README.Rmd

---
output: github_document
---

<!-- README.md is generated from README.Rmd. Please edit that file -->

```{r, echo = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "README-"
)
```


ri2 makes conducting randomization inference easy and (with the blessing of the original authors) is the successor package to [ri](https://cran.r-project.org/web/packages/ri/index.html).

ri2 has specific support for the following:

1. All randomization schemes in [randomizr](http://randomizr.declaredesign.org).
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

You can install ri2 is on CRAN
```{r, eval=FALSE}
install.packages("ri2")
```

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


```{r,eval=FALSE}
install.packages("devtools")
devtools::install_github("acoppock/ri2")
```

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

```{r}
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)
summary(ri_out)
```

The development of ri2 is supported by a Standards Grant from [EGAP](http://egap.org).

```{r, out.height = "50px", echo=FALSE}
knitr::include_graphics("man/figures/EGAP_logo.jpg")
```