* aGrUM
* Learning algorithm `gum::learning::MIIC` can use the weighted databases.
* Internal improvements for `act` tool, `cmake` and compilers (`clang`).
* pyAgrum
* New visualisation for `gum::DiscretizedVariable` + new config to select this visualisation.
* `pyAgrum.BNLearner` can use now the weighted databases for all learning algorithms.
* Documentation improvements.
* `pyAgrum.lib.bn2roc`
* adding new functions `get{ROC|PR}points()`.
* accepting `pandas.DataFrame` as data source (`datasrc`).
* adding Fbeta (beta!=1) scores to bn2roc.
* adding F-Beta threshold on ROC and PR curves.
* `bn2roc` functions now force many parameters to be keyword-arguments in order to prevent the risk of mixing arguments.
* adding new functions `anim{ROC|PR}`.
* `pyAgrum.skbn.Discretizer` can propose a set of labels (that includes the labels from the database) when `"NoDiscretization"` is selected. (see tutorial `52-Classifier_Discretizer`).