markdrisser/convoSPAT: Convolution-Based Nonstationary Spatial Modeling
Version 1.2.4

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

Maintainer
LicenseMIT + file LICENSE
Version1.2.4
URL http://github.com/markdrisser/convoSPAT
Package repositoryView on GitHub
Installation Install the latest version of this package by entering the following in R:
install.packages("devtools")
library(devtools)
install_github("markdrisser/convoSPAT")
markdrisser/convoSPAT documentation built on Aug. 15, 2017, 1:40 a.m.