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RDPOWER\n\nThe ``rdpower`` package provides Python, R, and Stata implementations of power, sample size, and minimum detectable effects calculations using robust bias-corrected local polynomial inference methods.\n\nThis work was supported by the National Science Foundation through grant [SES-1357561](https://www.nsf.gov/awardsearch/showAward?AWD_ID=1357561).\n\n## Website\n\nhttps://rdpackages.github.io/rdpower\n\n## Queries and Requests\n\nPlease email: [rdpackages@googlegroups.com](mailto:rdpackages@googlegroups.com)\n\n## Major Upgrades\n\nThis package was first released in Fall 2016, and had one major upgrade in Fall 2020.\n\n- _Fall 2020 new feature_: command/function `rdmde` for computing minimum detectable effects.\n\n## Python Implementation\n\nTo install/update in Python type:\n```\npip install rdpower\n```\n\n- Help: [PYPI repository](https://pypi.org/project/rdpower/).\n\n- Replication: [py-script](python/rdpower_illustration.py), [senate data](python/rdpower_senate.csv).\n\n## R Implementation\n\nTo install/update in R type:\n```\ninstall.packages('rdpower')\n```\n- Help: [R Manual](https://cran.r-project.org/web/packages/rdpower/rdpower.pdf), [CRAN repository](https://cran.r-project.org/package=rdpower).\n\n- Replication files: [R-script](R/rdpower_illustration.R), [data-senate](R/rdpower_senate.csv).\n\n## Stata Implementation\n\nTo install/update in Stata type:\n```\nnet install rdpower, from(https://raw.githubusercontent.com/rdpackages/rdpower/master/stata) replace\n```\n\n- Help: [rdpower](stata/rdpower.pdf), [rdsampsi](stata/rdsampsi.pdf), [rdmde](stata/rdmde.pdf).\n\n- Replication: [do-file](stata/rdpower_illustration.do), [data-senate](stata/rdpower_senate.dta).\n\n\n## References\n\nFor overviews and introductions, see [rdpackages website](https://rdpackages.github.io).\n\n### Software and Implementation\n\n- Cattaneo, Titiunik and Vazquez-Bare (2019): [Power Calculations for Regression Discontinuity Designs](https://rdpackages.github.io/references/Cattaneo-Titiunik-VazquezBare_2019_Stata.pdf).\u003cbr\u003e\n_Stata Journal_ 19(1): 210-245.\n\n### Technical and Methodological\n\n- Calonico, Cattaneo and Titiunik (2014): [Robust Nonparametric Confidence Intervals for Regression-Discontinuity Designs](https://rdpackages.github.io/references/Calonico-Cattaneo-Titiunik_2014_ECMA.pdf).\u003cbr\u003e\n_Econometrica_ 82(6): 2295-2326.\u003cbr\u003e\n[Supplemental Appendix](https://rdpackages.github.io/references/Calonico-Cattaneo-Titiunik_2014_ECMA--Supplemental.pdf).\n\n- Calonico, Cattaneo, Farrell and Titiunik (2019): [Regression Discontinuity Designs Using Covariates](https://rdpackages.github.io/references/Calonico-Cattaneo-Farrell-Titiunik_2019_RESTAT.pdf).\u003cbr\u003e\n_Review of Economics and Statistics_ 101(3): 442-451.\u003cbr\u003e\n[Supplemental Appendix](https://rdpackages.github.io/references/Calonico-Cattaneo-Farrell-Titiunik_2019_RESTAT--Supplement.pdf).\n\n- Calonico, Cattaneo and Farrell (2020): [Optimal Bandwidth Choice for Robust Bias Corrected Inference in Regression Discontinuity Designs](https://rdpackages.github.io/references/Calonico-Cattaneo-Farrell_2020_ECTJ.pdf).\u003cbr\u003e\n_Econometrics Journal_ 23(2): 192-210.\u003cbr\u003e\n[Supplemental Appendix](https://rdpackages.github.io/references/Calonico-Cattaneo-Farrell_2020_ECTJ--Supplement.pdf).\n\n\u003cbr\u003e\u003cbr\u003e\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frdpackages%2Frdpower","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Frdpackages%2Frdpower","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frdpackages%2Frdpower/lists"}