The R package
dgpsi provides R interface to Python package
dgpsi for deep and linked Gaussian process emulations.
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!
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.
- Check A Quick Guide to dgpsi to get started with the package.
- For experimental features, check out our website for the development version.
You can install the package from CRAN:
or its development version from GitHub:
After the installation, run
to install and activate the required Python environment. That’s it, the package is now ready to use!
library(dgpsi), telling R to invoke the required Python environment.
If you experience issues while running
init_py(), 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 restart the R and rerun:
Ming, D., Williamson, D., and Guillas, S. (2022) Deep Gaussian process emulation using stochastic imputation. Technometrics. 0(0), 1-12.
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.