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GaussianHMM1d
View on CRAN: Click
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Download and install GaussianHMM1d package within the R console
Install from CRAN:
install.packages("GaussianHMM1d")
Install from Github:
library("remotes")
install_github("cran/GaussianHMM1d")
Install by package version:
library("remotes")
install_version("GaussianHMM1d", "1.1.1")
Attach the package and use:
library("GaussianHMM1d")
Maintained by
Bouchra R. Nasri
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2019-03-07
Latest Update: 2023-07-08
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) .
How to cite:
Bouchra R. Nasri (2019). GaussianHMM1d: Inference, Goodness-of-Fit and Forecast for Univariate Gaussian Hidden Markov Models. R package version 1.1.1, https://cran.r-project.org/web/packages/GaussianHMM1d. Accessed 21 Nov. 2024.
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Complete documentation for GaussianHMM1d
Functions, R codes and Examples using
the GaussianHMM1d R package
Some associated functions: EstHMM1d . EstRegime . ForecastHMMPdf . ForecastHMMeta . GaussianMixtureCdf . GaussianMixtureInv . GaussianMixturePdf . GofHMM1d . Sim.HMM.Gaussian.1d . Sim.Markov.Chain . Sn . bootstrapfun . em.step .
Some associated R codes: EstHMM1d.R . EstRegime.R . ForecastHMMPdf.R . ForecastHMMeta.R . GaussianMixtureCdf.R . GaussianMixtureInv.R . GaussianMixturePdf.R . GofHMM1d.R . Sim.HMM.Gaussian.1d.R . Sim.Markov.Chain.R . Sn.R . bootstrapfun.R . em.step.R . Full GaussianHMM1d package functions and examples
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