Package: GaussianHMM1d 1.1.1
GaussianHMM1d: Inference, Goodness-of-Fit and Forecast for Univariate Gaussian Hidden Markov Models
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) <doi:10.1201/b14285>.
Authors:
GaussianHMM1d_1.1.1.tar.gz
GaussianHMM1d_1.1.1.zip(r-4.5)GaussianHMM1d_1.1.1.zip(r-4.4)GaussianHMM1d_1.1.1.zip(r-4.3)
GaussianHMM1d_1.1.1.tgz(r-4.4-x86_64)GaussianHMM1d_1.1.1.tgz(r-4.4-arm64)GaussianHMM1d_1.1.1.tgz(r-4.3-x86_64)GaussianHMM1d_1.1.1.tgz(r-4.3-arm64)
GaussianHMM1d_1.1.1.tar.gz(r-4.5-noble)GaussianHMM1d_1.1.1.tar.gz(r-4.4-noble)
GaussianHMM1d_1.1.1.tgz(r-4.4-emscripten)GaussianHMM1d_1.1.1.tgz(r-4.3-emscripten)
GaussianHMM1d.pdf |GaussianHMM1d.html✨
GaussianHMM1d/json (API)
| # Install 'GaussianHMM1d' in R: |
| install.packages('GaussianHMM1d', repos = c('https://bouchranasri.r-universe.dev', 'https://cloud.r-project.org')) |
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 1 years agofrom:91d43c8da6. Checks:OK: 4 NOTE: 5. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Nov 02 2024 |
| R-4.5-win-x86_64 | NOTE | Nov 02 2024 |
| R-4.5-linux-x86_64 | NOTE | Nov 02 2024 |
| R-4.4-win-x86_64 | NOTE | Nov 02 2024 |
| R-4.4-mac-x86_64 | NOTE | Nov 02 2024 |
| R-4.4-mac-aarch64 | NOTE | Nov 02 2024 |
| R-4.3-win-x86_64 | OK | Nov 02 2024 |
| R-4.3-mac-x86_64 | OK | Nov 02 2024 |
| R-4.3-mac-aarch64 | OK | Nov 02 2024 |
Exports:bootstrapfunEstHMM1dEstRegimeForecastHMMetaForecastHMMPdfGaussianMixtureCdfGaussianMixtureInvGaussianMixturePdfGofHMM1dSim.HMM.Gaussian.1dSim.Markov.ChainSimHMMGaussianInvSn
Dependencies:codetoolsdoParallelforeachiterators
