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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: | 1.1.1 |
Depends: | R (≥ 4.0) |
Imports: | dplyr |
Suggests: | ranger, mgcv, future, future.apply, testthat (≥ 3.0.0), spelling |
Published: | 2023-12-08 |
DOI: | 10.32614/CRAN.package.collinear |
Author: | Blas M. Benito [aut, cre, cph] |
Maintainer: | Blas M. Benito <blasbenito at gmail.com> |
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 |
Reference manual: | collinear.pdf |
Package source: | collinear_1.1.1.tar.gz |
Windows binaries: | r-devel: collinear_1.1.1.zip, r-release: collinear_1.1.1.zip, r-oldrel: collinear_1.1.1.zip |
macOS binaries: | r-release (arm64): collinear_1.1.1.tgz, r-oldrel (arm64): collinear_1.1.1.tgz, r-release (x86_64): collinear_1.1.1.tgz, r-oldrel (x86_64): collinear_1.1.1.tgz |
Old sources: | collinear archive |
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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