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HMC (version 1.1)

High Dimensional Mean Comparison with Projection and Cross-Fitting

Description

Provides interpretable High-dimensional Mean Comparison methods (HMC). For example, users can use them to assess the difference in gene expression between two treatment groups. It is not a gene-by-gene comparison. Instead, we focus on the interplay between features and are interested in those that are predictive of the group label. The methods are valid frequentist tests and give sparse estimates indicating which features contribute to the test results.

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Version

Install

install.packages('HMC')

Monthly Downloads

522

Version

1.1

License

GPL-2

Maintainer

Tianyu Zhang

Last Published

August 17th, 2024

Functions in HMC (1.1)

extract_pc

Extract the principle components from the output of simple_pc_testing() and debiased_pc_testing().
anchored_lasso_testing

Anchored test for two-sample mean comparison.
estimate_nuisance_pc

The function for nuisance parameter estimation in simple_pc_testing() and debiased_pc_testing().
extract_lasso_coef

Extract the lasso estimate from the output of anchored_lasso_testing().
estimate_nuisance_parameter_lasso

The function for nuisance parameter estimation in anchored_lasso_testing().
index_spliter

Split the sample index into n_folds many groups so that we can perform cross-fitting
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.
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().
evaluate_influence_function_multi_factor

Calculate the test statistics on the left-out samples. Called in debiased_pc_testing().
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.
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.