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hmm.discnp (version 3.0-9)

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

409

Version

3.0-9

License

GPL (>= 2)

Maintainer

Rolf Turner

Last Published

September 26th, 2022

Functions in hmm.discnp (3.0-9)

hydroDat

Canadian hydrological data sets.
ccprSim

Simulated monocyte counts and psychosis symptoms.
hmm.discnp-internal

Internal hmm.discnp functions.
cnvrtRho

Convert Rho between forms.
lesionCount

Multiple sclerosis lesion counts for three patients.
logLikHmm

Log likelihood of a hidden Markov model
SydColDisc

Discretised version of coliform counts in sea-water samples
fitted.hmm.discnp

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

Anova for hmm.discnp models
hmm

Fit a hidden Markov model to discrete data.
rhmm

Simulate discrete data from a non-parametric hidden Markov model.
update.hmm.discnp

Update a fitted hmm.discnp model.
nafracCalc

Calculate fractions of missing values.
pr

Probability of state sequences.
misstify

Insert missing values.
sp

Calculate the conditional state probabilities.
squantCI

Simulation-quantile based confidence intervals.
viterbi

Most probable state sequence.
scovmat

Simulation based covariance matrix.
predict.hmm.discnp

Predicted values of a discrete non-parametric hidden Markov model.
weissData

Data from “An Introduction to Discrete-Valued Time Series”
mps

Most probable states.