{ "_id": "6a1035b1acfb0bcc41c99c03", "Package": "HMC", "Title": "High-Dimensional Mean Comparison with Projection and\nCross-Fitting", "Version": "1.2", "Date": "2025-05-02", "Authors@R": "person(\"Tianyu\", \"Zhang\",\nrole = c(\"aut\", \"cre\", \"cph\"),\nemail = \"tianyuz3@andrew.cmu.edu\")", "Description": "Provides interpretable high-dimensional mean comparison\nmethods (HMC). For example, users can apply these methods to\nassess the difference in gene expression between two treatment\ngroups. It is not a gene-by-gene comparison. Instead, the\nmethods focus on the interplay between features and identify\nthose that are predictive of the group label. The tests are\nvalid frequentist procedures and yield sparse estimates\nindicating which features contribute to the group differences.", "License": "GPL-2", "Encoding": "UTF-8", "RoxygenNote": "7.3.2", "URL": "https://github.com/terrytianyuzhang/HMC/tree/main/HMC_package", "NeedsCompilation": "no", "Packaged": { "Date": "2026-05-12 07:06:41 UTC", "User": "root" }, "Author": "Tianyu Zhang [aut, cre, cph]", "Maintainer": "Tianyu Zhang ", "Repository": "https://cran.r-universe.dev", "Date/Publication": "2025-05-02 17:34:11 UTC", "RemoteUrl": "https://github.com/cran/HMC", "RemoteRef": "HEAD", "RemoteSha": "ed3faa52476d660076a1b469654ef507224aa580", "MD5sum": "640923bc4a8e7dada9d6b3f30de1f273", "_user": "cran", "_type": "src", "_file": "HMC_1.2.tar.gz", "_fileid": "6df2e7140e07eff3fbe1db4a0d09e9875c0f215e072bc0cfc5f6b2a96a27692e", "_filesize": 170506, "_sha256": "6df2e7140e07eff3fbe1db4a0d09e9875c0f215e072bc0cfc5f6b2a96a27692e", "_created": "2026-05-12T07:06:41.000Z", "_published": "2026-05-22T10:53:37.139Z", "_distro": "noble", "_jobs": [ { "job": 77365423359, "time": 130, "config": "linux-devel-x86_64", "r": "4.7.0", "check": "OK", "artifact": "6937825024" }, { "job": 77365423392, "time": 141, "config": "linux-release-x86_64", "r": "4.6.0", "check": "OK", "artifact": "6937826499" }, { "job": 77365422897, "time": 176, "config": "source", "r": "4.6.0", "check": "OK", "artifact": "6937782597" }, { "job": 77365422836, "time": 97, "config": "wasm-release", "r": "4.6.0", "check": "OK", "artifact": "7158778855" } ], "_buildurl": "https://github.com/r-universe/cran/actions/runs/25718989980", "_status": "success", "_host": "GitHub-Actions", "_upstream": "https://github.com/cran/HMC", "_commit": { "id": "ed3faa52476d660076a1b469654ef507224aa580", "author": "Tianyu Zhang ", "committer": "cran-robot ", "message": "version 1.2\n", "time": 1746207251 }, "_maintainer": { "name": "Tianyu Zhang", "email": "tianyuz3@andrew.cmu.edu" }, "_registered": true, "_dependencies": [ { "package": "glmnet", "role": "Imports" }, { "package": "irlba", "role": "Imports" }, { "package": "PMA", "role": "Imports" }, { "package": "MASS", "role": "Imports" }, { "package": "stats", "role": "Imports" }, { "package": "grpreg", "role": "Imports" } ], "_owner": "cran", "_selfowned": false, "_usedby": 0, "_updates": [], "_tags": [], "_stars": 0, "_userbio": { "uuid": 6899542, "type": "organization", "name": "cran", "description": "Unofficial read-only mirror of all CRAN R packages" }, "_downloads": { "count": 562, "source": "https://cranlogs.r-pkg.org/downloads/total/last-month/HMC" }, "_devurl": "https://github.com/terrytianyuzhang/hmc", "_searchresults": 41, "_rbuild": "4.6.0", "_assets": [ "extra/citation.cff", "extra/citation.html", "extra/citation.json", "extra/citation.txt", "extra/contents.json", "extra/HMC.html", "manual.pdf" ], "_realowner": "cran", "_cranurl": false, "_releases": [ { "version": "1.0", "date": "2024-03-09" }, { "version": "1.1", "date": "2024-08-17" }, { "version": "1.2", "date": "2025-05-02" } ], "_exports": [ "anchored_lasso_testing", "check_data_for_folds", "check_non_null_and_identical_colnames", "collect_active_features_proj", "combine_folds_mean_diff", "compute_predictive_contributions", "debiased_pc_testing", "estimate_leading_pc", "estimate_nuisance_parameter_lasso", "estimate_nuisance_pc", "evaluate_influence_function_multi_factor", "evaluate_pca_lasso_plug_in", "evaluate_pca_plug_in", "extract_lasso_coef", "extract_pc", "fit_lasso", "index_spliter", "mean_comparison_anchor", "normalize_and_split", "process_fold_mean_diff", "simple_pc_testing", "summarize_feature_name", "summarize_pc_name", "validate_and_convert_data" ], "_help": [ { "page": "anchored_lasso_testing", "title": "Anchored test for two-sample mean comparison.", "topics": [ "anchored_lasso_testing" ] }, { "page": "check_data_for_folds", "title": "Check that data has enough rows for cross-validation folds", "topics": [ "check_data_for_folds" ] }, { "page": "check_non_null_and_identical_colnames", "title": "Check non-null and consistent column names across datasets", "topics": [ "check_non_null_and_identical_colnames" ] }, { "page": "collect_active_features_proj", "title": "Collect active features and groups based on projection directions", "topics": [ "collect_active_features_proj" ] }, { "page": "combine_folds_mean_diff", "title": "Combine fold-level test statistics from cross-validation", "topics": [ "combine_folds_mean_diff" ] }, { "page": "compute_predictive_contributions", "title": "Compute predictive contributions of feature groups", "topics": [ "compute_predictive_contributions" ] }, { "page": "debiased_pc_testing", "title": "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.", "topics": [ "debiased_pc_testing" ] }, { "page": "estimate_leading_pc", "title": "Estimate the leading principal component", "topics": [ "estimate_leading_pc" ] }, { "page": "estimate_nuisance_parameter_lasso", "title": "The function for nuisance parameter estimation in anchored_lasso_testing().", "topics": [ "estimate_nuisance_parameter_lasso" ] }, { "page": "estimate_nuisance_pc", "title": "The function for nuisance parameter estimation in simple_pc_testing() and debiased_pc_testing().", "topics": [ "estimate_nuisance_pc" ] }, { "page": "evaluate_influence_function_multi_factor", "title": "Calculate the test statistics on the left-out samples. Called in debiased_pc_testing().", "topics": [ "evaluate_influence_function_multi_factor" ] }, { "page": "evaluate_pca_lasso_plug_in", "title": "Calculate the test statistics on the left-out samples. Called in anchored_lasso_testing().", "topics": [ "evaluate_pca_lasso_plug_in" ] }, { "page": "evaluate_pca_plug_in", "title": "Calculate the test statistics on the left-out samples. Called in simple_pc_testing().", "topics": [ "evaluate_pca_plug_in" ] }, { "page": "extract_lasso_coef", "title": "Extract the lasso estimate from the output of anchored_lasso_testing().", "topics": [ "extract_lasso_coef" ] }, { "page": "extract_pc", "title": "Extract the principle components from the output of simple_pc_testing() and debiased_pc_testing().", "topics": [ "extract_pc" ] }, { "page": "fit_lasso", "title": "Fit a (group) Lasso logistic regression classifier", "topics": [ "fit_lasso" ] }, { "page": "index_spliter", "title": "Split indices into folds", "topics": [ "index_spliter" ] }, { "page": "mean_comparison_anchor", "title": "High-dimensional two-sample mean comparison with anchored projection", "topics": [ "mean_comparison_anchor" ] }, { "page": "normalize_and_split", "title": "Normalize and split two datasets using pooled mean and standard deviation", "topics": [ "normalize_and_split" ] }, { "page": "process_fold_mean_diff", "title": "Process one cross-validation fold for mean difference testing", "topics": [ "process_fold_mean_diff" ] }, { "page": "simple_pc_testing", "title": "Simple plug-in test for two-sample mean comparison.", "topics": [ "simple_pc_testing" ] }, { "page": "summarize_feature_name", "title": "Summarize the features (e.g. genes) that contribute to the test result, i.e. those features consistently show up in Lasso vectors.", "topics": [ "summarize_feature_name" ] }, { "page": "summarize_pc_name", "title": "Summarize the features (e.g. genes) that contribute to the test result, i.e. those features consistently show up in the sparse principle components.", "topics": [ "summarize_pc_name" ] }, { "page": "validate_and_convert_data", "title": "Validate and convert input data", "topics": [ "validate_and_convert_data" ] } ], "_rundeps": [ "codetools", "foreach", "glmnet", "grpreg", "irlba", "iterators", "lattice", "MASS", "Matrix", "PMA", "Rcpp", "RcppEigen", "shape", "survival" ], "_score": 1.6127838567197355, "_indexed": true, "_nocasepkg": "hmc", "_universes": [ "cran", "terrytianyuzhang" ], "_binaries": [ { "r": "4.7.0", "os": "linux", "version": "1.2", "date": "2026-05-12T07:08:42.000Z", "distro": "noble", "commit": "ed3faa52476d660076a1b469654ef507224aa580", "fileid": "b281bd9f9202fc9d0ba89ad594c3ab4410b3438a402a5b7f52f9c667082895b9", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/cran/actions/runs/25718989980" }, { "r": "4.6.0", "os": "linux", "version": "1.2", "date": "2026-05-12T07:08:47.000Z", "distro": "noble", "commit": "ed3faa52476d660076a1b469654ef507224aa580", "fileid": "f7ea14d05308b6a1740b19a9ae82fdcb2480ff3fa8f8143498970c252f5b5257", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/cran/actions/runs/25718989980" }, { "r": "4.6.0", "os": "wasm", "version": "1.2", "date": "2026-05-22T10:53:22.000Z", "commit": "ed3faa52476d660076a1b469654ef507224aa580", "fileid": "07a2e821618fca04511a7737afe07b77af87fc4eb48155c44e552bb7eeb99f67", "status": "success", "buildurl": "https://github.com/r-universe/cran/actions/runs/25718989980" } ] }