R package CARBayes: Spatial Generalised Linear Mixed Models for Areal Unit Data. 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. The response variable can be binomial, Gaussian or Poisson. Spatial autocorrelation is modelled by a set of random effects, which are assigned a conditional autoregressive (CAR) prior distribution. A number of different CAR priors are available for the random effects, including a multivariate CAR (MCAR) model for multivariate spatial data. 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 was supported by the Engineering and Physical Science Research Council (EPSRC) grant EP/J017442/1.
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References in zbMATH (referenced in 4 articles , 1 standard article )
Showing results 1 to 4 of 4.
- Cowles, Mary Kathryn; Bonett, Stephen; Seedorff, Michael: Independent sampling for Bayesian normal conditional autoregressive models with OpenCL acceleration (2018)
- Terrance Savitsky: Bayesian Nonparametric Mixture Estimation for Time-Indexed Functional Data in R (2016)
- Edzer Pebesma; Roger Bivand; Paulo Ribeiro: Software for Spatial Statistics (2015)
- Duncan Lee: CARBayes: An R Package for Bayesian Spatial Modeling with Conditional Autoregressive Priors (2013)