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 | 
| 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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