Package: GaSP 1.0.6

William J. Welch

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.tar.gz(r-4.5-noble)GaSP_1.0.6.tar.gz(r-4.4-noble)
GaSP_1.0.6.tgz(r-4.4-emscripten)GaSP_1.0.6.tgz(r-4.3-emscripten)
GaSP.pdf |GaSP.html✨
GaSP/json (API)

# InstallGaSP in R:
install.packages('GaSP',repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Datasets:
  • borehole - Data for the borehole function

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

14 exports 0.23 score 0 dependencies 195 downloads

Last updated 8 days agofrom:38efb7ec4e

Exports:CrossValidateDescribeXFitGaSPModelPlotAllPlotJointEffectsPlotMainEffectsPlotPredictionsPlotQQPlotResidualsPlotStdResidualsPredictRMSEVisualize

Dependencies:

GaSP: Train and Apply a Gaussian Stochastic Process Model

Yilin Yang and William J. Welch

Rendered fromGaSP_vignette.Rmdusingknitr::rmarkdownon Jun 28 2024.

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