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The eclust package implements the methods developped in the paper An analytic approach for interpretable predictive models in high dimensional data, in the presence of interactions with exposures (2017+) Preprint. Breifly, eclust is a two-step procedure: 1a) a clustering stage where variables are clustered based on some measure of similarity, 1b) a dimension reduction stage where a summary measure is created for each of the clusters, and 2) a simultaneous variable selection and regression stage on the summarized cluster measures.


You can install the development version of eclust from GitHub with:



See the online vignette for example usage of the functions.


This package is makes use of several existing packages including:

  1. Park, M. Y., Hastie, T., & Tibshirani, R. (2007). Averaged gene expressions for regression. Biostatistics, 8(2), 212-227.
  2. Bühlmann, P., Rütimann, P., van de Geer, S., & Zhang, C. H. (2013). Correlated variables in regression: clustering and sparse estimation. Journal of Statistical Planning and Inference, 143(11), 1835-1858.


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You can see the most recent changes to the package in the file

Code of Conduct

Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.