# -------------------------------------------- # CITATION file created with {cffr} R package # See also: https://docs.ropensci.org/cffr/ # -------------------------------------------- cff-version: 1.2.0 message: 'To cite package "OptimalBinningWoE" in publications use:' type: software license: MIT title: 'OptimalBinningWoE: Optimal Binning and Weight of Evidence Framework for Modeling' version: 1.10.0 doi: 10.32614/CRAN.package.OptimalBinningWoE abstract: 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) . authors: - family-names: Lopes given-names: José Evandeilton email: evandeilton@gmail.com orcid: https://orcid.org/0009-0007-5887-4084 repository: https://evandeilton.r-universe.dev repository-code: https://github.com/evandeilton/OptimalBinningWoE commit: f3dbc2ea7394fac5b3ccafcea171730a2b19f555 url: https://evandeilton.github.io/OptimalBinningWoE date-released: '2026-05-17' contact: - family-names: Lopes given-names: José Evandeilton email: evandeilton@gmail.com orcid: https://orcid.org/0009-0007-5887-4084