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

Andrew O. Finley, Sudipto Banerjee and Alan E. Gelfand

Journal of Statistical Software, 2015, vol. 063, issue i13

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.

Date: 2015-02-13
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Persistent link: https://EconPapers.repec.org/RePEc:jss:jstsof:v:063:i13

DOI: 10.18637/jss.v063.i13

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