anchored_lasso_testing
                        Anchored test for two-sample mean comparison.
debiased_pc_testing     Debiased one-step test for two-sample mean
                        comparison. A small p-value tells us not only
                        there is difference in the mean vectors, but
                        can also indicates which principle component
                        the difference aligns with.
estimate_nuisance_parameter_lasso
                        The function for nuisance parameter estimation
                        in anchored_lasso_testing().
estimate_nuisance_pc    The function for nuisance parameter estimation
                        in simple_pc_testing() and
                        debiased_pc_testing().
evaluate_influence_function_multi_factor
                        Calculate the test statistics on the left-out
                        samples. Called in debiased_pc_testing().
evaluate_pca_lasso_plug_in
                        Calculate the test statistics on the left-out
                        samples. Called in anchored_lasso_testing().
evaluate_pca_plug_in    Calculate the test statistics on the left-out
                        samples. Called in simple_pc_testing().
extract_lasso_coef      Extract the lasso estimate from the output of
                        anchored_lasso_testing().
extract_pc              Extract the principle components from the
                        output of simple_pc_testing() and
                        debiased_pc_testing().
index_spliter           Split the sample index into n_folds many groups
                        so that we can perform cross-fitting
simple_pc_testing       Simple plug-in test for two-sample mean
                        comparison.
summarize_feature_name
                        Summarize the features (e.g. genes) that
                        contribute to the test result, i.e. those
                        features consistently show up in Lasso vectors.
summarize_pc_name       Summarize the features (e.g. genes) that
                        contribute to the test result, i.e. those
                        features consistently show up in the sparse
                        principle components.
