Package: EzGP
Title: Easy-to-Interpret Gaussian Process Models for Computer
        Experiments
Version: 0.1.0
Authors@R: c(
    person("Jiayi", "Li", email = "jiayili0123@outlook.com", role = c("cre", "aut")),
    person("Qian", "Xiao", email = "QIAN.XIAO@uga.edu", role = "aut"),
    person("Abhyuday", "Mandal", email = "abhyuday@uga.edu", role = "aut"),
    person("C. Devon", "Lin", email = "devon.lin@queensu.ca", role = "aut"),
    person("Xinwei", "Deng", email = "xdeng@vt.edu", role = "aut"))
Description: 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>.
License: GPL-2
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.2.0
Depends: R (>= 4.2.0), stats (>= 4.2.0)
Imports: methods (>= 4.2.0), nloptr (>= 2.0.3)
Suggests: testthat (>= 3.0.0)
NeedsCompilation: no
Packaged: 2026-06-23 10:05:23 UTC; root
Author: Jiayi Li [cre, aut], Qian Xiao [aut], Abhyuday Mandal [aut], C.
        Devon Lin [aut], Xinwei Deng [aut]
Maintainer: Jiayi Li <jiayili0123@outlook.com>
Repository: https://cran.r-universe.dev
Date/Publication: 2023-07-06 17:40:08 UTC
RemoteUrl: https://github.com/cran/EzGP
RemoteRef: HEAD
RemoteSha: b6dc0e5854758a00ddcfc9d98c7ab81fa45a8129