<p>The German Credit Data contains data on 20 variables and the classification whether
an applicant is considered a Good or a Bad credit risk for 1000 loan applicants.
Only 3 numeric variables are extracted (Duration of Credit (month),
Credit Amount and Age (years)) along with
good/bad indicator (Creditability) and renamed as:
qual (Creditability), maturity (Duration of Credit (month)), age (Age (years)),
amount (Credit Amount).</p>

datasets

Performs monotonic binning of numeric risk factor in credit rating models (PD, LGD, EAD)
development. All functions handle both binary and continuous target variable.
Functions that use isotonic regression in the first stage of binning process have an additional
feature for correction of minimum percentage of observations and minimum target rate per bin.
Additionally, monotonic trend can be identified based on raw data or, if known in advance,
forced by functions' argument. Missing values and other possible special values are treated
separately from so-called complete cases.

Andrija Djurovic

monobin

Monotonic Binning for Credit Rating Models

gcd function

<p>An object of class <code>data.frame</code> with 1000 rows and 4 columns.</p>

gcd: Excerpt from German Credit Data

Description

Usage

Arguments

Format