This R package provides a computationally efficient way of fitting
weighted linear fixed effects estimators for causal inference with
various weighting schemes.
Imai and Kim
(2011) show that weighted linear fixed effects
estimators can be used to estimate the average treatment effects
under different identification strategies. This includes
stratified randomized experiments, matching and stratification for
observational studies, first differencing, and
difference-in-differences. The package also provides various
robust standard errors and a specification test for standard
linear fixed effects estimators.