@article{JSSv055i13,
title={CARBayes: An R Package for Bayesian Spatial Modeling with Conditional Autoregressive Priors},
volume={55},
url={https://www.jstatsoft.org/index.php/jss/article/view/v055i13},
doi={10.18637/jss.v055.i13},
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 <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.},
number={13},
journal={Journal of Statistical Software},
author={Lee, Duncan},
year={2013},
pages={1–24}
}