did: An R package for difference-in-differences with multiple time periods
did R package provides tools to estimate and conduct asymptotically valid inference about average treatment effects in Difference-in-Differences models with multiple time periods and variation in treatment timing. In short, this package implements the causal inference tools proposed in Callaway and Sant’Anna (2018), “Difference-in-Differences with Multiple Time Periods and an Application on the Minimum Wage and Employment”
You can install the did package from CRAN with
Alternatively, you can install the most recent version of the did package from Github using
pstest: An R package to assess the goodness-of-fit of parametric propensity score models
pstest R package provides data-driven nonparametric diagnostic tools for detecting propensity score misspecification, that do not depend on tuning parameters, and do not suffer from the “curse of dimensionality’’. In short, this package implements the class of specifications tests for the propensity score proposed in Sant’Anna and Song, “Specification Tests for the Propensity Score”.
You can install the pstest package from CRAN with
install.packages("pstest"), though this may not be the most recent version.
Alternatively, you can install the most recent version of the pstest package from Github (this is what we recommend), using
kmte: An R package for treatment effects with censored outcomes
kmte R package includes a variety of policy evaluation tools suitable for right-censored (duration) outcomes. The content includes estimators and tests related to average, quantile, and distributional treatment effects under difference identifying assumptions including unconfoundedness, local treatment effects, and nonlinear difference-in-differences. In short, this package implement all estimators proposed in Sant’Anna (2016), “Program Evaluation with Right-Censored Data”, and all tests proposed in Sant’Anna (2017), “Nonparametric tests for Treatment Effect Heterogeneity with Duration Outcomes”.
The kmte package is still under development, so it is not yet on CRAN. Nonetheless, you can install its most recent version from Github, using