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dgpsi

R interface to 'dgpsi' for deep and linked Gaussian process emulations

https://github.com/mingdeyu/dgpsi-r

Science Score: 59.0%

This score indicates how likely this project is to be science-related based on various indicators:

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    CITATION.cff file
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    Found codemeta.json file
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    DOI references
    Found 4 DOI reference(s) in README
  • ✓
    Academic publication links
    Links to: arxiv.org
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    Committers with academic emails
    2 of 3 committers (66.7%) from academic institutions
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    Scientific vocabulary similarity
    Low similarity (13.7%) to scientific vocabulary

Keywords

deep-gaussian-processes emulation gaussian-processes package r surrogate-models
Last synced: 6 months ago · JSON representation

Repository

R interface to 'dgpsi' for deep and linked Gaussian process emulations

Basic Info
Statistics
  • Stars: 0
  • Watchers: 1
  • Forks: 4
  • Open Issues: 0
  • Releases: 6
Topics
deep-gaussian-processes emulation gaussian-processes package r surrogate-models
Created over 3 years ago · Last pushed 7 months ago
Metadata Files
Readme Changelog License

README.md

dgpsi

CRAN_Status_Badge Download R-CMD-check DOC python <!-- badges: end -->

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, heteroskedastic Gaussian, and Categorical).
  • 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.
  • Feature Badge Large-scale GP, DGP, and Linked (D)GP emulations.
  • Feature Badge Scalable DGP classification using Stochastic Imputation.
  • Feature Badge Bayesian optimization.

Getting started

Installation

You can install the package from CRAN:

r install.packages('dgpsi')

or its development version from GitHub:

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

After the installation, run

r library(dgpsi)

to load the package. To install or activate the required Python environment automatically, you can either run dgpsi::init_py() explicitly or simply call any function from the package. That's it - the package is 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:

r dgpsi::init_py(reinstall = T)

Or uninstall completely the Python environment:

r dgpsi::init_py(uninstall = T)

and then reinstall:

r 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.

Owner

  • Name: Deyu Ming
  • Login: mingdeyu
  • Kind: user
  • Company: University College London

GitHub Events

Total
  • Create event: 2
  • Issues event: 4
  • Release event: 1
  • Delete event: 1
  • Issue comment event: 5
  • Push event: 111
  • Pull request review event: 1
  • Pull request event: 5
  • Fork event: 2
Last Year
  • Create event: 2
  • Issues event: 4
  • Release event: 1
  • Delete event: 1
  • Issue comment event: 5
  • Push event: 111
  • Pull request review event: 1
  • Pull request event: 5
  • Fork event: 2

Committers

Last synced: almost 3 years ago

All Time
  • Total Commits: 158
  • Total Committers: 3
  • Avg Commits per committer: 52.667
  • Development Distribution Score (DDS): 0.019
Top Committers
Name Email Commits
Deyu Ming d****6@u****k 155
TJ McKinley t****y@e****k 2
TJ McKinley t****y@u****m 1
Committer Domains (Top 20 + Academic)
exeter.ac.uk: 1 ucl.ac.uk: 1

Issues and Pull Requests

Last synced: 6 months ago

All Time
  • Total issues: 2
  • Total pull requests: 5
  • Average time to close issues: about 19 hours
  • Average time to close pull requests: about 16 hours
  • Total issue authors: 2
  • Total pull request authors: 4
  • Average comments per issue: 1.5
  • Average comments per pull request: 0.4
  • Merged pull requests: 4
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 2
  • Pull requests: 3
  • Average time to close issues: about 19 hours
  • Average time to close pull requests: about 23 hours
  • Issue authors: 2
  • Pull request authors: 3
  • Average comments per issue: 1.5
  • Average comments per pull request: 0.33
  • Merged pull requests: 2
  • Bot issues: 0
  • Bot pull requests: 0
View more stats
Top Authors
Issue Authors
  • kaedonkers (1)
  • batistahpedro (1)
Pull Request Authors
  • mingdeyu (2)
  • tjmckinley (2)
  • BayesExeter (2)
  • timothee-bacri (1)
Top Labels
Issue Labels
Pull Request Labels

Packages

  • Total packages: 1
  • Total downloads:
    • cran 255 last-month
  • Total dependent packages: 0
  • Total dependent repositories: 0
  • Total versions: 6
  • Total maintainers: 1
cran.r-project.org: dgpsi

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

  • Versions: 6
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Downloads: 255 Last month
Rankings
Forks count: 21.9%
Dependent packages count: 29.8%
Stargazers count: 35.2%
Dependent repos count: 35.5%
Average: 36.7%
Downloads: 61.0%
Maintainers (1)
deyu.ming.16@ucl.ac.uk
Last synced: 6 months ago

Dependencies

.github/workflows/R-CMD-check.yaml actions
  • actions/checkout v2 composite
  • r-lib/actions/check-r-package v2 composite
  • r-lib/actions/setup-pandoc v2 composite
  • r-lib/actions/setup-r v2 composite
  • r-lib/actions/setup-r-dependencies v2 composite
.github/workflows/pkgdown.yaml actions
  • JamesIves/github-pages-deploy-action 4.1.4 composite
  • actions/checkout v2 composite
  • r-lib/actions/setup-pandoc v2 composite
  • r-lib/actions/setup-r v2 composite
  • r-lib/actions/setup-r-dependencies v2 composite
DESCRIPTION cran
  • R >= 4.0 depends
  • benchmarkme >= 1.0.8 imports
  • reticulate >= 1.25 imports
  • MASS * suggests
  • R.utils * suggests
  • knitr * suggests
  • rmarkdown * suggests
  • utils * suggests