Changes in version 0.1.0 (2026-06-22)                  

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.