  CircSpaceTime is the only R package currently available that implements
Bayesian models for spatial and spatio-temporal interpolation of circular data.
Such data are often found in applications where, among the many, wind
directions, animal movement directions, and wave directions are involved. To
analyze such data we need models for observations at locations s and times t,
as the so-called geostatistical models, providing structured dependence assumed
to decay in distance and time. The approach we take begins with Gaussian
processes defined for linear variables over space and time. Then, we use either
wrapping or projection to obtain processes for circular data. The models are
cast as hierarchical, with fitting and inference within a Bayesian framework.
Altogether, this package implements work developed by a series of papers; the
most relevant being Jona Lasinio, Gelfand, and Jona Lasinio (2012); Wang and
Gelfand (2014); Mastrantonio, Jona Lasinio, and Gelfand (2016). All procedures
are written using Rcpp. Estimates are obtained by MCMC allowing parallelized
multiple chains run. The implementation of the proposed models is considerably
improved on the simple routines adopted in the research papers. As original
running examples, for the spatial and spatio-temporal settings, we use wind
directions datasets over central Italy.
