This package implements Bayesian hierarchical spatial
areal unit models. In such models the spatial correlation is
modelled by a set of random effects, which are assigned a
conditional autoregressive (CAR) prior distribution. Examples
of the models included are the BYM model as well as a recently
developed localised spatial smoothing model.The creation of
this package was supported by the Economic and Social Research
Council (ESRC) grant RES-000-22-4256.
R (>= 2.10.0), coda, evd, MASS, lattice, MCMCpack, spam,
stats4, truncdist
boot, deldir, foreign, grid, maptools, Matrix, nlme,
shapefiles, sp, spdep, splines

This package implements Bayesian hierarchical spatial areal unit models. In such models the spatial correlation is modelled by  a set of random effects, which are assigned a conditional autoregressive (CAR) prior distribution. Examples of the models included are the BYM model as well as a recently developed localised spatial smoothing model.The creation of this package was supported by the Economic and Social Research Council (ESRC) grant RES-000-22-4256.

Implements Bayesian hierarchical spatial areal unit models. In such models the spatial autocorrelation is modelled by  a set of random effects, which are assigned a conditional autoregressive (CAR) prior distribution. Examples of the models included are the BYM model as well as a recently developed localised spatial smoothing model.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 is supported by the Engineering and Physical Science Research Council (EPSRC) grant EP/J017442/1.
boot, deldir, foreign, grid, maptools, Matrix, nlme,
shapefiles, spdep, splines

Implements a class of spatial generalised linear mixed models for areal unit data, with inference in a Bayesian setting using Markov chain Monte Carlo (McMC) simulation. The 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, and 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 is supported by the Engineering and Physical Science Research Council (EPSRC) grant EP/J017442/1.