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CCNR: Classifier Chain Network for Multi-Label Classification

Fits a Classifier Chain Network (CCN) for multi-label binary classification. The model jointly learns label dependencies through a chain structure and optimizes an Lq norm aggregated cross-entropy loss. Provides methods for prediction, model summary, and k-fold cross-validation with grid search over hyperparameters. The methodology is described in Touw and Van de Velden (2025) <doi:10.1016/j.eswa.2025.128048>.

Version: 0.2.0
Depends: R (≥ 4.4.0)
Imports: Rcpp (≥ 1.0.9), MASS, stats, utils
LinkingTo: Rcpp, RcppEigen
Suggests: testthat (≥ 3.0.0)
Published: 2026-11-04
DOI: 10.32614/CRAN.package.CCNR
Author: Daniel J.W. Touw ORCID iD [aut, cre], Michel Van de Velden ORCID iD [ths] (PhD advisor of Daniel J.W. Touw)
Maintainer: Daniel J.W. Touw <touw at ese.eur.nl>
BugReports: https://github.com/djwtouw/CCNR/issues
License: GPL-3
URL: https://github.com/djwtouw/CCNR
NeedsCompilation: yes
Citation: CCNR citation info
Materials: README, NEWS
CRAN checks: CCNR results

Documentation:

Reference manual: CCNR.html , CCNR.pdf

Downloads:

Package source: CCNR_0.2.0.tar.gz
Windows binaries: r-devel: CCNR_0.2.0.zip, r-release: CCNR_0.2.0.zip, r-oldrel: CCNR_0.2.0.zip
macOS binaries: r-release (arm64): CCNR_0.2.0.tgz, r-oldrel (arm64): CCNR_0.2.0.tgz, r-release (x86_64): CCNR_0.2.0.tgz, r-oldrel (x86_64): CCNR_0.2.0.tgz

Linking:

Please use the canonical form https://CRAN.R-project.org/package=CCNR 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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