dgpsi

The R package dgpsi provides R interface to Python package dgpsi for deep and linked Gaussian process emulations. The package currently has following features:

• Deep Gaussian process emulation with flexible architecture construction:
  • multiple layers;
  • multiple GP nodes;
  • separable or non-separable squared exponential and Matérn-2.5 kernels;
  • global input connections;
  • non-Gaussian likelihoods (Poisson, Negative-Binomial, heteroskedastic Gaussian, and more to come);
• Linked emulation of feed-forward systems of computer models:
  • linking GP emulators of deterministic individual computer models;
  • linking GP and DGP emulators of deterministic individual computer models;
• Multi-core predictions from GP, DGP, and Linked (D)GP emulators.
• Fast Leave-One-Out (LOO) and Out-Of-Sample (OOS) validations for GP, DGP, and linked (D)GP emulators.

Documentation

See https://mingdeyu.github.io/dgpsi-R to learn more about the package.

Installation

You can install development version of the package from the GitHub repo:

1. In your RStudio Console, type:

devtools::install_github('mingdeyu/dgpsi-R')

2. Restart your RStudio.

3. Load the package and initialize the required Python environment:

library(dgpsi)
init_py()

References

Ming, D., Williamson, D., and Guillas, S. (2022) Deep Gaussian process emulation using stochastic imputation. Technometrics (to appear).

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.