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estimatr (version 0.16)

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

15,736

Version

0.16

License

MIT + file LICENSE

Issues

70

Pull Requests

3

Stars

133

Forks

21

Maintainer

Graeme Blair

Last Published

March 2nd, 2019

Functions in estimatr (0.16)

permutations_to_condition_pr_mat

Builds condition probability matrices for Horvitz-Thompson estimation from permutation matrix
estimatr_tidiers

Tidy an estimatr object
estimatr

estimatr
extract.robust_default

Extract model data for texreg package
starprep

Prepare model fits for stargazer
reexports

Objects exported from other packages
alo_star_men

Replication data for Lin 2013
commarobust

Build lm_robust object from lm fit
estimatr_glancers

Glance at an estimatr object
lm_lin

Linear regression with the Lin (2013) covariate adjustment
iv_robust

Two-Stage Least Squares Instrumental Variables Regression
lm_robust

Ordinary Least Squares with Robust Standard Errors
gen_pr_matrix_cluster

Generate condition probability matrix given clusters and probabilities
horvitz_thompson

Horvitz-Thompson estimator for two-armed trials
lh_robust

Linear Hypothesis for Ordinary Least Squares with Robust Standard Errors
declaration_to_condition_pr_mat

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

Design-based difference-in-means estimator
lm_robust_fit

Internal method that creates linear fits
na.omit_detailed.data.frame

Extra logging on na.omit handler
predict.lm_robust

Predict method for lm_robust object