Other packages > Find by keyword >

mHMMbayes  

Multilevel Hidden Markov Models Using Bayesian Estimation
View on CRAN: Click here


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

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

Install by package version:
library("remotes")
install_version("mHMMbayes", "1.1.1")



Attach the package and use:
library("mHMMbayes")
Maintained by
Emmeke Aarts
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2019-10-25
Latest Update: 2025-07-11
Description:
An implementation of the multilevel (also known as mixed or random effects) hidden Markov model using Bayesian estimation in R. The multilevel hidden Markov model (HMM) is a generalization of the well-known hidden Markov model, for the latter see Rabiner (1989) . The multilevel HMM is tailored to accommodate (intense) longitudinal data of multiple individuals simultaneously, see e.g., de Haan-Rietdijk et al. . Using a multilevel framework, we allow for heterogeneity in the model parameters (transition probability matrix and conditional distribution), while estimating one overall HMM. The model can be fitted on multivariate data with either a categorical, normal, or Poisson distribution, and include individual level covariates (allowing for e.g., group comparisons on model parameters). Parameters are estimated using Bayesian estimation utilizing the forward-backward recursion within a hybrid Metropolis within Gibbs sampler. Missing data (NA) in the dependent variables is accommodated assuming MAR. The package also includes various visualization options, a function to simulate data, and a function to obtain the most likely hidden state sequence for each individual using the Viterbi algorithm.
How to cite:
Emmeke Aarts (2019). mHMMbayes: Multilevel Hidden Markov Models Using Bayesian Estimation. R package version 1.1.1, https://cran.r-project.org/web/packages/mHMMbayes. Accessed 21 Jul. 2026.
Previous versions and publish date:
(2026-07-09 06:27), 0.1.0 (2019-10-25 09:20), 0.1.1 (2019-10-30 12:30), 0.2.0 (2022-08-17 18:00), 1.0.0 (2023-10-02 17:10), 1.1.0 (2024-04-01 14:20)
Other packages that cited mHMMbayes R package
View mHMMbayes citation profile
Other R packages that mHMMbayes depends, imports, suggests or enhances
Complete documentation for mHMMbayes
Downloads during the last 30 days

Today's Hot Picks in Authors and Packages

beastier  
Call 'BEAST2'
'BEAST2' () is a widely used Bayesian phylogenetic tool, that uses DNA/RNA/ ...
Download / Learn more Package Citations See dependency  
OSTE  
Optimal Survival Trees Ensemble
Function for growing survival trees ensemble ('Naz Gul', 'Nosheen Faiz', 'Dan Brawn', 'Rafal Kulakow ...
Download / Learn more Package Citations See dependency  
quickcode  
Quick and Essential 'R' Tricks for Better Scripts
The NOT functions, 'R' tricks and a compilation of some simple quick plus often used 'R' codes to im ...
Download / Learn more Package Citations See dependency  
netgen  
Network Generator for Combinatorial Graph Problems
Methods for the generation of a wide range of network geographies, e.g., grid networks or clustered ...
Download / Learn more Package Citations See dependency  
splash  
Simple Process-Led Algorithms for Simulating Habitats
This program calculates bioclimatic indices and fluxes (radiation, evapotranspiration, soil moistur ...
Download / Learn more Package Citations See dependency  
truncnormbayes  
Estimates Moments for a Truncated Normal Distribution using 'Stan'
Finds the posterior modes for the mean and standard deviation for a truncated normal distribution wi ...
Download / Learn more Package Citations See dependency  

27,889

R Packages

239,283

Dependencies

74,019

Author Associations

27,890

Publication Badges

© Copyright since 2022. All right reserved, rpkg.net.  Based in Cambridge, Massachusetts, USA