collinear: Automated Multicollinearity Management

Effortless multicollinearity management in data frames with both numeric and categorical variables for statistical and machine learning applications. The package simplifies multicollinearity analysis by combining four robust methods: 1) target encoding for categorical variables (Micci-Barreca, D. 2001 <doi:10.1145/507533.507538>); 2) automated feature prioritization to prevent key variable loss during filtering; 3) pairwise correlation for all variable combinations (numeric-numeric, numeric-categorical, categorical-categorical); and 4) fast computation of variance inflation factors.

Version:	2.0.0
Depends:	R (≥ 4.0)
Imports:	progressr, future.apply, mgcv, rpart, ranger
Suggests:	future, testthat (≥ 3.0.0), spelling
Published:	2024-11-08
DOI:	10.32614/CRAN.package.collinear
Author:	Blas M. Benito [aut, cre, cph]
Maintainer:	Blas M. Benito <blasbenito at gmail.com>
BugReports:	https://github.com/blasbenito/collinear/issues
License:	MIT + file LICENSE
URL:	https://blasbenito.github.io/collinear/
NeedsCompilation:	no
Language:	en-US
Citation:	collinear citation info
Materials:	README NEWS
CRAN checks:	collinear results

Documentation:

Reference manual:

collinear.pdf

Downloads:

Package source:	collinear_2.0.0.tar.gz
Windows binaries:	r-devel: collinear_2.0.0.zip, r-release: collinear_2.0.0.zip, r-oldrel: collinear_2.0.0.zip
macOS binaries:	r-release (arm64): collinear_2.0.0.tgz, r-oldrel (arm64): collinear_2.0.0.tgz, r-release (x86_64): collinear_2.0.0.tgz, r-oldrel (x86_64): collinear_2.0.0.tgz
Old sources:	collinear archive

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