Published by the Foundation for Open Access Statistics Editors-in-chief: Bettina Grün, Torsten Hothorn, Rebecca Killick, Edzer Pebesma, Achim Zeileis    ISSN 1548-7660; CODEN JSSOBK
Authors: Duncan Lee
Title: CARBayes: An R Package for Bayesian Spatial Modeling with Conditional Autoregressive Priors
Abstract: Conditional autoregressive models are commonly used to represent spatial autocorrelation in data relating to a set of non-overlapping areal units, which arise in a wide variety of applications including agriculture, education, epidemiology and image analysis. Such models are typically specified in a hierarchical Bayesian framework, with inference based on Markov chain Monte Carlo (MCMC) simulation. The most widely used software to fit such models is WinBUGS or OpenBUGS, but in this paper we introduce the R package CARBayes. The main advantage of CARBayes compared with the BUGS software is its ease of use, because: (1) the spatial adjacency information is easy to specify as a binary neighbourhood matrix; and (2) given the neighbourhood matrix the models can be implemented by a single function call in R. This paper outlines the general class of Bayesian hierarchical models that can be implemented in the CARBayes software, describes their implementation via MCMC simulation techniques, and illustrates their use with two worked examples in the fields of house price analysis and disease mapping.

Page views:: 14825. Submitted: 2012-09-24. Published: 2013-11-20.
Paper: CARBayes: An R Package for Bayesian Spatial Modeling with Conditional Autoregressive Priors     Download PDF (Downloads: 19773)
CARBayes_1.6.tar.gz: R source package Download (Downloads: 712; 4MB)
v55i13.R: R example code from the paper Download (Downloads: 955; 5KB) Replication code for BUGS models Download (Downloads: 550; 12KB)

DOI: 10.18637/jss.v055.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.