API

Base Classes

base_variable_importance.BaseVariableImportance()	Base class for variable importance methods.
base_perturbation.BasePerturbation(...[, ...])

Feature Importance Classes

LOCO(estimator, loss, method, n_jobs)
CFI(estimator, loss, method, n_jobs, ...[, ...])
PFI(estimator, loss, method, n_jobs, ...)
D0CRT(estimator[, method, estimated_coef, ...])	Implements distilled conditional randomization test (dCRT) without interactions.

Feature Importance functions

clustered_inference(X_init, y, ward[, ...])	Clustered inference algorithm for statistical analysis of high-dimensional data.
clustered_inference_pvalue(n_samples, group, ...)	Compute corrected p-values at the cluster level and transform them back to feature level.
desparsified_lasso(X, y[, dof_ajdustement, ...])	Desparsified Lasso
desparsified_lasso_pvalue(n_samples, ...[, ...])	Calculate confidence intervals and p-values for desparsified lasso estimators.
desparsified_group_lasso_pvalue(beta_hat, ...)	Compute p-values for the desparsified group Lasso estimator using chi-squared or F tests
ensemble_clustered_inference(X_init, y, ward)	Ensemble clustered inference algorithm for high-dimensional statistical inference, as described in [Chevalier et al., 2022].
ensemble_clustered_inference_pvalue(...[, ...])	Compute and aggregate p-values across multiple bootstrap iterations using an aggregation method.
model_x_knockoff(X, y[, estimator, ...])	Model-X Knockoff

Samplers

conditional_sampling.ConditionalSampler([...])

Helper Functions

quantile_aggregation(pvals[, gamma, adaptive])	Implements the quantile aggregation method for p-values.
reid(X, y[, epsilon, tolerance, ...])	Residual sum of squares based estimators for noise standard deviation estimation.