R package fields: Tools for spatial data. Fields is for curve, surface and function fitting with an emphasis on splines, spatial data and spatial statistics. The major methods include cubic, and thin plate splines, Kriging and compact covariances for large data sets. The splines and Kriging methods are supporting by functions that can determine the smoothing parameter (nugget and sill variance) by cross validation and also by restricted maximum likelihood. A major feature is that any covariance function implemented in R and following a simple fields format can be used for spatial prediction. Some tailored optimization functions are supplied for finding the MLEs for the Matern family of covariances. There are also many useful functions for plotting and working with spatial data as images. This package also contains an implementation of sparse matrix methods for large spatial data sets and currently requires the sparse matrix (spam) package. But spam is not required for the standard spatial functions. Use help(fields) to get started and for an overview. The fields source code is heavily commented and provides useful explanations of numerical details in addition to the manual pages.
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References in zbMATH (referenced in 8 articles )
Showing results 1 to 8 of 8.
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- Mark D. Risser, Catherine A. Calder: Local likelihood estimation for covariance functions with spatially-varying parameters: the convoSPAT package for R (2015) arXiv
- Zachary D. Weller: spTest: An R Package Implementing Nonparametric Tests of Isotropy (2015) arXiv
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- Robinson, Andrew P.; Hamann, Jeff D.: Forest analytics with R (2011)
- Wang, Yizao; Stoev, Stilian A.: Conditional sampling for spectrally discrete max-stable random fields (2011)
- Ruppert, David; Wand, M.P.; Carroll, Raymond J.: Semiparametric regression during 2003--2007 (2009)
- Leitenstorfer, Florian; Tutz, Gerhard: Knot selection by boosting techniques (2007)