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 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 CRAN checks: shrinkGPR results Documentation: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=shrinkGPR to link to this page.
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