"topics": [
"morie_rlm"
]
},
{
"page": "morie_rls_phi",
"title": "RLS correlation matrix and normal equations",
"topics": [
"morie_rls_phi"
]
},
{
"page": "morie_rminsamp",
"title": "Uniform draws needed to hit the typical set once (eq. 29.19, p. 366)",
"topics": [
"morie_rminsamp"
]
},
{
"page": "morie_rmsdtr",
"title": "Kabsch optimal-superposition RMSD",
"topics": [
"morie_rmsdtr"
]
},
{
"page": "morie_rmse",
"title": "Root mean squared error (ch. 19 p. 566)",
"topics": [
"morie_rmse"
]
},
{
"page": "morie_rmsprop",
"title": "RMSProp optimiser step",
"topics": [
"morie_rmsprop"
]
},
{
"page": "morie_rmsse",
"title": "Root mean squared scaled error (ch. 19 p. 572)",
"topics": [
"morie_rmsse"
]
},
{
"page": "morie_rmst",
"title": "Restricted mean survival time",
"topics": [
"morie_rmst"
]
},
{
"page": "morie_rmst_difference",
"title": "Difference in restricted mean survival time between two groups",
"topics": [
"morie_rmst_difference"
]
},
{
"page": "morie_rnn_genomic",
"title": "Vanilla RNN genomic predictor (BPTT, base R)",
"topics": [
"morie_rnn_genomic"
]
},
{
"page": "morie_rob_huber",
"title": "Huber M-estimator regression",
"topics": [
"morie_rob_huber"
]
},
{
"page": "morie_rob_m",
"title": "General M-estimator regression",
"topics": [
"morie_rob_m"
]
},
{
"page": "morie_rob_mm",
"title": "MM-estimator regression",
"topics": [
"morie_rob_mm"
]
},
{
"page": "morie_rob_mm_alias",
"title": "MM-estimator regression - alias entry point",
"topics": [
"morie_rob_mm_alias"
]
},
{
"page": "morie_rob_qn",
"title": "Qn robust scale estimator",
"topics": [
"morie_rob_qn"
]
},
{
"page": "morie_rob_s",
"title": "S-estimator regression",
"topics": [
"morie_rob_s"
]
},
{
"page": "morie_rob_sens_slope",
"title": "Sen's slope for a time series",
"topics": [
"morie_rob_sens_slope"
]
},
{
"page": "morie_rob_sn",
"title": "Sn robust scale estimator",
"topics": [
"morie_rob_sn"
]
},
{
"page": "morie_rob_tau",
"title": "Tau-estimator regression",
"topics": [
"morie_rob_tau"
]
},
{
"page": "morie_rob_theil_sen",
"title": "Theil-Sen slope estimator",
"topics": [
"morie_rob_theil_sen"
]
},
{
"page": "morie_robust_anova",
"title": "Robust one-way designs",
"topics": [
"morie_robust_anova"
]
},
{
"page": "morie_robust_association",
"title": "Robust correlation, effect size and regression",
"topics": [
"morie_robust_association"
]
},
{
"page": "morie_robust_location",
"title": "Robust measures of location",
"topics": [
"morie_robust_location"
]
},
{
"page": "morie_robust_quantiles",
"title": "Robust quantiles and spread",
"topics": [
"morie_robust_quantiles"
]
},
{
"page": "morie_robust_tests",
"title": "Robust tests for one and two groups",
"topics": [
"morie_robust_tests"
]
},
{
"page": "morie_robust_vcov",
"title": "Heteroskedasticity-consistent covariance (White's sandwich)",
"topics": [
"morie_robust_se",
"morie_robust_vcov"
]
},
{
"page": "morie_roc_auc_score",
"title": "ROC curve and AUC (R parity)",
"topics": [
"morie_roc_auc_score"
]
},
{
"page": "morie_rollfeat",
"title": "Rolling-window features (ch. 6 p. 176)",
"topics": [
"morie_rollfeat"
]
},
{
"page": "morie_rosenb",
"title": "Rosenbaum sensitivity bounds over a Gamma grid",
"topics": [
"morie_rosenb"
]
},
{
"page": "morie_rpgad",
"title": "Convert an RDP curve to (epsilon, delta)-differential privacy",
"topics": [
"morie_rpgad"
]
},
{
"page": "morie_rq",
"title": "Quantile regression (Frisch-Newton interior point, native)",
"topics": [
"coef.morie_rq",
"fitted.morie_rq",
"logLik.morie_rq",
"morie_rq",
"predict.morie_rq",
"print.morie_rq",
"print.summary.morie_rq",
"residuals.morie_rq",
"summary.morie_rq",
"vcov.morie_rq"
]
},
{
"page": "morie_rsample_bootstraps",
"title": "Native set of bootstrap resamples (rsample-style)",
"topics": [
"morie_rsample_bootstraps"
]
},
{
"page": "morie_rsk_correspondence",
"title": "The Robinson-Schensted correspondence",
"topics": [
"morie_rsk_correspondence"
]
},
{
"page": "morie_rsk_insert",
"title": "Row-insert one value into a tableau, bumping",
"topics": [
"morie_rsk_insert"
]
},
{
"page": "morie_rsk_inverse",
"title": "Recover the permutation from its two tableaux",
"topics": [
"morie_rsk_inverse"
]
},
{
"page": "morie_rtwall",
"title": "Wallinga-Teunis case reproduction numbers",
"topics": [
"morie_rtwall"
]
},
{
"page": "morie_ruin_probability",
"title": "Absorption probability of a finite-capacity allocation",
"topics": [
"morie_ruin_probability"
]
},
{
"page": "morie_run_ebac_selection_ipw_analysis",
"title": "Run the eBAC selection-adjusted IPW workflow",
"topics": [
"morie_run_ebac_selection_ipw_analysis"
]
},
{
"page": "morie_run_length_bound",
"title": "Mean run length of a restarted detector with false-alarm probability alpha",
"topics": [
"morie_run_length_bound"
]
},
{
"page": "morie_run_morie_module",
"title": "Run one implemented MORIE module against CPADS data",
"topics": [
"morie_run_morie_module"
]
},
{
"page": "morie_run_morie_modules",
"title": "Run multiple implemented MORIE modules",
"topics": [
"morie_run_morie_modules"
]
},
{
"page": "morie_run_pipeline",
"title": "Run multiple workflow steps",
"topics": [
"morie_run_pipeline"
]
},
{
"page": "morie_run_propensity_ipw_analysis",
"title": "Run the CPADS propensity/IPW workflow",
"topics": [
"morie_run_propensity_ipw_analysis"
]
},
{
"page": "morie_run_treatment_effects_analysis",
"title": "Run an extended treatment-effects analysis (ATE / ATT / ATC + CATE)",
"topics": [
"morie_run_treatment_effects_analysis"
]
},
{
"page": "morie_run_weighted_logistic_analysis",
"title": "Run a weighted logistic-regression analysis",
"topics": [
"morie_run_weighted_logistic_analysis"
]
},
{
"page": "morie_run_workflow_step",
"title": "Run one project workflow step",
"topics": [
"morie_run_workflow_step"
]
},
{
"page": "morie_runs_pmf",
"title": "Null distributions of the two-sample runs counts",
"topics": [
"morie_runs_pmf"
]
},
{
"page": "morie_runs_test",
"title": "Wald-Wolfowitz runs test (native)",
"topics": [
"morie_runs_test"
]
},
{
"page": "morie_runs_updown",
"title": "Exact null distribution of runs up and down",
"topics": [
"morie_runs_updown"
]
},
{
"page": "morie_sacc",
"title": "Soft policy iteration (tabular soft actor-critic core)",
"topics": [
"morie_sacc"
]
},
{
"page": "morie_safe_factor",
"title": "Build a factor with explicit, verified levels",
"topics": [
"morie_safe_factor"
]
},
{
"page": "morie_safe_recode",
"title": "Recode a categorical column by explicit name-to-name mapping",
"topics": [
"morie_safe_recode"
]
},
{
"page": "morie_safe_relabel",
"title": "Relabel a categorical variable by name, never by position",
"topics": [
"morie_safe_relabel"
]
},
{
"page": "morie_sample",
"title": "Load a included MORIE reference sample by name",
"topics": [
"morie_sample"
]
},
{
"page": "morie_sample_acf",
"title": "Sample autocorrelation function",
"topics": [
"morie_sample_acf"
]
},
{
"page": "morie_sample_pacf",
"title": "Sample partial autocorrelation function",
"topics": [
"morie_sample_pacf"
]
},
{
"page": "morie_sample_size",
"title": "Required sample sizes and Bayesian criteria",
"topics": [
"morie_sample_size"
]
},
{
"page": "morie_sample_size_domain",
"title": "Sample size for a subpopulation (domain) within a larger frame",
"topics": [
"morie_sample_size_domain"
]
},
{
"page": "morie_sample_size_logistic",
"title": "Sample size for logistic regression (Hsieh, Bloch and Larsen 1998)",
"topics": [
"morie_sample_size_logistic"
]
},
{
"page": "morie_sample_size_proportion",
"title": "Sample size for a proportion under a complex design",
"topics": [
"morie_sample_size_proportion"
]
},
{
"page": "morie_sar_lag",
"title": "Spatial lag (SAR) model reduced form",
"topics": [
"morie_sar_lag"
]
},
{
"page": "morie_sarsa",
"title": "Tabular SARSA",
"topics": [
"morie_sarsa"
]
},
{
"page": "morie_sax_breakpoints",
"title": "Equiprobable Gaussian breakpoints for SAX",
"topics": [
"morie_sax_breakpoints"
]
},
{
"page": "morie_sax_mindist",
"title": "MINDIST between two SAX words",
"topics": [
"morie_sax_mindist"
]
},
{
"page": "morie_saxR",
"title": "SAX word for a series",
"topics": [
"morie_saxR"
]
},
{
"page": "morie_scaled_schoenfeld",
"title": "Scaled Schoenfeld residuals and the Grambsch-Therneau PH test",
"topics": [
"morie_scaled_schoenfeld"
]
},
{
"page": "morie_screen_design",
"title": "Two-phase screening design for a rare subpopulation",
"topics": [
"morie_screen_design"
]
},
{
"page": "morie_sctsne",
"title": "t-SNE embedding (Algorithm 1 of van der Maaten & Hinton 2008)",
"topics": [
"morie_sctsne"
]
},
{
"page": "morie_sd_forms",
"title": "Standard deviations of sums and means",
"topics": [
"morie_sd_forms"
]
},
{
"page": "morie_seasnaive",
"title": "Seasonal naive baseline (ch. 8 p. 219)",
"topics": [
"morie_seasnaive"
]
},
{
"page": "morie_second_moment_threshold",
"title": "The second moment method",
"topics": [
"morie_second_moment_threshold"
]
},
{
"page": "morie_selective_labels",
"title": "Selective labels: what a release rule's failure rate can be known from released cases",
"topics": [
"morie_selective_labels"
]
},
{
"page": "morie_selective_labels_judges",
"title": "Selective labels across judges ordered by leniency",
"topics": [
"morie_selective_labels_judges"
]
},
{
"page": "morie_semipar_bridge",
"title": "Semiparametric kernel primitives (R port)",
"topics": [
"morie_semipar_bridge"
]
},
{
"page": "morie_semipar_efficiency",
"title": "Semiparametric efficiency",
"topics": [
"morie_semipar_efficiency"
]
},
{
"page": "morie_semipartial_cor",
"title": "Semi-partial (part) correlation matrix (native)",
"topics": [
"morie_semipartial_cor"
]
},
{
"page": "morie_sensitivity_amplification",
"title": "Amplification of a sensitivity parameter: one Gamma is a curve of (Lambda, Delta)",
"topics": [
"morie_sensitivity_amplification"
]
},
{
"page": "morie_sensitivity_evalue",
"title": "E-values for the EValue dispatch family (extender)",
"topics": [
"morie_sensitivity_evalue"
]
},
{
"page": "morie_sensitivity_konfound",
"title": "Konfound robustness for a coefficient (native konfound)",
"topics": [
"morie_sensitivity_konfound"
]
},
{
"page": "morie_sensitivity_omitted_var_bias",
"title": "Omitted-variable bias on a fitted model (native sensemakr)",
"topics": [
"morie_sensitivity_omitted_var_bias"
]
},
{
"page": "morie_sensitivity_rosenbaum",
"title": "Rosenbaum bounds sensitivity analysis",
"topics": [
"morie_sensitivity_rosenbaum"
]
},
{
"page": "morie_sensitivity_tipping_point",
"title": "Tipping point for a single unmeasured confounder (native tipr)",
"topics": [
"morie_sensitivity_tipping_point"
]
},
{
"page": "morie_sentence_effect_bounds",
"title": "Worst-case identification bounds for a binary outcome under two sentences",
"topics": [
"morie_sentence_effect_bounds"
]
},
{
"page": "morie_sentence_effect_mtr",
"title": "Monotone-treatment-response bounds for a binary outcome under two sentences",
"topics": [
"morie_sentence_effect_mtr"
]
},
{
"page": "morie_sentence_effect_mts",
"title": "Monotone-treatment-selection bounds for a sentencing contrast",
"topics": [
"morie_sentence_effect_mts"
]
},
{
"page": "morie_sepset",
"title": "Smallest set that d-separates x from y, searched in a fixed order",
"topics": [
"morie_sepset"
]
},
{
"page": "morie_serial_mediation",
"title": "Serial (chain) mediation X -> M1 -> M2 -> Y",
"topics": [
"morie_serial_mediation"
]
},
{
"page": "morie_series_regression",
"title": "Series (sieve) regression",
"topics": [
"morie_series_regression"
]
},
{
"page": "morie_seriesdecomp",
"title": "Autoformer series decomposition block, eq. (1)",
"topics": [
"morie_seriesdecomp"
]
},
{
"page": "morie_sexbeta",
"title": "Dynamic-equilibrium fitness-variance factor (eq. 19.7, p. 271)",
"topics": [
"morie_sexbeta"
]
},
{
"page": "morie_sexdfdt",
"title": "Mean-fitness growth rate under recombination (eq. 19.13, p. 273)",
"topics": [
"morie_sexdfdt"
]
},
{
"page": "morie_sexfsol",
"title": "Closed-form fitness trajectory under recombination (eq. 19.14, p. 273)",
"topics": [
"morie_sexfsol"
]
},
{
"page": "morie_sgd_momentum",
"title": "SGD with heavy-ball momentum",
"topics": [
"morie_sgd_momentum"
]
},
{
"page": "morie_sgd_update",
"title": "Stochastic-gradient update on a mini-batch",
"topics": [
"morie_sgd_update"
]
},
{
"page": "morie_sgolay_smooth",
"title": "Savitzky-Golay smoothing filter",
"topics": [
"morie_sgolay_smooth"
]
},
{
"page": "morie_sgt_closeness_centrality",
"title": "Closeness centrality (Wasserman & Faust Ch 5.3.2, sgtclo)",
"topics": [
"morie_sgt_closeness_centrality"
]
},
{
"page": "morie_sha1",
"title": "Hash R objects with numeric rounding (digest's sha1 generic, natively)",
"topics": [
"morie_sha1"
]
},
{
"page": "morie_shapiro_wilk_test",
"title": "Shapiro-Wilk normality test",
"topics": [
"morie_shapiro_wilk_test"
]
},
{
"page": "morie_shrinkage_factor_error",
"title": "Error of the empirical-Bayes shrinkage factor when the total mass is estimated",
"topics": [
"morie_shrinkage_factor_error"
]
},
{
"page": "morie_shrinkage_loss",
"title": "Loss of the shrinkage estimator against a known truth",
"topics": [
"morie_shrinkage_loss"
]
},
{
"page": "morie_shrunk_targeting_unsafe",
"title": "The thing not to do: penalise the fluctuation",
"topics": [
"morie_shrunk_targeting_unsafe"
]
},
{
"page": "morie_si_estimators",
"title": "Simple random sampling estimators for proportions and infinite totals",
"topics": [
"morie_si_estimators"
]
},
{
"page": "morie_sibtest",
"title": "SIBTEST for differential item functioning",
"topics": [
"morie_sibtest"
]
},
{
"page": "morie_sieve_basis",
"title": "Sieve basis",
"topics": [
"morie_sieve_basis"
]
},
{
"page": "morie_sieve_iv",
"title": "Sieve (series) solution of the nonparametric IV equation",
"topics": [
"morie_sieve_iv"
]
},
{
"page": "morie_sigevid",
"title": "Log evidence for the noise level (eq. 24.13, p. 320)",
"topics": [
"morie_sigevid"
]
},
{
"page": "morie_sign_test_power",
"title": "Power function of the two-sided sign test (Gibbons Ch 5.4.4)",
"topics": [
"morie_sign_test_power"
]
},
{
"page": "morie_simple_random_sample",
"title": "Simple random sample from a data frame",
"topics": [
"morie_simple_random_sample"
]
},
{
"page": "morie_simpleboot_two",
"title": "Direct bridge to 'simpleboot::two.boot'",
"topics": [
"morie_simpleboot_two"
]
},
{
"page": "morie_simplicial_complex_faces",
"title": "Close maximal simplices under taking faces",
"topics": [
"morie_simplicial_complex_faces"
]
},
{
"page": "morie_simulate_longitudinal_panel",
"title": "Simulate a longitudinal panel and return a tidy long-format data.frame",
"topics": [
"morie_simulate_longitudinal_panel"
]
},
{
"page": "morie_singleton_bound",
"title": "The Singleton bound",
"topics": [
"morie_singleton_bound"
]
},
{
"page": "morie_singularity_transfer",
"title": "The basic transfer theorem",
"topics": [
"morie_singularity_transfer"
]
},
{
"page": "morie_siu_all_analyses",
"title": "Run every SIU descriptive analysis in this module",
"topics": [
"morie_siu_all_analyses"
]
},
{
"page": "morie_siu_analyze",
"title": "SIU descriptive analyses (R port of 'morie.siu.analyze')",
"topics": [
"morie_siu_analyze"
]
},
{
"page": "morie_siu_anomaly_check",
"title": "Per-field anomaly check: does the parser's extraction match the HTML?",
"topics": [
"morie_siu_anomaly_check"
]
},
{
"page": "morie_siu_audit_case",
"title": "Audit one SIU case end-to-end: parser output + raw HTML",
"topics": [
"morie_siu_audit_case"
]
},
{
"page": "morie_siu_audit_columns",
"title": "Per-column accuracy audit: estimate every SIU column's correctness",
"topics": [
"morie_siu_audit_columns"
]
},
{
"page": "morie_siu_audit_panel",
"title": "Mixture-of-Agents audit panel for one SIU report (native, any model)",
"topics": [
"morie_siu_audit_panel"
]
},
{
"page": "morie_siu_by_police_service",
"title": "SIU cases by police service",
"topics": [
"morie_siu_by_police_service"
]
},
{
"page": "morie_siu_by_year",
"title": "SIU cases by year",
"topics": [
"morie_siu_by_year"
]
},
{
"page": "morie_siu_cache_path",
"title": "Cache-path helper for the lightweight SIU scraper",
"topics": [
"morie_siu_cache_path"
]
},
{
"page": "morie_siu_case_counts",
"title": "SIU per-case team-size distribution",
"topics": [
"morie_siu_case_counts"
]
},
{
"page": "morie_siu_charges_by_year_chi2",
"title": "Chi-square: charges-recommended independent of year?",
"topics": [
"morie_siu_charges_by_year_chi2"
]
},
{
"page": "morie_siu_classify_mandela",
"title": "Apply the Sprott-Doob Mandela-Rules classifier to one SIU stay",
"topics": [
"morie_siu_classify_mandela"
]
},
{
"page": "morie_siu_cli",
"title": "SIU command-line front end inside R",
"topics": [
"morie_siu_cli"
]
},
{
"page": "morie_siu_compare",
"title": "Field-by-field SIU comparison against a user-supplied external table",
"topics": [
"morie_siu_compare"
]
},
{
"page": "morie_siu_decision_timing",
"title": "SIU decision-timing distributions",
"topics": [
"morie_siu_decision_timing"
]
},
{
"page": "morie_siu_demographics",
"title": "Affected-person demographics",
"topics": [
"morie_siu_demographics"
]
},
{
"page": "morie_siu_fetch",
"title": "Lightweight Ontario SIU Director's Reports scraper (R-native)",
"topics": [
"morie_siu_fetch",
"morie_siu_index_url"
]
},
{
"page": "morie_siu_fetch_cases",
"title": "Scrape Ontario SIU Director's Reports into a tidy CSV",
"topics": [
"morie_siu_fetch_cases"
]
},
{
"page": "morie_siu_fetch_dataframe",
"title": "Scrape Ontario SIU Director's Reports and return a data frame",
"topics": [
"morie_siu_fetch_dataframe"
]
},
{
"page": "morie_siu_index",
"title": "SIU drid ? case_number ? language index",
"topics": [
"morie_siu_index"
]
},
{
"page": "morie_siu_llm_extract",
"title": "Extract SIU report fields with an LLM (Gemini or Claude)",
"topics": [
"morie_siu_llm_extract"
]
},
{
"page": "morie_siu_mental_health_race_indicators",
"title": "Mental-health / race keyword indicators",
"topics": [
"morie_siu_mental_health_race_indicators"
]
},
{
"page": "morie_siu_panel",
"title": "Run the Mixture-of-Agents reading panel on SIU reports",
"topics": [
"morie_siu_panel"
]
},
{
"page": "morie_siu_parse_html",
"title": "Parse a SIU director's-report HTML page (pure-R)",
"topics": [
"morie_siu_parse_html"
]
},
{
"page": "morie_siu_parse_news_html",
"title": "Parse a SIU news-release HTML page (pure-R)",
"topics": [
"morie_siu_parse_news_html"
]
},
{
"page": "morie_siu_parse_report",
"title": "Parse one SIU director's report into the full 16-field schema",
"topics": [
"morie_siu_parse_report",
"morie_siu_parse_reports"
]
},
{
"page": "morie_siu_parser",
"title": "Pure-R SIU director's-report parser (port of 'morie.siu._parser')",
"topics": [
"morie_siu_parser"
]
},
{
"page": "morie_siu_record_correction",
"title": "Record a verified correction to the SIU parser's output",
"topics": [
"morie_siu_record_correction"
]
},
{
"page": "morie_siu_refresh_manifest",
"title": "Rebuild the Ontario SIU DRID manifest by probing the live site",
"topics": [
"morie_siu_refresh_manifest"
]
},
{
"page": "morie_siu_reports",
"title": "SIU director's-reports corpus: reviewed data first, fetch only what's new",
"topics": [
"morie_siu_reports"
]
},
{
"page": "morie_siu_resolve_so",
"title": "Resolve a subject-official count: verified corpus first, rules second",
"topics": [
"morie_siu_resolve_so"
]
},
{
"page": "morie_siu_sanity_check",
"title": "Row-level sanity check on a parsed SIU table (regex-only, no LLM)",
"topics": [
"morie_siu_sanity_check"
]
},
{
"page": "morie_siu_schema",
"title": "SIU schema, text, dates and report fetch (bricklayer or native core)",
"topics": [
"morie_siu_fetch_report",
"morie_siu_html_to_text",
"morie_siu_report_url",
"morie_siu_schema",
"morie_siu_strip_boilerplate",
"morie_siu_to_iso_date"
]
},
{
"page": "morie_siu_sprott_doob_feb2021",
"title": "Comprehensive replication of Sprott & Doob (Feb 2021)",
"topics": [
"morie_siu_sprott_doob_feb2021"
]
},
{
"page": "morie_siu_sprott_doob_iftene_table1",
"title": "Sprott-Doob-Iftene (May 2021) Table 1: IEDM-reviewed population",
"topics": [
"morie_siu_sprott_doob_iftene_table1"
]
},
{
"page": "morie_siu_sprott_doob_iftene_table10",
"title": "Sprott-Doob-Iftene (May 2021) Table 10: per-IEDM decision variance",
"topics": [
"morie_siu_sprott_doob_iftene_table10"
]
},
{
"page": "morie_siu_sprott_doob_iftene_table15",
"title": "Sprott-Doob-Iftene (May 2021) Table 15: long-stay no-IEDM cases",
"topics": [
"morie_siu_sprott_doob_iftene_table15"
]
},
{
"page": "morie_siu_sprott_doob_iftene_table9",
"title": "Sprott-Doob-Iftene (May 2021) Table 9: IEDM review outcomes",
"topics": [
"morie_siu_sprott_doob_iftene_table9"
]
},
{
"page": "morie_siu_sprott_doob_table11",
"title": "Sprott-Doob (Feb 2021) Table 11: Region x stay length",
"topics": [
"morie_siu_sprott_doob_table11"
]
},
{
"page": "morie_siu_sprott_doob_table12",
"title": "Sprott-Doob (Feb 2021) Table 12: regional over-/under-representation",
"topics": [
"morie_siu_sprott_doob_table12"
]
},
{
"page": "morie_siu_sprott_doob_table13",
"title": "Sprott-Doob (Feb 2021) Table 13: regional SIU person-stay rates",
"topics": [
"morie_siu_sprott_doob_table13"
]
},
{
"page": "morie_siu_sprott_doob_table15",
"title": "Sprott-Doob (Feb 2021) Table 15: Region x MH-flag",
"topics": [
"morie_siu_sprott_doob_table15"
]
},
{
"page": "morie_siu_sprott_doob_table19",
"title": "Sprott-Doob (Feb 2021) Table 19: Mandela-Rules classification",
"topics": [
"morie_siu_sprott_doob_table19"
]
},
{
"page": "morie_siu_sprott_doob_table22",
"title": "Sprott-Doob (Feb 2021) Table 22: Region x Mandela groups",
"topics": [
"morie_siu_sprott_doob_table22"
]
},
{
"page": "morie_siu_sprott_doob_table23",
"title": "Sprott-Doob (Feb 2021) Table 23: regional torture/solitary rates",
"topics": [
"morie_siu_sprott_doob_table23"
]
},
{
"page": "morie_siu_sprott_doob_table4",
"title": "Sprott-Doob (Feb 2021) Table 4: length-of-stay distribution",
"topics": [
"morie_siu_sprott_doob_table4"
]
},
{
"page": "morie_siu_translate",
"title": "Translate SIU report text into any target language via local LLM",
"topics": [
"morie_siu_translate",
"morie_siu_translate_fr_to_en"
]
},
{
"page": "morie_siu_verify_chi2",
"title": "Recompute Pearson chi-square from a 2D contingency table",
"topics": [
"morie_siu_verify_chi2"
]
},
{
"page": "morie_siu_verify_published_chi_squares",
"title": "Verify every published chi-square against its transcribed cells",
"topics": [
"morie_siu_verify_published_chi_squares"
]
},
{
"page": "MORIE_SIUIAP_AFFIDAVITS",
"title": "Federal Court affidavits / expert evidence indexed by 'morie'",
"topics": [
"MORIE_SIUIAP_AFFIDAVITS"
]
},
{
"page": "morie_siuiap_cite",
"title": "Build a citation string for an SIU IAP / CRIMSL / affidavit entry",
"topics": [
"morie_siuiap_cite"
]
},
{
"page": "MORIE_SIUIAP_CRIMSL_REPORTS",
"title": "CRIMSL UToronto Sprott / Doob / Iftene research reports (2020-2021)",
"topics": [
"MORIE_SIUIAP_CRIMSL_REPORTS"
]
},
{
"page": "MORIE_SIUIAP_ORIGINAL_PANEL_2019_2020",
"title": "Earlier (Doob-chaired) panel, established 2019, dissolved mid-2020",
"topics": [
"MORIE_SIUIAP_ORIGINAL_PANEL_2019_2020"
]
},
{
"page": "MORIE_SIUIAP_PANEL_MANDATE",
"title": "SIU IAP panel mandate (long-form prose)",
"topics": [
"MORIE_SIUIAP_PANEL_MANDATE"
]
},
{
"page": "MORIE_SIUIAP_PANEL_MEMBERS",
"title": "SIU IAP panel members (2021-2024 panel, Sapers-chaired)",
"topics": [
"MORIE_SIUIAP_PANEL_MEMBERS"
]
},
{
"page": "morie_siuiap_panel_summary",
"title": "Human-readable summary of the SIU IAP panel",
"topics": [
"morie_siuiap_panel_summary"
]
},
{
"page": "MORIE_SIUIAP_REPORTS",
"title": "SIU IAP panel reports (Public Safety Canada, 2022-2024)",
"topics": [
"MORIE_SIUIAP_REPORTS"
]
},
{
"page": "MORIE_SIUIAP_URL",
"title": "SIU IAP - Public Safety Canada landing page URL",
"topics": [
"MORIE_SIUIAP_URL"
]
},
{
"page": "morie_slice",
"title": "Univariate slice sampler",
"topics": [
"morie_slice"
]
},
{
"page": "morie_slidecv",
"title": "Sliding-window cross-validation (ch. 5 p. 128)",
"topics": [
"morie_slidecv"
]
},
{
"page": "morie_smape",
"title": "Symmetric mean absolute percentage error (ch. 19 p. 569)",
"topics": [
"morie_smape"
]
},
{
"page": "morie_smoothed_fU",
"title": "Smoothed deconvolution estimator of f_U",
"topics": [
"morie_smoothed_fU"
]
},
{
"page": "morie_sms_rate",
"title": "Rate of convergence of the smoothed maximum-score estimator",
"topics": [
"morie_sms_rate"
]
},
{
"page": "morie_snht",
"title": "Standard normal homogeneity test (Alexandersson 1986)",
"topics": [
"morie_snht"
]
},
{
"page": "morie_sobel_test",
"title": "Sobel test for an indirect (mediated) effect",
"topics": [
"morie_sobel_test"
]
},
{
"page": "morie_spatial",
"title": "Construct a spatial data object",
"topics": [
"morie_spatial"
]
},
{
"page": "morie_spatial_2sls",
"title": "Spatial two-stage least squares",
"topics": [
"morie_spatial_2sls"
]
},
{
"page": "morie_spatial_classical_mds_cpp",
"title": "morie_spatial_classical_mds_cpp",
"topics": [
"morie_spatial_classical_mds_cpp"
]
},
{
"page": "morie_spatial_cluster",
"title": "Spatial clustering of the coordinates",
"topics": [
"morie_spatial_cluster"
]
},
{
"page": "morie_spatial_coords",
"title": "Coordinate matrix of a spatial object",
"topics": [
"morie_spatial_coords"
]
},
{
"page": "morie_spatial_crs",
"title": "CRS of a spatial object (EPSG + WKT2-style label)",
"topics": [
"morie_spatial_crs"
]
},
{
"page": "morie_spatial_distance",
"title": "Distance matrix for a spatial object",
"topics": [
"morie_spatial_distance"
]
},
{
"page": "morie_spatial_econometrics",
"title": "Spatial autocorrelation and spatial-econometric estimators",
"topics": [
"morie_spatial_econometrics"
]
},
{
"page": "morie_spatial_emirt_theta_update_cpp",
"title": "morie_spatial_emirt_theta_update_cpp",
"topics": [
"morie_spatial_emirt_theta_update_cpp"
]
},
{
"page": "morie_spatial_krige",
"title": "Ordinary kriging predictions at new locations",
"topics": [
"morie_spatial_krige"
]
},
{
"page": "morie_spatial_lag_model",
"title": "Maximum-likelihood spatial regression models",
"topics": [
"morie_spatial_error_model",
"morie_spatial_lag_model"
]
},
{
"page": "morie_spatial_leaflet_spec",
"title": "Specification for an interactive (leaflet) map",
"topics": [
"morie_spatial_leaflet_spec"
]
},
{
"page": "morie_spatial_models",
"title": "Maximum-likelihood spatial models, point patterns and local privacy",
"topics": [
"morie_spatial_models"
]
},
{
"page": "morie_spatial_moran",
"title": "Global Moran's I on a spatial object",
"topics": [
"morie_spatial_moran"
]
},
{
"page": "morie_spatial_neighbors",
"title": "Construct spatial neighbours",
"topics": [
"morie_spatial_neighbors"
]
},
{
"page": "morie_spatial_nominate_iterate_cpp",
"title": "morie_spatial_nominate_iterate_cpp",
"topics": [
"morie_spatial_nominate_iterate_cpp"
]
},
{
"page": "morie_spatial_omission",
"title": "Spatial-lag omission is bias, spatial-error omission is inefficiency",
"topics": [
"morie_spatial_omission"
]
},
{
"page": "morie_spatial_sample",
"title": "Sample points from a spatial object, optionally by local density",
"topics": [
"morie_spatial_sample"
]
},
{
"page": "morie_spatial_smacof_step_cpp",
"title": "morie_spatial_smacof_step_cpp",
"topics": [
"morie_spatial_smacof_step_cpp"
]
},
{
"page": "morie_spatial_transform",
"title": "Reproject a spatial object between CRS (4326 <-> 3857)",
"topics": [
"morie_spatial_transform"
]
},
{
"page": "morie_spatial_variogram",
"title": "Empirical semivariogram",
"topics": [
"morie_spatial_variogram"
]
},
{
"page": "morie_spatial_variogram_fit",
"title": "Fit a variogram model by Gaussian maximum likelihood",
"topics": [
"morie_spatial_variogram_fit"
]
},
{
"page": "morie_spatial_voting_aldrich_mckelvey",
"title": "Aldrich-McKelvey scaling",
"topics": [
"morie_spatial_voting_aldrich_mckelvey"
]
},
{
"page": "morie_spatial_voting_alpha_nominate",
"title": "Alpha-NOMINATE ideal points (Carroll et al. 2013)",
"topics": [
"morie_spatial_voting_alpha_nominate"
]
},
{
"page": "morie_spatial_voting_anchoring_vignettes",
"title": "Anchoring vignettes for DIF correction",
"topics": [
"morie_spatial_voting_anchoring_vignettes"
]
},
{
"page": "morie_spatial_voting_bayesian_am",
"title": "Bayesian Aldrich-McKelvey scaling (Hare et al. 2015)",
"topics": [
"morie_spatial_voting_bayesian_am"
]
},
{
"page": "morie_spatial_voting_bayesian_irt_likelihood",
"title": "Bayesian IRT likelihood (deterministic part of CJR machinery)",
"topics": [
"morie_spatial_voting_bayesian_irt_likelihood"
]
},
{
"page": "morie_spatial_voting_bayesian_irt_posterior",
"title": "Posterior summaries for a Bayesian IRT chain",
"topics": [
"morie_spatial_voting_bayesian_irt_posterior"
]
},
{
"page": "morie_spatial_voting_bayesian_mds",
"title": "Bayesian metric multidimensional scaling (Bakker and Poole 2013)",
"topics": [
"morie_spatial_voting_bayesian_mds"
]
},
{
"page": "morie_spatial_voting_bayesian_unfolding",
"title": "Bayesian unfolding (Bakker and Poole 2013)",
"topics": [
"morie_spatial_voting_bayesian_unfolding"
]
},
{
"page": "morie_spatial_voting_blackbox",
"title": "Blackbox / Basic Space scaling",
"topics": [
"morie_spatial_voting_blackbox"
]
},
{
"page": "morie_spatial_voting_cjr_irt",
"title": "Clinton-Jackman-Rivers Bayesian IRT by Gibbs sampling",
"topics": [
"morie_spatial_voting_cjr_irt"
]
},
{
"page": "morie_spatial_voting_classical_mds",
"title": "Classical (metric) multidimensional scaling",
"topics": [
"morie_spatial_voting_classical_mds"
]
},
{
"page": "morie_spatial_voting_cutting_lines",
"title": "Cutting-line endpoints for Coombs-mesh plots",
"topics": [
"morie_spatial_voting_cutting_lines"
]
},
{
"page": "morie_spatial_voting_double_centering",
"title": "Double-center a distance matrix",
"topics": [
"morie_spatial_voting_double_centering"
]
},
{
"page": "morie_spatial_voting_dw_nominate",
"title": "DW-NOMINATE dynamic weighted ideal-point estimator",
"topics": [
"morie_spatial_voting_dw_nominate"
]
},
{
"page": "morie_spatial_voting_dynamic_irt",
"title": "Dynamic IRT: ideal points that drift by a random walk (Martin and Quinn 2002)",
"topics": [
"morie_spatial_voting_dynamic_irt"
]
},
{
"page": "morie_spatial_voting_em_irt",
"title": "EM algorithm for binary IRT",
"topics": [
"morie_spatial_voting_em_irt"
]
},
{
"page": "morie_spatial_voting_ideal_point_recovery",
"title": "Ideal-point recovery from unfolding output",
"topics": [
"morie_spatial_voting_ideal_point_recovery"
]
},
{
"page": "morie_spatial_voting_indscal",
"title": "INDSCAL: weighted MDS with individual differences",
"topics": [
"morie_spatial_voting_indscal"
]
},
{
"page": "morie_spatial_voting_mds_fit_stats",
"title": "MDS fit statistics (Mardia criterion)",
"topics": [
"morie_spatial_voting_mds_fit_stats"
]
},
{
"page": "morie_spatial_voting_mlsmu6",
"title": "MLSMU6 alternating least-squares unfolding",
"topics": [
"morie_spatial_voting_mlsmu6"
]
},
{
"page": "morie_spatial_voting_nominate_bootstrap",
"title": "Parametric bootstrap of NOMINATE standard errors",
"topics": [
"morie_spatial_voting_nominate_bootstrap"
]
},
{
"page": "morie_spatial_voting_nominate_loglik",
"title": "NOMINATE log-likelihood and GMP",
"topics": [
"morie_spatial_voting_nominate_loglik"
]
},
{
"page": "morie_spatial_voting_nominate_utility",
"title": "NOMINATE Gaussian utility",
"topics": [
"morie_spatial_voting_nominate_utility"
]
},
{
"page": "morie_spatial_voting_nominate_vote_prob",
"title": "Single NOMINATE vote probability",
"topics": [
"morie_spatial_voting_nominate_vote_prob"
]
},
{
"page": "morie_spatial_voting_nonmetric_mds",
"title": "Nonmetric MDS with isotonic regression",
"topics": [
"morie_spatial_voting_nonmetric_mds"
]
},
{
"page": "morie_spatial_voting_nonparametric_bootstrap",
"title": "Nonparametric bootstrap for AM / blackbox scaling positions",
"topics": [
"morie_spatial_voting_nonparametric_bootstrap"
]
},
{
"page": "morie_spatial_voting_normal_vectors",
"title": "Normal-vector projection of an external measure",
"topics": [
"morie_spatial_voting_normal_vectors"
]
},
{
"page": "morie_spatial_voting_optimal_classification",
"title": "Optimal Classification scaling",
"topics": [
"morie_spatial_voting_optimal_classification"
]
},
{
"page": "morie_spatial_voting_ordered_oc",
"title": "Ordered Optimal Classification for ordinal scales",
"topics": [
"morie_spatial_voting_ordered_oc"
]
},
{
"page": "morie_spatial_voting_ordinal_irt",
"title": "Ordinal IRT: the ordinal factor model of Quinn (2004)",
"topics": [
"morie_spatial_voting_ordinal_irt"
]
},
{
"page": "morie_spatial_voting_procrustes",
"title": "Procrustes rotation",
"topics": [
"morie_spatial_voting_procrustes"
]
},
{
"page": "morie_spatial_voting_smacof",
"title": "SMACOF stress minimisation",
"topics": [
"morie_spatial_voting_smacof"
]
},
{
"page": "morie_spatial_voting_smacof_unfolding",
"title": "SMACOF rectangular unfolding",
"topics": [
"morie_spatial_voting_smacof_unfolding"
]
},
{
"page": "morie_spatial_voting_unfolding_stress",
"title": "Compute unfolding stress",
"topics": [
"morie_spatial_voting_unfolding_stress"
]
},
{
"page": "morie_spatial_voting_wordfish",
"title": "Wordfish: Poisson IRT for document-term matrices",
"topics": [
"morie_spatial_voting_wordfish"
]
},
{
"page": "morie_spatial_weights",
"title": "Spatial weights from a neighbour matrix",
"topics": [
"morie_spatial_weights"
]
},
{
"page": "morie_spatial_wordfish_omega_update_cpp",
"title": "morie_spatial_wordfish_omega_update_cpp",
"topics": [
"morie_spatial_wordfish_omega_update_cpp"
]
},
{
"page": "morie_spearman_rho",
"title": "Spearman rank correlation",
"topics": [
"morie_spearman_rho"
]
},
{
"page": "morie_specs_from_df",
"title": "Build a list of column specs from a parsed CSV header",
"topics": [
"morie_specs_from_df"
]
},
{
"page": "morie_spectral_bandwidth",
"title": "Spectral bandwidth by two criteria",
"topics": [
"morie_spectral_bandwidth"
]
},
{
"page": "morie_spectral_cluster",
"title": "Spectral clustering via 'kernlab'",
"topics": [
"morie_spectral_cluster"
]
},
{
"page": "morie_spectral_density",
"title": "Welch power spectral density",
"topics": [
"morie_spectral_density"
]
},
{
"page": "morie_sperner_lemma_triangle",
"title": "Sperner's lemma on the standard subdivided triangle",
"topics": [
"morie_sperner_lemma_triangle"
]
},
{
"page": "morie_sperner_width",
"title": "Sperner's theorem: the largest antichain in the subset lattice",
"topics": [
"morie_sperner_width"
]
},
{
"page": "morie_spillover_effects",
"title": "Direct, spillover and net effects under a stated exposure mapping",
"topics": [
"morie_spillover_effects"
]
},
{
"page": "morie_spillover_exposure",
"title": "Three-level exposure from a treatment on a place network",
"topics": [
"morie_spillover_exposure"
]
},
{
"page": "morie_spillover_exposure_probs",
"title": "Exposure probabilities of a completely randomised deployment",
"topics": [
"morie_spillover_exposure_probs"
]
},
{
"page": "morie_spillover_ht",
"title": "Horvitz-Thompson totals and contrasts under a randomised deployment",
"topics": [
"morie_spillover_ht"
]
},
{
"page": "morie_spillover_ht_variance",
"title": "Design variance of the Horvitz-Thompson total (population formula)",
"topics": [
"morie_spillover_ht_variance"
]
},
{
"page": "morie_spillover_ring_bound",
"title": "Ring mis-specification bound for the spillover estimand",
"topics": [
"morie_spillover_ring_bound"
]
},
{
"page": "morie_spline_logit",
"title": "Cubic splines: piecewise, truncated power, basis and odds-ratio forms",
"topics": [
"morie_spline_logit"
]
},
{
"page": "morie_sprott_doob",
"title": "Sprott & Doob (CRIMSL UToronto) SIU analyses",
"topics": [
"morie_sprott_doob"
]
},
{
"page": "morie_standard_tableaux_count",
"title": "The hook length formula",
"topics": [
"morie_standard_tableaux_count"
]
},
{
"page": "morie_stars_bars",
"title": "Stars and bars count",
"topics": [
"morie_stars_bars"
]
},
{
"page": "morie_stat_bridge",
"title": "Bridge between external runners and the morie R command registry",
"topics": [
"morie_stat_bridge"
]
},
{
"page": "morie_stat_commands",
"title": "Central command registry for the morie R surface",
"topics": [
"morie_stat_commands"
]
},
{
"page": "morie_state_space_model",
"title": "Local-level state-space model (Kalman filter+smoother)",
"topics": [
"morie_state_space_model"
]
},
{
"page": "morie_std_coef",
"title": "Standardized coefficients for OLS and logistic models",
"topics": [
"morie_std_coef"
]
},
{
"page": "morie_steiner_triple_system",
"title": "Steiner triple systems, with the Bose construction verified",
"topics": [
"morie_steiner_triple_system"
]
},
{
"page": "morie_stirling_first",
"title": "Unsigned Stirling numbers of the first kind",
"topics": [
"morie_stirling_first"
]
},
{
"page": "morie_stirling_second",
"title": "Stirling numbers of the second kind",
"topics": [
"morie_stirling_second"
]
},
{
"page": "morie_stirling_series_error",
"title": "Stirling's series with its error bound checked",
"topics": [
"morie_stirling_series_error"
]
},
{
"page": "morie_stl_decompose",
"title": "STL seasonal-trend decomposition by loess",
"topics": [
"morie_stl_decompose"
]
},
{
"page": "morie_stlAn",
"title": "STL decomposition with MAD outlier flags",
"topics": [
"morie_stlAn"
]
},
{
"page": "morie_stldecomp",
"title": "Seasonal-trend decomposition (ch. 3 p. 64)",
"topics": [
"morie_stldecomp"
]
},
{
"page": "morie_stratified_proportion",
"title": "Stratified estimator of a proportion",
"topics": [
"morie_stratified_proportion"
]
},
{
"page": "morie_stratified_sample",
"title": "Proportional or fixed stratified random sample",
"topics": [
"morie_stratified_sample"
]
},
{
"page": "morie_stratsplt",
"title": "Stratified test split by proportional allocation (pp. 60-61)",
"topics": [
"morie_stratsplt"
]
},
{
"page": "morie_stsi_estimators",
"title": "Stratified simple random sampling estimators",
"topics": [
"morie_stsi_estimators"
]
},
{
"page": "morie_subcompositional_incoherence",
"title": "Spurious correlation of compositional parts, and its cure",
"topics": [
"morie_subcompositional_incoherence"
]
},
{
"page": "morie_sucrule",
"title": "Rule of succession (eq. 3.16, p. 52)",
"topics": [
"morie_sucrule"
]
},
{
"page": "morie_suggest_analysis_plan",
"title": "Suggest an analysis plan from a dataset profile",
"topics": [
"morie_suggest_analysis_plan"
]
},
{
"page": "morie_sukhatme_test",
"title": "Sukhatme two-sample scale test (Gibbons Ch 9.7)",
"topics": [
"morie_sukhatme_test"
]
},
{
"page": "morie_sum_density",
"title": "Density of the sum of two independent variables on grids",
"topics": [
"morie_sum_density"
]
},
{
"page": "morie_summarize_output_audit",
"title": "Summarize an output audit",
"topics": [
"morie_summarize_output_audit"
]
},
{
"page": "morie_survey_calibrate",
"title": "Raking calibration to known marginal totals (iterative proportional fitting)",
"topics": [
"morie_survey_calibrate"
]
},
{
"page": "morie_survey_categorical",
"title": "Survey-weighted categorical estimates",
"topics": [
"morie_survey_categorical"
]
},
{
"page": "morie_survey_complex_glm",
"title": "Complex-survey GLM in one call",
"topics": [
"morie_survey_complex_glm"
]
},
{
"page": "morie_survey_design",
"title": "Construct a survey design object",
"topics": [
"morie_survey_design"
]
},
{
"page": "morie_survey_glm",
"title": "Survey-weighted GLM with design-based SEs",
"topics": [
"morie_survey_glm"
]
},
{
"page": "morie_survey_hajek_mean",
"title": "Hajek (ratio) estimator of a population mean",
"topics": [
"morie_survey_hajek_mean"
]
},
{
"page": "morie_survey_ht_total",
"title": "Horvitz-Thompson estimator of a population total",
"topics": [
"morie_survey_ht_total"
]
},
{
"page": "morie_survey_mean",
"title": "Survey-weighted mean with its design-based standard error",
"topics": [
"morie_survey_mean"
]
},
{
"page": "morie_survey_poststratify",
"title": "Post-stratification weights (sample-to-population alignment)",
"topics": [
"morie_survey_poststratify"
]
},
{
"page": "morie_survey_ratio",
"title": "Ratio estimator of a population total using known X_pop",
"topics": [
"morie_survey_ratio"
]
},
{
"page": "morie_survey_subpop",
"title": "Subpopulation (domain) mean with Woodruff linearised SE",
"topics": [
"morie_survey_subpop"
]
},
{
"page": "morie_survey_variances",
"title": "Design-based vs model-based variance identities (chapter 26)",
"topics": [
"morie_survey_variances"
]
},
{
"page": "morie_survival",
"title": "Survival analysis: Kaplan-Meier, Nelson-Aalen, log-rank and Cox",
"topics": [
"morie_survival"
]
},
{
"page": "morie_survival_psi",
"title": "Kaplan-Meier Z-estimator map",
"topics": [
"morie_survival_psi"
]
},
{
"page": "morie_survvae_PRIMITIVES",
"title": "Accepted primitives for the DSM experts",
"topics": [
"morie_survvae_PRIMITIVES"
]
},
{
"page": "morie_sutvachk",
"title": "SUTVA: no interference between units, one version of treatment",
"topics": [
"morie_sutvachk"
]
},
{
"page": "morie_svc_train_cpp",
"title": "Binary C-SVC via SMO (compiled)",
"topics": [
"morie_svc_train_cpp"
]
},
{
"page": "morie_svm",
"title": "Support vector machines",
"topics": [
"morie_svm",
"morie_svm_beta",
"morie_svm_decision",
"morie_svm_dual_objective",
"morie_svm_fit_dual",
"morie_svm_intercept",
"morie_svm_label_matrix"
]
},
{
"page": "morie_svm_decision_cpp",
"title": "Decision values for new data given fitted SVM coefficients (compiled)",
"topics": [
"morie_svm_decision_cpp"
]
},
{
"page": "morie_svm_genomic",
"title": "Support-vector regression for genomic prediction",
"topics": [
"morie_svm_genomic"
]
},
{
"page": "morie_svm_hinge_primal",
"title": "Linear SVM (primal hinge loss) - R parity",
"topics": [
"morie_svm_hinge_primal"
]
},
{
"page": "morie_svm_kernel_trick",
"title": "Kernel SVM (RBF / poly / sigmoid) - R parity",
"topics": [
"morie_svm_kernel_trick"
]
},
{
"page": "morie_svr_train_cpp",
"title": "eps-SVR via SMO (compiled)",
"topics": [
"morie_svr_train_cpp"
]
},
{
"page": "morie_sync_rng",
"title": "Synchronised RNG seeded reproducibly for cross-language workflows",
"topics": [
"morie_sync_rng"
]
},
{
"page": "morie_synth_control",
"title": "Native synthetic control (Abadie-Diamond-Hainmueller)",
"topics": [
"morie_synth_control"
]
},
{
"page": "morie_synth_corrections_uof",
"title": "Build a synthetic Corrections-UoF data.frame for testing",
"topics": [
"morie_synth_corrections_uof"
]
},
{
"page": "morie_synth_otis",
"title": "Build a synthetic OTIS data.frame for a given publication id",
"topics": [
"morie_synth_otis"
]
},
{
"page": "morie_synth_otis_all",
"title": "Build the full 29-dataset OTIS synthetic list",
"topics": [
"morie_synth_otis_all"
]
},
{
"page": "morie_t_quantile",
"title": "Student-t quantile by bisection on the incomplete beta",
"topics": [
"morie_t_quantile"
]
},
{
"page": "morie_table_inertia",
"title": "Chi-square and total inertia of a two-way table",
"topics": [
"morie_table_inertia"
]
},
{
"page": "morie_tail_ratio",
"title": "Tail ratios that separate light from power-law count tails",
"topics": [
"morie_tail_ratio"
]
},
{
"page": "morie_taphonomy_bhm",
"title": "Bayesian hierarchical preservation model",
"topics": [
"morie_taphonomy_bhm"
]
},
{
"page": "morie_taphonomy_clr",
"title": "Centred log-ratio (CLR) transform of compositional data",
"topics": [
"morie_taphonomy_clr"
]
},
{
"page": "morie_taphonomy_decay_absorption",
"title": "Absorption analysis of a taphonomic decay chain",
"topics": [
"morie_taphonomy_decay_absorption"
]
},
{
"page": "morie_taphonomy_decay_chain",
"title": "Build a taphonomic decay Markov chain (DTMC)",
"topics": [
"morie_taphonomy_decay_chain"
]
},
{
"page": "morie_taphonomy_decay_delta",
"title": "Natural-vs-treated fate delta for a taphonomic decay chain",
"topics": [
"morie_taphonomy_decay_delta"
]
},
{
"page": "morie_taphonomy_decay_simulate",
"title": "Simulate one decay path through a taphonomic Markov chain",
"topics": [
"morie_taphonomy_decay_simulate"
]
},
{
"page": "morie_taphonomy_evidence_loglik",
"title": "Gaussian log-likelihood of forensic evidence under a model",
"topics": [
"morie_taphonomy_evidence_loglik"
]
},
{
"page": "morie_taphonomy_fetch_usgs_soil",
"title": "Fetch USGS National Geochemical Database soil geochemistry",
"topics": [
"morie_taphonomy_fetch_usgs_soil"
]
},
{
"page": "morie_taphonomy_ilr",
"title": "Isometric log-ratio (ILR) transform of compositional data",
"topics": [
"morie_taphonomy_ilr"
]
},
{
"page": "morie_taphonomy_likelihood_ratio",
"title": "Forensic likelihood ratio for two competing hypotheses",
"topics": [
"morie_taphonomy_likelihood_ratio"
]
},
{
"page": "morie_taphonomy_morphosource_fetch",
"title": "Download a MorphoSource media capsule",
"topics": [
"morie_taphonomy_morphosource_fetch"
]
},
{
"page": "morie_taphonomy_morphosource_search",
"title": "Search MorphoSource for 3D media or physical objects",
"topics": [
"morie_taphonomy_morphosource_search"
]
},
{
"page": "morie_taphonomy_pmi_schema",
"title": "STO-2022 taphonomic-observation schema (PMI nuisance variables)",
"topics": [
"morie_taphonomy_pmi_schema"
]
},
{
"page": "morie_taphonomy_preservation_delta",
"title": "Estimate the natural preservation \"delta\" and its E-value",
"topics": [
"morie_taphonomy_preservation_delta"
]
},
{
"page": "morie_taphonomy_preservation_lr",
"title": "Preservation likelihood ratio from measured evidence",
"topics": [
"morie_taphonomy_preservation_lr"
]
},
{
"page": "morie_taphonomy_schema",
"title": "Taphonomy data schema (zero-row template)",
"topics": [
"morie_taphonomy_schema"
]
},
{
"page": "morie_taphonomy_simulate_pxrf",
"title": "Simulate synthetic pXRF compositional data (Dirichlet)",
"topics": [
"morie_taphonomy_simulate_pxrf"
]
},
{
"page": "morie_tarone_ware",
"title": "Tarone-Ware and the weighted log-rank family",
"topics": [
"morie_tarone_ware"
]
},
{
"page": "morie_tau_to_theta",
"title": "Invert the Kendall's tau relation for a copula parameter",
"topics": [
"morie_tau_to_theta"
]
},
{
"page": "morie_taulep",
"title": "Explicit tau-leaping simulation (Gillespie 2001)",
"topics": [
"morie_taulep"
]
},
{
"page": "morie_tcc",
"title": "Test characteristic curve",
"topics": [
"morie_tcc"
]
},
{
"page": "morie_tcls",
"title": "t-closeness of a release, per equivalence class and overall",
"topics": [
"morie_tcls"
]
},
{
"page": "morie_terry_hoeffding_test",
"title": "Terry-Hoeffding (Fisher-Yates) two-sample normal-scores test (Gibbons Ch 8.3.1)",
"topics": [
"morie_terry_hoeffding_test"
]
},
{
"page": "morie_test_information",
"title": "Test information function",
"topics": [
"morie_test_information"
]
},
{
"page": "morie_text_ate",
"title": "Treatment effect adjusting for what the text reveals",
"topics": [
"morie_text_ate"
]
},
{
"page": "morie_tfidf",
"title": "Term frequency-inverse document frequency, computed natively",
"topics": [
"morie_tfidf"
]
},
{
"page": "morie_tftnet",
"title": "Temporal Fusion Transformer gating and variable selection",
"topics": [
"morie_tftnet"
]
},
{
"page": "morie_tgarch_model",
"title": "GJR-GARCH(1,1) threshold GARCH",
"topics": [
"morie_tgarch_model"
]
},
{
"page": "morie_theta_method",
"title": "Theta method",
"topics": [
"morie_theta_method"
]
},
{
"page": "morie_thinning_identification",
"title": "Thinning a Poisson count: the recording rate is not identified",
"topics": [
"morie_thinning_identification"
]
},
{
"page": "morie_threshold_autoregression",
"title": "Two-regime self-exciting threshold autoregressive (SETAR) model",
"topics": [
"morie_threshold_autoregression"
]
},
{
"page": "morie_tide",
"title": "TiDE dense encoder-decoder",
"topics": [
"morie_tide"
]
},
{
"page": "morie_tightness_check",
"title": "Asymptotic tightness / equicontinuity check",
"topics": [
"morie_tightness_check"
]
},
{
"page": "morie_tikhonov_iv",
"title": "Tikhonov regularisation for nonparametric IV",
"topics": [
"morie_tikhonov_iv"
]
},
{
"page": "morie_timeseries_robust",
"title": "Unit-root testing and robust regression",
"topics": [
"morie_timeseries_robust"
]
},
{
"page": "morie_tmldgp_cheatsheet",
"title": "Compact one-line summary of the tmldgp recipe",
"topics": [
"morie_tmldgp_cheatsheet"
]
},
{
"page": "morie_tmldta_cheatsheet",
"title": "Compact one-line summary of the tmldta recipe",
"topics": [
"morie_tmldta_cheatsheet"
]
},
{
"page": "morie_tmldyk_cheatsheet",
"title": "Compact one-line summary of the tmldyk recipe",
"topics": [
"morie_tmldyk_cheatsheet"
]
},
{
"page": "morie_tmldyn_cheatsheet",
"title": "Compact one-line summary of the tmldyn recipe",
"topics": [
"morie_tmldyn_cheatsheet"
]
},
{
"page": "morie_tmle_ate",
"title": "Targeted maximum likelihood estimate of the average treatment effect",
"topics": [
"morie_tmle_ate"
]
},
{
"page": "morie_tmle_data_adaptive",
"title": "Contrast between data-discovered exposure levels, done honestly",
"topics": [
"morie_tmle_data_adaptive"
]
},
{
"page": "morie_tmle_diff_kernel",
"title": "Differentially private TMLE of the ATE",
"topics": [
"morie_tmle_diff_kernel"
]
},
{
"page": "morie_tmle_dynamic_regime",
"title": "Mean outcome under the (V-)optimal dynamic treatment rule",
"topics": [
"morie_tmle_dynamic_regime"
]
},
{
"page": "morie_tmle_late",
"title": "TMLE for the instrumental-variable LATE",
"topics": [
"morie_tmle_late"
]
},
{
"page": "morie_tmle_longitudinal",
"title": "Longitudinal TMLE: always-treat minus never-treat",
"topics": [
"morie_tmle_longitudinal"
]
},
{
"page": "morie_tmle_mediation",
"title": "TMLE for natural direct and indirect mediation effects",
"topics": [
"morie_tmle_mediation"
]
},
{
"page": "morie_tmle_propensity_only",
"title": "Propensity-only TMLE: double robustness by construction",
"topics": [
"morie_tmle_propensity_only"
]
},
{
"page": "morie_tmle_quantile",
"title": "TMLE for quantile treatment effects",
"topics": [
"morie_tmle_quantile"
]
},
{
"page": "morie_tmle_rmst",
"title": "TMLE for the difference in restricted mean survival time",
"topics": [
"morie_tmle_rmst"
]
},
{
"page": "morie_tmle_sensitivity",
"title": "TMLE bounds under the marginal sensitivity model",
"topics": [
"morie_tmle_sensitivity"
]
},
{
"page": "morie_tmle_time_varying",
"title": "Sequentially targeted g-computation under time-varying confounding",
"topics": [
"morie_tmle_time_varying"
]
},
{
"page": "morie_tmle_truncation_sweep",
"title": "TMLE across a grid of propensity truncation levels",
"topics": [
"morie_tmle_truncation_sweep"
]
},
{
"page": "morie_tmlefp_cheatsheet",
"title": "Compact one-line summary of the tmlefp recipe",
"topics": [
"morie_tmlefp_cheatsheet"
]
},
{
"page": "morie_to_hood_crosswalk",
"title": "Load the included BIDIRECTIONAL 158 <-> 140 neighbourhood crosswalk",
"topics": [
"morie_to_hood_crosswalk"
]
},
{
"page": "morie_to_neighbourhoods",
"title": "Load a Toronto neighbourhood polygon layer",
"topics": [
"morie_to_neighbourhoods"
]
},
{
"page": "morie_tokenizer_decode",
"title": "Decode token IDs back to text",
"topics": [
"morie_tokenizer_decode"
]
},
{
"page": "morie_tokenizer_encode",
"title": "Encode text to a vector of token IDs",
"topics": [
"morie_tokenizer_encode"
]
},
{
"page": "morie_tokenizer_new",
"title": "Construct a MORIE tokenizer",
"topics": [
"morie_tokenizer_new"
]
},
{
"page": "morie_tokenizer_vocab_size",
"title": "Vocabulary size",
"topics": [
"morie_tokenizer_vocab_size"
]
},
{
"page": "morie_tolerance_beta",
"title": "Distribution-free tolerance coefficient for (X(r), X(s))",
"topics": [
"morie_tolerance_beta"
]
},
{
"page": "morie_tolerance_limits",
"title": "Distribution-free (Wilks) tolerance limits",
"topics": [
"morie_tolerance_limits"
]
},
{
"page": "morie_topological_torsions",
"title": "Every topological torsion in a molecule, with multiplicities",
"topics": [
"morie_topological_torsions"
]
},
{
"page": "morie_toptor_cheatsheet",
"title": "Compact one-line summary of the toptor recipe",
"topics": [
"morie_toptor_cheatsheet"
]
},
{
"page": "morie_toronto_neighbourhoods",
"title": "Toronto neighbourhood boundary versions",
"topics": [
"morie_toronto_neighbourhoods"
]
},
{
"page": "morie_torsion_similarity",
"title": "The paper's similarity score S = 2 D / (d_i + d_j)",
"topics": [
"morie_torsion_similarity"
]
},
{
"page": "morie_tox_antemortem_lr",
"title": "Likelihood ratio for antemortem ingestion versus postmortem artefact",
"topics": [
"morie_tox_antemortem_lr"
]
},
{
"page": "morie_tox_calibration",
"title": "Fit an internal-standard calibration curve and inverse-predict an unknown",
"topics": [
"morie_tox_calibration"
]
},
{
"page": "morie_tox_ethanol_congeners",
"title": "Adjudicate ethanol as antemortem ingestion versus postmortem production",
"topics": [
"morie_tox_ethanol_congeners"
]
},
{
"page": "morie_tox_left_censor_impute",
"title": "Impute left-censored (below-LOD) toxicology values",
"topics": [
"morie_tox_left_censor_impute"
]
},
{
"page": "morie_tox_matrix_reliability",
"title": "Rank specimen matrices by reliability under submersion and decomposition",
"topics": [
"morie_tox_matrix_reliability"
]
},
{
"page": "morie_tox_matrix_schema",
"title": "Typed zero-row schema for a forensic-toxicology sample table",
"topics": [
"morie_tox_matrix_schema"
]
},
{
"page": "morie_tox_pmr_ratio",
"title": "Flag postmortem redistribution from a central:peripheral concentration ratio",
"topics": [
"morie_tox_pmr_ratio"
]
},
{
"page": "morie_tps_add_hood_140_from_158",
"title": "Add an equivalent HOOD_140 column to a HOOD_158-keyed data.frame",
"topics": [
"morie_tps_add_hood_140_from_158"
]
},
{
"page": "morie_tps_add_hood_158_from_140",
"title": "Add an equivalent HOOD_158 column to a HOOD_140-keyed data.frame",
"topics": [
"morie_tps_add_hood_158_from_140"
]
},
{
"page": "morie_tps_aggregate_158_to_140",
"title": "Aggregate per-158 counts to per-140 counts (EXACT for pure cake-cuts)",
"topics": [
"morie_tps_aggregate_158_to_140"
]
},
{
"page": "morie_tps_analyze",
"title": "RichResult-emitting analyses for the 13 TPS crime datasets",
"topics": [
"morie_tps_analyze"
]
},
{
"page": "morie_tps_analyze_all",
"title": "Run the full TPS analysis capsule across many TPS data.frames",
"topics": [
"morie_tps_analyze_all"
]
},
{
"page": "morie_tps_analyze_assault",
"title": "Convenience alias: full TPS capsule on the Assault dataset",
"topics": [
"morie_tps_analyze_assault",
"morie_tps_analyze_autotheft",
"morie_tps_analyze_bicycletheft",
"morie_tps_analyze_breakandenter",
"morie_tps_analyze_communitysafetyindicators",
"morie_tps_analyze_hatecrimes",
"morie_tps_analyze_homicides",
"morie_tps_analyze_intimatepartnerandfamilyviolence",
"morie_tps_analyze_neighbourhoodcrimerates",
"morie_tps_analyze_robbery",
"morie_tps_analyze_shootingandfirearmdiscarges",
"morie_tps_analyze_theftfrommovingvehicle",
"morie_tps_analyze_theftover"
]
},
{
"page": "morie_tps_analyze_csi_from_dataframes",
"title": "Toronto CSI per-year + per-ward from a named list of TPS data.frames",
"topics": [
"morie_tps_analyze_csi_from_dataframes"
]
},
{
"page": "morie_tps_analyze_one",
"title": "Run the standard TPS analysis capsule on one data.frame",
"topics": [
"morie_tps_analyze_one"
]
},
{
"page": "morie_tps_arima_forecast",
"title": "ARIMA(1,1,1) monthly-count forecast",
"topics": [
"morie_tps_arima_forecast"
]
},
{
"page": "morie_tps_assert_hood_version",
"title": "Assert the HOOD_* schema version of a TPS data.frame",
"topics": [
"morie_tps_assert_hood_version"
]
},
{
"page": "morie_tps_available_formats",
"title": "List of formats this build can actually load",
"topics": [
"morie_tps_available_formats"
]
},
{
"page": "morie_tps_bivariate_moran",
"title": "Bivariate Moran's I between two attributes at the same polygons",
"topics": [
"morie_tps_bivariate_moran"
]
},
{
"page": "morie_tps_bivariate_morans_i",
"title": "Bivariate Moran's I between two TPS categories",
"topics": [
"morie_tps_bivariate_morans_i"
]
},
{
"page": "morie_tps_category_correlation_matrix",
"title": "Pearson correlation across TPS categories' per-hood counts",
"topics": [
"morie_tps_category_correlation_matrix"
]
},
{
"page": "morie_tps_changepoint_detection",
"title": "Pettitt-style change-point on yearly incident counts",
"topics": [
"morie_tps_changepoint_detection"
]
},
{
"page": "morie_tps_compare_hawkes_kernels",
"title": "Compare Hawkes models across kernel x baseline combinations",
"topics": [
"morie_tps_compare_hawkes_kernels"
]
},
{
"page": "morie_tps_composite_index",
"title": "Composite per-neighbourhood crime-risk index across TPS categories",
"topics": [
"morie_tps_composite_index"
]
},
{
"page": "morie_tps_crime_compare",
"title": "Compare counts and trends across multiple TPS categories",
"topics": [
"morie_tps_crime_compare"
]
},
{
"page": "morie_tps_criminal_network_graph",
"title": "Premise x neighbourhood co-occurrence network",
"topics": [
"morie_tps_criminal_network_graph"
]
},
{
"page": "MORIE_TPS_CSI_CATEGORIES",
"title": "Canonical CSI category names (the 9 TPS open-data feeds)",
"topics": [
"MORIE_TPS_CSI_CATEGORIES"
]
},
{
"page": "morie_tps_csi_per_neighbourhood",
"title": "CSI per neighbourhood (HOOD_158)",
"topics": [
"morie_tps_csi_per_neighbourhood"
]
},
{
"page": "morie_tps_csi_per_year",
"title": "Toronto CSI per fiscal year from per-category counts",
"topics": [
"morie_tps_csi_per_year"
]
},
{
"page": "morie_tps_csi_weight",
"title": "Return the CSI weight for a TPS open-data category",
"topics": [
"morie_tps_csi_weight"
]
},
{
"page": "morie_tps_data_dir",
"title": "Default project data directory for TPS open data",
"topics": [
"morie_tps_data_dir"
]
},
{
"page": "morie_tps_dbscan_clusters",
"title": "DBSCAN density clusters on lat/long",
"topics": [
"morie_tps_dbscan_clusters"
]
},
{
"page": "morie_tps_disaggregate_140_to_158",
"title": "Disaggregate per-140 counts to per-158 counts (uniform-density)",
"topics": [
"morie_tps_disaggregate_140_to_158"
]
},
{
"page": "morie_tps_district_for_centroid",
"title": "Return the pre-1998 borough name for a (lat, lon) centroid",
"topics": [
"morie_tps_district_for_centroid"
]
},
{
"page": "morie_tps_fetch_category",
"title": "Fetch one TPS category as a CSV, paging until exhausted",
"topics": [
"morie_tps_fetch_category"
]
},
{
"page": "morie_tps_fetch_dataframe",
"title": "Fetch a TPS category and return it as a 'data.frame'",
"topics": [
"morie_tps_fetch_dataframe"
]
},
{
"page": "morie_tps_figures",
"title": "Export the TPS stochastic diagnostic figures",
"topics": [
"morie_tps_figures"
]
},
{
"page": "morie_tps_fokker_planck_grid",
"title": "1-D Fokker-Planck density evolution under OU drift+diffusion",
"topics": [
"morie_tps_fokker_planck_grid"
]
},
{
"page": "morie_tps_getis_ord_g_star",
"title": "Local Getis-Ord Gi* statistic per neighbourhood",
"topics": [
"morie_tps_getis_ord_g_star"
]
},
{
"page": "morie_tps_gini_concentration",
"title": "Gini coefficient of a numeric vector",
"topics": [
"morie_tps_gini_concentration"
]
},
{
"page": "morie_tps_hawkes_advanced",
"title": "Non-stationary Hawkes with non-exponential kernels (R port)",
"topics": [
"morie_tps_hawkes_advanced"
]
},
{
"page": "morie_tps_hawkes_advanced_fit",
"title": "Fit a single (kernel, baseline) Hawkes specification",
"topics": [
"morie_tps_hawkes_advanced_fit"
]
},
{
"page": "morie_tps_hawkes_markovian_vs_nonmarkovian",
"title": "Focused 2x2 Markovian vs non-Markovian Hawkes comparison",
"topics": [
"morie_tps_hawkes_markovian_vs_nonmarkovian"
]
},
{
"page": "morie_tps_hawkes_temporal_fit",
"title": "Temporal exponential-kernel Hawkes fit",
"topics": [
"morie_tps_hawkes_temporal_fit"
]
},
{
"page": "morie_tps_inspection_game_phase",
"title": "Helbing-Szolnoki inspection-game phase diagram",
"topics": [
"morie_tps_inspection_game_phase"
]
},
{
"page": "morie_tps_kde_density",
"title": "2-D kernel density estimate of geocoded incidents",
"topics": [
"morie_tps_kde_density"
]
},
{
"page": "morie_tps_langevin_simulate",
"title": "Euler-Maruyama Ornstein-Uhlenbeck simulation",
"topics": [
"morie_tps_langevin_simulate"
]
},
{
"page": "morie_tps_layer_urls",
"title": "TPS ArcGIS layer URLs known to MORIE",
"topics": [
"MORIE_TPS_LAYER_URLS",
"morie_tps_layer_urls"
]
},
{
"page": "morie_tps_levy_flight_alpha",
"title": "Levy-flight tail exponent on consecutive-incident steps",
"topics": [
"morie_tps_levy_flight_alpha"
]
},
{
"page": "morie_tps_list_categories",
"title": "List TPS categories known to the fetcher",
"topics": [
"morie_tps_list_categories"
]
},
{
"page": "morie_tps_list_datasets",
"title": "List all TPS datasets as a 'data.frame'",
"topics": [
"morie_tps_list_datasets"
]
},
{
"page": "morie_tps_list_formats",
"title": "Map TPS format name -> path of the file that would be loaded",
"topics": [
"morie_tps_list_formats"
]
},
{
"page": "morie_tps_load",
"title": "Load TPS dataset 'name' in the given 'format'",
"topics": [
"morie_tps_load"
]
},
{
"page": "morie_tps_load_dataset",
"title": "Load one TPS dataset by category name (CSV thin path)",
"topics": [
"morie_tps_load_dataset"
]
},
{
"page": "morie_tps_load_tps",
"title": "Load a TPS hub layer (incident category or aggregate layer)",
"topics": [
"morie_tps_load_tps"
]
},
{
"page": "morie_tps_load_tps_dataset",
"title": "Load a TPS incident dataset for the statistical-physics suite",
"topics": [
"morie_tps_load_tps_dataset"
]
},
{
"page": "morie_tps_local_morans_i",
"title": "LISA - local Moran's Ii per neighbourhood",
"topics": [
"morie_tps_local_morans_i"
]
},
{
"page": "morie_tps_lotka_volterra_police_crime",
"title": "Lotka-Volterra predator-prey on yearly crime counts",
"topics": [
"morie_tps_lotka_volterra_police_crime"
]
},
{
"page": "morie_tps_moran_sweep_heatmap",
"title": "Sweep polygon Moran's I across (category x year)",
"topics": [
"morie_tps_moran_sweep_heatmap"
]
},
{
"page": "morie_tps_morans_i_neighbourhood",
"title": "Global Moran's I on neighbourhood-level incident counts",
"topics": [
"morie_tps_morans_i_neighbourhood"
]
},
{
"page": "morie_tps_neighbourhood_concentration",
"title": "How concentrated is crime across Toronto's 158 neighbourhoods?",
"topics": [
"morie_tps_neighbourhood_concentration"
]
},
{
"page": "morie_tps_offence_summary",
"title": "Offence-distribution rollup for a TPS crime data.frame",
"topics": [
"morie_tps_offence_summary"
]
},
{
"page": "morie_tps_polygon_morans_i",
"title": "Polygon-aware Moran's I on a value column",
"topics": [
"morie_tps_polygon_morans_i"
]
},
{
"page": "morie_tps_pretty_label",
"title": "Convert a SQL-style column name to a prose label",
"topics": [
"morie_tps_pretty_label"
]
},
{
"page": "morie_tps_project_xy",
"title": "Project (lat, lon) to rotated planar kilometres",
"topics": [
"morie_tps_project_xy"
]
},
{
"page": "morie_tps_psdp_layers",
"title": "List the TPS PSDP layers wrapped by morie",
"topics": [
"morie_tps_psdp_layers"
]
},
{
"page": "MORIE_TPS_REGISTRY",
"title": "Registry of TPS open-data categories",
"topics": [
"MORIE_TPS_REGISTRY"
]
},
{
"page": "morie_tps_render",
"title": "Toronto Police Service map rendering (R-side)",
"topics": [
"morie_tps_render"
]
},
{
"page": "morie_tps_render_choropleth",
"title": "Polygon choropleth map for Toronto (158 wards)",
"topics": [
"morie_tps_render_choropleth"
]
},
{
"page": "morie_tps_render_dbscan",
"title": "DBSCAN cluster figure on TPS-projected points",
"topics": [
"morie_tps_render_dbscan"
]
},
{
"page": "morie_tps_render_district_proportional",
"title": "District-level proportional-symbol map",
"topics": [
"morie_tps_render_district_proportional"
]
},
{
"page": "morie_tps_render_points",
"title": "Render a TPS point-pattern map (incident dots, optional DBSCAN)",
"topics": [
"morie_tps_render_points"
]
},
{
"page": "morie_tps_render_quad",
"title": "Four-panel composite of TPS rendering primitives",
"topics": [
"morie_tps_render_quad"
]
},
{
"page": "morie_tps_render_satscan_panel",
"title": "SaTScan-style spatial scan panel",
"topics": [
"morie_tps_render_satscan_panel"
]
},
{
"page": "morie_tps_render_yearly_grid",
"title": "Small-multiples panel of per-year TPS choropleths",
"topics": [
"morie_tps_render_yearly_grid"
]
},
{
"page": "morie_tps_resolve_hood_col",
"title": "Resolve which HOOD_* column to use on a TPS crime data.frame",
"topics": [
"morie_tps_resolve_hood_col"
]
},
{
"page": "morie_tps_ripley_k",
"title": "Ripley's K function at multiple radii",
"topics": [
"morie_tps_ripley_k"
]
},
{
"page": "morie_tps_sarima_forecast",
"title": "Seasonal ARIMA forecast on monthly incident counts",
"topics": [
"morie_tps_sarima_forecast"
]
},
{
"page": "morie_tps_sdb_reaction_diffusion",
"title": "Short-D'Orsogna-Brantingham 2008 hot-spot PDE",
"topics": [
"morie_tps_sdb_reaction_diffusion"
]
},
{
"page": "morie_tps_sdb_turing_demo",
"title": "Canonical Short-D'Orsogna-Brantingham Turing-pattern demo",
"topics": [
"morie_tps_sdb_turing_demo"
]
},
{
"page": "morie_tps_seasonal_pattern",
"title": "Seasonal / cyclic incident-time patterns",
"topics": [
"morie_tps_seasonal_pattern"
]
},
{
"page": "morie_tps_spatial_summary",
"title": "Spatial rollup for a TPS crime data.frame",
"topics": [
"morie_tps_spatial_summary"
]
},
{
"page": "morie_tps_statphysics",
"title": "Statistical physics of crime for TPS data",
"topics": [
"morie_tps_statphysics"
]
},
{
"page": "morie_tps_statphysics_analyze_all",
"title": "Run all four statistical-physics analyses on a list of categories",
"topics": [
"morie_tps_statphysics_analyze_all"
]
},
{
"page": "morie_tps_stochastic",
"title": "Stochastic-physics-of-crime analyses for TPS data",
"topics": [
"morie_tps_stochastic"
]
},
{
"page": "MORIE_TPS_SUPPORTED_FORMATS",
"title": "Format names that 'morie_tps_load()' knows how to dispatch",
"topics": [
"MORIE_TPS_SUPPORTED_FORMATS"
]
},
{
"page": "morie_tps_temporal",
"title": "Temporal analyses for TPS crime data",
"topics": [
"morie_tps_temporal"
]
},
{
"page": "morie_tps_temporal_summary",
"title": "Temporal rollup for a TPS crime data.frame",
"topics": [
"morie_tps_temporal_summary"
]
},
{
"page": "MORIE_TPS_TORONTO_POPULATION_BY_YEAR",
"title": "Toronto reference population by fiscal year (StatsCan 17-10-0009-01)",
"topics": [
"MORIE_TPS_TORONTO_POPULATION_BY_YEAR"
]
},
{
"page": "MORIE_TPS_TOTAL_CSI_WEIGHTS",
"title": "Total-CSI weights for the 9 TPS open-data categories",
"topics": [
"MORIE_TPS_TOTAL_CSI_WEIGHTS"
]
},
{
"page": "morie_tps_urban_scaling_beta",
"title": "Bettencourt urban-scaling exponent across the 158 Toronto wards",
"topics": [
"morie_tps_urban_scaling_beta"
]
},
{
"page": "morie_tps_use_of_force",
"title": "TPS Use-of-Force rate + type distribution",
"topics": [
"morie_tpsuof",
"morie_tps_use_of_force"
]
},
{
"page": "MORIE_TPS_VIOLENT_CSI_WEIGHTS",
"title": "Violent-CSI weights for the 9 TPS open-data categories",
"topics": [
"MORIE_TPS_VIOLENT_CSI_WEIGHTS"
]
},
{
"page": "morie_tps_year_over_year_trend",
"title": "Year-over-year linear trend on incident counts",
"topics": [
"morie_tps_year_over_year_trend"
]
},
{
"page": "morie_tps_year_to_hood_version",
"title": "Recommended HOOD_* schema version for a given OCC_YEAR",
"topics": [
"morie_tps_year_to_hood_version"
]
},
{
"page": "morie_tps_yoy_panel",
"title": "Side-by-side year-over-year panel across TPS categories",
"topics": [
"morie_tps_yoy_panel"
]
},
{
"page": "morie_tqang_angular_difference",
"title": "Signed shortest difference 'a - b', in '[-pi, pi)'",
"topics": [
"morie_tqang_angular_difference"
]
},
{
"page": "morie_tqang_cheatsheet",
"title": "One-line rationale mirroring the Python cheatsheet",
"topics": [
"morie_tqang_cheatsheet"
]
},
{
"page": "morie_tqang_quantize_angles",
"title": "Uniform quantisation of angles to '2^bits' equal sectors",
"topics": [
"morie_tqang_quantize_angles",
"morie_tqang_tqang",
"morie_tqang_turboquant_angle_quantization"
]
},
{
"page": "morie_tqang_wrap_angle",
"title": "Wrap an angle to '[-pi, pi)'",
"topics": [
"morie_tqang_wrap_angle"
]
},
{
"page": "morie_tqlld_cheatsheet",
"title": "One-line rationale mirroring the Python cheatsheet",
"topics": [
"morie_tqlld_cheatsheet"
]
},
{
"page": "morie_tqlld_lloyd_max_codebook",
"title": "Lloyd-Max optimal scalar codebook",
"topics": [
"morie_tqlld_lloyd_max_codebook",
"morie_tqlld_tqlld",
"morie_tqlld_turboquant_lloyd_max_codebook"
]
},
{
"page": "morie_tqlld_quantize_with_codebook",
"title": "Quantise samples with a fixed codebook",
"topics": [
"morie_tqlld_quantize_with_codebook"
]
},
{
"page": "morie_transfer_entropy_gaussian",
"title": "Gaussian transfer entropy / Granger conditional mutual information",
"topics": [
"morie_transfer_entropy_gaussian"
]
},
{
"page": "morie_transfer_function",
"title": "Transfer function and coherence",
"topics": [
"morie_transfer_function"
]
},
{
"page": "morie_transfer_verify",
"title": "Verify a categorical variable that crossed from one program to another",
"topics": [
"morie_transfer_verify"
]
},
{
"page": "morie_transform_asymptotics",
"title": "Asymptotic properties of Horowitz's T_n and F_n",
"topics": [
"morie_transform_asymptotics"
]
},
{
"page": "morie_transform_prediction",
"title": "Prediction from a fitted transformation model",
"topics": [
"morie_transform_prediction"
]
},
{
"page": "morie_transform_T_F",
"title": "Horowitz's nonparametric estimators of T and F",
"topics": [
"morie_transform_T_F"
]
},
{
"page": "morie_transformer_genomic",
"title": "Transformer (1-head self-attention) genomic predictor (base R)",
"topics": [
"morie_transformer_genomic"
]
},
{
"page": "morie_tree_fit_cpp",
"title": "Grow one regression / second-order tree (compiled)",
"topics": [
"morie_tree_fit_cpp"
]
},
{
"page": "morie_tree_predict_cpp",
"title": "Predict from a flattened tree (compiled)",
"topics": [
"morie_tree_predict_cpp"
]
},
{
"page": "morie_trend_vector",
"title": "The trend vector T = (1/N) sum (a_i - A) S_i and its randomisation test",
"topics": [
"morie_trend_vector"
]
},
{
"page": "morie_trimmed_mean",
"title": "Trimming and Winsorizing",
"topics": [
"morie_trimmed_mean",
"morie_winsorize",
"morie_winsorized_mean",
"morie_winsorized_variance"
]
},
{
"page": "morie_trimmed_mean_anova",
"title": "Heteroscedastic one-way designs",
"topics": [
"morie_brunner_dette_munk",
"morie_trimmed_mean_anova"
]
},
{
"page": "morie_trimmed_mean_se",
"title": "Inference for a single trimmed mean",
"topics": [
"morie_trimmed_mean_ci",
"morie_trimmed_mean_se"
]
},
{
"page": "morie_trkassign",
"title": "Minimum-cost detection-to-track assignment (p. 476)",
"topics": [
"morie_trkassign"
]
},
{
"page": "morie_ts",
"title": "Construct a validated time series",
"topics": [
"morie_ts"
]
},
{
"page": "morie_ts_accuracy",
"title": "Forecast accuracy metrics",
"topics": [
"morie_ts_accuracy"
]
},
{
"page": "morie_ts_acf",
"title": "Ordered autocorrelation / autocovariance of a series",
"topics": [
"morie_ts_acf"
]
},
{
"page": "morie_ts_arima",
"title": "Fit an ARIMA / SARIMA model",
"topics": [
"morie_ts_arima"
]
},
{
"page": "morie_ts_backtest",
"title": "Back-test a forecasting configuration on a holdout tail",
"topics": [
"morie_ts_backtest"
]
},
{
"page": "morie_ts_decompose",
"title": "Decompose a seasonal time series",
"topics": [
"morie_ts_decompose"
]
},
{
"page": "morie_ts_difference",
"title": "Difference a series, retaining the information to reverse it",
"topics": [
"morie_ts_difference"
]
},
{
"page": "morie_ts_forecast",
"title": "Forecast from a fitted time-series model",
"topics": [
"morie_ts_forecast"
]
},
{
"page": "morie_ts_select",
"title": "Select ARIMA orders by information criterion",
"topics": [
"morie_ts_select"
]
},
{
"page": "morie_ts_stationarity",
"title": "Test a series for stationarity",
"topics": [
"morie_ts_stationarity"
]
},
{
"page": "morie_ts_trim_forecast",
"title": "Trim a forecast to a maximum prediction-interval width",
"topics": [
"morie_ts_trim_forecast"
]
},
{
"page": "morie_ts_undifference",
"title": "Reverse 'morie_ts_difference()'",
"topics": [
"morie_ts_undifference"
]
},
{
"page": "morie_tsboot",
"title": "Native block bootstrap for time series",
"topics": [
"morie_tsboot"
]
},
{
"page": "morie_tsimpute",
"title": "Missing-data imputation for time series (ch. 2 pp. 44-52)",
"topics": [
"morie_tsimpute"
]
},
{
"page": "morie_tsmixer",
"title": "TSMixer time-mixing and feature-mixing, all-MLP",
"topics": [
"morie_tsmixer"
]
},
{
"page": "morie_tsne_reduction",
"title": "t-SNE for non-linear dimension reduction (R parity)",
"topics": [
"morie_tsne_reduction"
]
},
{
"page": "morie_tsregmat",
"title": "Time series recast as a regression problem (ch. 5 p. 118)",
"topics": [
"morie_tsregmat"
]
},
{
"page": "morie_ttsAn",
"title": "Seasonal Hybrid ESD anomaly detection",
"topics": [
"morie_ttsAn"
]
},
{
"page": "morie_ttsplit",
"title": "Stable train/test split by identifier hash (p. 58)",
"topics": [
"morie_ttsplit"
]
},
{
"page": "morie_turan_graph",
"title": "The Turan graph: complete r-partite with parts as equal as possible",
"topics": [
"morie_turan_graph"
]
},
{
"page": "morie_turan_number",
"title": "Turan's theorem: most edges with no clique on r+1 vertices",
"topics": [
"morie_turan_number"
]
},
{
"page": "morie_turning_point_test",
"title": "Turning-point test (native)",
"topics": [
"morie_turning_point_test"
]
},
{
"page": "morie_tvtsplit",
"title": "Three-way train/validation/test split by identifier hash",
"topics": [
"morie_tvtsplit"
]
},
{
"page": "morie_twelvefold_way",
"title": "The twelvefold way",
"topics": [
"morie_twelvefold_way"
]
},
{
"page": "morie_two_boot",
"title": "Native two-sample bootstrap",
"topics": [
"morie_two_boot"
]
},
{
"page": "morie_two_group_binomial",
"title": "Two-group binomial tests and the odds ratio Wald interval",
"topics": [
"morie_two_group_binomial"
]
},
{
"page": "morie_two_point_bound",
"title": "Le Cam's two-point lower bound on a finite sample space",
"topics": [
"morie_two_point_bound"
]
},
{
"page": "morie_two_sample_coverage",
"title": "Two-sample placement coverage (Gibbons Ch 2.11.2)",
"topics": [
"morie_two_sample_coverage"
]
},
{
"page": "morie_two_sample_t_test",
"title": "Two-sample t-test with tidy output",
"topics": [
"morie_two_sample_t_test"
]
},
{
"page": "morie_two_step_additive",
"title": "Two-step oracle-efficient additive model estimator",
"topics": [
"morie_two_step_additive"
]
},
{
"page": "morie_twostage_design",
"title": "Optimal two-stage design sizes",
"topics": [
"morie_twostage_design"
]
},
{
"page": "morie_typset",
"title": "Typical-set membership test (eq. 4.29, p. 80)",
"topics": [
"morie_typset"
]
},
{
"page": "morie_u_process",
"title": "U-statistic of order m",
"topics": [
"morie_u_process"
]
},
{
"page": "morie_ucbb",
"title": "UCB1 bandit policy on a realized-reward table",
"topics": [
"morie_ucbb"
]
},
{
"page": "morie_unclr_gaussian",
"title": "morie_unclr_phi",
"topics": [
"morie_unclr_Phi",
"morie_unclr_phi"
]
},
{
"page": "morie_uniform_matroid",
"title": "The uniform matroid U(k, n)",
"topics": [
"morie_uniform_matroid"
]
},
{
"page": "morie_union_bound_exists",
"title": "First moment / union bound",
"topics": [
"morie_union_bound_exists"
]
},
{
"page": "morie_unobserved_components",
"title": "Unobserved-components decomposition (trend + seasonal + irregular)",
"topics": [
"morie_unobserved_components"
]
},
{
"page": "morie_urnpost",
"title": "Posterior over a discrete urn hypothesis (eq. 2.25-2.26, p. 28)",
"topics": [
"morie_urnpost"
]
},
{
"page": "morie_urnpred",
"title": "Posterior predictive probability for the next draw (eq. 2.29-2.31, p. 29)",
"topics": [
"morie_urnpred"
]
},
{
"page": "morie_userguide",
"title": "Get path to an MORIE userguide",
"topics": [
"morie_userguide"
]
},
{
"page": "morie_validate_cpads_data",
"title": "Validate a CPADS analysis data frame",
"topics": [
"morie_validate_cpads_data"
]
},
{
"page": "morie_validate_outputs_manifest",
"title": "Validate outputs manifest structure",
"topics": [
"morie_validate_outputs_manifest"
]
},
{
"page": "morie_van_der_waerden_test",
"title": "Van der Waerden two-sample normal-scores location test (Gibbons Ch 8.3.2)",
"topics": [
"morie_van_der_waerden_test"
]
},
{
"page": "morie_vanr1",
"title": "VanRaden Method 1 genomic relationship matrix",
"topics": [
"morie_vanr1"
]
},
{
"page": "morie_var_backtest",
"title": "Joint VaR backtest: coverage plus independence",
"topics": [
"morie_var_backtest"
]
},
{
"page": "morie_var_cpp",
"title": "morie_var_cpp",
"topics": [
"morie_var_cpp"
]
},
{
"page": "morie_variogram_composite_likelihood",
"title": "Composite-likelihood semivariogram estimation",
"topics": [
"morie_variogram_composite_likelihood"
]
},
{
"page": "morie_variogram_design",
"title": "Variogram-sampling design criteria",
"topics": [
"morie_variogram_design"
]
},
{
"page": "morie_vcov_cl",
"title": "One-way clustered covariance (native, CR0 / CR1)",
"topics": [
"morie_vcov_cl"
]
},
{
"page": "morie_vcov_hac",
"title": "HAC (Newey-West) covariance (native)",
"topics": [
"morie_vcov_hac"
]
},
{
"page": "morie_vcov_hc",
"title": "Heteroskedasticity-consistent covariance (native, HC0-HC5)",
"topics": [
"morie_vcov_hc"
]
},
{
"page": "morie_vcov_robust",
"title": "Unified robust-covariance dispatcher (native)",
"topics": [
"morie_vcov_robust"
]
},
{
"page": "morie_vecm",
"title": "Vector error-correction model (VECM)",
"topics": [
"morie_vecm"
]
},
{
"page": "morie_veil_of_darkness",
"title": "The veil-of-darkness contrast as a sign test with an identified bias ratio",
"topics": [
"morie_veil_of_darkness"
]
},
{
"page": "morie_verify_pollution",
"title": "Run the pollution-to-health causal pipeline and report it",
"topics": [
"morie_verify_pollution"
]
},
{
"page": "morie_verify_statistical_output",
"title": "Verify that a serialised statistical output meets minimum quality gates",
"topics": [
"morie_verify_statistical_output"
]
},
{
"page": "morie_vertex_access_token",
"title": "Fetch and cache a Google Cloud access token via gcloud",
"topics": [
"morie_vertex_access_token"
]
},
{
"page": "morie_vertex_ask_gemini",
"title": "Send a single-turn prompt to Gemini via Vertex AI",
"topics": [
"morie_vertex_ask_gemini"
]
},
{
"page": "morie_vertex_health_check",
"title": "Tiny smoke test for the Vertex AI client",
"topics": [
"morie_vertex_health_check"
]
},
{
"page": "morie_vertex_resolve_config",
"title": "Resolve Vertex AI configuration from environment variables",
"topics": [
"morie_vertex_resolve_config"
]
},
{
"page": "morie_vic_indigenous_ratio",
"title": "Indigenous victimisation rate ratio from the Victorian tables",
"topics": [
"morie_vic_indigenous_ratio"
]
},
{
"page": "morie_vic_lga_rates",
"title": "Rank Victorian local government areas by offence rate",
"topics": [
"morie_vic_lga_rates"
]
},
{
"page": "morie_vic_offence_trend",
"title": "Offence-division trend from the Victorian criminal incidents table",
"topics": [
"morie_vic_offence_trend"
]
},
{
"page": "morie_vif_tolerance",
"title": "Tolerance and variance inflation factor",
"topics": [
"morie_vif_tolerance"
]
},
{
"page": "morie_vol_garman_klass",
"title": "Garman-Klass OHLC volatility estimator",
"topics": [
"morie_vol_garman_klass"
]
},
{
"page": "morie_vol_harmonic",
"title": "Harmonic-mean aggregation of per-period volatilities",
"topics": [
"morie_vol_harmonic"
]
},
{
"page": "morie_vol_noise",
"title": "Microstructure noise variance from high-frequency returns",
"topics": [
"morie_vol_noise"
]
},
{
"page": "morie_vol_parkinson",
"title": "Parkinson range volatility estimator",
"topics": [
"morie_vol_parkinson"
]
},
{
"page": "morie_volsabr",
"title": "SABR model Black implied volatility",
"topics": [
"morie_volsabr"
]
},
{
"page": "morie_vpc",
"title": "Variance partition coefficient (latent-variable method)",
"topics": [
"morie_vpc"
]
},
{
"page": "morie_vpd_download_instructions",
"title": "Print step-by-step VPD GeoDASH download instructions",
"topics": [
"morie_vpd_download_instructions"
]
},
{
"page": "morie_wald_iv",
"title": "The Wald instrumental-variable ratio on a finite population",
"topics": [
"morie_wald_iv"
]
},
{
"page": "morie_walkfwd",
"title": "Walk-forward validation (ch. 5 p. 126)",
"topics": [
"morie_walkfwd"
]
},
{
"page": "morie_wasserman_aic",
"title": "AIC of a fitted model (Ch 13.6, wsmaic)",
"topics": [
"morie_wasserman_aic"
]
},
{
"page": "morie_wasserman_bic",
"title": "BIC of a fitted model (Ch 13.6, wsmbic)",
"topics": [
"morie_wasserman_bic"
]
},
{
"page": "morie_wasserman_boosting",
"title": "AdaBoost.M1 with decision stumps (Ch 22, wsmbst)",
"topics": [
"morie_wasserman_boosting"
]
},
{
"page": "morie_wasserman_bootstrap_percentile",
"title": "Bootstrap percentile interval (Ch 8, wsmbpc)",
"topics": [
"morie_wasserman_bootstrap_percentile"
]
},
{
"page": "morie_wasserman_bootstrap_pivotal",
"title": "Bootstrap pivotal interval (Ch 8, wsmbpv)",
"topics": [
"morie_wasserman_bootstrap_pivotal"
]
},
{
"page": "morie_wasserman_char_fn",
"title": "Empirical characteristic function (Ch 3, wsmcfn)",
"topics": [
"morie_wasserman_char_fn"
]
},
{
"page": "morie_wasserman_chebyshev_ineq",
"title": "Chebyshev bound P(|X-mu| >= k sigma) <= 1/k^2 (Ch 4, wsmcby)",
"topics": [
"morie_wasserman_chebyshev_ineq"
]
},
{
"page": "morie_wasserman_chi_sq_gof",
"title": "Chi-square goodness-of-fit (Ch 10, wsmchi)",
"topics": [
"morie_wasserman_chi_sq_gof"
]
},
{
"page": "morie_wasserman_clt",
"title": "CLT standardised mean (Ch 5, wsmclt)",
"topics": [
"morie_wasserman_clt"
]
},
{
"page": "morie_wasserman_covariance",
"title": "Covariance Cov(X,Y) = E[XY] - EXE[Y] (Ch 4, wsmcov)",
"topics": [
"morie_wasserman_covariance"
]
},
{
"page": "morie_wasserman_cramer_rao",
"title": "Cramer-Rao lower bound (Ch 9 Thm 9.23, wsmcrl)",
"topics": [
"morie_wasserman_cramer_rao"
]
},
{
"page": "morie_wasserman_credible_interval",
"title": "Equal-tail credible interval (Ch 11, wsmbcr)",
"topics": [
"morie_wasserman_credible_interval"
]
},
{
"page": "morie_wasserman_delta_method",
"title": "Delta method standard error (Ch 5, wsmdlm)",
"topics": [
"morie_wasserman_delta_method"
]
},
{
"page": "morie_wasserman_directed_graph",
"title": "Directed graphical model factorisation (Ch 17, wsmdir)",
"topics": [
"morie_wasserman_directed_graph"
]
},
{
"page": "morie_wasserman_dkw_cb",
"title": "DKW confidence band for F (Ch 7 Thm 7.5, wsmcb)",
"topics": [
"morie_wasserman_dkw_cb"
]
},
{
"page": "morie_wasserman_em_algorithm",
"title": "EM for a two-component normal mixture (Ch 9, wsmemt)",
"topics": [
"morie_wasserman_em_algorithm"
]
},
{
"page": "morie_wasserman_empirical_cdf",
"title": "Empirical distribution function (Ch 7, wsmcdf)",
"topics": [
"morie_wasserman_empirical_cdf"
]
},
{
"page": "morie_wasserman_empirical_quantile",
"title": "Empirical quantile, Hyndman-Fan type 1 (Ch 7, wsmqtl)",
"topics": [
"morie_wasserman_empirical_quantile"
]
},
{
"page": "morie_wasserman_entropy",
"title": "Entropy in nats (Ch 23, wsment)",
"topics": [
"morie_wasserman_entropy"
]
},
{
"page": "morie_wasserman_expectation",
"title": "Expectation E[X] = int x f(x) dx by trapezoid (Ch 3, wsmexp)",
"topics": [
"morie_wasserman_expectation"
]
},
{
"page": "morie_wasserman_fisher_info",
"title": "Fisher information by numeric curvature (Ch 9, wsmfis)",
"topics": [
"morie_wasserman_fisher_info"
]
},
{
"page": "morie_wasserman_gibbs_sampler",
"title": "Gibbs sampler for a bivariate normal (Ch 24.4, wsmgib)",
"topics": [
"morie_wasserman_gibbs_sampler"
]
},
{
"page": "morie_wasserman_gmm_em",
"title": "Two-component Gaussian mixture via EM (Ch 19, wsmgmm)",
"topics": [
"morie_wasserman_gmm_em"
]
},
{
"page": "morie_wasserman_graphical_model",
"title": "Normalised clique-potential model with its mode (Ch 17, wsmgrp)",
"topics": [
"morie_wasserman_graphical_model"
]
},
{
"page": "morie_wasserman_hmm_forward",
"title": "HMM forward algorithm with per-step scaling (Rabiner 1989; Wasserman Ch 23 covers the underlying Markov chains) (wsmhmm)",
"topics": [
"morie_wasserman_hmm_forward"
]
},
{
"page": "morie_wasserman_hoeffding",
"title": "Hoeffding bound for bounded variables (Ch 4, wsmhfd)",
"topics": [
"morie_wasserman_hoeffding"
]
},
{
"page": "morie_wasserman_influence_function",
"title": "Empirical influence via the sensitivity curve (Ch 7, wsmifn)",
"topics": [
"morie_wasserman_influence_function"
]
},
{
"page": "morie_wasserman_kernel_regression",
"title": "Nadaraya-Watson kernel regression (Ch 20 Def 20.21, wsmcrk)",
"topics": [
"morie_wasserman_kernel_regression"
]
},
{
"page": "morie_wasserman_kfold_cv",
"title": "K-fold cross-validated squared error (Ch 13.6, wsmcvr)",
"topics": [
"morie_wasserman_kfold_cv"
]
},
{
"page": "morie_wasserman_kmeans",
"title": "K-means by Lloyd's algorithm (Ch 19, wsmkmn)",
"topics": [
"morie_wasserman_kmeans"
]
},
{
"page": "morie_wasserman_kullback_leibler",
"title": "Kullback-Leibler divergence (Ch 23, wsmkbk)",
"topics": [
"morie_wasserman_kullback_leibler"
]
},
{
"page": "morie_wasserman_lasso",
"title": "Lasso by cyclic coordinate descent (Ch 13, wsmlas)",
"topics": [
"morie_wasserman_lasso"
]
},
{
"page": "morie_wasserman_least_squares",
"title": "Ordinary least squares with classical errors (Ch 13 Thm 13.4, wsmlsr)",
"topics": [
"morie_wasserman_least_squares"
]
},
{
"page": "morie_wasserman_likelihood",
"title": "Likelihood L(theta) = prod f(X_i; theta) (Ch 9, wsmlik)",
"topics": [
"morie_wasserman_likelihood"
]
},
{
"page": "morie_wasserman_lln",
"title": "Law of large numbers: running means (Ch 5, wsmlln)",
"topics": [
"morie_wasserman_lln"
]
},
{
"page": "morie_wasserman_local_polynomial",
"title": "Local polynomial regression (Ch 20, wsmlpr)",
"topics": [
"morie_wasserman_local_polynomial"
]
},
{
"page": "morie_wasserman_log_likelihood",
"title": "Log-likelihood with per-observation terms (Ch 9, wsmllk)",
"topics": [
"morie_wasserman_log_likelihood"
]
},
{
"page": "morie_wasserman_log_linear",
"title": "Saturated log-linear model for a two-way table (Ch 17, wsmlgc)",
"topics": [
"morie_wasserman_log_linear"
]
},
{
"page": "morie_wasserman_logistic_regression",
"title": "Logistic regression MLE by Newton-Raphson (Ch 13.7, wsmlgr)",
"topics": [
"morie_wasserman_logistic_regression"
]
},
{
"page": "morie_wasserman_markov_ineq",
"title": "Markov bound P(X >= a) <= EX/a for X >= 0 (Ch 4, wsmmrk)",
"topics": [
"morie_wasserman_markov_ineq"
]
},
{
"page": "morie_wasserman_mcmc_metropolis",
"title": "Random-walk Metropolis sampler (Ch 24.3, wsmmcm)",
"topics": [
"morie_wasserman_mcmc_metropolis"
]
},
{
"page": "morie_wasserman_mgf",
"title": "Empirical MGF M(t) = (1/n) sum e^{t X_i} (Ch 3, wsmmgf)",
"topics": [
"morie_wasserman_mgf"
]
},
{
"page": "morie_wasserman_minimax",
"title": "Minimax value of a finite decision problem (Ch 12, wsmmin)",
"topics": [
"morie_wasserman_minimax"
]
},
{
"page": "morie_wasserman_mle_asymptotic",
"title": "MLE asymptotic standard error and Wald interval (Ch 9 Thm 9.18, wsmasm)",
"topics": [
"morie_wasserman_mle_asymptotic"
]
},
{
"page": "morie_wasserman_mutual_info",
"title": "Mutual information of two discrete samples (Ch 23, wsmmtl)",
"topics": [
"morie_wasserman_mutual_info"
]
},
{
"page": "morie_wasserman_nonparametric_boot",
"title": "Nonparametric bootstrap standard error (Ch 8, wsmnpb)",
"topics": [
"morie_wasserman_nonparametric_boot"
]
},
{
"page": "morie_wasserman_odds_ratio",
"title": "Odds ratio with Woolf interval (Ch 16, wsmodd)",
"topics": [
"morie_wasserman_odds_ratio"
]
},
{
"page": "morie_wasserman_parametric_boot",
"title": "Parametric bootstrap (Ch 8, wsmprb)",
"topics": [
"morie_wasserman_parametric_boot"
]
},
{
"page": "morie_wasserman_pca",
"title": "Principal component analysis (Ch 14, wsmpca)",
"topics": [
"morie_wasserman_pca"
]
},
{
"page": "morie_wasserman_poisson_regression",
"title": "Poisson log-linear regression MLE (Ch 13, wsmpsr)",
"topics": [
"morie_wasserman_poisson_regression"
]
},
{
"page": "morie_wasserman_posterior",
"title": "Grid posterior p(theta | x) (Ch 11, wsmbay)",
"topics": [
"morie_wasserman_posterior"
]
},
{
"page": "morie_wasserman_posterior_mean",
"title": "Posterior mean from a grid posterior (Ch 11, wsmpst1)",
"topics": [
"morie_wasserman_posterior_mean"
]
},
{
"page": "morie_wasserman_relative_risk",
"title": "Relative risk with Katz interval (Ch 16, wsmrrr)",
"topics": [
"morie_wasserman_relative_risk"
]
},
{
"page": "morie_wasserman_ridge",
"title": "Ridge regression (Ch 13, wsmrgr)",
"topics": [
"morie_wasserman_ridge"
]
},
{
"page": "morie_wasserman_smoothing_spline",
"title": "Discrete smoothing spline (Ch 20.5, wsmsmp)",
"topics": [
"morie_wasserman_smoothing_spline"
]
},
{
"page": "morie_wasserman_svm",
"title": "Linear SVM by pairwise dual ascent (Ch 22, wsmsvm)",
"topics": [
"morie_wasserman_svm"
]
},
{
"page": "morie_wasserman_undirected_graph",
"title": "Exact undirected clique model (Ch 17, wsmund)",
"topics": [
"morie_wasserman_undirected_graph"
]
},
{
"page": "morie_wasserman_variance",
"title": "Variance Var(X) = E[X^2] - E[X]^2 (Wasserman Ch 3, morie.fn wsmvar)",
"topics": [
"morie_wasserman_variance"
]
},
{
"page": "morie_wasserman_viterbi",
"title": "Viterbi decoding in log space (Ch 23, wsmvit)",
"topics": [
"morie_wasserman_viterbi"
]
},
{
"page": "morie_wasserman_white_huber",
"title": "White-Huber sandwich covariance (Ch 9, wsmwhz)",
"topics": [
"morie_wasserman_white_huber"
]
},
{
"page": "morie_wasserstein_test",
"title": "Two-sample permutation test on the 1-Wasserstein distance",
"topics": [
"morie_wasserstein_test"
]
},
{
"page": "morie_wavelet_time_series",
"title": "Discrete wavelet decomposition for a time series",
"topics": [
"morie_wavelet_time_series"
]
},
{
"page": "morie_wayback_url",
"title": "Resolve a Wayback Machine snapshot URL (via rmoriebricklayer)",
"topics": [
"morie_wayback_url"
]
},
{
"page": "morie_weight_cbps",
"title": "Covariate-balancing propensity score (Imai & Ratkovic 2014)",
"topics": [
"morie_weight_cbps"
]
},
{
"page": "morie_weight_diagnostic",
"title": "Balance diagnostics for a morie_weight object",
"topics": [
"morie_weight_diagnostic"
]
},
{
"page": "morie_weight_entropy",
"title": "Entropy-balancing weights (Hainmueller 2012)",
"topics": [
"morie_weight_entropy"
]
},
{
"page": "morie_weight_ow",
"title": "Overlap weights (Li, Morgan & Zaslavsky 2018)",
"topics": [
"morie_weight_ow"
]
},
{
"page": "morie_weight_ps",
"title": "Propensity-score weights (logistic base learner)",
"topics": [
"morie_weight_ps"
]
},
{
"page": "morie_weight_stabilized",
"title": "Stabilized IPW (Austin 2009 / Robins et al. 2000)",
"topics": [
"morie_weight_stabilized"
]
},
{
"page": "morie_weight_super",
"title": "SuperLearner-style ensemble propensity weights",
"topics": [
"morie_weight_super"
]
},
{
"page": "morie_weight_trimming",
"title": "Trim extreme weights from a morie_weight object",
"topics": [
"morie_weight_trimming"
]
},
{
"page": "morie_weighted_logrank",
"title": "Weighted log-rank tests (Peto-Peto, Gehan-Wilcoxon, Tarone-Ware)",
"topics": [
"morie_weighted_logrank"
]
},
{
"page": "morie_weightit",
"title": "Balancing weights by WeightIt's rules, natively",
"topics": [
"morie_weightit",
"print.morie_weightit"
]
},
{
"page": "morie_weights_bootstrap",
"title": "Bootstrap replicate weights (Rao-Wu rescaling within strata)",
"topics": [
"morie_weights_bootstrap"
]
},
{
"page": "morie_weights_brr",
"title": "Balanced Repeated Replication (BRR) weights",
"topics": [
"morie_weights_brr"
]
},
{
"page": "morie_weights_calibrate_to_totals",
"title": "Dispatch helper - calibrate to totals via \"raking\" or \"greg\"",
"topics": [
"morie_weights_calibrate_to_totals"
]
},
{
"page": "morie_weights_combined",
"title": "Combined design x nonresponse x post-strat (x trim) pipeline",
"topics": [
"morie_weights_combined"
]
},
{
"page": "morie_weights_deff",
"title": "Kish design effect (n / ESS)",
"topics": [
"morie_weights_deff"
]
},
{
"page": "morie_weights_design",
"title": "Design weights from inclusion probabilities",
"topics": [
"morie_weights_design"
]
},
{
"page": "morie_weights_detect_extreme",
"title": "Detect extreme weights at +/- k * IQR or by absolute percentile",
"topics": [
"morie_weights_detect_extreme"
]
},
{
"page": "morie_weights_diagnostics",
"title": "Comprehensive weight diagnostics",
"topics": [
"morie_weights_diagnostics"
]
},
{
"page": "morie_weights_ess",
"title": "Kish effective sample size: (sum w_i)^2 / sum w_i^2",
"topics": [
"morie_weights_ess"
]
},
{
"page": "morie_weights_fay_brr",
"title": "Fay's BRR weights with perturbation coefficient 'fay_coefficient' in [0,1)",
"topics": [
"morie_weights_fay_brr"
]
},
{
"page": "morie_weights_greg",
"title": "Generalised regression (GREG) calibration",
"topics": [
"morie_weights_greg"
]
},
{
"page": "morie_weights_jackknife",
"title": "Jackknife replicate weights (JK1 delete-1 or JKn stratified delete-n)",
"topics": [
"morie_weights_jackknife"
]
},
{
"page": "morie_weights_multiframe",
"title": "Multi-frame (dual-frame) survey weights (Hartley compositing)",
"topics": [
"morie_weights_multiframe"
]
},
{
"page": "morie_weights_nonresponse",
"title": "Non-response adjustment within cells",
"topics": [
"morie_weights_nonresponse"
]
},
{
"page": "morie_weights_normalize",
"title": "Normalise weights so they sum to n (sample) or N (population)",
"topics": [
"morie_weights_normalize"
]
},
{
"page": "morie_weights_poststratify",
"title": "Post-stratification weight adjustment",
"topics": [
"morie_weights_poststratify"
]
},
{
"page": "morie_weights_propensity_nonresponse",
"title": "Propensity-score non-response weights (logistic)",
"topics": [
"morie_weights_propensity_nonresponse"
]
},
{
"page": "morie_weights_rake",
"title": "Raking calibration (iterative proportional fitting)",
"topics": [
"morie_weights_rake"
]
},
{
"page": "morie_weights_replicate_variance",
"title": "Variance estimation from replicate estimates",
"topics": [
"morie_weights_replicate_variance"
]
},
{
"page": "morie_weights_sdr",
"title": "Successive Difference Replication (SDR) weights",
"topics": [
"morie_weights_sdr"
]
},
{
"page": "morie_weights_smooth",
"title": "Smooth survey weights via shrinkage toward the mean (or log-mean)",
"topics": [
"morie_weights_smooth"
]
},
{
"page": "morie_weights_totals",
"title": "Spatial weights constants",
"topics": [
"morie_weights_totals"
]
},
{
"page": "morie_weights_trim",
"title": "Trim extreme weights at percentile cutpoints",
"topics": [
"morie_weights_trim"
]
},
{
"page": "morie_welch_test",
"title": "Welch's and Yuen's tests for two groups",
"topics": [
"morie_welch_test",
"morie_yuen_paired",
"morie_yuen_test"
]
},
{
"page": "morie_wilcox_test",
"title": "Native Wilcoxon rank-sum permutation test",
"topics": [
"morie_wilcox_test"
]
},
{
"page": "morie_wilcoxon_power",
"title": "Monte-Carlo power of the Wilcoxon signed-rank test (Gibbons Ch 5.7.3)",
"topics": [
"morie_wilcoxon_power"
]
},
{
"page": "morie_wilcoxon_signed_rank_test",
"title": "Wilcoxon signed-rank test (paired)",
"topics": [
"morie_wilcoxon_signed_rank_test"
]
},
{
"page": "morie_winkler",
"title": "Winkler interval score (ch. 17)",
"topics": [
"morie_winkler"
]
},
{
"page": "morie_wnominate_fit",
"title": "W-NOMINATE ideal points by alternating optimisation",
"topics": [
"morie_wnominate_fit"
]
},
{
"page": "morie_write_audit_markdown",
"title": "Write a Markdown audit report",
"topics": [
"morie_write_audit_markdown"
]
},
{
"page": "morie_write_parquet",
"title": "Write a data frame to Parquet",
"topics": [
"morie_write_parquet"
]
},
{
"page": "morie_write_synthetic_data",
"title": "Write synthetic epidemiology-style data to CSV",
"topics": [
"morie_write_synthetic_data"
]
},
{
"page": "morie_wsm_admissible",
"title": "Admissibility of decision rules by risk dominance",
"topics": [
"morie_wsm_admissible"
]
},
{
"page": "morie_wsm_bagging",
"title": "Bagging",
"topics": [
"morie_wsm_bagging"
]
},
{
"page": "morie_wsm_bootstrap",
"title": "Bootstrap variance estimator",
"topics": [
"morie_wsm_bootstrap"
]
},
{
"page": "morie_wsm_importance_sampling",
"title": "Importance sampling",
"topics": [
"morie_wsm_importance_sampling"
]
},
{
"page": "morie_wsm_kde",
"title": "Kernel density estimate with Silverman's window-width rules",
"topics": [
"morie_wsm_kde"
]
},
{
"page": "morie_wsm_mle",
"title": "Maximum likelihood as an M-estimator",
"topics": [
"morie_wsm_mle"
]
},
{
"page": "morie_wsm_plug_in",
"title": "Plug-in estimator of a statistical functional",
"topics": [
"morie_wsm_plug_in"
]
},
{
"page": "morie_xgboost_objective",
"title": "XGBoost regularized objective (R parity)",
"topics": [
"morie_xgboost_objective"
]
},
{
"page": "morie_xml_sax",
"title": "Parse XML natively with SAX-style callbacks (pure R)",
"topics": [
"morie_xml_sax"
]
},
{
"page": "morie_xpehh1",
"title": "Cross-population EHH (XP-EHH)",
"topics": [
"morie_xpehh1"
]
},
{
"page": "morie_yangr",
"title": "Yang et al. realized relationship matrix",
"topics": [
"morie_yangr"
]
},
{
"page": "morie_yule_walker",
"title": "Yule-Walker AR estimation",
"topics": [
"morie_yule_walker"
]
},
{
"page": "morie_zero_altered_poisson",
"title": "Zero-altered Poisson models",
"topics": [
"morie_zap_link",
"morie_zap_loglik",
"morie_zap_mean_variance",
"morie_zap_mle",
"morie_zap_predict",
"morie_zero_altered_poisson"
]
},
{
"page": "morie_zip_vuong_boundary",
"title": "Vuong's test of zero-inflated against plain Poisson at the boundary",
"topics": [
"morie_zip_vuong_boundary"
]
},
{
"page": "morie_zonal_ebm",
"title": "Zonal energy-balance model (Budyko-Sellers)",
"topics": [
"morie_zonal_ebm"
]
},
{
"page": "Morieclrp",
"title": "Conditional p-value for the CLR statistic",
"topics": [
"Morieclrp"
]
},
{
"page": "MorphologicalOpening",
"title": "Morphological opening",
"topics": [
"MorphologicalOpening"
]
},
{
"page": "Motfom",
"title": "FIMO-style position weight matrix scan",
"topics": [
"Motfom"
]
},
{
"page": "motfsr_alphabet_of",
"title": "motfsr_alphabet_of",
"topics": [
"motfsr_alphabet_of"
]
},
{
"page": "motfsr_bayes_threshold",
"title": "motfsr_bayes_threshold",
"topics": [
"motfsr_bayes_threshold"
]
},
{
"page": "motfsr_cheatsheet",
"title": "motfsr_cheatsheet",
"topics": [
"motfsr_cheatsheet"
]
},
{
"page": "motfsr_lambda_grid",
"title": "motfsr_lambda_grid",
"topics": [
"motfsr_lambda_grid"
]
},
{
"page": "motfsr_log_component",
"title": "motfsr_log_component",
"topics": [
"motfsr_log_component"
]
},
{
"page": "motfsr_log_odds_matrix",
"title": "motfsr_log_odds_matrix",
"topics": [
"motfsr_log_odds_matrix"
]
},
{
"page": "motfsr_mm_fit",
"title": "motfsr_mm_fit",
"topics": [
"motfsr_mm_fit"
]
},
{
"page": "motfsr_mu",
"title": "motfsr_mu",
"topics": [
"motfsr_mu"
]
},
{
"page": "motfsr_normalise_windows",
"title": "motfsr_normalise_windows",
"topics": [
"motfsr_normalise_windows"
]
},
{
"page": "motfsr_prepare",
"title": "motfsr_prepare",
"topics": [
"motfsr_prepare"
]
},
{
"page": "motfsr_run",
"title": "motfsr_run",
"topics": [
"motfsr_run"
]
},
{
"page": "motfsr_score_sequence",
"title": "motfsr_score_sequence",
"topics": [
"motfsr_score_sequence"
]
},
{
"page": "motfsr_theta_from_subsequence",
"title": "motfsr_theta_from_subsequence",
"topics": [
"motfsr_theta_from_subsequence"
]
},
{
"page": "motfsr_uniform_theta",
"title": "motfsr_uniform_theta",
"topics": [
"motfsr_uniform_theta"
]
},
{
"page": "MothFlameOptimizer",
"title": "Moth-flame optimization",
"topics": [
"MothFlameOptimizer"
]
},
{
"page": "mpfn_cheatsheet",
"title": "mpfn_cheatsheet",
"topics": [
"mpfn_cheatsheet"
]
},
{
"page": "mpfn_is_permutation_invariant",
"title": "mpfn_is_permutation_invariant",
"topics": [
"mpfn_is_permutation_invariant"
]
},
{
"page": "mpfn_message",
"title": "mpfn_message",
"topics": [
"mpfn_message"
]
},
{
"page": "mpfn_message_passing",
"title": "mpfn_message_passing",
"topics": [
"mpfn_message_passing"
]
},
{
"page": "mpfn_readout",
"title": "mpfn_readout",
"topics": [
"mpfn_readout"
]
},
{
"page": "mpfn_sig",
"title": "mpfn_sig",
"topics": [
"mpfn_sig"
]
},
{
"page": "mpfn_update_gru",
"title": "mpfn_update_gru",
"topics": [
"mpfn_update_gru"
]
},
{
"page": "mqtmpl_cheatsheet",
"title": "mqtmpl_cheatsheet",
"topics": [
"mqtmpl_cheatsheet"
]
},
{
"page": "mqtmpl_check_method",
"title": "mqtmpl_check_method",
"topics": [
"mqtmpl_check_method"
]
},
{
"page": "mqtmpl_genotype_probabilities",
"title": "Qk left, qk+1 right",
"topics": [
"mqtmpl_genotype_probabilities"
]
},
{
"page": "mqtmpl_haldane",
"title": "mqtmpl_haldane",
"topics": [
"mqtmpl_haldane"
]
},
{
"page": "mqtmpl_hmm_genotype_probabilities",
"title": "mqtmpl_hmm_genotype_probabilities",
"topics": [
"mqtmpl_hmm_genotype_probabilities"
]
},
{
"page": "mqtmpl_imputation_weights",
"title": "mqtmpl_imputation_weights",
"topics": [
"mqtmpl_imputation_weights"
]
},
{
"page": "mqtmpl_kw_n_imp",
"title": "mqtmpl_kw_n_imp",
"topics": [
"mqtmpl_kw_n_imp"
]
},
{
"page": "mqtmpl_lod_support_interval",
"title": "mqtmpl_lod_support_interval",
"topics": [
"mqtmpl_lod_support_interval"
]
},
{
"page": "mqtmpl_method_status",
"title": "mqtmpl_method_status",
"topics": [
"mqtmpl_method_status"
]
},
{
"page": "mqtmpl_permutation_threshold",
"title": "mqtmpl_permutation_threshold",
"topics": [
"mqtmpl_permutation_threshold"
]
},
{
"page": "mqtmpl_sample_genotypes",
"title": "mqtmpl_sample_genotypes",
"topics": [
"mqtmpl_sample_genotypes"
]
},
{
"page": "mqtmpl_scan_cim",
"title": "mqtmpl_scan_cim",
"topics": [
"mqtmpl_scan_cim"
]
},
{
"page": "mqtmpl_scan_imp",
"title": "mqtmpl_scan_imp",
"topics": [
"mqtmpl_scan_imp"
]
},
{
"page": "mqtmpl_scanone",
"title": "mqtmpl_scanone",
"topics": [
"mqtmpl_scanone"
]
},
{
"page": "mqtmpl_single_marker",
"title": "mqtmpl_single_marker",
"topics": [
"mqtmpl_single_marker"
]
},
{
"page": "mrm_admissions",
"title": "Admissions implied by a population and a length of stay",
"topics": [
"mrm_admissions"
]
},
{
"page": "mrm_adp",
"title": "Average daily population",
"topics": [
"mrm_adp"
]
},
{
"page": "mrm_adp_from_counts",
"title": "Person-days estimated from periodic headcounts",
"topics": [
"mrm_adp_from_counts"
]
},
{
"page": "mrm_alos",
"title": "Average length of stay",
"topics": [
"mrm_alos"
]
},
{
"page": "mrm_anova_bonferroni",
"title": "One-way ANOVA with pairwise Bonferroni-adjusted t-tests",
"topics": [
"mrm_anova_bonferroni"
]
},
{
"page": "mrm_anova_oneway",
"title": "One-way ANOVA + Tukey HSD post-hoc",
"topics": [
"mrm_anova_oneway"
]
},
{
"page": "mrm_anova_power",
"title": "Power of one-way ANOVA given Cohen's f",
"topics": [
"mrm_anova_power"
]
},
{
"page": "mrm_assumptions_check",
"title": "Composite Rubin-style identifiability assumption check",
"topics": [
"mrm_assumptions_check"
]
},
{
"page": "mrm_causal_design",
"title": "Designed-experiment convenience wrapper around the morie causal estimator family",
"topics": [
"mrm_causal_design"
]
},
{
"page": "mrm_check_balancing",
"title": "Composite balance verdict using the Imbens-Rubin %SMD criterion",
"topics": [
"mrm_check_balancing"
]
},
{
"page": "mrm_check_overlap",
"title": "Propensity-score support overlap diagnostic (Cole-Hernan 2008)",
"topics": [
"mrm_check_overlap"
]
},
{
"page": "mrm_classify_mandela",
"title": "Mandela Rules classifier for solitary-confinement placements",
"topics": [
"mrm_classify_mandela"
]
},
{
"page": "mrm_clt_demo",
"title": "Central Limit Theorem demonstrator",
"topics": [
"mrm_clt_demo"
]
},
{
"page": "mrm_design",
"title": "Experimental-design callables (designexptr-inspired)",
"topics": [
"mrm_design"
]
},
{
"page": "mrm_diagnostics",
"title": "Causal-inference diagnostics (R parity)",
"topics": [
"mrm_diagnostics"
]
},
{
"page": "mrm_doe",
"title": "Design-of-Experiments toolkit (R parity)",
"topics": [
"mrm_doe"
]
},
{
"page": "mrm_factorial_2k",
"title": "2^k factorial-design analysis with main effects + interactions",
"topics": [
"mrm_factorial_2k"
]
},
{
"page": "mrm_fractional_factorial",
"title": "Fractional 2^(k-p) factorial: main effects + alias structure",
"topics": [
"mrm_fractional_factorial"
]
},
{
"page": "mrm_gentrification",
"title": "Baseline-conditional gentrification coding (MRM primitive)",
"topics": [
"mrm_gentrification"
]
},
{
"page": "mrm_gentrification_panel",
"title": "Construct a baseline-conditional 3-level gentrification factor",
"topics": [
"mrm_gentrification_panel"
]
},
{
"page": "mrm_graeco_latin",
"title": "Graeco-Latin square four-way ANOVA (row, col, Latin, Greek)",
"topics": [
"mrm_graeco_latin"
]
},
{
"page": "mrm_kulldorff",
"title": "Kulldorff space-time scan statistic on TPS event data",
"topics": [
"mrm_kulldorff"
]
},
{
"page": "mrm_latin_square",
"title": "Latin-square three-way ANOVA (row, col, treatment)",
"topics": [
"mrm_latin_square"
]
},
{
"page": "mrm_lisa",
"title": "LISA (Local Indicators of Spatial Association) on polygon-level crime data + per-year polygon Moran's I time series",
"topics": [
"mrm_lisa"
]
},
{
"page": "mrm_mathstats",
"title": "Mathematical-statistics / simulation / computation toolkit (R parity)",
"topics": [
"mrm_mathstats"
]
},
{
"page": "mrm_mc_power",
"title": "Empirical Monte-Carlo power",
"topics": [
"mrm_mc_power"
]
},
{
"page": "mrm_median_causal_effect",
"title": "Median causal effect via 1:1 nearest-neighbour PS matching",
"topics": [
"mrm_median_causal_effect"
]
},
{
"page": "mrm_morans_i",
"title": "Moran's I statistic for residual spatial autocorrelation",
"topics": [
"mrm_morans_i"
]
},
{
"page": "mrm_oneprop_test",
"title": "One-proportion test (binomial exact + Wald approximation)",
"topics": [
"mrm_oneprop_test"
]
},
{
"page": "mrm_otis",
"title": "MRM-framework analyses on Ontario OTIS data",
"topics": [
"mrm_otis"
]
},
{
"page": "mrm_otis_mandela_spectrum",
"title": "Mandela Rules apples-to-apples spectrum on OTIS b01",
"topics": [
"mrm_otis_mandela_spectrum"
]
},
{
"page": "mrm_otis_mortification_cooccurrence",
"title": "Pairwise Cramer's V of OTIS b01 alert columns (mortification proxy)",
"topics": [
"mrm_otis_mortification_cooccurrence"
]
},
{
"page": "mrm_otis_placement_concentration",
"title": "Per-individual segregation-placement-count concentration on OTIS b09",
"topics": [
"mrm_otis_placement_concentration"
]
},
{
"page": "mrm_otis_region_locality",
"title": "OTIS b01 region locality: chi-square + diagonal-share",
"topics": [
"mrm_otis_region_locality"
]
},
{
"page": "mrm_otis_seg_duration_km",
"title": "Kaplan-Meier survival on OTIS b01 segregation-placement durations",
"topics": [
"mrm_otis_seg_duration_km"
]
},
{
"page": "mrm_otis_stock_flow",
"title": "Stock and flow across the OTIS strata",
"topics": [
"mrm_otis_stock_flow"
]
},
{
"page": "mrm_period_days",
"title": "Period length in days, from dates",
"topics": [
"mrm_period_days"
]
},
{
"page": "mrm_perm_block",
"title": "Block-permutation test for treatment effect",
"topics": [
"mrm_perm_block"
]
},
{
"page": "mrm_pit",
"title": "Probability Integral Transform (PIT)",
"topics": [
"mrm_pit"
]
},
{
"page": "mrm_primitives_ordinal",
"title": "Threshold-specific ordinal-logit primitive (MRM)",
"topics": [
"mrm_primitives_ordinal"
]
},
{
"page": "mrm_qq_plot",
"title": "Q-Q plot coordinates against a reference distribution",
"topics": [
"mrm_qq_plot"
]
},
{
"page": "mrm_random_latin",
"title": "Generate a random k x k Latin square",
"topics": [
"mrm_random_latin"
]
},
{
"page": "mrm_rcbd",
"title": "Randomised complete block design (RCBD) two-way ANOVA",
"topics": [
"mrm_rcbd"
]
},
{
"page": "mrm_response_surface",
"title": "Second-order response-surface fit (Box-Wilson 1951)",
"topics": [
"mrm_response_surface"
]
},
{
"page": "mrm_samples",
"title": "Included reference data samples and dataset fetchers",
"topics": [
"mrm_samples"
]
},
{
"page": "mrm_siu",
"title": "MRM-framework analyses on Ontario SIU (Special Investigations Unit) data",
"topics": [
"mrm_siu"
]
},
{
"page": "mrm_siu_case_to_decision_km",
"title": "KM survival of SIU case-to-decision gap per police service",
"topics": [
"mrm_siu_case_to_decision_km"
]
},
{
"page": "mrm_siu_outcome_classifier",
"title": "Tabulate SIU Director's-decision outcomes",
"topics": [
"mrm_siu_outcome_classifier"
]
},
{
"page": "mrm_siu_per_service_rate",
"title": "Per-police-service SIU case-rate summary",
"topics": [
"mrm_siu_per_service_rate"
]
},
{
"page": "mrm_spatial_spillover",
"title": "Spatial Durbin / SAR direct-indirect-total decomposition (MRM primitive)",
"topics": [
"mrm_spatial_spillover"
]
},
{
"page": "mrm_spatial_spillover_decomposition",
"title": "SDM direct / indirect / total decomposition",
"topics": [
"mrm_spatial_spillover_decomposition"
]
},
{
"page": "mrm_standardised_difference",
"title": "Imbens-Rubin standardised %SMD per covariate",
"topics": [
"mrm_standardised_difference"
]
},
{
"page": "mrm_stay_summary",
"title": "Length of stay with its distribution and interval",
"topics": [
"mrm_stay_summary"
]
},
{
"page": "mrm_stock_flow",
"title": "Stock and flow side by side, with the decomposition",
"topics": [
"mrm_stock_flow"
]
},
{
"page": "mrm_synthetic_area_exposure",
"title": "Compute the synthetic exposure offset for each area",
"topics": [
"mrm_synthetic_area_exposure"
]
},
{
"page": "mrm_synthetic_exposure",
"title": "Synthetic small-area-estimated exposure offset (MRM primitive)",
"topics": [
"mrm_synthetic_exposure"
]
},
{
"page": "mrm_threshold_coefficient",
"title": "Extract coefficient(s) for one covariate across all thresholds",
"topics": [
"mrm_threshold_coefficient"
]
},
{
"page": "mrm_threshold_specific_ordinal",
"title": "Fit a threshold-specific cumulative-logit ordinal regression",
"topics": [
"mrm_threshold_specific_ordinal"
]
},
{
"page": "mrm_tps",
"title": "MRM-framework analyses on Toronto Police Service (TPS) open data",
"topics": [
"mrm_tps"
]
},
{
"page": "mrm_tps_kulldorff_scan",
"title": "Run a 3-d (lat, lon, time) Kulldorff scan with MC inference",
"topics": [
"mrm_tps_kulldorff_scan"
]
},
{
"page": "mrm_tps_levy_scaling",
"title": "Levy-flight Hill-MLE exponent on TPS inter-incident step lengths",
"topics": [
"mrm_tps_levy_scaling"
]
},
{
"page": "mrm_tps_lisa",
"title": "Local Moran's I per polygon + quadrant + 999-permutation significance",
"topics": [
"mrm_tps_lisa"
]
},
{
"page": "mrm_tps_load_hawkes_refit",
"title": "Load the precomputed per-category TPS Hawkes refit manifest",
"topics": [
"mrm_tps_load_hawkes_refit"
]
},
{
"page": "mrm_tps_moran_clustering",
"title": "Global Moran's I + DBSCAN summary on TPS lat/long event data",
"topics": [
"mrm_tps_moran_clustering"
]
},
{
"page": "mrm_tps_neighbourhood_recurrence_km",
"title": "Kaplan-Meier inter-event recurrence on TPS by neighbourhood",
"topics": [
"mrm_tps_neighbourhood_recurrence_km"
]
},
{
"page": "mrm_tps_polygon_moran_per_year",
"title": "Per-year global Moran's I time series across a polygon surface",
"topics": [
"mrm_tps_polygon_moran_per_year"
]
},
{
"page": "mrm_two_treatment_test",
"title": "Two-treatment outcome comparison with three assumption regimes",
"topics": [
"mrm_two_treatment_test"
]
},
{
"page": "mrm_twoprop_test",
"title": "Two-proportion test (chi-square + Fisher exact + Wald)",
"topics": [
"mrm_twoprop_test"
]
},
{
"page": "mrm_uof",
"title": "Generic Multilevel Reconciliation Methodology (MRM) Use-of-Force callables",
"topics": [
"mrm_uof"
]
},
{
"page": "mrm_uof_data_quality_audit",
"title": "Schema, null, and suspect-value audit",
"topics": [
"mrm_uof_data_quality_audit"
]
},
{
"page": "mrm_uof_demographic_disparity",
"title": "Demographic disparity in outcome rates with risk-ratio CIs",
"topics": [
"mrm_uof_demographic_disparity"
]
},
{
"page": "mrm_uof_force_concentration",
"title": "Concentration of UoF incidents across forces / services",
"topics": [
"mrm_uof_force_concentration"
]
},
{
"page": "mrm_uof_region_locality",
"title": "Region-at-time vs region-now locality contingency",
"topics": [
"mrm_uof_region_locality"
]
},
{
"page": "mrm_uof_weapon_diversity",
"title": "Weapon-by-force contingency test",
"topics": [
"mrm_uof_weapon_diversity"
]
},
{
"page": "mrm_uof_yoy_change",
"title": "Year-on-year change in incident counts",
"topics": [
"mrm_uof_yoy_change"
]
},
{
"page": "mrm_var_test",
"title": "Chi-square test for variance (Wilks 1962)",
"topics": [
"mrm_var_test"
]
},
{
"page": "Mrr",
"title": "Rank-based score that only cares where the first hit lands",
"topics": [
"Mrr"
]
},
{
"page": "Mscmcl",
"title": "Impute the untreated counterfactual panel by nuclear-norm completion",
"topics": [
"Mscmcl"
]
},
{
"page": "Msetst",
"title": "Mean squared error of a set of predictions",
"topics": [
"Msetst"
]
},
{
"page": "Mskbnd2",
"title": "Manski no-assumption ATE bounds computed within covariate strata",
"topics": [
"Mskbnd2"
]
},
{
"page": "Msm001",
"title": "Statistical learning model, systematic plus random part",
"topics": [
"Msm001"
]
},
{
"page": "Msm002",
"title": "Single-mean model for a one-way layout",
"topics": [
"Msm002"
]
},
{
"page": "Msm003",
"title": "One-way fixed-effects model",
"topics": [
"Msm003"
]
},
{
"page": "Msm004",
"title": "eq. (1.3) p.16 (re-export)",
"topics": [
"Msm004"
]
},
{
"page": "Msm005",
"title": "Reparameterized one-way model",
"topics": [
"Msm005"
]
},
{
"page": "Msm006",
"title": "One-way random-effects (mixed) model",
"topics": [
"Msm006"
]
},
{
"page": "Msm007",
"title": "Generalized precision",
"topics": [
"Msm007"
]
},
{
"page": "Msm008",
"title": "Generalized sensitivity and specificity",
"topics": [
"Msm008"
]
},
{
"page": "Msm009",
"title": "Brier score for categorical data",
"topics": [
"Msm009"
]
},
{
"page": "Msm010",
"title": "General univariate linear mixed model",
"topics": [
"Msm010"
]
},
{
"page": "Msm011",
"title": "Marginal likelihood of the linear mixed model",
"topics": [
"Msm011"
]
},
{
"page": "Msm012",
"title": "eq. (5.2) p.142 (re-export)",
"topics": [
"Msm012"
]
},
{
"page": "Msm013",
"title": "eq. (5.1) p.142 (re-export)",
"topics": [
"Msm013"
]
},
{
"page": "Msm014",
"title": "eq. (5.1) p.142 (re-export)",
"topics": [
"Msm014"
]
},
{
"page": "Msm019",
"title": "eq. (5.1) p.142 (re-export)",
"topics": [
"Msm019"
]
},
{
"page": "Msm026",
"title": "Multi-trait genomic linear mixed model",
"topics": [
"Msm026"
]
},
{
"page": "Msm027",
"title": "eq. (5.1) p.142 (re-export)",
"topics": [
"Msm027"
]
},
{
"page": "Msm028",
"title": "Multi-trait model with extra fixed effects",
"topics": [
"Msm028"
]
},
{
"page": "Msm029",
"title": "eq. (5.5) p.153 (re-export)",
"topics": [
"Msm029"
]
},
{
"page": "Msm030",
"title": "eq. (5.5) p.153 (re-export)",
"topics": [
"Msm030"
]
},
{
"page": "Msm031",
"title": "eq. (5.5) p.153 (re-export)",
"topics": [
"Msm031"
]
},
{
"page": "Msm042",
"title": "Normal linear regression under the Bayesian paradigm",
"topics": [
"Msm042"
]
},
{
"page": "Msm043",
"title": "Non-informative prior for the linear model",
"topics": [
"Msm043"
]
},
{
"page": "Msm044",
"title": "eq. (6.1)-(6.2) pp.172 (re-export)",
"topics": [
"Msm044"
]
},
{
"page": "Msm045",
"title": "eq. (6.1)-(6.2) pp.172 (re-export)",
"topics": [
"Msm045"
]
},
{
"page": "Msm047",
"title": "eq. (6.2) p.172 (re-export)",
"topics": [
"Msm047"
]
},
{
"page": "Msm057",
"title": "eq. (6.1)-(6.2) pp.172 (re-export)",
"topics": [
"Msm057"
]
},
{
"page": "Msm058",
"title": "eq. (6.1)-(6.2) pp.172 (re-export)",
"topics": [
"Msm058"
]
},
{
"page": "Msm059",
"title": "eq. (6.1)-(6.2) pp.172 (re-export)",
"topics": [
"Msm059"
]
},
{
"page": "Msm060",
"title": "eq. (6.1)-(6.2) pp.172 (re-export)",
"topics": [
"Msm060"
]
},
{
"page": "Msm077",
"title": "eq. (6.2) p.172 (re-export)",
"topics": [
"Msm077"
]
},
{
"page": "Msm083",
"title": "eq. (1.2) p.15 (re-export)",
"topics": [
"Msm083"
]
},
{
"page": "Msm084",
"title": "eq. (1.2) p.15 (re-export)",
"topics": [
"Msm084"
]
},
{
"page": "Msm092",
"title": "Ordinal latent predictor with environment, markers and interaction",
"topics": [
"Msm092"
]
},
{
"page": "Msm093",
"title": "eq. (7.3) p.219 (re-export)",
"topics": [
"Msm093"
]
},
{
"page": "Msm097",
"title": "eq. (7.3) p.219 (re-export)",
"topics": [
"Msm097"
]
},
{
"page": "Msm098",
"title": "Ordinal predictor with environment and genetic effects",
"topics": [
"Msm098"
]
},
{
"page": "Msm099",
"title": "eq. (7.3) p.219 (re-export)",
"topics": [
"Msm099"
]
},
{
"page": "Msm101",
"title": "eq. (7.5) p.221 (re-export)",
"topics": [
"Msm101"
]
},
{
"page": "Msm102",
"title": "eq. (7.5) p.221 (re-export)",
"topics": [
"Msm102"
]
},
{
"page": "Msm103",
"title": "eq. (7.3) p.219 (re-export)",
"topics": [
"Msm103"
]
},
{
"page": "Msm109",
"title": "Penalized multinomial log-likelihood (ridge)",
"topics": [
"Msm109"
]
},
{
"page": "Msm111",
"title": "eq. (7.7) p.226 (re-export)",
"topics": [
"Msm111"
]
},
{
"page": "Msm114",
"title": "eq. (7.7) p.226 (re-export)",
"topics": [
"Msm114"
]
},
{
"page": "Msm115",
"title": "Lasso-penalized multinomial log-likelihood",
"topics": [
"Msm115"
]
},
{
"page": "Msm117",
"title": "eq. (7.7) p.226 (re-export)",
"topics": [
"Msm117"
]
},
{
"page": "Msm118",
"title": "eq. (7.7) p.226 (re-export)",
"topics": [
"Msm118"
]
},
{
"page": "Msm119",
"title": "eq. (7.10) p.227 (re-export)",
"topics": [
"Msm119"
]
},
{
"page": "Msm123",
"title": "RKHS optimization problem",
"topics": [
"Msm123"
]
},
{
"page": "Msm124",
"title": "eq. (8.1) p.253 (re-export)",
"topics": [
"Msm124"
]
},
{
"page": "Msm127",
"title": "eq. (8.1) p.253 (re-export)",
"topics": [
"Msm127"
]
},
{
"page": "Msm128",
"title": "RKHS estimation in the frequentist framework",
"topics": [
"Msm128"
]
},
{
"page": "Msm129",
"title": "eq. (8.3) p.254 (re-export)",
"topics": [
"Msm129"
]
},
{
"page": "Msm130",
"title": "eq. (8.3) p.254 (re-export)",
"topics": [
"Msm130"
]
},
{
"page": "Msm133",
"title": "eq. (8.3) p.254 (re-export)",
"topics": [
"Msm133"
]
},
{
"page": "Msm134",
"title": "eq. (8.3) p.254 (re-export)",
"topics": [
"Msm134"
]
},
{
"page": "Msm159",
"title": "eq. (1.2) p.15 (re-export)",
"topics": [
"Msm159"
]
},
{
"page": "Msm160",
"title": "eq. (1.2) p.15 (re-export)",
"topics": [
"Msm160"
]
},
{
"page": "Msm236",
"title": "eq. (1.3) p.16 (re-export)",
"topics": [
"Msm236"
]
},
{
"page": "Msm237",
"title": "eq. (1.3) p.16 (re-export)",
"topics": [
"Msm237"
]
},
{
"page": "Msm238",
"title": "eq. (1.3) p.16 (re-export)",
"topics": [
"Msm238"
]
},
{
"page": "Msm239",
"title": "eq. (1.3) p.16 (re-export)",
"topics": [
"Msm239"
]
},
{
"page": "Msm249",
"title": "Sum-of-squares loss",
"topics": [
"Msm249"
]
},
{
"page": "Msm262",
"title": "Basis expansion of the coefficient function (MVSML eq. 14.2)",
"topics": [
"Msm262"
]
},
{
"page": "Msm315",
"title": "eq. (1.2) p.15 (re-export)",
"topics": [
"Msm315"
]
},
{
"page": "Msm316",
"title": "eq. (1.2) p.15 (re-export)",
"topics": [
"Msm316"
]
},
{
"page": "Msm317",
"title": "eq. (1.2) p.15 (re-export)",
"topics": [
"Msm317"
]
},
{
"page": "Msm318",
"title": "eq. (1.2) p.15 (re-export)",
"topics": [
"Msm318"
]
},
{
"page": "Msm319",
"title": "eq. (1.2) p.15 (re-export)",
"topics": [
"Msm319"
]
},
{
"page": "Msm320",
"title": "eq. (1.2) p.15 (re-export)",
"topics": [
"Msm320"
]
},
{
"page": "Msm321",
"title": "eq. (1.2) p.15 (re-export)",
"topics": [
"Msm321"
]
},
{
"page": "Msm322",
"title": "eq. (1.2) p.15 (re-export)",
"topics": [
"Msm322"
]
},
{
"page": "Msm323",
"title": "Nonparametric links of the zero-altered Poisson forest",
"topics": [
"Msm323"
]
},
{
"page": "Msm324",
"title": "eq. (15.1) p.651 (re-export)",
"topics": [
"Msm324"
]
},
{
"page": "Msm325",
"title": "Zero-truncated Poisson splitting criterion",
"topics": [
"Msm325"
]
},
{
"page": "Msm326",
"title": "eq. (15.2) p.651 (re-export)",
"topics": [
"Msm326"
]
},
{
"page": "Msm327",
"title": "ZAP random-forest prediction",
"topics": [
"Msm327"
]
},
{
"page": "Msm328",
"title": "eq. (15.3) p.652 (re-export)",
"topics": [
"Msm328"
]
},
{
"page": "Msm329",
"title": "ZAPC random-forest prediction",
"topics": [
"Msm329"
]
},
{
"page": "Msm330",
"title": "eq. (15.4) p.652 (re-export)",
"topics": [
"Msm330"
]
},
{
"page": "Msm331",
"title": "eq. (15.4) p.652 (re-export)",
"topics": [
"Msm331"
]
},
{
"page": "Msm332",
"title": "Linear multiple regression fitted by OLS",
"topics": [
"Msm332"
]
},
{
"page": "Msm333",
"title": "eq. (3.1) p.71 with the OLS solution pp.72-73 (re-export)",
"topics": [
"Msm333"
]
},
{
"page": "Msm334",
"title": "Expected prediction error of the OLS fit",
"topics": [
"Msm334"
]
},
{
"page": "mtdrl_bandit_tasks",
"title": "mtdrl_bandit_tasks",
"topics": [
"mtdrl_bandit_tasks"
]
},
{
"page": "mtdrl_cheatsheet",
"title": "mtdrl_cheatsheet",
"topics": [
"mtdrl_cheatsheet"
]
},
{
"page": "mtdrl_history_features",
"title": "mtdrl_history_features",
"topics": [
"mtdrl_history_features"
]
},
{
"page": "mtdrl_run",
"title": "mtdrl_run",
"topics": [
"mtdrl_run"
]
},
{
"page": "mtdrl_TabularHistoryAgent",
"title": "mtdrl_TabularHistoryAgent",
"topics": [
"mtdrl_TabularHistoryAgent"
]
},
{
"page": "Mtlmmfit",
"title": "Multi-trait linear mixed model",
"topics": [
"Mtlmmfit"
]
},
{
"page": "Mtrbnd",
"title": "Manski-Pepper monotone treatment response bounds, either direction",
"topics": [
"Mtrbnd"
]
},
{
"page": "Mtrbound",
"title": "Monotone treatment response bounds",
"topics": [
"Mtrbound"
]
},
{
"page": "Mtsbnd",
"title": "Monotone treatment selection bounds (alias of Mtsbound)",
"topics": [
"Mtsbnd"
]
},
{
"page": "Mtsbound",
"title": "Monotone treatment selection bounds",
"topics": [
"Mtsbound"
]
},
{
"page": "Mtsmtv",
"title": "Intersection of the MTS and the MTR bound on E[Y(d)]",
"topics": [
"Mtsmtv"
]
},
{
"page": "MultiExp",
"title": "Multinomial theorem expansion",
"topics": [
"MultiExp"
]
},
{
"page": "Multinom",
"title": "Multinomial coefficient",
"topics": [
"Multinom"
]
},
{
"page": "MultinomialDist",
"title": "Multinomial distribution",
"topics": [
"MultinomialDist"
]
},
{
"page": "MultM",
"title": "Specific and joint natural indirect effects through parallel mediators",
"topics": [
"MultM"
]
},
{
"page": "MuscleAlign",
"title": "MUSCLE-style multiple sequence alignment",
"topics": [
"MuscleAlign",
"SumOfPairsScore"
]
},
{
"page": "Mutinf",
"title": "Mutual information of a 2-D joint pmf",
"topics": [
"Mutinf"
]
},
{
"page": "MuY",
"title": "Mean of the linear model output",
"topics": [
"MuY"
]
},
{
"page": "muzero_add_noise",
"title": "muzero_add_noise",
"topics": [
"muzero_add_noise"
]
},
{
"page": "muzero_backup",
"title": "muzero_backup",
"topics": [
"muzero_backup"
]
},
{
"page": "muzero_cheatsheet",
"title": "muzero_cheatsheet",
"topics": [
"muzero_cheatsheet"
]
},
{
"page": "muzero_gamma_rv",
"title": "muzero_gamma_rv",
"topics": [
"muzero_gamma_rv"
]
},
{
"page": "muzero_MinMax",
"title": "muzero_MinMax",
"topics": [
"muzero_MinMax"
]
},
{
"page": "muzero_Node",
"title": "muzero_Node",
"topics": [
"muzero_Node"
]
},
{
"page": "muzero_search",
"title": "muzero_search",
"topics": [
"muzero_search"
]
},
{
"page": "muzero_select",
"title": "muzero_select",
"topics": [
"muzero_select"
]
},
{
"page": "MvNormDens",
"title": "Multivariate normal density",
"topics": [
"MvNormDens"
]
},
{
"page": "MvSkewNorm",
"title": "Multivariate skew-normal density",
"topics": [
"MvSkewNorm"
]
},
{
"page": "MvtDens",
"title": "Multivariate t density",
"topics": [
"MvtDens"
]
},
{
"page": "Mznstep",
"title": "MuZero n-step bootstrapped value targets",
"topics": [
"Mznstep"
]
},
{
"page": "Mzpuct",
"title": "MuZero pUCT action selection with min-max normalised values",
"topics": [
"Mzpuct"
]
},
{
"page": "Mzreanal",
"title": "MuZero Reanalyze targets and prioritised-replay weights",
"topics": [
"Mzreanal"
]
},
{
"page": "Mzrecur",
"title": "MuZero recurrent inference step",
"topics": [
"Mzrecur"
]
},
{
"page": "Mzvalue",
"title": "MuZero categorical value head and its invertible scaling",
"topics": [
"Mzvalue"
]
},
{
"page": "Mzworld",
"title": "MuZero world model: representation plus dynamics rollout",
"topics": [
"Mzworld"
]
},
{
"page": "n_effective_tests",
"title": "Effective number of independent tests from a correlation matrix",
"topics": [
"n_effective_tests"
]
},
{
"page": "n_stat_commands",
"title": "Total number of registered commands (excluding aliases)",
"topics": [
"n_stat_commands"
]
},
{
"page": "Naday",
"title": "Nadaraya-Watson kernel regression (alias of hrzk2)",
"topics": [
"Naday"
]
},
{
"page": "Nadwat",
"title": "Nadaraya-Watson and local linear kernel regression (ESL eqs. 2.40-2.42)",
"topics": [
"Nadwat"
]
},
{
"page": "Naivefc",
"title": "Naive forecast",
"topics": [
"Naivefc"
]
},
{
"page": "Nakagami",
"title": "Nakagami distribution",
"topics": [
"Nakagami"
]
},
{
"page": "NaoStationIndex",
"title": "Climate indices and CO2 curve fitting",
"topics": [
"Co2CurveFit",
"NaoStationIndex"
]
},
{
"page": "NaturalNeighbourWeights",
"title": "Natural-neighbour interpolation (Sibson and Laplace coordinates)",
"topics": [
"NaturalNeighbourWeights",
"NnCrossValidation",
"NnGradientInterpolate",
"NnInDomain",
"NnInterpolate"
]
},
{
"page": "Ndcg",
"title": "Normalised discounted cumulative gain at k",
"topics": [
"Ndcg"
]
},
{
"page": "Nde",
"title": "Pearl's natural direct effect, estimated from a linear SEM",
"topics": [
"Nde"
]
},
{
"page": "Neffsrs",
"title": "Effective simple-random-sample size of a weighted design",
"topics": [
"Neffsrs"
]
},
{
"page": "Negctc",
"title": "Fit the effect of D on a known-null outcome and test it for zero",
"topics": [
"Negctc"
]
},
{
"page": "NeighbourCardinality",
"title": "Neighbour counts and their frequency table",
"topics": [
"NeighbourCardinality"
]
},
{
"page": "NelderMead",
"title": "Nelder-Mead simplex minimisation",
"topics": [
"NelderMead"
]
},
{
"page": "nested_cross_validate",
"title": "Nested cross-validation with inner-loop grid search",
"topics": [
"nested_cross_validate"
]
},
{
"page": "nestvc",
"title": "Variance components of a two-stage nested design",
"topics": [
"nestvc"
]
},
{
"page": "Netattack",
"title": "Error and attack tolerance of a network",
"topics": [
"Netattack"
]
},
{
"page": "Netbtw",
"title": "Freeman betweenness centrality of a node",
"topics": [
"Netbtw"
]
},
{
"page": "Netclocent",
"title": "Closeness centrality of a node (re-export)",
"topics": [
"Netclocent"
]
},
{
"page": "Netdeg",
"title": "Freeman degree centrality of a node, normalised",
"topics": [
"Netdeg"
]
},
{
"page": "Neteigcent",
"title": "Eigenvector centrality of a network (re-export)",
"topics": [
"Neteigcent"
]
},
{
"page": "Netparf",
"title": "Network attributable fraction with spillover",
"topics": [
"Netparf"
]
},
{
"page": "Newraf",
"title": "Newton-Raphson minimisation",
"topics": [
"Newraf"
]
},
{
"page": "NeymanScottProcess",
"title": "Neyman-Scott cluster process: moments and simulation",
"topics": [
"NeymanScottProcess"
]
},
{
"page": "Ngppr",
"title": "Normalized gamma process prior (the Dirichlet process)",
"topics": [
"Ngppr"
]
},
{
"page": "Niccgg",
"title": "Nakagawa-Schielzeth marginal R-squared for a random-intercept LMM",
"topics": [
"Niccgg"
]
},
{
"page": "Nie",
"title": "Natural indirect effect (Pearl 2001)",
"topics": [
"Nie"
]
},
{
"page": "Nignst",
"title": "Conjugate N-Inv-chi2 posterior update for a normal sample",
"topics": [
"Nignst"
]
},
{
"page": "nlsgn",
"title": "Nonlinear least squares by Gauss-Newton",
"topics": [
"nlsgn"
]
},
{
"page": "Nndist",
"title": "Border-corrected nearest-neighbour distance CDF G(r)",
"topics": [
"Nndist"
]
},
{
"page": "Nnet1lay",
"title": "Single-layer feed-forward network as an adaptive basis expansion (ESL 2.45)",
"topics": [
"Nnet1lay"
]
},
{
"page": "nnlsq",
"title": "Non-negative least squares",
"topics": [
"nnlsq"
]
},
{
"page": "nobs.morie_lm",
"title": "Number of observations in method for 'morie_lm' objects",
"topics": [
"nobs.morie_lm"
]
},
{
"page": "node2v_alpha_pq",
"title": "node2v_alpha_pq",
"topics": [
"node2v_alpha_pq"
]
},
{
"page": "node2v_cheatsheet",
"title": "node2v_cheatsheet",
"topics": [
"node2v_cheatsheet"
]
},
{
"page": "node2v_dist",
"title": "node2v_dist",
"topics": [
"node2v_dist"
]
},
{
"page": "node2v_skipgram_pairs",
"title": "node2v_skipgram_pairs",
"topics": [
"node2v_skipgram_pairs"
]
},
{
"page": "node2v_transition_probabilities",
"title": "node2v_transition_probabilities",
"topics": [
"node2v_transition_probabilities"
]
},
{
"page": "node2v_walk",
"title": "node2v_walk",
"topics": [
"node2v_walk"
]
},
{
"page": "noise_schedule",
"title": "Linear noise schedule, scaled down",
"topics": [
"noise_schedule"
]
},
{
"page": "normality_suite",
"title": "Run a suite of normality tests",
"topics": [
"normality_suite"
]
},
{
"page": "NormalScore",
"title": "Sequential Gaussian and indicator simulation",
"topics": [
"BackTransform",
"MarkovChainSimulate",
"NormalScore",
"SgsCrossValidation",
"SgsSimulate",
"SimulationSummary",
"SisSimulate",
"TransitionMatrix"
]
},
{
"page": "Normlap",
"title": "Normalised Laplacian in Chung's sense",
"topics": [
"Normlap"
]
},
{
"page": "normtl",
"title": "Tolerance factor for normally distributed populations",
"topics": [
"normtl"
]
},
{
"page": "Novlt",
"title": "How surprising a recommendation is, in bits",
"topics": [
"Novlt"
]
},
{
"page": "Npbcl",
"title": "Posterior-mode partition of a Dirichlet-process normal mixture",
"topics": [
"Npbcl"
]
},
{
"page": "Npbcox",
"title": "Cox regression with a gamma-process baseline hazard",
"topics": [
"Npbcox"
]
},
{
"page": "Npbqr",
"title": "Quantile regression with a Dirichlet-process scale mixture",
"topics": [
"Npbqr"
]
},
{
"page": "Npbsr",
"title": "Nonparametric Bayes survival via a beta process",
"topics": [
"Npbsr"
]
},
{
"page": "Nprphet",
"title": "Additive forecast decomposition: trend, seasonality, autoregression",
"topics": [
"Nprphet"
]
},
{
"page": "Nrgmwd",
"title": "Mean functional of a normalized random measure",
"topics": [
"Nrgmwd"
]
},
{
"page": "Nrmprc",
"title": "Normalized inverse-Gaussian process",
"topics": [
"Nrmprc"
]
},
{
"page": "Nsamp",
"title": "Sample size for a stated half-width, with the finite-population step",
"topics": [
"Nsamp"
]
},
{
"page": "ntrtau",
"title": "Sample size for testing Kendall's tau (Noether)",
"topics": [
"ntrtau"
]
},
{
"page": "Nucleolus",
"title": "Nucleolus and prenucleolus of a transferable-utility game",
"topics": [
"Nucleolus"
]
},
{
"page": "number_needed_to_harm",
"title": "Number needed to harm (NNH) - sign-reversed NNT",
"topics": [
"number_needed_to_harm"
]
},
{
"page": "number_needed_to_treat",
"title": "Number needed to treat (NNT) = 1 / |RD|",
"topics": [
"number_needed_to_treat"
]
},
{
"page": "nw_regression",
"title": "Nadaraya-Watson kernel regression",
"topics": [
"nw_regression"
]
},
{
"page": "Nystromap",
"title": "Nystrom low-rank approximation of a kernel matrix",
"topics": [
"Nystromap"
]
},
{
"page": "Objfair",
"title": "Individual fairness audit against a Lipschitz constraint",
"topics": [
"Objfair"
]
},
{
"page": "OceanChlorophyll",
"title": "Marine remote sensing and ocean physics",
"topics": [
"BareSoilIndex",
"Co2Flux",
"DepthInvariantIndex",
"EuphoticDepth",
"FloatingAlgaeIndex",
"MangroveVegetationIndex",
"OceanChlorophyll",
"OilSpillDrift",
"OxygenSolubility",
"PlumeConcentration",
"PracticalSalinity",
"SedimentTransport",
"SplitWindowSst",
"VgpmProduction"
]
},
{
"page": "Ocmtmd",
"title": "Outcome-model diagnostic for a marginal structural model",
"topics": [
"Ocmtmd"
]
},
{
"page": "ocrwit_cheatsheet",
"title": "ocrwit_cheatsheet",
"topics": [
"ocrwit_cheatsheet"
]
},
{
"page": "ocrwit_mask_units",
"title": "ocrwit_mask_units",
"topics": [
"ocrwit_mask_units"
]
},
{
"page": "ocrwit_normalise_bbox",
"title": "ocrwit_normalise_bbox",
"topics": [
"ocrwit_normalise_bbox"
]
},
{
"page": "ocrwit_patch_of_box",
"title": "ocrwit_patch_of_box",
"topics": [
"ocrwit_patch_of_box"
]
},
{
"page": "ocrwit_segment_layout_boxes",
"title": "ocrwit_segment_layout_boxes",
"topics": [
"ocrwit_segment_layout_boxes"
]
},
{
"page": "odds_ratio",
"title": "Odds ratio for a 2x2 table [[a, b], [c, d]]",
"topics": [
"odds_ratio"
]
},
{
"page": "odds_ratio_table",
"title": "Odds-ratio table from a fitted logistic GLM",
"topics": [
"odds_ratio_table"
]
},
{
"page": "Oddsrat-gibbons",
"title": "Odds ratio by Woolf's logit method (Woolf 1955) - NOT from Gibbons",
"topics": [
"Oddsrat"
]
},
{
"page": "OddsRat",
"title": "Cross-product odds ratio for a 2 x 2 table, with a Woolf interval",
"topics": [
"OddsRat"
]
},
{
"page": "offlrl_as_dist",
"title": "offlrl_as_dist",
"topics": [
"offlrl_as_dist"
]
},
{
"page": "offlrl_cheatsheet",
"title": "offlrl_cheatsheet",
"topics": [
"offlrl_cheatsheet"
]
},
{
"page": "offlrl_logsumexp",
"title": "offlrl_logsumexp",
"topics": [
"offlrl_logsumexp"
]
},
{
"page": "offlrl_lookup",
"title": "offlrl_lookup",
"topics": [
"offlrl_lookup"
]
},
{
"page": "offlrl_safe_max_key",
"title": "offlrl_safe_max_key",
"topics": [
"offlrl_safe_max_key"
]
},
{
"page": "offlrl_softmax",
"title": "offlrl_softmax",
"topics": [
"offlrl_softmax"
]
},
{
"page": "Ogkcv",
"title": "Orthogonalized Gnanadesikan-Kettenring robust scatter",
"topics": [
"Ogkcv"
]
},
{
"page": "OlcAssembly",
"title": "Read assembly and protein disorder",
"topics": [
"OlcAssembly",
"ProteinDisorder"
]
},
{
"page": "Olsfit",
"title": "Linear model fit by least squares (ESL eq. 2.6)",
"topics": [
"Olsfit"
]
},
{
"page": "Olsnormeq",
"title": "Ordinary least squares through the normal equations",
"topics": [
"Olsnormeq"
]
},
{
"page": "omega_squared",
"title": "Omega-squared - less biased than eta-squared",
"topics": [
"omega_squared"
]
},
{
"page": "Omegah",
"title": "Share of total score variance due to the single general factor",
"topics": [
"Omegah"
]
},
{
"page": "omitted_variable_bias",
"title": "Omitted-variable bias analysis (sensemakr framework)",
"topics": [
"omitted_variable_bias"
]
},
{
"page": "one_proportion_ztest",
"title": "One-sample z-test for a proportion",
"topics": [
"one_proportion_ztest"
]
},
{
"page": "one_sample_ttest",
"title": "One-sample Student's t-test",
"topics": [
"one_sample_ttest"
]
},
{
"page": "one_way_anova",
"title": "One-way between-subjects ANOVA",
"topics": [
"one_way_anova"
]
},
{
"page": "OneMExp",
"title": "The (1 - x)^n to e^(-nx) step of the Poisson limit",
"topics": [
"OneMExp"
]
},
{
"page": "OniIndex",
"title": "Climate indices: ONI, PDO, QBO, degree heating weeks, cyclone energy, Hadley edge, scaling and downscaling",
"topics": [
"BcsdDownscale",
"CcScaling",
"CycloneEnergy",
"DegreeHeatingWeeks",
"EmpiricalQuantileMap",
"HadleyEdge",
"HurricaneTrack",
"OniIndex",
"PdoIndex",
"ProbabilityRatio",
"QboIndex",
"SeaLevelSemiEmpirical"
]
},
{
"page": "open_record",
"title": "Open a sealed record",
"topics": [
"open_record"
]
},
{
"page": "Ops.morie_gr_dual",
"title": "Arithmetic on dual numbers",
"topics": [
"Ops.morie_gr_dual"
]
},
{
"page": "Ops.morie_gvar",
"title": "Arithmetic on reverse-mode tape nodes",
"topics": [
"Ops.morie_gvar"
]
},
{
"page": "optimal_duration",
"title": "Release time maximising utility",
"topics": [
"optimal_duration"
]
},
{
"page": "OptimalCuttingLines",
"title": "Roll-call analysis",
"topics": [
"OptimalCuttingLines"
]
},
{
"page": "Opttre",
"title": "Optimal treatment regime by exhaustive tree search",
"topics": [
"Opttre"
]
},
{
"page": "or_to_d",
"title": "Convert odds ratio to Cohen's d (Hasselblad & Hedges, 1995)",
"topics": [
"or_to_d"
]
},
{
"page": "or_to_r",
"title": "Convert OR to Pearson r via d",
"topics": [
"or_to_r"
]
},
{
"page": "OrderedLogis",
"title": "Ordered logistic distribution",
"topics": [
"OrderedLogis"
]
},
{
"page": "Ordprobs",
"title": "Category probabilities of the ordinal threshold model",
"topics": [
"Ordprobs"
]
},
{
"page": "OrdRep",
"title": "Ordered sampling with replacement",
"topics": [
"OrdRep"
]
},
{
"page": "OrdSubs",
"title": "Ordered subgroups without repetition",
"topics": [
"OrdSubs"
]
},
{
"page": "OrInt",
"title": "Odds ratio with an interaction term",
"topics": [
"OrInt"
]
},
{
"page": "Otbar",
"title": "Average measures the way transport says they should be averaged",
"topics": [
"Otbar"
]
},
{
"page": "Otbarfree",
"title": "Barycentre whose support is found rather than fixed in advance",
"topics": [
"Otbarfree"
]
},
{
"page": "Otbarmap",
"title": "Barycentric projection of a transport plan",
"topics": [
"Otbarmap"
]
},
{
"page": "Otbreg",
"title": "Alternate KL projections of a Gibbs kernel onto the two marginals",
"topics": [
"Otbreg"
]
},
{
"page": "Otcostlp",
"title": "l_p ground cost matrix",
"topics": [
"Otcostlp"
]
},
{
"page": "Otcostsq",
"title": "Squared-Euclidean ground cost matrix",
"topics": [
"Otcostsq"
]
},
{
"page": "Otcw",
"title": "Test whether a matching can be improved by re-shuffling a cycle",
"topics": [
"Otcw"
]
},
{
"page": "Otdiv",
"title": "Entropic transport cost with its own bias subtracted off",
"topics": [
"Otdiv"
]
},
{
"page": "Otdwd",
"title": "Rescale a positive matrix to doubly stochastic form",
"topics": [
"Otdwd"
]
},
{
"page": "Otemd",
"title": "Exact earth mover's distance between two histograms",
"topics": [
"Otemd"
]
},
{
"page": "Otentreg",
"title": "Entropic regularisation term of a coupling",
"topics": [
"Otentreg"
]
},
{
"page": "Otfgw",
"title": "One coupling that has to satisfy the features and the structure at once",
"topics": [
"Otfgw"
]
},
{
"page": "Otfreeen",
"title": "Primal-dual gap of the entropic OT problem",
"topics": [
"Otfreeen"
]
},
{
"page": "Otgws",
"title": "Couple two spaces that have no common ground metric",
"topics": [
"Otgws"
]
},
{
"page": "Otker",
"title": "Transport in a feature space, with the cost read off a kernel",
"topics": [
"Otker"
]
},
{
"page": "Otlogpot",
"title": "Dual potentials from Sinkhorn scalings",
"topics": [
"Otlogpot"
]
},
{
"page": "Otmarsh",
"title": "Solve transport once part of the source marginal has been deleted",
"topics": [
"Otmarsh"
]
},
{
"page": "Otmcluster",
"title": "Lloyd's algorithm with transport in place of Euclidean distance",
"topics": [
"Otmcluster"
]
},
{
"page": "Otmd",
"title": "Ground cost that measures distance in units of the data's own spread",
"topics": [
"Otmd"
]
},
{
"page": "Otmm",
"title": "Average the transport cost over subsamples instead of solving once",
"topics": [
"Otmm"
]
},
{
"page": "Otmot",
"title": "Couple more than two histograms at once",
"topics": [
"Otmot"
]
},
{
"page": "Otmqd",
"title": "Distortion of a codebook, computed as transport to its atoms",
"topics": [
"Otmqd"
]
},
{
"page": "Otmsw",
"title": "Take the worst direction rather than the average one",
"topics": [
"Otmsw"
]
},
{
"page": "Otmxh",
"title": "Transport between mixtures, restricted to plans that stay Gaussian",
"topics": [
"Otmxh"
]
},
{
"page": "Otnegent",
"title": "Discrete entropy of a coupling matrix",
"topics": [
"Otnegent"
]
},
{
"page": "Ototk",
"title": "Value of the dual objective for a candidate pair of potentials",
"topics": [
"Ototk"
]
},
{
"page": "Otpr",
"title": "Transport only 'm' units of mass and leave the rest in place",
"topics": [
"Otpr"
]
},
{
"page": "Otpushfw",
"title": "Push-forward of a density through a smooth bijective map",
"topics": [
"Otpushfw"
]
},
{
"page": "Otreg",
"title": "Dual potentials of the entropic transport problem",
"topics": [
"Otreg"
]
},
{
"page": "Otsd",
"title": "Sliced distance computed on a shared quantile grid",
"topics": [
"Otsd"
]
},
{
"page": "Otsinkh",
"title": "Entropic-regularised OT by Sinkhorn scaling",
"topics": [
"Otsinkh"
]
},
{
"page": "Otsinkit",
"title": "Iterations Sinkhorn needs to reach a marginal tolerance",
"topics": [
"Otsinkit"
]
},
{
"page": "Otsinktol",
"title": "Marginal violation of a coupling, in the sup norm",
"topics": [
"Otsinktol"
]
},
{
"page": "Otsobm",
"title": "Replace W_1 by a Hilbert norm on the difference of the measures",
"topics": [
"Otsobm"
]
},
{
"page": "Otsoftas",
"title": "Soft assignment matrix from an entropic transport plan",
"topics": [
"Otsoftas"
]
},
{
"page": "Otsw",
"title": "Average the one-dimensional distance over many directions",
"topics": [
"Otsw"
]
},
{
"page": "Otunbal",
"title": "Transport between histograms of unequal mass",
"topics": [
"Otunbal"
]
},
{
"page": "Otws",
"title": "Optimal transport cost on the line, which needs no solver",
"topics": [
"Otws"
]
},
{
"page": "Otws2",
"title": "Wasserstein distance on the line, by sorting",
"topics": [
"Otws2"
]
},
{
"page": "Otwsg",
"title": "Wasserstein distance between Gaussians, without solving anything",
"topics": [
"Otwsg"
]
},
{
"page": "Outbrkdet",
"title": "Outbreak detection by online changepoint analysis of counts",
"topics": [
"Outbrkdet"
]
},
{
"page": "Outmad",
"title": "MAD-median outlier rule",
"topics": [
"Outmad"
]
},
{
"page": "OutMah",
"title": "Outliers by robust Mahalanobis distance",
"topics": [
"OutMah"
]
},
{
"page": "Outms",
"title": "Mean/standard-deviation outlier rule",
"topics": [
"Outms"
]
},
{
"page": "Outsplit",
"title": "Split coordinates into outlier and non-outlier sets, bits allocated apart",
"topics": [
"Outsplit"
]
},
{
"page": "Ovbias",
"title": "Omitted variable bias, classical and partial-R2 form",
"topics": [
"Ovbias"
]
},
{
"page": "Ovbnk",
"title": "Oster bound on bias from omitted variables",
"topics": [
"Ovbnk"
]
},
{
"page": "Owltrn",
"title": "Outcome-weighted learning for an optimal treatment regime",
"topics": [
"Owltrn"
]
},
{
"page": "Paa",
"title": "Piecewise aggregate approximation",
"topics": [
"Paa"
]
},
{
"page": "Pagehink",
"title": "Page-Hinkley sequential test for a shift in the mean",
"topics": [
"Pagehink"
]
},
{
"page": "Pagrk",
"title": "PageRank",
"topics": [
"Pagrk"
]
},
{
"page": "paired_permutation_test",
"title": "Paired permutation test (sign-flipping)",
"topics": [
"paired_permutation_test"
]
},
{
"page": "paired_ttest",
"title": "Paired-sample t-test",
"topics": [
"paired_ttest"
]
},
{
"page": "Paligi",
"title": "ALiBi attention with per-head linear positional bias",
"topics": [
"Paligi"
]
},
{
"page": "PandInd",
"title": "And-rule for independent events",
"topics": [
"PandInd"
]
},
{
"page": "panel_differences",
"title": "Post-minus-pre panel differences",
"topics": [
"panel_differences"
]
},
{
"page": "PanelBinaryChoice",
"title": "Panel binary choice models",
"topics": [
"PanelBinaryChoice"
]
},
{
"page": "PanelWithin",
"title": "Panel-data diagnostics",
"topics": [
"BaltagiLiTest",
"ConleyVcov",
"CrossSectionDependence",
"PanelResiduals",
"PanelSerialTest",
"PanelVarianceComponents",
"PanelWithin",
"UnobservedEffectsTest"
]
},
{
"page": "papadk",
"title": "Papadakis nearest-neighbour adjustment for field trials",
"topics": [
"papadk"
]
},
{
"page": "Paracompare",
"title": "Compare parametric survival families by information criterion",
"topics": [
"morie_survival_compare_parametric",
"Paracompare"
]
},
{
"page": "parametric_bootstrap",
"title": "Parametric bootstrap",
"topics": [
"parametric_bootstrap"
]
},
{
"page": "Parasurv",
"title": "Parametric survival fit by maximum likelihood",
"topics": [
"morie_survival_parametric",
"Parasurv"
]
},
{
"page": "Pareff",
"title": "Population attributable fraction (Levin)",
"topics": [
"Pareff"
]
},
{
"page": "partial_correlation",
"title": "Partial Pearson correlation controlling for covariates",
"topics": [
"partial_correlation"
]
},
{
"page": "partial_eta_squared",
"title": "Partial eta-squared",
"topics": [
"partial_eta_squared"
]
},
{
"page": "PartisanGerrymanderMeasures",
"title": "Partisan symmetry, swing, competitiveness and disproportionality",
"topics": [
"Disproportionality",
"DistrictCompetitiveness",
"ElectoralSwing",
"Malapportionment",
"PartisanGerrymanderMeasures"
]
},
{
"page": "PartyNicheness",
"title": "Meyer and Miller (2015) nicheness of the parties in one election",
"topics": [
"PartyNicheness"
]
},
{
"page": "PartyPositions",
"title": "Party positions",
"topics": [
"PartyPositions"
]
},
{
"page": "PartySystemIndices",
"title": "Party systems and roll-call utilities",
"topics": [
"PartyDivergence",
"PartySystemIndices",
"RollcallFilter",
"RollcallMatrix",
"RollcallSummary"
]
},
{
"page": "PascalId",
"title": "Pascal's addition rule",
"topics": [
"PascalId"
]
},
{
"page": "PatchMetrics",
"title": "Landscape metrics of categorical rasters (FRAGSTATS, landscapemetrics conventions)",
"topics": [
"ClassMetrics",
"LandscapeMetrics",
"PatchMetrics"
]
},
{
"page": "PatchStructure",
"title": "Further FRAGSTATS landscape metrics: shape, core, proximity and information theory",
"topics": [
"ClassStructure",
"LandscapeInformation",
"PatchStructure",
"ProximityMetrics"
]
},
{
"page": "PBCI",
"title": "Percentile bootstrap interval from ordered bootstrap values",
"topics": [
"PBCI"
]
},
{
"page": "pburg",
"title": "Burg autoregressive power spectral density",
"topics": [
"pburg"
]
},
{
"page": "Pcadim",
"title": "Principal component compression of a marker matrix",
"topics": [
"Pcadim"
]
},
{
"page": "Pcasnps",
"title": "EIGENSTRAT principal components of a genotype matrix",
"topics": [
"Pcasnps"
]
},
{
"page": "PCenter",
"title": "p-center location",
"topics": [
"PCenter"
]
},
{
"page": "Pcfunc",
"title": "Pair correlation function g(r) from the derivative of Ripley's K",
"topics": [
"Pcfunc"
]
},
{
"page": "pcgenv",
"title": "PCG Shannon-energy envelope",
"topics": [
"pcgenv"
]
},
{
"page": "pcgmur",
"title": "PCG murmur likelihood score",
"topics": [
"pcgmur"
]
},
{
"page": "pcgseg-signal",
"title": "PCG S1/S2 heart-sound segmentation",
"topics": [
"pcgseg"
]
},
{
"page": "pchnrm",
"title": "Pearson chi-square test of normality",
"topics": [
"pchnrm"
]
},
{
"page": "pearson_correlation",
"title": "Pearson product-moment correlation",
"topics": [
"pearson_correlation"
]
},
{
"page": "Pearsonr",
"title": "Sample correlation, its t test, and a Fisher-z interval",
"topics": [
"Pearsonr"
]
},
{
"page": "Pelt",
"title": "PELT changepoint detection (pruned exact linear time)",
"topics": [
"Pelt"
]
},
{
"page": "Permanova",
"title": "PERMANOVA pseudo-F from a distance matrix",
"topics": [
"Permanova"
]
},
{
"page": "permutation_fdr",
"title": "Permutation-based FDR control via empirical null p-value distribution",
"topics": [
"permutation_fdr"
]
},
{
"page": "permutation_fwer",
"title": "Permutation-based FWER control via step-down max-T (Westfall-Young)",
"topics": [
"permutation_fwer"
]
},
{
"page": "permutation_test",
"title": "Two-sample permutation test",
"topics": [
"permutation_test"
]
},
{
"page": "Perplex",
"title": "Perplexity of a language model",
"topics": [
"Perplex"
]
},
{
"page": "Perturb",
"title": "Perturbation of one composition by another",
"topics": [
"Perturb"
]
},
{
"page": "Pettitt",
"title": "Pettitt's non-parametric single change-point test",
"topics": [
"Pettitt"
]
},
{
"page": "pettitt_changepoint",
"title": "Pettitt non-parametric change-point detection",
"topics": [
"pettitt_changepoint"
]
},
{
"page": "pfd",
"title": "Petrosian fractal dimension",
"topics": [
"pfd"
]
},
{
"page": "Pgrank",
"title": "Stationary importance of a node under a random surfer",
"topics": [
"Pgrank"
]
},
{
"page": "PgSigmas",
"title": "Atmospheric dispersion: Pasquill-Gifford sigmas, Briggs plume rise, Gaussian plume and puff, Eulerian and Lagrangian models",
"topics": [
"AdvectionDiffusion2d",
"BriggsPlumeRise",
"GaussianPlume",
"GaussianPuff",
"LagrangianParticles",
"PgSigmas"
]
},
{
"page": "ph_assumption_test",
"title": "Proportional-hazards assumption test (Schoenfeld-style)",
"topics": [
"ph_assumption_test"
]
},
{
"page": "Pheno2",
"title": "Normalise a phenotype, then flag what is still extreme",
"topics": [
"Pheno2"
]
},
{
"page": "phi_coefficient",
"title": "Phi coefficient for a 2x2 contingency table",
"topics": [
"phi_coefficient"
]
},
{
"page": "Phylog",
"title": "Root-to-tip regression dating (TempEst)",
"topics": [
"Phylog"
]
},
{
"page": "Phylotr",
"title": "Neighbor-joining phylogenetic tree (Saitou-Nei 1987)",
"topics": [
"Phylotr"
]
},
{
"page": "piecewise_trend",
"title": "Evaluate the piecewise-linear trend (Eq. 4 of Taylor & Letham 2018)",
"topics": [
"piecewise_trend"
]
},
{
"page": "Piepar",
"title": "Effect of shifting the exposure distribution, not eliminating it",
"topics": [
"Piepar"
]
},
{
"page": "PiidInt",
"title": "Intersection of k independent events of common probability",
"topics": [
"PiidInt"
]
},
{
"page": "PivotedCholesky",
"title": "Pivoted (incomplete) Cholesky factorisation",
"topics": [
"PivotedCholesky"
]
},
{
"page": "placebo_did",
"title": "Pre-period placebo DiD",
"topics": [
"placebo_did"
]
},
{
"page": "plackt",
"title": "Plackett's bivariate distribution",
"topics": [
"plackt"
]
},
{
"page": "Plncf",
"title": "Planck blackbody spectral radiance, wavelength form",
"topics": [
"Plncf"
]
},
{
"page": "plot.morie_bayes_fit",
"title": "Plot method for 'morie_bayes_fit' objects",
"topics": [
"plot.morie_bayes_fit"
]
},
{
"page": "plot.morie_cluster",
"title": "Default 2-D cluster plot",
"topics": [
"plot.morie_cluster"
]
},
{
"page": "plot.morie_lm",
"title": "Default diagnostic plot for a morie_lm",
"topics": [
"plot.morie_lm"
]
},
{
"page": "plot.morie_rich_result",
"title": "Default plot for morie rich-result objects",
"topics": [
"plot.morie_rich_result"
]
},
{
"page": "plot.morie_spatial",
"title": "Plot method for 'morie_spatial' objects",
"topics": [
"plot.morie_spatial"
]
},
{
"page": "plot.morie_ts_forecast",
"title": "Plot method for 'morie_ts_forecast' objects",
"topics": [
"plot.morie_ts_forecast"
]
},
{
"page": "PluralityCompetition",
"title": "Hotelling-Downs plurality competition on a line",
"topics": [
"PluralityCompetition"
]
},
{
"page": "Pmedex",
"title": "Indirect effect divided by an independently supplied total",
"topics": [
"Pmedex"
]
},
{
"page": "PMedian",
"title": "p-median location",
"topics": [
"PMedian"
]
},
{
"page": "PmfVar",
"title": "Variance of a discrete pmf",
"topics": [
"PmfVar"
]
},
{
"page": "PnaAgg",
"title": "Principal Neighbourhood Aggregation (Corso et al. 2020, eq. 7)",
"topics": [
"PnaAgg"
]
},
{
"page": "point_biserial_correlation",
"title": "Point-biserial correlation",
"topics": [
"point_biserial_correlation"
]
},
{
"page": "Poisdev",
"title": "Deviance and Pearson goodness of fit for a Poisson regression",
"topics": [
"Poisdev"
]
},
{
"page": "Poisdir",
"title": "Pitman-Yor stick-breaking weights and the Gibbs factor V_{n,k}",
"topics": [
"Poisdir"
]
},
{
"page": "PoisExactCI",
"title": "Exact Poisson interval",
"topics": [
"PoisExactCI"
]
},
{
"page": "PoisGauss",
"title": "Gaussian limit of the Poisson distribution",
"topics": [
"PoisGauss"
]
},
{
"page": "Poislo",
"title": "Poisson loss for count outcomes",
"topics": [
"Poislo"
]
},
{
"page": "PoisMean",
"title": "Mean of the Poisson distribution by its series",
"topics": [
"PoisMean"
]
},
{
"page": "PoisMode",
"title": "Mode of the Poisson distribution",
"topics": [
"PoisMode"
]
},
{
"page": "PoisNorm",
"title": "Poisson normalisation via the exponential series",
"topics": [
"PoisNorm"
]
},
{
"page": "PoisPeak",
"title": "Peak height of the Poisson distribution",
"topics": [
"PoisPeak"
]
},
{
"page": "Poispen",
"title": "Penalized Poisson log-linear regression",
"topics": [
"Poispen"
]
},
{
"page": "PoisPmf",
"title": "Poisson probability mass function",
"topics": [
"PoisPmf"
]
},
{
"page": "Poispred",
"title": "Conjugate Poisson analysis with its negative-binomial predictive",
"topics": [
"Poispred"
]
},
{
"page": "PoissonGammaEb",
"title": "Poisson-gamma empirical Bayes relative risks",
"topics": [
"PoissonGammaEb"
]
},
{
"page": "PoissonProcessFit",
"title": "Log-linear Poisson point process: Berman-Turner fit and simulation",
"topics": [
"PoissonProcessFit"
]
},
{
"page": "PoisStirC",
"title": "Poisson-Stirling pmf in centred variables",
"topics": [
"PoisStirC"
]
},
{
"page": "PoisStirl",
"title": "Poisson pmf under Stirling's factorial approximation",
"topics": [
"PoisStirl"
]
},
{
"page": "PoisSum1",
"title": "The Poisson pmf sums to one",
"topics": [
"PoisSum1"
]
},
{
"page": "PoisVar",
"title": "Variance of the Poisson distribution by its series",
"topics": [
"PoisVar"
]
},
{
"page": "PoisZero",
"title": "Poisson probability of no events",
"topics": [
"PoisZero"
]
},
{
"page": "PolarizationIndices",
"title": "Polarization indices",
"topics": [
"EarthMoversDistance",
"EstebanRayIndex",
"IssueConstraint",
"PolarizationIndices",
"PolarizationTrend"
]
},
{
"page": "PolarizationMeasures",
"title": "Polarization measures",
"topics": [
"PolarizationMeasures"
]
},
{
"page": "PolicyMotivatedEquilibrium",
"title": "Equilibrium of two policy-motivated candidates under electoral uncertainty",
"topics": [
"PolicyMotivatedEquilibrium"
]
},
{
"page": "PollardRho",
"title": "Numerical and discrete algorithms",
"topics": [
"LegendrePolynomials",
"LogitProportion",
"McStandardError",
"PanterDiteBound",
"PolarTransform",
"PollardRho",
"ResolutionRefutation"
]
},
{
"page": "PolrFit",
"title": "Proportional odds model by maximum likelihood",
"topics": [
"PolrFit"
]
},
{
"page": "PolygonBoolean",
"title": "Vector and raster geoprocessing",
"topics": [
"ElevationProfile",
"PolygonArea",
"PolygonBoolean",
"ProximityBands"
]
},
{
"page": "PolygonCompactness",
"title": "Polygon compactness",
"topics": [
"PolygonCompactness"
]
},
{
"page": "PolygonContiguity",
"title": "Contiguity neighbours of polygons",
"topics": [
"PolygonContiguity"
]
},
{
"page": "Polykern",
"title": "Polynomial kernel matrix",
"topics": [
"Polykern"
]
},
{
"page": "PopulationDensitySurface",
"title": "Population density surface",
"topics": [
"PopulationDensitySurface"
]
},
{
"page": "PopVar",
"title": "Population variance of a data set",
"topics": [
"PopVar"
]
},
{
"page": "posterior_mean",
"title": "DDPM posterior mean",
"topics": [
"posterior_mean"
]
},
{
"page": "Postrt",
"title": "Post-stratification as a weight adjustment",
"topics": [
"Postrt"
]
},
{
"page": "Poststs",
"title": "Post-stratified estimator of a population mean",
"topics": [
"Poststs"
]
},
{
"page": "PottsGibbs",
"title": "Potts and Ising lattice models",
"topics": [
"PottsExact",
"PottsGibbs"
]
},
{
"page": "PowerDist",
"title": "Power function distribution",
"topics": [
"PowerDist"
]
},
{
"page": "PowerIndices",
"title": "Voting power indices",
"topics": [
"PowerIndices"
]
},
{
"page": "Powering",
"title": "Power a composition by a real scalar",
"topics": [
"Powering"
]
},
{
"page": "PowerLawField",
"title": "Gaussian field with a prescribed power spectrum by FFT filtering",
"topics": [
"PowerLawField"
]
},
{
"page": "PowerTTest",
"title": "Power analysis for t, proportion and ANOVA F tests",
"topics": [
"CalculateInteractionPower",
"PowerAnova",
"PowerPropTest",
"PowerTTest"
]
},
{
"page": "PowExpAp2",
"title": "Second-order (1+a)^n approximation",
"topics": [
"PowExpAp2"
]
},
{
"page": "PowExpApx",
"title": "First-order (1+a)^n approximation",
"topics": [
"PowExpApx"
]
},
{
"page": "PowLogSer",
"title": "The (1+a)^n product expansion from the log series",
"topics": [
"PowLogSer"
]
},
{
"page": "Powsrv",
"title": "Power of a two-sided test under a complex survey design",
"topics": [
"Powsrv"
]
},
{
"page": "Ppcrep",
"title": "Posterior predictive p-value comparing T(y, theta) to T(y_rep, theta)",
"topics": [
"Ppcrep"
]
},
{
"page": "Ppmean",
"title": "Posterior predictive mean, sd and interval from replicated datasets",
"topics": [
"Ppmean"
]
},
{
"page": "Ppsamp",
"title": "PPS design quantities for a frame of units",
"topics": [
"Ppsamp"
]
},
{
"page": "Ppssamp",
"title": "Draw n units with probability proportional to size, and estimate",
"topics": [
"Ppssamp"
]
},
{
"page": "Pptest",
"title": "Phillips-Perron test for a unit root, trend included",
"topics": [
"Pptest"
]
},
{
"page": "Pptrend",
"title": "Phillips-Perron unit-root test with an explicit trend switch",
"topics": [
"Pptrend"
]
},
{
"page": "Ppunit",
"title": "Phillips-Perron unit-root test",
"topics": [
"Ppunit"
]
},
{
"page": "PrecK",
"title": "Precision at k",
"topics": [
"PrecK"
]
},
{
"page": "Predacc",
"title": "Genomic selection accuracy: Pearson correlation of observed and predicted",
"topics": [
"Predacc"
]
},
{
"page": "Predcomp",
"title": "Expected code length of a predictive model (the wrong-code bound)",
"topics": [
"Predcomp"
]
},
{
"page": "predict.morie_cluster",
"title": "Assign new data to existing clusters without re-fitting",
"topics": [
"predict.morie_cluster"
]
},
{
"page": "predict.morie_gam",
"title": "Predictions from a native generalized additive model",
"topics": [
"predict.morie_gam"
]
},
{
"page": "predict.morie_lm",
"title": "Predict from a morie_lm, optionally with intervals",
"topics": [
"predict.morie_lm"
]
},
{
"page": "predict.morie_ml_fit",
"title": "Predict from a trained model",
"topics": [
"predict.morie_ml_fit"
]
},
{
"page": "prediction_interval",
"title": "Prediction interval for a new study (random-effects meta)",
"topics": [
"prediction_interval"
]
},
{
"page": "Prefixev",
"title": "Evaluate a Polish-notation expression, right to left",
"topics": [
"Prefixev"
]
},
{
"page": "Prehay",
"title": "Specific and total indirect effects with percentile bootstrap CIs",
"topics": [
"Prehay"
]
},
{
"page": "Prevratio",
"title": "Prevalence ratio",
"topics": [
"Prevratio"
]
},
{
"page": "print.morie_arsau_analysis_result",
"title": "Print method for 'morie_arsau_analysis_result' objects",
"topics": [
"print.morie_arsau_analysis_result"
]
},
{
"page": "print.morie_arsau_result",
"title": "Print method for 'morie_arsau_result' objects",
"topics": [
"print.morie_arsau_result"
]
},
{
"page": "print.morie_audit_result",
"title": "Print method for audit results",
"topics": [
"print.morie_audit_result"
]
},
{
"page": "print.morie_bayes_fit",
"title": "Print method for 'morie_bayes_fit' objects",
"topics": [
"print.morie_bayes_fit"
]
},
{
"page": "print.morie_city_profile",
"title": "Print method for 'morie_city_profile' objects",
"topics": [
"print.morie_city_profile"
]
},
{
"page": "print.morie_cluster",
"title": "Print method for 'morie_cluster' objects",
"topics": [
"print.morie_cluster"
]
},
{
"page": "print.morie_cluster_spec",
"title": "Print method for 'morie_cluster_spec' objects",
"topics": [
"print.morie_cluster_spec"
]
},
{
"page": "print.morie_distribution",
"title": "Print method for 'morie_distribution' objects",
"topics": [
"print.morie_distribution"
]
},
{
"page": "print.morie_dml_clustered",
"title": "Print method for 'morie_dml_clustered' objects",
"topics": [
"print.morie_dml_clustered"
]
},
{
"page": "print.morie_eda_table",
"title": "Print method for 'morie_eda_table' objects",
"topics": [
"print.morie_eda_table"
]
},
{
"page": "print.morie_fairness_result",
"title": "Print method for 'morie_fairness_result' objects",
"topics": [
"print.morie_fairness_result"
]
},
{
"page": "print.morie_hawkes_fit",
"title": "Print method for 'morie_hawkes_fit' objects",
"topics": [
"print.morie_hawkes_fit"
]
},
{
"page": "print.morie_hawkes_st_fit",
"title": "Print method for 'morie_hawkes_st_fit' objects",
"topics": [
"print.morie_hawkes_st_fit"
]
},
{
"page": "print.morie_laniyonu_ard_result",
"title": "Print method for 'morie_laniyonu_ard_result' objects",
"topics": [
"print.morie_laniyonu_ard_result"
]
},
{
"page": "print.morie_laniyonu_gp_result",
"title": "Print method for 'morie_laniyonu_gp_result' objects",
"topics": [
"print.morie_laniyonu_gp_result"
]
},
{
"page": "print.morie_laniyonu_smi_result",
"title": "Print method for 'morie_laniyonu_smi_result' objects",
"topics": [
"print.morie_laniyonu_smi_result"
]
},
{
"page": "print.morie_lm",
"title": "Print method for 'morie_lm' objects",
"topics": [
"print.morie_lm"
]
},
{
"page": "print.morie_lm_spec",
"title": "Print method for 'morie_lm_spec' objects",
"topics": [
"print.morie_lm_spec"
]
},
{
"page": "print.morie_lmm",
"title": "Print a morie mixed model",
"topics": [
"print.morie_lmm"
]
},
{
"page": "print.morie_ml_fit",
"title": "Print method for 'morie_ml_fit' objects",
"topics": [
"print.morie_ml_fit"
]
},
{
"page": "print.morie_ml_model",
"title": "Print method for 'morie_ml_model' objects",
"topics": [
"print.morie_ml_model"
]
},
{
"page": "print.morie_ml_recipe",
"title": "Print method for 'morie_ml_recipe' objects",
"topics": [
"print.morie_ml_recipe"
]
},
{
"page": "print.morie_ml_split",
"title": "Print method for 'morie_ml_split' objects",
"topics": [
"print.morie_ml_split"
]
},
{
"page": "print.morie_mrm_result",
"title": "Print method for 'morie_mrm_result' objects",
"topics": [
"print.morie_mrm_result"
]
},
{
"page": "print.morie_mrm_uof_result",
"title": "Print method for 'morie_mrm_uof_result' objects",
"topics": [
"print.morie_mrm_uof_result"
]
},
{
"page": "print.morie_multiple_testing_result",
"title": "Print method for 'morie_multiple_testing_result' objects",
"topics": [
"print.morie_multiple_testing_result"
]
},
{
"page": "print.morie_otis_analysis_result",
"title": "Print method for 'morie_otis_analysis_result' objects",
"topics": [
"print.morie_otis_analysis_result"
]
},
{
"page": "print.morie_otis_result",
"title": "Print method for 'morie_otis_result' objects",
"topics": [
"print.morie_otis_result"
]
},
{
"page": "print.morie_semipar_kernels",
"title": "Print method for 'morie_semipar_kernels' objects",
"topics": [
"print.morie_semipar_kernels"
]
},
{
"page": "print.morie_spatial",
"title": "Print method for 'morie_spatial' objects",
"topics": [
"print.morie_spatial"
]
},
{
"page": "print.morie_stat_command",
"title": "Print method for 'morie_stat_command' objects",
"topics": [
"print.morie_stat_command"
]
},
{
"page": "print.morie_test_result",
"title": "Print method for 'morie_test_result' objects",
"topics": [
"print.morie_test_result"
]
},
{
"page": "print.morie_tokenizer",
"title": "Print method for morie tokenizer",
"topics": [
"print.morie_tokenizer"
]
},
{
"page": "print.morie_tps_result",
"title": "Pretty-print method for 'morie_tps_result' objects",
"topics": [
"print.morie_tps_result"
]
},
{
"page": "print.morie_tps_spatial_advanced_result",
"title": "Print method for 'morie_tps_spatial_advanced_result' objects",
"topics": [
"print.morie_tps_spatial_advanced_result"
]
},
{
"page": "print.morie_tps_spatial_result",
"title": "Print method for 'morie_tps_spatial_result' objects",
"topics": [
"print.morie_tps_spatial_result"
]
},
{
"page": "print.morie_tps_stochastic_result",
"title": "Print method for 'morie_tps_stochastic_result' objects",
"topics": [
"print.morie_tps_stochastic_result"
]
},
{
"page": "print.morie_tps_temporal_result",
"title": "Print method for 'morie_tps_temporal_result' objects",
"topics": [
"print.morie_tps_temporal_result"
]
},
{
"page": "print.morie_ts_forecast",
"title": "Print method for 'morie_ts_forecast' objects",
"topics": [
"print.morie_ts_forecast"
]
},
{
"page": "print.morie_ts_model",
"title": "Print method for 'morie_ts_model' objects",
"topics": [
"print.morie_ts_model"
]
},
{
"page": "print.morie_variable_taxonomy",
"title": "Print method for taxonomy entries",
"topics": [
"print.morie_variable_taxonomy"
]
},
{
"page": "print.summary.morie_lmm",
"title": "Print a morie mixed-model summary",
"topics": [
"print.summary.morie_lmm"
]
},
{
"page": "Prnkpg",
"title": "PageRank - alias of 'Pgrank'",
"topics": [
"Prnkpg"
]
},
{
"page": "probabilistic_bias_analysis",
"title": "Probabilistic (Monte Carlo) sensitivity analysis",
"topics": [
"probabilistic_bias_analysis"
]
},
{
"page": "ProbabilisticVote",
"title": "Probabilistic voting and candidate competition",
"topics": [
"BaronFerejohn",
"CandidateEquilibrium",
"HotellingPriceLocation",
"KalaiSmorodinsky",
"PcaIdealPoints",
"ProbabilisticVote",
"SalopCircle",
"VoteTradingRikerBrams"
]
},
{
"page": "ProbabilityKriging",
"title": "Probability kriging",
"topics": [
"ProbabilityKriging"
]
},
{
"page": "ProcrustesFit",
"title": "Procrustes analysis (vegan procrustes and protest conventions)",
"topics": [
"GeneralizedProcrustes",
"ProcrustesFit"
]
},
{
"page": "ProcrustesOblique",
"title": "Oblique Procrustes rotation and configuration orientation checks",
"topics": [
"MdsAnisotropy",
"MdsFlip",
"MdsPolarity",
"MdsReflect",
"ProcrustesOblique"
]
},
{
"page": "ProductVariance",
"title": "Probability toolkit: product variance, Weibull and folded-normal laws, evidence, CRPS, maximum entropy, EV simulation, DDM, KING",
"topics": [
"BvLogisticSimulate",
"Chisq1Cdf",
"CrpsCdf",
"DdmDrift",
"FoldedNormal",
"HarmonicMeanEvidence",
"KingKinship",
"MaxEntropyDiscrete",
"ProductVariance",
"WeibullMoments"
]
},
{
"page": "Propalloc",
"title": "Allocate n units to strata in proportion to stratum size",
"topics": [
"Propalloc"
]
},
{
"page": "prophet_design",
"title": "Stack trend, seasonality and holiday columns into one design",
"topics": [
"prophet_design"
]
},
{
"page": "prophet_predict",
"title": "Forecast at new times, reusing the fitted coefficients",
"topics": [
"prophet_predict"
]
},
{
"page": "PropMd",
"title": "Share of the total effect that runs through the mediator",
"topics": [
"PropMd"
]
},
{
"page": "Propme",
"title": "Proportion mediated and mediated-to-direct ratio",
"topics": [
"Propme"
]
},
{
"page": "prphet_cheatsheet",
"title": "prphet cheatsheet",
"topics": [
"prphet_cheatsheet"
]
},
{
"page": "prtcl_cheatsheet",
"title": "prtcl cheatsheet",
"topics": [
"prtcl_cheatsheet"
]
},
{
"page": "Prtdid",
"title": "Goodman-Bacon partition of the two-way fixed-effects DiD",
"topics": [
"Prtdid"
]
},
{
"page": "prxgms_cheatsheet",
"title": "prxgms cheatsheet",
"topics": [
"prxgms_cheatsheet"
]
},
{
"page": "Pseudo",
"title": "Path-specific effect of x on y in a linear SEM",
"topics": [
"Pseudo"
]
},
{
"page": "Pseudor2",
"title": "Pseudo coefficients of determination for a logistic regression",
"topics": [
"Pseudor2"
]
},
{
"page": "psis_loo",
"title": "PSIS leave-one-out cross-validation",
"topics": [
"psis_loo"
]
},
{
"page": "Psoop",
"title": "Particle swarm optimisation on a deterministic low-discrepancy stream",
"topics": [
"Psoop"
]
},
{
"page": "Ptmcmc",
"title": "Parallel tempering (replica exchange) MCMC",
"topics": [
"Ptmcmc"
]
},
{
"page": "Ptnulltst",
"title": "Bayes factor for H0: p = p* against a nonparametric alternative",
"topics": [
"Ptnulltst"
]
},
{
"page": "Ptotal",
"title": "Law of total probability",
"topics": [
"Ptotal"
]
},
{
"page": "PTukey",
"title": "Studentized range distribution function",
"topics": [
"PTukey"
]
},
{
"page": "Pwpgt",
"title": "Prentice-Williams-Peterson conditional gap-time model",
"topics": [
"Pwpgt"
]
},
{
"page": "PwrRTest",
"title": "Power of the correlation test and Cohen's benchmarks",
"topics": [
"CohenRMagnitude",
"PwrRTest"
]
},
{
"page": "PysrRegression",
"title": "Symbolic regression with a Pareto front (PySR scheme)",
"topics": [
"PysrRegression"
]
},
{
"page": "Qcritset",
"title": "Criterion-function set estimate for moment inequalities",
"topics": [
"Qcritset"
]
},
{
"page": "Qlearn",
"title": "Tabular Q-learning on an explicit finite MDP",
"topics": [
"Qlearn"
]
},
{
"page": "Qmds",
"title": "Empirical quantile-mapping bias correction (QUANT)",
"topics": [
"Qmds"
]
},
{
"page": "QnScale",
"title": "Rousseeuw & Croux (1993) Qn robust scale",
"topics": [
"QnScale"
]
},
{
"page": "Qrf",
"title": "Quantile (pinball) scoring function for point forecasts",
"topics": [
"Qrf"
]
},
{
"page": "qrntcq_cheatsheet",
"title": "qrntcq cheatsheet",
"topics": [
"qrntcq_cheatsheet"
]
},
{
"page": "QTukey",
"title": "Studentized range quantile",
"topics": [
"QTukey"
]
},
{
"page": "QuadraticUtilityLagrange",
"title": "Utility maximisation under constraints and the VCG mechanism",
"topics": [
"QuadraticUtilityConstrained",
"QuadraticUtilityLagrange",
"VcgMechanism"
]
},
{
"page": "QuantileRegressionLp",
"title": "Quantile regression by linear programming and spatial two-stage quantile regression",
"topics": [
"QuantileRegressionLp",
"SpatialQuantileIv"
]
},
{
"page": "quarantine_efficacy",
"title": "Quarantine efficacy (eq. 1 of Ashcroft et al. 2021)",
"topics": [
"quarantineefficacy",
"quarantine_efficacy"
]
},
{
"page": "r_effect_size",
"title": "Pearson r as an effect size with Fisher-z CI",
"topics": [
"r_effect_size"
]
},
{
"page": "r_squared",
"title": "Coefficient of determination R^2",
"topics": [
"r_squared"
]
},
{
"page": "r_to_d",
"title": "Convert Pearson r to Cohen's d",
"topics": [
"r_to_d"
]
},
{
"page": "r_to_or",
"title": "Convert Pearson r to OR via d",
"topics": [
"r_to_or"
]
},
{
"page": "R0",
"title": "Basic reproduction number R0",
"topics": [
"R0"
]
},
{
"page": "R0bayse",
"title": "Basic reproduction number - alias of 'R0'",
"topics": [
"R0bayse"
]
},
{
"page": "R2FTest",
"title": "F test that all slopes are zero, from R-squared",
"topics": [
"R2FTest"
]
},
{
"page": "RadialBasis",
"title": "Radial basis function surfaces",
"topics": [
"RadialBasis",
"RbfGrid",
"RbfInterpolate",
"RbfLoocv",
"RbfMultiscale"
]
},
{
"page": "Radj",
"title": "Adjusted coefficient of determination for the linear model",
"topics": [
"Radj"
]
},
{
"page": "Raklng",
"title": "Raking ratio estimation by iterative proportional fitting",
"topics": [
"Raklng"
]
},
{
"page": "ramsey_reset_test",
"title": "Ramsey RESET test for functional-form misspecification",
"topics": [
"ramsey_reset_test"
]
},
{
"page": "random_effects_meta",
"title": "Random-effects (DerSimonian-Laird) meta-analytic pooling",
"topics": [
"random_effects_meta"
]
},
{
"page": "RandomPhaseField",
"title": "Random-phase spectral simulation of an isotropic Gaussian random field",
"topics": [
"RandomPhaseField"
]
},
{
"page": "RandomSpatialSample",
"title": "Spatial sampling designs and weights",
"topics": [
"AdaptiveClusterSample",
"CellDeclusteringWeights",
"HexagonalGridSample",
"MapQualityIndices",
"QuadtreeGrid",
"RandomSpatialSample",
"SpatialThinning",
"VoronoiDeclusteringWeights"
]
},
{
"page": "Randres",
"title": "Recover a population rate from deliberately noised answers",
"topics": [
"Randres"
]
},
{
"page": "RandW",
"title": "Random-walk graph kernel between two graphs",
"topics": [
"RandW"
]
},
{
"page": "Randwk",
"title": "Distribution of a simple random walk after a fixed number of steps",
"topics": [
"Randwk"
]
},
{
"page": "rank_biserial_correlation",
"title": "Rank-biserial correlation for two independent samples",
"topics": [
"rank_biserial_correlation"
]
},
{
"page": "RankedProbabilityScore",
"title": "Ranked probability score",
"topics": [
"RankedProbabilityScore"
]
},
{
"page": "Ranksum",
"title": "Wilcoxon rank-sum test, normal approximation with a tie correction",
"topics": [
"Ranksum"
]
},
{
"page": "RaoScott",
"title": "Rao-Scott corrections and the Thomas-Rao F test",
"topics": [
"RaoScott"
]
},
{
"page": "Rasscale",
"title": "RAS matrix scaling to prescribed margins",
"topics": [
"Rasscale"
]
},
{
"page": "RasterAggregate",
"title": "Raster aggregation, resampling, zonal statistics, masks and distances",
"topics": [
"CostDistance",
"DistanceTransform",
"RasterAggregate",
"RasterDisaggregate",
"RasterMask",
"RasterResample",
"ZonalStatistics"
]
},
{
"page": "rate_ratio",
"title": "Incidence rate ratio (IRR)",
"topics": [
"rate_ratio"
]
},
{
"page": "Ratioest",
"title": "Ratio estimate of a mean or total, using an auxiliary variable",
"topics": [
"Ratioest"
]
},
{
"page": "RayleighDist",
"title": "Rayleigh distribution",
"topics": [
"RayleighDist"
]
},
{
"page": "Rbfk",
"title": "Radial basis (Gaussian) kernel",
"topics": [
"Rbfk"
]
},
{
"page": "Rbfkern",
"title": "Gaussian (radial basis) kernel matrix",
"topics": [
"Rbfkern"
]
},
{
"page": "Rddmanip",
"title": "McCrary's test for a discontinuity in the density at the cutoff",
"topics": [
"Rddmanip"
]
},
{
"page": "Rdfzzy",
"title": "Fuzzy regression discontinuity: the Wald ratio at the cutoff",
"topics": [
"Rdfzzy"
]
},
{
"page": "Rdkfp",
"title": "RDKit path-based (subgraph) topological fingerprint",
"topics": [
"Rdkfp"
]
},
{
"page": "Rdkkin",
"title": "Regression kink design: the ratio of slope changes at the threshold",
"topics": [
"Rdkkin"
]
},
{
"page": "Rdksrn",
"title": "Sharp regression discontinuity by local linear regression",
"topics": [
"Rdksrn"
]
},
{
"page": "Rdmcbw",
"title": "Plug-in MSE-optimal bandwidth for a sharp RD design",
"topics": [
"Rdmcbw"
]
},
{
"page": "Rdpc",
"title": "Renyi differential privacy of the Gaussian mechanism",
"topics": [
"Rdpc"
]
},
{
"page": "reassigned_spectrogram",
"title": "Reassigned spectrogram",
"topics": [
"reassigned_spectrogram"
]
},
{
"page": "Redcap",
"title": "REDCAP regionalization",
"topics": [
"Redcap"
]
},
{
"page": "RedEdgePosition",
"title": "Red edge, reflectance, temperature and albedo conversions",
"topics": [
"FractionalVegetationCover",
"LandSurfaceTemperature",
"NdviEmissivity",
"RedEdgePosition",
"ShortwaveAlbedo",
"ToaReflectance"
]
},
{
"page": "Redund",
"title": "Redundancy of a source",
"topics": [
"Redund"
]
},
{
"page": "Reffec",
"title": "Effective reproduction number from susceptible depletion",
"topics": [
"Reffec"
]
},
{
"page": "RegimeWeights",
"title": "Regime (block-diagonal) weights",
"topics": [
"RegimeWeights"
]
},
{
"page": "register_stat_command",
"title": "Register a stat_command in the package-level registry",
"topics": [
"register_stat_command"
]
},
{
"page": "regression_table",
"title": "Side-by-side regression table for multiple model fits",
"topics": [
"regression_table"
]
},
{
"page": "RegularGrid",
"title": "Grid generation and grid-based sampling",
"topics": [
"RegularGrid",
"SpaceTimeSample",
"SystematicSample",
"TransfiniteGrid",
"TriangularGrid"
]
},
{
"page": "relative_efficiency",
"title": "Relative efficiency of m imputations",
"topics": [
"relative_efficiency"
]
},
{
"page": "relative_utility",
"title": "Utility of one standard quarantine relative to another",
"topics": [
"relative_utility"
]
},
{
"page": "Reluact",
"title": "Rectifier linear unit activation and its gradient",
"topics": [
"Reluact"
]
},
{
"page": "Remlik",
"title": "Restricted (residual) maximum likelihood log-likelihood of a mixed model",
"topics": [
"Remlik"
]
},
{
"page": "Renent",
"title": "Renyi entropy of order alpha",
"topics": [
"Renent"
]
},
{
"page": "repeated_cv",
"title": "Repeated K-fold cross-validation",
"topics": [
"repeated_cv"
]
},
{
"page": "repeated_measures_anova",
"title": "One-way repeated-measures ANOVA",
"topics": [
"repeated_measures_anova"
]
},
{
"page": "Reportm",
"title": "Report Noisy Max",
"topics": [
"Reportm"
]
},
{
"page": "Resaln",
"title": "Resultant of two polynomials",
"topics": [
"Resaln"
]
},
{
"page": "residuals.morie_lm",
"title": "Extract residuals from method for 'morie_lm' objects",
"topics": [
"residuals.morie_lm"
]
},
{
"page": "residuals.morie_lmm",
"title": "Residuals of a morie mixed model",
"topics": [
"residuals.morie_lmm"
]
},
{
"page": "resolve_stat_command",
"title": "Resolve a command by canonical name or alias",
"topics": [
"resolve_stat_command"
]
},
{
"page": "RetestIQ",
"title": "IQ test-retest worked example",
"topics": [
"RetestIQ"
]
},
{
"page": "RetestR",
"title": "Test-retest correlation",
"topics": [
"RetestR"
]
},
{
"page": "RevSlope",
"title": "Reverse regression slope",
"topics": [
"RevSlope"
]
},
{
"page": "rhobin",
"title": "Lower bound on the equicorrelation of exchangeable binary data",
"topics": [
"rhobin"
]
},
{
"page": "RhoBounds",
"title": "Eigenvalue bounds on the autoregressive parameter",
"topics": [
"RhoBounds"
]
},
{
"page": "RhumbLine",
"title": "Geodesy and sampling: rhumb lines, ECEF and Helmert datum transformation, spatial CV folds, stratified samples, nested grids",
"topics": [
"BlockCvFolds",
"EcefToGeodetic",
"GeodeticToEcef",
"HelmertTransform",
"KmeansCvFolds",
"NestedGrid",
"RhumbLine",
"TemporalStratifiedSample"
]
},
{
"page": "RiceDist",
"title": "Rice distribution",
"topics": [
"RiceDist"
]
},
{
"page": "Ridgeobj",
"title": "Penalized residual sum of squares of ridge regression",
"topics": [
"Ridgeobj"
]
},
{
"page": "Ridgesol",
"title": "Closed-form ridge regression estimator",
"topics": [
"Ridgesol"
]
},
{
"page": "RischIntegration",
"title": "Symbolic calculus: limits, symbolic matrices, first-order ODEs and the rational Risch algorithm",
"topics": [
"MatrixSymbolic",
"OdeSymbolic",
"RischIntegration",
"SymbolicLimit"
]
},
{
"page": "risk_difference",
"title": "Risk difference for a 2x2 table",
"topics": [
"risk_difference"
]
},
{
"page": "risk_ratio",
"title": "Risk ratio (relative risk) for a 2x2 table",
"topics": [
"risk_ratio"
]
},
{
"page": "Riskdf",
"title": "Risk difference between two proportions",
"topics": [
"Riskdf"
]
},
{
"page": "Riskrt",
"title": "Risk ratio between two proportions",
"topics": [
"Riskrt"
]
},
{
"page": "Rkhsbayes",
"title": "Bayesian kernel BLUP: the closed-form conditional mean of the kernel effects",
"topics": [
"Rkhsbayes"
]
},
{
"page": "Rkhsnorm",
"title": "Squared RKHS norm of a kernel expansion",
"topics": [
"Rkhsnorm"
]
},
{
"page": "Rmsetst",
"title": "Root mean squared error of a set of predictions",
"topics": [
"Rmsetst"
]
},
{
"page": "Rmst",
"title": "Restricted mean survival time",
"topics": [
"morie_survival_rmst",
"Rmst"
]
},
{
"page": "Rmstdiff",
"title": "Difference in restricted mean survival time between two groups",
"topics": [
"morie_survival_rmst_diff",
"Rmstdiff"
]
},
{
"page": "Robcov",
"title": "Standard errors that survive the wrong variance assumption",
"topics": [
"Robcov"
]
},
{
"page": "RobinsonProject",
"title": "Further map projections and geodesy",
"topics": [
"GeoidHeight",
"MapUnproject",
"NormalGravity",
"RobinsonProject",
"RotatedGrid",
"RotatedPole",
"WebMercator"
]
},
{
"page": "RobustLda",
"title": "Robust linear discriminant analysis",
"topics": [
"RobustLda"
]
},
{
"page": "RoganGladen",
"title": "Prevalence with a misclassifying test",
"topics": [
"RoganGladen"
]
},
{
"page": "RollcallCohesion",
"title": "Roll-call analysis: cohesion and agreement, Snyder-Groseclose party influence, logistic ideal points",
"topics": [
"LogitIdealPoints",
"PartyInfluence",
"RollcallCohesion"
]
},
{
"page": "Ropedy",
"title": "Rotary position embedding rescaled to reach past the trained length",
"topics": [
"Ropedy"
]
},
{
"page": "Rosenb",
"title": "Rosenbaum sensitivity bounds over a grid of Gamma",
"topics": [
"Rosenb"
]
},
{
"page": "rosenbaum_bounds",
"title": "Rosenbaum sensitivity analysis for matched-pair designs",
"topics": [
"rosenbaum_bounds"
]
},
{
"page": "rotate_dek",
"title": "Rotate a DEK: re-encrypt one record",
"topics": [
"rotate_dek"
]
},
{
"page": "rotate_kek",
"title": "Rotate the KEK: re-wrap every DEK, no record ciphertext touched",
"topics": [
"rotate_kek"
]
},
{
"page": "rotation_cost",
"title": "Cost model: single-key vs envelope rotation",
"topics": [
"rotation_cost"
]
},
{
"page": "Rownorm",
"title": "Row-normalised spatial weights",
"topics": [
"Rownorm"
]
},
{
"page": "rpmcor",
"title": "Correlation between and within subjects for repeated measurements",
"topics": [
"rpmcor"
]
},
{
"page": "Rrand",
"title": "The same mechanism, read as a differential-privacy guarantee",
"topics": [
"Rrand"
]
},
{
"page": "rrexct",
"title": "Exact confidence interval and test for the ratio of two Poisson rates",
"topics": [
"rrexct"
]
},
{
"page": "rrint",
"title": "RR interval series from R-peak sample indices",
"topics": [
"rrint"
]
},
{
"page": "Rtrenew",
"title": "Renewal-equation reproduction number with reporting delay",
"topics": [
"Rtrenew"
]
},
{
"page": "RtSi",
"title": "Cori et al. (2013) instantaneous reproduction number",
"topics": [
"RtSi"
]
},
{
"page": "rubins_rules",
"title": "Pool multiply imputed estimates by Rubin's rules",
"topics": [
"rubins_rules"
]
},
{
"page": "run_stat_command",
"title": "Run a command's REPL handler with positional/keyword arguments",
"topics": [
"run_stat_command"
]
},
{
"page": "runs_test",
"title": "Wald-Wolfowitz runs test for randomness",
"topics": [
"runs_test"
]
},
{
"page": "Rwlap",
"title": "Random-walk Laplacian L_rw = I - P, with P the transition matrix",
"topics": [
"Rwlap"
]
},
{
"page": "Sacc",
"title": "Soft policy iteration on an explicit finite MDP",
"topics": [
"Sacc"
]
},
{
"page": "Sacdet",
"title": "SAC (SARAR) model diagnostics",
"topics": [
"Sacboot",
"Sacconv",
"Sacdet",
"Sacimp",
"Sacjac",
"Saclrt",
"Sacres",
"Sacrob",
"Sacvar",
"Sacwald"
]
},
{
"page": "SalvoExchange",
"title": "Salvo model and Colonel Blotto games",
"topics": [
"BlottoBestResponse",
"BlottoPayoff",
"SalvoExchange"
]
},
{
"page": "sampen",
"title": "Sample entropy",
"topics": [
"sampen"
]
},
{
"page": "SampfordDesign",
"title": "Sampford's unequal-probability sampling without replacement",
"topics": [
"SampfordDesign"
]
},
{
"page": "sample_mean",
"title": "Sample moments, errors and ensemble statistics",
"topics": [
"central_moment",
"ensemble_average",
"ensemble_variance",
"esl_total_sum_squares",
"kurtosis_coeff",
"mean_squared_error",
"popvar",
"raw_moment",
"rms_value",
"root_mean_squared_error",
"sample_mean",
"skewness_coeff"
]
},
{
"page": "SampleLmoments",
"title": "Flood, low-flow and flow-duration frequency analysis",
"topics": [
"FloodFrequency",
"FlowDurationCurve",
"LowFlowFrequency",
"SampleLmoments"
]
},
{
"page": "SampleVariogram",
"title": "Sample variogram (gstat variogram conventions)",
"topics": [
"SampleVariogram"
]
},
{
"page": "SampleVariogramNd",
"title": "Variogram extras",
"topics": [
"AnisotropicLag",
"SampleVariogramNd",
"VariogramCloudBox",
"VariogramEnvelope",
"VariogramFractal",
"VariogramJackknife",
"WindowedSemivariance",
"ZonalSemivariance"
]
},
{
"page": "SampMean",
"title": "Sample mean",
"topics": [
"SampMean"
]
},
{
"page": "SampR",
"title": "Sample correlation coefficient",
"topics": [
"SampR"
]
},
{
"page": "SampVar",
"title": "Unbiased sample variance",
"topics": [
"SampVar"
]
},
{
"page": "SampVarVar",
"title": "Variance of the sample variance",
"topics": [
"SampVarVar"
]
},
{
"page": "Sardet",
"title": "Spatial lag (SAR) model diagnostics",
"topics": [
"Sarboot",
"Sardet",
"Sarfilt",
"Sarimp",
"Sarjac",
"Sarlrt",
"Sarr2",
"Sarres",
"Sarsc",
"Sarsim",
"Sarspil",
"Sarvar",
"Sarwald"
]
},
{
"page": "SarPoisson",
"title": "Spatial count regression",
"topics": [
"BymVarianceFraction",
"CountModelIc",
"SarNegbin",
"SarPoisson",
"SarPoissonLmTest",
"SarZinb",
"SarZip"
]
},
{
"page": "SarProbitGibbs",
"title": "Bayesian spatial autoregressive models by Gibbs sampling",
"topics": [
"SarOrderedProbitGibbs",
"SarProbitGibbs",
"SarTobitGibbs",
"SpatialBayesGibbs"
]
},
{
"page": "Sarsa",
"title": "Tabular SARSA on an explicit finite MDP",
"topics": [
"Sarsa"
]
},
{
"page": "SavingsRoutes",
"title": "Vehicle routing extensions and trip generation",
"topics": [
"SavingsRoutes",
"TripGeneration"
]
},
{
"page": "SaxtonRawls",
"title": "Soil hydraulic pedotransfer functions and retention curves",
"topics": [
"SaxtonRawls",
"VanGenuchten"
]
},
{
"page": "Sbcrank",
"title": "Rank statistics for simulation-based calibration, and a uniformity test",
"topics": [
"Sbcrank"
]
},
{
"page": "Scanstat",
"title": "Kulldorff spatial scan statistic",
"topics": [
"Scanstat"
]
},
{
"page": "ScanZones",
"title": "Spatial cluster detection: circular scan, Besag-Newell, Tango and Stone",
"topics": [
"BesagNewell",
"KulldorffScan",
"ScanZones",
"StoneTest",
"TangoTest"
]
},
{
"page": "Scbsft",
"title": "Semiparametric-efficient treatment effect adjusting for baseline",
"topics": [
"Scbsft"
]
},
{
"page": "Scdisp",
"title": "Spatial count model tests",
"topics": [
"Scdisp",
"Scnblrt",
"Scpboot",
"Scpflx"
]
},
{
"page": "ScheffeCI",
"title": "Scheffe interval for a contrast",
"topics": [
"ScheffeCI"
]
},
{
"page": "Schoenfeld",
"title": "Schoenfeld residuals and the proportional-hazards test",
"topics": [
"morie_survival_schoenfeld",
"Schoenfeld"
]
},
{
"page": "schomg",
"title": "Scored chi-square test of homogeneity",
"topics": [
"schomg"
]
},
{
"page": "score_data_quality",
"title": "Multi-dimensional data quality scores",
"topics": [
"score_data_quality"
]
},
{
"page": "score_test",
"title": "Score (Lagrange multiplier) test",
"topics": [
"score_test"
]
},
{
"page": "Scoredist",
"title": "Distortion of an inner-product score under TurboQuant_prod",
"topics": [
"Scoredist"
]
},
{
"page": "Scoretest",
"title": "Score (Rao) test of H0: p = p0 for a binomial proportion",
"topics": [
"Scoretest"
]
},
{
"page": "scpmf",
"title": "Impacts, predictions and Wald test of spatial-lag count models",
"topics": [
"scnbmf",
"scpmf",
"scpprd",
"scpwld"
]
},
{
"page": "SdAvgBin",
"title": "Standard deviations of a binomial total and its average",
"topics": [
"SdAvgBin"
]
},
{
"page": "SdBinom",
"title": "Standard deviation of a binomial count",
"topics": [
"SdBinom"
]
},
{
"page": "Sdcdis",
"title": "Geomasking: spatial data distortion for privacy protection",
"topics": [
"Sdcdis"
]
},
{
"page": "SdCoinSum",
"title": "Standard deviation of a fair-coin Heads count",
"topics": [
"SdCoinSum"
]
},
{
"page": "SdemML",
"title": "Spatial Durbin error, general nesting and CAR regression with tests",
"topics": [
"CarML",
"GnsML",
"LogJacobian",
"ResidualMoran",
"SdemML",
"SpatialLrTest",
"SpatialWaldTest"
]
},
{
"page": "SdFromVar",
"title": "Standard deviation from a variance",
"topics": [
"SdFromVar"
]
},
{
"page": "SdIidSum",
"title": "Standard deviation of a sum of n i.i.d. variables",
"topics": [
"SdIidSum"
]
},
{
"page": "Sdmdet",
"title": "Spatial Durbin (SDM) model diagnostics",
"topics": [
"Gnsres",
"Sdemres",
"Sdmboot",
"Sdmcf",
"Sdmconv",
"Sdmdet",
"Sdmflt",
"Sdmjac",
"Sdmlrt",
"Sdmolsi",
"Sdmr2",
"Sdmres",
"Sdmvar",
"Sdmwald",
"Sdmwx"
]
},
{
"page": "SdmML",
"title": "Spatial Durbin ML with spillover indices, and Breusch-Pagan tests",
"topics": [
"BreuschPagan",
"SdmML",
"SpatialBPTest"
]
},
{
"page": "SdSum",
"title": "Standard deviation of a sum of independent variables",
"topics": [
"SdSum"
]
},
{
"page": "SdXbar",
"title": "Standard deviation of the sample mean",
"topics": [
"SdXbar"
]
},
{
"page": "seal_record",
"title": "Seal a record under its DEK",
"topics": [
"seal_record"
]
},
{
"page": "SegregationEvenness",
"title": "Segregation evenness: Theil, Atkinson and Gini",
"topics": [
"SegregationEvenness"
]
},
{
"page": "Seiarp",
"title": "SEIRA: SEIR with a parallel asymptomatic infectious compartment",
"topics": [
"Seiarp"
]
},
{
"page": "Seirep",
"title": "SEIR compartmental model with an exposed (latent) class",
"topics": [
"Seirep"
]
},
{
"page": "semi_partial_correlation",
"title": "Semi-partial (part) correlation",
"topics": [
"semi_partial_correlation"
]
},
{
"page": "SemiparKernels",
"title": "Object-style wrapper for the semiparametric kernel toolkit",
"topics": [
"SemiparKernels"
]
},
{
"page": "Semjac",
"title": "Spatial error (SEM) model diagnostics",
"topics": [
"Semboot",
"Semconv",
"Semflt",
"Semjac",
"Semlm",
"Semlrt",
"Semres",
"Semsc",
"Semspec",
"Semvar",
"Semwald"
]
},
{
"page": "Semsro",
"title": "SEM residual covariance matrix and its summary fit indices",
"topics": [
"Semsro"
]
},
{
"page": "sensitivity",
"title": "Sensitivity analysis for causal inference assumptions",
"topics": [
"sensitivity"
]
},
{
"page": "sensitivity_rosenbaum",
"title": "Rosenbaum bounds sensitivity analysis (data-frame interface)",
"topics": [
"sensitivity_rosenbaum"
]
},
{
"page": "sensitivity_summary",
"title": "Generate a comprehensive sensitivity-analysis summary",
"topics": [
"sensitivity_summary"
]
},
{
"page": "Sepcons",
"title": "Hellinger-affinity bound from strong delta-separation at stage k",
"topics": [
"Sepcons"
]
},
{
"page": "SequentialInhibition",
"title": "Simple sequential inhibition process",
"topics": [
"SequentialInhibition"
]
},
{
"page": "SequentialLinearProgramming",
"title": "Sequential linear programming",
"topics": [
"SequentialLinearProgramming"
]
},
{
"page": "SequentialQuadraticProgramming",
"title": "Sequential quadratic programming",
"topics": [
"SequentialQuadraticProgramming"
]
},
{
"page": "SerialCorrelation",
"title": "Linear mixed model with serial correlation and measurement error",
"topics": [
"LmmSerialCovariance",
"LmmSerialFit",
"LmmSerialLoglik",
"SerialCorrelation"
]
},
{
"page": "ServiceCoverage",
"title": "Service coverage",
"topics": [
"ServiceCoverage"
]
},
{
"page": "ServR",
"title": "Serendipity of a recommendation list",
"topics": [
"ServR"
]
},
{
"page": "set_transformer",
"title": "Back-compatible wrapper over 'setT' (old name)",
"topics": [
"set_transformer"
]
},
{
"page": "SetCoveringLocation",
"title": "Location set covering",
"topics": [
"SetCoveringLocation"
]
},
{
"page": "setT",
"title": "PMA_k attention pooling from Lee et al. (2019)",
"topics": [
"setT"
]
},
{
"page": "Sfbnds",
"title": "Balke-Pearl sharp bounds on the average causal effect under an instrument",
"topics": [
"Sfbnds"
]
},
{
"page": "sfgetis",
"title": "Eigenvector and Getis spatial filtering utilities",
"topics": [
"sfgetis",
"sfloc",
"sfmemb",
"sfmi",
"sforth",
"sfredu",
"spatial_filter"
]
},
{
"page": "sgcar",
"title": "Conditional autoregressive (CAR) model, fitted by maximum likelihood",
"topics": [
"sgcar"
]
},
{
"page": "SpatialGlmVariogram",
"title": "Spatial GLM, robust semivariogram, loss and weight normalisation",
"topics": [
"Sgcrh",
"Sglm",
"Sglss",
"Sgrwn"
]
},
{
"page": "Sgntest",
"title": "Exact sign test of H0: median = md",
"topics": [
"Sgntest"
]
},
{
"page": "sgolay",
"title": "Savitzky-Golay smoothing (direct alias)",
"topics": [
"sgolay"
]
},
{
"page": "SgsBlockSimulate",
"title": "Geostatistical simulation: SGS with block data, ensembles, p-field, LMC and collocated co-simulation, SIS, SNESIM, annealing",
"topics": [
"CollocatedCosimulate",
"ConditionalEnsemble",
"LmcConditionalSimulate",
"PfieldSimulate",
"SgsBlockSimulate",
"SisMarkovBayes",
"SnesimSimulate"
]
},
{
"page": "Sgtadj",
"title": "Adjacency and non-backtracking matrices of a graph",
"topics": [
"Sgtadj",
"Sgtnbe"
]
},
{
"page": "Sgtcheegerbound",
"title": "Exact Sgtcheegerbound constant, with the eigenvalue bounds it satisfies",
"topics": [
"Sgtcheegerbound"
]
},
{
"page": "Sgtcheegerconstant",
"title": "Sgtcheegerconstant constant by a sweep over the Fiedler vector",
"topics": [
"Sgtcheegerconstant"
]
},
{
"page": "Sgteigcent",
"title": "Eigenvector centrality from the leading eigenvector (re-export)",
"topics": [
"Sgteigcent"
]
},
{
"page": "Sgtgrn",
"title": "One symmetric-normalised graph convolution",
"topics": [
"Sgtgrn"
]
},
{
"page": "Sgtlap2",
"title": "Laplacian eigenmaps: embed a graph in k dimensions",
"topics": [
"Sgtlap2"
]
},
{
"page": "Sgtleid",
"title": "One Leiden refinement pass over an existing partition",
"topics": [
"Sgtleid"
]
},
{
"page": "Sgtmix",
"title": "Mixing time of a random walk from the absolute spectral gap",
"topics": [
"Sgtmix"
]
},
{
"page": "Sgtmodq",
"title": "Newman-Girvan modularity Q of a labelled partition",
"topics": [
"Sgtmodq"
]
},
{
"page": "Sgtpgr",
"title": "PageRank by power iteration - alias of 'Pgrank'",
"topics": [
"Sgtpgr"
]
},
{
"page": "Sgtrwk",
"title": "Random-walk kernel of a graph as a resolvent",
"topics": [
"Sgtrwk"
]
},
{
"page": "Sgtsbnd",
"title": "Kesten-Stigum detectability threshold for the symmetric SBM",
"topics": [
"Sgtsbnd"
]
},
{
"page": "Sgtsck",
"title": "Ng-Jordan-Weiss k-way spectral clustering",
"topics": [
"Sgtsck"
]
},
{
"page": "Sgtwlk",
"title": "Refine node colours until neighbourhoods stop distinguishing them",
"topics": [
"Sgtwlk"
]
},
{
"page": "Shanent",
"title": "Shannon entropy of a discrete distribution",
"topics": [
"Shanent"
]
},
{
"page": "shapiro_wilk",
"title": "Shapiro-Wilk test for normality",
"topics": [
"shapiro_wilk"
]
},
{
"page": "ShapleyOwen",
"title": "Shapley-Owen value",
"topics": [
"ShapleyOwen"
]
},
{
"page": "Shewh",
"title": "Shewhart control chart: the three-sigma decision, and what it costs",
"topics": [
"Shewh"
]
},
{
"page": "Shfflm",
"title": "Privacy amplification by shuffling: local DP to central DP",
"topics": [
"Shfflm"
]
},
{
"page": "Shfrm",
"title": "Shared gamma frailty model with marginal (population-averaged) output",
"topics": [
"Shfrm"
]
},
{
"page": "ShiftSchedule",
"title": "Shift scheduling, relief factors and workload staffing",
"topics": [
"ReliefFactor",
"ShiftSchedule",
"WorkloadStaffing"
]
},
{
"page": "ShortestPath",
"title": "Shortest paths, k-shortest paths and travelling salesman tours",
"topics": [
"KShortestPaths",
"ShortestPath",
"TspTour"
]
},
{
"page": "ShortestPathLengths",
"title": "Network measures (igraph conventions)",
"topics": [
"Centralities",
"ModularityScore",
"NetworkConnectivity",
"NetworkSummary",
"NodeVulnerability",
"ShortestPathLengths"
]
},
{
"page": "Siaepi",
"title": "Network SIR epidemic, individual-based mean field",
"topics": [
"Siaepi"
]
},
{
"page": "sidak",
"title": "Sidak FWER correction",
"topics": [
"sidak"
]
},
{
"page": "Sieg",
"title": "Siegel repeated-median line: the 50 percent breakdown robust regression",
"topics": [
"Sieg"
]
},
{
"page": "Siepid",
"title": "Network SI epidemic, individual-based mean field",
"topics": [
"Siepid"
]
},
{
"page": "Sietrt",
"title": "Network SIS epidemic, individual-based mean field",
"topics": [
"Sietrt"
]
},
{
"page": "Sigbasis",
"title": "Sigmoid basis transformation of a design matrix (ESL eq. 2.31)",
"topics": [
"Sigbasis"
]
},
{
"page": "Siglip",
"title": "Contrastive loss that scores each pair on its own",
"topics": [
"Siglip"
]
},
{
"page": "SilhouetteWidths",
"title": "Silhouette widths and Dunn index",
"topics": [
"DunnIndex",
"SilhouetteWidths"
]
},
{
"page": "silverman_bandwidth",
"title": "Silverman rule-of-thumb bandwidth",
"topics": [
"silverman_bandwidth"
]
},
{
"page": "simes_combined",
"title": "Simes test for the global null",
"topics": [
"simes_combined"
]
},
{
"page": "SineCosineAlgorithm",
"title": "Sine cosine algorithm",
"topics": [
"SineCosineAlgorithm"
]
},
{
"page": "Singgw",
"title": "Single-step relationship matrix H (ssGBLUP)",
"topics": [
"Singgw"
]
},
{
"page": "Singsd",
"title": "Singular spectrum analysis of a univariate series",
"topics": [
"Singsd"
]
},
{
"page": "singular_value_adjust",
"title": "SVD cap on the prewhitening matrix",
"topics": [
"singular_value_adjust"
]
},
{
"page": "SinhArcsinh",
"title": "Sinh-arcsinh distribution",
"topics": [
"SinhArcsinh"
]
},
{
"page": "Sinkdist",
"title": "Dual-Sinkhorn divergence between two discrete measures",
"topics": [
"Sinkdist"
]
},
{
"page": "SinkhornWeights",
"title": "Doubly-stochastic (Sinkhorn) scaling of spatial weights",
"topics": [
"SinkhornWeights"
]
},
{
"page": "Sirepi",
"title": "SIR compartmental model by fourth-order Runge-Kutta",
"topics": [
"Sirepi"
]
},
{
"page": "Sirstn",
"title": "Stochastic SIR by Gillespie's direct method",
"topics": [
"Sirstn"
]
},
{
"page": "Sirtdy",
"title": "Age-structured SIR driven by a social contact matrix",
"topics": [
"Sirtdy"
]
},
{
"page": "Skater",
"title": "SKATER regionalization",
"topics": [
"Skater"
]
},
{
"page": "Slope1",
"title": "Slope One and Weighted Slope One collaborative-filtering predictors",
"topics": [
"Slope1"
]
},
{
"page": "SlopeProd",
"title": "Product of the forward and reverse regression slopes",
"topics": [
"SlopeProd"
]
},
{
"page": "SLXRegression",
"title": "SLX regression with impacts and a Wald test",
"topics": [
"SLXRegression"
]
},
{
"page": "Slxwx",
"title": "Spatial lag of X (SLX) model diagnostics",
"topics": [
"Slxboot",
"Slxflt",
"Slxres",
"Slxwx"
]
},
{
"page": "SmacofIndDiff",
"title": "Three-way SMACOF (INDSCAL, IDIOSCAL, replicated MDS)",
"topics": [
"SmacofIndDiff"
]
},
{
"page": "SmacofMds",
"title": "Symmetric SMACOF multidimensional scaling",
"topics": [
"SmacofMds"
]
},
{
"page": "SmartsMatch",
"title": "SMARTS search, Crippen logP and PAINS alerts",
"topics": [
"ClogpEstimate",
"PainsFilter",
"SmartsMatch"
]
},
{
"page": "SmilesMolecularWeight",
"title": "Molecular descriptors from SMILES",
"topics": [
"LipinskiDescriptors",
"SmilesHba",
"SmilesHbd",
"SmilesMolecularWeight",
"SmilesRotatableBonds",
"SmilesTpsa"
]
},
{
"page": "Smplqc",
"title": "Per-sample genotype quality control",
"topics": [
"Smplqc"
]
},
{
"page": "Smrind",
"title": "Standardised mortality ratio, indirect standardisation",
"topics": [
"Smrind"
]
},
{
"page": "SmtSolver",
"title": "Decision and factoring algorithms",
"topics": [
"ShorFactoring",
"SmtSolver"
]
},
{
"page": "Snaivefc",
"title": "Seasonal naive forecast",
"topics": [
"Snaivefc"
]
},
{
"page": "Snmlin",
"title": "G-estimation of a linear structural nested mean model",
"topics": [
"Snmlin"
]
},
{
"page": "Snpblr",
"title": "SNP-BLUP additive genomic prediction",
"topics": [
"Snpblr"
]
},
{
"page": "Snpblup",
"title": "SNP-BLUP (ridge regression BLUP) marker effects and breeding values",
"topics": [
"Snpblup"
]
},
{
"page": "Snr2",
"title": "Snijders-Bosker level-1 explained variance",
"topics": [
"Snr2"
]
},
{
"page": "Snr2u",
"title": "Snijders-Bosker level-2 explained variance",
"topics": [
"Snr2u"
]
},
{
"page": "SnScale",
"title": "Rousseeuw & Croux (1993) Sn robust scale",
"topics": [
"SnScale"
]
},
{
"page": "Sntmn",
"title": "Mandatory minimum sentence analysis",
"topics": [
"Sntmn"
]
},
{
"page": "sobol_sequence",
"title": "Sobol low-discrepancy sequence in [0, 1)^dim",
"topics": [
"sobol_sequence"
]
},
{
"page": "Socpts",
"title": "Second-order cone programming by the barrier method",
"topics": [
"Socpts"
]
},
{
"page": "soft_threshold",
"title": "Soft-thresholding",
"topics": [
"soft_threshold"
]
},
{
"page": "soft_value_iteration",
"title": "Soft value iteration on a deterministic tabular MDP",
"topics": [
"soft_value_iteration"
]
},
{
"page": "SoilChemistry",
"title": "Soil chemistry, carbon, texture and Sobel filtering",
"topics": [
"SobelFilter",
"SoilCarbon",
"SoilChemistry",
"UsdaTexture"
]
},
{
"page": "SpaceTimeInteractionTest",
"title": "Monte Carlo test of space-time interaction",
"topics": [
"SpaceTimeInteractionTest"
]
},
{
"page": "SpaceTimeK",
"title": "Space-time K function of Diggle, Chetwynd, Haggkvist and Morris (1995)",
"topics": [
"SpaceTimeK"
]
},
{
"page": "spanis",
"title": "Geometric anisotropy: correcting direction dependence by a linear map",
"topics": [
"spanis"
]
},
{
"page": "Sparseattn",
"title": "Sparse (masked) scaled dot-product attention",
"topics": [
"Sparseattn"
]
},
{
"page": "spatial_sir",
"title": "Spatial epidemic and cluster models",
"topics": [
"spatial_cure_rate",
"spatial_frailty",
"spatial_sir"
]
},
{
"page": "SpatialAccessibility",
"title": "Spatial accessibility",
"topics": [
"SpatialAccessibility"
]
},
{
"page": "SpatialBargaining",
"title": "Spatial bargaining",
"topics": [
"SpatialBargaining"
]
},
{
"page": "SpatialConcentration",
"title": "Spatial concentration indices",
"topics": [
"SpatialConcentration"
]
},
{
"page": "SpatialCorrelogram",
"title": "Spatial correlogram over neighbour-graph lag orders",
"topics": [
"SpatialCorrelogram"
]
},
{
"page": "SpatialGlmmFit",
"title": "Spatial GLMM fitting, prediction, residuals and scoring",
"topics": [
"CrpsGaussian",
"CrpsPoisson",
"CrpsSample",
"GlmmResiduals",
"SpatialGlmmFit",
"SpatialGlmmPredict"
]
},
{
"page": "SpatialImpacts",
"title": "Direct, indirect and total impacts of a spatial lag or Durbin model",
"topics": [
"SpatialImpacts"
]
},
{
"page": "SpatialKde",
"title": "Bivariate Gaussian kernel density of a point pattern",
"topics": [
"SpatialKde"
]
},
{
"page": "SpatialPanelMl",
"title": "Spatial panel data models",
"topics": [
"InformationCriteria",
"SpatialPanelMl",
"SpatialPanelReLag"
]
},
{
"page": "SpatialProbit",
"title": "Spatial probit, logit, Poisson, ZIP and panel models (entry points)",
"topics": [
"SpatialLogit",
"SpatialPanelFe",
"SpatialPanelRe",
"SpatialPoisson",
"SpatialProbit",
"SpatialZip"
]
},
{
"page": "SpatialRegressionML",
"title": "Spatial lag, spatial error and SAC models by maximum likelihood",
"topics": [
"SpatialRegressionML"
]
},
{
"page": "SpatialTwoStageLS",
"title": "Spatial two-stage least squares for the spatial lag model",
"topics": [
"SpatialTwoStageLS"
]
},
{
"page": "SpatialWeights",
"title": "Spatial weight matrix from point coordinates",
"topics": [
"SpatialWeights"
]
},
{
"page": "Spatrend",
"title": "Spatial trend surface: linear mean plus correlated error",
"topics": [
"Spatrend"
]
},
{
"page": "spbayr",
"title": "Bayesian hierarchical spatial models for disease mapping",
"topics": [
"spbayr"
]
},
{
"page": "spbesf",
"title": "Modified Bessel function of the second kind, K_nu",
"topics": [
"spbesf"
]
},
{
"page": "spbino",
"title": "Binomial point process: a FIXED number of independent uniform points",
"topics": [
"spbino"
]
},
{
"page": "spblup",
"title": "Best linear unbiased predictor: ordinary kriging",
"topics": [
"spblup"
]
},
{
"page": "spbym",
"title": "Besag-York-Mollie convolution model for disease mapping",
"topics": [
"spbym"
]
},
{
"page": "spcar",
"title": "Conditional autoregressive model: the conditional specification",
"topics": [
"spcar"
]
},
{
"page": "spcirc",
"title": "Circular semivariogram model",
"topics": [
"spcirc"
]
},
{
"page": "spcmp",
"title": "Likelihood-ratio tests and AIC for nested spatial covariance models",
"topics": [
"spcmp"
]
},
{
"page": "spcont",
"title": "Mean-square continuity, decided by the covariance at the origin",
"topics": [
"spcont"
]
},
{
"page": "spconv",
"title": "Convolution representation of a stationary random field",
"topics": [
"spconv"
]
},
{
"page": "spcovf",
"title": "Empirical covariance function of a second-order stationary field",
"topics": [
"spcovf"
]
},
{
"page": "spcsr",
"title": "Complete spatial randomness, and a quadrat check of it",
"topics": [
"spcsr"
]
},
{
"page": "spearman_correlation",
"title": "Spearman rank correlation",
"topics": [
"spearman_correlation"
]
},
{
"page": "Specclus",
"title": "Spectral clustering: embed by the bottom eigenvectors, then k-means",
"topics": [
"Specclus"
]
},
{
"page": "specification_curve",
"title": "Specification curve analysis",
"topics": [
"specification_curve"
]
},
{
"page": "SpectralGRF",
"title": "Gaussian random field simulation by circulant embedding",
"topics": [
"SpectralGRF"
]
},
{
"page": "SpectralIndex",
"title": "Spectral indices of reflectance bands",
"topics": [
"SpectralIndex"
]
},
{
"page": "spexp",
"title": "Exponential semivariogram model",
"topics": [
"spexp"
]
},
{
"page": "spffun",
"title": "F-function: the empty space (point-to-nearest-event) distribution",
"topics": [
"spffun"
]
},
{
"page": "spgaus",
"title": "Gaussian semivariogram model",
"topics": [
"spgaus"
]
},
{
"page": "spgfun",
"title": "G-function: the nearest-neighbour distance distribution",
"topics": [
"spgfun"
]
},
{
"page": "spglmk",
"title": "Spatial prediction in a generalized linear model",
"topics": [
"spglmk"
]
},
{
"page": "spglmm",
"title": "Conditional specification of a spatial GLMM",
"topics": [
"spglmm"
]
},
{
"page": "spgls",
"title": "Generalised least squares with a known covariance matrix",
"topics": [
"spgls"
]
},
{
"page": "spglsv",
"title": "Semivariogram fit by generalized least squares (Cressie 1985)",
"topics": [
"spglsv"
]
},
{
"page": "spgwr",
"title": "Geographically weighted regression: locally varying coefficients",
"topics": [
"spgwr"
]
},
{
"page": "spgwrb",
"title": "GWR bandwidth selection by cross-validation or corrected AIC",
"topics": [
"spgwrb"
]
},
{
"page": "spgwrk",
"title": "GWR kernel weight functions",
"topics": [
"spgwrk"
]
},
{
"page": "spicar",
"title": "Intrinsic CAR (ICAR) prior: precision structure, not a fit",
"topics": [
"spicar"
]
},
{
"page": "spiso",
"title": "Isotropy check by comparing directional semivariograms",
"topics": [
"spiso"
]
},
{
"page": "spkcrs",
"title": "Cross K-function for bivariate point patterns",
"topics": [
"spkcrs"
]
},
{
"page": "spkfnn",
"title": "Leave-one-out cross-validation of a kriging model",
"topics": [
"spkfnn"
]
},
{
"page": "spkfun",
"title": "Ripley's K-function",
"topics": [
"spkfun"
]
},
{
"page": "spkrnv",
"title": "Parametric kernel semivariogram",
"topics": [
"spkrnv"
]
},
{
"page": "spkwt",
"title": "Solve the kriging system for the weights",
"topics": [
"spkwt"
]
},
{
"page": "splfun",
"title": "L-function, the variance-stabilised K-function",
"topics": [
"splfun"
]
},
{
"page": "splgk",
"title": "Lognormal kriging",
"topics": [
"splgk"
]
},
{
"page": "spmatr",
"title": "Matern covariance function class",
"topics": [
"spmatr"
]
},
{
"page": "spmenv",
"title": "Moments of Moran's I under Gaussianity and under randomization",
"topics": [
"spmenv"
]
},
{
"page": "Spmi",
"title": "Simultaneous pairwise marginal independence (SPMI)",
"topics": [
"Spmi"
]
},
{
"page": "spmidw",
"title": "Inverse distance weighted interpolation",
"topics": [
"spmidw"
]
},
{
"page": "spml",
"title": "Covariance parameters by maximum or restricted maximum likelihood",
"topics": [
"spml"
]
},
{
"page": "spmsd",
"title": "Mean-square differentiability, from the even derivatives of C at 0",
"topics": [
"spmsd"
]
},
{
"page": "spmsim",
"title": "Multiscale GWR: one bandwidth per covariate",
"topics": [
"spmsim"
]
},
{
"page": "spmwst",
"title": "Moving-window kriging with locally re-estimated semivariograms",
"topics": [
"spmwst"
]
},
{
"page": "spnest",
"title": "Nested semivariogram: the linear model of regionalization",
"topics": [
"spnest"
]
},
{
"page": "spnpsv",
"title": "Nonparametric (Shapiro-Botha) semivariogram",
"topics": [
"spnpsv"
]
},
{
"page": "spnscl",
"title": "Neyman-Scott cluster process: second-order behaviour",
"topics": [
"spnscl"
]
},
{
"page": "spnsr",
"title": "Effects of nugget, sill and range on a kriging prediction",
"topics": [
"spnsr"
]
},
{
"page": "spnst",
"title": "Parametric non-stationary correlation: the point-source model",
"topics": [
"spnst"
]
},
{
"page": "spnug",
"title": "Nugget effect in the semivariogram: discontinuity at the origin",
"topics": [
"spnug"
]
},
{
"page": "spols",
"title": "Ordinary least squares fitting of a semivariogram model",
"topics": [
"spols"
]
},
{
"page": "SpPanelDynamic",
"title": "Dynamic spatial panel (spatial ARX) on the fixed-effects spatial lag model",
"topics": [
"SpPanelDynamic"
]
},
{
"page": "spperiod",
"title": "Periodogram of a process observed on a rectangular lattice",
"topics": [
"spperiod"
]
},
{
"page": "sppfe",
"title": "Spatial panel models, tests and resampling",
"topics": [
"sppbe",
"sppboot",
"sppcov",
"sppdiag",
"sppdyn",
"sppfe",
"sppgmm",
"spphaus",
"sppiv",
"spplm",
"sppre",
"sppres",
"sppsac",
"sppsar",
"sppsdm",
"sppsem"
]
},
{
"page": "sppois",
"title": "Homogeneous Poisson process (HPP), the CSR reference model",
"topics": [
"sppois"
]
},
{
"page": "sppow",
"title": "Power semivariogram model (unbounded)",
"topics": [
"sppow"
]
},
{
"page": "sppql",
"title": "Fit a spatial GLMM by pseudo-likelihood",
"topics": [
"sppql"
]
},
{
"page": "spprmf",
"title": "Spatial probit and logit: impacts, probabilities, simulated ML and panels",
"topics": [
"splgtmf",
"splgtml",
"spprmf",
"spprml",
"spprprd",
"sprmfdi",
"sptfx",
"sptrx"
]
},
{
"page": "SprawlEntropy",
"title": "Sprawl entropy",
"topics": [
"SprawlEntropy"
]
},
{
"page": "spreml",
"title": "Restricted maximum likelihood (REML) for semivariogram parameters",
"topics": [
"spreml"
]
},
{
"page": "sprfss",
"title": "Which stationarity assumption the data can support",
"topics": [
"sprfss"
]
},
{
"page": "spsar",
"title": "Spatial autoregressive (lag) model",
"topics": [
"spsar"
]
},
{
"page": "spsemv",
"title": "Empirical semivariogram: half the mean squared difference",
"topics": [
"spsemv"
]
},
{
"page": "spskrg",
"title": "Simple kriging: the mean is known",
"topics": [
"spskrg"
]
},
{
"page": "spspec",
"title": "Sum-of-sinusoids (spectral) representation, eqs (2.26)-(2.27)",
"topics": [
"spspec"
]
},
{
"page": "spsph",
"title": "Spherical semivariogram model",
"topics": [
"spsph"
]
},
{
"page": "spssoc",
"title": "Covariance and semivariogram under second-order stationarity",
"topics": [
"spssoc"
]
},
{
"page": "spstcn",
"title": "Non-separable spatio-temporal covariance functions",
"topics": [
"spstcn"
]
},
{
"page": "spstcv",
"title": "Separable spatio-temporal covariance functions",
"topics": [
"spstcv"
]
},
{
"page": "spstp",
"title": "Spatio-temporal point process intensity",
"topics": [
"spstp"
]
},
{
"page": "spstvg",
"title": "Empirical spatio-temporal semivariogram",
"topics": [
"spstvg"
]
},
{
"page": "sptent",
"title": "Tent semivariogram model",
"topics": [
"sptent"
]
},
{
"page": "sptgk",
"title": "Trans-Gaussian kriging",
"topics": [
"sptgk"
]
},
{
"page": "spthom",
"title": "Thomas process: the Neyman-Scott process with Gaussian offspring",
"topics": [
"spthom"
]
},
{
"page": "spwkth",
"title": "Spectral density of a stationary field from its covariance function",
"topics": [
"spwkth"
]
},
{
"page": "spwls",
"title": "Weighted least squares fitting of a semivariogram model",
"topics": [
"spwls"
]
},
{
"page": "Srfintp",
"title": "Interpolate a scattered surface onto a grid by GP regression",
"topics": [
"Srfintp"
]
},
{
"page": "Srstotal",
"title": "Estimate a population total from a simple random sample",
"topics": [
"Srstotal"
]
},
{
"page": "Sscompv",
"title": "Wolbers concordance for competing-risks predictions",
"topics": [
"Sscompv"
]
},
{
"page": "Ssello",
"title": "Sum-of-squared-error loss for continuous outcomes",
"topics": [
"Ssello"
]
},
{
"page": "Ssintc",
"title": "Nonparametric survival when each event time is only bracketed",
"topics": [
"Ssintc"
]
},
{
"page": "Sstrlf",
"title": "Left-truncated survival adjustment",
"topics": [
"Sstrlf"
]
},
{
"page": "standardized_coefficients",
"title": "Standardised regression coefficients (beta weights)",
"topics": [
"standardized_coefficients"
]
},
{
"page": "StarBars",
"title": "Stars and bars",
"topics": [
"StarBars"
]
},
{
"page": "StarBRec",
"title": "Stars-and-bars recursion",
"topics": [
"StarBRec"
]
},
{
"page": "stat_bridge_exec",
"title": "Execute a single command and return the resulting text",
"topics": [
"stat_bridge_exec"
]
},
{
"page": "stat_bridge_fn_info",
"title": "Inspect a single command by name",
"topics": [
"stat_bridge_fn_info"
]
},
{
"page": "stat_bridge_fn_search",
"title": "Search the registry for matching commands",
"topics": [
"stat_bridge_fn_search"
]
},
{
"page": "stat_bridge_help",
"title": "Formatted text dump of the command registry",
"topics": [
"stat_bridge_help"
]
},
{
"page": "stat_bridge_main",
"title": "Command-line dispatcher",
"topics": [
"stat_bridge_main"
]
},
{
"page": "stat_bridge_registry_json",
"title": "JSON enumeration of all registered commands",
"topics": [
"stat_bridge_registry_json"
]
},
{
"page": "stat_bridge_verify",
"title": "Self-test enumeration helper",
"topics": [
"stat_bridge_verify"
]
},
{
"page": "stat_command",
"title": "Construct a stat_command entry",
"topics": [
"stat_command"
]
},
{
"page": "statistics",
"title": "Comprehensive hypothesis testing suite for epidemiological research",
"topics": [
"statistics"
]
},
{
"page": "Stbciw",
"title": "Stabilized inverse-probability-of-censoring weights",
"topics": [
"Stbciw"
]
},
{
"page": "StCokriging",
"title": "Space-time cokriging and the diffusion covariance",
"topics": [
"DiffusionStCovariance",
"StCokriging"
]
},
{
"page": "STCovariance",
"title": "Space-time covariance models (gstat vgmST conventions)",
"topics": [
"STCovariance"
]
},
{
"page": "StFit",
"title": "Fit a space-time variogram model and space-time kriging variants",
"topics": [
"StBlockKriging",
"StFit",
"StKrigingDiagnostics",
"StLeaveHOut",
"StLocalKriging",
"StPredictGrid",
"StSimulate",
"StSmoothness",
"StUniversalKriging"
]
},
{
"page": "StGetisOrd",
"title": "Space-time Gi*, bivariate Moran and space-time trend surfaces",
"topics": [
"BivariateMoran",
"StGetisOrd",
"StTrendSurface"
]
},
{
"page": "STKriging",
"title": "Space-time kriging prediction and variance",
"topics": [
"STKriging"
]
},
{
"page": "STKrigingCV",
"title": "Leave-one-out cross-validation of space-time kriging",
"topics": [
"STKrigingCV"
]
},
{
"page": "StLag",
"title": "Space-time ARMA models: STACF, STPACF, STAR and STARMA fits, forecasts, portmanteau test",
"topics": [
"StAcf",
"StarFit",
"StarmaFit",
"StarmaForecast",
"StLag",
"StPacf",
"StPortmanteau"
]
},
{
"page": "StModelVariogram",
"title": "Space-time variograms and covariance families",
"topics": [
"StCovarianceFamily",
"StLinearCombination",
"StModelVariogram"
]
},
{
"page": "storey_q",
"title": "Storey q-value procedure (adaptive FDR)",
"topics": [
"storey_q"
]
},
{
"page": "stouffer_combined",
"title": "Stouffer's z-score method",
"topics": [
"stouffer_combined"
]
},
{
"page": "Stratdes",
"title": "Allocate a stratified sample and report the variance it achieves",
"topics": [
"Stratdes"
]
},
{
"page": "Stratmean",
"title": "Stratified estimate of the population mean, with its variance",
"topics": [
"Stratmean"
]
},
{
"page": "Strdis",
"title": "DeltaCon structural distance between two graphs",
"topics": [
"Strdis",
"structural_distance"
]
},
{
"page": "StreamOrder",
"title": "Stream order and topographic indices",
"topics": [
"StreamOrder",
"StreamPowerIndex",
"TopographicWetnessIndex"
]
},
{
"page": "StreamSegments",
"title": "Stream networks, channel form, groundwater flow and depression breaching",
"topics": [
"BreachDepressions",
"ChannelSlope",
"DarcyFlow",
"HeightAboveDrainage",
"MeanderMetrics",
"StreamSegments"
]
},
{
"page": "StripMean",
"title": "Strip mean from the reverse regression line",
"topics": [
"StripMean"
]
},
{
"page": "Strtwt",
"title": "Stabilized inverse-probability-of-treatment weights within strata",
"topics": [
"Strtwt"
]
},
{
"page": "Stsmod",
"title": "Linear Gaussian state-space model: Kalman filter and smoother",
"topics": [
"Stsmod"
]
},
{
"page": "Studrs",
"title": "Residuals rescaled by a variance the point itself did not inflate",
"topics": [
"Studrs"
]
},
{
"page": "STVariogram",
"title": "Empirical space-time variogram on a full space-time grid",
"topics": [
"STVariogram"
]
},
{
"page": "Subcomp",
"title": "Form the subcomposition on a selected subset of parts",
"topics": [
"Subcomp"
]
},
{
"page": "subsampling",
"title": "Subsampling inference (Politis, Romano & Wolf)",
"topics": [
"subsampling"
]
},
{
"page": "subscale_ea_ave",
"title": "Subscale average variance extracted and composite reliability",
"topics": [
"subscale_ea_ave",
"subscale_ea_composite_rel",
"subscale_ee_ave",
"subscale_ee_composite_rel",
"subscale_er_ave",
"subscale_er_composite_rel",
"subscale_ua_ave",
"subscale_ua_composite_rel"
]
},
{
"page": "substance_categories",
"title": "Substance Categories",
"topics": [
"substance_categories"
]
},
{
"page": "Suffstat",
"title": "Sufficient statistics for the Bernoulli and Normal models",
"topics": [
"Suffstat"
]
},
{
"page": "SumDens",
"title": "Convolution density of a sum",
"topics": [
"SumDens"
]
},
{
"page": "SumDensP",
"title": "Strip probability for a sum density",
"topics": [
"SumDensP"
]
},
{
"page": "SumInts",
"title": "Sum of the first N integers",
"topics": [
"SumInts"
]
},
{
"page": "summary_statistics_table",
"title": "Descriptive statistics for a set of variables",
"topics": [
"summary_statistics_table"
]
},
{
"page": "summary.morie_cluster",
"title": "Summarise method for 'morie_cluster' objects",
"topics": [
"summary.morie_cluster"
]
},
{
"page": "summary.morie_gam",
"title": "Summary of a native generalized additive model",
"topics": [
"summary.morie_gam"
]
},
{
"page": "summary.morie_lm",
"title": "Summarise method for 'morie_lm' objects",
"topics": [
"summary.morie_lm"
]
},
{
"page": "summary.morie_lmm",
"title": "Summarise a morie mixed model",
"topics": [
"summary.morie_lmm"
]
},
{
"page": "summary.morie_ml_fit",
"title": "Summarise method for 'morie_ml_fit' objects",
"topics": [
"summary.morie_ml_fit"
]
},
{
"page": "Superlrn",
"title": "Weight candidate predictions by cross-validated risk, on the simplex",
"topics": [
"Superlrn"
]
},
{
"page": "Surpris",
"title": "Surprisal (self-information) of one or more outcomes",
"topics": [
"Surpris"
]
},
{
"page": "Survbri",
"title": "Brier score for survival prediction",
"topics": [
"Survbri"
]
},
{
"page": "Survci2",
"title": "Uno C-index for censored data",
"topics": [
"Survci2"
]
},
{
"page": "Survip",
"title": "Design-corrected p-value for a naively computed chi-square statistic",
"topics": [
"Survip"
]
},
{
"page": "Survipa",
"title": "Index of prediction accuracy (scaled Brier score)",
"topics": [
"Survipa"
]
},
{
"page": "Survlnk",
"title": "Discrete-time survival regression with a chosen hazard link",
"topics": [
"Survlnk"
]
},
{
"page": "Survrls",
"title": "Restricted mean survival time over a fixed horizon",
"topics": [
"Survrls"
]
},
{
"page": "Survtdc",
"title": "Truncated time-dependent concordance index",
"topics": [
"Survtdc"
]
},
{
"page": "Svariog",
"title": "Sample (empirical) semivariogram",
"topics": [
"Svariog"
]
},
{
"page": "Svmep",
"title": "Epsilon-insensitive support vector regression",
"topics": [
"Svmep"
]
},
{
"page": "Svmhp",
"title": "Maximum margin (hard margin) hyperplane",
"topics": [
"Svmhp"
]
},
{
"page": "Svmsl",
"title": "Support vector classifier with slack variables (soft margin)",
"topics": [
"Svmsl"
]
},
{
"page": "Svmwolfe",
"title": "Wolfe dual objective of the support vector machine",
"topics": [
"Svmwolfe"
]
},
{
"page": "Svymed",
"title": "Survey median",
"topics": [
"Svymed"
]
},
{
"page": "Svyqtl",
"title": "Survey quantile by inversion of the weighted CDF",
"topics": [
"Svyqtl"
]
},
{
"page": "Svytbl",
"title": "Weighted two-way table with a design-corrected test of independence",
"topics": [
"Svytbl"
]
},
{
"page": "swdist",
"title": "Spatial weights: builders, lags and graph operations",
"topics": [
"swadapt",
"swblk",
"swdist",
"swgab",
"swinv",
"swkern",
"swknn",
"swlag",
"swlag2",
"swlagf",
"swpath",
"swpower",
"swspars",
"swsph",
"swtri"
]
},
{
"page": "SwigluBlock",
"title": "SwiGLU feed-forward block",
"topics": [
"swiglu"
]
},
{
"page": "Swinmw",
"title": "Swin window multi-head self-attention with relative position bias",
"topics": [
"Swinmw"
]
},
{
"page": "SymbolicIntegrate",
"title": "Symbolic integration and differentiation",
"topics": [
"SymbolicDiff",
"SymbolicIntegrate"
]
},
{
"page": "synthetic_abc",
"title": "Synthetic-likelihood ABC, Meeds & Welling Algorithm 1",
"topics": [
"synthetic_abc"
]
},
{
"page": "synthetic_log_likelihood",
"title": "Synthetic log-likelihood",
"topics": [
"synthetic_log_likelihood"
]
},
{
"page": "Sysamp",
"title": "Every k-th unit: all k systematic samples and the exact variance",
"topics": [
"Sysamp"
]
},
{
"page": "Sysrs",
"title": "Draw one systematic sample with a random start, and estimate from it",
"topics": [
"Sysrs"
]
},
{
"page": "systematic_resample",
"title": "Systematic resampling",
"topics": [
"systematic_resample"
]
},
{
"page": "table1",
"title": "Table 1 (baseline characteristics) stratified by group",
"topics": [
"table1"
]
},
{
"page": "tables_pub",
"title": "Publication-ready table generation",
"topics": [
"tables_pub"
]
},
{
"page": "tagRC_adapted_pagerank",
"title": "tagRC_adapted_pagerank",
"topics": [
"tagRC_adapted_pagerank"
]
},
{
"page": "tagRC_cheatsheet",
"title": "tagRC_cheatsheet",
"topics": [
"tagRC_cheatsheet"
]
},
{
"page": "tagRC_preference_vector",
"title": "tagRC_preference_vector",
"topics": [
"tagRC_preference_vector"
]
},
{
"page": "tagRC_tripartite_graph",
"title": "tagRC_tripartite_graph",
"topics": [
"tagRC_tripartite_graph"
]
},
{
"page": "taphonomy",
"title": "Taphonomic preservation as a causal-inference problem",
"topics": [
"taphonomy"
]
},
{
"page": "TasseledCap",
"title": "Multispectral image processing (RStoolbox conventions)",
"topics": [
"AtgpEndmembers",
"BandPca",
"CloudMask",
"CloudShadowMask",
"EstimateHaze",
"FparFromNdvi",
"GaussianMlClassify",
"KmeansClassify",
"MnfTransform",
"PanSharpen",
"RadiometricCorrection",
"SpectralAngleClassify",
"TasseledCap",
"TopographicCorrection",
"Tvdi"
]
},
{
"page": "Taxass",
"title": "Kraken-style taxonomic classification by RTL path scoring",
"topics": [
"Taxass"
]
},
{
"page": "TeachingLearningOptimizer",
"title": "Teaching-learning-based optimization",
"topics": [
"TeachingLearningOptimizer"
]
},
{
"page": "temporal_validate",
"title": "Train on earlier data, test on later data",
"topics": [
"temporal_validate"
]
},
{
"page": "TerrainIndices",
"title": "Terrain derivatives of a DEM (terra terrain conventions)",
"topics": [
"TerrainIndices"
]
},
{
"page": "Testcond",
"title": "Check the three conditions that deliver a posterior contraction rate",
"topics": [
"Testcond"
]
},
{
"page": "Tfcells",
"title": "Cell counts N_epsilon for a tail-free or Polya-tree partition",
"topics": [
"Tfcells"
]
},
{
"page": "ThinPlateSpline",
"title": "Thin-plate spline interpolation and smoothing in two dimensions",
"topics": [
"ThinPlateSpline"
]
},
{
"page": "Thomp",
"title": "Beta-Bernoulli Thompson sampling",
"topics": [
"Thomp"
]
},
{
"page": "tippett_combined",
"title": "Tippett's minimum-p method",
"topics": [
"tippett_combined"
]
},
{
"page": "tipping_point_analysis",
"title": "Tipping-point analysis for missing-data sensitivity",
"topics": [
"tipping_point_analysis"
]
},
{
"page": "Tldepl",
"title": "Lower tail dependence coefficient",
"topics": [
"Tldepl"
]
},
{
"page": "Tldepu",
"title": "Upper tail dependence coefficient",
"topics": [
"Tldepu"
]
},
{
"page": "Tmlbas",
"title": "TMLE whose initial outcome model uses baseline covariates only",
"topics": [
"Tmlbas"
]
},
{
"page": "Tmldis",
"title": "How much of a group gap survives equalising the covariates",
"topics": [
"Tmldis"
]
},
{
"page": "Tmleboot",
"title": "Nonparametric bootstrap interval for the targeted risk difference",
"topics": [
"Tmleboot"
]
},
{
"page": "Tmlecat",
"title": "Target E[Y_a] at every level of a categorical treatment",
"topics": [
"Tmlecat"
]
},
{
"page": "Tmlecde",
"title": "Controlled direct effect: the treatment effect with the mediator fixed",
"topics": [
"Tmlecde"
]
},
{
"page": "Tmleinf",
"title": "Standard error, interval and score check from an influence curve",
"topics": [
"Tmleinf"
]
},
{
"page": "Tmleor",
"title": "Targeted marginal odds ratio, with inference on the log scale",
"topics": [
"Tmleor"
]
},
{
"page": "Tmleqs",
"title": "How far the initial fit was from solving the efficient score equation",
"topics": [
"Tmleqs"
]
},
{
"page": "Tmlerd",
"title": "Targeted estimate of the marginal risk difference E[Y_1] - E[Y_0]",
"topics": [
"Tmlerd"
]
},
{
"page": "Tmlestab",
"title": "Stabilize a targeted estimate by truncating the propensity score",
"topics": [
"Tmlestab"
]
},
{
"page": "Tmlevar",
"title": "Target the variance parameter sigma^2 = E[D*^2] and bound it",
"topics": [
"Tmlevar"
]
},
{
"page": "Tmlfed",
"title": "Pool site-level TMLEs without moving any row between sites",
"topics": [
"Tmlfed"
]
},
{
"page": "Tmlhrz",
"title": "TMLE for the marginal hazard ratio when proportional hazards fails",
"topics": [
"Tmlhrz"
]
},
{
"page": "Tmlitr",
"title": "TMLE for the value of the estimated optimal individualized rule",
"topics": [
"Tmlitr"
]
},
{
"page": "Tmlmct",
"title": "TMLE for a vector-valued (multivariate) binary treatment",
"topics": [
"Tmlmct"
]
},
{
"page": "TmlMd",
"title": "Natural direct and indirect effects, both targeted",
"topics": [
"TmlMd"
]
},
{
"page": "Tmlmda",
"title": "TMLE for the ATE with a missing-at-random outcome",
"topics": [
"Tmlmda"
]
},
{
"page": "Tmlmlt",
"title": "TMLE for a multi-arm treatment: arm means and pairwise contrasts",
"topics": [
"Tmlmlt"
]
},
{
"page": "Tmlmnl",
"title": "Cross-fitted TMLE with pluggable machine learners for Q and g",
"topics": [
"Tmlmnl"
]
},
{
"page": "Tmlmpc",
"title": "TMLE for a multi-state cause-specific cumulative hazard contrast",
"topics": [
"Tmlmpc"
]
},
{
"page": "Tmlmpi",
"title": "TMLE for the marginal probabilistic index",
"topics": [
"Tmlmpi"
]
},
{
"page": "Tmlmrk",
"title": "TMLE for the long-run value of a policy in a finite Markov decision process",
"topics": [
"Tmlmrk"
]
},
{
"page": "Tmlnde2",
"title": "Direct effect with the mediator drawn from a fixed reference arm",
"topics": [
"Tmlnde2"
]
},
{
"page": "Tmlnsm",
"title": "TMLE for a non-smooth functional: the counterfactual median difference",
"topics": [
"Tmlnsm"
]
},
{
"page": "Tmlnte",
"title": "Total effect by TMLE, with the mediator held out of the model",
"topics": [
"Tmlnte"
]
},
{
"page": "Tmlper",
"title": "Point-treatment TMLE with seasonality in both nuisance models",
"topics": [
"Tmlper"
]
},
{
"page": "Tmlphd",
"title": "High-dimensional TMLE with L1-penalised nuisance models",
"topics": [
"Tmlphd"
]
},
{
"page": "Tmlpse",
"title": "TMLE for a path-specific effect through a chosen subset of mediators",
"topics": [
"Tmlpse"
]
},
{
"page": "Tmlqlc",
"title": "Backward-targeted Q-learning for a multi-stage treatment regime",
"topics": [
"Tmlqlc"
]
},
{
"page": "Tmlrct",
"title": "RCT-assisted TMLE for the trial-population ATE",
"topics": [
"Tmlrct"
]
},
{
"page": "Tmlrec",
"title": "TMLE for the marginal recurrent-event rate ratio",
"topics": [
"Tmlrec"
]
},
{
"page": "Tmlres",
"title": "TMLE with an estimated second-order remainder",
"topics": [
"Tmlres"
]
},
{
"page": "Tmlric",
"title": "Targeted maximum likelihood for a rare binary outcome",
"topics": [
"Tmlric"
]
},
{
"page": "Tmlsbg",
"title": "TMLE for the average treatment effect inside a named subgroup",
"topics": [
"Tmlsbg"
]
},
{
"page": "Tmlsbs",
"title": "Sample-split TMLE on a data-adaptively selected covariate subset",
"topics": [
"Tmlsbs"
]
},
{
"page": "Tmlspl",
"title": "TMLE for a direct effect under interference, at a fixed network exposure",
"topics": [
"Tmlspl"
]
},
{
"page": "Tmltrn",
"title": "TMLE transporting a treatment effect to a target population",
"topics": [
"Tmltrn"
]
},
{
"page": "Tmscore",
"title": "Structure similarity whose scale does not drift with chain length",
"topics": [
"Tmscore"
]
},
{
"page": "Topdown",
"title": "Top-down disaggregation of a hierarchical forecast",
"topics": [
"Topdown"
]
},
{
"page": "tox",
"title": "Forensic toxicology: matrix-aware quantitation and antemortem inference",
"topics": [
"tox"
]
},
{
"page": "tps_crime",
"title": "Cross-category crime analyses for Toronto Police Service (TPS) datasets",
"topics": [
"tps_crime"
]
},
{
"page": "tps_csi",
"title": "Statistics Canada Crime Severity Index (CSI) weights for TPS data",
"topics": [
"tps_csi"
]
},
{
"page": "tps_spatial",
"title": "Spatial analyses for TPS crime data",
"topics": [
"tps_spatial"
]
},
{
"page": "tps_spatial_advanced",
"title": "Heavyweight spatial statistics for TPS data",
"topics": [
"tps_spatial_advanced"
]
},
{
"page": "Tqdist",
"title": "Smallest sketch size meeting an inner-product distortion target",
"topics": [
"Tqdist"
]
},
{
"page": "Tqhs",
"title": "Quantize a key embedding to one bit per sketch row",
"topics": [
"Tqhs"
]
},
{
"page": "Tqmom",
"title": "The l-th absolute moment of a zero-mean normal",
"topics": [
"Tqmom"
]
},
{
"page": "Tqorth",
"title": "Replace a Gaussian sketch by one with orthonormal rows",
"topics": [
"Tqorth"
]
},
{
"page": "Tqrot",
"title": "Draw a rotation that spreads outlier energy over all coordinates",
"topics": [
"Tqrot"
]
},
{
"page": "TrackSteps",
"title": "Animal movement and spatial networks",
"topics": [
"CorePeriphery",
"CrwKalman",
"MovementHmm",
"MovementNetwork",
"NetworkRobustness",
"TrackSteps"
]
},
{
"page": "train_bpe",
"title": "Train a BPE model: merge the most frequent adjacent pair greedily",
"topics": [
"train_bpe"
]
},
{
"page": "TrajectoryMetrics",
"title": "Movement trajectory analysis",
"topics": [
"FirstPassageTime",
"MeanSquaredDisplacement",
"OdMatrix",
"SimulateWalk",
"TrajectoryMetrics"
]
},
{
"page": "TransformedField",
"title": "Non-Gaussian random fields",
"topics": [
"AnisotropicCoords",
"MaxStableField",
"TransformedField"
]
},
{
"page": "TransportationProblem",
"title": "Transportation problem",
"topics": [
"TransportationProblem"
]
},
{
"page": "TravellingSalesman",
"title": "Travelling salesman tour",
"topics": [
"TravellingSalesman"
]
},
{
"page": "Trcwgt",
"title": "Truncated product of treatment and censoring weights",
"topics": [
"Trcwgt"
]
},
{
"page": "treatment_effect_table",
"title": "Summary table of causal effect estimates from multiple estimators",
"topics": [
"treatment_effect_table"
]
},
{
"page": "Treedepth",
"title": "How often the NUTS trajectory hit the tree-depth cap",
"topics": [
"Treedepth"
]
},
{
"page": "trend_matrix",
"title": "Design columns for k, m and each delta_j",
"topics": [
"trend_matrix"
]
},
{
"page": "TrimCI",
"title": "Tukey-McLaughlin inference for a trimmed mean",
"topics": [
"TrimCI"
]
},
{
"page": "Trimit",
"title": "Weight trimming with the excess redistributed",
"topics": [
"Trimit"
]
},
{
"page": "TrimSE",
"title": "Standard error of the trimmed mean",
"topics": [
"TrimSE"
]
},
{
"page": "TripNoRep",
"title": "Ordered triples without repetition",
"topics": [
"TripNoRep"
]
},
{
"page": "Trmwgt",
"title": "Weight truncation at a percentile of the weight distribution",
"topics": [
"Trmwgt"
]
},
{
"page": "TruncatedProductPvalue",
"title": "Effect modification in matched observational studies",
"topics": [
"SubmaxComparisons",
"SubmaxTest",
"TruncatedProductPvalue",
"WilcoxonSensitivityMoments"
]
},
{
"page": "Tsallen",
"title": "Tsallis entropy of the empirical distribution of a sample",
"topics": [
"Tsallen"
]
},
{
"page": "Tsalls",
"title": "Tsallis entropy of a probability vector that is already known",
"topics": [
"Tsalls"
]
},
{
"page": "TukeyKramer",
"title": "Tukey-Kramer intervals for all pairwise differences",
"topics": [
"TukeyKramer"
]
},
{
"page": "TukeyLambda",
"title": "Tukey lambda distribution",
"topics": [
"TukeyLambda"
]
},
{
"page": "Tukeyw",
"title": "Biweight M-estimate of location, with redescending weights",
"topics": [
"Tukeyw"
]
},
{
"page": "Tukrho",
"title": "The biweight loss and its derivative, evaluated pointwise",
"topics": [
"Tukrho"
]
},
{
"page": "Tukrr",
"title": "Robust regression by iteratively reweighted least squares",
"topics": [
"Tukrr"
]
},
{
"page": "Turnbull",
"title": "Turnbull estimator for interval-censored data",
"topics": [
"morie_survival_turnbull",
"Turnbull"
]
},
{
"page": "TurningBands",
"title": "Turning bands simulation of a Gaussian random field",
"topics": [
"TurningBands"
]
},
{
"page": "TurningBandsSpherical",
"title": "Spatial simulation and interpolation",
"topics": [
"AnnealingSimulation",
"DeformationFieldSimulate",
"DlaAggregate",
"MovingLeastSquares",
"TurningBandsSpherical"
]
},
{
"page": "Tvsbn",
"title": "Asymmetric set similarity with separate penalties per side",
"topics": [
"Tvsbn"
]
},
{
"page": "two_proportion_ztest",
"title": "Two-sample z-test for the difference in proportions",
"topics": [
"two_proportion_ztest"
]
},
{
"page": "two_sample_ttest",
"title": "Independent two-sample t-test (equal or unequal variance)",
"topics": [
"two_sample_ttest"
]
},
{
"page": "two_way_anova",
"title": "Two-way factorial ANOVA (Type-II SS)",
"topics": [
"two_way_anova"
]
},
{
"page": "TwoCoinVar",
"title": "Variance of the sum of two fair coin flips",
"topics": [
"TwoCoinVar"
]
},
{
"page": "Twostage",
"title": "Two-stage sample: PSUs drawn, then elements within each PSU",
"topics": [
"Twostage"
]
},
{
"page": "Twostg",
"title": "Cheng-Wei-Ying two-stage transformation-model estimator",
"topics": [
"Twostg"
]
},
{
"page": "Ucbb",
"title": "UCB1 index policy on a fixed reward table",
"topics": [
"Ucbb"
]
},
{
"page": "UhiIntensity",
"title": "Surface urban heat island intensity",
"topics": [
"UhiIntensity"
]
},
{
"page": "unescape_whitespace",
"title": "Invert the whitespace escape",
"topics": [
"unescape_whitespace"
]
},
{
"page": "Unitl",
"title": "Unit-length (L2) normalization",
"topics": [
"Unitl"
]
},
{
"page": "Unobts",
"title": "Unobserved components (basic structural) model by the Kalman filter",
"topics": [
"Unobts"
]
},
{
"page": "unwrap_dek",
"title": "Unwrap a DEK, optionally logging the call",
"topics": [
"unwrap_dek"
]
},
{
"page": "utility",
"title": "Utility = efficacy per person-day in quarantine (eq. 4)",
"topics": [
"utility"
]
},
{
"page": "Vaccthresh",
"title": "Critical vaccination threshold for herd immunity",
"topics": [
"Vaccthresh"
]
},
{
"page": "Vaean",
"title": "Anomaly detection by VAE reconstruction probability",
"topics": [
"Vaean"
]
},
{
"page": "Vaeber",
"title": "Variational autoencoder evidence lower bound (SGVB estimator)",
"topics": [
"Vaeber"
]
},
{
"page": "Vaecf",
"title": "Multinomial variational autoencoder for collaborative filtering",
"topics": [
"Vaecf"
]
},
{
"page": "validate_schema",
"title": "Validate a data frame against a list of column rules",
"topics": [
"validate_schema"
]
},
{
"page": "validation",
"title": "Data and model validation framework",
"topics": [
"validation"
]
},
{
"page": "vancouver_crime_adjacent",
"title": "Included Vancouver Open Data crime-adjacent civic datasets",
"topics": [
"morie_datasets_vancouver_community_centres",
"morie_datasets_vancouver_community_food_markets",
"morie_datasets_vancouver_disability_parking",
"morie_datasets_vancouver_fire_halls",
"morie_datasets_vancouver_graffiti",
"morie_datasets_vancouver_homeless_shelters",
"morie_datasets_vancouver_noise_control_areas",
"morie_datasets_vancouver_property_use_inspection_districts",
"morie_datasets_vancouver_public_art",
"vancouver_crime_adjacent"
]
},
{
"page": "VandIE",
"title": "The four-way decomposition expressed as shares of the total",
"topics": [
"VandIE"
]
},
{
"page": "Vanr1",
"title": "VanRaden method 1 genomic relationship matrix",
"topics": [
"Vanr1"
]
},
{
"page": "Vanr2",
"title": "VanRaden method 2 genomic relationship matrix (marker weighted)",
"topics": [
"Vanr2"
]
},
{
"page": "Varatr",
"title": "Value at Risk and expected shortfall from a GARCH(1,1) volatility",
"topics": [
"Varatr"
]
},
{
"page": "Vardec",
"title": "VAR forecast error variance decomposition - alias of 'Fevdc'",
"topics": [
"Vardec"
]
},
{
"page": "Varest",
"title": "VAR(p) vector autoregression by multivariate least squares",
"topics": [
"Varest"
]
},
{
"page": "VarF",
"title": "VAR(p) multivariate autoregression - alias of 'Varest'",
"topics": [
"VarF"
]
},
{
"page": "vargha_delaney_a",
"title": "Vargha-Delaney A statistic",
"topics": [
"vargha_delaney_a"
]
},
{
"page": "Vargpc",
"title": "Scalable variational Gaussian process classification (SVGP)",
"topics": [
"Vargpc"
]
},
{
"page": "variable_taxonomy",
"title": "Per-variable taxonomy + dispatcher (R mirror of morie.variable_taxonomy)",
"topics": [
"variable_taxonomy"
]
},
{
"page": "variance_equality_suite",
"title": "Run a suite of homogeneity-of-variance tests",
"topics": [
"variance_equality_suite"
]
},
{
"page": "variance_ratio",
"title": "Variance ratio (F-test for equality of variances)",
"topics": [
"variance_ratio"
]
},
{
"page": "VariationRatio",
"title": "Association measures and affine moments",
"topics": [
"AffineMoments",
"TostCorrelation",
"VariationRatio",
"YuleAssociation"
]
},
{
"page": "Varimp",
"title": "VAR impulse response function - alias of 'Irfun'",
"topics": [
"Varimp"
]
},
{
"page": "VariogramLoglik",
"title": "Gaussian log-likelihood and ML/REML fitting of a covariance model (geoR likfit)",
"topics": [
"Likfit",
"SelectVariogramModel",
"VariogramLoglik"
]
},
{
"page": "VariogramMap",
"title": "Variogram map",
"topics": [
"VariogramMap"
]
},
{
"page": "Varrd",
"title": "Variance reduction criterion for regression tree splitting",
"topics": [
"Varrd"
]
},
{
"page": "VarSum",
"title": "Variance of a sum of independent variables",
"topics": [
"VarSum"
]
},
{
"page": "VarXbar",
"title": "Variance of the sample mean",
"topics": [
"VarXbar"
]
},
{
"page": "Vbinfp",
"title": "Barber-Agakov variational lower bound on mutual information",
"topics": [
"Vbinfp"
]
},
{
"page": "Vbnopt",
"title": "Variational inference by ELBO maximisation - alias of 'Vinfer'",
"topics": [
"Vbnopt"
]
},
{
"page": "Vbnpc",
"title": "Variational Bayes for a Dirichlet process mixture (truncated stick-breaking)",
"topics": [
"Vbnpc"
]
},
{
"page": "vcov.morie_lm",
"title": "Extract the covariance matrix of method for 'morie_lm' objects",
"topics": [
"vcov.morie_lm"
]
},
{
"page": "vcov.morie_lmm",
"title": "Covariance of the fixed-effect estimates",
"topics": [
"vcov.morie_lmm"
]
},
{
"page": "Vcquant",
"title": "Quantize a value cache with the inner-product variant of TurboQuant",
"topics": [
"Vcquant"
]
},
{
"page": "VecchiaLoglik",
"title": "Scalable approximations to kriging",
"topics": [
"FixedRankKriging",
"NngpPredict",
"SparseGpKrige",
"TaperedKriging",
"VecchiaLoglik"
]
},
{
"page": "VehicleRoutingSavings",
"title": "Vehicle routing by Clarke-Wright savings",
"topics": [
"VehicleRoutingSavings"
]
},
{
"page": "verify_chain",
"title": "Verify a chain, returning the FIRST bad index (not just a boolean)",
"topics": [
"verify_chain"
]
},
{
"page": "verify_inclusion",
"title": "Recompute the root from a leaf and an audit path",
"topics": [
"verify_inclusion"
]
},
{
"page": "vgdrift",
"title": "Expected Matheron semivariogram under a linear drift",
"topics": [
"vgdrift"
]
},
{
"page": "VgmSemivariance",
"title": "Variogram models (gstat vgm shapes and more)",
"topics": [
"VgmCorrelogram",
"VgmCovariance",
"VgmSemivariance"
]
},
{
"page": "VgmSpectralDensity",
"title": "Spectral density of Matern, exponential and Gaussian covariances",
"topics": [
"VgmSpectralDensity"
]
},
{
"page": "Vgrm",
"title": "Empirical (Matheron) variogram",
"topics": [
"variogram",
"Vgrm"
]
},
{
"page": "Vilbrt",
"title": "ViLBERT two-stream co-attention",
"topics": [
"Vilbrt"
]
},
{
"page": "Vinasc",
"title": "Score a docked pose with the Vina free-energy function",
"topics": [
"Vinasc"
]
},
{
"page": "Vinfer",
"title": "Mean-field variational inference by coordinate ascent on the ELBO",
"topics": [
"Vinfer"
]
},
{
"page": "Virtloss",
"title": "Virtual loss for parallel Monte-Carlo tree search",
"topics": [
"Virtloss"
]
},
{
"page": "Viterb",
"title": "Most likely hidden state sequence, in one forward pass",
"topics": [
"Viterb"
]
},
{
"page": "viterbi_segment",
"title": "Unigram LM segmentation by Viterbi over the split lattice",
"topics": [
"viterbi_segment"
]
},
{
"page": "Vitlnorm",
"title": "Layer normalisation",
"topics": [
"Vitlnorm"
]
},
{
"page": "Vitscn",
"title": "Scaled cosine attention (Swin Transformer V2)",
"topics": [
"Vitscn"
]
},
{
"page": "Vivkt",
"title": "Show how the classical two-way split sits inside the four-way one",
"topics": [
"Vivkt"
]
},
{
"page": "VmfDens",
"title": "Von Mises-Fisher density",
"topics": [
"VmfDens"
]
},
{
"page": "vol_engle_lagrange",
"title": "Volatility diagnostics: ARCH-LM and multi-horizon distributional accuracy",
"topics": [
"vol_corradi_swan_persistence",
"vol_engle_lagrange"
]
},
{
"page": "Volsv",
"title": "Quasi-likelihood SV(1) via the Kalman filter",
"topics": [
"Volsv"
]
},
{
"page": "VoronoiCells",
"title": "Voronoi cells of a point pattern inside a convex window",
"topics": [
"VoronoiCells"
]
},
{
"page": "VoteProbability",
"title": "Spatial vote probabilities",
"topics": [
"VoteProbability"
]
},
{
"page": "VoterUtility",
"title": "Spatial voting utilities",
"topics": [
"VoterUtility"
]
},
{
"page": "VoteShares",
"title": "Party competition: vote shares, Hotelling-Downs equilibria, strategic voting, entry, bargaining, committees",
"topics": [
"BestResponseEquilibrium",
"CommitteeOutcome",
"EffectiveNumberParties",
"EntryGame",
"LaverDynamics",
"MedianParty",
"MergeSplitParties",
"RubinsteinBargaining",
"SetterOutcome",
"StrategicVoteShares",
"VoteShares"
]
},
{
"page": "Vrmed",
"title": "Fraction of explained variance attributable to the mediator",
"topics": [
"Vrmed"
]
},
{
"page": "Vstrength",
"title": "Vertex strengths of a weighted graph",
"topics": [
"Vstrength"
]
},
{
"page": "Waicd",
"title": "Predictive criterion computed from the posterior, pointwise",
"topics": [
"Waicd"
]
},
{
"page": "wald_test",
"title": "Wald test for linear restrictions on parameters",
"topics": [
"wald_test"
]
},
{
"page": "Waldstat",
"title": "Wald test of H0: theta = theta0 against a two-sided alternative",
"topics": [
"Waldstat"
]
},
{
"page": "Wassdt",
"title": "1-Wasserstein distance on the line",
"topics": [
"Wassdt"
]
},
{
"page": "Watstro",
"title": "Rewire a ring lattice and watch the diameter collapse",
"topics": [
"Watstro"
]
},
{
"page": "Wave",
"title": "Daubechies orthonormal wavelet basis for a periodic signal",
"topics": [
"Wave"
]
},
{
"page": "Weibull3",
"title": "Three-parameter Weibull distribution",
"topics": [
"Weibull3"
]
},
{
"page": "WeibullFrailtyFit",
"title": "Spatial survival and SPDE fields: Weibull gamma-frailty model and SPDE (Matern) GMRF precision",
"topics": [
"SpdePrecisionGrid",
"WeibullFrailtyFit"
]
},
{
"page": "WeightInit",
"title": "Neural network weight initialisation (Philox)",
"topics": [
"WeightInit",
"Xavir",
"Xvrig"
]
},
{
"page": "WeightsStyle",
"title": "Spatial weights coding schemes (spdep nb2listw styles)",
"topics": [
"WeightsStyle"
]
},
{
"page": "WeightsSummary",
"title": "Summary of a spatial weights matrix",
"topics": [
"WeightsSummary"
]
},
{
"page": "Weisz",
"title": "Iteratively reweighted mean converging on the geometric median",
"topics": [
"Weisz"
]
},
{
"page": "welch",
"title": "Welch power spectral density",
"topics": [
"welch"
]
},
{
"page": "welch_ttest",
"title": "Welch's t-test (convenience wrapper)",
"topics": [
"welch_ttest"
]
},
{
"page": "Welshw",
"title": "Gaussian-shaped weight that decays smoothly and never reaches zero",
"topics": [
"Welshw"
]
},
{
"page": "Wfrep",
"title": "Weighted frequency distribution",
"topics": [
"Wfrep"
]
},
{
"page": "Wgtlap",
"title": "Assemble the weighted Laplacian from an edge list",
"topics": [
"Wgtlap"
]
},
{
"page": "WhaleOptimization",
"title": "Whale optimization algorithm",
"topics": [
"WhaleOptimization"
]
},
{
"page": "WideD",
"title": "Wide & Deep jointly trained classifier",
"topics": [
"WideD"
]
},
{
"page": "wilcoxon_signed_rank",
"title": "Wilcoxon signed-rank test (one-sample or paired)",
"topics": [
"wilcoxon_signed_rank"
]
},
{
"page": "wild_bootstrap",
"title": "Wild bootstrap for linear regression with heteroskedasticity",
"topics": [
"wild_bootstrap"
]
},
{
"page": "Winz",
"title": "Mean after pulling the tails in to the quantiles",
"topics": [
"Winz"
]
},
{
"page": "WishartDens",
"title": "Wishart and inverse Wishart densities",
"topics": [
"WishartDens"
]
},
{
"page": "WishartMoments",
"title": "Spatio-temporal model building blocks",
"topics": [
"ArmaAcf",
"CarrollStCorrelation",
"HarmonicOzoneDlm",
"NormalGammaDlm",
"PartitionedCovariance",
"ProcessConvolutionCovariance",
"SeparablePrewhiten",
"WishartMoments"
]
},
{
"page": "Wlwmm",
"title": "Wei-Lin-Weissfeld marginal model for multivariate failure times",
"topics": [
"Wlwmm"
]
},
{
"page": "Wmeansr",
"title": "Horvitz-Thompson weighted (Hajek) survey mean",
"topics": [
"Wmeansr"
]
},
{
"page": "Wmtwgt",
"title": "Spatial weights matrix from point coordinates",
"topics": [
"Wmtwgt"
]
},
{
"page": "Wordscores",
"title": "Wordscores",
"topics": [
"Wordscores"
]
},
{
"page": "Wpiece",
"title": "WordPiece subword tokenizer",
"topics": [
"Wpiece"
]
},
{
"page": "wrap_angle",
"title": "Wrap to [-pi, pi)",
"topics": [
"wrap_angle"
]
},
{
"page": "wrap_dek",
"title": "Wrap a DEK under a KEK",
"topics": [
"wrap_dek"
]
},
{
"page": "WrapCauchy",
"title": "Wrapped Cauchy distribution",
"topics": [
"WrapCauchy"
]
},
{
"page": "WrapNorm",
"title": "Wrapped normal distribution",
"topics": [
"WrapNorm"
]
},
{
"page": "Wsdt2d",
"title": "Transport distance between equal-size clouds by optimal assignment",
"topics": [
"Wsdt2d"
]
},
{
"page": "Wvar",
"title": "Weighted variance and weighted mean",
"topics": [
"Wvar"
]
},
{
"page": "Wvltdb",
"title": "Multi-level Daubechies wavelet decomposition of a periodic signal",
"topics": [
"Wvltdb"
]
},
{
"page": "Xpehh1",
"title": "Cross-population extended haplotype homozygosity (XP-EHH)",
"topics": [
"Xpehh1"
]
},
{
"page": "Yangr",
"title": "Yang et al. realized genomic relationship matrix",
"topics": [
"Yangr"
]
},
{
"page": "Yarn",
"title": "YaRN context-window scaling: NTK-by-parts + ramp + temperature",
"topics": [
"Yarn"
]
},
{
"page": "Yolk",
"title": "Yolk of a spatial majority game",
"topics": [
"Yolk"
]
},
{
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