**estimatr** is an `R`

package providing a range of commonly-used linear estimators, designed for speed and for ease-of-use. Users can easily recover robust, cluster-robust, and other design appropriate estimates. We include two functions that implement means estimators, `difference_in_means()`

and `horvitz_thompson()`

, and three linear regression estimators, `lm_robust()`

, `lm_lin()`

, and `iv_robust()`

. In each case, users can choose an estimator to reflect cluster-randomized, block-randomized, and block-and-cluster-randomized designs. The Getting Started Guide describes each estimator provided by **estimatr** and how it can be used in your analysis.

You can also see the multiple ways you can get regression tables out of estimatr using commonly used `R`

packages such as `texreg`

and `stargazer`

. Fast estimators also enable fast simulation of research designs to learn about their properties (see DeclareDesign).

## Installing estimatr

To install the latest stable release of **estimatr**, please ensure that you are running version 3.4 or later of R and run the following code:

`install.packages("estimatr")`

If you would like to use the latest development release of **estimatr**, please ensure that you are running version 3.4 or later of R and run the following code:

```
install.packages("estimatr", dependencies = TRUE,
repos = c("http://r.declaredesign.org", "https://cloud.r-project.org"))
```

## Easy to use

Once the package is installed, getting appropriate estimates and standard errors is now both fast and easy.

```
library(estimatr)
# sample data from cluster-randomized experiment
library(fabricatr)
library(randomizr)
dat <- fabricate(
N = 100,
y = rnorm(N),
clusterID = sample(letters[1:10], size = N, replace = TRUE),
z = cluster_ra(clusterID)
)
# robust standard errors
res_rob <- lm_robust(y ~ z, data = dat)
# tidy dataframes on command!
tidy(res_rob)
#> term estimate std.error statistic p.value conf.low conf.high df
#> 1 (Intercept) 0.27 0.16 1.7 0.089 -0.041 0.580 98
#> 2 z -0.42 0.21 -2.0 0.044 -0.833 -0.012 98
#> outcome
#> 1 y
#> 2 y
# cluster robust standard errors
res_cl <- lm_robust(y ~ z, data = dat, clusters = clusterID)
# standard summary view also available
summary(res_cl)
#>
#> Call:
#> lm_robust(formula = y ~ z, data = dat, clusters = clusterID)
#>
#> Standard error type: CR2
#>
#> Coefficients:
#> Estimate Std. Error t value Pr(>|t|) CI Lower CI Upper DF
#> (Intercept) 0.269 0.164 1.64 0.20 -0.255 0.793 2.99
#> z -0.422 0.250 -1.69 0.14 -1.027 0.182 6.30
#>
#> Multiple R-squared: 0.041 , Adjusted R-squared: 0.0312
#> F-statistic: 2.86 on 1 and 9 DF, p-value: 0.125
# matched-pair design learned from blocks argument
data(sleep)
res_dim <- difference_in_means(extra ~ group, data = sleep, blocks = ID)
```

The Getting Started Guide has more examples and uses, as do the reference pages. The Mathematical Notes provide more information about what each estimator is doing under the hood.

## Fast to use

Getting estimates and robust standard errors is also faster than it used to be. Compare our package to using `lm()`

and the `sandwich`

package to get HC2 standard errors. More speed comparisons are available here. Furthermore, with many blocks (or fixed effects), users can use the `fixed_effects`

argument of `lm_robust`

with HC1 standard errors to greatly improve estimation speed. More on fixed effects here.

```
dat <- data.frame(X = matrix(rnorm(2000*50), 2000), y = rnorm(2000))
library(microbenchmark)
library(lmtest)
library(sandwich)
mb <- microbenchmark(
`estimatr` = lm_robust(y ~ ., data = dat),
`lm + sandwich` = {
lo <- lm(y ~ ., data = dat)
coeftest(lo, vcov = vcovHC(lo, type = 'HC2'))
}
)
```

estimatr | median run-time (ms) |
---|---|

estimatr | 20 |

lm + sandwich | 45 |

This project is generously supported by a grant from the Laura and John Arnold Foundation and seed funding from Evidence in Governance and Politics (EGAP).