{ "_id": "6a1eec6eb401979e734125c3", "Package": "STOPES", "Type": "Package", "Title": "Selection Threshold Optimized Empirically via Splitting", "Version": "0.2", "Date": "2022-05-20", "Author": "Marinela Capanu [aut, cre], Mihai Giurcanu [aut, ctb], Colin\nBegg [aut], Mithat Gonen [aut]", "Maintainer": "Marinela Capanu ", "Authors@R": "c(person(\"Marinela\", \"Capanu\", role = c(\"aut\", \"cre\"), email = \"capanum@mskcc.org\"),\nperson(\"Mihai\", \"Giurcanu\", role = c(\"aut\", \"ctb\")),\nperson(\"Colin\", \"Begg\", role = \"aut\"),\nperson(\"Mithat\", \"Gonen\", role = \"aut\"))", "Description": "Implements variable selection procedures for low to\nmoderate size generalized linear regressions models. It\nincludes the STOPES functions for linear regression (Capanu M,\nGiurcanu M, Begg C, Gonen M, Optimized variable selection via\nrepeated data splitting, Statistics in Medicine, 2020,\n19(6):2167-2184) as well as subsampling based optimization\nmethods for generalized linear regression models (Marinela\nCapanu, Mihai Giurcanu, Colin B Begg, Mithat Gonen, Subsampling\nbased variable selection for generalized linear models).", "License": "GPL-2", "NeedsCompilation": "no", "Packaged": { "Date": "2026-05-19 09:24:37 UTC", "User": "root" }, "Repository": "https://capanum.r-universe.dev", "Date/Publication": "2022-05-27 07:40:13 UTC", "RemoteUrl": "https://github.com/cran/STOPES", "RemoteRef": "HEAD", "RemoteSha": "5a0d8ee2df4d92f3382b64d51c8cf6cd0ae0c322", "MD5sum": "2f1f7b7266e10f6ee211b50afb9eab19", "_user": "capanum", "_type": "src", "_file": "STOPES_0.2.tar.gz", "_fileid": "555f6a410123821aedd4b99f4b86b8c03cecdbb639606b15b27a2aaba3be55eb", "_filesize": 91072, "_sha256": 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