Description Usage Arguments Value Author(s)

View source: R/normalizeGaussian.R

Converts a random variable `x`

extracted by a population represented by the sample `data`

or `sample`

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

is `TRUE`

1 2 3 |

`x` |
value or vector of values 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` |
a character string or |

the normalized variable or its inverse

@note This function makes a Marginal Gaussianization. See the R code for further details

Emanuele Cordano, Emanuele Eccel

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