shrinkGPR: Scalable Gaussian Process Regression with Hierarchical ShrinkagePriors
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: | 1.1.1 |
| Depends: | R (≥ 4.1.0) |
| Imports: | gsl,progress,rlang, utils, methods,torch |
| Suggests: | testthat (≥ 3.0.0),shrinkTVP,plotly |
| Published: | 2025-10-01 |
| 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 seehttps://cran.r-project.org/web/packages/torch/vignettes/installation.html |
| Materials: | NEWS |
| CRAN checks: | shrinkGPR results |
Documentation:
| Reference manual: | shrinkGPR.html ,shrinkGPR.pdf |
Downloads:
| Package source: | shrinkGPR_1.1.1.tar.gz |
| Windows binaries: | r-devel:shrinkGPR_1.1.1.zip, r-release:shrinkGPR_1.1.1.zip, r-oldrel:shrinkGPR_1.1.1.zip |
| macOS binaries: | r-release (arm64):shrinkGPR_1.1.1.tgz, r-oldrel (arm64):shrinkGPR_1.1.1.tgz, r-release (x86_64):shrinkGPR_1.1.1.tgz, r-oldrel (x86_64):shrinkGPR_1.1.1.tgz |
| Old sources: | shrinkGPR archive |
Linking:
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