  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.
