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roclab  

ROC-Optimizing Binary Classifiers
View on CRAN: Click here


Download and install roclab package within the R console
Install from CRAN:
install.packages("roclab")

Install from Github:
library("remotes")
install_github("cran/roclab")

Install by package version:
library("remotes")
install_version("roclab", "0.1.4")



Attach the package and use:
library("roclab")
Maintained by
Gimun Bae
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2025-11-04
Latest Update: 2025-11-04
Description:
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).
How to cite:
Gimun Bae (2025). roclab: ROC-Optimizing Binary Classifiers. R package version 0.1.4, https://cran.r-project.org/web/packages/roclab. Accessed 21 Aug. 2026.
Previous versions and publish date:
(2026-07-09 06:52), 0.1.3 (2025-10-28 09:20)
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Complete documentation for roclab
Functions, R codes and Examples using the roclab R package
Full roclab package functions and examples
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