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
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

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Reference manual: GADAG.pdf
Package source: GADAG_0.99.0.tar.gz
Windows binaries: r-devel: GADAG_0.99.0.zip, r-devel-gcc8: GADAG_0.99.0.zip, r-release: GADAG_0.99.0.zip, r-oldrel: GADAG_0.99.0.zip
OS X binaries: r-release: GADAG_0.99.0.tgz, r-oldrel: GADAG_0.99.0.tgz

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