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shrinkGPR  

Scalable Gaussian Process Regression with Hierarchical Shrinkage Priors
View on CRAN: Click here


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

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

Install by package version:
library("remotes")
install_version("shrinkGPR", "2.1.0")



Attach the package and use:
library("shrinkGPR")
Maintained by
Peter Knaus
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2025-01-30
Latest Update: 2025-01-30
Description:
Efficient variational inference methods for fully Bayesian Gaussian Process Regression (GPR) models with hierarchical shrinkage priors, including the triple gamma prior for effective variable selection and covariance shrinkage in high-dimensional settings. The package leverages normalizing flows to approximate complex posterior distributions. For details on implementation, see Knaus (2025) <doi:10.48550/arXiv.2501.13173>.
How to cite:
Peter Knaus (2025). shrinkGPR: Scalable Gaussian Process Regression with Hierarchical Shrinkage Priors. R package version 2.1.0, https://cran.r-project.org/web/packages/shrinkGPR. Accessed 04 Oct. 2026.
Previous versions and publish date:
(2026-09-09 16:10), 1.0.0 (2025-01-30 20:00), 1.1.1 (2025-10-01 18:40), 1.1 (2025-08-19 14:20), 2.0.0 (2026-03-30 16:10)
Other packages that cited shrinkGPR R package
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Complete documentation for shrinkGPR
Functions, R codes and Examples using the shrinkGPR R package
Full shrinkGPR package functions and examples
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