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.


Reference manual

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0.1.1 by Ben O'Neill, a year ago

Browse source code at

Authors: Ben O'Neill [aut, cre]

Documentation:   PDF Manual  

MIT + file LICENSE license

Imports graphics, grDevices

Suggests ggplot2, gridExtra, mvtnorm

See at CRAN