Package: LatticeKrig
Version: 3.3
Date: May 24, 2013
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: 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.
License: GPL (>= 2)
URL: http://www.r-project.org
Depends: R (>= 3.0.1), spam, fields (>= 6.9.1)
Packaged: 2014-07-11 03:30:33 UTC; nychka
NeedsCompilation: yes
Repository: CRAN
Date/Publication: 2014-07-11 06:47:16

Package: LatticeKrig
Version: 3.4
Date: August 27, 2014
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: 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.
License: GPL (>= 2)
URL: http://www.r-project.org
Depends: R (>= 3.0.1), spam, fields (>= 6.9.1)
Packaged: 2014-08-28 02:33:24 UTC; nychka
NeedsCompilation: yes
Repository: CRAN
Date/Publication: 2014-08-28 07:01:38