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Spatial data relating to non-overlapping areal units are prevalent in fields such as economics, environmental science, epidemiology and social science, and a large suite of modeling tools have been developed for analysing these data. Many utilize conditional autoregressive (CAR) priors to capture the spatial autocorrelation inherent in these data, and software packages such as CARBayes and R-INLA have been developed to make these models easily accessible to others. Such spatial data are typically available for multiple time periods, and the development of methodology for capturing temporally changing spatial dynamics is the focus of much current research. A sizeable proportion of this literature has focused on extending CAR priors to the spatio-temporal domain, and this article presents the R package CARBayesST, which is the first dedicated software package for spatio-temporal areal unit modeling with conditional autoregressive priors. The software package allows to fit a range of models focused on different aspects of spacetime modeling, including estimation of overall space and time trends, and the identification of clusters of areal units that exhibit elevated values. This paper outlines the class of models that the software package implement, before applying them to simulated and two real examples from the fields of epidemiology and housing market analysis.

Spatio-Temporal Areal Unit Modeling in R with Conditional Autoregressive Priors Using the CARBayesST Package