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OptimalBinningWoE  

Optimal Binning and Weight of Evidence Framework for Modeling
View on CRAN: Click here


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

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

Install by package version:
library("remotes")
install_version("OptimalBinningWoE", "1.0.8")



Attach the package and use:
library("OptimalBinningWoE")
Maintained by
José Evandeilton Lopes
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2026-01-23
Latest Update: 2026-01-29
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) <doi:10.1002/9781119201731> and Navas-Palencia (2020) <doi:10.48550/arXiv.2001.08025>.
How to cite:
José Evandeilton Lopes (2026). OptimalBinningWoE: Optimal Binning and Weight of Evidence Framework for Modeling. R package version 1.0.8, https://cran.r-project.org/web/packages/OptimalBinningWoE. Accessed 11 Mar. 2026.
Previous versions and publish date:
1.0.3 (2026-01-23 22:10)
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Functions, R codes and Examples using the OptimalBinningWoE R package
Full OptimalBinningWoE package functions and examples
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