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: 1.0.3
Depends: methods
Imports: Rcpp (≥ 0.12.16), RcppProgress (≥ 0.1), Rtsne (≥ 0.11.0)
LinkingTo: Rcpp, RcppProgress
Suggests: knitr, rmarkdown
Published: 2019-04-11
DOI: 10.32614/CRAN.package.HDclust
Author: Yevhen Tupikov [aut], Lin Lin [aut], Lixiang Zhang [aut], Jia Li [aut, cre]
Maintainer: Jia Li <jiali 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_1.0.3.tar.gz
Windows binaries: r-devel: HDclust_1.0.3.zip, r-release: HDclust_1.0.3.zip, r-oldrel: HDclust_1.0.3.zip
macOS binaries: r-release (arm64): HDclust_1.0.3.tgz, r-oldrel (arm64): HDclust_1.0.3.tgz, r-release (x86_64): HDclust_1.0.3.tgz, r-oldrel (x86_64): HDclust_1.0.3.tgz
Old sources: HDclust archive

Reverse dependencies:

Reverse suggests: OTclust


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