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>.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
0.1.0 |
rolling source/ R- | EzGP_0.1.0.tar.gz |
676.7 KiB |
0.1.0 |
latest source/ R- | EzGP_0.1.0.tar.gz |
676.7 KiB |
0.1.0 |
2026-04-23 source/ R- | EzGP_0.1.0.tar.gz |
676.7 KiB |
0.1.0 |
2026-04-09 windows/windows R-4.5 | EzGP_0.1.0.zip |
781.6 KiB |