Paulo J. Ribeiro Jr., Ole F. Christensen, Peter J. Diggle
geoR and geoRglm: Software for Model-Based Geostatistics
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The packages geoR and geoRglm are contributed packages to the statistical
software system R, implementing methods for model-based geostatistical
data-analysis. In this paper we focus on the capabilities of the packages,
the computational implementation and related issues, and indicate directions
for future developments.

geoR implements methods for Gaussian and transformed Gaussian models. The
package includes functions and methods for reading and preparing the data,
exploratory analysis, inference on model parameters including variogram based
and likelihood based methods, and spatial interpolation. The generic term
"kriging" is used in the geostatistical literature in connection with several
methods of spatial interpolation/prediction. geoR implements classical "kriging
flavours", simple, ordinary, universal and external trend kriging and algorithms
for conditional simulation. The package also implements Bayesian methods which
take the parameter uncertainty into account when predicting at specified locations.

The package geoRglm is an extension of geoR for inference in generalised linear
spatial models using Markov chain Monte Carlo (MCMC) methods. geoRglm implements
conditional simulation and Bayesian inference for the Poisson and Binomial
generalised linear models.