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ts.extend (version 0.1.1)

Stationary Gaussian ARMA Processes and Other Time-Series Utilities

Description

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.

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install.packages('ts.extend')

Monthly Downloads

10

Version

0.1.1

License

MIT + file LICENSE

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Maintainer

Ben O'Neill

Last Published

November 14th, 2020

Functions in ts.extend (0.1.1)

spectrum.test

Permutation-spectrum test for time-series data
plot.spectrum.test

Plot of the Permutation-Spectrum Test
plot.intensity

Plot scatterplot matrix of intensity vectors
rGARMA

Generate random vectors from the stationary GARMA distribution
plot.time.series

Plot scatterplot matrix of time-series vectors
pGARMA

Cumulative distribution function for the stationary GARMA distribution
intensity

Compute the spectral intensity of a time-series vector/matrix
dGARMA

Density function for the stationary GARMA distribution
garma

Simulated example data set
ARMA.var

Covariance/correlation matrix for the stationary ARMA model
ARMA.autocov

Auto-covariance/auto-correlation function for the stationary ARMA model