Statistics, Department of

 

The R Journal

Date of this Version

12-2014

Document Type

Article

Citation

The R Journal (December 2014) 6(2); Editor: Deepayan Sarkar

Comments

Copyright 2014, The R Foundation. Open access material. License: CC BY 3.0 Unported

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.

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