Package: BayesNSGP
Title: Bayesian Analysis of Non-Stationary Gaussian Process Models
Description: Enables off-the-shelf functionality for fully Bayesian, nonstationary Gaussian process modeling. The approach to nonstationary modeling involves a closed-form, convolution-based covariance function with spatially-varying 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 post-processing step.
Version: 0.1.1
Date: 2019-10-12
Maintainer: Daniel Turek <dbt1@williams.edu>
Author: Daniel Turek, Mark Risser
Depends: R (>= 3.4.0),nimble
Imports: FNN,Matrix,methods,StatMatch
License: GPL-3
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.1.1
NeedsCompilation: no
Packaged: 2019-10-12 09:24:14 UTC; dturek
Repository: CRAN
Date/Publication: 2019-10-12 09:50:02 UTC
