- Major extensions of the unit test framework which result in a coverage >98% (a summary is given in [82](https://github.com/DoubleML/doubleml-for-py/pull/82))
- In the PLR one can now also specify classifiers for ``ml_m`` in case of a binary treatment variable with values 0 and 1 (see [86](https://github.com/DoubleML/doubleml-for-py/pull/86) for details)
- The joint Python and R docu and user guide is now served to [https://docs.doubleml.org](https://docs.doubleml.org) from a separate repo [https://github.com/DoubleML/doubleml-docs](https://github.com/DoubleML/doubleml-docs)
- Generate and upload a unit test coverage report to codecov [https://app.codecov.io/gh/DoubleML/doubleml-for-py](https://app.codecov.io/gh/DoubleML/doubleml-for-py) [#76](https://github.com/DoubleML/doubleml-for-py/pull/76)
- Run lint checks with flake8 [78](https://github.com/DoubleML/doubleml-for-py/pull/78), align code with PEP8 standards [#79](https://github.com/DoubleML/doubleml-for-py/pull/79), activate code quality checks at codacy [#80](https://github.com/DoubleML/doubleml-for-py/pull/80)
- Refactoring (reduce code redundancy) of the code for tuning of the ML learners used for approximation the nuisance functions [81](https://github.com/DoubleML/doubleml-for-py/pull/81)
- Minor updates, bug fixes and improvements of the exception handling (contained in [82](https://github.com/DoubleML/doubleml-for-py/pull/82) & [#89](https://github.com/DoubleML/doubleml-for-py/pull/89))