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hmm.discnp (version 2.1-5)

Hidden Markov Models with Discrete Non-Parametric Observation Distributions

Description

Fits hidden Markov models with discrete non-parametric observation distributions to data sets. The observations may be univariate or bivariate. 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. Auxiliary predictors are accommodated in the univariate setting.

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Version

Install

install.packages('hmm.discnp')

Monthly Downloads

328

Version

2.1-5

License

GPL (>= 2)

Maintainer

Rolf Turner

Last Published

November 26th, 2018

Functions in hmm.discnp (2.1-5)

ccprSim

Simulated monocyte counts and psychosis symptoms.
SydColDisc

Discretised version of coliform counts in sea-water samples
lesionCount

Multiple sclerosis lesion counts for three patients.
predict.hmm.discnp

Predicted values of a discrete non-parametric hidden Markov model.
anova.hmm.discnp

Anova for hmm.discnp models
logLikHmm

Log likelihood of a hidden Markov model
scovmat

Simulation based covariance matrix.
misstify

Insert missing values.
mps

Most probable states.
rhmm

Simulate discrete data from a non-parametric hidden Markov model.
sp

Calculate the conditional state probabilities.
viterbi

Most probable state sequence.
hmm.discnp-internal

Internal hmm.discnp functions.
hydroDat

Canadian hydrological data sets.
squantCI

Simulation-quantile based confidence intervals.
weissData

Data from “An Introduction to Discrete-Valued Time Series”
update.hmm.discnp

Update a fitted hmm.discnp model.
fitted.hmm.discnp

Fitted values of a discrete non-parametric hidden Markov model.
hmm

Fit a hidden Markov model to discrete data.
nafracCalc

Calculate fractions of missing values.
pr

Probability of state sequences.
cnvrtRho

Convert Rho between forms.