Learn R Programming

⚠️There's a newer version (3.0-9) of this package.Take me there.

hmm.discnp (version 3.0-7)

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.

Copy Link

Version

Install

install.packages('hmm.discnp')

Monthly Downloads

330

Version

3.0-7

License

GPL (>= 2)

Maintainer

Rolf Turner

Last Published

February 9th, 2022

Functions in hmm.discnp (3.0-7)

fitted.hmm.discnp

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

Internal hmm.discnp functions.
hydroDat

Canadian hydrological data sets.
ccprSim

Simulated monocyte counts and psychosis symptoms.
lesionCount

Multiple sclerosis lesion counts for three patients.
SydColDisc

Discretised version of coliform counts in sea-water samples
cnvrtRho

Convert Rho between forms.
logLikHmm

Log likelihood of a hidden Markov model
hmm

Fit a hidden Markov model to discrete data.
anova.hmm.discnp

Anova for hmm.discnp models
predict.hmm.discnp

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

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

Probability of state sequences.
nafracCalc

Calculate fractions of missing values.
viterbi

Most probable state sequence.
misstify

Insert missing values.
mps

Most probable states.
scovmat

Simulation based covariance matrix.
sp

Calculate the conditional state probabilities.