Gaussian ARMA models in the ts.extend package
Abstract
This paper introduces and describes the R package ts.extend, which adds probability functions for stationary Gaussian ARMA models and some related utility functions for timeseries. We show how to use the package to compute the density and distributions functions for models in this class, and generate random vectors from this model. The package allows the user to use marginal or conditional models using a simple syntax for conditioning variables and marginalised elements. This allows users to simulate timeseries vectors from any stationary Gaussian ARMA model, even if some elements are conditional values or omitted values. We also show how to use the package to compute the spectral intensity of a timeseries vector and implement the permutationspectrum test for a timeseries vector to detect the presence of a periodic signal.
 Publication:

arXiv eprints
 Pub Date:
 September 2021
 arXiv:
 arXiv:2109.12416
 Bibcode:
 2021arXiv210912416O
 Keywords:

 Statistics  Computation