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, . 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", .


News

Reference manual

It appears you don't have a PDF plugin for this browser. You can click here to download the reference manual.

install.packages("GaSP")

1.0.0 by William J. Welch, 6 months ago


Browse source code at https://github.com/cran/GaSP


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


Documentation:   PDF Manual  


GPL-3 license


Suggests testthat


See at CRAN