The `recipes`

package can be used to create design matrices for modeling
and to conduct preprocessing of variables. It is meant to be a more
extensive framework that R's formula method. Some differences between
simple formula methods and recipes are that

Variables can have arbitrary roles in the analysis beyond predictors and outcomes.

A recipe consists of one or more steps that define actions on the variables.

Recipes can be defined sequentially using pipes as well as being modifiable and extensible.

## Basic Functions

The three main functions are `recipe()`

, `prep()`

,
and `bake()`

.

`recipe()`

defines the operations on the data and the associated
roles. Once the preprocessing steps are defined, any parameters are
estimated using `prep()`

. Once the data are ready for
transformation, the `bake()`

function applies the operations.

## Step Functions

These functions are used to add new actions to the recipe and have the
naming convention `"step_action"`

. For example,
`step_center()`

centers the data to have a zero mean and
`step_dummy()`

is used to create dummy variables.