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: |
0.1.2 |
| 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 |
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