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.

Getting started

Package details

AuthorMark D. Risser [aut, cre]
MaintainerMark D. Risser <>
LicenseMIT + file LICENSE
Package repositoryView on CRAN
Installation Install the latest version of this package by entering the following in R:

Try the convoSPAT package in your browser

Any scripts or data that you put into this service are public.

convoSPAT documentation built on Jan. 16, 2021, 5:31 p.m.