spBayes for Large Univariate and Multivariate Point-Referenced Spatio-Temporal Data Models

• Main Authors: Andrew O. Finley, Sudipto Banerjee, Alan E. Gelfand
• Format: Article
• Language: English
• Published: Foundation for Open Access Statistics 2015-02-01
• Series: Journal of Statistical Software
• Online Access: http://www.jstatsoft.org/index.php/jss/article/view/2228

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.