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.
Please email: email@example.com
This package was first released in Fall 2016, and had one major upgrade in Fall 2020.
rdmdefor computing minimum detectable effects.
To install/update in Python type:
pip install rdpower
To install/update in R type:
To install/update in Stata type:
net install rdpower, from(https://raw.githubusercontent.com/rdpackages/rdpower/master/stata) replace
For source code and related files, visit
For overviews and introductions, see rdpackages website.
Calonico, Cattaneo and Titiunik (2014): Robust Nonparametric Confidence Intervals for Regression-Discontinuity Designs.
Econometrica 82(6): 2295-2326.
Calonico, Cattaneo and Farrell (2020): Optimal Bandwidth Choice for Robust Bias Corrected Inference in Regression Discontinuity Designs.
Econometrics Journal 23(2): 192-210.