Dependent Mixture Models - Hidden Markov Models of GLMs and Other Distributions in S4
Methods to simulate from (dep-)mix models
Compute the stationary distribution of a transition probability matrix...
Methods for creating depmix transition and initial probability models
Parameter standard errors
Class "depmix"
DepmixS4 internal functions
'depmix' and 'mix' methods.
Fit 'depmix' or 'mix' models
Class "depmix.fitted" (and "depmix.fitted.classLik")
Dependent Mixture Model Specifiction
Class "depmix.sim"
depmixS4 provides classes for specifying and fitting hidden Markov mod...
Control parameters for the EM algorithm
Format percentage for level in printing confidence interval
Forward and backward variables
Methods for creating depmix response models
Log likelihood ratio test on two fitted models
Dependent Mixture Model Specifiction: full control and adding response...
Class "mix"
Class "mix.fitted" (and "mix.fitted.classLik")
Mixture Model Specifiction
Class "mix.sim"
Methods to fit a (dep-)mix model using multiple sets of starting value...
Posterior state/class probabilities and classification
Class "response"
Class "GLMresponse" and class "transInit"
Response models currently implemented in depmix.
Viterbi algorithm for decoding the most likely state sequence
Fits latent (hidden) Markov models on mixed categorical and continuous (time series) data, otherwise known as dependent mixture models, see Visser & Speekenbrink (2010, <DOI:10.18637/jss.v036.i07>).