GPGame: Solving Complex Game Problems using Gaussian Processes

Sequential strategies for finding a game equilibrium are proposed in a black-box setting (expensive pay-off evaluations, no derivatives). The algorithm handles noiseless or noisy evaluations. Two acquisition functions are available. Graphical outputs can be generated automatically. V. Picheny, M. Binois, A. Habbal (2018) <doi:10.1007/s10898-018-0688-0>. M. Binois, V. Picheny, P. Taillandier, A. Habbal (2020) <doi:10.48550/arXiv.1902.06565>.

Version:	1.2.0
Imports:	Rcpp (≥ 0.12.5), DiceKriging, GPareto, KrigInv, DiceDesign, MASS, mnormt, mvtnorm, methods, matrixStats
LinkingTo:	Rcpp
Suggests:	DiceOptim, testthat
Published:	2022-01-23
DOI:	10.32614/CRAN.package.GPGame
Author:	Victor Picheny [aut, cre], Mickael Binois [aut]
Maintainer:	Victor Picheny <victor.picheny at inra.fr>
BugReports:	https://github.com/vpicheny/GPGame/issues
License:	GPL-3
URL:	https://github.com/vpicheny/GPGame
NeedsCompilation:	yes
Materials:	README NEWS
CRAN checks:	GPGame results

Documentation:

Reference manual:

GPGame.pdf

Downloads:

Package source:	GPGame_1.2.0.tar.gz
Windows binaries:	r-devel: GPGame_1.2.0.zip, r-release: GPGame_1.2.0.zip, r-oldrel: GPGame_1.2.0.zip
macOS binaries:	r-release (arm64): GPGame_1.2.0.tgz, r-oldrel (arm64): GPGame_1.2.0.tgz, r-release (x86_64): GPGame_1.2.0.tgz, r-oldrel (x86_64): GPGame_1.2.0.tgz
Old sources:	GPGame archive

Linking:

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