loans

<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.
Name of the columns are used as give in the source file.
Note that subset of those data is available also in 'monobin' package (gcd) and used for some examples in 'PDtoolkit' package.</p>

datasets

The goal of this package is to cover the most common steps in probability of default (PD) rating model development and validation.
The main procedures available are those that refer to univariate, bivariate, multivariate analysis, calibration and validation.
Along with accompanied 'monobin' and 'monobinShiny' packages, 'PDtoolkit' provides functions which are suitable for different
data transformation and modeling tasks such as:
imputations, monotonic binning of numeric risk factors, binning of categorical risk factors, weights of evidence (WoE) and
information value (IV) calculations, WoE coding (replacement of risk factors modalities with WoE values), risk factor clustering,
area under curve (AUC) calculation and others. Additionally, package provides set of validation functions for testing homogeneity,
heterogeneity, discriminatory and predictive power of the model.

Andrija Djurovic

PDtoolkit

Collection of Tools for PD Rating Model Development and
Validation

loans function

<p>An object of class <code>data.frame</code> with 1000 rows and 21 columns.</p>

loans: German Credit Data

Description

Usage

Arguments

Format