CGP: Composite Gaussian process models

Share:

Fit composite Gaussian process (CGP) models as described in Ba and Joseph (2012) "Composite Gaussian Process Models for Emulating Expensive Functions", Annals of Applied Statistics. The CGP model is capable of approximating complex surfaces that are not second-order stationary. Important functions in this package are CGP, print.CGP, summary.CGP, predict.CGP and plotCGP.

Author
Shan Ba and V. Roshan Joseph
Date of publication
2014-09-21 07:43:39
Maintainer
Shan Ba <shanbatr@gmail.com>
License
LGPL-2.1
Version
2.0-2

View on CRAN

Man pages

CGP
Composite Gaussian process models
CGP.default
Default method for the CGP class
CGPEst
Estimate composite Gaussian process models
CGP-package
The composite Gaussian process model package
plotCGP
Jackknife (leave-one-out) actual by predicted diagnostic plot
predict.CGP
Predict from the composite Gaussian process model
print.CGP
CGP model summary information
summary.CGP
CGP model summary information

Files in this package

CGP
CGP/NAMESPACE
CGP/R
CGP/R/CGP.default.R
CGP/R/summary.CGP.R
CGP/R/CGPEst.R
CGP/R/predict.CGP.R
CGP/R/plotCGP.R
CGP/R/CGP.R
CGP/R/print.CGP.R
CGP/MD5
CGP/DESCRIPTION
CGP/man
CGP/man/print.CGP.Rd
CGP/man/CGP-package.Rd
CGP/man/summary.CGP.Rd
CGP/man/CGP.Rd
CGP/man/CGPEst.Rd
CGP/man/CGP.default.Rd
CGP/man/plotCGP.Rd
CGP/man/predict.CGP.Rd