Learn R Programming
⚠️There's a newer version (4.0) of this package.Take me there.

CARBayesST (version 1.1)

Poisson Log-linear Models with Spatio-temporal Random Effects

Description

Implements a series of Poisson log-linear models with spatio-temporal random effects. The spatial and temporal autocorrelation in the random effects are induced using the class of Conditional AutoRegressive (CAR) priors, which are a special case of Gaussian Markov Random Fields (GMRF). All models are fitted in a Bayesian context using Markov chain Monte Carlo (McMC) simulation.

Copy Link

Version

Install

install.packages('CARBayesST')

Monthly Downloads

437

Version

1.1

License

GPL (>= 2)

Maintainer

Duncan Lee

Last Published

November 14th, 2014

Functions in CARBayesST (1.1)

ST.ARCAR

Fit the space-time Poisson log-linear model proposed by Rushworth et al. (2014b) with an autoregressive spatio-temporal correlation stucture.
print.carbayesST

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

Fit the space-time Poisson log-linear model proposed by Lee and Lawson (2014) with a clustering componennt and an autoregressive spatio-temporal correlation stucture.
results.summarise

Summarise a matrix of Markov chain Monte Carlo samples.
ST.cluster

Fit the space-time Poisson log-linear clustering model proposed by Lee and Lawson (2014).
CARBayesST-package

Poisson Log-linear Models with Spatio-temporal Random Effects
ST.KHinteraction

Fit the space-time Poisson log-linear model proposed by Knorr-Held (2000) with separable spatio-temporal main effects and an independent space-time interaction.
ST.ARCARadaptive

Fit the space-time Poisson log-linear model proposed by Rushworth et al. (2014a) with an adaptive autoregressive spatio-temporal correlation stucture.
ST.KHmain

Fit the space-time Poisson log-linear model proposed by Knorr-Held and Besag (1998) with separable spatio-temporal main effects.