Package: DBHC 0.0.3

Gabriel Budel

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.

Authors:Gabriel Budel [aut, cre], Flavius Frasincar [aut]

DBHC_0.0.3.tar.gz
DBHC_0.0.3.zip(r-4.5)DBHC_0.0.3.zip(r-4.4)DBHC_0.0.3.zip(r-4.3)
DBHC_0.0.3.tgz(r-4.4-any)DBHC_0.0.3.tgz(r-4.3-any)
DBHC_0.0.3.tar.gz(r-4.5-noble)DBHC_0.0.3.tar.gz(r-4.4-noble)
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DBHC.pdf |DBHC.html
DBHC/json (API)
NEWS

# Install 'DBHC' in R:
install.packages('DBHC', repos = c('https://gabybudel.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/gabybudel/dbhc/issues

On CRAN:

2.70 score 1 stars 3 scripts 176 downloads 13 exports 45 dependencies

Last updated 2 years agofrom:b071dee901. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKOct 09 2024
R-4.5-winOKOct 09 2024
R-4.5-linuxOKOct 09 2024
R-4.4-winOKOct 09 2024
R-4.4-macOKOct 09 2024
R-4.3-winOKOct 09 2024
R-4.3-macOKOct 09 2024

Exports:assign.clusterscluster.biccount.parametersemission.heatmaphmm.clustmodel.llpartition.bicselect.seedsseq2hmm.llsize.searchsmooth.hmmsmooth.probabilitiestransition.heatmap

Dependencies:bootcliclustercolorspacecpp11fansifarverggplot2gluegridBasegtableigraphisobandlabelinglatticelifecyclemagrittrMASSMatrixmgcvmunsellnlmenloptrnumDerivpermutepillarpkgconfigplyrR6RColorBrewerRcppRcppArmadilloreshape2rlangscalesseqHMMstringistringrtibbleTraMineRutf8vctrsveganviridisLitewithr