Package: shrinkGPR
Type: Package
Title: Scalable Gaussian Process Regression with Hierarchical Shrinkage
        Priors
Version: 2.0.0
Authors@R: c(
  person("Peter", "Knaus", email = "peter.knaus@wu.ac.at",
    role = c("aut", "cre"), comment = c(ORCID = "0000-0001-6498-7084")))
Maintainer: Peter Knaus <peter.knaus@wu.ac.at>
Description: 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>.
License: GPL (>= 2)
Encoding: UTF-8
Depends: R (>= 4.1.0)
Imports: gsl, progress, rlang, utils, methods, torch (>= 0.16.0), mniw
RoxygenNote: 7.3.3
Suggests: testthat (>= 3.0.0), shrinkTVP, plotly
Config/testthat/edition: 3
SystemRequirements: torch backend, for installation guide see
        https://cran.r-project.org/web/packages/torch/vignettes/installation.html
NeedsCompilation: no
Packaged: 2026-03-30 12:24:26 UTC; knaus
Author: Peter Knaus [aut, cre] (ORCID: <https://orcid.org/0000-0001-6498-7084>)
Repository: CRAN
Date/Publication: 2026-03-30 14:10:03 UTC
Built: R 4.6.0; ; 2026-04-26 04:50:32 UTC; unix
