BlasBenito/collinear
R package to manage multicollinearity in modeling data frames.
When building predictive models, `collinear` helps data scientists and analysts prepare their datasets by identifying and managing redundant variables. It takes a dataset with various predictor types (numeric, categorical) and a target variable, then outputs a filtered set of predictors that are less correlated with each other, improving model stability and interpretability. This tool is designed for anyone working with statistical or machine learning models who needs to clean up their data.
Use this if you are developing predictive models (like regression or classification) and suspect that some of your input variables might be too similar, leading to less reliable model results.
Not ideal if your primary goal is descriptive analysis where retaining all original variables is crucial, or if you're not working with predictive models at all.
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Last pushed
Jan 07, 2026
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