eclust: Environment Based Clustering for Interpretable Predictive Models in High Dimensional Data

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) <doi:10.1101/102475>. This package includes an algorithm for clustering high dimensional data that can be affected by an environmental factor.

Version: 0.1.0
Depends: R (≥ 3.3.1)
Imports: caret, data.table, dynamicTreeCut, magrittr, pacman, WGCNA, stringr, pander, stats
Suggests: cluster, earth, ncvreg, knitr, rmarkdown, protoclust, factoextra, ComplexHeatmap, circlize, pheatmap, viridis, pROC, glmnet
Published: 2017-01-26
Author: Sahir Rai Bhatnagar [aut, cre] (
Maintainer: Sahir Rai Bhatnagar <sahir.bhatnagar at>
License: MIT + file LICENSE
NeedsCompilation: no
Materials: README NEWS
CRAN checks: eclust results


Reference manual: eclust.pdf
Vignettes: Introduction to eclust
Package source: eclust_0.1.0.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
OS X El Capitan binaries: r-release: not available
OS X Mavericks binaries: r-oldrel: eclust_0.1.0.tgz


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