hmm.discnp v0.2-4

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Hidden Markov Models with Discrete Non-Parametric Observation Distributions

Fits hidden Markov models with discrete non-parametric observation distributions to data sets. Simulates data from such models. Finds most probable underlying hidden states, the most probable sequences of such states, and the log likelihood of a collection of observations given the parameters of the model.

Functions in hmm.discnp

Name Description
colifCount Coliform counts in sea-water samples
pr Probability of state sequences.
sim.hmm Simulate discrete data from a hidden Markov model.
viterbi Most probable state sequence.
mps Most probable states.
fitted.hmm.discnp Fitted values of a discrete non-parametric hidden Markov model.
hmm.discnp-internal Internal hmm.discnp functions.
logLikHmm Log likelihood of a hidden Markov model
sp Calculate the conditional state probabilities.
hmm Fit a hidden Markov model to discrete data.
lesionCount Multiple sclerosis lesion counts for three patients.
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Details

Date 2016-04-08
LazyData true
License GPL (>= 2)
NeedsCompilation yes
Packaged 2016-04-08 08:59:22 UTC; rolf
Repository CRAN
Date/Publication 2016-04-08 11:33:08

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