ClusterR: Gaussian Mixture Models, K-Means, Mini-Batch-Kmeans and K-Medoids Clustering

Gaussian mixture models, k-means, mini-batch-kmeans and k-medoids clustering with the option to plot, validate, predict (new data) and estimate the optimal number of clusters. The package takes advantage of 'RcppArmadillo' to speed up the computationally intensive parts of the functions.

Version: 1.0.5
Depends: R (≥ 3.2.3), gtools
Imports: Rcpp (≥ 0.12.5), OpenImageR, graphics, grDevices, utils, gmp, FD, stats, ggplot2
LinkingTo: Rcpp, RcppArmadillo (≥ 0.7.2)
Suggests: testthat, covr, knitr, rmarkdown
Published: 2017-02-11
Author: Lampros Mouselimis
Maintainer: Lampros Mouselimis <mouselimislampros at gmail.com>
BugReports: https://github.com/mlampros/ClusterR/issues
License: MIT + file LICENSE
URL: https://github.com/mlampros/ClusterR
NeedsCompilation: yes
Materials: README NEWS
CRAN checks: ClusterR results

Downloads:

Reference manual: ClusterR.pdf
Vignettes: Functionality of the ClusterR package
Package source: ClusterR_1.0.5.tar.gz
Windows binaries: r-devel: ClusterR_1.0.5.zip, r-release: ClusterR_1.0.5.zip, r-oldrel: ClusterR_1.0.5.zip
OS X El Capitan binaries: r-release: not available
OS X Mavericks binaries: r-oldrel: ClusterR_1.0.5.tgz
Old sources: ClusterR archive

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