ri2

ri2 makes conducting randomization inference easy and (with the blessing of the original authors) is the successor package to ri.

ri2 has specific support for the following:

  1. All randomization schemes in randomizr.
  2. Difference-in-means and OLS-adjusted estimates of ATE estimates using R-native formula syntax.
  3. Multi-arm trials.
  4. ANOVA-style hypothesis tests (e.g., testing interaction term under null of constant effects),

Additionally, ri2 provides:

  1. Accommodation for arbitrary randomization schemes
  2. Accommodation for arbitrary (scalar) test statistics

ri2 is on CRAN:

install.packages("ri2")

If you’d like to install the most current development release, you can use the following code:

install.packages("devtools")
devtools::install_github("acoppock/ri2")

Here is the basic syntax for a two-arm trial:

library(ri2)
N <- 100
declaration <- declare_ra(N = N, m = 50)

Z <- conduct_ra(declaration)
X <- rnorm(N)
Y <- .9 * X + .2 * Z + rnorm(N)
dat <- data.frame(Y, X, Z)

ri_out <-
  conduct_ri(
    formula = Y ~ Z,
    declaration = declaration,
    assignment = "Z",
    sharp_hypothesis = 0,
    data = dat
  )

plot(ri_out)
Histogram of the randomization distribution of the estimated ATE under the sharp null of no effect, with a vertical line at the observed estimate.
summary(ri_out)
#>   term  estimate two_tailed_p_value
#> 1    Z 0.1235293              0.658

The development of ri2 is supported by a Standards Grant from EGAP.

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