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 analyse 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, by Jona Lasinio, Mastrantonio, Wang, and Gelfand. 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.
CircSpaceTime: an R package for spatial and spatio-temporal modelling of circular data / Jona Lasinio, Giovanna; Santoro, Mario; Mastrantonio, Gianluca. - In: JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION. - ISSN 0094-9655. - 90:7(2020), pp. 1315-1345.
|Titolo:||CircSpaceTime: an R package for spatial and spatio-temporal modelling of circular data|
|Data di pubblicazione:||2020|
|Digital Object Identifier (DOI):||http://dx.doi.org/10.1080/00949655.2020.1725008|
|Appare nelle tipologie:||1.1 Articolo in rivista|
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