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:
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)
DBHC_0.0.3.tgz(r-4.4-emscripten)DBHC_0.0.3.tgz(r-4.3-emscripten)
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')) |
Bug tracker:https://github.com/gabybudel/dbhc/issues
Last updated 2 years agofrom:b071dee901. Checks:OK: 7. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Oct 09 2024 |
R-4.5-win | OK | Oct 09 2024 |
R-4.5-linux | OK | Oct 09 2024 |
R-4.4-win | OK | Oct 09 2024 |
R-4.4-mac | OK | Oct 09 2024 |
R-4.3-win | OK | Oct 09 2024 |
R-4.3-mac | OK | Oct 09 2024 |
Exports:assign.clusterscluster.biccount.parametersemission.heatmaphmm.clustmodel.llpartition.bicselect.seedsseq2hmm.llsize.searchsmooth.hmmsmooth.probabilitiestransition.heatmap
Dependencies:bootcliclustercolorspacecpp11fansifarverggplot2gluegridBasegtableigraphisobandlabelinglatticelifecyclemagrittrMASSMatrixmgcvmunsellnlmenloptrnumDerivpermutepillarpkgconfigplyrR6RColorBrewerRcppRcppArmadilloreshape2rlangscalesseqHMMstringistringrtibbleTraMineRutf8vctrsveganviridisLitewithr
Readme and manuals
Help Manual
Help page | Topics |
---|---|
Cluster Assignment | assign.clusters |
HMM BIC | cluster.bic |
Count HMM Parameters | count.parameters |
Heatmap Emission Probabilities | emission.heatmap |
DBHC Algorithm | hmm.clust |
Get HMM Log Likelihood | model.ll |
Partition BIC | partition.bic |
Seed Selection Procedure | select.seeds |
Sequence-to-HMM Likelihood | seq2hmm.ll |
Size Search Algorithm | size.search |
Smooth HMM Parameters | smooth.hmm |
Smooth Probabilities | smooth.probabilities |
Heatmap Transition Probabilities | transition.heatmap |