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# CITATION file created with {cffr} R package
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cff-version: 1.2.0
message: 'To cite package "HRTnomaly" in publications use:'
type: software
license: AGPL-3.0-only
title: 'HRTnomaly: Historical, Relational, and Tail Anomaly-Detection Algorithms'
version: 25.2.25
doi: 10.32614/CRAN.package.HRTnomaly
abstract: The presence of outliers in a dataset can substantially bias the results
  of statistical analyses. To correct for outliers, micro edits are manually performed
  on all records. A set of constraints and decision rules is typically used to aid
  the editing process. However, straightforward decision rules might overlook anomalies
  arising from disruption of linear relationships. Computationally efficient methods
  are provided to identify historical, tail, and relational anomalies at the data-entry
  level (Sartore et al., 2024; <https://doi.org/10.6339/24-JDS1136>). A score statistic
  is developed for each anomaly type, using a distribution-free approach motivated
  by the Bienaymé-Chebyshev's inequality, and fuzzy logic is used to detect cellwise
  outliers resulting from different types of anomalies. Each data entry is individually
  scored and individual scores are combined into a final score to determine anomalous
  entries. In contrast to fuzzy logic, Bayesian bootstrap and a Bayesian test based
  on empirical likelihoods are also provided as studied by Sartore et al. (2024; <https://doi.org/10.3390/stats7040073>).
  These algorithms allow for a more nuanced approach to outlier detection, as it can
  identify outliers at data-entry level which are not obviously distinct from the
  rest of the data. --- This research was supported in part by the U.S. Department
  of Agriculture, National Agriculture Statistics Service. The findings and conclusions
  in this publication are those of the authors and should not be construed to represent
  any official USDA, or US Government determination or policy.
authors:
- family-names: Sartore
  given-names: Luca
  email: luca.sartore@usda.gov
- family-names: Sartore
  given-names: Luca
  email: drwolf85@gmail.com
- family-names: Chen
  given-names: Lu
  email: lu.chen@usda.gov
- family-names: Wart
  given-names: Justin
  name-particle: van
  email: justin.vanwart@usda.gov
- family-names: Dau
  given-names: Andrew
  email: andrew.dau@usda.gov
- family-names: Bejleri
  given-names: Valbona
  email: valbona.bejleri@usda.gov
repository: https://drwolf85.r-universe.dev
commit: ea019761e28634192c2283b7728747e67785c078
date-released: '2025-02-25'
contact:
- family-names: Sartore
  given-names: Luca
  email: drwolf85@gmail.com