{ "_id": "6a103faeacfb0bcc41c9ceb5", "Package": "sgs", "Title": "Sparse-Group SLOPE: Adaptive Bi-Level Selection with FDR Control", "Version": "0.3.7", "Date": "2025-05-06", "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.", "Roxygen": "list(markdown = TRUE)", "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", "Config/pak/sysreqs": "libicu-dev", "Repository": "https://ff1201.r-universe.dev", "Date/Publication": "2025-06-12 15:11:28 UTC", "RemoteUrl": "https://github.com/ff1201/sgs", "RemoteRef": "HEAD", "RemoteSha": "405aab0d80388f2765a01ca4bc4089ecaf9801e4", "NeedsCompilation": "yes", "Packaged": { "Date": "2026-05-11 08:40:07 UTC", "User": "root" }, "Author": "Fabio Feser [aut, cre] (ORCID: )", "MD5sum": "6f1499aa02f41627361d73c0216afd75", "_user": "ff1201", "_type": "src", "_file": "sgs_0.3.7.tar.gz", "_fileid": "28b55efd2e7638e0cd66a0bee6b8279623ce943470ada7c7c9d3b8558748d47a", "_filesize": 966723, "_sha256": 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"405aab0d80388f2765a01ca4bc4089ecaf9801e4", "author": "Fabio Feser <79450502+ff1201@users.noreply.github.com>", "committer": "GitHub ", "message": "Merge pull request #3 from jolars/new-slope-workarounds\n\nWork around incoming breaking changes in SLOPE", "time": 1749741088 }, "_maintainer": { "name": "Fabio Feser", "email": "ff120@ic.ac.uk", "login": "ff1201", "uuid": 79450502 }, "_registered": true, "_dependencies": [ { "package": "Rcpp", "role": "LinkingTo" }, { "package": "RcppArmadillo", "role": "LinkingTo" }, { "package": "Matrix", "role": "Imports" }, { "package": "MASS", "role": "Imports" }, { "package": "caret", "role": "Imports" }, { "package": "grDevices", "role": "Imports" }, { "package": "graphics", "role": "Imports" }, { "package": "methods", "role": "Imports" }, { "package": "stats", "role": "Imports" }, { "package": "SLOPE", "role": "Imports" }, { "package": "Rlab", "role": "Imports" }, { "package": "Rcpp", "version": ">= 1.0.10", "role": "Imports" }, { "package": 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"fit_gslope_cv", "fit_sgo", "fit_sgo_cv", "fit_sgs", "fit_sgs_cv", "gen_pens", "gen_toy_data", "scaled_sgs" ], "_help": [ { "page": "arma_mv", "title": "Matrix Product in RcppArmadillo.", "topics": [ "arma_mv" ] }, { "page": "arma_sparse", "title": "Matrix Product in RcppArmadillo.", "topics": [ "arma_sparse" ] }, { "page": "as_sgs", "title": "Fits the adaptively scaled SGS model (AS-SGS).", "concept": [ "SGS-methods", "model-selection" ], "topics": [ "as_sgs" ] }, { "page": "atos", "title": "Adaptive three operator splitting (ATOS).", "topics": [ "atos" ] }, { "page": "coef.sgs", "title": "Extracts coefficients for one of the following object types: '\"sgs\"', '\"sgs_cv\"', '\"gslope\"', '\"gslope_cv\"'.", "concept": [ "SGS-methods", "gSLOPE-methods" ], "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" ] }, { "page": "scaled_sgs", "title": "Fits a scaled regression SLOPE-based.", "concept": [ "SGS-methods", "model-selection" ], "topics": [ "scaled_sgs" ] } ], "_readme": "https://github.com/ff1201/sgs/raw/HEAD/README.md", "_rundeps": [ "BH", 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