Package: CARBayesST
Type: Package
Title: Spatio-Temporal Generalised Linear Mixed Models for Areal Unit
        Data
Version: 4.0
Date: 2023-10-31
Author: Duncan Lee, Alastair Rushworth, Gary Napier and William Pettersson 
Maintainer: Duncan Lee <Duncan.Lee@glasgow.ac.uk>
Description: 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 Rushworth et al. (2014) <doi:10.1016/j.sste.2014.05.001>. 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.
License: GPL (>= 2)
Depends: MASS, R (>= 3.5.0), Rcpp (>= 0.11.5)
Imports: CARBayesdata, coda, dplyr, GGally, ggplot2, gridExtra, gtools,
        leaflet, matrixStats, MCMCpack, parallel, sf, spam, spdep,
        stats, truncdist, truncnorm, utils
LinkingTo: Rcpp
LazyLoad: yes
ByteCompile: yes
URL: https://github.com/duncanplee/CARBayesST
BugReports: https://github.com/duncanplee/CARBayesST/issues
NeedsCompilation: yes
Packaged: 2023-10-30 15:43:55 UTC; duncanlee
Repository: CRAN
Date/Publication: 2023-10-30 16:40:02 UTC
Built: R 4.6.0; aarch64-apple-darwin23; 2026-04-25 13:36:23 UTC; unix
Archs: CARBayesST.so.dSYM
