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 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: 1.0.0
Depends: R (≥ 3.5.0)
Suggests: testthat
Published: 2021-06-07
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

Downloads:

Reference manual: GaSP.pdf
Package source: GaSP_1.0.0.tar.gz
Windows binaries: r-devel: GaSP_1.0.0.zip, r-devel-UCRT: GaSP_1.0.0.zip, r-release: GaSP_1.0.0.zip, r-oldrel: GaSP_1.0.0.zip
macOS binaries: r-release (arm64): GaSP_1.0.0.tgz, r-release (x86_64): GaSP_1.0.0.tgz, r-oldrel: GaSP_1.0.0.tgz

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