0.0.4

Added

• Group importance: new group_importance() method on the base Explainer class for computing group-level feature importance with uncertainty. Aggregates per-sample UEIFs within user-defined feature groups and returns importance, standard errors, z-scores, and p-values.
  • Accepts groups as a dict of index lists, a 1-D label array, or a binary pandas.DataFrame indicator matrix (features may belong to multiple groups).
  • Optional null-feature thresholding zeros out per-feature UEIFs with negative mean before aggregation.
  • Finite-sample SE correction (se_adjustment parameter) for conservative inference.
• Per-sample UEIFs (ueifs_X, ueifs_Z) are now stored as instance attributes after calling OTExplainer, EOTExplainer, and FlowExplainer, enabling downstream group aggregation.
• Crossfitting: new cross-fitted DFI explainer for valid inference at small sample sizes. Wraps any explainer class (OTExplainer, EOTExplainer, FlowExplainer) and performs K-fold cross-fitting so that the disentanglement map is never evaluated on its own training data.
• Flexible cv parameter accepts an int (shorthand for KFold) or any scikit-learn splitter instance (StratifiedKFold, ShuffleSplit, RepeatedKFold, GroupKFold, custom, etc.).
• Optional y and groups parameters for stratified and group-aware splitters.
• Overlapping test set handling: splitters like ShuffleSplit and RepeatedKFold that assign samples to multiple test sets are handled by per-sample UEIF averaging.
• Ensemble prediction on new data: cf(X_new) averages importance from all fold explainers.
• Crossfitting inherits conf_int() and summary() from the base Explainer class.
• Crossfitting exported from fdfi top-level package.
• 17 new tests covering init, OT/EOT/Flow cross-fitting, all splitter types, conf_int, summary, and ensemble prediction.