collinear: Seamless Multicollinearity Management for Categorical and Numeric Variables

System for seamless management of multicollinearity in data frames with numeric and/or categorical variables for statistical analysis and machine learning modeling. The package combines bivariate correlation (Pearson, Spearman, and Cramer's V) with variance inflation factor analysis, target encoding to transform categorical variables into numeric (Micci-Barreca, D. 2001 <doi:10.1145/507533.507538>), and a flexible feature prioritization method, to deliver a comprehensive multicollinearity management tool covering a wide range of use cases.

Version: 1.0.1
Depends: R (≥ 4.0)
Imports: dplyr
Suggests: ranger, mgcv, future, future.apply, testthat (≥ 3.0.0), spelling
Published: 2023-10-24
Author: Blas Benito ORCID iD [aut, cre, cph]
Maintainer: Blas Benito <blasbenito at>
License: GPL (≥ 3)
NeedsCompilation: no
Language: en-US
Materials: README NEWS
CRAN checks: collinear results


Reference manual: collinear.pdf


Package source: collinear_1.0.1.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): collinear_1.0.1.tgz, r-oldrel (arm64): collinear_1.0.1.tgz, r-release (x86_64): collinear_1.0.1.tgz, r-oldrel (x86_64): not available


Please use the canonical form to link to this page.