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# CITATION.cff file created with {cffr} R package
# See also: https://docs.ropensci.org/cffr/
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cff-version: 1.2.0
message: 'To cite package "labelnoise" in publications use:'
type: software
license: MIT
title: 'labelnoise: Find Label Errors in Classification Data with Confident Learning'
version: 0.1.0
doi: 10.32614/CRAN.package.labelnoise
abstract: 'Finds examples whose given class label is likely wrong, using out-of-sample
  predicted probabilities from any classifier. Implements confident learning (Northcutt,
  Jiang and Chuang, 2021) <https://doi.org/10.1613/jair.1.12125>: per-class confidence
  thresholds, the confident joint, calibrated estimates of the joint distribution
  of given and true labels, noise transition matrices, and five rank-and-prune rules
  for flagging label issues. Also provides model-agnostic label quality scores (Kuan
  and Mueller, 2022) <https://doi.org/10.5281/zenodo.18168246> and a per-class label
  health summary. Inputs can be a label vector with a probability matrix, a data frame
  of predictions, resampling results from ''tune'', predictions from ''mlr3'', or
  a fitted ''caret'' model.'
authors:
- family-names: Al Farizal P
  given-names: Ridson
  email: ridsonalfarizal15@gmail.com
  orcid: https://orcid.org/0000-0003-0617-0214
repository: https://ridsonap.r-universe.dev
repository-code: https://github.com/ridsonap/labelnoise
commit: ec6b1091189f00dda9f4d5a04b4220b723028237
url: https://ridsonap.is-a.dev/labelnoise/
date-released: '2026-10-28'
contact:
- family-names: Al Farizal P
  given-names: Ridson
  email: ridsonalfarizal15@gmail.com
  orcid: https://orcid.org/0000-0003-0617-0214