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# CITATION.cff 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 "deepICMGP" in publications use:'
type: software
license: LGPL-2.0-only
title: 'deepICMGP: Deep Intrinsic Coregionalization Gaussian Processes for Multiple
  Outputs'
version: 0.1.1
doi: 10.1615/Int.J.UncertaintyQuantification.2026062570
identifiers:
- type: doi
  value: 10.32614/CRAN.package.deepICMGP
abstract: Fits two-layer deep intrinsic coregionalization Gaussian process surrogate
  models for multiple outputs observed at shared input locations. Uses Markov chain
  Monte Carlo sampling for kernel parameters and latent coordinates, with plug-in
  coregionalization estimates. Provides burn-in removal and thinning, posterior predictive
  means and within-location output covariances, and sequential experimental design
  using an active learning criterion. The method is described by Chang and Sung (2026)
  <https://doi.org/10.1615/Int.J.UncertaintyQuantification.2026062570>. Adapts code
  from the 'deepgp' package, with underlying deep Gaussian process methodology described
  by Sauer, Gramacy, and Higdon (2023) <https://doi.org/10.1080/00401706.2021.2008505>.
authors:
- family-names: Chang
  given-names: Chun-Yi
  email: chang103@msu.edu
- family-names: Sung
  given-names: Chih-Li
  email: sungchih@msu.edu
preferred-citation:
  type: article
  title: Deep Intrinsic Coregionalization Multi-Output Gaussian Process Surrogate
    with Active Learning
  authors:
  - family-names: Chang
    given-names: Chun-Yi
    email: chang103@msu.edu
  - family-names: Sung
    given-names: Chih-Li
    email: sungchih@msu.edu
  journal: International Journal for Uncertainty Quantification
  year: '2026'
  volume: '16'
  issue: '4'
  doi: 10.1615/Int.J.UncertaintyQuantification.2026062570
  start: '29'
  end: '52'
repository: https://jennychang32.r-universe.dev
commit: f355b8ad21cc51dc5e39ce3b9404c4e42fce3737
date-released: '2026-11-03'
contact:
- family-names: Chang
  given-names: Chun-Yi
  email: chang103@msu.edu
references:
- type: article
  title: Active Learning for Deep Gaussian Process Surrogates
  authors:
  - family-names: Sauer
    given-names: Annie
  - family-names: Gramacy
    given-names: Robert B.
  - family-names: Higdon
    given-names: David
  journal: Technometrics
  year: '2023'
  volume: '65'
  issue: '1'
  doi: 10.1080/00401706.2021.2008505
  start: '4'
  end: '18'