geoR and geoRglm: Software for Model-Based Geostatistics

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.