Gemclus

+ Adding a new sparse logistic regression model trained with mutual information instead of MMD GEMINI: `gemclus.sparse.SparseLinearMI`
+ Adding new package containing methods for unsupervised tree clustering with end-to-end training: `gemclus.tree`. The package features a CART-like algorithm for clustering named `Kauri` and a differentiable tree named `Douglas`
+ *Experimental*: A method for adding constraints of type must-link cannot-link to discriminative models: `gemclus.add_mlcl_constraint`
+ Minor fixes in documentation
+ Better compatibility with scikit learn 1.3.0 regarding parameter constraint check

+ Fixing the ABCMeta parameter validation problem for the `draw_gmm` method for retrocompatibility with Python 3.8.
+ Constraining the package to Python>=3.8 to respect the requirements of the package.
+ Minor fix on the `get_selection` method for the Linear sparse models to respect the 1d output shape of the array.

+ Isolating the definition of GEMINIs in a separate classes for external usages: `gemini.MMDOvO`, `gemini.WassersteinOvA` etc.
+ Adding the nonparametric models in package `gemclus.nonparametric` with 2 additional examples for its usage in graph node clustering.
+ Fixing control variables in the `path` method for spars models.

+ Adding the Gaussian+Student-t mixture dataset: `gstm`
+ Method for sampling multivariate student-t distributions: `multivariate_student_t`
+ Adding tests for `data` package
+ Adding the possibility of a precomputed kernel/distance passed to `fit`
+ Adding batch size parameters
+ Fixing zero division in sparse linear model proximal gradient