Changes in version 0.3.7 (2026-04-29)                  

Documentation

  - The vignette previously mixed defensive requireNamespace() checks
    with bare library(<suggests_pkg>) calls; the bare calls would
    hard-error if the suggested package was not installed, defeating the
    defensive checks elsewhere in the same vignette. The
    tidymodels-interop chunk now carries eval =
    requireNamespace("recipes", quietly = TRUE) &&
    requireNamespace("yardstick", quietly = TRUE) in its chunk header,
    so the chunk is skipped (rather than erroring) during vignette build
    when those Suggests packages are absent. The parallel-setup chunk
    gains a brief comment documenting that future is a Suggests
    dependency. A regression test (test-vignette-suggests.R) walks the
    vignette and asserts that every chunk-level library(<suggests_pkg>)
    call is inside an appropriately gated chunk.

API improvements (no behavior change)

  - predict_guard() is now also accessible through the standard
    [stats::predict()] generic via a registered S3 method
    predict.GuardFit(). Calling predict(fit, newdata) on a GuardFit
    object dispatches to predict.GuardFit() and yields output that is
    bit-identical to the legacy predict_guard(fit, newdata).
    predict_guard() is preserved as a thin backward-compatible alias, so
    existing code continues to work without modification. methods(class
    = "GuardFit") now returns print, summary, and predict, restoring the
    standard R idiom for transformer objects.

  - Added show() / print() methods to the public result classes that
    previously only had summary():
    
      - LeakFit: new show() (S4) — brief auto-print giving task,
        outcome, learners, fold count, and fold-status one-liner.
      - LeakAudit: new show() (S4) — brief auto-print giving task,
        outcome, permutation-gap statistics, and component row counts
        (batch association, target leakage, duplicates).
      - LeakTune: new print() (S3) — brief auto-print giving outer-fold
        success rate, tuning-grid size, selection rule, and refit
        status. Each method ends with a one-line hint pointing to
        summary(<obj>) for the full diagnostic report. methods(class =
        ...) now returns show/print alongside summary for all three
        classes.

Renames (no behavior change)

  - .guard_fit() is renamed to guard_fit() and .guard_ensure_levels() is
    renamed to guard_ensure_levels(). Leading-dot prefixes on exported
    functions are unconventional and were causing the renamed helpers to
    appear awkwardly in help(package = "bioLeak"). Behavior, arguments,
    and return values are unchanged; only the names move from the
    dot-prefixed form to ordinary names. Internal callers
    (fit_resample(), impute_guarded(), predict_guard()'s documentation,
    and the package vignette) are updated to use the new names.

New features

  - Added public accessor functions for the S4 result classes so that
    downstream code (replication scripts, vignettes, and end-user
    analyses) can read components of LeakFit, LeakAudit, and
    LeakDeltaLSI objects without reaching into S4 internals via @. The
    new accessors are purely additive; slot definitions are unchanged
    and existing code that uses @ continues to work.
      - LeakFit: fit_metrics().
      - LeakAudit: audit_perm_gap(), audit_batch_assoc(),
        audit_target_assoc(), audit_duplicates(), audit_info().
      - LeakDeltaLSI: dlsi_metric(), dlsi_robust(), dlsi_ci(),
        dlsi_p_value(), dlsi_tier(), dlsi_R_eff(), dlsi_repeats(). Each
        accessor performs an is(x, "<Class>") validation and emits an
        informative error when called on the wrong object.

                 Changes in version 0.3.5 (2026-03-26)                  

Breaking changes

  - delta_lsi(): inference tier strings renamed to accurately reflect
    what each tier provides. "C_point_only" → "C_signflip" (the
    sign-flip p-value is available at this tier, not just point
    estimates); "B_ci_only" → "B_signflip_ci" (both the sign-flip
    p-value and BCa CI are available). Code that compares result@tier
    against the old string literals must be updated.

New features

  - delta_lsi() gains a block_size argument and makes exchangeability
    actionable for "blocked_time" inputs. When exchangeability =
    "blocked_time", the sign-flip test now uses a block procedure that
    flips contiguous blocks of repeats together, preserving serial
    autocorrelation under the null. block_size is auto-estimated from
    the AR(1) of the repeat-level deltas when NULL (default) and capped
    at floor(R/3) to guarantee at least three independent blocks. The
    @info slot gains block_size_used and n_blocks fields. If the block
    structure yields fewer than five independent blocks, @p_value is set
    to NA and a warning is issued.
  - delta_lsi() now emits an explicit warning when exchangeability is
    "by_group" or "within_batch", informing users that those modes are
    stored but inference still uses the iid sign-flip procedure.
    Previously these values were accepted silently without affecting
    computation.

Bug fixes and improvements

  - fit_resample(): compact + combined mode now correctly excludes
    constraint-axis violations from training sets. Previously the
    compact fallback used setdiff(all, test), ignoring multi-axis
    constraints declared via make_split_plan(constraints = ...). The
    same fix is applied in the as_rsample() conversion path for
    consistency.
  - Guarded preprocessing: lasso and t-test feature selection now uses
    name-based column selection in the transform step, preventing index
    misalignment when constant columns are removed during fitting.
  - delta_lsi(): R_eff and the inference tier are now recomputed after
    repeat-level intersection, so that dropped all-NA repeats correctly
    reduce the effective sample size and select the appropriate tier.
  - fit_resample(): fold error messages are now correctly captured when
    running in parallel via future.apply. Previously <<- mutations
    inside worker processes were silently lost; errors are now attached
    as result attributes and extracted after the parallel map.
  - tune_resample(): fold-ID columns (id, id2, .notes) no longer leak
    into hyperparameter aggregation in the internal select_config()
    helper.
  - summary.LeakFit() now returns object@metric_summary invisibly,
    matching the documented return value (previously returned the object
    itself).
  - Fixed vignette (bioLeak-intro) referencing a shadowed data frame for
    sample count; now reads from fit_safe@splits@info$coldata.
  - Fixed audit_leakage() roxygen documenting a duplicates column named
    in_train_test; the actual column name is cross_fold.
  - make_split_plan(): time-series mode now warns and skips folds with
    fewer than 3 test samples instead of producing degenerate folds.
  - fit_resample(): added bounds checking for repeat_id in compact fold
    resolution to produce a clear error instead of a cryptic index
    failure.
  - show() and summary() for LeakDeltaLSI now label the sign-flip
    p-value as testing mean(Δr) (delta_metric), not delta_lsi, making
    the estimator–inference pairing explicit.
  - summary() prints a diagnostic note when the sign-flip p-value and
    BCa CI lead to qualitatively different conclusions (one significant,
    one spanning zero), which can occur when outlier repeats pull the
    arithmetic mean away from the Huber estimate.
  - summary() prints the block size and number of blocks used when
    exchangeability = "blocked_time".

                 Changes in version 0.3.0 (2026-03-05)                  

New features

  - Added N-axis combined splitting via constraints in
    make_split_plan(), generalizing beyond two-axis combined CV while
    preserving train/test exclusion across all declared axes.
  - Added compact = TRUE split storage (fold assignments) for large
    datasets to reduce split object memory footprint.
  - Added check_split_overlap() for explicit overlap-invariant
    validation across fold/group axes.
  - Added cv_ci() (with Nadeau-Bengio correction) and integrated CI
    columns into fit_resample() and tune_resample() metric summaries
    (*_ci_lo, *_ci_hi).
  - Added guard_to_recipe() to map guarded preprocessing configurations
    to recipes pipelines with explicit fallback/warning behavior.
  - Added benchmark_leakage_suite() for reproducible
    modality-by-mechanism benchmark grids and detection-rate summaries.
  - Expanded audit_leakage() diagnostics with mechanism taxonomy fields
    (mechanism_class, taxonomy, mechanism_summary) and richer risk
    attribution outputs.
  - Added FDR-aware target scan outputs (p_value_adj, flag_fdr) with
    selectable multiple-testing correction (target_p_adjust,
    target_alpha).
  - Added feature_space (raw/rank) and duplicate_scope (train_test/all)
    controls for duplicate diagnostics.
  - Strengthened permutation auditing with explicit perm_mode handling
    for rsample-derived splits and safer perm_refit = "auto" behavior.
  - Extended tidymodels interoperability: rsample conversion and
    metadata inference are more robust (split_cols = "auto",
    mode/perm-mode propagation, stricter compatibility checks).
  - Improved nested tuning safety in tune_resample(): final refit now
    aggregates hyperparameters across outer folds (median/majority)
    instead of selecting a single best outer fold.
  - Added binomial threshold tuning support in tune_resample() using
    inner-fold predictions (tune_threshold, threshold_grid,
    threshold_metric).
  - Added structured fold-status tracking (fold_status) and elapsed
    timing in both fitting and tuning paths for better failure-mode
    observability.
  - Added strict-mode and validation-policy infrastructure
    (bioLeak.strict, bioLeak.validation_mode) with structured condition
    classes for safer recipe and workflow guardrails.
  - Added provenance capture (.bio_capture_provenance) and attached
    provenance metadata to LeakFit, LeakAudit, and LeakTune.
  - Improved summary.LeakAudit() output with explicit Mechanism Risk
    Assessment reporting.
  - Hardened recipe preprocessing in fit_resample() to avoid fold-time
    failures when recipes reference split metadata columns (for example
    subject).
  - Updated simulation defaults and audit settings for more practical
    runtime (simulate_leakage_suite() default B, auto refit cap
    handling).
  - Updated manuscript/simulation assets under paper/ with refreshed
    large-scale simulation outputs and case-study artifacts.

                 Changes in version 0.2.0 (2026-02-11)                  

New features

  - Leak-safe hyperparameter tuning via tune_resample(): nested
    cross-validation using tidymodels tune/dials with leakage-aware
    outer splits.
  - Tidymodels interoperability: fit_resample() now accepts rsample
    rset/rsplit objects as splits, recipes::recipe for preprocessing,
    workflows::workflow as learner, and yardstick::metric_set for
    metrics. as_rsample() converts LeakSplits to an rsample rset.
  - Parsnip model specs accepted directly as the learner argument in
    fit_resample().
  - Diagnostics polish: new calibration_summary() and plot_calibration()
    for probability calibration checks; confounder_sensitivity() and
    plot_confounder_sensitivity() for sensitivity analysis.
  - Simulation utility simulate_leakage_suite() for generating
    controlled leakage scenarios and benchmarking audit sensitivity.
  - HTML audit report via audit_report(): renders a self-contained HTML
    summary of all audit results for sharing and review.
  - Multi-learner auditing with audit_leakage_by_learner() to audit each
    learner in a multi-model fit separately.
  - Multivariate target leakage scan enabled by default in
    audit_leakage() for supported tasks, complementing the existing
    univariate scan.
  - Refit-based permutations (perm_refit = TRUE or "auto") in
    audit_leakage() for a more powerful permutation gap test when refit
    data are available.
  - Class weights support in fit_resample() for imbalanced
    classification tasks.
  - New plotting functions: plot_fold_balance(), plot_overlap_checks(),
    plot_perm_distribution(), plot_time_acf().

Improvements

  - S4 classes (LeakSplits, LeakFit, LeakAudit) now include setValidity
    checks for slot consistency.
  - summary() methods for LeakFit, LeakAudit, and LeakTune improved with
    clearer console output and edge-case handling.
  - impute_guarded() gains enhanced diagnostics and RNG safety.
  - .guard_fit() and .guard_ensure_levels() made more robust with better
    error messages.
  - Permutation label factory (permute_labels) gains verbose mode,
    digest-based caching, and improved stratification safety.
  - audit_leakage() handles NA metrics gracefully and enriches trail
    metadata.
  - make_split_plan() improved stratification logic and reproducible
    seeding.
  - audit_report() now renders from a temporary copy of the Rmd template
    to avoid write failures on read-only file systems (e.g. during R CMD
    check).
  - Comprehensive vignette (bioLeak-intro) rewritten with guided
    workflow and leaky-vs-correct comparisons.

Bug fixes

  - Fixed fit_resample() result aggregation when folds fail during
    preprocessing.
  - Fixed missForest preprocessing dropping rows.
  - Fixed single-level factors causing errors in guarded preprocessing.
  - Fixed filter keep-column alignment by name.
  - Fixed glmnet folds receiving non-numeric design matrices.
  - Fixed constant imputation for categorical data.
  - Fixed RANN self-neighbour filter in duplicate detection.
  - Fixed various edge cases in outcome extraction and hashing
    utilities.
  - Resolved multiple CRAN check issues (Rd formatting, example runtime,
    read-only file-system writes).

                 Changes in version 0.1.0 (2026-02-06)                  

  - Initial release.
  - Core pipeline: make_split_plan() for leakage-aware splitting
    (subject-grouped, batch-blocked, study leave-out, time-ordered);
    fit_resample() for cross-validated fitting with built-in guarded
    preprocessing (train-only imputation, normalisation, filtering,
    feature selection).
  - Leakage auditing: audit_leakage() with label-permutation gap test,
    batch/study association tests, univariate target leakage scan, and
    near-duplicate detection.
  - Guarded preprocessing helpers: impute_guarded(), predict_guard(),
    .guard_fit(), .guard_ensure_levels().
  - S4 class system: LeakSplits, LeakFit, LeakAudit.
  - Support for binomial, multiclass, regression, and survival tasks.
  - Built-in learners: glm, glmnet, ranger, xgboost (via
    custom_learners).
  - SummarizedExperiment input support.
  - Vignette and comprehensive documentation.