ROC-Optimizing Binary Classifiers R package roclab version 0.1.4 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). Software Imports: stats, graphics, utils, ggplot2, fastDummies, kernlab, pracma, rsample, dplyr, caret, pROC Suggests: mlbench, knitr, rmarkdown, testthat (>= 3.0.0) Gimun Bae <gimunbae0201@gmail.com> Comprehensive R Archive Network (CRAN) Gimun Bae [aut, cre], Seung Jun Shin [aut] MIT + file LICENSE (https://CRAN.R-project.org/package=roclab/LICENSE) 2025-11-04 application/tgz https://CRAN.R-project.org/package=roclab doi:10.32614/CRAN.package.roclab