The commarobust pacakge does two things:

1. With the commarobust() function, you can easily estimate robust standard errors on your model objects. Almost as easy as Stata!

2. You can easily prepare your standard errors for inclusion in a stargazer table with makerobustseslist(). I’m open to better names for this function.

Install from Github!

install.packages("devtools")
devtools::install_github("acoppock/commarobust")

Check it out:

library(commarobust)
library(randomizr) # For easy random assignments
#> Warning: package 'randomizr' was built under R version 3.3.2
Z <- complete_ra(100)
Y <- 5 + 10*Z + rnorm(100)
fit <- lm(Y ~ Z)
commarobust(fit)
#>              Estimate Std. Error  t value     Pr(>|t|)
#> (Intercept)  4.779192  0.1677829 28.48439 3.322561e-49
#> Z           10.264458  0.2212777 46.38723 1.698068e-68

And now in Stargazer. See how the intercept doesn’t have stars even though the control group mean is statistically significantly larger than zero? Nice!

library(stargazer)
Z_1 <- complete_ra(100)
Y_1 <- 10 + 5*Z_1 + rnorm(100)
Z_2 <- complete_ra(100)
Y_2 <- 10 + 2*Z_2 + rnorm(100)

fit_1 <- lm(Y_1 ~ Z_1)
fit_2 <- lm(Y_2 ~ Z_2)

stargazer(fit_1, fit_2,
se = makerobustseslist(fit_1, fit_2),
p = makerobustpslist(fit_1, fit_2), type = "html")
 Dependent variable: Y_1 Y_2 (1) (2) Z_1 4.723*** (0.175) Z_2 2.341*** (0.174) Constant 10.173 9.813 (0.111) (0.112) Observations 100 100 R2 0.882 0.648 Adjusted R2 0.881 0.644 Residual Std. Error (df = 98) 0.873 0.871 F Statistic (df = 1; 98) 731.660*** 180.343*** Note: p<0.1; p<0.05; p<0.01