Package: OptimalBinningWoE Type: Package Title: Optimal Binning and Weight of Evidence Framework for Modeling Version: 1.10.0 Date: 2026-05-17 Authors@R: person(given = "José Evandeilton", family = "Lopes", role = c("aut", "cre", "cph"), email = "evandeilton@gmail.com", comment = c(ORCID = "0009-0007-5887-4084")) Description: 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) and Navas-Palencia (2020) . License: MIT + file LICENSE URL: https://github.com/evandeilton/OptimalBinningWoE, https://evandeilton.github.io/OptimalBinningWoE BugReports: https://github.com/evandeilton/OptimalBinningWoE/issues Depends: R (>= 4.1.0) Encoding: UTF-8 Language: en-US Imports: Rcpp, recipes, rlang, tibble, dials LinkingTo: Rcpp, RcppEigen, RcppNumerical Suggests: testthat (>= 3.0.0), dplyr, generics, knitr, rmarkdown, tidymodels, workflows, parsnip, pROC, scorecard Config/testthat/edition: 3 SystemRequirements: C++17 RoxygenNote: 7.3.3 VignetteBuilder: knitr Config/pak/sysreqs: libicu-dev Repository: https://evandeilton.r-universe.dev Date/Publication: 2026-05-20 22:18:03 UTC RemoteUrl: https://github.com/evandeilton/optimalbinningwoe RemoteRef: HEAD RemoteSha: f3dbc2ea7394fac5b3ccafcea171730a2b19f555 NeedsCompilation: yes Packaged: 2026-06-19 07:00:14 UTC; root Author: José Evandeilton Lopes [aut, cre, cph] (ORCID: ) Maintainer: José Evandeilton Lopes