spBayes for large univariate and multivariate point-referenced spatio-temporal data models

In this paper we detail the reformulation and rewrite of core functions in the spbayes r package. these efforts have focused on improving computational efficiency,flexibility,and usability for point-referenced data models. attention is given to algorithm and computing developments that result in improved sampler convergence rate and efficiency by reducing parameter space; decreased sampler run-time by avoiding expensive matrix computations,and; increased scalability to large datasets by implementing a class of predictive process models that attempt to overcome computational hurdles by representing spatial processes in terms of lower-dimensional realizations. beyond these general computational improvements for existing model functions,we detail new functions for modeling data indexed in both space and time. these new functions implement a class of dynamic spatio-temporal models for settings where space is viewed as continuous and time is taken as discrete. © 2015,american statistical association. all rights reserved.