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woeBinning
View on CRAN: Click
here
Download and install woeBinning package within the R console
Install from CRAN:
install.packages("woeBinning")
Install from Github:
library("remotes")
install_github("cran/woeBinning") Install by package version:
library("remotes")
install_version("woeBinning", "0.1.6") Attach the package and use:
library("woeBinning")
Maintained by
Thilo Eichenberg
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[Scholar Profile | Author Map]
All associated links for this package
First Published: 2017-02-21
Latest Update: 2018-07-28
Description:
Implements an automated binning of numeric variables and factors with respect to a dichotomous target variable. Two approaches are provided: An implementation of fine and coarse classing that merges granular classes and levels step by step. And a tree-like approach that iteratively segments the initial bins via binary splits. Both procedures merge, respectively split, bins based on similar weight of evidence (WOE) values and stop via an information value (IV) based criteria. The package can be used with single variables or an entire data frame. It provides flexible tools for exploring different binning solutions and for deploying them to (new) data.
How to cite:
Thilo Eichenberg (2017). woeBinning: Supervised Weight of Evidence Binning of Numeric Variables and Factors. R package version 0.1.6, https://cran.r-project.org/web/packages/woeBinning. Accessed 09 Mar. 2026.
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Complete documentation for woeBinning
Functions, R codes and Examples using
the woeBinning R package
Some associated functions: germancredit . woe.binning.deploy . woe.binning . woe.binning.plot . woe.binning.table . woe.tree.binning . woeBinning .
Some associated R codes: germancredit.R . woe.binning.R . woe.binning.deploy.R . woe.binning.plot.R . woe.binning.table.R . woe.tree.binning.R . Full woeBinning package functions and examples
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