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.) Moreover, 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. An extension of this version over previous ones ( < 5.4 ) is the support for different geometries besides a rectangular domain. One benefit of the LatticeKrig model/approach is the facility to do unconditional and conditional simulation of the field for large numbers of arbitrary points. There is also the flexibility for estimating non-stationary 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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