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This package is under active development
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:
install.packages("pacman")
::p_install_gh("sahirbhatnagar/eclust") pacman
See the online vignette for example usage of the functions.
This package is makes use of several existing packages including:
glmnet
for lasso and elasticnet regressionearth
for MARS modelsWGCNA
for topological overlap matricesYou 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.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.