CARBayesST: Spatio-Temporal Generalised Linear Mixed Models for Areal Unit Data

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, including Bernardinelli et al. (1995) <doi:10.1002/sim.4780142112>, Rushworth et al. (2014) <doi:10.1016/j.sste.2014.05.001> and Lee et al. (2016) <doi:10.1214/16-AOAS941>. Full details are given in the vignette accompanying this package. 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.

Package details

AuthorDuncan Lee, Alastair Rushworth and Gary Napier
MaintainerDuncan Lee <[email protected]>
LicenseGPL (>= 2)
Package repositoryView on CRAN
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CARBayesST documentation built on Dec. 16, 2019, 1:36 a.m.