Package: HMC 1.1

Tianyu Zhang

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

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.

Authors:Tianyu Zhang [aut, cre, cph]

HMC_1.1.tar.gz
HMC_1.1.tar.gz(r-4.5-noble)HMC_1.1.tar.gz(r-4.4-noble)
HMC_1.1.tgz(r-4.4-emscripten)HMC_1.1.tgz(r-4.3-emscripten)
HMC.pdf |HMC.html✨
HMC/json (API)

# Install 'HMC' in R:
install.packages('HMC', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

13 exports 0.09 score 13 dependencies 5 scripts 195 downloads

Last updated 1 months agofrom:cb8dab0e35. Checks:OK: 2. Indexed: yes.

TargetResultDate
Doc / VignettesOKAug 20 2024
R-4.5-linuxOKAug 20 2024

Exports:anchored_lasso_testingdebiased_pc_testingestimate_nuisance_parameter_lassoestimate_nuisance_pcevaluate_influence_function_multi_factorevaluate_pca_lasso_plug_inevaluate_pca_plug_inextract_lasso_coefextract_pcindex_splitersimple_pc_testingsummarize_feature_namesummarize_pc_name

Dependencies:codetoolsforeachglmnetirlbaiteratorslatticeMASSMatrixPMARcppRcppEigenshapesurvival

Readme and manuals

Help Manual

Help pageTopics
Anchored test for two-sample mean comparison.anchored_lasso_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.debiased_pc_testing
The function for nuisance parameter estimation in anchored_lasso_testing().estimate_nuisance_parameter_lasso
The function for nuisance parameter estimation in simple_pc_testing() and debiased_pc_testing().estimate_nuisance_pc
Calculate the test statistics on the left-out samples. Called in debiased_pc_testing().evaluate_influence_function_multi_factor
Calculate the test statistics on the left-out samples. Called in anchored_lasso_testing().evaluate_pca_lasso_plug_in
Calculate the test statistics on the left-out samples. Called in simple_pc_testing().evaluate_pca_plug_in
Extract the lasso estimate from the output of anchored_lasso_testing().extract_lasso_coef
Extract the principle components from the output of simple_pc_testing() and debiased_pc_testing().extract_pc
Split the sample index into n_folds many groups so that we can perform cross-fittingindex_spliter
Simple plug-in test for two-sample mean comparison.simple_pc_testing
Summarize the features (e.g. genes) that contribute to the test result, i.e. those features consistently show up in Lasso vectors.summarize_feature_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.summarize_pc_name