API

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
desparsified_lasso(X, y[, dof_ajdustement, ...])	Desparsified Lasso
desparsified_lasso_pvalue(n_samples, ...[, ...])	Calculate confidence intervals and p-values for desparsified lasso estimators. This function computes confidence intervals for the desparsified lasso estimator beta_hat. It can also return p-values derived from these confidence intervals. Parameters ---------- n_samples : float The number of samples beta_hat : ndarray, shape (n_features,) The desparsified lasso coefficient estimates. sigma_hat : float Estimated noise level. precision_diagonal : ndarray, shape (n_features,) Diagonal elements of the precision matrix estimate. confidence : float, default=0.95 Confidence level for intervals, must be in [0, 1]. distribution : str, default="norm" Distribution to use for p-value calculation. Currently only "norm" supported. epsilon : float, default=1e-14 Small value to avoid numerical issues in p-value calculation. Returns ------- pval : ndarray, shape (n_features,) P-values pval_corr : ndarray, shape (n_features,) Corrected p-values one_minus_pval : ndarray, shape (n_features,) 1 - p-values one_minus_pval_corr : ndarray, shape (n_features,) 1 - corrected p-values confidence_bound_min : ndarray, shape (n_features,) Lower bounds of confidence intervals confidence_bound_max : ndarray, shape (n_features,) Upper bounds of confidence intervals.
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
reid(X, y[, epsilon, tolerance, ...])	Residual sum of squares based estimators for noise standard deviation estimation.
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
permutation_test(X, y, estimator[, ...])	Permutation test
permutation_test_pval(weights, ...)	Compute p-value from 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.
zscore_from_pval(pval[, one_minus_pval, ...])	Computing z-scores from one-sided p-values.

Classes

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