{ "_id": "6a185c7eacfb0bcc41dbbbfe", "Package": "oscar", "Type": "Package", "Title": "Optimal Subset Cardinality Regression (OSCAR) Models Using the\nL0-Pseudonorm", "Version": "1.2.1", "Date": "2023-10-02", "Authors@R": "c(\nperson(given=c(\"Teemu\", \"Daniel\"), family=\"Laajala\", role=c(\"aut\", \"cre\"), email=\"teelaa@utu.fi\", comment = c(ORCID = \"0000-0002-7016-7354\")),\nperson(given=\"Kaisa\", family=\"Joki\", role=c(\"aut\"), email=\"kjjoki@utu.fi\"),\nperson(given=\"Anni\", family=\"Halkola\", role=c(\"aut\"), email=\"ansuha@utu.fi\"))", "Description": "Optimal Subset Cardinality Regression (OSCAR) models offer\nregularized linear regression using the L0-pseudonorm,\nconventionally known as the number of non-zero coefficients.\nThe package estimates an optimal subset of features using the\nL0-penalization via cross-validation, bootstrapping and visual\ndiagnostics. Effective Fortran implementations are offered\nalong the package for finding optima for the DC-decomposition,\nwhich is used for transforming the discrete L0-regularized\noptimization problem into a continuous non-convex optimization\ntask. These optimization modules include DBDC ('Double Bundle\nmethod for nonsmooth DC optimization' as described in Joki et\nal. (2018) ) and LMBM ('Limited Memory\nBundle Method for large-scale nonsmooth optimization' as in\nHaarala et al. (2004) ). The\nOSCAR models are comprehensively exemplified in Halkola et al.\n(2023) ). Multiple regression\nmodel families are supported: Cox, logistic, and Gaussian.", "License": "GPL-3", "LazyData": "true", "URL": "https://github.com/Syksy/oscar", "BugReports": "https://github.com/Syksy/oscar/issues", "NeedsCompilation": "yes", "VignetteBuilder": "knitr", "Encoding": "UTF-8", "RoxygenNote": "7.2.3", "Packaged": { "Date": "2026-05-28 15:11:03 UTC", "User": "root" }, "Author": "Teemu Daniel Laajala [aut, cre]\n(), Kaisa Joki [aut],\nAnni Halkola [aut]", "Maintainer": "Teemu Daniel Laajala ", "Repository": "https://cran.r-universe.dev", "Date/Publication": "2023-10-02 15:31:15 UTC", "RemoteUrl": "https://github.com/cran/oscar", "RemoteRef": "HEAD", "RemoteSha": "86316f02b44ee45c7c072388fe840c2ea599b518", "MD5sum": "0c7fd02042985c38c24a7b764649c17b", "_user": "cran", "_type": "src", "_file": "oscar_1.2.1.tar.gz", "_fileid": "d7472cc48f5bc0846c8befe3c9069863fea005f890f38279740ad16eee4f0158", "_filesize": 659903, "_sha256": 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"_status": "success", "_host": "GitHub-Actions", "_upstream": "https://github.com/cran/oscar", "_commit": { "id": "86316f02b44ee45c7c072388fe840c2ea599b518", "author": "Teemu Daniel Laajala ", "committer": "cran-robot ", "message": "version 1.2.1\n", "time": 1696260675 }, "_maintainer": { "name": "Teemu Daniel Laajala", "email": "teelaa@utu.fi", "login": "syksy", "orcid": "0000-0002-7016-7354", "description": "Finn with a knack for bioinformatics (mostly cancer), artificial intelligence, machine learning, game jams, and all that jazz.", "uuid": 36551395 }, "_registered": true, "_dependencies": [ { "package": "R", "version": ">= 4.1.0", "role": "Depends" }, { "package": "graphics", "role": "Imports" }, { "package": "grDevices", "role": "Imports" }, { "package": "hamlet", "role": "Imports" }, { "package": "Matrix", "role": "Imports" }, { "package": "methods", "role": "Imports" }, { "package": "stats", "role": "Imports" }, { "package": "survival", "role": "Imports" }, { "package": 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"class": [ "matrix", "array" ], "fields": [ "BMI", "HEIGHTBL", "WEIGHTBL", "SYSTOLICBP", "DIASTOLICBP", "PULSE", "HB", "HEMAT", "RBC", "WBC", "PLT", "LYMperLEU", "MONOperLEU", "NEU", "POT", "MONO", "BASOperLEU", "EOSperLEU", "NEUperLEU", "NA.", "CCRC", "LDH", "AST", "ALB", "ALP", "PSA", "CREAT", "CREACL", "LYM", "PHOS", "CA", "ALT", "TPRO", "EOS", "TESTO", "GLU", "MG", "TBILI" ], "rows": 22, "table": true, "tojson": true }, { "name": "ex_X", "title": "Example data from TYKS / HUSLAB", "object": "ex", "class": [ "matrix", "array" ], "fields": [ "BMI", "HEIGHTBL", "WEIGHTBL", "SYSTOLICBP", "DIASTOLICBP", "PULSE", "HB", "HEMAT", "RBC", "WBC", "PLT", "LYMperLEU", "MONOperLEU", "NEU", "POT", "MONO", "BASOperLEU", "EOSperLEU", "NEUperLEU", "NA.", "CCRC", "LDH", "AST", "ALB", "ALP", "PSA", "CREAT", "CREACL", "LYM", "PHOS", "CA", "ALT", "TPRO", "EOS", "TESTO", "GLU", "MG", "TBILI" ], "rows": 650, "table": true, "tojson": true }, { "name": "ex_Y", "title": "Example data from TYKS / HUSLAB", "object": "ex", "class": [ "matrix", "array" ], "fields": [ "Time", "Event" ], "rows": 650, "table": true, "tojson": true } ], "_help": [ { "page": "oscar-package", "title": "oscar: Optimal Subset Cardinality Regression", "topics": [ "oscar-package" ] }, { "page": "oscar-methods", "title": "Extract coefficients of oscar-objects", "topics": [ "coef,oscar-method", "plot,oscar-method", "predict,oscar-method" ] }, { "page": "cost", "title": "Return total cost of model fit based on provided kit/variable costs vector", "topics": [ "cost", "cost,oscar-method", "cost,oscar-methods" ] }, { "page": "ex", "title": "Example data from TYKS / HUSLAB", "topics": [ "ex_c", "ex_K", "ex_X", "ex_Y" ] }, { "page": "feat", "title": "Return named vector of feature indices with a given k that are non-zero", "topics": [ "feat", "feat,oscar-method", "feat,oscar-methods" ] }, { "page": "kits", "title": "Return named vector of indices for kits with a given k that are non-zero", "topics": [ "kits", "kits,oscar-method", "kits,oscar-methods" ] }, { "page": "oscar", "title": "Main OSCAR fitting function", "topics": [ "oscar" ] }, { "page": "oscar-class", "title": "S4-class for oscar", "topics": [ "oscar-class" ] }, { "page": "oscar.binarize", "title": "Binary logical indicator matrix representation of an oscar object's coefficients (zero vs. non-zero, i.e. feature inclusion)", "topics": [ "oscar.binarize" ] }, { "page": "oscar.binplot", "title": "Visualize binary indicator matrix optionally coupled with cross-validation performance for oscar models", "topics": [ "oscar.binplot" ] }, { "page": "oscar.bs", "title": "Bootstrapping for oscar-fitted model objects", "topics": [ "oscar.bs" ] }, { "page": "oscar.bs.boxplot", "title": "Bootstrap visualization with boxplot, percentage of new additions", "topics": [ "oscar.bs.boxplot" ] }, { "page": "oscar.bs.k", "title": "Reformatting bootstrap output for cardinality k rows", "topics": [ "oscar.bs.k" ] }, { "page": "oscar.bs.plot", "title": "Bootstrap heatmap plot for oscar models", "topics": [ "oscar.bs.plot" ] }, { "page": "oscar.bs.visu", "title": "Visualize bootstrapping of a fit oscar object", "topics": [ "oscar.bs.visu" ] }, { "page": "oscar.control", "title": "Control OSCAR optimizer parameters", "topics": [ "oscar.control" ] }, { "page": "oscar.cost.after", "title": "Return total cost of model fits if the cost is not included in the oscar object", "topics": [ "oscar.cost.after" ] }, { "page": "oscar.cv", "title": "Cross-validation for oscar-fitted model objects over k-range", "topics": [ "oscar.cv" ] }, { "page": "oscar.cv.visu", "title": "Visualize cross-validation as a function of k", "topics": [ "oscar.cv.visu" ] }, { "page": "oscar.pareto", "title": "Retrieve a set of pareto-optimal points for an oscar-model based on model goodness-of-fit or cross-validation", "topics": [ "oscar.pareto" ] }, { "page": "oscar.pareto.visu", "title": "Visualize oscar model pareto front", "topics": [ "oscar.pareto.visu" ] }, { "page": "oscar.sparsify", "title": "Create a sparse matrix representation of betas as a function of k", "topics": [ "oscar.sparsify" ] }, { "page": "oscar.visu", "title": "Target function value and total kit cost as a function of number of kits included", "topics": [ "oscar.visu" ] }, { "page": "generics", "title": "Showing oscar-objects", "topics": [ "show,oscar-method" ] } ], "_rundeps": [ "hamlet", "lattice", "Matrix", "pROC", "Rcpp", "survival" ], "_sysdeps": [ { "shlib": "libgfortran", "package": "libgfortran5", "source": "gcc", "version": "14.2.0-4ubuntu2~24.04.1", "name": "fortran", "homepage": "http://gcc.gnu.org/", "description": "Runtime library for GNU Fortran applications" } ], "_vignettes": [ { "source": "example.Rmd", "filename": "example.html", "title": "Example use of the OSCAR package", "author": "Teemu Daniel Laajala", "engine": "knitr::rmarkdown", "headings": [ "OSCAR modelling", "Cox regression (simulated TYKS real-world PCa data)", "Goodness of fit vs. model 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