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: | 0.0.2 |
| Imports: | seqHMM (≥ 1.0.8), TraMineR (≥ 2.0-7), reshape2 (≥ 1.2.1), ggplot2 (≥ 2.2.1) |
| Published: | 2018-04-13 |
| 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_0.0.2.tar.gz |
| Windows binaries: | r-devel: DBHC_0.0.2.zip, r-release: DBHC_0.0.2.zip, r-oldrel: DBHC_0.0.2.zip |
| OS X binaries: | r-release: DBHC_0.0.2.tgz, r-oldrel: DBHC_0.0.2.tgz |
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