Package for Multi-Criteria Decision Analysis with Objective Criteria Weighting
Project description
objective-weights-for-mcda
This is Python 3 library dedicated for multi-criteria decision analysis with criteria weights determined by objective weighting methods.
Installation
Downloading and installation of objective-weights-mcda
package can be done with using pip
pip install objective-weights-mcda
Methods
mcda_methods
includes:
vikor
with VIKOR method
Other modules include:
-
additions
includerank_preference
method for ranking alternatives according to MCDA score -
correlations
include:- Spearman rank correlation coefficient
spearman
, - Weighted Spearman rank correlation coefficient
weighted_spearman
, - Pearson correlation coefficient
pearson_coeff
- Spearman rank correlation coefficient
-
normalizations
with methods for decision matrix normalization:linear_normalization
- Linear normalization,minmax_normalization
- Minimum- Maximum normalization,max_normalization
- Maximum normalization,sum_normalization
- Sum normalization,vector_normalization
- Vector normalization
-
weighting_methods
include 11 objective weighting methods for determination of criteria weights (significance) without decision-maker involvement:equal_weighting
- Equal weighting methodentropy_weighting
- Entropy weighting methodstd_weighting
- Standard deviation weighting methodcritic_weighting
- CRITIC weighting methodgini_weighting
- Gini coefficient-based weighting methodmerec_weighting
- MEREC weighting methodstat_var_weighting
- Statistical variance weighting methodcilos_weighting
- CILOS weighting methodidocriw_weighting
- IDOCRIW weighting methodangle_weighting
- Angle weighting methodcoeff_var_weighting
- Coefficient of variation weighting method
Examples of usage of objective_weights_mcda
are provided on GitHub in examples
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