High Dimensional Mean Comparison with Projection and Cross-Fitting

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Documentation for package ‘HMC’ version 1.0

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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.