Fast Estimators for Design-Based Inference
Build lm_robust object from lm fit
Builds condition probability matrices for Horvitz-Thompson estimation ...
Design-based difference-in-means estimator
estimatr
Glance at an estimatr object
Tidy an estimatr object
Extract model data for texreg package
Generate condition probability matrix given clusters and probabilities
Horvitz-Thompson estimator for two-armed trials
Two-Stage Least Squares Instrumental Variables Regression
Linear Hypothesis for Ordinary Least Squares with Robust Standard Erro...
Linear regression with the Lin (2013) covariate adjustment
Ordinary Least Squares with Robust Standard Errors
Internal method that creates linear fits
Extra logging on na.omit handler
Builds condition probability matrices for Horvitz-Thompson estimation ...
Predict method for lm_robust object
Objects exported from other packages
Prepare model fits for stargazer
Fast procedures for small set of commonly-used, design-appropriate estimators with robust standard errors and confidence intervals. Includes estimators for linear regression, instrumental variables regression, difference-in-means, Horvitz-Thompson estimation, and regression improving precision of experimental estimates by interacting treatment with centered pre-treatment covariates introduced by Lin (2013) <doi:10.1214/12-AOAS583>.
Useful links