Learn R Programming

depmixS4 (version 1.5-4)

Dependent Mixture Models - Hidden Markov Models of GLMs and Other Distributions in S4

Description

Fits latent (hidden) Markov models on mixed categorical and continuous (time series) data, otherwise known as dependent mixture models, see Visser & Speekenbrink (2010, ).

Copy Link

Version

Install

install.packages('depmixS4')

Monthly Downloads

717

Version

1.5-4

License

GPL (>= 2)

Maintainer

Ingmar Visser

Last Published

August 24th, 2026

Functions in depmixS4 (1.5-4)

Format percentage for level in printing confidence interval

mix.fitted-class

Class "mix.fitted" (and "mix.fitted.classLik")

Control parameters for the EM algorithm

Class "mix.sim"

Mixture Model Specifiction

Log likelihood ratio test on two fitted models

Speed Accuracy Switching Data

Compute the stationary distribution of a transition probability matrix.

Methods for creating depmix transition and initial probability models

Methods to simulate from (dep-)mix models

Posterior state/class probabilities and classification

Standard & Poor's 500 index

response-classes

Class "GLMresponse" and class "transInit"

Response models currently implemented in depmix.

Parameter standard errors

Viterbi algorithm for decoding the most likely state sequence

Class "response"

depmix-internal

DepmixS4 internal functions

forwardbackward

Forward and backward variables

depmix.sim-class

Class "depmix.sim"

Methods for creating depmix response models

Fit 'depmix' or 'mix' models

Dependent Mixture Model Specifiction: full control and adding response models

Balance Scale Data

'depmix' and 'mix' methods.

depmixS4-package

depmixS4 provides classes for specifying and fitting hidden Markov models

depmix.fitted-class

Class "depmix.fitted" (and "depmix.fitted.classLik")

Methods to fit a (dep-)mix model using multiple sets of starting values

Dependent Mixture Model Specifiction