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Fits convolution-based nonstationary Gaussian process models to point-referenced spatial data. The nonstationary covariance function allows the user to specify the underlying correlation structure and which spatial dependence parameters should be allowed to vary over space: the anisotropy, nugget variance, and process variance. The parameters are estimated via maximum likelihood, using a local likelihood approach. Also provided are functions to fit stationary spatial models for comparison, calculate the Kriging predictor and standard errors, and create various plots to visualize nonstationarity.
Version: | 1.2.7 |
Depends: | R (≥ 3.1.2) |
Imports: | stats, graphics, ellipse, fields, MASS, plotrix, StatMatch |
Published: | 2021-01-16 |
DOI: | 10.32614/CRAN.package.convoSPAT |
Author: | Mark D. Risser [aut, cre] |
Maintainer: | Mark D. Risser <markdrisser at gmail.com> |
License: | MIT + file LICENSE |
URL: | http://github.com/markdrisser/convoSPAT |
NeedsCompilation: | no |
Citation: | convoSPAT citation info |
CRAN checks: | convoSPAT results |
Reference manual: | convoSPAT.pdf |
Package source: | convoSPAT_1.2.7.tar.gz |
Windows binaries: | r-devel: convoSPAT_1.2.7.zip, r-release: convoSPAT_1.2.7.zip, r-oldrel: convoSPAT_1.2.7.zip |
macOS binaries: | r-release (arm64): convoSPAT_1.2.7.tgz, r-oldrel (arm64): convoSPAT_1.2.7.tgz, r-release (x86_64): convoSPAT_1.2.7.tgz, r-oldrel (x86_64): convoSPAT_1.2.7.tgz |
Old sources: | convoSPAT archive |
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.