Hidden Markov Models


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Documentation for package ‘HiddenMarkov’ version 1.2-7

User Guides and Package Vignettes

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Help Pages

backward Forward and Backward Probabilities
BaumWelch Estimate Parameters Using Baum-Welch Algorithm
bwcontrol Control Parameters for the Baum Welch Algorithm
Changes Changes Made to Package HiddenMarkov
compdelta Compute Marginal Distribution of Stationary Markov Chain
Demonstration Demonstration Examples
dthmm Discrete Time HMM Object
Estep E Step of EM Algorithm
Estep.mmpp Markov Modulated Poisson Process - 2nd Level Functions
forward Forward and Backward Probabilities
forwardback Forward and Backward Probabilities
forwardback.mmpp Markov Modulated Poisson Process - 2nd Level Functions
HiddenMarkov Overview of Package HiddenMarkov
logLik Log Likelihood of Hidden Markov Model
mchain Markov Chain Object
mmglm Markov Modulated GLM Object
mmpp Markov Modulated Poisson Process Object
mmpp-2nd-level-functions Markov Modulated Poisson Process - 2nd Level Functions
Mstep M Step of EM Algorithm
neglogLik Negative Log-Likelihood
Pi2vector Transform Transition or Rate Matrices to Vector
probhmm Conditional Distribution Function
Q2vector Transform Transition or Rate Matrices to Vector
residuals Residuals of Hidden Markov Models
simulate Simulate Various HMM Processes
summary Summary Methods for Hidden Markov Model Objects
Transform.Parameters Transform Transition or Rate Matrices to Vector
vector2Pi Transform Transition or Rate Matrices to Vector
vector2Q Transform Transition or Rate Matrices to Vector
Viterbi Viterbi Algorithm for Hidden Markov Model Objects