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BayesNSGP  

Bayesian Analysis of Non-Stationary Gaussian Process Models
View on CRAN: Click here


Download and install BayesNSGP package within the R console
Install from CRAN:
install.packages("BayesNSGP")

Install from Github:
library("remotes")
install_github("cran/BayesNSGP")

Install by package version:
library("remotes")
install_version("BayesNSGP", "0.1.2")



Attach the package and use:
library("BayesNSGP")
Maintained by
Daniel Turek
[Scholar Profile | Author Map]
First Published: 2019-08-29
Latest Update: 2022-01-09
Description:
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) ). 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.
How to cite:
Daniel Turek (2019). BayesNSGP: Bayesian Analysis of Non-Stationary Gaussian Process Models. R package version 0.1.2, https://cran.r-project.org/web/packages/BayesNSGP. Accessed 11 Apr. 2025.
Previous versions and publish date:
0.1.0 (2019-08-29 14:50), 0.1.1 (2019-10-12 11:50)
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Complete documentation for BayesNSGP
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