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) <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.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
0.2.0 |
rolling linux/jammy R-4.5 | BayesNSGP_0.2.0.tar.gz |
44.7 KiB |
0.2.0 |
rolling linux/noble R-4.5 | BayesNSGP_0.2.0.tar.gz |
44.7 KiB |
0.2.0 |
rolling source/ R- | BayesNSGP_0.2.0.tar.gz |
44.7 KiB |
0.2.0 |
latest linux/jammy R-4.5 | BayesNSGP_0.2.0.tar.gz |
44.7 KiB |
0.2.0 |
latest linux/noble R-4.5 | BayesNSGP_0.2.0.tar.gz |
44.7 KiB |
0.2.0 |
latest source/ R- | BayesNSGP_0.2.0.tar.gz |
44.7 KiB |
0.2.0 |
2026-04-26 source/ R- | BayesNSGP_0.2.0.tar.gz |
44.7 KiB |
0.2.0 |
2026-04-23 source/ R- | BayesNSGP_0.2.0.tar.gz |
44.7 KiB |
0.1.2 |
2025-04-20 source/ R- | BayesNSGP_0.1.2.tar.gz |
33.7 KiB |