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BayesGP  

Efficient Implementation of Gaussian Process in Bayesian Hierarchical Models
View on CRAN: Click here


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

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

Install by package version:
library("remotes")
install_version("BayesGP", "0.1.3")



Attach the package and use:
library("BayesGP")
Maintained by
Ziang Zhang
[Scholar Profile | Author Map]
First Published: 2024-11-12
Latest Update: 2024-11-12
Description:
Implements Bayesian hierarchical models with flexible Gaussian process priors, focusing on Extended Latent Gaussian Models and incorporating various Gaussian process priors for Bayesian smoothing. Computations leverage finite element approximations and adaptive quadrature for efficient inference. Methods are detailed in Zhang, Stringer, Brown, and Stafford (2023) <doi:10.1177/09622802221134172>; Zhang, Stringer, Brown, and Stafford (2024) <doi:10.1080/10618600.2023.2289532>; Zhang, Brown, and Stafford (2023) <doi:10.48550/arXiv.2305.09914>; and Stringer, Brown, and Stafford (2021) <doi:10.1111/biom.13329>.
How to cite:
Ziang Zhang (2024). BayesGP: Efficient Implementation of Gaussian Process in Bayesian Hierarchical Models. R package version 0.1.3, https://cran.r-project.org/web/packages/BayesGP. Accessed 17 Feb. 2025.
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