The 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.
This work was supported by the National Science Foundation through grant SES-1357561.
https://rdpackages.github.io/rdpower
Please email: [email protected]
This package was first released in Fall 2016, and had one major upgrade in Fall 2020.
- Fall 2020 new feature: command/function
rdmdefor computing minimum detectable effects.
To install/update in Python type:
pip install rdpower
-
Help: PYPI repository.
-
Replication: py-script, senate data.
To install/update in R type:
install.packages('rdpower')
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Help: R Manual, CRAN repository.
-
Replication files: R-script, data-senate.
To install/update in Stata type:
net install rdpower, from(https://raw.githubusercontent.com/rdpackages/rdpower/master/stata) replace
-
Replication: do-file, data-senate.
For overviews and introductions, see rdpackages website.
- Cattaneo, Titiunik and Vazquez-Bare (2019): Power Calculations for Regression Discontinuity Designs.
Stata Journal 19(1): 210-245.
-
Calonico, Cattaneo and Titiunik (2014): Robust Nonparametric Confidence Intervals for Regression-Discontinuity Designs.
Econometrica 82(6): 2295-2326.
Supplemental Appendix. -
Calonico, Cattaneo, Farrell and Titiunik (2019): Regression Discontinuity Designs Using Covariates.
Review of Economics and Statistics 101(3): 442-451.
Supplemental Appendix. -
Calonico, Cattaneo and Farrell (2020): Optimal Bandwidth Choice for Robust Bias Corrected Inference in Regression Discontinuity Designs.
Econometrics Journal 23(2): 192-210.
Supplemental Appendix.