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MatrixHMM  

Parsimonious Families of Hidden Markov Models for Matrix-Variate Longitudinal Data
View on CRAN: Click here


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

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

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



Attach the package and use:
library("MatrixHMM")
Maintained by
Salvatore D. Tomarchio
[Scholar Profile | Author Map]
First Published: 2024-08-28
Latest Update: 2024-08-28
Description:
Implements three families of parsimonious hidden Markov models (HMMs) for matrix-variate longitudinal data using the Expectation-Conditional Maximization (ECM) algorithm. The package supports matrix-variate normal, t, and contaminated normal distributions as emission distributions. For each hidden state, parsimony is achieved through the eigen-decomposition of the covariance matrices associated with the emission distribution. This approach results in a comprehensive set of 98 parsimonious HMMs for each type of emission distribution. Atypical matrix detection is also supported, utilizing the fitted (heavy-tailed) models.
How to cite:
Salvatore D. Tomarchio (2024). MatrixHMM: Parsimonious Families of Hidden Markov Models for Matrix-Variate Longitudinal Data. R package version 1.0.0, https://cran.r-project.org/web/packages/MatrixHMM. Accessed 27 Feb. 2025.
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