R: Spatial Generalised Linear Mixed Models for Areal Unit Data

# Spatial Generalised Linear Mixed Models for Areal Unit Data

## Documentation for package ‘CARBayes’ version 6.1.1

- 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
fitted.CARBayes | Extract the fitted values from a model.
highlight.borders | Creates an sf data.frame object (from the sf package) 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.
S.glm | Fit a generalised linear model to data.
S.RAB | Fit a spatial generalised linear model with anisotropic basis functions to data for computationally efficient localised spatial smoothing, where the parameters are estimated by penalised maximum likelihood estimation with a ridge regression penalty.