rsdv 0.1.0

Initial CRAN release.

Synthesizer

• Gaussian copula synthesizer (gaussian_copula_synthesizer()) that fits a single joint copula over all modeled columns: numerical, categorical, and boolean.
• Parametric marginal fitting for numerical columns with best-fit selection among norm, beta, gamma, truncnorm, and uniform by Kolmogorov-Smirnov distance. Per-column overrides via numerical_distributions; global default via default_distribution.
• Categorical and boolean columns are embedded into the copula via their cumulative-frequency intervals, preserving cross-column dependence (numeric↔︎categorical and categorical↔︎categorical).
• sample() for unconditional generation and sample_conditions() for conditional generation on categorical or boolean values via rejection sampling.
• Per-column missingness rates from training data are reproduced in synthetic output.

Metadata and constraints

• Column-type metadata system (metadata(), set_column_type(), set_primary_key()) with auto-detection and JSON serialization (metadata_to_json(), save_metadata()).
• Declarative constraint system: equality, inequality, fixed-combinations, and custom row-level predicates (add_constraint(), check_constraints()), enforced via rejection sampling.

Evaluation

• quality_report() aggregates metrics into the two-property hierarchy used by the Python SDMetrics library:
  • Column Shapes — per-column marginal fidelity (KS similarity for numerical, TVD similarity for categorical).
  • Column Pair Trends — pairwise dependence (correlation_similarity() for numerical pairs, contingency_similarity() for categorical pairs). ML efficacy (train-on-synthetic / test-on-real, TSTR/TRTR) is reported separately, not folded into the overall score.
• diagnostic_report() checks structural validity: boundary adherence (numerical ranges), category adherence (categorical values), and key uniqueness for primary keys.
• privacy_report() reports the nearest-neighbour distance ratio (NNDR) and, optionally, attribute disclosure risk.
• autoplot() methods for quality, diagnostic, and privacy reports.

Data

• Bundled dataset adult_income — a 500-row sample of the UCI Adult Income dataset used in examples and vignettes.

Vignettes

• “Getting Started with rsdv” — practitioner-oriented guide covering metadata, fitting, conditional sampling, quality and diagnostic reports, privacy evaluation, constraints, and missing-data handling.
• “Migrating from synthpop” — side-by-side comparison and feature table.