riskutility 0.2.0

Bug fixes and one API addition, all surfaced while preparing the JSS software paper. Three of these change reported values, so results produced with 0.1.0 are not directly comparable.

New

• tcap() gains cont_bins (default 10), matching dcap(). A continuous target is now discretised into quantile bins before matching. Previously an exact match on a continuous value essentially never occurred and every TCAP score collapsed to zero.
• tcap() now reports all three TCAP variants in use in the literature and gains a kind argument ("certain", the default; "matched"; "conditional") that selects which one print()/summary() highlight – reported values are unchanged. tcap_certain keeps riskutility’s strict definition (the key uniquely determines the target in both the original and the synthetic data). The new tcap_matched reproduces synthpop’s TCAP as defined up to synthpop 1.9-2; the new tcap_conditional implements the definition of Little, Allmendinger & Elliot (2025, Journal of Official Statistics 41(1), 255-308), which synthpop adopts from version 1.9-3. The previous documentation claim that tcap_certain equals synthpop’s TCAP held only on data where every disclosive synthetic key class is also unambiguous in the original data; the documentation now states the exact mapping.

Bug fixes (change reported values)

• propscore(): the synthetic-side kernel density was indexed with length(ps) – a scalar – rather than nrow(p), so the index ran backwards and every density diagnostic reported by summary() (kl, density_ratio, mean_ratio, sd_ratio and their Bayes-space counterparts) was computed on the wrong records.
• tstr(): R-squared is now clamped at 0, and the TSTR/TRTR ratio is returned as NA with a warning when the train-on-real model has no predictive power. Previously two negative R-squared values could divide to a large positive ratio and be reported as excellent utility.
• tail_fidelity(): the Jensen-Shannon divergence is now computed in density space. It previously used Bayes-space (clr) ordinates, which are sign-indefinite, and could return values far outside [0, log 2].

Other

• dcr() now warns when the training and holdout sets differ in size. The dcr_share reference value of 0.5 and the 0.55 rule used by privacy_pass assume equal sizes; with an unequal split the expected share under no memorisation is n_train / (n_train + n_holdout).
• Tests that compare riskutility against synthpop output are now version-aware (synthpop 1.9-3 redefines its TCAP measure) and are skipped on CRAN, as requested by the synthpop maintainers and the CRAN team. They continue to run in local devtools::test() runs.
• print.tcap() no longer errors on a result with zero matched records.
• compare_feature_importance(importance_type = "permutation") no longer requires the vip package (archived from CRAN on 2026-07-08 and therefore flagged by the CRAN incoming checks): permutation importance is computed internally, and the documented caret-style metric names ("Accuracy", "Kappa", "ROC AUC", "RMSE", "MAE", "MAPE", "R-squared") as well as custom metric functions are handled directly, with error metrics sign-flipped so that larger importance always means a more influential feature. vip was removed from Suggests.

riskutility 0.1.0

Initial release: a comprehensive framework for measuring disclosure risk and data utility of anonymized and synthetic data. All measures share a consistent S3 API (print(), summary(), plot()) and feed a multivariate Risk-Utility (R-U) map.

Disclosure risk

• Attribution-based (CAP family): dcap() (reports both the raw mean CAP and the differential CAP = mean CAP minus baseline), tcap(), weap(), disco().
• ML-based: rapid() (Risk of Attribute Prediction-Induced Disclosure; random-forest default, also lm/cart/gbm/logit) with confint(), permutation test, threshold selection, synthesizer cross-validation, and six plot types.
• Distance-based (holdout): dcr(), nndr(), ims(), repu(), including the DCR-Delusion caveat and null-distribution diagnostics.
• Membership inference: domias(), nnaa(), mia_classifier().
• Classical SDC privacy models: kanonymity(), ldiversity() (distinct/entropy/recursive), tcloseness() (EMD), suda(), individual_risk(), population_uniqueness() (Pitman/Zayatz/SNB), epsilon_identifiability(), delta_presence(), hitting_rate(), singling_out(), linkability(), attacker_risk() (prosecutor/journalist/marketer), drisk().
• Record linkage: recordLinkage() with deterministic, probabilistic (Fellegi-Sunter), PRAM, predictive, random-forest, RBRL, robust-Mahalanobis, and embedding (autoencoder) methods; independent, bijective (Hungarian / GDBRL), and optimal-transport (Sinkhorn) matching; blocking and per-record accessors. All eight methods share a single re-identification-risk definition — the probability of identifying the true match within the attacker’s candidate set. For the random-forest and embedding methods, the nearest-neighbour similarity (their former risk value) is now retained in an nn_similarity diagnostic column. na_anon (ignore/match/mismatch) is honored consistently across all methods (PRAM no longer reports an artificial zero risk for records with a missing key). New options: compute_baseline = TRUE reports the no-perturbation reference risk (with risk_reduction), and expected_risk = TRUE reports a perturbation-aware expected PRAM risk over the transition distribution. User-supplied m_probs/u_probs are validated and clamped to the open interval (0,1).
• Reporting: disclosure_report() produces a comprehensive multi-metric report.

Data utility

• Propensity-score utility: propscore(), pMSE(), specks().
• Global / interval: gower(), mqs(), ci_overlap(), ci_proximity().
• Distributional and structural: compare_wasserstein(), compare_ks_test(), compare_chisq_gof(), compare_pca(), compare_embedding(), compare_correlation_matrices(), hellinger(), energy_distance(), mmd(), copula_fidelity(), tail_fidelity(), contingency_fidelity().
• Downstream / model-based: tstr() (train on synthetic, test on real), compare_feature_importance(), compare_model_performance(), regression_fidelity(), subgroup_utility().
• Information-theoretic: KLDiv(), JSDiv(), CrossEntropy(), entropy and mutual-information helpers, privacy_score().

Multivariate Risk-Utility map

• rumap(): normalized multivariate R-U evaluation with Pareto-frontier identification, internal-consistency metrics, and seven visualizations (scatter, heatmap, dot plot, parallel coordinates, radial, PCA biplot, blockwise PCA).

Integration

• synth_pair() container plus from_synthpop() and from_simPop() converters; most measures dispatch on synth_pair objects as well as plain data frames.