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>.
| Version: |
0.1.0 |
| Depends: |
R (≥ 4.2.0), stats (≥ 4.2.0) |
| Imports: |
methods (≥ 4.2.0), nloptr (≥ 2.0.3) |
| Suggests: |
testthat (≥ 3.0.0) |
| Published: |
2023-07-06 |
| Author: |
Jiayi Li [cre, aut],
Qian Xiao [aut],
Abhyuday Mandal [aut],
C. Devon Lin [aut],
Xinwei Deng [aut] |
| Maintainer: |
Jiayi Li <jiayili0123 at outlook.com> |
| License: |
GPL-2 |
| NeedsCompilation: |
no |
| Materials: |
NEWS |
| CRAN checks: |
EzGP results |
Documentation:
Downloads:
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