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  "Title": "Convolution-Based Nonstationary Spatial Modeling",
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  "Date": "2017-12-13",
  "Authors@R": "person(\"Mark D.\", \"Risser\", email = \"markdrisser@gmail.com\",\nrole = c(\"aut\", \"cre\"))",
  "Description": "Fits convolution-based nonstationary Gaussian process\nmodels to point-referenced spatial data. The nonstationary\ncovariance function allows the user to specify the underlying\ncorrelation structure and which spatial dependence parameters\nshould be allowed to vary over space: the anisotropy, nugget\nvariance, and process variance. The parameters are estimated\nvia maximum likelihood, using a local likelihood approach. Also\nprovided are functions to fit stationary spatial models for\ncomparison, calculate the Kriging predictor and standard\nerrors, and create various plots to visualize nonstationarity.",
  "License": "MIT + file LICENSE",
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  "URL": "http://github.com/markdrisser/convoSPAT",
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