{ "_id": "6a1040f0acfb0bcc41c9d39a", "Package": "glmtrans", "Type": "Package", "Title": "Transfer Learning under Regularized Generalized Linear Models", "Version": "2.1.0", "Authors@R": "c(person(\"Ye\", \"Tian\", role = c(\"aut\", \"cre\"), email = \"ye.t@columbia.edu\"), person(\"Yang\", \"Feng\", role = \"aut\", email = \"yang.feng@nyu.edu\"))", "Description": "We provide an efficient implementation for two-step\nmulti-source transfer learning algorithms in high-dimensional\ngeneralized linear models (GLMs). The elastic-net penalized GLM\nwith three popular families, including linear, logistic and\nPoisson regression models, can be fitted. To avoid negative\ntransfer, a transferable source detection algorithm is\nproposed. We also provides visualization for the transferable\nsource detection results. The details of methods can be found\nin \"Tian, Y., & Feng, Y. (2023). Transfer learning under\nhigh-dimensional generalized linear models. Journal of the\nAmerican Statistical Association, 118(544), 2684-2697.\".", "License": "GPL-2", "Encoding": "UTF-8", "RoxygenNote": "7.3.2", "VignetteBuilder": "knitr", "NeedsCompilation": "no", "Packaged": { "Date": "2026-05-11 08:04:58 UTC", "User": "root" }, "Author": "Ye Tian [aut, cre], Yang Feng [aut]", "Maintainer": "Ye Tian ", "Config/pak/sysreqs": "libicu-dev", "Repository": "https://ytstat.r-universe.dev", "Date/Publication": "2025-03-01 01:40:08 UTC", "RemoteUrl": "https://github.com/cran/glmtrans", "RemoteRef": "HEAD", "RemoteSha": "c27f1f5f3aa531d8ca4071ad359d47a7e3160635", "MD5sum": "17075e7de9045c869892144690fa3b5b", "_user": "ytstat", "_type": "src", "_file": "glmtrans_2.1.0.tar.gz", "_fileid": "334ece82dfff89fd22e42e465aa4383233a9ba6e20d4f14972d13dd5ec0d7219", "_filesize": 532479, "_sha256": "334ece82dfff89fd22e42e465aa4383233a9ba6e20d4f14972d13dd5ec0d7219", "_created": "2026-05-11T08:04:58.000Z", "_published": "2026-05-22T11:41:36.366Z", "_distro": "noble", "_jobs": [ 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