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:

Reference manual: EzGP.pdf

Downloads:

Package source: EzGP_0.1.0.tar.gz
Windows binaries: r-prerel: EzGP_0.1.0.zip, r-release: EzGP_0.1.0.zip, r-oldrel: EzGP_0.1.0.zip
macOS binaries: r-prerel (arm64): EzGP_0.1.0.tgz, r-release (arm64): EzGP_0.1.0.tgz, r-oldrel (arm64): EzGP_0.1.0.tgz, r-prerel (x86_64): EzGP_0.1.0.tgz, r-release (x86_64): EzGP_0.1.0.tgz

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