PARSE: Model-Based Clustering with Regularization Methods for High-Dimensional Data

Model-based clustering and identifying informative features based on regularization methods. The package includes three regularization methods - PAirwise Reciprocal fuSE (PARSE) penalty proposed by Wang, Zhou and Hoeting (2016), the adaptive L1 penalty (APL1) and the adaptive pairwise fusion penalty (APFP). Heatmaps are included to shown the identification of informative features.

Version: 0.1.0
Depends: R (≥ 3.0.0)
Imports: stats, mvtnorm, gplots, foreach, doParallel, grDevices, utils
Published: 2016-06-11
Author: Lulu Wang, Wen Zhou, Jennifer Hoeting
Maintainer: Lulu Wang <wanglulu at stat.colostate.edu>
License: CC0
NeedsCompilation: no
CRAN checks: PARSE results

Downloads:

Reference manual: PARSE.pdf
Package source: PARSE_0.1.0.tar.gz
Windows binaries: r-devel: PARSE_0.1.0.zip, r-release: PARSE_0.1.0.zip, r-oldrel: PARSE_0.1.0.zip
OS X El Capitan binaries: r-release: PARSE_0.1.0.tgz
OS X Mavericks binaries: r-oldrel: PARSE_0.1.0.tgz

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