Package: GADAG 0.99.1

Magali Champion

GADAG: A Genetic Algorithm for Learning Directed Acyclic Graphs

Learns sparse large Directed Acyclic Graphs with a combination of a convex program and a tailored genetic algorithm.

Authors:Magali Champion [aut, cre], Victor Picheny [aut], Matthieu Vignes [aut]

GADAG_0.99.1.tar.gz
GADAG_0.99.1.zip(r-4.7-x86_64)GADAG_0.99.1.zip(r-4.7-arm64)GADAG_0.99.1.zip(r-4.6-x86_64)GADAG_0.99.1.zip(r-4.6-arm64)GADAG_0.99.1.zip(r-4.5-x86_64)
GADAG_0.99.1.tgz(r-4.6-x86_64)GADAG_0.99.1.tgz(r-4.6-arm64)GADAG_0.99.1.tgz(r-4.5-x86_64)GADAG_0.99.1.tgz(r-4.5-arm64)
GADAG_0.99.1.tar.gz(r-4.7-arm64)GADAG_0.99.1.tar.gz(r-4.7-x86_64)GADAG_0.99.1.tar.gz(r-4.6-arm64)GADAG_0.99.1.tar.gz(r-4.6-x86_64)
GADAG_0.99.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
GADAG/json (API)

# Install 'GADAG' in R:
install.packages('GADAG', repos = c('https://magalichampion.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/magalichampion/gadag/issues

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
Datasets:

On CRAN:

Conda:

openblascpp

2.30 score 6 scripts 435 downloads 6 exports 17 dependencies

Last updated from:77ab48a443. Checks:15 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK120
linux-devel-x86_64OK114
source / vignettesOK207
linux-release-arm64OK117
linux-release-x86_64OK112
macos-release-arm64OK157
macos-release-x86_64OK265
macos-oldrel-arm64OK179
macos-oldrel-x86_64OK313
windows-devel-arm64OK339
windows-devel-x86_64OK149
windows-release-arm64OK379
windows-release-x86_64OK102
windows-oldrel-x86_64OK104
wasm-releaseOK98

Exports:evaluationfitnessGADAG_AnalyzeGADAG_CVGADAG_RungenerateToyData

Dependencies:clicpp11cvToolsDEoptimRglueigraphlatticelifecyclemagrittrMASSMatrixpkgconfigRcppRcppArmadillorlangrobustbasevctrs