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CARBayesST  

Spatio-Temporal Generalised Linear Mixed Models for Areal Unit Data
View on CRAN: Click here


Download and install CARBayesST package within the R console
Install from CRAN:
install.packages("CARBayesST")

Install from Github:
library("remotes")
install_github("cran/CARBayesST")

Install by package version:
library("remotes")
install_version("CARBayesST", "4.0")



Attach the package and use:
library("CARBayesST")
Maintained by
Duncan Lee
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2014-08-11
Latest Update: 2023-01-17
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) . 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.
How to cite:
Duncan Lee (2014). CARBayesST: Spatio-Temporal Generalised Linear Mixed Models for Areal Unit Data. R package version 4.0, https://cran.r-project.org/web/packages/CARBayesST. Accessed 22 Dec. 2024.
Previous versions and publish date:
1.0 (2014-08-11 08:38), 1.1 (2014-11-14 18:12), 2.0 (2015-07-06 18:51), 2.1 (2015-08-26 14:14), 2.2 (2016-02-25 18:03), 2.3 (2016-06-15 17:26), 2.4 (2016-07-28 17:28), 2.5.1 (2017-08-14 14:20), 2.5.2 (2018-04-17 11:50), 2.5 (2017-03-16 12:26), 3.0.1 (2019-01-08 12:50), 3.0.2 (2019-12-12 11:50), 3.0 (2018-09-27 17:30), 3.1.1 (2021-02-04 16:30), 3.1 (2020-03-09 16:10), 3.2.1 (2021-05-31 09:30), 3.2.2 (2022-03-16 17:00), 3.2.3 (2022-04-26 12:20), 3.2 (2021-03-31 02:10), 3.3.1 (2023-01-17 14:30), 3.3 (2022-05-12 18:30)
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