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# CITATION file created with {cffr} R package
# See also: https://docs.ropensci.org/cffr/
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cff-version: 1.2.0
message: 'To cite package "shrinkGPR" in publications use:'
type: software
license: GPL-2.0-or-later
title: 'shrinkGPR: Scalable Gaussian Process Regression with Hierarchical Shrinkage
  Priors'
version: 1.0.1
doi: 10.32614/CRAN.package.shrinkGPR
abstract: 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) <https://doi.org/10.48550/arXiv.2501.13173>.
authors:
- family-names: Knaus
  given-names: Peter
  email: peter.knaus@wu.ac.at
  orcid: https://orcid.org/0000-0001-6498-7084
repository: https://neferkareii.r-universe.dev
commit: a5d1b6832a17083b14b275b2d7de09a7038457b1
contact:
- family-names: Knaus
  given-names: Peter
  email: peter.knaus@wu.ac.at
  orcid: https://orcid.org/0000-0001-6498-7084