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], Yilin Yang [aut]
Maintainer:	William J. Welch <will at stat.ubc.ca>
License:	GPL-3
NeedsCompilation:	yes
Materials:	README
CRAN checks:	GaSP results

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