Converts several samples `x`

random variable extracted by populations represented by the columns of `data`

respectively or `sample`

to a normally-distributed samples with assinged mean and standard deviation or vice versa in case `inverse`

is `TRUE`

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`x` |
value to be converted |

`data` |
a sample of data on which a non-parametric probability distribution is estimated |

`cpf` |
cumulative probability distribution. If |

`mean` |
mean (expected value) of the normalized random variable. Default is 0. |

`sd` |
standard deviation of the normalized random variable. Default is 1. |

`inverse` |
logical value. If |

`step` |
vector of values in which step discontinuities of the cumulative probability function occur. Default is |

`prec` |
amplitude of the neighbourhood of the step discontinuities where cumulative probability function is treated as non-continuous. |

`type` |
see |

`extremes` |
logical variable.
If
where |

`sample` |
information on how to sample |

`origin_x` |
date corresponding to the first row of |

`origin_data` |
date corresponding to the first row of |

a matrix with the normalized variable or its inverse

It applies `normalizeGaussian`

for each column of `x`

and `data`

.
See the R code for further details

Emanuele Cordano, Emanuele Eccel

`normalizeGaussian`

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