ts.extend: Stationary Gaussian ARMA Processes and Other Time-Series Utilities

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

Documentation:

Reference manual:

ts.extend.pdf

Downloads:

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 (arm64): ts.extend_0.1.1.tgz, r-oldrel (arm64): ts.extend_0.1.1.tgz, r-release (x86_64): ts.extend_0.1.1.tgz, r-oldrel (x86_64): ts.extend_0.1.1.tgz
Old sources:	ts.extend archive

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