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.
Package details |
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Author | Blas M. Benito [aut, cre, cph] (<https://orcid.org/0000-0001-5105-7232>) |
Maintainer | Blas M. Benito <blasbenito@gmail.com> |
License | MIT + file LICENSE |
Version | 2.0.0 |
URL | https://blasbenito.github.io/collinear/ |
Package repository | View on CRAN |
Installation |
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