Implements a class of univariate and multivariate spatial generalised linear mixed models for areal unit data, with inference in a Bayesian setting using Markov chain Monte Carlo (MCMC) simulation using a single or multiple Markov chains. The response variable can be binomial, Gaussian, multinomial, Poisson or zero-inflated Poisson (ZIP), and spatial autocorrelation is modelled by a set of random effects that are assigned a conditional autoregressive (CAR) prior distribution. A number of different models are available for univariate spatial data, including models with no random effects as well as random effects modelled by different types of CAR prior, including the BYM model (Besag et al., 1991, <doi:10.1007/BF00116466>) and Leroux model (Leroux et al., 2000, <doi:10.1007/978-1-4612-1284-3_4>). Additionally,  a multivariate CAR (MCAR) model for multivariate spatial data is available, as is a two-level hierarchical model for modelling data relating to individuals within areas. Full details are given in the vignette accompanying this package. The initial creation of this package was supported by the Economic and Social Research Council (ESRC) grant RES-000-22-4256, and on-going development has been supported by the Engineering and Physical Science Research Council (EPSRC) grant EP/J017442/1, ESRC grant ES/K006460/1, Innovate UK / Natural Environment Research Council (NERC) grant NE/N007352/1 and the TB Alliance.

Spatio-temporal data from Scotland used in the vignettes accompanying the CARBayes (spatial modelling) and CARBayesST (spatio-temporal modelling) packages. Most of the data relate to the set of 271 Intermediate Zones (IZ)  that make up the 2001 definition of the  Greater Glasgow and Clyde health board.

Implements a class of univariate and multivariate spatio-temporal generalised linear mixed models for areal unit data, with inference in a Bayesian setting using Markov chain Monte Carlo (MCMC) simulation. The response variable can be binomial, Gaussian, or Poisson, but for some models only the binomial and Poisson data likelihoods are available. The spatio-temporal autocorrelation is modelled by  random effects, which are assigned conditional autoregressive (CAR) style prior distributions. A number of different random effects structures are available, including models similar to Rushworth et al. (2014) <doi:10.1016/j.sste.2014.05.001>. Full details are given in the vignette accompanying this package. The creation and development of this package was supported by the Engineering and Physical Sciences Research Council  (EPSRC) grants EP/J017442/1 and EP/T004878/1 and the Medical Research Council (MRC) grant MR/L022184/1.