RDLOCRAND

The rdlocrand package provides Python, R and Stata implementations of statistical inference and graphical procedures for Regression Discontinuity designs employing local randomization methods. It provides point estimators, confidence intervals estimators, binomial manipulation testing, windows selectors, automatic plots, sensitivity analysis, and other related features.

This work was supported in part by the National Science Foundation through grants SES-1357561.

Queries and Requests

Please email: rdpackages@googlegroups.com

Major Upgrades

This package was first released in Spring 2016, and had one major upgrade in Spring 2021.

• Spring 2021 new features include: (i) speed improvements; (ii) mass points in running variable adjustments; and (iii) asymmetric randomization window selection.

Python Implementation

To install/update in R type:

pip install rdlocrand

• Help: PyPI repository.

• Replication: py-script, senate data.

R Implementation

To install/update in R type:

install.packages('rdlocrand')

• Help: R Manual, CRAN repository.

• Replication: R-script, senate data, R illustration.

Stata Implementation

To install/update in Stata type:

net install rdlocrand, from(https://raw.githubusercontent.com/rdpackages/rdlocrand/master/stata) replace

• Help: rdrandinf, rdwinselect, rdsensitivity, rdrbounds.

• Replication: do-file, senate data.

Repository

For source code and related files, visit rdlocrand repository.

References

For overviews and introductions, see rdpackages website.

Software and Implementation

• Cattaneo, Titiunik and Vazquez-Bare (2016): Inference in Regression Discontinuity Designs under Local Randomization.
  Stata Journal 16(2): 331-367.

Technical and Methodological

• Cattaneo, Frandsen and Titiunik (2015): Randomization Inference in the Regression Discontinuity Design: An Application to Party Advantages in the U.S. Senate.
  Journal of Causal Inference 3(1): 1-24.

• Cattaneo, Titiunik and Vazquez-Bare (2017): Comparing Inference Approaches for RD Designs: A Reexamination of the Effect of Head Start on Child Mortality.
  Journal of Policy Analysis and Management 36(3): 643-681.