Aplr

Changed the default value of m from 20000 to 3000 in order to have less computationally demanding default hyperparameters.

Removed an unnecessary C++ constructor parameter and updated the documentation.

Modified default hyperparameters based on empirical tests on several openml and pmlb datasets. Learning rate (v) was increased to 0.5 from 0.1, min_observations_in_split was decreased to 4 from 20, ineligible_boosting_steps_added was increased to 15 from 10 and max_eligible_terms was increased to 7 from 5.

Also added the APLRTuner object which simplifies tuning of APLR. Se the [example folder](https://github.com/ottenbreit-data-science/aplr/tree/main/examples).

Fixed a bug that caused too slow convergence when using the logit link. This also affected APLRClassifier since it uses underlying logit models.

- Improved sklearn compatibility for APLRClassifier by adding a _classes__ field and a _predict_proba_ method.
- Changed the default value for the maximum number of boosting steps to try, _m_, from 3000 to 20000 to ensure convergence in most cases (if this gets too slow then you can increase the learning rate _v_ and reduce _m_).

APLR now also works with numpy 2.0.0.