Published by the Foundation for Open Access Statistics
Editors-in-chief: Bettina Grün, Torsten Hothorn, Edzer Pebesma, Achim Zeileis    ISSN 1548-7660; CODEN JSSOBK
spBayes for Large Univariate and Multivariate Point-Referenced Spatio-Temporal Data Models | Finley | Journal of Statistical Software
Authors: Andrew O. Finley, Sudipto Banerjee, Alan E. Gelfand
Title: spBayes for Large Univariate and Multivariate Point-Referenced Spatio-Temporal Data 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.

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Paper: spBayes for Large Univariate and Multivariate Point-Referenced Spatio-Temporal Data Models     Download PDF (Downloads: 2357)
Supplements:
spBayes_0.3-9.tar.gz: R source package Download (Downloads: 192; 480KB)
v63i13.R: R example code from the paper Download (Downloads: 217; 14KB)

DOI: 10.18637/jss.v063.i13

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Paper: Creative Commons Attribution 3.0 Unported License
Code: GNU General Public License (at least one of version 2 or version 3) or a GPL-compatible license.