# Welcome to pyrepo-mcda documentation!

pyrepo-mcda is Python 3 library for Multi-Criteria Decision Analysis. This library includes:

• MCDA methods:

• `TOPSIS`

• `CODAS`

• `MABAC`

• `MULTIMOORA`

• `MOORA`

• `VIKOR`

• `WASPAS`

• `EDAS`

• `SPOTIS`

• `AHP`

• `ARAS`

• `COPRAS`

• `CRADIS`

• `MARCOS`

• `PROMETHEE II`

• `PROSA C`

• `SAW`

• Distance metrics:

• `euclidean` (Euclidean distance)

• `manhattan` (Manhattan distance)

• `hausdorff` (Hausdorff distance)

• `correlation` (Correlation distance)

• `chebyshev` (Chebyshev distance)

• `std_euclidean` (Standardized Euclidean distance)

• `cosine` (Cosine distance)

• `csm` (Cosine similarity measure)

• `squared_euclidean` (Squared Euclidean distance)

• `bray_curtis` (Sorensen or Bray-Curtis distance)

• `canberra` (Canberra distance)

• `lorentzian` (Lorentzian distance)

• `jaccard` (Jaccard distance)

• `dice` (Dice distance)

• `bhattacharyya` (Bhattacharyya distance)

• `hellinger` (Hellinger distance)

• `matusita` (Matusita distance)

• `squared_chord` (Squared-chord distance)

• `pearson_chi_square` (Pearson chi square distance)

• `squared_chi_square` (Sqaured chi square distance)

• Correlation coefficients:

• `spearman` (Spearman rank correlation coefficient)

• `weighted_spearman` (Weighted Spearman rank correlation coefficient)

• `pearson_coeff` (Pearson correlation coefficient)

• `WS_coeff` (Similarity rank coefficient - WS coefficient)

• Methods for normalization of decision matrix:

• `linear_normalization` (Linear normalization)

• `minmax_normalization` (Minimum-Maximum normalization)

• `max_normalization` (Maximum normalization)

• `sum_normalization` (Sum normalization)

• `vector_normalization` (Vector normalization)

• `multimoora_normalization` (Normalization method dedicated for the MULTIMOORA method)

• Objective weighting methods for determining criteria weights required by Multi-Criteria Decision Analysis (MCDA) methods:

• `equal_weighting` (Equal weighting method)

• `entropy_weighting` (Entropy weighting method)

• `std_weighting` (Standard deviation weighting method)

• `critic_weighting` (CRITIC weighting method)

• `gini_weighting` (Gini coefficient-based weighting method)

• `merec_weighting` (MEREC weighting method)

• `stat_var_weighting` (Statistical variance weighting method)

• `cilos_weighting` (CILOS weighting method)

• `idocriw_weighting` (IDOCRIW weighting method)

• `angle_weighting` (Angle weighting method)

• `coeff_var_weighting` (Coefficient of variation weighting method)

• Stochastic Multicriteria Acceptability Analysis Method - SMAA combined with VIKOR (`VIKOR_SMAA`)

• Methods for determination of compromise rankings based on several rankings obtained with different MCDA methods:

• `copeland` (the Copeland method for compromise ranking)

• `dominance_directed_graph` (Dominance Directed Graph for compromise ranking)

• `rank_position_method` (Rank Position Method for compromise ranking)

• `improved_borda_rule` (Improved Borda Rule method for compromise for MULTIMOORA method)

• Methods for sensitivity analysis:

• `Sensitivity_analysis_weights_percentages` (Method for sensitivity analysis considering percentage modification of criteria weights)

• `Sensitivity_analysis_weights_values` (Method for sensitivity analysis considering setting different values as chosen criterion weight)

• `rank_preferences` (Method for ordering alternatives according to their preference values obtained with MCDA methods)