I've created the following open source tools and packages:

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StructureBoost: This is a package for gradient boosting that enables the algorithm to take into account the structure of categorical variables.  Plus it has a bunch of other nifty features.

Github: https://www.github.com/numeristical/structureboost

Documentation: https://structureboost.readthedocs.io

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SplineCalib: A tool for probability calibration using splines.  The most advanced and feature-rich tool for effectively calibrating your model.  SplineCalib is contained in the ML-Insights package.

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ML-Insights: Along with SplineCalib, ML-Insights contains advanced ICE-plot visualization for understanding the performance of your model.  It also contains the "histogram_pair" function for data exploration in classification problems.

Github: https://www.github.com/numeristical/introspective

Documentation: https://ml-insights.readthedocs.io