NEWS.md
In bioLeak: Leakage-Safe Modeling and Auditing for Genomic and Clinical Data

bioLeak 0.3.0

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.

bioLeak 0.2.0

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().

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.

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).

bioLeak 0.1.0

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.