convoSPAT: 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.
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