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.
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!