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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) <doi:10.48550/arXiv.1702.00434>). 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: | 0.1.2 |
| Depends: | R (≥ 3.4.0), nimble |
| Imports: | FNN, Matrix, methods, StatMatch |
| Published: | 2022-01-09 |
| Author: | Daniel Turek, Mark Risser |
| Maintainer: | Daniel Turek <danielturek at gmail.com> |
| License: | GPL-3 |
| NeedsCompilation: | no |
| CRAN checks: | BayesNSGP results |
| Reference manual: | BayesNSGP.pdf |
| Package source: | BayesNSGP_0.1.2.tar.gz |
| Windows binaries: | r-devel: BayesNSGP_0.1.2.zip, r-release: BayesNSGP_0.1.2.zip, r-oldrel: BayesNSGP_0.1.2.zip |
| macOS binaries: | r-release (arm64): BayesNSGP_0.1.2.tgz, r-oldrel (arm64): BayesNSGP_0.1.2.tgz, r-release (x86_64): BayesNSGP_0.1.2.tgz, r-oldrel (x86_64): BayesNSGP_0.1.2.tgz |
| Old sources: | BayesNSGP archive |
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