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shrinkGPR: Scalable Gaussian Process Regression with Hierarchical Shrinkage Priors
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>.
Getting started
Browse package contents
Package details |
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| Author | Peter Knaus [aut, cre] (<https://orcid.org/0000-0001-6498-7084>) |
| Maintainer | Peter Knaus <peter.knaus@wu.ac.at> |
| License | GPL (>= 2) |
| Version | 1.0.0 |
| Package repository | View on CRAN |
| Installation |
Install the latest version of this package by entering the following in R:
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