diceR: Diverse Cluster Ensemble in R

Performs cluster analysis using an ensemble clustering framework. Results from a diverse set of algorithms are pooled together using methods such as majority voting, K-Modes, LinkCluE, and CSPA. There are options to compare cluster assignments across algorithms using internal and external indices, visualizations such as heatmaps, and significance testing for the existence of clusters.

Version: 0.1.0
Depends: R (≥ 3.2.0)
Imports: abind, assertthat, dplyr, magrittr, tidyr, purrr, ggplot2, gplots, grDevices, Hmisc, flux, NMF, apcluster, kernlab, mclust, infotheo, blockcluster, caret, class, clue, cluster, clusterCrit, clValid, klaR, RColorBrewer, quantable, RankAggreg, sigclust, methods, Rcpp
LinkingTo: Rcpp
Suggests: testthat, knitr, rmarkdown, covr
Published: 2017-06-21
Author: Derek Chiu [aut, cre], Aline Talhouk [aut], Johnson Liu [ctb, com]
Maintainer: Derek Chiu <dchiu at bccrc.ca>
BugReports: https://github.com/AlineTalhouk/diceR/issues
License: MIT + file LICENSE
URL: https://github.com/AlineTalhouk/diceR
NeedsCompilation: yes
Materials: README
CRAN checks: diceR results


Reference manual: diceR.pdf
Vignettes: Cluster Analysis using 'diceR'
Package source: diceR_0.1.0.tar.gz
Windows binaries: r-devel: diceR_0.1.0.zip, r-release: diceR_0.1.0.zip, r-oldrel: diceR_0.1.0.zip
OS X El Capitan binaries: r-release: diceR_0.1.0.tgz
OS X Mavericks binaries: r-oldrel: diceR_0.1.0.tgz


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