Optimal Binning and Weight of Evidence Framework for Modeling R package OptimalBinningWoE version 1.0.8 High-performance implementation of 36 optimal binning algorithms (16 categorical, 20 numerical) for Weight of Evidence ('WoE') transformation, credit scoring, and risk modeling. Includes advanced methods such as Mixed Integer Linear Programming ('MILP'), Genetic Algorithms, Simulated Annealing, and Monotonic Regression. Features automatic method selection based on Information Value ('IV') maximization, strict monotonicity enforcement, and efficient handling of large datasets via 'Rcpp'. Fully integrated with the 'tidymodels' ecosystem for building robust machine learning pipelines. Based on methods described in Siddiqi (2006) <doi:10.1002/9781119201731> and Navas-Palencia (2020) <doi:10.48550/arXiv.2001.08025>. Software Depends: R (>= 4.1.0) Imports: Rcpp, recipes, rlang, tibble, dials LinkingTo: Rcpp, RcppEigen, RcppNumerical Suggests: testthat (>= 3.0.0), dplyr, generics, knitr, rmarkdown, tidymodels, workflows, parsnip, pROC, scorecard José Evandeilton Lopes <evandeilton@gmail.com> Comprehensive R Archive Network (CRAN) José Evandeilton Lopes [aut, cre, cph] (ORCID: <https://orcid.org/0009-0007-5887-4084>) MIT + file LICENSE (https://CRAN.R-project.org/package=OptimalBinningWoE/LICENSE) 2026-01-29 application/tgz https://CRAN.R-project.org/package=OptimalBinningWoE doi:10.32614/CRAN.package.OptimalBinningWoE en-US