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.
Package details |
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Author | Mark D. Risser [aut, cre] |
Maintainer | Mark D. Risser <markdrisser@gmail.com> |
License | MIT + file LICENSE |
Version | 1.2.7 |
URL | http://github.com/markdrisser/convoSPAT |
Package repository | View on CRAN |
Installation |
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