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# CITATION file created with {cffr} R package
# See also: https://docs.ropensci.org/cffr/
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cff-version: 1.2.0
message: 'To cite package "LatticeKrig" in publications use:'
type: software
license: GPL-2.0-or-later
title: 'LatticeKrig: Multi-Resolution Kriging Based on Markov Random Fields'
version: 9.3.0
doi: 10.5065/D6HD7T1R
identifiers:
- type: doi
  value: 10.32614/CRAN.package.LatticeKrig
abstract: Methods for the interpolation of large spatial datasets. This package uses
  a basis function approach that provides a surface fitting method that can approximate
  standard spatial data models. 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 sparse linear algebra. 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 R 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. See the 'LatticeKrigRPackage' GitHub repository for
  a vignette of this package. Development of this package was supported in part by
  the National Science Foundation Grant 1417857 and the National Center for Atmospheric
  Research.
authors:
- family-names: Nychka
  given-names: Douglas
  email: nychka@mines.edu
- family-names: Hammerling
  given-names: Dorit
- family-names: Sain
  given-names: Stephan
- family-names: Lenssen
  given-names: Nathan
- family-names: Smirniotis
  given-names: Colette
- family-names: Iverson
  given-names: Matthew
- family-names: Sikorski
  given-names: Antony
preferred-citation:
  type: generic
  title: 'LatticeKrig: Multiresolution Kriging Based on Markov Random Fields'
  authors:
  - family-names: Nychka
    given-names: Douglas
    email: nychka@ucar.edu
  - family-names: Hammerling
    given-names: Dorit
    email: hammerling@samsi.info
  - family-names: Sain
    given-names: Stephan
    email: ssain@ucar.edu
  - family-names: Lenssen
    given-names: Nathan
    email: lenssen@ucar.edu
  - family-names: Sikorski
    given-names: Antony
    email: asikorski@mines.edu
  notes: R package version 9.3.0
  location:
    name: Golden, CO, USA
  year: '2024'
  doi: 10.5065/D6HD7T1R
  url: https://github.com/dnychka/LatticeKrigRPackage
repository: https://CRAN.R-project.org/package=LatticeKrig
url: https://www.r-project.org
date-released: '2024-10-07'
contact:
- family-names: Nychka
  given-names: Douglas
  email: nychka@mines.edu