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estimatr (version 0.10.0)

Fast Estimators for Design-Based Inference

Description

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) .

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Install

install.packages('estimatr')

Monthly Downloads

8,795

Version

0.10.0

License

MIT + file LICENSE

Issues

69

Pull Requests

3

Stars

133

Forks

20

Maintainer

Graeme Blair

Last Published

July 12th, 2018

Functions in estimatr (0.10.0)

tidy

Tidy the result of an estimator into a data.frame
na.omit_detailed.data.frame

Extra logging on na.omit handler
starprep

Prepare model fits for stargazer
permutations_to_condition_pr_mat

Builds condition probability matrices for Horvitz-Thompson estimation from permutation matrix
extract.robust_default

Extract model data for texreg package
lm_robust_fit

Internal method that creates linear fits
lm_robust

Ordinary Least Squares with Robust Standard Errors
declaration_to_condition_pr_mat

Builds condition probability matrices for Horvitz-Thompson estimation from randomizr declaration
gen_pr_matrix_cluster

Generate condition probability matrix given clusters and probabilities
difference_in_means

Design-based difference-in-means estimator
commarobust

Build lm_robust object from lm fit
estimatr

estimatr
alo_star_men

Replication data for Lin 2013
iv_robust

Two-Stage Least Squares Instrumental Variables Regression
horvitz_thompson

Horvitz-Thompson estimator for two-armed trials
lm_lin

Linear regression with the Lin (2013) covariate adjustment
predict.lm_robust

Predict method for lm_robust object