Stationary Gaussian ARMA processes and the stationary 'GARMA' distribution are fundamental in time series analysis. Here we give utilities to compute the auto-covariance/auto-correlation for a stationary Gaussian ARMA process, as well as the probability functions (density, cumulative distribution, random generation) for random vectors from this distribution. We also give functions for the spectral intensity, and the permutation-spectrum test for testing a time-series vector for the presence of a signal.
| Version: | 0.1.1 |
| Imports: | graphics, grDevices |
| Suggests: | ggplot2, gridExtra, mvtnorm |
| Published: | 2020-11-14 |
| Author: | Ben O'Neill [aut, cre] |
| Maintainer: | Ben O'Neill <ben.oneill at hotmail.com> |
| License: | MIT + file LICENSE |
| URL: | https://github.com/ben-oneill/ts.extend |
| NeedsCompilation: | no |
| CRAN checks: | ts.extend results |
| Reference manual: | ts.extend.pdf |
| Package source: | ts.extend_0.1.1.tar.gz |
| Windows binaries: | r-devel: ts.extend_0.1.1.zip, r-release: ts.extend_0.1.1.zip, r-oldrel: ts.extend_0.1.1.zip |
| macOS binaries: | r-release: ts.extend_0.1.1.tgz, r-oldrel: ts.extend_0.1.1.tgz |
| Old sources: | ts.extend archive |
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