https://gimunbae.r-universe.devRecent package updates in gimunbaeR-universehttps://github.com/gimunbae.pnghttps://gimunbae.r-universe.devTue, 04 Nov 2025 07:22:27 GMTgimunbae0201@gmail.com (Gimun Bae)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).https://github.com/r-universe/gimunbae/actions/runs/26810214449Tue, 04 Nov 2025 07:22:27 GMTroclab0.1.4successhttps://gimunbae.r-universe.devhttps://github.com/gimunbae/roclabroclab-intro.Rmdroclab-intro.htmlIntroduction to roclab2025-09-11 03:06:042025-11-04 07:22:27