  We exploit Gaussian copulas to specify a class of multivariate circular
distributions and obtain parametric models for the analysis of correlated
circular data. This approach provides a straightforward extension of
traditional multivariate normal models to the circular setting, without
imposing restrictions on the marginal data distribution nor requiring
overwhelming routines for parameter estimation. The proposal is illustrated on
two case studies of animal orientation and sea currents, where we propose an
autoregressive model for circular time series and a geostatistical model for
circular spatial series.
