# Spatial Generalised Linear Mixed Models for Areal Unit Data

## Documentation for package ‘CARBayes’ version 5.0

- DESCRIPTION file.
- User guides, package vignettes and other documentation.

## Help Pages

CARBayes-package | Spatial Generalised Linear Mixed Models for Areal Unit Data
CARBayes | Spatial Generalised Linear Mixed Models for Areal Unit Data
coef.CARBayes | Extract the regression coefficients from a model.
combine.data.shapefile | Combines a data frame with a shapefile to create a SpatialPolygonsDataFrame object.
fitted.CARBayes | Extract the fitted values from a model.
highlight.borders | Creates a SpatialPoints object identifying a subset of borders between neighbouring areas.
logLik.CARBayes | Extract the estimated loglikelihood from a fitted model.
model.matrix.CARBayes | Extract the Model (design) matrix from a model.
MVS.CARleroux | Fit a multivariate spatial generalised linear mixed model to data, where the random effects are modelled by a multivariate conditional autoregressive model.
print.CARBayes | Print a summary of a fitted CARBayes model to the screen.
residuals.CARBayes | Extract the residuals from a model.
S.CARbym | Fit a spatial generalised linear mixed model to data, where the random effects have a BYM conditional autoregressive prior.
S.CARdissimilarity | Fit a spatial generalised linear mixed model to data, where the random effects have a localised conditional autoregressive prior.
S.CARleroux | Fit a spatial generalised linear mixed model to data, where the random effects have a Leroux conditional autoregressive prior.
S.CARlocalised | Fit a spatial generalised linear mixed model to data, where a set of spatially smooth random effects are augmented with a piecewise constant intercept process.
S.CARmultilevel | Fit a spatial generalised linear mixed model to multi-level areal unit data, where the spatial random effects have a Leroux conditional autoregressive prior and there are also individual or small group level random effects.
S.glm | Fit a spatial generalised linear model to data.
summarise.lincomb | Compute the posterior distribution for a linear combination of the covariates from the linear predictor.
summarise.samples | Summarise a matrix of Markov chain Monte Carlo samples.