Companion package to the paper: An analytic approach for
interpretable predictive models in high dimensional data, in the presence of
interactions with exposures. Bhatnagar, Yang, Khundrakpam, Evans, Blanchette, Bouchard, Greenwood (2017)
This package is under active development
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:
glmnetfor lasso and elasticnet regression
earthfor MARS models
WGCNAfor topological overlap matrices
You can see the most recent changes to the package in the NEWS.md file
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.
NEWS.mdfile to track changes to the package.