{ "_id": "6a116a84acfb0bcc41cf3941", "Package": "TransHDM", "Title": "High-Dimensional Mediation Analysis via Transfer Learning", "Version": "1.0.1", "Authors@R": "c(\nperson(given=\"Huer\", family=\"Gao\", email=\"gaohuer@proton.me\", role=c(\"aut\", \"cre\",\"cph\")),\nperson(given=\"Lulu\", family=\"Pan\", email=\"25111020061@m.fudan.edu.cn\", role = c(\"aut\",\"cph\")),\nperson(given=\"Yongfu\", family=\"Yu\", role = c(\"ctb\",\"cph\")),\nperson(given=\"Guoyou\", family=\"Qin\", role=c(\"ctb\",\"cph\"))\n)", "Description": "Provides a framework for high-dimensional mediation\nanalysis using transfer learning. The main function TransHDM()\nintegrates large-scale source data to improve the detection\npower of potential mediators in small-sample target studies. It\naddresses data heterogeneity via transfer regularization and\ndebiased estimation while controlling the false discovery rate.\nThe package also includes utilities for data generation\n(gen_simData_homo(), gen_simData_hetero()), baseline methods\nsuch as lasso() and dblasso(), sure independence screening via\nSIS(), and model diagnostics through source_detection(). The\nmethodology is described in Pan et al. (2025)\n.", "License": "GPL (>= 3)", "URL": "https://github.com/Gaohuer/TransHDM", "Encoding": "UTF-8", "Roxygen": "list(markdown = TRUE)", "RoxygenNote": "7.3.3", "Language": "en-US", "VignetteBuilder": "knitr", "Config/pak/sysreqs": "libicu-dev", "Repository": "https://gaohuer.r-universe.dev", "Date/Publication": "2026-03-24 10:13:59 UTC", "RemoteUrl": "https://github.com/gaohuer/transhdm", "RemoteRef": "HEAD", "RemoteSha": "06b1d7b919217342e9e8137a3a196d7bc03e7211", "NeedsCompilation": "no", "Packaged": { "Date": "2026-05-23 08:47:39 UTC", "User": "root" }, "Author": "Huer Gao [aut, cre, cph],\nLulu Pan [aut, cph],\nYongfu Yu [ctb, cph],\nGuoyou Qin [ctb, cph]", "Maintainer": "Huer Gao ", "MD5sum": "04cafbafb22471acff664a6e6a897120", "_user": "gaohuer", "_type": "src", "_file": "TransHDM_1.0.1.tar.gz", "_fileid": "0dadd8ccb5bf1039c4460f2f05abcfffdfeeeb9116a961bc4afff6c365ce973f", "_filesize": 661955, "_sha256": "0dadd8ccb5bf1039c4460f2f05abcfffdfeeeb9116a961bc4afff6c365ce973f", 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Models for Transfer Learning", "Lasso for transfer learning", "Debiased Lasso for transfer learning" ], "created": "2026-03-12 13:43:31", "modified": "2026-03-12 13:43:31", "commits": 1 } ], "_score": 4, "_indexed": true, "_nocasepkg": "transhdm", "_universes": [ "gaohuer" ], "_binaries": [ { "r": "4.7.0", "os": "linux", "version": "1.0.1", "date": "2026-05-23T08:50:25.000Z", "distro": "noble", "commit": "06b1d7b919217342e9e8137a3a196d7bc03e7211", "fileid": "57dd31d8b603e6c27a600cbdbb2057b2a43e50ed81bddc27efa8517b265c13e6", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/gaohuer/actions/runs/26328391731" }, { "r": "4.6.0", "os": "linux", "version": "1.0.1", "date": "2026-05-23T08:50:01.000Z", "distro": "noble", "commit": "06b1d7b919217342e9e8137a3a196d7bc03e7211", "fileid": "b766d0a8df7a15e63f09e6672e3724777726f92b7aa5725cb55eebb6fee5d98a", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/gaohuer/actions/runs/26328391731" }, 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