Installation
About
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.
Citation | convoSPAT citation info |
github.com/markdrisser/convoSPAT |
Key Metrics
Downloads
Yesterday | 7 0% |
Last 7 days | 52 -29% |
Last 30 days | 255 +5% |
Last 90 days | 740 -19% |
Last 365 days | 3.084 -20% |