Genieclust

* [BACKWARD INCOMPATIBILITY] [Python and R] Inequality measures
are no longer referred to as inequity measures.

* [BACKWARD INCOMPATIBILITY] [Python and R]
Some external cluster validity measures were renamed
(as per the major revision of <https://doi.org/10.48550/arXiv.2209.02935>):
`adjusted_asymmetric_accuracy` -> `normalized_clustering_accuracy`,
`normalized_accuracy` -> `normalized_pivoted_accuracy`.

* [BACKWARD INCOMPATIBILITY] [Python] `compare_partitions2` has been removed,
as `compare_partitions` and other partition similarity scores
now support both pairs of label vectors `(x, y)` and confusion matrices
`(x=C, y=None)`.

* [Python and R] New parameter to `pair_sets_index`: `clipped`.

* In `normalizing_permutation` and external cluster validity measures,
the input matrices can now be of the type `double`.

* [BUGFIX] [Python] 80: Fixed adjustment for `nmslib_n_neighbors`
in small samples.

* [BUGFIX] [Python] 82: `cluster_validity` submodule not imported.

* [BUGFIX] Some external cluster validity measures
now handle NaNs better and are slightly less prone to round-off errors.

* [Python] The GIc algorithm is no longer marked as experimental;
its description will be provided in a forthcoming paper; see
<https://doi.org/10.48550/arXiv.2303.05679>.

* [R] `mst.default` now throws an error if any element in the input matrix
is missing/infinite.

* [Python] Fixed the call to `mlpack.emst` that stopped working
with the new version of `mlpack`.

* [Python and R] `adjusted_asymmetric_accuracy`
now accepts confusion matrices with fewer columns than rows.
Such "missing" columns are now treated as if they were filled with 0s.

* [Python and R] `pair_sets_index`, and `normalized_accuracy` return
the same results for non-symmetric confusion matrices and transposes thereof.

* [Python] 75: `nmslib` is now optional.

* [BUILD TIME]: The use of `ssize_t` was not portable.

* [Python and R] New function: `adjusted_asymmetric_accuracy`.

* [Python and R] Implementations of the so-called internal cluster
validity measures discussed in
DOI: [10.1016/j.ins.2021.10.004](https://doi.org/10.1016/j.ins.2021.10.004);
see our (GitHub-only) [CVI](https://github.com/gagolews/optim_cvi) package
for R. In particular, the generalised Dunn indices are based on the code
originally authored by Maciej Bartoszuk. Thanks.

Functions added (`cluster_validity` module):
`calinski_harabasz_index`,
`dunnowa_index`,
`generalised_dunn_index`,
`negated_ball_hall_index`,
`negated_davies_bouldin_index`,
`negated_wcss_index`,
`silhouette_index`,
`silhouette_w_index`,
`wcnn_index`.

These cluster validity measures are discussed
in more detail at <https://clustering-benchmarks.gagolewski.com>.

* [BACKWARD INCOMPATIBILITY] `normalized_confusion_matrix`
now solves the maximal assignment problem instead of applying
the somewhat primitive partial pivoting.

* [Python and R] New function: `normalizing_permutation`

* [R] New function: `normalized_confusion_matrix`.

* [Python and R] New parameter to `pair_sets_index`: `simplified`.

* [Python] New parameters to `plots.plot_scatter`:
`axis`, `title`, `xlabel`, `ylabel`, `xlim`, `ylim`.