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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.
Health stats visible at Monitor.