anchored_lasso_testing
                        Anchored test for two-sample mean comparison.
check_data_for_folds    Check that data has enough rows for
                        cross-validation folds
check_non_null_and_identical_colnames
                        Check non-null and consistent column names
                        across datasets
collect_active_features_proj
                        Collect active features and groups based on
                        projection directions
combine_folds_mean_diff
                        Combine fold-level test statistics from
                        cross-validation
compute_predictive_contributions
                        Compute predictive contributions of feature
                        groups
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_leading_pc     Estimate the leading principal component
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().
fit_lasso               Fit a (group) Lasso logistic regression
                        classifier
index_spliter           Split indices into folds
mean_comparison_anchor
                        High-dimensional two-sample mean comparison
                        with anchored projection
normalize_and_split     Normalize and split two datasets using pooled
                        mean and standard deviation
process_fold_mean_diff
                        Process one cross-validation fold for mean
                        difference testing
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.
validate_and_convert_data
                        Validate and convert input data
