Slise

Add `nogil=True`to the jitted functions and handle `NUMBA_DISABLE_JIT=1`.
Mention optional Numba dependencies in the `README.md`.

- Added option for limiting the number of threads used.
- Added new helpful warnings (such as warning about potentially bad `numba.threading_layer()`).
- Deprecate (only warning for now) `get_params()` in favour of `coefficients`.

Changes:
- Add optional weights to the algorithm.
- Do not normalise logits.
- Make some fields "private".
- Increase version to match the R variant.

SLISE is now available on PyPI and can be installed with:
sh
pip install slise

This release fixes a bug when using `normalise=True` in SLISE-regression.

Furthermore, the impact calculated from normalised and unnormalised values tells different stories. Unnormalised impact lets you reconstruct the original prediction, while normalised impact is more of a comparison to the rest of the data. Thus, they are given separate rows when using the built in `print` and `plot_dist` functions.

Some of the built-in plotting functions have been improved. Warnings from intermediate optimisation steps are now hidden by default (warnings from the last optimisation step are still shown). Finally, the examples have been extended and improved, especially in regards to how to interpret the results.