Model-diagnostics

Some highlights:
- Confidence intervals for `plot_reliability_diagram` via arguments `n_bootstrap` and `confidence_level` (PR 32).
- New option `diagram_type = "bias"` for `plot_reliability_diagram`, which is roughly a 45 degree rotated plot (PR 35).
- Better visualisation of uncertainty/standard errors in `plot_bias` and distinction between numerical and categorical features (PR 37).
- Consistently sorted output, i.e. the different (model) columns of `y_pred` (PR 37).
- Number of effective (output) bins is now always at most `n_bins` in `compute_bias` and `plot_bias` (PR 37).
- Switch to [ruff](https://beta.ruff.rs/) (PR #34)

A new module `scoring` containing:
- Add strictly consistent, homogeneous scoring functions
- `HomogeneousExpectileScore` for mean an expectiles
- `HomogeneousQuantileScore` for quantiles
- `SquaredError`, `PoissonDeviance`, `GammaDeviance` and `PinballLoss` for convenience
- Add `LogLoss`
- Add score decomposition `decompose` 🚀
To my knowledge, this is the first time the score decomposition into miscalibration, discrimination (or resolution) is available in Python. R users can use the wonderful [reliabilitydiag package](https://cran.r-project.org/package=reliabilitydiag) of aijordan for quite some time now.

- Added support for case weights.
- Use of the fantastic https://www.pola.rs/ library (instead of pyarrow and pandas).

First public release