3.0.0 - 2024-04-27

Breaking changes:

• All arguments to :class:~glum.GeneralizedLinearRegressorBase, :class:~glum.GeneralizedLinearRegressor and :class:~glum.GeneralizedLinearRegressorCV are now keyword-only.
• All arguments to public methods of :class:~glum.GeneralizedLinearRegressorBase, :class:~glum.GeneralizedLinearRegressor or :class:~glum.GeneralizedLinearRegressorCV except X, y, sample_weight and offset are now keyword-only.
• :class:~glum.GeneralizedLinearRegressor's default value for alpha is now 0, i.e. no regularization.
• :class:~glum.GammaDistribution, :class:~glum.InverseGaussianDistribution, :class:~glum.NormalDistribution and :class:~glum.PoissonDistribution no longer inherit from :class:~glum.TweedieDistribution.
• The power parameter of :class:~glum.TweedieLink has been renamed from p to power, in line with :class:~glum.TweedieDistribution.
• :class:~glum.TweedieLink no longer instantiates :class:~glum.IdentityLink or :class:~glum.LogLink for power=0 and power=1, respectively. On the other hand, :class:~glum.TweedieLink is now compatible with power=0 and power=1.

New features:

• Added a formula interface for specifying models.
• Improved feature name handling. Feature names are now created for non-pandas input matrices too. Furthermore, the format of categorical features can be specified by the user.
• Term names are now stored in the model's attributes. This is useful for categorical features, where they refer to the whole variable, not just single levels.
• Added more options for treating missing values in categorical columns. They can either raise a ValueError ("fail"), be treated as all-zero indicators ("zero") or represented as a new category ("convert").
• meth:GeneralizedLinearRegressor.wald_test can now perform tests based on a formula string and term names.
• :class:~glum.InverseGaussianDistribution gains a :meth:~glum.InverseGaussianDistribution.log_likelihood method.