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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) <https://jmlr.org/papers/v18/16-342.html>.

Version: 1.0.4
Depends: methods
Imports: Rcpp (≥ 0.12.16), RcppProgress (≥ 0.1), Rtsne (≥ 0.11.0)
LinkingTo: Rcpp, RcppProgress
Suggests: knitr, rmarkdown
Published: 2024-09-20
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
Materials: NEWS
CRAN checks: HDclust results

Documentation:

Reference manual: HDclust.pdf
Vignettes: A quick tour of HDclust (source, R code)

Downloads:

Package source: HDclust_1.0.4.tar.gz
Windows binaries: r-devel: HDclust_1.0.4.zip, r-release: HDclust_1.0.4.zip, r-oldrel: HDclust_1.0.4.zip
macOS binaries: r-release (arm64): HDclust_1.0.4.tgz, r-oldrel (arm64): HDclust_1.0.4.tgz, r-release (x86_64): HDclust_1.0.4.tgz, r-oldrel (x86_64): HDclust_1.0.4.tgz
Old sources: HDclust archive

Reverse dependencies:

Reverse suggests: OTclust

Linking:

Please use the canonical form https://CRAN.R-project.org/package=HDclust to link to this page.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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