R package collinear

Description

Multicollinearity hinders the interpretability of linear and machine learning models.

The R package collinear, available on CRAN, combines four methods for easy management of multicollinearity in modelling data frames with numeric and categorical variables:

• Target Encoding: Transforms categorical predictors to numeric using a numeric response as reference.
• Preference Order: Ranks predictors by their association with a response variable to preserve important ones in multicollinearity filtering.
• Pairwise Correlation Filtering: Automated multicollinearity filtering of numeric and categorical predictors based

Main Improvements in Version 2.0.0

1. Expanded Functionality: Functions collinear() and preference_order() support both categorical and numeric responses and predictors, and can handle several responses at once.
2. Robust Selection Algorithms: Enhanced selection in vif_select() and cor_select().
3. Enhanced Functionality to Rank Predictors: New functions to compute association between response and predictors covering most use-cases, and automated function selection depending on data features.
4. Simplified Target Encoding: Streamlined and parallelized for better efficiency, and new default is “loo” (leave-one-out).
5. Parallelization and Progress Bars: Utilizes future and progressr for enhanced performance and user experience.on pairwise correlations.

• Variance Inflation Factor Filtering: Automated multicollinearity filtering of numeric predictors based on Variance Inflation Factors.

The article How It Works explains how the package works in detail.

Citation

If you find this package useful, please cite it as:

Blas M. Benito (2024). collinear: R Package for Seamless Multicollinearity Management. Version 2.0.0. doi: 10.5281/zenodo.10039489