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GaussianHMM1d (version 1.1.2)

Inference, Goodness-of-Fit and Forecast for Univariate Gaussian Hidden Markov Models

Description

Inference, goodness-of-fit test, and prediction densities and intervals for univariate Gaussian Hidden Markov Models (HMM). The goodness-of-fit is based on a Cramer-von Mises statistic and uses parametric bootstrap to estimate the p-value. The description of the methodology is taken from Chapter 10.2 of Remillard (2013) .

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Version

Install

install.packages('GaussianHMM1d')

Monthly Downloads

175

Version

1.1.2

License

GPL (>= 2)

Maintainer

Bouchra Nasri

Last Published

February 5th, 2025

Functions in GaussianHMM1d (1.1.2)

bootstrapfun

Function to perform parametric bootstrap
Sim.HMM.Gaussian.1d

Simulation of a univariate Gaussian Hidden Markov Model (HMM)
ForecastHMMeta

Estimated probabilities of the regimes given new observations
EstHMM1d

Estimation of a univariate Gaussian Hidden Markov Model (HMM)
ForecastHMMPdf

Density function of a Gaussian HMM at time n+k
EstRegime

Estimated Regimes for the univariate Gaussian HMM
GaussianMixtureInv

Inverse distribution function of a mixture of Gaussian univariate distributions
Sim.Markov.Chain

Simulation of a finite Markov chain
GaussianMixturePdf

Density function of a mixture of Gaussian univariate distributions
GaussianMixtureCdf

Distribution function of a mixture of Gaussian univariate distributions
GofHMM1d

Goodness-of-fit test of a univariate Gaussian Hidden Markov Model
SimHMMGaussianInv

Simulation of a univariate Gaussian Hidden Markov Model (HMM)
Sn

Cramer-von Mises statistic for goodness-of-fit of the null hypothesis of a univariate uniform distrubtion over [0,1]