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The documentation is provided [here](https://distance-metrics-for-mcda.readthedocs.io/en/latest/)\n\n# Installation\n\n```\npip install distance-metrics-mcda\n```\n\n# Methods\n\nThis is Python 3 library providing package `distance_metrics_mcda` that includes metrics that can measure alternatives distance from \nreference solutions in multi-criteria decision analysis. This library contains module `weighting_methods` with the following distance metrics:\n\n- Euclidean distance `euclidean`\n- Manhattan (Taxicab) distance `manhattan`\n- Hausdorff distance `hausdorff`\n- Correlation distance `correlation`\n- Chebyshev distance `chebyshev`\n- Standardized euclidean distance `std_euclidean`\n- Cosine distance `cosine`\n- Cosine similarity measure `csm`\n- Squared Euclidean distance `squared_euclidean`\n- Sorensen or Bray-Curtis distance `bray_curtis`\n- Canberra distance `canberra`\n- Lorentzian distance `lorentzian`\n- Jaccard distance `jaccard`\n- Dice distance `dice`\n- Bhattacharyya distance `bhattacharyya`\n- Hellinger distance `hellinger`\n- Matusita distance `matusita`\n- Squared-chord distance `squared_chord`\n- Pearson chi-square distance `pearson_chi_square`\n- Squared chi-square distance `squared_chi_square`\n\nThe library also provides other methods necessary for multi-criteria decision analysis, which are as follows: The TOPSIS method for \nmulti-criteria decision analysis TOPSIS in module `mcda_methods`. The TOPSIS method is based on measuring the distance of alternatives from \nPositive Ideal Solution and Negative Ideal Solution using `distance_metrics` mentioned above.\n\nNormalization techniques:\n\n- Linear `linear_normalization`\n- Minimum-Maximum `minmax_normalization`\n- Maximum `max_normalization`\n- Sum `sum_normalization`\n- Vector `vector_normalization`\n\nCorrelation coefficients:\n\n- Spearman rank correlation coefficient rs `spearman`\n- Weighted Spearman rank correlation coefficient rw `weighted_spearman`\n- Pearson coefficent `pearson_coeff`\n\nObjective weighting methods:\n\n- Entropy weighting method `entropy_weighting`\n- CRITIC weighting method `critic_weighting`\n\nExample of usage of `distance-metrics-mcda` are provided on [GitHub](https://github.com/energyinpython/distance-metrics-for-mcda) in [examples](https://github.com/energyinpython/distance-metrics-for-mcda/tree/main/examples)","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fenergyinpython%2Fdistance-metrics-for-mcda","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fenergyinpython%2Fdistance-metrics-for-mcda","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fenergyinpython%2Fdistance-metrics-for-mcda/lists"}