When using the package, please acknowledge:

Ming, D. and 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., and Guillas, S. (2023) Deep Gaussian process emulation using stochastic imputation, Technometrics. (65)2, 150-161.

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

Ming, D. and Williamson, D. (2025) dgpsi: An R package powered by Python for modelling linked deep Gaussian processes, R package version 2.6.0. 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  @Unpublished{,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 2.6.0},
    url = {https://CRAN.R-project.org/package=dgpsi},
    year = {2025},
  }