CGP: Composite Gaussian Process Models

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.

Version:	2.1-1
Published:	2018-06-12
Author:	Shan Ba and V. Roshan Joseph
Maintainer:	Shan Ba <shanbatr at gmail.com>
License:	LGPL-2.1
NeedsCompilation:	no
CRAN checks:	CGP results

Documentation:

Reference manual:

CGP.pdf

Downloads:

Package source:	CGP_2.1-1.tar.gz
Windows binaries:	r-devel: CGP_2.1-1.zip, r-release: CGP_2.1-1.zip, r-oldrel: CGP_2.1-1.zip
macOS binaries:	r-release (arm64): CGP_2.1-1.tgz, r-oldrel (arm64): CGP_2.1-1.tgz, r-release (x86_64): CGP_2.1-1.tgz, r-oldrel (x86_64): CGP_2.1-1.tgz
Old sources:	CGP archive

Reverse dependencies:

Reverse suggests:

IGP

Linking:

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