Package: EzGP 0.1.0

Jiayi Li

EzGP: Easy-to-Interpret Gaussian Process Models for Computer Experiments

Fit model for datasets with easy-to-interpret Gaussian process modeling, predict responses for new inputs. The input variables of the datasets can be quantitative, qualitative/categorical or mixed. The output variable of the datasets is a scalar (quantitative). The optimization of the likelihood function can be chosen by the users (see the documentation of EzGP_fit()). The modeling method is published in "EzGP: Easy-to-Interpret Gaussian Process Models for Computer Experiments with Both Quantitative and Qualitative Factors" by Qian Xiao, Abhyuday Mandal, C. Devon Lin, and Xinwei Deng (2022) <doi:10.1137/19M1288462>.

Authors:Jiayi Li [cre, aut], Qian Xiao [aut], Abhyuday Mandal [aut], C. Devon Lin [aut], Xinwei Deng [aut]

EzGP_0.1.0.tar.gz
EzGP_0.1.0.tar.gz(r-4.5-noble)EzGP_0.1.0.tar.gz(r-4.4-noble)
EzGP_0.1.0.tgz(r-4.4-emscripten)EzGP_0.1.0.tgz(r-4.3-emscripten)
EzGP.pdf |EzGP.html✨
EzGP/json (API)
NEWS

# Install 'EzGP' in R:
install.packages('EzGP', repos = 'https://cloud.r-project.org')
Datasets:
  • EzGP_data - Dataset for the example in function 'EzGP_fit'
  • LEzGP_data - Dataset for the example in function 'LEzGP_fit'

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

1.00 score 123 downloads 7 exports 1 dependencies

Last updated 2 years agofrom:b6dc0e5854. Checks:1 OK, 2 NOTE. Indexed: yes.

TargetResultLatest binary
Doc / VignettesOKMar 24 2025
R-4.5-linuxNOTEMar 24 2025
R-4.4-linuxNOTEMar 24 2025

Exports:cov_mEEzGP_fitEEzGP_predictEzGP_fitEzGP_predictLEzGP_fitLLF_gradients

Dependencies:nloptr

Citation

To cite package ‘EzGP’ in publications use:

Li J, Xiao Q, Mandal A, Lin C, Deng X (2023). EzGP: Easy-to-Interpret Gaussian Process Models for Computer Experiments. R package version 0.1.0, https://CRAN.R-project.org/package=EzGP.

Corresponding BibTeX entry: 

  @Manual{,
    title = {EzGP: Easy-to-Interpret Gaussian Process Models for
      Computer Experiments},
    author = {Jiayi Li and Qian Xiao and Abhyuday Mandal and C. Devon
      Lin and Xinwei Deng},
    year = {2023},
    note = {R package version 0.1.0},
    url = {https://CRAN.R-project.org/package=EzGP},
  }