mcdabench: Benchmarking for Multi-Criteria Decision Analysis

Performs and benchmarks various Multi-Criteria Decision Analysis (MCDA) methods. MCDA is a decision-making framework used to evaluate and rank alternatives based on multiple conflicting criteria using normalization, weighting, and aggregation techniques. The package implements a wide range of MCDA methods including ARAS (Additive Ratio Assessment), AROMAN (Alternative Ranking Order Method Accounting for two-step Normalization), COCOSO (Combined Compromise Solution), CODAS (Combinative Distance-based Assessment), COPRAS (Complex Proportional Assessment), EDAS (Evaluation based on Distance from Average Solution), ELECTRE (Elimination and Choice Expressing Reality) family (I-IV), FUCA (Faire Un Choix Adequat), GRA (Grey Relational Analysis), MABAC (Multi-Attributive Border Approximation Area Comparison), MAIRCA (Multi-Attributive Ideal-Real Comparative Analysis), MARCOS (Measurement of Alternatives and Ranking according to Compromise Solution), MAUT (Multi-Attribute Utility Theory), MAVT (Multi-Attribute Value Theory), MEGAN (Multi-criteria Evaluation with Gradual-weighting and Aggregation of Normalized distance matrices), MOORA (Multi-Objective Optimization on the basis of Ratio Analysis), OCRA (Operational Competitiveness Rating Analysis), ORESTE (Organisation, Rangement Et Synthese De Donnees Relationnelles), PROMETHEE (Preference Ranking Organization Method for Enrichment Evaluations I-VI), RAM (Root Assessment Method), ROV (Range of Value), SMART (Simple Multi-Attribute Rating Technique), TOPSIS (Technique for Order Preference by Similarity to Ideal Solution), VIKOR (VlseKriterijumska Optimizacija I Kompromisno Resenje), WASPAS (Weighted Aggregated Sum Product Assessment), WPM (Weighted Product Model), and WSM (Weighted Sum Model). The package computes comparative evaluation measures including Spearman rank correlation (Spearman, 1904) <doi:10.2307/1412107>, Salabun-Urbaniak's weight similarity index (Salabun and Urbaniak, 2020)<doi:10.1007/978-3-030-50417-5_47>, Wilcoxon signed-rank test (Wilcoxon, 1945)<doi:10.2307/3001968>, and permutation- and bootstrap- based entropy difference tests for pairwise method comparisons using Jensen-Shannon divergence (Lin, 1991)<doi:10.1109/18.61115>. It also provides sensitivity and stability analysis of MCDA results. Weight sensitivity analysis is implemented through deterministic and stochastic perturbation of criterion weights, and is also integrated as a built-in step within the MEGAN method framework (Cebeci, 2026)<doi:10.7717/peerj-cs.3819>.

Version:	1.1.1
Depends:	R (≥ 4.5.0)
Imports:	factoextra, ggplot2, gplots, igraph, monochromeR, networkD3
Suggests:	knitr, rmarkdown
Published:	2026-05-12
DOI:	10.32614/CRAN.package.mcdabench
Author:	Cagatay Cebeci [aut, cre]
Maintainer:	Cagatay Cebeci <cebecicagatay at gmail.com>
License:	GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation:	no
Citation:	mcdabench citation info
Materials:	README, NEWS
CRAN checks:	mcdabench results

Documentation:

Reference manual:	mcdabench.html , mcdabench.pdf
Vignettes:	Comparison of Multi-Criteria Decision Making Methods with mcdabench (source, R code) Multi-Criteria Decision Making Using MEGAN Algorithm in mcdabench Package in R (source, R code) Sensitivity & Stability Analysis for MCDA Methods (source, R code)

Downloads:

Package source:	mcdabench_1.1.1.tar.gz
Windows binaries:	r-devel: not available, r-release: mcdabench_1.1.1.zip, r-oldrel: not available
macOS binaries:	r-release (arm64): mcdabench_1.1.1.tgz, r-oldrel (arm64): mcdabench_1.1.1.tgz, r-release (x86_64): mcdabench_1.1.1.tgz, r-oldrel (x86_64): mcdabench_1.1.1.tgz

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