DBHC: Sequence Clustering with Discrete-Output HMMs

Provides an implementation of a mixture of hidden Markov models (HMMs) for discrete sequence data in the Discrete Bayesian HMM Clustering (DBHC) algorithm. The DBHC algorithm is an HMM Clustering algorithm that finds a mixture of discrete-output HMMs while using heuristics based on Bayesian Information Criterion (BIC) to search for the optimal number of HMM states and the optimal number of clusters.

Version: 0.0.3
Imports: seqHMM (≥ 1.0.8), TraMineR (≥ 2.0-7), reshape2 (≥ 1.2.1), ggplot2 (≥ 2.2.1), methods (≥ 4.2.2)
Suggests: testthat (≥ 3.0.0)
Published: 2022-12-22
DOI: 10.32614/CRAN.package.DBHC
Author: Gabriel Budel [aut, cre], Flavius Frasincar [aut]
Maintainer: Gabriel Budel <gabysp_budel at hotmail.com>
BugReports: https://github.com/gabybudel/DBHC/issues
License: GPL (≥ 3)
URL: https://github.com/gabybudel/DBHC
NeedsCompilation: no
Materials: README NEWS
CRAN checks: DBHC results


Reference manual: DBHC.pdf


Package source: DBHC_0.0.3.tar.gz
Windows binaries: r-devel: DBHC_0.0.3.zip, r-release: DBHC_0.0.3.zip, r-oldrel: DBHC_0.0.3.zip
macOS binaries: r-release (arm64): DBHC_0.0.3.tgz, r-oldrel (arm64): DBHC_0.0.3.tgz, r-release (x86_64): DBHC_0.0.3.tgz, r-oldrel (x86_64): DBHC_0.0.3.tgz
Old sources: DBHC archive


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