{ "_id": "6ae1e29795ca2370cc8cc092", "Package": "sgs", "Title": "Sparse-Group SLOPE: Adaptive Bi-Level Selection with FDR Control", "Version": "0.3.9", "Date": "2025-09-30", "Authors@R": "person(\"Fabio\", \"Feser\", role = c(\"aut\", \"cre\"), email = \"ff120@ic.ac.uk\",comment = c(ORCID = \"0009-0007-3088-9727\"))", "Maintainer": "Fabio Feser ", "Description": "Implementation of Sparse-group SLOPE (SGS) (Feser and\nEvangelou (2023) ) models.\nLinear and logistic regression models are supported, both of\nwhich can be fit using k-fold cross-validation. Dense and\nsparse input matrices are supported. In addition, a general\nAdaptive Three Operator Splitting (ATOS) (Pedregosa and Gidel\n(2018) ) implementation is\nprovided. Group SLOPE (gSLOPE) (Brzyski et al. (2019)\n) and group-based OSCAR\nmodels (Feser and Evangelou (2024)\n) are also implemented. All\nmodels are available with strong screening rules (Feser and\nEvangelou (2024) ) for\ncomputational speed-up.", "RoxygenNote": "7.3.1", "License": "GPL (>= 3)", "Encoding": "UTF-8", "URL": "https://github.com/ff1201/sgs", "BugReports": "https://github.com/ff1201/sgs/issues", "VignetteBuilder": "knitr", "NeedsCompilation": "yes", "Packaged": { "Date": "2026-10-28 11:33:32 UTC", "User": "root" }, "Author": "Fabio Feser [aut, cre] (ORCID:\n)", "Repository": "https://cran.r-universe.dev", "Date/Publication": "2025-09-30 13:50:02 UTC", "RemoteUrl": "https://github.com/cran/sgs", "RemoteRef": "HEAD", "RemoteSha": "520c238631e7e2a46018e58909b3ef455013e329", "_user": "cran", "_type": "src", "_file": "sgs_0.3.9.tar.gz", "_fileid": "https://r2.ropensci.org/22f3466ffbc27b27330a81926de2611e3bd1b316be58bcf761e807320ba4fe84", "_filesize": 966077, "_sha256": "22f3466ffbc27b27330a81926de2611e3bd1b316be58bcf761e807320ba4fe84", "_expires": "2027-02-05T11:55:01.000Z", "_created": "2026-10-28T11:33:32.000Z", "_published": "2026-10-28T11:55:03.367Z", "_upstream": "https://github.com/cran/sgs", "_status": "success", "_maintainer": { "name": "Fabio Feser", "email": "ff120@ic.ac.uk", "orcid": "0009-0007-3088-9727" }, "_jobs": [ { "job": 122791332103, "time": 311, "config": "linux-devel-arm64", "r": "4.7.0", "check": "OK", "artifact": "12812832815" }, { "job": 122791332182, "time": 737, "config": "linux-devel-x86_64", "r": "4.7.0", "check": "OK", "artifact": "12814020517" }, { "job": 122791332166, "time": 194, "config": "linux-release-arm64", "r": "4.6.1", "check": "OK", "artifact": "12812054327" }, { "job": 122791332315, "time": 535, "config": "linux-release-x86_64", "r": "4.6.1", "check": "OK", "artifact": "12813485786" }, { "job": 122789000330, "time": 320, "config": "source", "r": "4.6.1", "check": "OK", "artifact": "12812019138" }, { "job": 122791332149, "time": 146, "config": "wasm-release", "r": "4.6.0", "check": "OK", "artifact": "12811809745" }, { "job": 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"topics": [ "coef.sgs" ] }, { "page": "fit_goscar", "title": "Fit a gOSCAR model.", "concept": [ "gSLOPE-methods" ], "topics": [ "fit_goscar" ] }, { "page": "fit_goscar_cv", "title": "Fit a gOSCAR model using k-fold cross-validation.", "concept": [ "gSLOPE-methods", "model-selection" ], "topics": [ "fit_goscar_cv" ] }, { "page": "fit_gslope", "title": "Fit a gSLOPE model.", "concept": [ "gSLOPE-methods" ], "topics": [ "fit_gslope" ] }, { "page": "fit_gslope_cv", "title": "Fit a gSLOPE model using k-fold cross-validation.", "concept": [ "gSLOPE-methods", "model-selection" ], "topics": [ "fit_gslope_cv" ] }, { "page": "fit_sgo", "title": "Fit an SGO model.", "concept": [ "SGS-methods" ], "topics": [ "fit_sgo" ] }, { "page": "fit_sgo_cv", "title": "Fit an SGO model using k-fold cross-validation.", "concept": [ "SGS-methods", "model-selection" ], "topics": [ "fit_sgo_cv" ] }, { "page": "fit_sgs", "title": "Fit an SGS model.", "concept": [ "SGS-methods" ], "topics": [ "fit_sgs" ] }, { "page": "fit_sgs_cv", "title": "Fit an SGS model using k-fold cross-validation.", "concept": [ "SGS-methods", "model-selection" ], "topics": [ "fit_sgs_cv" ] }, { "page": "gen_pens", "title": "Generate penalty sequences for SGS.", "topics": [ "gen_pens" ] }, { "page": "gen_toy_data", "title": "Generate toy data.", "topics": [ "gen_toy_data" ] }, { "page": "plot.sgs", "title": "Plot models of the following object types: '\"sgs\"', '\"sgs_cv\"', '\"gslope\"', '\"gslope_cv\"'.", "concept": [ "SGS-methods", "gSLOPE-methods" ], "topics": [ "plot.sgs" ] }, { "page": "predict.sgs", "title": "Predict using one of the following object types: '\"sgs\"', '\"sgs_cv\"', '\"gslope\"', '\"gslope_cv\"'.", "concept": [ "SGS-methods", "gSLOPE-methods" ], "topics": [ "predict.sgs" ] }, { "page": "print.sgs", "title": "Prints information for one of the following object types: '\"sgs\"', '\"sgs_cv\"', '\"gslope\"', '\"gslope_cv\"'.", "concept": [ "SGS-methods", "gSLOPE-methods" ], "topics": [ "print.sgs" ] 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