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.
|Author||Mark D. Risser [aut, cre]|
|Date of publication||2017-11-03 16:53:59 UTC|
|Maintainer||Mark D. Risser <[email protected]>|
|License||MIT + file LICENSE|
|Package repository||View on CRAN|
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