Package: shrinkGPR
Type: Package
Title: Scalable Gaussian Process Regression with Hierarchical Shrinkage
        Priors
Version: 2.1.1
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
Roxygen: list(markdown = TRUE)
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
Config/roxygen2/version: 8.0.0
RoxygenNote: 7.3.3
Config/pak/sysreqs: libgsl0-dev
Repository: https://neferkareii.r-universe.dev
Date/Publication: 2026-10-23 13:17:50 UTC
RemoteUrl: https://github.com/neferkareii/shrinkgpr
RemoteRef: HEAD
RemoteSha: 20a822dce80831163d2cb81fe40137f822341042
NeedsCompilation: no
Packaged: 2026-10-23 18:45:22 UTC; root
Author: Peter Knaus [aut, cre] (ORCID: <https://orcid.org/0000-0001-6498-7084>)