Easy-to-Interpret Gaussian Process Models for Computer Experiments R package EzGP version 0.1.0 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>. Software Depends: R (>= 4.2.0), stats (>= 4.2.0) Imports: methods (>= 4.2.0), nloptr (>= 2.0.3) Suggests: testthat (>= 3.0.0) Jiayi Li <jiayili0123@outlook.com> Comprehensive R Archive Network (CRAN) Jiayi Li [cre, aut], Qian Xiao [aut], Abhyuday Mandal [aut], C. Devon Lin [aut], Xinwei Deng [aut] GPL-2 2023-07-06 application/tgz https://CRAN.R-project.org/package=EzGP doi:10.32614/CRAN.package.EzGP