Changes in version 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")).

                 Changes in version 0.1.0 (2026-06-08)                  

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.