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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!


dgpsi currently has following features:

Getting started


You can install the package from CRAN:


or its development version from GitHub:


After the installation, run


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!

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:


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.


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.