Package: CCNR 0.2.0

Daniel J.W. Touw

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>.

Authors:Daniel J.W. Touw [aut, cre], Michel Van de Velden [ths]

CCNR_0.2.0.tar.gz
CCNR_0.2.0.zip(r-4.7-x86_64)CCNR_0.2.0.zip(r-4.7-arm64)CCNR_0.2.0.zip(r-4.6-x86_64)CCNR_0.2.0.zip(r-4.6-arm64)
CCNR_0.2.0.tar.gz(r-4.7-arm64)CCNR_0.2.0.tar.gz(r-4.7-x86_64)CCNR_0.2.0.tar.gz(r-4.6-arm64)CCNR_0.2.0.tar.gz(r-4.6-x86_64)
CCNR_0.2.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
CCNR/json (API)

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

Bug tracker:https://github.com/djwtouw/ccnr/issues

Uses libs:
  • c++– GNU Standard C++ Library v3
Datasets:
  • amphibians - Presence of amphibian species at Polish water reservoirs

On CRAN:

Conda:

cpp

1.70 score 12 exports 3 dependencies

Last updated from:9f665b7348. Checks:10 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK103
linux-devel-x86_64OK130
source / vignettesOK206
linux-release-arm64OK108
linux-release-x86_64OK143
windows-devel-arm64OK206
windows-devel-x86_64OK153
windows-release-arm64OK193
windows-release-x86_64OK160
wasm-releaseOK100

Exports:ccnccn_cvccn_exact_matchccn_hamming_accuracyccn_log_likelihoodccn_macro_f1ccn_mean_aucccn_micro_f1conditional_entropy_matrixentropy_label_ordergenerate_datasetmultilabel_stratified_folds

Dependencies:MASSRcppRcppEigen

Readme and manuals