The objective of this package is to perform statistical inference using an expressive statistical grammar that coheres with the `tidyverse`

design framework.

To install the current stable version of `infer`

from CRAN:

To install the developmental version of `infer`

, make sure to install `remotes`

first. The `pkgdown`

website for this developmental version is at https://infer.netlify.com.

To install the cutting edge version of `infer`

(do so at your own risk), make sure to install `remotes`

first. This version was last updated on 2018-09-19 13:22:06.

To see the things we are working on with the package as vignettes/Articles, check out the developmental `pkgdown`

site at https://infer-dev.netlify.com.

We welcome others helping us make this package as user friendly and efficient as possible. Please review our contributing and conduct guidelines. Of particular interest is helping us to write `testthat`

tests and in building vignettes that show how to (and how NOT to) use the package. By participating in this project you agree to abide by its terms.

These examples assume that `mtcars`

has been overwritten so that the variables `cyl`

, `vs`

, `am`

, `gear`

, and `carb`

are `factor`

s.

```
mtcars <- as.data.frame(mtcars) %>%
mutate(cyl = factor(cyl),
vs = factor(vs),
am = factor(am),
gear = factor(gear),
carb = factor(carb))
```

Hypothesis test for a difference in proportions (using the formula interface `y ~ x`

in `specify()`

):

```
mtcars %>%
specify(am ~ vs, success = "1") %>%
hypothesize(null = "independence") %>%
generate(reps = 100, type = "permute") %>%
calculate(stat = "diff in props", order = c("1", "0"))
```

Confidence interval for a difference in means (using the non-formula interface giving both the `response`

and `explanatory`

variables in `specify()`

):

```
mtcars %>%
specify(response = mpg, explanatory = am) %>%
generate(reps = 100, type = "bootstrap") %>%
calculate(stat = "diff in means", order = c("1", "0"))
```

Note that the formula and non-formula interfaces work for all implemented inference procedures in `infer`

. Use whatever is more natural for you. If you will be doing modeling using functions like `lm()`

and `glm()`

, we recommend you begin to use the formula `y ~ x`

notation as soon as possible though.

Other examples are available in the package vignettes.