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.
ngspatial, CARBayes, spdep, Rcpp, RcppArmadillo, batchmeans
Spatial, NumericalMathematics, Econometrics, HighPerformanceComputing
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For attribution, please cite this work as
Hughes, "ngspatial: A Package for Fitting the Centered Autologistic and Sparse Spatial Generalized Linear Mixed Models for Areal Data", The R Journal, 2015
BibTeX citation
@article{RJ-2014-026,
author = {Hughes, John},
title = {ngspatial: A Package for Fitting the Centered Autologistic and Sparse Spatial Generalized Linear Mixed Models for Areal Data},
journal = {The R Journal},
year = {2015},
note = {https://doi.org/10.32614/RJ-2014-026},
doi = {10.32614/RJ-2014-026},
volume = {6},
issue = {2},
issn = {2073-4859},
pages = {81-95}
}