**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).

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"))
```

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.

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 | 22 | | lm + sandwich | 43 |

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).

graemeblair/DDestimate documentation built on Sept. 10, 2019, 7:38 p.m.

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