roclab: ROC-Optimizing Binary Classifiers

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:	0.1.3
Imports:	stats, graphics, utils, ggplot2, fastDummies, kernlab, pracma, rsample, dplyr, caret
Suggests:	mlbench, knitr, rmarkdown, testthat (≥ 3.0.0)
Published:	2025-10-28
DOI:	10.32614/CRAN.package.roclab (may not be active yet)
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

Documentation:

Reference manual:	roclab.html , roclab.pdf
Vignettes:	Introduction to roclab (source, R code)

Downloads:

Package source:	roclab_0.1.3.tar.gz
Windows binaries:	r-devel: not available, r-release: roclab_0.1.3.zip, r-oldrel: not available
macOS binaries:	r-release (arm64): roclab_0.1.3.tgz, r-oldrel (arm64): roclab_0.1.3.tgz, r-release (x86_64): roclab_0.1.3.tgz, r-oldrel (x86_64): roclab_0.1.3.tgz

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