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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>.
Getting started
Browse package contents
Package details |
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| 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@stat.ubc.ca> |
| License | GPL-3 |
| Version | 1.0.4 |
| Package repository | View on CRAN |
| Installation |
Install the latest version of this package by entering the following in R:
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