Spatio-Temporal Generalised Linear Mixed Models for Areal Unit Data

Implements a class of univariate and multivariate 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 models similar to Bernardinelli et al. (1995) and Rushworth et al. (2014) . Full details are given in the vignette accompanying this package. The creation and development of this package was supported by the Engineering and Physical Sciences Research Council (EPSRC) grants EP/J017442/1 and EP/T004878/1 and the Medical Research Council (MRC) grant MR/L022184/1.


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3.2.1 by Duncan Lee, 8 months ago

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Authors: Duncan Lee , Alastair Rushworth , Gary Napier and William Pettersson

Documentation:   PDF Manual  

Task views: Handling and Analyzing Spatio-Temporal Data

GPL (>= 2) license

Imports CARBayesdata, coda, dplyr, GGally, ggplot2, gridExtra, gtools, leaflet, matrixStats, MCMCpack, rgdal, sp, spam, spdep, stats, truncdist, truncnorm, utils

Depends on MASS, Rcpp

Linking to Rcpp

See at CRAN