Package: CCNR 0.2.0

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.zip(r-4.5-x86_64)
CCNR_0.2.0.tgz(r-4.6-x86_64)CCNR_0.2.0.tgz(r-4.6-arm64)CCNR_0.2.0.tgz(r-4.5-x86_64)CCNR_0.2.0.tgz(r-4.5-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://djwtouw.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

3.18 score 6 downloads 12 exports 3 dependencies

Last updated from:8919cbce0a. Checks:15 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK138
linux-devel-x86_64OK150
source / vignettesOK186
linux-release-arm64OK115
linux-release-x86_64OK160
macos-release-arm64OK248
macos-release-x86_64OK220
macos-oldrel-arm64OK259
macos-oldrel-x86_64OK519
windows-devel-arm64OK364
windows-devel-x86_64OK123
windows-release-arm64OK360
windows-release-x86_64OK118
windows-oldrel-x86_64OK78
wasm-releaseOK103

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

Help Manual

Help pageTopics
CCNR: Classifier Chain Network for Multi-Label ClassificationCCNR-package CCNR
Presence of amphibian species at Polish water reservoirsamphibians
Fit a Classifier Chain Networkccn
Cross-validation for a Classifier Chain Networkccn_cv
Exact match ratio scoring functionccn_exact_match
Hamming accuracy scoring functionccn_hamming_accuracy
Mean log-likelihood scoring functionccn_log_likelihood
Macro-F1 scoring functionccn_macro_f1
Mean AUC scoring functionccn_mean_auc
Micro-F1 scoring functionccn_micro_f1
Extract coefficients from a Classifier Chain Networkcoef.ccn
Extract Coefficients from a Cross-validated Classifier Chain Networkcoef.ccn_cv
Pairwise Conditional Entropies of the Labelsconditional_entropy_matrix
Chain Order Derived from the Entropy of the Labelsentropy_label_order
Extract fitted values from a Classifier Chain Networkfitted.ccn
Extract Fitted Values from a Cross-validated Classifier Chain Networkfitted.ccn_cv
Generate a synthetic multilabel classification datasetgenerate_dataset
Stratified fold assignments for multi-label datamultilabel_stratified_folds
Predict from a Classifier Chain Networkpredict.ccn
Predict from a Cross-validated Classifier Chain Networkpredict.ccn_cv
Print a Classifier Chain Networkprint.ccn
Print a Classifier Chain Network Cross-validationprint.ccn_cv
Extract residuals from a Classifier Chain Networkresiduals.ccn
Summarize a Classifier Chain Networksummary.ccn
Summarize Classifier Chain Network Cross-Validation Resultssummary.ccn_cv