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shrinkGPR: Scalable Gaussian Process Regression with Hierarchical Shrinkage Priors
Efficient variational inference methods for fully Bayesian univariate and multivariate Gaussian and t-process regression models. Hierarchical shrinkage priors, including the triple gamma prior, are used 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] (ORCID: <https://orcid.org/0000-0001-6498-7084>) |
| Maintainer | Peter Knaus <peter.knaus@wu.ac.at> |
| License | GPL (>= 2) |
| Version | 2.0.0 |
| Package repository | View on CRAN |
| Installation |
Install the latest version of this package by entering the following in R:
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