Package: GaSP 1.0.6

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>.

Authors:William J. Welch [aut, cre, cph], Yilin Yang [aut]

GaSP_1.0.6.tar.gz
GaSP_1.0.6.zip(r-4.7)GaSP_1.0.6.zip(r-4.6)GaSP_1.0.6.zip(r-4.5)
GaSP_1.0.6.tgz(r-4.6-x86_64)GaSP_1.0.6.tgz(r-4.6-arm64)GaSP_1.0.6.tgz(r-4.5-x86_64)GaSP_1.0.6.tgz(r-4.5-arm64)
GaSP_1.0.6.tar.gz(r-4.7-arm64)GaSP_1.0.6.tar.gz(r-4.7-x86_64)GaSP_1.0.6.tar.gz(r-4.6-arm64)GaSP_1.0.6.tar.gz(r-4.6-x86_64)
GaSP_1.0.6.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
GaSP/json (API)

# Install 'GaSP' in R:
install.packages('GaSP', repos = c('https://wsqlab.r-universe.dev', 'https://cloud.r-project.org'))
Datasets:
  • borehole - Data for the borehole function

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

2.70 score 8 scripts 137 downloads 1 mentions 14 exports 0 dependencies

Last updated from:38efb7ec4e. Checks:11 WARNING, 2 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64WARNING176
linux-devel-x86_64WARNING127
source / vignettesOK161
linux-release-arm64WARNING137
linux-release-x86_64WARNING127
macos-release-arm64WARNING159
macos-release-x86_64WARNING344
macos-oldrel-arm64WARNING231
macos-oldrel-x86_64WARNING430
windows-develWARNING147
windows-releaseWARNING132
windows-oldrelWARNING137
wasm-releaseOK100

Exports:CrossValidateDescribeXFitGaSPModelPlotAllPlotJointEffectsPlotMainEffectsPlotPredictionsPlotQQPlotResidualsPlotStdResidualsPredictRMSEVisualize

Dependencies:

GaSP: Train and Apply a Gaussian Stochastic Process Model

Yilin Yang and William J. Welch

Rendered fromGaSP_vignette.Rmdusingknitr::rmarkdownon Jun 02 2026.

Last update: 2023-05-18
Started: 2022-01-18