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DBHC  

Sequence Clustering with Discrete-Output HMMs
View on CRAN: Click here


Download and install DBHC package within the R console
Install from CRAN:
install.packages("DBHC")

Install from Github:
library("remotes")
install_github("cran/DBHC")

Install by package version:
library("remotes")
install_version("DBHC", "0.0.3")



Attach the package and use:
library("DBHC")
Maintained by
Gabriel Budel
[Scholar Profile | Author Map]
First Published: 2018-04-13
Latest Update: 2022-12-22
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.
How to cite:
Gabriel Budel (2018). DBHC: Sequence Clustering with Discrete-Output HMMs. R package version 0.0.3, https://cran.r-project.org/web/packages/DBHC. Accessed 06 May. 2025.
Previous versions and publish date:
0.0.2 (2018-04-13 13:09)
Other packages that cited DBHC R package
View DBHC citation profile
Other R packages that DBHC depends, imports, suggests or enhances
Complete documentation for DBHC
Functions, R codes and Examples using the DBHC R package
Some associated functions: assign.clusters . cluster.bic . count.parameters . emission.heatmap . hmm.clust . model.ll . partition.bic . select.seeds . seq2hmm.ll . size.search . smooth.hmm . smooth.probabilities . transition.heatmap . 
Some associated R codes: DBHC.R . hmmclustering.R . plotfunctions.R .  Full DBHC package functions and examples
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