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
Z <- complete_ra(100)
Y <- 5 + 10*Z + rnorm(100)
fit <- lm(Y ~ Z)
commarobust(fit)
#>              Estimate Std. Error  t value     Pr(>|t|)
#> (Intercept)  4.797376  0.1349279 35.55510 8.201968e-58
#> Z           10.290971  0.2028367 50.73526 3.696001e-72

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	5.407\*\*\*
	(0.206)
Z\_2		1.899\*\*\*
		(0.207)
Constant	9.767	10.039
	(0.150)	(0.151)
Observations	100	100
R2	0.875	0.463
Adjusted R2	0.874	0.458
Residual Std. Error (df = 98)	1.030	1.033
F Statistic (df = 1; 98)	688.468\*\*\*	84.532\*\*\*
Note:	*p<0.1; **p<0.05; ***p<0.01