Enables offtheshelf functionality for fully Bayesian, nonstationary Gaussian process modeling. The approach to nonstationary modeling involves a closedform, convolutionbased covariance function with spatiallyvarying parameters; these parameter processes can be specified either deterministically (using covariates or basis functions) or stochastically (using approximate Gaussian processes). Stationary Gaussian processes are a special case of our methodology, and we furthermore implement approximate Gaussian process inference to account for very large spatial data sets (Finley, et al (2017) <arXiv:1702.00434v2>). Bayesian inference is carried out using Markov chain Monte Carlo methods via the 'nimble' package, and posterior prediction for the Gaussian process at unobserved locations is provided as a postprocessing step.
Package details 


Author  Daniel Turek, Mark Risser 
Maintainer  Daniel Turek <danielturek@gmail.com> 
License  GPL3 
Version  0.1.2 
Package repository  View on CRAN 
Installation 
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