creditmodel: Toolkit for Credit Modeling, Analysis and Visualization

Provides a highly efficient R tool suite for Credit Modeling, Analysis and Visualization.Contains infrastructure functionalities such as data exploration and preparation, missing values treatment, outliers treatment, variable derivation, variable selection, dimensionality reduction, grid search for hyper parameters, data mining and visualization, model evaluation, strategy analysis etc. This package is designed to make the development of binary classification models (machine learning based models as well as credit scorecard) simpler and faster. The references including: 1 Refaat, M. (2011, ISBN: 9781447511199). Credit Risk Scorecard: Development and Implementation Using SAS; 2 Bezdek, James C.FCM: The fuzzy c-means clustering algorithm. Computers & Geosciences (0098-3004),<doi:10.1016/0098-3004(84)90020-7>.

Version:	1.3.1
Depends:	R (≥ 2.10)
Imports:	data.table, dplyr, ggplot2, foreach, doParallel, glmnet, rpart, cli, xgboost
Suggests:	pdp, pmml, XML, knitr, gbm, randomForest, rmarkdown
Published:	2022-01-07
Author:	Dongping Fan [aut, cre]
Maintainer:	Dongping Fan <fdp at pku.edu.cn>
License:	AGPL-3
NeedsCompilation:	no
Materials:	README NEWS
In views:	MissingData
CRAN checks:	creditmodel results

Documentation:

Reference manual:	creditmodel.pdf
Vignettes:	Introduction to creditmodel

Downloads:

Package source:	creditmodel_1.3.1.tar.gz
Windows binaries:	r-devel: creditmodel_1.3.1.zip, r-release: creditmodel_1.3.1.zip, r-oldrel: creditmodel_1.3.1.zip
macOS binaries:	r-release (arm64): creditmodel_1.3.1.tgz, r-oldrel (arm64): creditmodel_1.3.1.tgz, r-release (x86_64): creditmodel_1.3.1.tgz, r-oldrel (x86_64): creditmodel_1.3.1.tgz
Old sources:	creditmodel archive

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