Implements ROC (Receiver Operating Characteristic)–Optimizing Binary Classifiers, supporting both linear and kernel models. Both model types provide a variety of surrogate loss functions. In addition, linear models offer multiple regularization penalties, whereas kernel models support a range of kernel functions. Scalability for large datasets is achieved through approximation-based options, which accelerate training and make fitting feasible on large data. Utilities are provided for model training, prediction, and cross-validation. The implementation builds on the ROC-Optimizing Support Vector Machines. For more information, see Hernàndez-Orallo, José, et al. (2004) <doi:10.1145/1046456.1046489>, presented in the ROC Analysis in AI Workshop (ROCAI-2004).
| Version: | 0.1.4 |
| Imports: | stats, graphics, utils, ggplot2, fastDummies, kernlab, pracma, rsample, dplyr, caret, pROC |
| Suggests: | mlbench, knitr, rmarkdown, testthat (≥ 3.0.0) |
| Published: | 2025-11-04 |
| DOI: | 10.32614/CRAN.package.roclab |
| Author: | Gimun Bae [aut, cre], Seung Jun Shin [aut] |
| Maintainer: | Gimun Bae <gimunbae0201 at gmail.com> |
| BugReports: | https://github.com/gimunBae/roclab/issues |
| License: | MIT + file LICENSE |
| URL: | https://github.com/gimunBae/roclab |
| NeedsCompilation: | no |
| Materials: | README |
| CRAN checks: | roclab results |
| Reference manual: | roclab.html , roclab.pdf |
| Vignettes: |
Introduction to roclab (source, R code) |
| Package source: | roclab_0.1.4.tar.gz |
| Windows binaries: | r-devel: roclab_0.1.4.zip, r-release: roclab_0.1.4.zip, r-oldrel: roclab_0.1.4.zip |
| macOS binaries: | r-release (arm64): roclab_0.1.4.tgz, r-oldrel (arm64): roclab_0.1.4.tgz, r-release (x86_64): roclab_0.1.4.tgz, r-oldrel (x86_64): roclab_0.1.4.tgz |
| Old sources: | roclab archive |
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