bioLeak 0.3.5

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".

bioLeak 0.3.0

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.

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.