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) <arXiv:1902.06565v2>.

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
Author: Victor Picheny ORCID iD [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

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