hetGP: Heteroskedastic Gaussian Process Modeling and Design under Replication

Performs Gaussian process regression with heteroskedastic noise following Binois, M., Gramacy, R., Ludkovski, M. (2016) <arXiv:1611.05902>. The input dependent noise is modeled as another Gaussian process. Replicated observations are encouraged as they yield computational savings. Sequential design procedures based on the integrated mean square prediction error and lookahead heuristics are provided, and notably fast update functions when adding new observations.

Version: 1.1.2
Depends: R (≥ 2.10)
Imports: Rcpp (≥ 0.12.3), MASS, methods, DiceDesign
LinkingTo: Rcpp
Suggests: knitr, monomvn, lhs, colorspace
Published: 2019-10-24
Author: Mickael Binois, Robert B. Gramacy
Maintainer: Mickael Binois <mickael.binois at inria.fr>
License: LGPL-2 | LGPL-2.1 | LGPL-3 [expanded from: LGPL]
NeedsCompilation: yes
Materials: NEWS
CRAN checks: hetGP results

Downloads:

Reference manual: hetGP.pdf
Vignettes: a guide to the hetGP package
Package source: hetGP_1.1.2.tar.gz
Windows binaries: r-devel: hetGP_1.1.2.zip, r-release: hetGP_1.1.2.zip, r-oldrel: hetGP_1.1.2.zip
macOS binaries: r-release: hetGP_1.1.2.tgz, r-oldrel: hetGP_1.1.2.tgz
Old sources: hetGP archive

Reverse dependencies:

Reverse imports: activegp, quantkriging

Linking:

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