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# CITATION 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 "mcdabench" in publications use:'
type: software
license: GPL-2.0-or-later
title: 'mcdabench: Benchmarking for Multi-Criteria Decision Analysis'
version: 1.1.1
doi: 10.7717/peerj-cs.3819
identifiers:
- type: doi
  value: 10.32614/CRAN.package.mcdabench
abstract: Performs and benchmarks various Multi-Criteria Decision Analysis (MCDA)
  methods. The methods are designed to evaluate and rank alternatives based on multiple
  criteria, applying various normalization, weighting, and aggregation algorithms.
  The MCDA methods includes ARAS, AROMAN, COCOSO, CODAS, COPRAS, EDAS, ELECTRE family
  (I-IV), FUCA, GRA, MABAC, MAIRCA, MARCOS, MAUT, MAVT, MEGAN, MOORA, OCRA, ORETES,
  PROMETHEE family (I - VI), RAM, ROV, SMART, TOPSIS, VIKOR, WASPAS, WPM, WSM and
  others, facilitating flexible and efficient analyses for multi-criteria problems.
  Constructs the common comparison measures such as Spearman rank correlations, Salabun-Urbaniak
  weight similarities (WS), index of agreement, Wilcoxon rank sum test, Jensen-Shannon
  Divergence based permutation tests and entropy differences based bootstrap tests
  for pairwise comparisons in addition to various sensitivity and stability analyses.
  The weight sensitivity analysis is made using either gradual and random weight modification
  method, and this is builti-in step with MEGAN method.
authors:
- family-names: Cebeci
  given-names: Cagatay
  email: cebecicagatay@gmail.com
preferred-citation:
  type: article
  title: 'Multi-criteria evaluation with gradual-weighting and aggregation of normalised
    distance matrices: a case study in renewable energy grid selection'
  authors:
  - family-names: Cebeci
    given-names: Cagatay
    email: cebecicagatay@gmail.com
  year: '2026'
  journal: PeerJ Computer Science
  volume: '12'
  doi: 10.7717/peerj-cs.3819
  notes: 'Cebeci, C. (2026). Multi-criteria evaluation with gradual-weighting and
    aggregation of normalised distance matrices: a case study in renewable energy
    grid selection. PeerJ Computer Science, 12:e3819. https://doi.org/10.7717/peerj-cs.3819'
  start: e3819
repository: https://cagataycebeci.r-universe.dev
commit: 86d71551a476b40e953424f3610960e1457afc05
date-released: '2026-04-15'
contact:
- family-names: Cebeci
  given-names: Cagatay
  email: cebecicagatay@gmail.com