Description: Runs a minimum-hypergeometric (mHG) test as described in: Eden, E. (2007). Discovering Motifs in Ranked Lists of DNA Sequences. Haifa.

Package: mHMMbayes
Type: Package
Title: Multilevel Hidden Markov Models Using Bayesian Estimation
Version: 0.1.0
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: 2019-10-21 12:35:52 UTC; 0446564
Author: Emmeke Aarts [aut, cre]
Repository: CRAN
Date/Publication: 2019-10-25 07:20:02 UTC