API

Estimators

Functions

quantile_aggregation(pvals[, gamma, adaptive])	Implements the quantile aggregation method for p-values.
clustered_inference(X_init, y, ward, n_clusters)	Clustered inference algorithm for statistical analysis of high-dimensional data.
dcrt_zero(X, y[, estimated_coef, sigma_X, ...])	Implements distilled conditional randomization test (dCRT) without interactions.
dcrt_pvalue(selection_features, X_res, ...)	Calculate p-values and identify significant features using the dCRT test statistics.
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, ...)	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.
reid(X, y[, epsilon, tolerance, ...])	Residual sum of squares based estimators for noise standard deviation estimation.
model_x_knockoff(X, y[, estimator, ...])	Model-X Knockoff
permutation_test(X, y, estimator[, ...])	Permutation test
reid(X, y[, epsilon, tolerance, ...])	Residual sum of squares based estimators for noise standard deviation estimation.
empirical_thresholding(X, y[, linear_estimator])	Perform empirical thresholding on the input data and target using a linear estimator.

Classes

BasePerturbation(estimator, loss, ...)
LOCO(estimator, loss, method, n_jobs)
CPI(estimator, loss, method, n_jobs, ...[, ...])
PermutationImportance(estimator, loss, ...)