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.