Convolution-Based Nonstationary Spatial Modeling

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.


Reference manual

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1.2.4 by Mark D. Risser, a year ago

Browse source code at

Authors: Mark D. Risser [aut, cre]

Documentation:   PDF Manual  

MIT + file LICENSE license

Imports stats, graphics, ellipse, fields, geoR, MASS, plotrix, StatMatch

See at CRAN