Glum

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**Bug fix:**

- Added cython compiler directive legacy_implicit_noexcept = True to fix performance regression with cython 3.

**Other changes:**

- Require Python>=3.9 in line with `NEP 29 <https://numpy.org/neps/nep-0029-deprecation_policy.html#support-table>`.
- Build and test with Python 3.12 in CI.
- Added line search stopping criterion for tiny loss improvements based on gradient information.
- Added warnings about breaking changes in future versions.

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**New features:**

- Added the complementary log-log (``cloglog``) link function.
- Added the option to store the covariance matrix after estimating it. In this case, the covariance matrix does not have to be recomputed when calling inference methods.
- Added methods for performing Wald tests based on a restriction matrix, feature names or term names.
- Added a method for creating a coefficient table with confidence intervals and p-values.

**Bug fix:**

- Fixed :meth:`~glum.GeneralizedLinearRegressorBase.covariance_matrix` mutating feature names when called with a data frame. See `here <https://github.com/Quantco/glum/issues/669>`_.

**Other changes:**

- When computing the covariance matrix, check whether the design matrix is ill-conditioned for all types of input. Furthermore, do it in a more efficient way.
- Pin ``tabmat<4.0.0`` (the new release will bring breaking changes).

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**Bug fix**

- Fix the ``glm_benchmarks_analyze`` command line tool. See `here <https://github.com/Quantco/glum/issues/642>`_.
- Fixed a bug in :class:`~glum.GeneralizedLinearRegressor` when fit on a data set with a constant column and ``warm_start=True``. See `here <https://github.com/Quantco/glum/issues/645>`_.

**Other changes:**

- Remove dev dependency on ``dask_ml``.
- We now pin ``llvm-openmp=11`` when creating the wheel for macOS in line with what scikit-learn does.

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**Other changes:**

- Better error message when the number of input features is different between the fit and predict methods.

**Bug fix:**

- We fixed a bug in the computation of :meth:`~glum.distribution.NegativeBinomialDistribution.log_likelihood`. Previously, this method just returned ``None``.

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**New feature:**

- Added Negative Binomial distribution by setting the ``'family'`` parameter of
:class:`~glum.GeneralizedLinearRegressor` and :class:`~glum.GeneralizedLinearRegressorCV`
to ``'negative.binomial'``.

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**Bug fixes:**

- Fixed an issue with :meth:`~glum.ExponentialDispersionModel._score_matrix` which failed when called with a tabmat matrix input.

**Other changes**:

- Removes unused scikit-learn cython imports.