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It implements a new procedure of variable selection in the context of redundancy between explanatory variables, which holds true with high dimensional data (Grimonprez et al. (2023) <doi:10.18637/jss.v106.i03>).
Version: | 1.0.0 |
Imports: | gglasso, MASS, Matrix, fastcluster, FactoMineR, parallelDist |
Published: | 2023-03-15 |
DOI: | 10.32614/CRAN.package.MLGL |
Author: | Quentin Grimonprez [aut, cre], Samuel Blanck [ctb], Alain Celisse [ths], Guillemette Marot [ths], Yi Yang [ctb], Hui Zou [ctb] |
Maintainer: | Quentin Grimonprez <quentingrim at yahoo.fr> |
BugReports: | https://github.com/modal-inria/MLGL/issues |
License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
Copyright: | Inria |
NeedsCompilation: | no |
Citation: | MLGL citation info |
Materials: | NEWS |
CRAN checks: | MLGL results |
Reference manual: | MLGL.pdf |
Package source: | MLGL_1.0.0.tar.gz |
Windows binaries: | r-devel: MLGL_1.0.0.zip, r-release: MLGL_1.0.0.zip, r-oldrel: MLGL_1.0.0.zip |
macOS binaries: | r-release (arm64): MLGL_1.0.0.tgz, r-oldrel (arm64): MLGL_1.0.0.tgz, r-release (x86_64): MLGL_1.0.0.tgz, r-oldrel (x86_64): MLGL_1.0.0.tgz |
Old sources: | MLGL archive |
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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.