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
DOI: 10.32614/CRAN.package.CGP
Author: Shan Ba and V. Roshan Joseph
Maintainer: Shan Ba <shanbatr at>
License: LGPL-2.1
NeedsCompilation: no
CRAN checks: CGP results


Reference manual: CGP.pdf


Package source: CGP_2.1-1.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
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


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