garma

SERIES

SERIES1

<p>A simulated set of 16 time series over 30 time points.</p>

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.

Ben O'Neill

garma: Simulated example data set

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