Welcome to crispyn documentation!
===============================================

``crispyn`` is Python 3 library dedicated to multi-criteria decision analysis with criteria weights determined by objective weighting methods.
This library includes:

- The VIKOR method ``VIKOR``

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

	- ``AHP_WEIGHTING`` (AHP weighting method)
	- ``swara_weighting`` (SWARA weighting method)
	- ``lbwa_weighting`` (LBWA weighting method)
	- ``sapevo_weighting`` (SAPEVO weighting method)
	
- Stochastic Multicriteria Acceptability Analysis Method - SMAA combined with VIKOR (``VIKOR_SMAA``)
	
- Correlation coefficients:

	- ``spearman`` (Spearman rank correlation coefficient)
	- ``weighted_spearman`` (Weighted Spearman rank correlation coefficient)
	- ``pearson_coeff`` (Pearson correlation 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)
	
- additions:

	- ``rank_preferences`` (Method for ordering alternatives according to their preference values obtained with MCDA methods)
	
Check out the :doc:`usage` section for further information, including
how to :ref:`installation` the project.

.. note::

   This project is under active development.

Contents
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.. toctree::
	:maxdepth: 2

	usage
	example
	autoapi/index