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CARBayesST (version 2.0)

Spatio-Temporal Generalised Linear Mixed Models for Areal Unit Data

Description

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. The spatio-temporal autocorrelation is modelled by random effects, which are assigned conditional autoregressive (CAR) 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 below. The creation of this package was supported by the Engineering and Physical Science Research Council (EPSRC) grant EP/J017442/1 and the Medical Research Council (MRC) grant MR/L022184/1.

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Version

Install

install.packages('CARBayesST')

Monthly Downloads

437

Version

2.0

License

GPL (>= 2)

Maintainer

Duncan Lee

Last Published

July 6th, 2015

Functions in CARBayesST (2.0)

print.carbayesST

Print a summary of the fitted model to the screen.
ST.CARanova

Fit a spatio-temporal generalised linear mixed model to data, with spatial and temporal main effects and a spatio-temporal interaction.
ST.CARlocalised

Fit a spatio-temporal generalised linear mixed model to data, with a spatio-temporal autoregressive process and a piecewise constant intercept term.
ST.CARar

Fit a spatio-temporal generalised linear mixed model to data, with a spatio-temporal autoregressive process.
ST.CARlinear

Fit a spatio-temporal generalised linear mixed model to data, where the spatial units have linear time trends with spatially varying intercepts and slopes.
CARBayesST-package

Spatio-Temporal Generalised Linear Mixed Models For Areal Unit Data
ST.CARadaptive

Fit a spatio-temporal generalised linear mixed model to data with a spatio-temporal autoregressive process that has an adaptive autocorrelation stucture.