fastml version 0.7.7

New features

Feature Importance Stability Analysis: Added explain_stability() function to analyze feature importance stability across cross-validation folds. This helps identify features that are consistently important vs. those whose importance varies across different data subsets.
Store Fold Models: Added store_fold_models parameter to fastml() to optionally store models trained on each CV fold, enabling stability analysis with explain_stability().
S3 Methods for Stability Objects: Added print.fastml_stability() and plot.fastml_stability() methods for convenient display of stability analysis results.

Unified Explainer Infrastructure: Added fastml_prepare_explainer_inputs() helper function providing consistent data preparation across all explainer methods (explain_dalex(), explain_ale(), plot_ice(), interaction_strength(), surrogate_tree()).
Positive Class Resolution: Added resolve_positive_class() helper for consistent positive class handling across explainer functions, respecting event_class settings.
Enhanced explain_dalex(): Major refactoring with robust preprocessing (“baking”) helper that handles three scenarios: no preprocessor, successful baking, and fallback validation for already-processed data.
Enhanced plot_ice(): Added target_class parameter for classification, improved feature validation with informative error messages, and added warnings for multiclass problems.
Improved Resampling Metrics Aggregation: Resampling results now properly compute CV statistics (mean and SD across folds) instead of pooled metrics. Fixed grouping attributes that could carry over from fold processing.
Better Model Validation in Predictions: Added valid_model() helper to properly validate workflow and native survival model types during prediction.

Fixed algorithm name matching in predict.fastml() to correctly resolve base algorithm names to their full “algorithm (engine)” format.
Fixed fold metrics aggregation in guarded resampling to properly ungroup and convert results to plain tibbles.
Fixed various edge cases in explainer functions when preprocessing pipelines are absent or data is already processed.
Fixed unit tests across multiple test files for improved reliability and stability.
Prevented Rplots.pdf files from being created during test execution by adding graphics device suppression to plotting tests.
Added Rplots.pdf to .gitignore to prevent accidental tracking.

Removed incomplete or unstable survival backends where correct, leakage-safe behavior could not be guaranteed.

Full Survival Analysis Support: Added training, resampling, prediction, metric computation, and model summarization for time-to-event outcomes.
Guarded Survival Resampling: Introduced a workflow enforcing leakage-safe preprocessing, imputation, and model fitting within each resampling split.
Integrated Brier Score (IBS): Added IBS and expanded survival metric support with flexible time handling and user-configurable summary outputs.
New Survival Engines: Added support for parametric and semi-parametric models, including Cox, penalized Cox, Royston–Parmar, and flexible parametric survival models.
Advanced Resampling Strategies: Implemented grouped, blocked, rolling, stratified, and unbiased nested cross-validation.
Fold-wise Imputation: Added support for advanced imputation during resampling while preventing outcome leakage.
Engine Parameters: Introduced an engine_params argument to allow passing engine-specific options in a consistent way.
S3 Methods: Added explicit S3 method annotations for fastml generics.

Multiclass ROC AUC now defaults to macro averaging (tidymodels) and can be configured via multiclass_auc to use macro_weighted class-prevalence weighting.
Improved robustness of survival predictions, including risk scores, survival probabilities, quantiles, medians, and time estimates.
Enhanced survival summary outputs with clearer metric alignment and better handling of stratified and time-varying Cox models.
Improved extraction of predictions and summaries for parametric survival engines.
Strengthened recipe validation and sandboxing to harden preprocessing isolation and reduce user-induced leakage.
Improved handling of novel and missing categorical levels during prediction.
Integrated resampling metadata more tightly into training workflows and summaries.
Added survival_metric_convention to align survival evaluation defaults with tidymodels conventions when desired.
Parallel tuning now uses explicit RNG seeding to keep results stable across core counts.

Fixed multiple issues in survival label validation, prediction post-processing, and metric computation.
Corrected survival risk and probability calculations for several engines and model types.
Fixed log-rank calculation for time-varying Cox models.
Fixed summary formatting when confidence intervals are unavailable.
Removed inappropriate confusion matrix warnings for non-classification tasks.
Fixed edge cases leading to NA survival predictions and early exits during survival time computation.
Addressed naming collisions and alignment issues in tuning grids and metric selection.