{ "_id": "6a17fcd6acfb0bcc41daa7bf", "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": "2026-05-28 08:22:02 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", "MD5sum": "4da0a9afc20743a4632eb509f9a6f988", "_user": "cran", "_type": "src", "_file": "sgs_0.3.9.tar.gz", "_fileid": "0e9103a6bc81bbd41797600c6b3ffdf5a298073617bf8e727abb098d237bec4a", "_filesize": 964633, "_sha256": "0e9103a6bc81bbd41797600c6b3ffdf5a298073617bf8e727abb098d237bec4a", "_created": "2026-05-28T08:22:02.000Z", "_published": "2026-05-28T08:29:10.894Z", "_distro": "noble", "_jobs": [ { "job": 78251995939, "time": 274, "config": "linux-devel-arm64", "r": "4.7.0", "check": "OK", "artifact": "7261179953" }, { "job": 78251995914, "time": 282, "config": "linux-devel-x86_64", "r": "4.7.0", "check": "OK", "artifact": "7261182681" }, { "job": 78251995865, "time": 365, "config": "linux-release-arm64", "r": "4.6.0", "check": "OK", "artifact": "7261211243" }, { "job": 78251995862, "time": 251, "config": "linux-release-x86_64", "r": "4.6.0", "check": "OK", "artifact": "7261172044" }, { "job": 78251122112, "time": 332, "config": "source", "r": "4.6.0", "check": "OK", "artifact": "7261087406" }, { "job": 78251995826, "time": 154, "config": "wasm-release", "r": "4.6.0", "check": "OK", "artifact": "7261139521" } ], "_buildurl": "https://github.com/r-universe/cran/actions/runs/26563048169", "_status": "success", "_host": "GitHub-Actions", "_upstream": "https://github.com/cran/sgs", "_commit": { "id": "520c238631e7e2a46018e58909b3ef455013e329", "author": "Fabio Feser ", "committer": "cran-robot ", "message": "version 0.3.9\n", "time": 1759240202 }, "_maintainer": { "name": "Fabio Feser", "email": "ff120@ic.ac.uk", "login": "ff1201", "description": "PhD student on the StatML programme at Imperial & Oxford", "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": "SGL", "role": "Suggests" }, { "package": "gglasso", "role": "Suggests" }, { "package": "glmnet", "role": "Suggests" }, { "package": "testthat", "role": "Suggests" }, { "package": "knitr", "role": "Suggests" }, { "package": "grpSLOPE", "role": "Suggests" }, { "package": "rmarkdown", "role": "Suggests" } ], "_owner": "cran", "_selfowned": false, "_usedby": 1, "_updates": [ { "week": "2025-24", "n": 1 }, { "week": "2025-40", "n": 1 } ], "_tags": [ { "name": "0.3.8", "date": "2025-06-12" }, { "name": "0.3.9", "date": "2025-09-30" } ], "_stars": 1, "_contributors": [ { "user": "ff1201", "count": 13, "uuid": 79450502 } ], "_userbio": { "uuid": 6899542, "type": "organization", "name": "cran", "description": "Unofficial read-only mirror of all CRAN R packages" }, "_downloads": { "count": 177, "source": "https://cranlogs.r-pkg.org/downloads/total/last-month/sgs" }, "_devurl": "https://github.com/ff1201/sgs", "_searchresults": 13, "_topics": [ "openblas", "cpp", "openmp" ], "_rbuild": "4.6.0", "_assets": [ "extra/citation.cff", "extra/citation.html", "extra/citation.json", "extra/citation.txt", "extra/contents.json", "extra/readme.html", "extra/readme.md", "extra/sgs.html", "manual.pdf" ], "_homeurl": "https://github.com/ff1201/sgs", "_realowner": "ff1201", "_cranurl": false, "_releases": [ { "version": "0.1.0", "date": "2023-05-19" }, { "version": "0.1.1", "date": "2023-08-24" }, { "version": "0.2.0", "date": "2024-07-14" }, { "version": "0.3.0", "date": "2024-09-18" }, { "version": "0.3.1", "date": "2024-11-16" }, { "version": "0.3.2", "date": "2024-11-28" }, { "version": "0.3.3", "date": "2025-02-03" }, { "version": "0.3.4", "date": "2025-02-06" }, { "version": "0.3.5", "date": "2025-03-04" }, { "version": "0.3.6", "date": "2025-04-22" }, { "version": "0.3.7", "date": "2025-05-06" }, { "version": "0.3.8", "date": "2025-06-12" }, { "version": "0.3.9", "date": "2025-10-03" } ], "_exports": [ "arma_mv", "arma_sparse", "as_sgs", "atos", "fit_goscar", "fit_goscar_cv", "fit_gslope", "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/cran/sgs/raw/HEAD/README.md", "_rundeps": [ "BH", "bigmemory", 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"homepage": "https://www.openblas.net/", "description": "Optimized BLAS (linear algebra) library (shared lib, pthread)" }, { "shlib": "libstdc++", "package": "libstdc++6", "source": "gcc", "version": "14.2.0-4ubuntu2~24.04.1", "name": "c++", "homepage": "http://gcc.gnu.org/", "description": "GNU Standard C++ Library v3" }, { "shlib": "libgomp", "package": "libgomp1", "source": "gcc", "version": "14.2.0-4ubuntu2~24.04.1", "name": "openmp", "homepage": "http://gcc.gnu.org/", "description": "GCC OpenMP (GOMP) support library" } ], "_vignettes": [ { "source": "reproducible_example.Rmd", "filename": "reproducible_example.html", "title": "sgs reproducible example", "author": "Fabio Feser", "engine": "knitr::rmarkdown", "headings": [ "Introduction", "Sparse-group SLOPE", "Linear regression", "Data", "Fitting an SGS model", "Output of model", "Cross validation", "Plot", "Prediction", "Logistic regression", "Fitting and prediction", "Group SLOPE", "Screening", "Reference" ], "created": 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