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FourWayHMM  

Parsimonious Hidden Markov Models for Four-Way Data
View on CRAN: Click here


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

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

Install by package version:
library("remotes")
install_version("FourWayHMM", "1.0.0")



Attach the package and use:
library("FourWayHMM")
Maintained by
Salvatore D. Tomarchio
[Scholar Profile | Author Map]
First Published: 2021-11-30
Latest Update: 2021-11-30
Description:
Implements parsimonious hidden Markov models for four-way data via expectation- conditional maximization algorithm, as described in Tomarchio et al. (2020) . The matrix-variate normal distribution is used as emission distribution. For each hidden state, parsimony is reached via the eigen-decomposition of the covariance matrices of the emission distribution. This produces a family of 98 parsimonious hidden Markov models.
How to cite:
Salvatore D. Tomarchio (2021). FourWayHMM: Parsimonious Hidden Markov Models for Four-Way Data. R package version 1.0.0, https://cran.r-project.org/web/packages/FourWayHMM. Accessed 03 May. 2025.
Previous versions and publish date:
No previous versions
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Complete documentation for FourWayHMM
Functions, R codes and Examples using the FourWayHMM R package
Some associated functions: HMM.fit . HMM.init . simX . 
Some associated R codes: HMM_fit.R . HMM_init.R . Misc.R . simX.R .  Full FourWayHMM package functions and examples
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