R: High-Dimensional Mean Comparison with Projection and Cross-Fitting

# High-Dimensional Mean Comparison with Projection and Cross-Fitting

## Documentation for package ‘HMC’ version 1.2

- DESCRIPTION file

## Help Pages

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