R package 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 or Poisson. Spatial autocorrelation is modelled by a set of random effects, which are assigned a conditional autoregressive (CAR) prior distribution. A number of different CAR priors are available for the random effects, including a multivariate CAR (MCAR) model for multivariate spatial data. 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 was supported by the Engineering and Physical Science Research Council (EPSRC) grant EP/J017442/1.

References in zbMATH (referenced in 15 articles , 1 standard article )

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  1. Edgar Santos-Fernandez, Jay M. Ver Hoef, James M. McGree, Daniel J. Isaak, Kerrie Mengersen, Erin E. Peterson: SSNbayes: An R package for Bayesian spatio-temporal modelling on stream networks (2022) arXiv
  2. Sahu, Sujit K.: Bayesian modeling of spatio-temporal data with R (2022)
  3. Chen, Wanfang; Castruccio, Stefano; Genton, Marc G.: Assessing the risk of disruption of wind turbine operations in Saudi Arabia using Bayesian spatial extremes (2021)
  4. Ferreira, Marco A. R.; Porter, Erica M.; Franck, Christopher T.: Fast and scalable computations for Gaussian hierarchical models with intrinsic conditional autoregressive spatial random effects (2021)
  5. Fofana, Demba; George, E. O.; Bowman, Dale: Combining assumptions and graphical network into gene expression data analysis (2021)
  6. Francisco Palmí-Perales, Virgilio Gómez-Rubio, Miguel A. Martinez-Beneito: Bayesian Multivariate Spatial Models for Lattice Data with INLA (2021) not zbMATH
  7. Lawson, Andrew B.: Using R for Bayesian spatial and spatio-temporal health modeling (2021)
  8. Keefe, Matthew J.; Ferreira, Marco A. R.; Franck, Christopher T.: Objective Bayesian analysis for Gaussian hierarchical models with intrinsic conditional autoregressive priors (2019)
  9. Mihai Tivadar: OasisR: An R Package to Bring Some Order to the World of Segregation Measurement (2019) not zbMATH
  10. Prates, Marcos Oliveira; Assunção, Renato Martins; Rodrigues, Erica Castilho: Alleviating spatial confounding for areal data problems by displacing the geographical centroids (2019)
  11. Cowles, Mary Kathryn; Bonett, Stephen; Seedorff, Michael: Independent sampling for Bayesian normal conditional autoregressive models with OpenCL acceleration (2018)
  12. Duncan Lee; Alastair Rushworth; Gary Napier: Spatio-Temporal Areal Unit Modeling in R with Conditional Autoregressive Priors Using the CARBayesST Package (2018) not zbMATH
  13. Terrance Savitsky: Bayesian Nonparametric Mixture Estimation for Time-Indexed Functional Data in R (2016) not zbMATH
  14. Edzer Pebesma; Roger Bivand; Paulo Ribeiro: Software for Spatial Statistics (2015) not zbMATH
  15. Duncan Lee: CARBayes: An R Package for Bayesian Spatial Modeling with Conditional Autoregressive Priors (2013) not zbMATH