The hardware and bandwidth for this mirror is donated by METANET, the Webhosting and Full Service-Cloud Provider.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]metanet.ch.
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 |
| DOI: | 10.32614/CRAN.package.BayesNSGP |
| 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 |
Please use the canonical form https://CRAN.R-project.org/package=BayesNSGP to link to this page.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.