Compute mid-cumulative probabilities and mid-quantiles

1 2 | ```
midecdf(x, na.rm = FALSE)
midquantile(x, probs = 1:3/4, na.rm = FALSE)
``` |

`x` |
numeric vector of observations used to estimate the mid-cumulative distribution or the mid-quantiles. |

`probs` |
numeric vector of probabilities with values in [0,1]. |

`na.rm` |
logical value indicating whether NA values should be stripped before the computation proceeds. |

An object of class `class`

`midecdf`

or `midquantile`

with mid-cumulative probabilities and mid-quantiles. For `midecdf`

, this is a list that contains:

`x` |
unique values of the vector |

`y` |
estimated mid-cumulative probabilities. |

`fn` |
interpolating function of the points |

`data` |
input values. |

For `midquantile`

, this is a list that contains:

`x` |
probabilities |

`y` |
estimated mid-cumulative probabilities. |

`fn` |
interpolating function of the points |

`data` |
input values. |

Marco Geraci

Ma Y., Genton M., and Parzen E. Asymptotic properties of sample quantiles of discrete distributions. Annals of the Institute of Statistical Mathematics 2011;63(2):227-243

Parzen E. Quantile probability and statistical data modeling. Statistical Science 2004;19(4):652-62.

`confint.midquantile`

, `plot.midquantile`

1 2 | ```
x <- rpois(100, lambda = 3)
midquantile(x)
``` |

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