R package 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.
References in zbMATH (referenced in 1 article )
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- Mark D. Risser, Catherine A. Calder: Local likelihood estimation for covariance functions with spatially-varying parameters: the convoSPAT package for R (2015) arXiv