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rsdv 0.2.0

A correctness and robustness release driven by a code review of 0.1.0 (see issue #12 for the full catalogue). Two changes alter previously-returned numeric output and are called out separately below.

⚠ Default-output changes (potentially breaking)

• nndr() now standardises (z-scores) each numerical column by the real-data mean and standard deviation before the nearest-neighbour distance is computed. Without this, a single large-scale column (e.g. income in dollars) dominated the Euclidean distance and the score moved with measurement units rather than with row similarity. Pass normalize = FALSE to recover the previous behaviour exactly.
• correlation_similarity() and contingency_similarity() now return score = NA_real_ (rather than 1) when there are fewer than two columns of the relevant type, and diagnostic_report() returns NA_real_ per column when the synthetic column is entirely NA. Aggregated property scores in quality_report() / diagnostic_report() skip these NAs (na.rm = TRUE) so they no longer overstate fidelity with a synthetic “1” where there is no signal to measure.

New features

• equality_constraint() gains a tolerance argument: with tolerance > 0 on numeric columns, the check is abs(a - b) <= tolerance instead of exact ==. Default 0 preserves prior behaviour.
• custom_constraint() gains a vectorized argument: when TRUE, the predicate is called once with the whole data frame instead of once per row. Substantially faster on large synthetic samples for vectorisable predicates.
• ml_efficacy() gains a seed argument for reproducible train/test splits. The caller’s global RNG state is restored on exit, so callers using set.seed() elsewhere are unaffected.
• nndr() gains a normalize argument (default TRUE) — see the default-output note above.
• print() methods for equality_constraint, inequality_constraint, fixed_combinations_constraint, and custom_constraint.

Robustness and correctness

• metadata_to_json() / metadata_from_json() now round-trip the structural constraint types (equality, inequality, and fixed_combinations). custom_constraint cannot be serialised — it holds an R closure — and is dropped with a warning. Previously metadata_to_json() crashed on any constraint, so save_metadata() was effectively broken for non-trivial metadata.
• check_constraint.equality_constraint and check_constraint.inequality_constraint now return FALSE (not NA) for rows containing NA. This prevents NA from propagating into the row selector used by sample()’s rejection loop, which previously inserted phantom NA-only rows.
• sample_conditions() now honours metadata constraints alongside the user-supplied conditions (previously it filtered only on the conditions).
• tvd_similarity() now strips NAs from both sides and divides by the non-NA count on each side; previously NA-padding inflated TVD.
• ks_similarity() now suppresses the ks.test() “p-value will be approximate in the presence of ties” warning, which it leaked to users on any tied integer column (very common in tables with integer ages, capital gains, etc.).
• fixed_combinations_constraint now uses a collision-free length-prefix key encoding ("<nchar>:<value>"), removing a theoretical separator collision in the previous paste-based comparison.

Clearer errors and validations

• fit.gaussian_copula_synthesizer() errors clearly when a modeled column is entirely NA or when no row is complete across all modeled columns. Previously the user saw a cryptic 'dim' must be an integer (>= 2) from inside copula::normalCopula.
• ml_efficacy() validates target_col (must be a column of real) and test_fraction (must be strictly between 0 and 1) up front.
• attribute_disclosure_risk() validates that known_cols are present and numeric (one-hot encode categorical knowns first); previously triggered a cryptic FNN::knnx.index error.
• gaussian_copula_synthesizer() cross-checks numerical_distributions names against the metadata’s numerical columns; silently-ignored typos like list(capitl_gain = "gamma") now raise a clear error.
• sample_conditions() validates that .n values are positive whole numbers (was silently truncating or accepting negatives).
• privacy_report() errors when only one of sensitive_col / known_cols is supplied (previously silently dropped disclosure-risk computation).
• set_primary_key() emits an advisory warning when the column’s metadata type is not "id", since the column would otherwise be modeled as ordinary data and the diagnostic key-uniqueness check would typically fail.

Documentation

• set_column_type() docstring documents the level-ordering rule for categorical columns — factor keeps levels() order, character is sorted lexicographically (c("2", "10") becomes levels c("10", "2")).

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.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.