utiml: Utilities for Multi-Label Learning

Multi-label learning strategies and others procedures to support multi- label classification in R. The package provides a set of multi-label procedures such as sampling methods, transformation strategies, threshold functions, pre-processing techniques and evaluation metrics. A complete overview of the matter can be seen in Zhang, M. and Zhou, Z. (2014) <doi:10.1109/TKDE.2013.39> and Gibaja, E. and Ventura, S. (2015) A Tutorial on Multi-label Learning.

Version:	0.1.7
Depends:	R (≥ 3.0.0), mldr (≥ 0.4.0), parallel, ROCR
Imports:	stats, utils, methods
Suggests:	C50, e1071, infotheo, kknn, knitr, randomForest, rmarkdown, markdown, rpart, testthat, xgboost (≥ 0.6-4)
Published:	2021-05-31
Author:	Adriano Rivolli [aut, cre]
Maintainer:	Adriano Rivolli <rivolli at utfpr.edu.br>
BugReports:	https://github.com/rivolli/utiml
License:	GPL-3
URL:	https://github.com/rivolli/utiml
NeedsCompilation:	no
Materials:	README NEWS
CRAN checks:	utiml results

Documentation:

Reference manual:	utiml.pdf
Vignettes:	utiml: Utilities for Multi-label Learning

Downloads:

Package source:	utiml_0.1.7.tar.gz
Windows binaries:	r-devel: utiml_0.1.7.zip, r-release: utiml_0.1.7.zip, r-oldrel: utiml_0.1.7.zip
macOS binaries:	r-release (arm64): utiml_0.1.7.tgz, r-oldrel (arm64): utiml_0.1.7.tgz, r-release (x86_64): utiml_0.1.7.tgz, r-oldrel (x86_64): utiml_0.1.7.tgz
Old sources:	utiml archive

Linking:

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