Changelog

Multiple testing correction: conf_int() and summary() now support a multitest_method parameter (mimicking statsmodels) for controlling FDR/FWER. Adjusted p-values are returned as pvalue_adj.
Unified Group Importance: conf_int() now supports a groups argument to compute group-level feature importance with uncertainty, replacing the separate group_importance() logic (now deprecated).
- 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 (threshold_null=True) zeros out per-feature UEIFs with negative mean before aggregation.
Improved API Consistency: Renamed phi_hat to score in conf_int() output for better clarity.
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.