DoubleML is a community effort. Everyone is welcome to contribute. All contributors should adhere to this contributing guidelines and our code of conduct. The contributing guidelines are particularly helpful to get started for your first contribution.
To submit a bug report, you can use our issue template for bug reports.
library(DoubleML) library(mlr3) library(mlr3learners) library(data.table) set.seed(2) ml_g = lrn("regr.ranger", num.trees = 10, max.depth = 2) ml_m = ml_g$clone() obj_dml_data = make_plr_CCDDHNR2018(alpha = 0.5) dml_plr_obj = DoubleMLPLR$new(obj_dml_data, ml_g, ml_m) dml_plr_obj$fit() dml_plr_obj$summary()
State the result you would have expected and the result you actually got. In case of an exception the full traceback is appreciated.
State the versions of your code by running the following lines and copy-paste the result.
sessionInfo() packageVersion('DoubleML') packageVersion('mlr3')
We welcome feature requests and suggestions towards improving and/or extending the DoubleML package. For feature requests you can use the corresponding issue template.
We use GitHub Discussions to give the community a platform for asking questions about the DoubleML package and for discussions on topics related to the package.
Everyone is welcome to contribute to the DoubleML code base. The following guidelines and hints help you to get started.
In the following, the recommended way to contribute to DoubleML is described in detail. If you are just starting to work with Git and GitHub, we recommend to read Happy Git and GitHub for the useR and the chapter on Git and GitHub in Hadley Wickham’s book R Packages. The most important steps are: To fork the repo, then add your changes and finally submit a pull-request. 1. Fork the DoubleML repo by clicking on the Fork button (this requires a GitHub account).
This allows you to easily keep your repository in synch via
$ git add your_new_file your_modified_file $ git commit -m "A commit message which briefly summarizes the changes made" $ git push origin my_feature_branch
The title of the pull request summarizes the changes made.
The PR contains a detailed description of all changes and additions (you may want to comment on the diff in GitHub).
References to related issues or PRs are added.
The code passes
R CMD check and all (unit) tests. To check your code for common problems, run
By default, this runs all tests. In case you only want to run the tests, run
If you add an enhancements or new feature, unit tests (with a certain level of coverage) are mandatory for getting the PR merged.
Check whether your changes adhere to the “mlr-style” standards. For the check you can use the following code
require(styler) remotes::install_github("pat-s/styler@mlr-style") styler::style_pkg(style = styler::mlr_style) # entire package styler::style_file(<file>, style = styler::mlr_style) # specific file
If your PR is still work in progress, please consider marking it a draft PR (see also here).
We use the testthat package for unit testing. Unit testing is considered to be a fundamental part of the development workflow. We recommend to read the chapter on testing of Hadley Wickham’s book R Packages. The tests are located in the
tests/testthat subfolder. The test coverage is determined with the
covr package. Coverage reports for the package, PRs, branches etc. are available from codecov. It is mandatory to equip new features with an appropriate level of unit test coverage. To run all unit tests (for further option see the devtools docu) call
For a unit test coverage report you can run
The DoubleML package is particularly designed in a flexible way to make it easily extendable with regard to new model classes. Contributions in this direction are very much welcome, and we are happy to help authors to integrate their models in the DoubleML OOP structure. If you need assistance, just open an issue or contact one of the maintainers @MalteKurz or @PhilippBach.
The abstract base class
DoubleML implements all core functionalities based on a linear Neyman orthogonal score function. To contribute a new model class, you only need to specify all nuisance functions that need to be estimated for the new model class (e.g. regressions or classifications). Furthermore, the score components for the Neyman orthogonal score function need to be implemented. All other functionality is automatically available via inheritance from the abstract base class.
The documentation of DoubleML is generated with roxygen2. The corresponding website for the R API documentation is generated using pkgdown and hosted at https://docs.doubleml.org/r/stable. The website https:://docs.doubleml.org is built with sphinx. The source code for the website, user guide, example gallery, etc. is available in a separate repository https://github.com/DoubleML/doubleml-docs.
The documentation of DoubleML is generated with roxygen2. The corresponding website for the R API documentation is generated using pkgdown. To build the documentation of the package run
To build the documentation website, run (for more details, see the pkgdown documentation)
The documentation of DoubleML is hosted at https://docs.doubleml.org. The source code for the website, user guide, example gallery, etc. is available in a separate repository doubleml-docs. Changes, issues and PRs for the documentation (except the API documentation) should be discussed in the doubleml-docs repo. We welcome contributions to the user guide, especially case studies for the example gallery. A step-by-step guide for contributions to the example gallery is available here.