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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 "BayesGP" in publications use:'
type: software
license: GPL-3.0-or-later
title: 'BayesGP: Efficient Implementation of Gaussian Process in Bayesian Hierarchical
  Models'
version: 0.1.3
doi: 10.32614/CRAN.package.BayesGP
abstract: Implements Bayesian hierarchical models with flexible Gaussian process priors,
  focusing on Extended Latent Gaussian Models and incorporating various Gaussian process
  priors for Bayesian smoothing. Computations leverage finite element approximations
  and adaptive quadrature for efficient inference. Methods are detailed in Zhang,
  Stringer, Brown, and Stafford (2023) <https://doi.org/10.1177/09622802221134172>;
  Zhang, Stringer, Brown, and Stafford (2024) <https://doi.org/10.1080/10618600.2023.2289532>;
  Zhang, Brown, and Stafford (2023) <https://doi.org/10.48550/arXiv.2305.09914>; and
  Stringer, Brown, and Stafford (2021) <https://doi.org/10.1111/biom.13329>.
authors:
- family-names: Zhang
  given-names: Ziang
  email: ziangzhang@uchicago.edu
- family-names: Lin
  given-names: Yongwei
- family-names: Stringer
  given-names: Alex
- family-names: Brown
  given-names: Patrick
repository: https://CRAN.R-project.org/package=BayesGP
date-released: '2024-11-12'
contact:
- family-names: Zhang
  given-names: Ziang
  email: ziangzhang@uchicago.edu