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

Documentation:

Reference manual: GaSP.pdf
Vignettes: GaSP: Train and Apply a Gaussian Stochastic Process Model

Downloads:

Package source: GaSP_1.0.6.tar.gz
Windows binaries: r-devel: GaSP_1.0.6.zip, r-release: GaSP_1.0.6.zip, r-oldrel: GaSP_1.0.6.zip
macOS binaries: r-release (arm64): GaSP_1.0.6.tgz, r-oldrel (arm64): GaSP_1.0.6.tgz, r-release (x86_64): GaSP_1.0.6.tgz, r-oldrel (x86_64): GaSP_1.0.6.tgz
Old sources: GaSP archive

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