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constraints in make_split_plan(), generalizing
beyond two-axis combined CV while preserving train/test exclusion across
all declared axes.compact = TRUE split storage (fold assignments)
for large datasets to reduce split object memory footprint.check_split_overlap() for explicit
overlap-invariant validation across fold/group axes.cv_ci() (with Nadeau-Bengio correction) and
integrated CI columns into fit_resample() and
tune_resample() metric summaries (*_ci_lo,
*_ci_hi).guard_to_recipe() to map guarded preprocessing
configurations to recipes pipelines with explicit
fallback/warning behavior.benchmark_leakage_suite() for reproducible
modality-by-mechanism benchmark grids and detection-rate summaries.audit_leakage() diagnostics with mechanism
taxonomy fields (mechanism_class, taxonomy,
mechanism_summary) and richer risk attribution
outputs.p_value_adj,
flag_fdr) with selectable multiple-testing correction
(target_p_adjust, target_alpha).feature_space
(raw/rank) and duplicate_scope
(train_test/all) controls for duplicate
diagnostics.perm_mode handling for rsample-derived splits and safer
perm_refit = "auto" behavior.split_cols = "auto",
mode/perm-mode propagation, stricter compatibility checks).tune_resample(): final
refit now aggregates hyperparameters across outer folds
(median/majority) instead of selecting a single best outer fold.tune_resample() using inner-fold predictions
(tune_threshold, threshold_grid,
threshold_metric).fold_status) and
elapsed timing in both fitting and tuning paths for better failure-mode
observability.bioLeak.strict, bioLeak.validation_mode) with
structured condition classes for safer recipe and workflow
guardrails..bio_capture_provenance) and
attached provenance metadata to LeakFit,
LeakAudit, and LeakTune.summary.LeakAudit() output with explicit
Mechanism Risk Assessment reporting.fit_resample() to
avoid fold-time failures when recipes reference split metadata columns
(for example subject).simulate_leakage_suite() default B,
auto refit cap handling).paper/ with
refreshed large-scale simulation outputs and case-study artifacts.tune_resample(): nested cross-validation using tidymodels
tune/dials with leakage-aware outer
splits.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.learner argument in fit_resample().calibration_summary() and plot_calibration()
for probability calibration checks;
confounder_sensitivity() and
plot_confounder_sensitivity() for sensitivity
analysis.simulate_leakage_suite() for generating controlled leakage
scenarios and benchmarking audit sensitivity.audit_report():
renders a self-contained HTML summary of all audit results for sharing
and review.audit_leakage_by_learner() to audit each learner in a
multi-model fit separately.audit_leakage() for supported tasks, complementing the
existing univariate scan.perm_refit = TRUE or "auto") in
audit_leakage() for a more powerful permutation gap test
when refit data are available.fit_resample() for imbalanced classification tasks.plot_fold_balance(),
plot_overlap_checks(),
plot_perm_distribution(),
plot_time_acf().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.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).bioLeak-intro) rewritten with
guided workflow and leaky-vs-correct comparisons.fit_resample() result aggregation when folds fail
during preprocessing.missForest preprocessing dropping rows.glmnet folds receiving non-numeric design
matrices.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).audit_leakage() with
label-permutation gap test, batch/study association tests, univariate
target leakage scan, and near-duplicate detection.impute_guarded(), predict_guard(),
.guard_fit(), .guard_ensure_levels().LeakSplits, LeakFit,
LeakAudit.glm, glmnet,
ranger, xgboost (via
custom_learners).SummarizedExperiment input support.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.