EzGP: Easy-to-Interpret Gaussian Process Models for ComputerExperiments
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 |
| DOI: | 10.32614/CRAN.package.EzGP |
| 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 |
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