Package: LatticeKrig
Version: 5.5
Date: 2016-05-21
Title: Multiresolution Kriging Based on Markov Random Fields
Author: Douglas Nychka [aut, cre], Dorit Hammerling [aut], Stephan Sain [aut], Nathan Lenssen [aut]
Authors@R: c(
person("Douglas", "Nychka", role = c("aut", "cre"),
email = "nychka@ucar.edu"),
person("Dorit", "Hammerling", role = c("aut"),
email = "hammerling@samsi.info"),
person("Stephan", "Sain", role = "aut",
email = "ssain@ucar.edu"),
person("Nathan", "Lenssen", role = "aut",
email = "lenssen@ucar.edu"))
Maintainer: Douglas Nychka <nychka@ucar.edu>
Description: 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 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
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.
License: GPL (>= 2)
URL: http://www.r-project.org
Depends: R (>= 3.0.1), methods, spam, fields (>= 6.9.1)
Packaged: 2016-05-21 14:22:35 UTC; nychka
NeedsCompilation: yes
Repository: CRAN
Date/Publication: 2016-05-22 09:20:48