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title: 'wfe: Weighted Linear Fixed Effects Regression Models for Causal Inference'
version: 1.9.1
doi: 10.32614/CRAN.package.wfe
abstract: Provides a computationally efficient way of fitting weighted linear fixed
  effects estimators for causal inference with various weighting schemes. 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 implements methods described in Imai and Kim (2017) "When should We
  Use Linear Fixed Effects Regression Models for Causal Inference with Longitudinal
  Data?", available at <https://imai.fas.harvard.edu/research/FEmatch.html>.
authors:
- family-names: Kim
  given-names: In Song
  email: insong@mit.edu
- family-names: Imai
  given-names: Kosuke
  email: imai@Harvard.edu
repository: https://insongkim.r-universe.dev
repository-code: https://github.com/insongkim/wfe
commit: d7c0b7668a3cb62bbd0b048667924f8e4df75449
url: https://github.com/insongkim/wfe
date-released: '2019-02-13'
contact:
- family-names: Kim
  given-names: In Song
  email: insong@mit.edu