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>.

Getting started

Package details

AuthorWilliam J. Welch [aut, cre, cph] (<https://orcid.org/0000-0002-4575-3124>), Yilin Yang [aut] (<https://orcid.org/0000-0003-0885-6017>)
MaintainerWilliam J. Welch <will@stat.ubc.ca>
LicenseGPL-3
Version1.0.0
Package repositoryView on CRAN
Installation Install the latest version of this package by entering the following in R:
install.packages("GaSP")

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GaSP documentation built on June 7, 2021, 5:08 p.m.