HDclust: Clustering High Dimensional Data with Hidden Markov Model on Variable Blocks

Clustering of high dimensional data with Hidden Markov Model on Variable Blocks (HMM-VB) fitted via Baum-Welch algorithm. Clustering is performed by the Modal Baum-Welch algorithm (MBW), which finds modes of the density function. Lin Lin and Jia Li (2017) <http://jmlr.org/papers/v18/16-342.html>.

Version: 1.0
Depends: methods
Imports: Rcpp (≥ 0.12.16), RcppProgress (≥ 0.1)
LinkingTo: Rcpp, RcppProgress
Suggests: knitr, rmarkdown
Published: 2018-07-29
Author: Yevhen Tupikov [aut, cre], Lin Lin [aut], Jia Li [aut]
Maintainer: Yevhen Tupikov <yzt116 at psu.edu>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: yes
CRAN checks: HDclust results


Reference manual: HDclust.pdf
Vignettes: A quick tour of HDclust
Package source: HDclust_1.0.tar.gz
Windows binaries: r-devel: HDclust_1.0.zip, r-release: HDclust_1.0.zip, r-oldrel: HDclust_1.0.zip
OS X binaries: r-release: HDclust_1.0.tgz, r-oldrel: HDclust_1.0.tgz


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