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.

Total

1,904

Last month

279

Last week

61

Average per day

9

Daily downloads

Total downloads

Description file content

Package
DBHC
Type
Package
Title
Sequence Clustering with Discrete-Output HMMs
Version
0.0.2
Date
2018-04-10
Author
Gabriel Budel [aut, cre], Flavius Frasincar [aut]
Maintainer
Gabriel Budel
Description
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.
License
GPL (>= 3)
Encoding
UTF-8
URL
BugReports
https://github.com/gabybudel/DBHC/issues
LazyData
true
Imports
seqHMM (>= 1.0.8), TraMineR (>= 2.0-7), reshape2 (>= 1.2.1), ggplot2 (>= 2.2.1)
NeedsCompilation
no
Repository
CRAN
RoxygenNote
6.0.1
Packaged
2018-04-13 08:55:24 UTC; gabys
Date/Publication
2018-04-13 11:09:20 UTC

install.packages('DBHC')

0.0.2

7 months ago

https://github.com/gabybudel/DBHC

Gabriel Budel

GPL (>= 3)

Imports

seqHMM (>= 1.0.8), TraMineR (>= 2.0-7), reshape2 (>= 1.2.1), ggplot2 (>= 2.2.1)

Discussions