# Multilevel Hidden Markov Models Using Bayesian Estimation

## Documentation for package ‘mHMMbayes’ version 1.1.1

- DESCRIPTION file
- User guides, package vignettes and other documentation
- Package NEWS
- Package README

## Help Pages

int_to_prob | Transforming a set of Multinomial logit regression intercepts to probabilities
mHMM | Multilevel hidden Markov model using Bayesian estimation
nonverbal | Nonverbal communication of patients and therapist
nonverbal_cov | Predictors of nonverbal communication
obtain_emiss | Obtain the emission distribution probabilities for a fitted multilevel HMM
obtain_gamma | Obtain the transition probabilities gamma for a fitted multilevel HMM
pd_RW_emiss_cat | Proposal distribution settings RW Metropolis sampler for mHMM categorical emission distribution(s)
pd_RW_emiss_count | Proposal distribution settings RW Metropolis sampler for mHMM Poisson-lognormal emission distribution(s)
pd_RW_gamma | Proposal distribution settings RW Metropolis sampler for mHMM transition probability matrix gamma
plot.mHMM | Plotting the posterior densities for a fitted multilevel HMM
plot.mHMM_gamma | Plotting the transition probabilities gamma for a fitted multilevel HMM
prior_emiss_cat | Specifying informative hyper-prior on the categorical emission distribution(s) of the multilevel hidden Markov model
prior_emiss_cont | Specifying informative hyper-prior on the continuous emission distribution(s) of the multilevel hidden Markov model
prior_emiss_count | Specifying informative hyper-priors on the count emission distribution(s) of the multilevel hidden Markov model
prior_gamma | Specifying informative hyper-prior on the transition probability matrix gamma of the multilevel hidden Markov model
prob_to_int | Transforming a set of probabilities to Multinomial logit regression intercepts
sim_mHMM | Simulate data using a multilevel hidden Markov model
var_to_logvar | Transform the between-subject variance in the positive scale to the logvariance in the logarithmic scale
vit_mHMM | Obtain hidden state sequence for each subject using the Viterbi algorithm