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>.
| Version: |
2.0.0 |
| Depends: |
R (≥ 4.1.0) |
| Imports: |
gsl, progress, rlang, utils, methods, torch (≥ 0.16.0), mniw |
| Suggests: |
testthat (≥ 3.0.0), shrinkTVP, plotly |
| Published: |
2026-03-30 |
| DOI: |
10.32614/CRAN.package.shrinkGPR |
| Author: |
Peter Knaus  [aut,
cre] |
| Maintainer: |
Peter Knaus <peter.knaus at wu.ac.at> |
| License: |
GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: |
no |
| SystemRequirements: |
torch backend, for installation guide see
https://cran.r-project.org/web/packages/torch/vignettes/installation.html |
| Materials: |
NEWS |
| CRAN checks: |
shrinkGPR results |
Documentation:
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