prclust: Penalized Regression-Based Clustering Method

Clustering is unsupervised and exploratory in nature. Yet, it can be performed through penalized regression with grouping pursuit. In this package, we provide two algorithms for fitting the penalized regression-based clustering (PRclust) with non-convex grouping penalties, such as group truncated lasso, MCP and SCAD. One algorithm is based on quadratic penalty and difference convex method. Another algorithm is based on difference convex and ADMM, called DC-ADD, which is more efficient. Generalized cross validation and stability based method were provided to select the tuning parameters. Rand index, adjusted Rand index and Jaccard index were provided to estimate the agreement between estimated cluster memberships and the truth.

Version: 1.3
Depends: R (≥ 3.1.1)
Imports: Rcpp (≥ 0.12.1), parallel
LinkingTo: Rcpp
Published: 2016-12-13
Author: Chong Wu, Wei Pan
Maintainer: Chong Wu <wuxx0845 at umn.edu>
License: GPL-2 | GPL-3
NeedsCompilation: yes
CRAN checks: prclust results

Downloads:

Reference manual: prclust.pdf
Package source: prclust_1.3.tar.gz
Windows binaries: r-devel: prclust_1.2.zip, r-release: prclust_1.2.zip, r-oldrel: prclust_1.2.zip
OS X Mavericks binaries: r-release: prclust_1.2.tgz, r-oldrel: prclust_1.2.tgz
Old sources: prclust archive

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