When using the package, please acknowledge:

Ming D, Guillas S (2021). “Linked Gaussian process emulation for systems of computer models using Matérn kernels and adaptive design.” SIAM/ASA Journal on Uncertainty Quantification, 9(4), 1615–1642.

Ming D, Williamson D, Guillas S (2023). “Deep Gaussian process emulation using stochastic imputation.” Technometrics, 65(2), 150–161.

Ming D, Williamson D (2023). “Linked deep Gaussian process emulation for model networks.” arXiv:2306.01212.

Ming D, Williamson D (2024). dgpsi: An R package powered by Python for modelling linked deep Gaussian processes. R package version 2.4.0, https://CRAN.R-project.org/package=dgpsi.

Corresponding BibTeX entries:

  @Article{,
    title = {Linked Gaussian process emulation for systems of computer
      models using Matérn kernels and adaptive design},
    author = {Deyu Ming and Serge Guillas},
    journal = {SIAM/ASA Journal on Uncertainty Quantification},
    year = {2021},
    volume = {9},
    number = {4},
    pages = {1615--1642},
  }

  @Article{,
    title = {Deep Gaussian process emulation using stochastic
      imputation},
    author = {Deyu Ming and Daniel Williamson and Serge Guillas},
    journal = {Technometrics},
    year = {2023},
    volume = {65},
    number = {2},
    pages = {150--161},
  }

  @Unpublished{,
    title = {Linked deep Gaussian process emulation for model
      networks},
    author = {Deyu Ming and Daniel Williamson},
    note = {arXiv:2306.01212},
    year = {2023},
  }

  @Manual{,
    title = {dgpsi: An R package powered by Python for modelling linked
      deep Gaussian processes},
    author = {Deyu Ming and Daniel Williamson},
    note = {R package version 2.4.0},
    url = {https://CRAN.R-project.org/package=dgpsi},
    year = {2024},
  }