NEWS

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crimlab 0.0.0.9000

• cntreg() fits a count on a group by Poisson and negative binomial regression (the iterations of glm() and MASS::glm.nb()), with adjustment columns, an exposure offset and clusters, and reports the rate ratios with Wald intervals: model-based, cluster-robust (the sandwich of sandwich::vcovCL()), and from an exchangeable GEE (as 'geepack' estimates it). proof("cntreg") names the Lean theorems on swapping the groups.
• grprr() gives rates or proportions by group and their ratios and differences against a reference group: Wilson, Clopper-Pearson and exact Poisson intervals, log-normal and exact conditional intervals of the ratio, Newcombe's interval of a difference of proportions, and each group's over-representation among the events. dastd() gives directly standardised rates with Fay and Feuer's interval, and their ratios and differences; sirex() gives standardised incidence ratios with exact intervals, funnel limits and Marshall's empirical Bayes shrinkage.
• ctabx() cross-tabulates labels (counting rows, summing a count column, taking the largest or smallest value, or counting distinct ids per cell) and tests the table: Pearson's chi-square (with Yates' correction on request), the likelihood-ratio test, Fisher's exact test with the conditional odds ratio of a 2 x 2 table, Cramer's V with its noncentral chi-square interval and Bergsma's bias correction, the contingency coefficient, phi and the diagonal share. proof("ctabx") names the Lean theorems on the 2 x 2 statistic.
• clprep() maps a published dataset's columns onto a generic function's arguments from inst/presets/presets.tsv (one row per preset and argument: column, recode, filter, units, note, source), with recipes for derived columns, so that generic(clprep(data, preset)) gives the published analysis. The presets cover the Ontario segregation and deaths-in-custody tables, the race-based use-of-force files, the Special Investigations Unit cases, the Structured Intervention Unit tables and the Toronto incident layers. Presets also feed the risk-tool audits: fairdf(), tmpaud(), calaud() and scrdsp() take a clprep() result as their first argument, for a generic file of area, risk, outcome and group, for fairsim() records, and for the layouts of two published studies of risk classification.
• Audits of risk tools and predictive policing. fairdf() gives the parity gap and disparate impact ratio between groups with Newcombe and Katz intervals, the gaps in true and false positive rates and the average odds difference with their intervals, and the bias amplification score with a bootstrap interval; tmpaud() the same by city and period with each city's range. calaud() rolls records up to areas and asks whether the order of predicted risk matches the order of recorded outcomes (Spearman's rho with the AS 89 and large-sample p-values) and whether the mean rank gap of each group's areas departs from chance (a permutation test); scrdsp() a score's spread across groups with the one-way analysis of variance and Welch's test. lipaud() is the Lipschitz audit of individual fairness. nsbnd() bounds a true base rate behind a noisily recorded one for noise rates in boxes, and says which groups' order survives every admissible noise; impfpr() is Chouldechova's identity; rnkres() the pairs a noisy ranking orders. oaxbd() is the Oaxaca-Blinder decomposition of a gap at either group's coefficients and the threefold one, with delta-method standard errors. fairsim() simulates records with a known disparity and nordet() the noisy-OR chance of detection by patrols. proof() names the Lean theorems behind nsbnd(), impfpr(), rnkres() and oaxbd().
• relchk() recomputes a reported odds ratio, risk ratio or risk difference from its table under every labelling (each reference group, each outcome as the event, groups in rows or in columns) and says which readings reproduce the value as rounded: the labelling the report states, another one, or none. Zero cells give Inf or NaN, never a correction. proof("relchk") names the Lean theorems on how the contrasts change under relabelling.
• catchk() audits categorical inputs before an analysis: storage type, levels and counts, missing values, unused and rare levels, labels that differ only by case or white space, 0/1 and logical codings, numbers that look like category codes, value labels that disagree with the values, and the reference level a default would take and why. It can check a stated reference level, and changes nothing.
• Disparity tests: vodtst(), orrisk(), outtst(), dspbm(), dyadrt().
• Forensic statistics: gtmp(), lrpost(), cllr(), cllrmin(), lrcal(), mixlr(), kinidx(), patidx(), dbsrch(), tipplt(), eceplt().
• clrsmp() splits a change in a clearance rate into rate and mix effects and flags Simpson's paradox.
• expbnd() bounds rates and disparities when exposure is known only through a proxy.
• misrd() gives the risk difference when group membership is misrecorded.
• rtmhs() measures regression to the mean at selected hot spots.
• incap() gives the crime rate and share prevented under incapacitation.
• incapr() gives crimes prevented under desistance and replacement.
• sellab() bounds the failure rate of a release rule from released defendants alone.
• losbnd() bounds the mean time in custody from completed and open stays.
• sntbnd() bounds the effect of a sentence on a binary outcome without assumptions.
• hzdsel() shows how period-two hazards differ after selective depletion.
• Networks: coofg() builds the co-offending graph of an incident table; ntdeg(), ntbtw(), ntcls() and ntevc() give degree, betweenness, closeness and eigenvector centrality; ntcore() cores, ntcmp() components, ntlvn() and ntmod() Louvain communities and modularity, ntbrk() Gould-Fernandez brokerage and ntchg() the Cheeger bounds. Results are by node label in order of first appearance.
• Spatial: hvdist() and eqproj()/eqinv() for longitude and latitude; cgstat(), cgmedn(), webpt(), sdell() and cvhull() for centres, spread and hulls; nnidx(), ripk() and ripenv() for nearest neighbours and K functions; fzmode(), kmpart(), nnhclu() and stacel() for hot spots; mantst() and stmavg() in space and time; jtdecay(), jtsurf(), jtcal(), jtacc() and bjtc() for journey to crime. Points come as vectors, tables or sf points; longitude and latitude only with lonlat = TRUE.
• Spatial weights and autocorrelation: spwt() (k nearest, distance band, contiguity; binary or inverse distance), gearc() (Geary's C), gorg() (the Getis-Ord G), lmorn() (local Moran with conditional permutations), locgi() (local G and G*) and dirmn() (directional mean).
• hdbang() (head-bang smoothing, weighted or not), crwalk() (a correlated random walk of a series: fit, expected displacement and simulation) and rnnhcl() (nearest-neighbour clustering against a population at risk). nnidx(), ripk(), fzmode() and mantst() take metric = "manhattan"; fzmode(), kmpart(), nnhclu() and stacel() take zones as counts at centroids (count).
• fair() (fairness of a risk tool by group), tlvc() and tlvlr() (trace evidence), aorwt() (aoristic weights), bdig() and bnft() (Benford screens), skp() (Scott-Knott grouping), rptc() (constant reporters), rswp() and rshl() (Rosenbaum bounds), zgll(), zgem(), mvtll() and mvtem() (group-based trajectories), tfrp() (glass transfer), lwss() (lowess), chmb() (tool marks), gstr() (grid Gi*) and sptst() (Andresen's point pattern test).
• proof() names the Lean theorem behind each function; the proofs ship in inst/lean.
• A vector longer than the largest integer is refused with an error, by the exported functions and by the compiled entry points alike; the compiled code used to truncate its length.
• eceplt() gives the calibrated curve its limits at extreme prior odds: a calibrated ratio of 0 or infinity used to give NaN, or an error.
• incapr() refuses a t0 + s beyond the length of lam however large the two are; past the largest integer it used to warn of an integer overflow and stop with an unrelated error.
• sntbnd() orders the two values of z by Unicode code point, so the default treat and the order of the terms are the same on every machine and in every locale ("B" sorts before "a").
• Every error message starts with the name of the function that raised it ("outtst: `q` must be at most 1."); the words after it are unchanged.
• A matrix with more than one row and column is refused where a vector is expected, naming the argument; it used to be read as one long vector.
• eceplt() labels its vertical axis in bits.
• proof() names the theorems now proved for gtmp(), cllr(), dbsrch(), mixlr(), kinidx(), patidx(), misrd(), incap(), lrcal() and cllrmin(). The help pages of misrd(), cllr() and cllrmin() say what is proved and what is not.
• vignette("crimlab") walks through one example per family.
• Arguments that hold one value per group are matched by name. Named inputs whose names come in another order were read by position, a wrong answer: vodtst(c(white = 600, black = 400), c(black = 300, white = 700)) read 300 as the white night count. Now every later argument is put in the order of the first by name; names that differ, or a name given twice, are an error that lists them. Unnamed inputs are still matched by position. This holds in vodtst(), dspbm(), dyadrt() (the row and column names of count too), clrsmp(), expbnd(), incap(), gtmp(), hzdsel(), and for loci in kinidx() and patidx(); per-unit arguments of outtst(), rtmhs(), lrpost(), sellab() and sntbnd() are matched by name when every one has unique names.
• Per-group arguments also accept a list of single numbers and a one-row data frame; a one-row or one-column matrix gives its names.
• vodtst() takes one table of groups by day and night (a matrix, 2-d table or data frame with columns named day and night) with night left out.
• vodtst(), dspbm() and expbnd() return a ref column marking the reference group; a factor ref is read by its label.
• sntbnd() no longer picks the treatment by sorting the values of z: treat is required unless z is logical (TRUE is the treatment) or 0 and 1 (1 is).
• A labelled vector (with a labels attribute, as haven makes) is refused with a request to decode it first: its numbers are category codes.
• Learning: enpath(), the elastic net path by coordinate descent with cross-validation over given folds (the objective and scale of glmnet); lgtreg(), logistic regression; rfor(), random forests for regression and probabilities.
• Causal estimates: dmlx(), double machine learning by cross-fitting for the partially linear, interactive, IV, difference-in-differences and sample-selection models, with AIPW and TMLE from the same nuisances.
• enpath() screens binomial paths by the sequential strong rule (scr); rfor() grows honest trees (hon), limits split imbalance (imb) and takes mtry by the rule of grf by default; dmlx() adds two-way clusters, the experimental DiD scores (score = "exp") and passes tol, scr and hon to its learners.