{"id":20807043,"url":"https://github.com/doubleml/doubleml-for-r","last_synced_at":"2025-04-08T09:13:47.007Z","repository":{"id":37867168,"uuid":"294118558","full_name":"DoubleML/doubleml-for-r","owner":"DoubleML","description":"DoubleML - Double Machine Learning in R","archived":false,"fork":false,"pushed_at":"2025-03-07T09:24:06.000Z","size":3127,"stargazers_count":141,"open_issues_count":30,"forks_count":26,"subscribers_count":5,"default_branch":"main","last_synced_at":"2025-04-01T08:37:51.652Z","etag":null,"topics":["causal-inference","data-science","double-machine-learning","econometrics","machine-learning","mlr3","r","statistics"],"latest_commit_sha":null,"homepage":"https://docs.doubleml.org","language":"R","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"other","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/DoubleML.png","metadata":{"files":{"readme":"README.Rmd","changelog":null,"contributing":"CONTRIBUTING.md","funding":null,"license":"LICENSE","code_of_conduct":"CODE_OF_CONDUCT.md","threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2020-09-09T13:17:03.000Z","updated_at":"2025-03-27T06:09:43.000Z","dependencies_parsed_at":"2024-12-01T21:01:16.871Z","dependency_job_id":"3f5dc5ba-d169-4ca9-b754-e9366028cc04","html_url":"https://github.com/DoubleML/doubleml-for-r","commit_stats":{"total_commits":1167,"total_committers":6,"mean_commits":194.5,"dds":0.4532990574121679,"last_synced_commit":"ba452aba8f6c71df2da560e6d009fcb565079dd6"},"previous_names":[],"tags_count":17,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/DoubleML%2Fdoubleml-for-r","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/DoubleML%2Fdoubleml-for-r/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/DoubleML%2Fdoubleml-for-r/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/DoubleML%2Fdoubleml-for-r/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/DoubleML","download_url":"https://codeload.github.com/DoubleML/doubleml-for-r/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":247809964,"owners_count":20999816,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["causal-inference","data-science","double-machine-learning","econometrics","machine-learning","mlr3","r","statistics"],"created_at":"2024-11-17T19:30:14.704Z","updated_at":"2025-04-08T09:13:46.986Z","avatar_url":"https://github.com/DoubleML.png","language":"R","funding_links":[],"categories":[],"sub_categories":[],"readme":"---\noutput: github_document\n---\n\n\u003c!-- README.md is generated from README.Rmd. Please edit that file --\u003e\n\n# DoubleML - Double Machine Learning in R \u003ca href=\"https://docs.doubleml.org\"\u003e\u003cimg src=\"man/figures/logo.png\" align=\"right\" width = \"120\" /\u003e\u003c/a\u003e\n\n\n[![build](https://github.com/DoubleML/doubleml-for-r/workflows/build/badge.svg)](https://github.com/DoubleML/doubleml-for-r/actions?query=workflow%3Abuild)\n[![codecov](https://codecov.io/gh/DoubleML/doubleml-for-r/branch/main/graph/badge.svg?token=C5aiyo2MVL)](https://app.codecov.io/gh/DoubleML/doubleml-for-r/)\n[![CodeFactor](https://www.codefactor.io/repository/github/doubleml/doubleml-for-r/badge)](https://www.codefactor.io/repository/github/doubleml/doubleml-for-r)\n[![CRAN Version](https://www.r-pkg.org/badges/version/DoubleML)](https://cran.r-project.org/package=DoubleML)\n\nThe R package **DoubleML** provides an implementation of the double / debiased machine learning framework of [Chernozhukov et al. (2018)](https://arxiv.org/abs/1608.00060). It is built on top of [mlr3](https://mlr3.mlr-org.com/) and the [mlr3 ecosystem](https://github.com/mlr-org/mlr3/wiki/Extension-Packages) (Lang et al., 2019). \n\nNote that the R package was developed together with a python twin based on [scikit-learn](https://scikit-learn.org/). The python package is also available on [GitHub](https://github.com/DoubleML/doubleml-for-py) and [![PyPI version](https://badge.fury.io/py/DoubleML.svg)](https://badge.fury.io/py/DoubleML). \n\n## Documentation and maintenance \n\nDocumentation of functions in R: [https://docs.doubleml.org/r/stable/reference/index.html](https://docs.doubleml.org/r/stable/reference/index.html)\n\nUser guide: [https://docs.doubleml.org](https://docs.doubleml.org)\n\n**DoubleML** is currently maintained by [`@PhilippBach`](https://github.com/PhilippBach) and [`@SvenKlaassen`](https://github.com/SvenKlaassen). \n\n## Main Features\n\nDouble / debiased machine learning framework of [Chernozhukov et al. (2018)](https://arxiv.org/abs/1608.00060) for \n\n* Partially linear regression models (PLR)\n* Partially linear IV regression models (PLIV)\n* Interactive regression models (IRM)\n* Interactive IV regression models (IIVM)\n\n\nThe object-oriented implementation of **DoubleML** that is based on the [R6 package for R](https://r6.r-lib.org/) is very flexible. The model classes `DoubleMLPLR`, `DoubleMLPLIV`, `DoubleMLIRM` and `DoubleIIVM` implement the estimation of the nuisance functions via machine learning methods and the computation of the Neyman orthogonal score function. All other functionalities are implemented in the abstract base class `DoubleML`. In particular functionalities to estimate double machine learning models and to perform statistical inference via the methods `fit`, `bootstrap`, `confint`, `p_adjust` and `tune`. This object-oriented implementation allows a high flexibility for the model specification in terms of ...\n\n* ... the machine learning methods for estimation of the nuisance functions,\n* ... the resampling schemes,\n* ... the double machine learning algorithm,\n* ... the Neyman orthogonal score functions,\n* ...\n\nIt further can be readily extended with regards to\n\n* ... new model classes that come with Neyman orthogonal score functions being linear in the target parameter,\n* ... alternative score functions via callables,\n* ... alternative resampling schemes,\n* ...\n\n![OOP structure of the DoubleML package](man/figures/oop.svg?raw=true)\n\n## Installation \n\nInstall the latest release from CRAN:\n\n```{r, eval = FALSE}\nremotes::packages(\"DoubleML\")\n```\n\nInstall the development version from GitHub:\n\n```{r, eval = FALSE}\nremotes::install_github(\"DoubleML/doubleml-for-r\")\n```\n\n**DoubleML** requires \n\n* R (\u003e= 3.5.0)\n* R6 (\u003e= 2.4.1)\n* data.table (\u003e= 1.12.8)\n* stats\n* checkmate\n* mlr3 (\u003e= 0.5.0)\n* mlr3tuning (\u003e= 0.3.0)\n* mlr3learners (\u003e= 0.3.0)\n* mvtnorm\n* utils\n* clusterGeneration\n* readstata13\n\n## Contributing\nDoubleML is a community effort.\nEveryone is welcome to contribute.\nTo get started for your first contribution we recommend reading our\n[contributing guidelines](https://github.com/DoubleML/doubleml-for-r/blob/main/CONTRIBUTING.md)\nand our\n[code of conduct](https://github.com/DoubleML/doubleml-for-r/blob/main/CODE_OF_CONDUCT.md).\n\n## Citation\n\nIf you use the DoubleML package a citation is highly appreciated:\n\nBach, P., Chernozhukov, V., Kurz, M. S., and Spindler, M. (2021),\nDoubleML - An Object-Oriented Implementation of Double Machine Learning in R,\narXiv:[2103.09603](https://arxiv.org/abs/2103.09603). \n\nBibtex-entry:\n\n```\n@misc{DoubleML2020,\n      title={{DoubleML} -- {A}n Object-Oriented Implementation of Double Machine Learning in {R}}, \n      author={P. Bach and V. Chernozhukov and M. S. Kurz and M. Spindler and Sven Klaassen},\n      year={2024},\n      journal={Journal of Statistical Software},\n      volume={108},\n      number={3},\n      pages= {1-56},\n      doi={10.18637/jss.v108.i03},\n      note={arXiv:\\href{https://arxiv.org/abs/2103.09603}{2103.09603} [stat.ML]}\n}\n```\n\n## Acknowledgements\n\nFunding by the Deutsche Forschungsgemeinschaft (DFG, German Research\nFoundation) is acknowledged – Project Number 431701914.\n\n## References\n\n* Bach, P., Chernozhukov, V., Kurz, M. S., Spindler, M. and Klaassen, S. (2024), DoubleML - An Object-Oriented Implementation of Double Machine Learning in R, Journal of Statistical Software, 108(3): 1-56, \u003cdoi:10.18637/jss.v108.i03\u003e, arXiv:[2103.09603](https://arxiv.org/abs/2103.09603).\n\n* Chernozhukov, V., Chetverikov, D., Demirer, M., Duflo, E., Hansen, C., Newey, W. and Robins, J. (2018), Double/debiased machine learning for treatment and structural parameters. The Econometrics Journal, 21: C1-C68, \u003chttps://doi.org/10.1111/ectj.12097\u003e.\n\n* Lang, M., Binder, M., Richter, J., Schratz, P., Pfisterer, F., Coors, S., Au, Q., Casalicchio, G., Kotthoff, L., Bischl, B. (2019), mlr3: A modern object-oriented machine learing framework in R. Journal of Open Source Software, \u003chttps://doi.org/10.21105/joss.01903\u003e.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdoubleml%2Fdoubleml-for-r","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fdoubleml%2Fdoubleml-for-r","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdoubleml%2Fdoubleml-for-r/lists"}