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

TL;DR

This paper enhances the spBayes R package for large-scale univariate and multivariate spatio-temporal data, focusing on computational efficiency, scalability, and new dynamic modeling capabilities.

Contribution

It introduces computational improvements, new functions for spatio-temporal modeling, and scalable methods for large datasets in the spBayes package.

Findings

Improved sampler convergence and efficiency.

Reduced runtime by avoiding expensive matrix operations.

Implemented scalable dynamic spatio-temporal models.

Abstract

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.