Moptipy

We now also provide the CMA-ES algorithm variants from the library [`cmaes`](https://pypi.org/project/cmaes/), which is developed by Masashi Shibata and Masahiro Nomura at <https://github.com/CyberAgent/cmaes>.
These algorithms are wrapped into the `moptipy` API and can now be accesses and experimented on in the same way as any other numerical optimization algorithm in our package.
This is shown in the small example [`continuous_optimization.py`](https://thomasweise.github.io/moptipy/examples/continuous_optimization.html).

Fix of 0.9.23 to Comply with New `ruff` Rules

We now provide a wrapper around Powell's "Bound Optimization BY Quadratic Approximation" algorithm (BOBYQA) offered by the library "Powell's Derivative-Free Optimization solvers" ([`pdfo`](https://www.pdfo.net)). This means that another highly efficient algorithm for numerical/continuous optimization is now available out of the box under our `moptipy` API.

We also included the first draft of an example for [continuous optimization](https://thomasweise.github.io/moptipy/examples/continuous_optimization.html).

A set of strange bugs were fixed in [`StatRun`](https://thomasweise.github.io/moptipy/moptipy.evaluation.html#moptipy.evaluation.stat_run.StatRun) and [`Ert`](https://thomasweise.github.io/moptipy/moptipy.evaluation.html#moptipy.evaluation.ert.Ert).
There, we removed the `numba` jitting where it was not useful and problematic and fixed issues the accidental mismatch of Python `int`s and `numpy` `int`s.
We also better deal with the special case where [`StatRun`](https://thomasweise.github.io/moptipy/moptipy.evaluation.html#moptipy.evaluation.stat_run.StatRun)s only have single values.

minor improvements and documentation fix

All vector spaces are now bounded by finite box constraints.
We removed the bounds.py utility module and merged the functionality into the corresponding spaces.
The numerical optimization algorithms imported from SciPy have been updated accordingly.
This should lead to a more reasonable and maintainable API for numerical / continuous optimization.