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mldr.resampling: Resampling Algorithms for Multi-Label Datasets

Collection of the state of the art multi-label resampling algorithms. The objective of these algorithms is to achieve balance in multi-label datasets.

Version: 0.2.3
Imports: data.table, e1071, mldr, pbapply, vecsets
Suggests: parallel
Published: 2023-08-22
DOI: 10.32614/CRAN.package.mldr.resampling
Author: Miguel Ángel Dávila [cre], Francisco Charte ORCID iD [aut], María José Del Jesus ORCID iD [aut], Antonio Rivera ORCID iD [aut]
Maintainer: Miguel Ángel Dávila <madr0008 at red.ujaen.es>
License: MIT + file LICENSE
NeedsCompilation: no
Materials: README NEWS
CRAN checks: mldr.resampling results

Documentation:

Reference manual: mldr.resampling.pdf

Downloads:

Package source: mldr.resampling_0.2.3.tar.gz
Windows binaries: r-devel: mldr.resampling_0.2.3.zip, r-release: mldr.resampling_0.2.3.zip, r-oldrel: mldr.resampling_0.2.3.zip
macOS binaries: r-release (arm64): mldr.resampling_0.2.3.tgz, r-oldrel (arm64): mldr.resampling_0.2.3.tgz, r-release (x86_64): mldr.resampling_0.2.3.tgz, r-oldrel (x86_64): mldr.resampling_0.2.3.tgz
Old sources: mldr.resampling archive

Linking:

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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.