Package: mHMMbayes
Type: Package
Title: Multilevel Hidden Markov Models Using Bayesian Estimation
Version: 0.1.1
Depends: R (>= 3.5.0)
Imports: MCMCpack, mvtnorm, stats, Rdpack
Authors@R: person("Emmeke", "Aarts", email = "e.aarts@uu.nl",
                  role = c("aut", "cre"))
Maintainer: Emmeke Aarts <e.aarts@uu.nl>
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) <doi:10.1109/5.18626>. The 
    multilevel HMM is tailored to accommodate (intense) longitudinal data of 
    multiple individuals simultaneously, see e.g., de Haan-Rietdijk et al. 
    <doi:10.1080/00273171.2017.1370364>. 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 a categorical 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. 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.
URL: https://github.com/emmekeaarts/mHMMbayes
BugReports: https://github.com/emmekeaarts/mHMMbayes/issues
License: GPL-3
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.1.1
Suggests: knitr, rmarkdown, alluvial, grDevices, RColorBrewer, testthat
        (>= 2.1.0)
VignetteBuilder: knitr
RdMacros: Rdpack
NeedsCompilation: no
Packaged: 2019-10-30 10:53:19 UTC; emmeke
Author: Emmeke Aarts [aut, cre]
Repository: CRAN
Date/Publication: 2019-10-30 11:30:08 UTC
