Scikit-criteria

- Support for Python 3.10.
- All the objects of the project are now immutable by design, and can only
be mutated troughs the `object.copy()` method.
- Dominance analysis tools (`DecisionMatrix.dominance`).
- The method `DecisionMatrix.describe()` was deprecated and will be removed
in version *1.0*.
- New statistics functionalities `DecisionMatrix.stats` accessor.
- The accessors are now cached in the `DecisionMatrix`.

- Tutorial for dominance and satisfaction analysis.

- TOPSIS now support hyper-parameters to select different metrics.
- Generalize the idea of accessors in scikit-criteria througth a common
framework (`skcriteria.utils.accabc` module).
- New deprecation mechanism through the
- `skcriteria.utils.decorators.deprecated` decorator.

In this version scikit-criteria was rewritten from scratch. Among other things:

- The model implementation API was simplified.
- The `Data` object was removed in favor of `DecisionMatrix` which implements many more useful features for MCDA.
- Plots were completely re-implemented using [Seaborn](http://seaborn.pydata.org/).
- Coverage was increased to 100%.
- Pipelines concept was added (Thanks to [Scikit-learn](https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html)).
- New documentation. The quick start is totally rewritten!

**Full Changelog**: https://github.com/quatrope/scikit-criteria/commits/0.5

In this version scikit-criteria was rewritten from scratch. Among other things:

- The model implementation API was simplified.
- The `Data` object was removed in favor of `DecisionMatrix` which implements many more useful features for MCDA.
- Plots were completely re-implemented using [Seaborn](http://seaborn.pydata.org/).
- Coverage was increased to 100%.
- Pipelines concept was added (Thanks to [Scikit-learn](https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html)).

**Full Changelog**: https://github.com/quatrope/scikit-criteria/commits/0.5.dev0