CARBayes

Spatial Generalised Linear Mixed Models for Areal Unit Data

Implements a class of univariate and multivariate spatial 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, multinomial, Poisson or zero-inflated Poisson (ZIP), and spatial autocorrelation is modelled by a set of random effects that are assigned a conditional autoregressive (CAR) prior distribution. A number of different models are available for univariate spatial data, including models with no random effects as well as random effects modelled by different types of CAR prior, including the BYM model (Besag et al. (1991) <doi:10.1007/BF00116466>), the Leroux model (Leroux et al. (2000) <doi:10.1007/978-1-4612-1284-3_4>) and the localised model (Lee et al. (2015) <doi:10.1002/env.2348>). Additionally, a multivariate CAR (MCAR) model for multivariate spatial data is available, as is a two-level hierarchical model for modelling data relating to individuals within areas. Full details are given in the vignette accompanying this package. The initial creation of this package was supported by the Economic and Social Research Council (ESRC) grant RES-000-22-4256, and on-going development has been supported by the Engineering and Physical Science Research Council (EPSRC) grant EP/J017442/1, ESRC grant ES/K006460/1, Innovate UK / Natural Environment Research Council (NERC) grant NE/N007352/1 and the TB Alliance.

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Description file content

Package
CARBayes
Type
Package
Title
Spatial Generalised Linear Mixed Models for Areal Unit Data
Version
5.1.1
Date
2018-12-06
Author
Duncan Lee
Maintainer
Duncan Lee
Description
Implements a class of univariate and multivariate spatial 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, multinomial, Poisson or zero-inflated Poisson (ZIP), and spatial autocorrelation is modelled by a set of random effects that are assigned a conditional autoregressive (CAR) prior distribution. A number of different models are available for univariate spatial data, including models with no random effects as well as random effects modelled by different types of CAR prior, including the BYM model (Besag et al. (1991) ), the Leroux model (Leroux et al. (2000) ) and the localised model (Lee et al. (2015) ). Additionally, a multivariate CAR (MCAR) model for multivariate spatial data is available, as is a two-level hierarchical model for modelling data relating to individuals within areas. Full details are given in the vignette accompanying this package. The initial creation of this package was supported by the Economic and Social Research Council (ESRC) grant RES-000-22-4256, and on-going development has been supported by the Engineering and Physical Science Research Council (EPSRC) grant EP/J017442/1, ESRC grant ES/K006460/1, Innovate UK / Natural Environment Research Council (NERC) grant NE/N007352/1 and the TB Alliance.
License
GPL (>= 2)
Depends
MASS, R (>= 3.0.0), Rcpp (>= 0.11.5)
Imports
CARBayesdata, coda, leaflet, matrixcalc, MCMCpack, rgdal, sp, spam, spdep, stats, truncnorm, utils
Suggests
boot, deldir, foreign, grid, maptools, Matrix, nlme, shapefiles, splines
LinkingTo
Rcpp
LazyLoad
yes
ByteCompile
yes
URL
BugReports
http://github.com/duncanplee/CARBayes/issues
NeedsCompilation
yes
Packaged
2018-12-06 11:26:50 UTC; duncanlee
Repository
CRAN
Date/Publication
2018-12-06 11:50:03 UTC

install.packages('CARBayes')

5.1.1

3 days ago

http://github.com/duncanplee/CARBayes

Duncan Lee

GPL (>= 2)

Depends on

MASS, R (>= 3.0.0), Rcpp (>= 0.11.5)

Imports

CARBayesdata, coda, leaflet, matrixcalc, MCMCpack, rgdal, sp, spam, spdep, stats, truncnorm, utils

Suggests

boot, deldir, foreign, grid, maptools, Matrix, nlme, shapefiles, splines

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