Doubleml

- Implement a new score function `score = 'IV-type'` for the PLIV model (for details see 161)
--> **API change** from `DoubleMLPLIV$new(obj_dml_data, ml_g, ml_m, ml_r [, ...])` to `DoubleMLPLIV$new(obj_dml_data, ml_g, ml_m, ml_r, ml_g [, ...])`
- Adapt the nuisance estimation for the `'IV-type'` score for the PLR model (for details see 161)
--> **API change** from `DoubleMLPLR$new(obj_dml_data, ml_g, ml_m [, ...])` to `DoubleMLPLR$new(obj_dml_data, ml_l, ml_m, ml_g [, ...])`
- Use `task_type` instead of `learner_class` to identify whether a learner is meant to regress or classify (this change makes it possible to easily integrate pipelines from `mlr3pipelines` as learner for the nuisance functions) 141
- Add [Contribution Guidelines](https://github.com/DoubleML/doubleml-for-r/blob/master/CONTRIBUTING.md), issue templates, a pull request template and a [discussion forum](https://github.com/DoubleML/doubleml-for-r/discussions) to the R package repository #142 146 147
- Allow the usage of classifiers for binary outcome variables in the model classes IRM and IIVM 114
- Bug fixes and maintenance 155 156 157 158 160 163

- Prevent usage of `glmnet` learner for unit testing as recommended by CRAN (failing tests on Solaris) 137
- Prepare for the upcoming release of `checkmate` which is not backward compatible with our unit tests 134

- **Release highlight:** Clustered standard errors for double machine learning models 119
- Apply styler as described in the wiki (https://github.com/DoubleML/doubleml-for-r/wiki/Style-Guidelines) and add a corresponding CI on github actions #120 122
- Other refactoring, bug fixes and documentation updates 127 129 130 131 132 133

* Initialize all numeric matrices, vectors and arrays with the correct data type by using `NA_real_` instead of `NA` 115
* Replace a `print()` call with `cat()` 115

- Use active bindings in the R6 OOP implementation 106 & 93
- Fix the aggregation formula for standard errors from repeated cross-fitting 94 & 95
- Always use the same bootstrap algorithm independent of `dml1` vs `dml2` and consistent with docu and paper 98 & 99
- Initialize predictions with NA and make sure that there are no missleading entries in the evaluated score functions 96 & 105
- Avoid overriding learner parameters during turing 83 & 84
- Fixes in the exception handling and extension of the unit tests for the score function choice 82
- Prevent overwriting parameters from initialization when calling set_ml_nuisance_params 87 & 89
- Major refactoring and cleanup and extension of the unit test framework 101
- Extension and reorganization of exception handling for `DoubleMLData` objects 63 & 90
- Introduce style guide and clean up code 80 & 81
- Adaption to be compatible with an API change in the next `mlr3` release 103
- Run unit tests with mlr3 in dev version on github actions 104
- Updated the citation info 78, 79 & 86
- Added a short version of and a reference to the arXiv paper as vignette 110 & 113
- Prevent using the subclassed methods check_score and check_data when constructing DoubleML objects 107
- Other refactoring and minor adaptions 91, 92, 102 & 108

- 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).