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>.
| Version: |
1.0.0 |
| Depends: |
R (≥ 4.0.0) |
| Imports: |
gsl, progress, rlang, utils, methods, torch |
| Suggests: |
testthat (≥ 3.0.0) |
| Published: |
2025-01-30 |
| 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 |
| CRAN checks: |
shrinkGPR results |
Documentation:
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