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GADAG: A Genetic Algorithm for Learning Directed Acyclic Graphs

Sparse large Directed Acyclic Graphs learning with a combination of a convex program and a tailored genetic algorithm (see Champion et al. (2017) <https://hal.archives-ouvertes.fr/hal-01172745v2/document>).

Version:0.99.0
Depends:igraph,MASS
Imports:Rcpp (≥ 0.12.5)
LinkingTo:Rcpp,RcppArmadillo
Published:2017-04-11
DOI:10.32614/CRAN.package.GADAG
Author:Magali Champion, Victor Picheny and Matthieu Vignes
Maintainer:Magali Champion <magali.champion at parisdescartes.fr>
License:GPL-2
NeedsCompilation:yes
CRAN checks:GADAG results

Documentation:

Reference manual:GADAG.html ,GADAG.pdf

Downloads:

Package source: GADAG_0.99.0.tar.gz
Windows binaries: r-devel:GADAG_0.99.0.zip, r-release:GADAG_0.99.0.zip, r-oldrel:GADAG_0.99.0.zip
macOS binaries: r-release (arm64):GADAG_0.99.0.tgz, r-oldrel (arm64):GADAG_0.99.0.tgz, r-release (x86_64):GADAG_0.99.0.tgz, r-oldrel (x86_64):GADAG_0.99.0.tgz

Linking:

Please use the canonical formhttps://CRAN.R-project.org/package=GADAGto link to this page.


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