Package: OptimalBinningWoE 1.0.8

José Evandeilton Lopes

OptimalBinningWoE: Optimal Binning and Weight of Evidence Framework for Modeling

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>.

Authors:José Evandeilton Lopes [aut, cre, cph]

OptimalBinningWoE_1.0.8.tar.gz
OptimalBinningWoE_1.0.8.tar.gz(r-4.7-arm64)OptimalBinningWoE_1.0.8.tar.gz(r-4.7-x86_64)OptimalBinningWoE_1.0.8.tar.gz(r-4.6-arm64)OptimalBinningWoE_1.0.8.tar.gz(r-4.6-x86_64)
OptimalBinningWoE_1.0.8.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
OptimalBinningWoE/json (API)
NEWS

# Install 'OptimalBinningWoE' in R:
install.packages('OptimalBinningWoE', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/evandeilton/optimalbinningwoe/issues

Uses libs:
  • c++– GNU Standard C++ Library v3
  • openmp– GCC OpenMP (GOMP) support library

On CRAN:

Conda:

cppopenmp

3.00 score 9 scripts 138 downloads 61 exports 68 dependencies

Last updated from:438cd2af25. Checks:6 OK. Indexed: no.

TargetResultTimeFilesSyslog
linux-devel-arm64OK346
linux-devel-x86_64OK342
source / vignettesOK534
linux-release-arm64OK364
linux-release-x86_64OK327
wasm-releaseOK314

Exports:.categorical_only_algorithms.numerical_only_algorithms.universal_algorithms.valid_algorithmscontrol.obwoefit_logistic_regressionob_apply_woe_catob_apply_woe_numob_categorical_cmob_categorical_dmivob_categorical_dpob_categorical_fetbob_categorical_gmbob_categorical_ivbob_categorical_jediob_categorical_jedi_mwoeob_categorical_mbaob_categorical_milpob_categorical_mobob_categorical_sabob_categorical_sblpob_categorical_sketchob_categorical_swbob_categorical_udtob_check_distinctsob_cutpoints_catob_cutpoints_numob_gains_tableob_gains_table_featureob_numerical_bbob_numerical_cmob_numerical_dmivob_numerical_dpob_numerical_ewbob_numerical_fast_mdlpob_numerical_fetbob_numerical_irob_numerical_jediob_numerical_jedi_mwoeob_numerical_kmbob_numerical_ldbob_numerical_lpdbob_numerical_mblpob_numerical_mdlpob_numerical_mobob_numerical_mrblpob_numerical_oslpob_numerical_sketchob_numerical_ubsdob_numerical_udtob_preprocessobcorrobwoeobwoe_algorithmobwoe_algorithmsobwoe_applyobwoe_bin_cutoffobwoe_gainsobwoe_max_binsobwoe_min_binsstep_obwoe

Dependencies:classcliclockcodetoolscpp11data.tablediagramdialsDiceDesigndigestdplyrfarverfuturefuture.applygenericsggplot2globalsgluegowergtablehardhatipredisobandKernSmoothlabelinglatticelavalifecyclelistenvlubridatemagrittrMASSMatrixnnetnumDerivparallellypillarpkgconfigprodlimprogressrpurrrR6RColorBrewerRcppRcppEigenRcppNumericalrecipesrlangrpartS7scalessfdshapesparsevctrsSQUAREMstringistringrsurvivaltibbletidyrtidyselecttimechangetimeDatetzdbutf8vctrsviridisLitewithr

OptimalBinningWoE: Practical Guide for Credit Risk Modeling

José Evandeilton Lopes

Rendered fromintroduction.Rmdusingknitr::rmarkdownon May 29 2026.

Last update: 2026-01-29
Started: 2026-01-23

Readme and manuals

Help Manual

Help pageTopics
Categorical-Only Algorithms.categorical_only_algorithms
Numerical-Only Algorithms.numerical_only_algorithms
Universal Algorithms.universal_algorithms
Valid Binning Algorithms.valid_algorithms
Apply the Optimal Binning Transformationbake.step_obwoe
Control Parameters for Optimal Binning Algorithmscontrol.obwoe
Fit Logistic Regression Modelfit_logistic_regression
Apply Optimal Weight of Evidence (WoE) to a Categorical Featureob_apply_woe_cat
Apply Optimal Weight of Evidence (WoE) to a Numerical Featureob_apply_woe_num
Optimal Binning for Categorical Variables using Enhanced ChiMerge Algorithmob_categorical_cm
Optimal Binning for Categorical Variables using Divergence Measuresob_categorical_dmiv
Optimal Binning for Categorical Variables using Dynamic Programmingob_categorical_dp
Optimal Binning for Categorical Variables using Fisher's Exact Testob_categorical_fetb
Optimal Binning for Categorical Variables using Greedy Merge Algorithmob_categorical_gmb
Optimal Binning for Categorical Variables using Information Value Dynamic Programmingob_categorical_ivb
Optimal Binning for Categorical Variables using JEDI Algorithmob_categorical_jedi
Optimal Binning for Categorical Variables with Multinomial Target using JEDI-MWoEob_categorical_jedi_mwoe
Optimal Binning for Categorical Variables using Monotonic Binning Algorithmob_categorical_mba
Optimal Binning for Categorical Variables using Heuristic Algorithmob_categorical_milp
Optimal Binning for Categorical Variables using Monotonic Optimal Binning (MOB)ob_categorical_mob
Optimal Binning for Categorical Variables using Simulated Annealingob_categorical_sab
Optimal Binning for Categorical Variables using SBLPob_categorical_sblp
Optimal Binning for Categorical Variables using Sketch-based Algorithmob_categorical_sketch
Optimal Binning for Categorical Variables using Sliding Window Binning (SWB)ob_categorical_swb
Optimal Binning for Categorical Variables using a User-Defined Technique (UDT)ob_categorical_udt
Binning Categorical Variables using Custom Cutpointsob_cutpoints_cat
Binning Numerical Variables using Custom Cutpointsob_cutpoints_num
Compute Comprehensive Gains Table from Binning Resultsob_gains_table
Compute Gains Table for a Binned Feature Vectorob_gains_table_feature
Optimal Binning for Numerical Variables using Branch and Bound Algorithmob_numerical_bb
Optimal Binning for Numerical Variables using Enhanced ChiMerge Algorithmob_numerical_cm
Optimal Binning using Metric Divergence Measures (Zeng, 2013)ob_numerical_dmiv
Optimal Binning for Numerical Variables using Dynamic Programmingob_numerical_dp
Hybrid Optimal Binning using Equal-Width Initialization and IV Optimizationob_numerical_ewb
Optimal Binning using MDLP with Monotonicity Constraintsob_numerical_fast_mdlp
Optimal Binning using Fisher's Exact Testob_numerical_fetb
Optimal Binning using Isotonic Regression (PAVA)ob_numerical_ir
Optimal Binning using Joint Entropy-Driven Interval Discretization (JEDI)ob_numerical_jedi
Optimal Binning for Multiclass Targets using JEDI M-WOEob_numerical_jedi_mwoe
Optimal Binning using K-means Inspired Initialization (KMB)ob_numerical_kmb
Optimal Binning for Numerical Variables using Local Density Binningob_numerical_ldb
Optimal Binning using Local Polynomial Density Binning (LPDB)ob_numerical_lpdb
Optimal Binning for Numerical Features Using Monotonic Binning via Linear Programmingob_numerical_mblp
Optimal Binning for Numerical Features using Minimum Description Length Principleob_numerical_mdlp
Optimal Binning for Numerical Features using Monotonic Optimal Binningob_numerical_mob
Optimal Binning for Numerical Features using Monotonic Risk Binning with Likelihood Ratio Pre-binningob_numerical_mrblp
Optimal Binning for Numerical Variables using Optimal Supervised Learning Partitioningob_numerical_oslp
Optimal Binning for Numerical Variables using Sketch-based Algorithmob_numerical_sketch
Optimal Binning for Numerical Variables using Unsupervised Binning with Standard Deviationob_numerical_ubsd
Optimal Binning for Numerical Variables using Entropy-Based Partitioningob_numerical_udt
Data Preprocessor for Optimal Binningob_preprocess
Compute Multiple Robust Correlations Between Numeric Variablesobcorr
Unified Optimal Binning and Weight of Evidence Transformationobwoe
Binning Algorithm Parameterobwoe_algorithm
List Available Algorithmsobwoe_algorithms
Apply Weight of Evidence Transformations to New Dataobwoe_apply
Bin Cutoff Parameterobwoe_bin_cutoff
Gains Table Statistics for Credit Risk Scorecard Evaluationobwoe_gains
Maximum Bins Parameterobwoe_max_bins
Minimum Bins Parameterobwoe_min_bins
Plot Method for obwoe Objectsplot.obwoe
Plot Gains Tableplot.obwoe_gains
Prepare the Optimal Binning Stepprep.step_obwoe
Print Method for obwoe Objectsprint.obwoe
Print Method for step_obwoeprint.step_obwoe
Required Packages for step_obwoerequired_pkgs.step_obwoe
Optimal Binning and WoE Transformation Stepstep_obwoe
Summary Method for obwoe Objectssummary.obwoe
Tidy Method for step_obwoetidy.step_obwoe
Tunable Parameters for step_obwoetunable.step_obwoe