GaSP: Train and Apply a Gaussian Stochastic Process Model
Train a Gaussian stochastic process model of an unknown function, possibly observed with error, via maximum likelihood or maximum a posteriori (MAP) estimation, run model diagnostics, and make predictions, following Sacks, J., Welch, W.J., Mitchell, T.J., and Wynn, H.P. (1989) "Design and Analysis of Computer Experiments", Statistical Science, <doi:10.1214/ss/1177012413>. Perform sensitivity analysis and visualize low-order effects, following Schonlau, M. and Welch, W.J. (2006), "Screening the Input Variables to a Computer Model Via Analysis of Variance and Visualization", <doi:10.1007/0-387-28014-6_14>.
| Version: |
1.0.6 |
| Depends: |
R (≥ 3.5.0) |
| Suggests: |
markdown, rmarkdown, knitr, testthat |
| Published: |
2024-06-27 |
| DOI: |
10.32614/CRAN.package.GaSP |
| Author: |
William J. Welch 
[aut, cre, cph] (<https://orcid.org/0000-0002-4575-3124>),
Yilin Yang [aut]
(<https://orcid.org/0000-0003-0885-6017>) |
| Maintainer: |
William J. Welch <will at stat.ubc.ca> |
| License: |
GPL-3 |
| NeedsCompilation: |
yes |
| Materials: |
README |
| CRAN checks: |
GaSP results |
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