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GPareto: Gaussian Processes for Pareto Front Estimation and Optimization

Gaussian process regression models, a.k.a. Kriging models, are applied to global multi-objective optimization of black-box functions. Multi-objective Expected Improvement and Step-wise Uncertainty Reduction sequential infill criteria are available. A quantification of uncertainty on Pareto fronts is provided using conditional simulations.

Version: 1.1.8
Depends: DiceKriging, emoa
Imports: Rcpp (≥ 0.12.15), methods, rgenoud, pbivnorm, pso, randtoolbox, KrigInv, MASS, DiceDesign, ks, rgl
LinkingTo: Rcpp
Suggests: knitr, DiceOptim
Published: 2024-01-26
DOI: 10.32614/CRAN.package.GPareto
Author: Mickael Binois, Victor Picheny
Maintainer: Mickael Binois <mickael.binois at inria.fr>
BugReports: https://github.com/mbinois/GPareto/issues
License: GPL-3
URL: https://github.com/mbinois/GPareto
NeedsCompilation: yes
Citation: GPareto citation info
Materials: README NEWS
In views: Optimization
CRAN checks: GPareto results

Documentation:

Reference manual: GPareto.pdf
Vignettes: a guide to the GPareto package

Downloads:

Package source: GPareto_1.1.8.tar.gz
Windows binaries: r-devel: GPareto_1.1.8.zip, r-release: GPareto_1.1.8.zip, r-oldrel: GPareto_1.1.8.zip
macOS binaries: r-release (arm64): GPareto_1.1.8.tgz, r-oldrel (arm64): GPareto_1.1.8.tgz, r-release (x86_64): GPareto_1.1.8.tgz, r-oldrel (x86_64): GPareto_1.1.8.tgz
Old sources: GPareto archive

Reverse dependencies:

Reverse imports: GPGame
Reverse suggests: biopixR, DiceOptim

Linking:

Please use the canonical form https://CRAN.R-project.org/package=GPareto to link to this page.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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