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.


News

Reference manual

It appears you don't have a PDF plugin for this browser. You can click here to download the reference manual.

install.packages("convoSPAT")

1.2.4 by Mark D. Risser, a year ago


http://github.com/markdrisser/convoSPAT


Browse source code at https://github.com/cran/convoSPAT


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