Package: GaSP
Type: Package
Title: Train and Apply a Gaussian Stochastic Process Model
Version: 1.0.1
Authors@R: c(
    person(given = "William J.",
           family = "Welch",
           role = c("aut", "cre", "cph"),
           email = "will@stat.ubc.ca",
           comment = c(ORCID = "0000-0002-4575-3124")),
    person(given = "Yilin",
           family = "Yang",
           role = c("aut"),
           email = "yangyl17@students.cs.ubc.ca",
           comment = c(ORCID = "0000-0003-0885-6017"))
           )
Description: 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>.
Depends: R (>= 3.5.0)
Suggests: markdown, rmarkdown, knitr, testthat
License: GPL-3
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.1.2
VignetteBuilder: knitr
NeedsCompilation: yes
Packaged: 2022-01-18 04:26:53 UTC; will
Author: William J. Welch [aut, cre, cph]
    (<https://orcid.org/0000-0002-4575-3124>),
  Yilin Yang [aut] (<https://orcid.org/0000-0003-0885-6017>)
Maintainer: William J. Welch <will@stat.ubc.ca>
Repository: CRAN
Date/Publication: 2022-01-18 07:42:53 UTC
Built: R 4.0.5; x86_64-w64-mingw32; 2022-04-21 02:30:39 UTC; windows
Archs: i386, x64
