{ "_id": "6a1ea1691d7bb097a0a70d4f", "Package": "roclab", "Title": "ROC-Optimizing Binary Classifiers", "Version": "0.1.4", "Authors@R": "c(\nperson(\"Gimun\", \"Bae\",\nemail = \"gimunbae0201@gmail.com\",\nrole = c(\"aut\", \"cre\")),\nperson(\"Seung Jun\", \"Shin\",\nemail = \"sjshin@korea.ac.kr\",\nrole = \"aut\")\n)", "Description": "Implements ROC (Receiver Operating\nCharacteristic)–Optimizing Binary Classifiers, supporting both\nlinear and kernel models. Both model types provide a variety of\nsurrogate loss functions. In addition, linear models offer\nmultiple regularization penalties, whereas kernel models\nsupport a range of kernel functions. Scalability for large\ndatasets is achieved through approximation-based options, which\naccelerate training and make fitting feasible on large data.\nUtilities are provided for model training, prediction, and\ncross-validation. The implementation builds on the\nROC-Optimizing Support Vector Machines. For more information,\nsee Hernàndez-Orallo, José, et al. (2004)\n, presented in the ROC Analysis in\nAI Workshop (ROCAI-2004).", "License": "MIT + file LICENSE", "Encoding": "UTF-8", "Roxygen": "list(markdown = TRUE)", "RoxygenNote": "7.3.2", "Config/testthat/edition": "3", "VignetteBuilder": "knitr", "URL": "https://github.com/gimunBae/roclab", "BugReports": "https://github.com/gimunBae/roclab/issues", "Config/pak/sysreqs": "libicu-dev", "Repository": "https://gimunbae.r-universe.dev", "Date/Publication": "2025-11-04 07:22:27 UTC", "RemoteUrl": "https://github.com/gimunbae/roclab", "RemoteRef": "HEAD", "RemoteSha": "109e27c6128c80a453d66bcf1fff505533739cc0", "NeedsCompilation": "no", "Packaged": { "Date": "2026-06-02 09:20:05 UTC", "User": "root" }, "Author": "Gimun Bae [aut, cre],\nSeung Jun Shin [aut]", "Maintainer": "Gimun Bae ", "MD5sum": "ecbae01387abee6f901e8b3816040bd3", "_user": "gimunbae", "_type": "src", "_file": "roclab_0.1.4.tar.gz", "_fileid": "dbb1d703d5351719f84033fdf132c6d1ff7b68c5ef51ed2ffa6f3316bb6c20d6", "_filesize": 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