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monobin 0.2.4

The goal of the monobin R package is to perform monotonic binning of numeric risk factor in credit rating models (PD, LGD, EAD) development. All functions handle both binary and continuous target variable. Missing values and other possible special values are treated separately from so-called complete cases.

Installation

You can install the released version of monobin from the CRAN executing the following code in R session:

install.packages("monobin")

In order to install latest version from github, you can use the following code:

library(devtools)
install_github("andrija-djurovic/monobin")

Example

This is a basic example which shows you how to solve a problem of monotonic binning of numeric risk factors:

suppressMessages(library(monobin))
data(gcd)
amount.bin <- cum.bin(x = gcd$amount, y = gcd$qual)
amount.bin[[1]]
gcd$amount.bin <- amount.bin[[2]]
gcd %>% group_by(amount.bin) %>% summarise(n = n(), y.avg = mean(qual))
#increase default number of groups (g = 20)
amount.bin.1 <- cum.bin(x = gcd$amount, y = gcd$qual, g = 20)
amount.bin.1[[1]]
#force trend to decreasing
cum.bin(x = gcd$amount, y = gcd$qual, g = 20, force.trend = "d")[[1]]

For more examples and package functions check the help page:

help(package = monobin)

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Version

Install

install.packages('monobin')

Monthly Downloads

329

Version

0.2.4

License

GPL (>= 3)

Issues

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Maintainer

Andrija Djurovic

Last Published

July 21st, 2022

Functions in monobin (0.2.4)

gcd

Excerpt from German Credit Data
mdt.bin

Monotonic binning driven by decision tree
woe.bin

Four-stage monotonic binning procedure with WoE threshold
pct.bin

Monotonic binning based on percentiles
sts.bin

Four-stage monotonic binning procedure with statistical test correction
iso.bin

Three-stage monotonic binning procedure
cum.bin

Monotonic binning based on maximum cumulative target rate (MAPA)
ndr.bin

Four-stage monotonic binning procedure including regression with nested dummies