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bioLeak 0.2.0

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).

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.

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.