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>.