Fgclustering

Major changes in release:
- github actions miniconda installation error fix
- bug fix pandas groupby error for new pandas version
- enable selection of only top n features for plotting functions
- add multiple testing correction to p-value calculation across clusters
- bug fix calculation of chi square test for multiple categories over multiple clusters

This is a minor release with small bug fix: fix pytest.

This is a minor release with small bug fix: adding missing import statement.

This is a major release where we changed:

- new functions added that are speeding up the code
- new package for kmedoids calculation is used which enables faster computation
- parallelization of cluster optimization step
- introduction of new parameters such as n_jobs for number of parallel jobs to execute or verbose for printing the output
- update of the documentation
- updated and extended tutorial
- minor bug fixes - fgc runs now on categorical target inputs; use metric = precomputed for the k-medoids calculation
- added extra function calculate_statistics which enables visualisations with features not seen by the random forest or fgc

This is a minor release, where we changed:

- change plot_boxplots() into plot_distribution() and display categorical features as barplots
- merge plot_heatmap() and plot_boxplots() into plot_decision_paths(), as both plots are used to derive decision rules from Random Forests
- minor bug fix: when computing the global feature, copy X such that original X is not modified

The first version of the package was developed by lisa-sousa and DoTha .

**Full Changelog**: https://github.com/HelmholtzAI-Consultants-Munich/fg-clustering/commits/v0.2.0