convoSPAT (version 1.2.7)
Convolution-Based Nonstationary Spatial Modeling
Description
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.