estimatr v0.14


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Fast Estimators for Design-Based Inference

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>.

Functions in estimatr

Name Description
declaration_to_condition_pr_mat Builds condition probability matrices for Horvitz-Thompson estimation from randomizr declaration
lm_robust_fit Internal method that creates linear fits Extra logging on na.omit handler
extract.robust_default Extract model data for texreg package
gen_pr_matrix_cluster Generate condition probability matrix given clusters and probabilities
estimatr estimatr
estimatr_tidiers Tidy an estimatr object
reexports Objects exported from other packages
lm_lin Linear regression with the Lin (2013) covariate adjustment
lm_robust Ordinary Least Squares with Robust Standard Errors
starprep Prepare model fits for stargazer
alo_star_men Replication data for Lin 2013
commarobust Build lm_robust object from lm fit
horvitz_thompson Horvitz-Thompson estimator for two-armed trials
iv_robust Two-Stage Least Squares Instrumental Variables Regression
permutations_to_condition_pr_mat Builds condition probability matrices for Horvitz-Thompson estimation from permutation matrix
predict.lm_robust Predict method for lm_robust object
difference_in_means Design-based difference-in-means estimator
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Last month downloads


Type Package
Date 2018-10-29
License MIT + file LICENSE
LinkingTo Rcpp, RcppEigen
RoxygenNote 6.1.0
LazyData true
NeedsCompilation yes
Packaged 2018-11-05 18:35:07 UTC; luke
Repository CRAN
Date/Publication 2018-11-06 12:30:03 UTC

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