Doubleml

- Always use the same bootstrap algorithm independent of `dml1` vs `dml2` and consistent with docu and paper 101 & 102
- Added an exception handling to assure that an IV variable is specified when using a PLIV or IIVM model 107
- Improve exception handling for externally provided sample splitting 110
- Minor update of the str representation of `DoubleMLData` objects 112
- Code refactorings and unit test extensions 103, 105, 106, 111 & 113

- IIVM model: Added a subgroups option to adapt to cases with and without the subgroups of always-takers and never-takers (96).
- Add checks for the intersections of `y_col`, `d_cols`, `x_cols`, `z_cols` (84, 97). This also fixes 83 (with intersection between `x_cols` and `d_cols` a column could have been added multiple times to the covariate matrix).
- Added checks and exception handling for duplicate entries in `d_cols`, `x_cols` or `z_cols` (100).
- Check the datatype of `data` when initializing `DoubleMLData` objects. Also check for duplicate column names (100).
- Fix bug 95 in 97: It occurred when `x_cols` where inferred via setdiff and `y_col` was a string with multiple characters.
- We updated the citation info to refer to the arXiv paper (98): Bach, P., Chernozhukov, V., Kurz, M. S., and Spindler, M. (2021), DoubleML - An Object-Oriented Implementation of Double Machine Learning in Python, [arXiv:2104.03220](https://arxiv.org/abs/2104.03220).

- Provide an option to store & export the first-stage predictions [91](https://github.com/DoubleML/doubleml-for-py/pull/91)
- Added the package logo to the doc

- 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))

- Fixed a compatibility issue with `scikit-learn` 0.24, which only affected some unit tests ([70](https://github.com/DoubleML/doubleml-for-py/issues/70), [#71](https://github.com/DoubleML/doubleml-for-py/pull/71))
- Added scheduled unit tests on github-action (three times a week) [69](https://github.com/DoubleML/doubleml-for-py/pull/69)
- Split up estimation of nuisance functions and computation of score function components. Further introduced a private method `_est_causal_pars_and_se()`, see [72](https://github.com/DoubleML/doubleml-for-py/pull/72). This is needed for the DoubleML-Serverless project: https://github.com/DoubleML/doubleml-serverless.

- Bug fix in the drawing of bootstrap weights for the multiple treatment case 66 (see also DoubleML/doubleml-for-r28)
- Update install instructions as DoubleML is now listed on pypi
- Prepare submission to conda-forge: Include LICENSE file in source distribution
- Documentation is now served with HTTPS https://docs.doubleml.org