The R Journal: article published in 2014, volume 6:2

ngspatial: A Package for Fitting the Centered Autologistic and Sparse Spatial Generalized Linear Mixed Models for Areal Data
John Hughes , The R Journal (2014) 6:2, pages 81-95.

Abstract Two important recent advances in areal modeling are the centered autologistic model and the sparse spatial generalized linear mixed model (SGLMM), both of which are reparameterizations of traditional models. The reparameterizations improve regression inference by alleviating spatial confounding, and the sparse SGLMM also greatly speeds computing by reducing the dimension of the spatial random effects. Package ngspatial (’ng’ = non-Gaussian) provides routines for fitting these new models. The package supports composite likelihood and Bayesian inference for the centered autologistic model, and Bayesian inference for the sparse SGLMM.

Received: 2014-04-18; online 2015-01-04
CRAN packages: ngspatial, CARBayes, spdep, Rcpp, RcppArmadillo, batchmeans
CRAN Task Views implied by cited CRAN packages: Spatial, NumericalMathematics, Econometrics, HighPerformanceComputing

This article is licensed under a Creative Commons Attribution 3.0 Unported license .

@article{RJ-2014-026,
  author = {John Hughes},
  title = {{ngspatial: A Package for Fitting the Centered Autologistic
          and Sparse Spatial Generalized Linear Mixed Models for Areal
          Data}},
  year = {2014},
  journal = {{The R Journal}},
  doi = {10.32614/RJ-2014-026},
  url = {https://doi.org/10.32614/RJ-2014-026},
  pages = {81--95},
  volume = {6},
  number = {2}
}