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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.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 form https://CRAN.R-project.org/package=GADAG 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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