A step distribution is one where the cdf
and quantile function are step functions.
This includes empirical distributions.
`stepdst()`

facilitates the creation of
such a distribution by specifying the
observations/breakpoints, along with their weights.

stepdst(y, data, weights = 1, ...)
is_stepdst(object)
is.stepdst(object)

## Arguments

y |
Outcomes to comprise the distribution. Should either
evaluate to a vector, or be a name in the specified data. |

data |
Data frame, list, or environment
containing the outcome name in `y` . If missing,
`y` will be evaluated in the parent frame. |

weights |
Weights corresponding to the outcomes in `y` .
Must not be negative, but need not sum to 1. If `data`
is provided, the data will be searched for the name provided in
this argument. |

... |
Additional arguments, currently not used. |

object |
Object to check |

## Value

A "stepdst" object, which is also a "dst" object.
The cdf is a right-continuous step function,
and the quantile function is a left-continuous step function.

## Examples

#> Error in K(disp - 150): could not find function "K"

#> Error in plot(cond, "cdf", n = 1001, lty = 2, add = TRUE): object 'cond' not found