This repository contains some supplemental material in this top level and see the subdirectory LatticeKrig for the "head" of the standard R package. The most current package on CRAN is listed here as LatticeKrig_VERSION.tar.gz . At the time of writing the version is 7.0. To create a possibly new version from this repository download the LatticeKrig subdirectory and in UNIX
R CMD build --force LatticeKrig
To install this version for your system try
R CMD INSTALL LatticeKrig
or from the tar ball
R CMD INSTALL LatticeKrig_VERSION.tar.gz
where VERSION are the correct version numbers in this file.
Methods 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 and this package makes of the R spam package for this. An extension of this version over previous ones ( < 5.4 ) is the support for different geometries besides a rectangular domain. The Markov random field approach combined with a basis function representation makes the implementation of different geometries simple where only a few specific functions need to be added with most of the computation and evaluation done by generic routines that have been tuned to be efficient. One benefit of this package's 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 and also the case when the observations are a linear combination (e.g. an integral) of the spatial process. Included are generic methods for prediction, standard errors for prediction, plotting of the estimated surface and conditional and unconditional simulation.
The reference DOI 10.5065/D6W957CTis linked to the specific package version 5.5: Versions/LatticeKrig_5.5.tar.gz
MD5 check sum: ebefee1efcf3b6da395d7c9c540fee4a
For the most recent, distributed version of LatticeKrig use a CRAN mirror site such as R studio CRAN mirror site to download and install the package. Use citation("LatticeKrig") in R to generate a citation for this package with the current version number.
Package contact: Doug Nychka nychka@ucar.edu