Interface to 'dgpsi' for Deep and Linked Gaussian Process Emulations.

dgpsi

The R package dgpsi provides R interface to Python package dgpsi for deep and linked Gaussian process emulations using stochastic imputation (SI).

Hassle-free Python Setup
You don't need prior knowledge of Python to start using the package, all you need is a single click in R (see Installation section below) that automatically installs and activates the required Python environment for you!

Features

dgpsi currently has following features:

• Gaussian process emulations with separable or non-separable squared exponential and Matérn-2.5 kernels.
• Deep Gaussian process emulations with flexible structures including:
  • 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, and heteroskedastic Gaussian).
• Linked emulations of feed-forward systems of computer models by linking (D)GP emulators of deterministic individual computer models.
• Fast Leave-One-Out (LOO) and Out-Of-Sample (OOS) validations for GP, DGP, and linked (D)GP emulators.
• Multi-core predictions and validations for GP, DGP, and Linked (D)GP emulators.
• Sequential designs for (D)GP emulators and bundles of (D)GP emulators.
• Automatic pruning of DGP emulators, both statically and dynamically.

Getting started

• Check A Quick Guide to dgpsi to get started with the package.
• For experimental features, check out our website for the development version.

Installation

You can install the package from CRAN:

install.packages('dgpsi')

or its development version from GitHub:

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

After the installation, run

library(dgpsi)

to load the package. To install or activate the required Python environment automatically, simply run a function from the package. That's it, the package is now ready to use!

Note
After loading dgpsi, the package may take some time to compile and initiate the underlying Python environment the first time a function from dgpsi is executed. Any subsequent function calls won't require re-compiling or re-activation of the Python environment, and will be faster.

If you experience Python related issues while using the package, please try to reinstall the Python environment:

dgpsi::init_py(reinstall = T)

Or uninstall completely the Python environment:

dgpsi::init_py(uninstall = T)

and then reinstall:

dgpsi::init_py()

Research Notice

This package is part of an ongoing research initiative. For detailed information about the research aspects and guidelines for use, please refer to our Research Notice.

References

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

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

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.