Paulo J. Ribeiro Jr., Ole F. Christensen and Peter J. Diggle
Geostatistical software - geoR and geoRglm
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The packages geoR and geoRglm are contributed packages to the statistical
software system R, implementing methods for 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 some of the
classical "kriging flavours" such as simple, ordinary, universal and external
trend kriging and includes 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.