Package: OptimalBinningWoE
Type: Package
Title: Optimal Binning and Weight of Evidence Framework for Modeling
Version: 1.0.8
Date: 2026-01-28
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) <doi:10.1002/9781119201731> 
    and Navas-Palencia (2020) <doi:10.48550/arXiv.2001.08025>.
License: MIT + file LICENSE
URL: https://github.com/evandeilton/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
NeedsCompilation: yes
Packaged: 2026-01-28 16:06:00 UTC; evand
Author: José Evandeilton Lopes [aut, cre, cph] (ORCID:
    <https://orcid.org/0009-0007-5887-4084>)
Maintainer: José Evandeilton Lopes <evandeilton@gmail.com>
Repository: CRAN
Date/Publication: 2026-01-29 08:00:02 UTC
Built: R 4.7.0; x86_64-w64-mingw32; 2026-05-09 03:29:41 UTC; windows
Archs: x64
