Implements a class of 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, and full details are given in the vignette accompanying this package and the references in the help files. The creation of this package was supported by the Engineering and Physical Sciences Research Council (EPSRC) grant EP/J017442/1 and the Medical Research Council (MRC) grant MR/L022184/1.

Maintainer: Duncan Lee <Duncan.Lee at glasgow.ac.uk>

Author(s): Duncan Lee, Alastair Rushworth and Gary Napier

Install package and any missing dependencies by running this line in your R console:

install.packages("CARBayesST")

Depends MASS, R (>= 3.0.0), Rcpp(>=0.11.5)
Imports CARBayesdata, coda, dplyr, matrixcalc, sp, spam, spdep, stats, testthat, truncdist, truncnorm, utils
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Package CARBayesST
Materials
URL http://github.com/duncanplee/CARBayesST
Task Views SpatioTemporal
Version 2.5
Published 2017-03-16
License GPL (>= 2)
BugReports http://github.com/duncanplee/CARBayesST/issues
SystemRequirements
NeedsCompilation yes
Citation
CRAN checks CARBayesST check results
Package source CARBayesST_2.5.tar.gz