LatticeKrig: Multiresolution Kriging based on Markov random fields

Functions for the interpolation of large spatial datasets. This package follows a "fixed rank Kriging" approach using a large number of basis functions and provides spatial estimates that are comparable to standard families of covariance functions. Using a large number of basis functions allows for estimates that can come close to interpolating the observations (a spatial model with a small nugget variance.) The covariance model for this method can approximate the Matern covariance family but also allows for a multi-resolution model and supports efficient computation of the profile likelihood for estimating covariance parameters. This is accomplished through compactly supported basis functions and a Markov random field model for the basis coefficients. These features lead to sparse matrices for the computations. One benefit of this approach is the facilty to do unconditional and conditional simulation of the field for large numbers of arbitrary points. There is also the flexibility for estimating nonstationary covariances. Included are generic methods for prediction, standard errors for prediction, plotting of the estimated surface and conditional and unconditional simulation.

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