API Documentation

Estimators

Functions

aggregate_quantiles(list_one_sided_pval[, ...])	Aggregation of survival function values by adaptive quantile procedure
clustered_inference(X_init, y, ward, n_clusters)	Clustered inference algorithm
data_simulation
desparsified_lasso(X, y[, dof_ajdustement, ...])	Desparsified Lasso with confidence intervals
ensemble_clustered_inference(X_init, y, ...)	Ensemble clustered inference algorithm
group_reid(X, Y[, stationary, method, ...])	Estimation of the covariance matrix using group Reid procedure
hd_inference(X, y, method[, n_jobs, memory, ...])	Wrap-up high-dimensional inference procedures
knockoff_aggregation(X, y[, centered, ...])	Aggregation of Multiple knockoffs
model_x_knockoff(X, y[, fdr, offset, ...])	Model-X Knockoff
multivariate_1D_simulation([n_samples, ...])	Generate 1D data with Toeplitz design matrix
permutation_test(X, y, estimator[, ...])	Permutation test
permutation_test_pval(weights, ...)	Compute p-value from permutation test
reid(X, y[, eps, tol, max_iter, n_jobs, seed])	Estimation of noise standard deviation using Reid procedure
empirical_thresholding(X, y[, linear_estimator])	Perform empirical thresholding on the input data and target using a linear estimator.
zscore_from_pval(pval[, one_minus_pval, distrib])	Computing z-scores from one-sided p-values.

Classes

LOCO(estimator, loss, method, random_state, ...)	Leave-One-Covariate-Out (LOCO)
CPI(estimator, imputation_model, ...)	Conditional Permutation Importance (CPI) algorithm.
PermutationImportance(estimator, ...)