TY - JOUR
AU - Lee, Duncan
PY - 2013/11/20
Y2 - 2022/05/26
TI - CARBayes: An R Package for Bayesian Spatial Modeling with Conditional Autoregressive Priors
JF - Journal of Statistical Software
JA - J. Stat. Soft.
VL - 55
IS - 13
SE - Articles
DO - 10.18637/jss.v055.i13
UR - https://www.jstatsoft.org/index.php/jss/article/view/v055i13
SP - 1 - 24
AB - 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 <b>WinBUGS</b> or <b>OpenBUGS</b>, but in this paper we introduce the R package <b>CARBayes</b>. The main advantage of <b>CARBayes</b> 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 <b>CARBayes</b> 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.
ER -