BayesNSGP: Bayesian Analysis of Non-Stationary Gaussian Process Models

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
Depends: R (≥ 3.4.0), nimble
Imports: FNN, Matrix, methods, StatMatch
Published: 2019-10-12
Author: Daniel Turek, Mark Risser
Maintainer: Daniel Turek <dbt1 at williams.edu>
License: GPL-3
NeedsCompilation: no
CRAN checks: BayesNSGP results

Downloads:

Reference manual: BayesNSGP.pdf
Package source: BayesNSGP_0.1.1.tar.gz
Windows binaries: r-devel: BayesNSGP_0.1.1.zip, r-devel-gcc8: BayesNSGP_0.1.1.zip, r-release: BayesNSGP_0.1.1.zip, r-oldrel: BayesNSGP_0.1.1.zip
OS X binaries: r-release: BayesNSGP_0.1.1.tgz, r-oldrel: BayesNSGP_0.1.1.tgz
Old sources: BayesNSGP archive

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