Scalable Gaussian Process Regression with Hierarchical Shrinkage Priors R package shrinkGPR version 2.0.0 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>. Software Depends: R (>= 4.1.0) Imports: gsl, progress, rlang, utils, methods, torch (>= 0.16.0), mniw Suggests: testthat (>= 3.0.0), shrinkTVP, plotly Peter Knaus <peter.knaus@wu.ac.at> Comprehensive R Archive Network (CRAN) Peter Knaus [aut, cre] (ORCID: <https://orcid.org/0000-0001-6498-7084>) GPL (>= 2) 2026-03-30 application/tgz https://CRAN.R-project.org/package=shrinkGPR doi:10.32614/CRAN.package.shrinkGPR