Functions

The functions, by family. The API reference has a page for each.

• Disparities: Veil of darkness, odds ratios and risks, the outcome test, benchmarks and dyadic rates.
• Forensics: DNA match statistics, the weight and calibration of likelihood ratios, kinship, paternity, mixtures and database searches.
• Policing: Clearance rates, exposure proxies, misrecorded groups and regression to the mean.
• Corrections: Incapacitation, release rules, time in custody, sentence effects and hazards after selection.
• Networks: Co-offending graphs, centrality, cores, components, communities, brokerage and the Cheeger bounds.
• Spatial: Distances and projection, centrography, nearest neighbours and K functions, hot spots, space-time tests and journey to crime.
• Dossier methods: Fairness of risk tools, trace evidence, aoristic time, Benford screens, glass grouping and transfer, constant reporters, sensitivity bounds, trajectory models, lowess, tool marks, grid Gi* and the point pattern test.
• Risk tools and predictive policing: Differences between groups with intervals, over time and place; calibration of an area ranking and a score’s spread across groups; individual fairness; bounds under label noise, Chouldechova’s identity and the resolution of a ranking; the Oaxaca-Blinder decomposition; simulated records with a known disparity.
• Checks: Which labelling of a table reproduces a reported contrast, and an audit of categorical inputs.
• Counts, rates and tables: Rate ratios from count regression with exposure and clusters; rates and proportions by group with exact and approximate intervals; direct and indirect standardisation; cross-tabulation with tests and measures of association.
• Presets: A published dataset’s columns onto a generic function’s arguments.
• Learning: The elastic net with cross-validation, logistic regression and random forests; the learners of the cross-fitted estimators.
• Causal estimates: Double machine learning by cross-fitting, with the learners above; augmented inverse probability weighting and targeted maximum likelihood from the same nuisances.
• Proofs: Machine-checked proofs behind the package.
• Package: crimlab: Criminology and Sociolegal Statistics.