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depmixS4 (version 1.5-0)

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, ).

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Version

Install

install.packages('depmixS4')

Monthly Downloads

1,630

Version

1.5-0

License

GPL (>= 2)

Maintainer

Ingmar Visser

Last Published

May 12th, 2021

Functions in depmixS4 (1.5-0)

depmix-class

Class "depmix"
depmix-internal

DepmixS4 internal functions
depmix.fitted-class

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

Methods for creating depmix response models
depmixS4-package

depmixS4 provides classes for specifying and fitting hidden Markov models
fit

Fit 'depmix' or 'mix' models
depmix-methods

'depmix' and 'mix' methods.
depmix

Dependent Mixture Model Specifiction
balance

Balance Scale Data
depmix.sim-class

Class "depmix.sim"
mix.sim-class

Class "mix.sim"
formatperc

Format percentage for level in printing confidence interval
em.control

Control parameters for the EM algorithm
multistart

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

Log likelihood ratio test on two fitted models
sp500

Standard & Poor's 500 index
simulate

Methods to simulate from (dep-)mix models
forwardbackward

Forward and backward variables
response-classes

Class "GLMresponse" and class "transInit"
responses

Response models currently implemented in depmix.
mix-class

Class "mix"
viterbi

Viterbi algorithm for decoding the most likely state sequence
transInit

Methods for creating depmix transition and initial probability models
vcov

Parameter standard errors
makeDepmix

Dependent Mixture Model Specifiction: full control and adding response models
mix.fitted-class

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

Mixture Model Specifiction
speed

Speed Accuracy Switching Data
posterior

Posterior state/class probabilities and classification
response-class

Class "response"
stationary

Compute the stationary distribution of a transition probability matrix.