1. R qte Package [Website][CRAN] [Github]

The R qte package provides many methods for estimating the Quantile Treatment Effect (QTE) or the Quantile Treatment Effect on the Treated (QTET). These include estimating the QTE and QTET under the assumption of selection on observables (Firpo, 2007), the QTET in the Changes in Changes model (Athey and Imbens, 2006), the QTET in the Quantile Difference in Differences model, and the QTET under a Distributional Difference in Differences assumption (Callaway and Li, 2016; Callaway, Li, Oka (2017)).

2. R csabounds Package[Website] [CRAN] [Github]

The csabounds package contains functions written for my project “Job Displacement during the Great Recession: Tight Bounds on Distributional Treatment Effect Parameters using Panel Data.” The main functions are csa.bounds which computes bounds on the distribution and quantile of the treatment effect and attcpo which computes the average treatment effect conditional on the previous outcome.

3. R ccfa Package[Website] [CRAN] [Github]

The ccfa package contains functions for computing counterfactual distributions with a continuous treatment variable. Weige Huang and I developed this package in conjunction with our project “Intergenerational Income Mobility: Counterfactual Distributions with a Continuous Treatment.”

4. R BMisc Package [Website][CRAN] [Github]

The BMisc package contains various functions that I have found useful in my research and in writing R packages. In particular, it contains functions for working with panel data, quantiles, and printing results.

5. R TempleMetrics Package [Website][CRAN] [Github]

The TempleMetrics package contains various functions that members of the Econometrics Reading Group at Temple University have written and have found useful across projects. At the moment, it mainly contains code for distribution regression.

6. R did Package [Website][CRAN] [Github]

Contains tools for computing average treatment effect parameters in Difference in Differences models with more than two periods and with variation in treatment timing. The main parameters are group-time average treatment effects which are the average treatment effect for a particular group at a a particular time. These can be aggregated into a fewer number of treatment effect parameters, and the package deals with the cases where there is selective treatment timing, dynamic treatment effects, calendar time effects, or combinations of these. There are also functions for testing the Difference in Differences assumption, and plotting group-time average treatment effects.