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 TRUEConverts 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
normalizeGaussian(x = 0, data = x, cpf = NULL, mean = 0, sd = 1,
inverse = FALSE, step = NULL, prec = 10^-4, type = 3,
extremes = TRUE, sample = NULL)value or vector of values to be converted
a sample of data on which a non-parametric probability distribution is estimated
cumulative probability distribution. If NULL (default) is calculated as ecdf(data)
mean (expected value) of the normalized random variable. Default is 0.
standard deviation of the normalized random variable. Default is 1.
logical value. If TRUE the function works inversely (the opposite way). Default is FALSE.
vector of values in which step discontinuities of the cumulative probability function occur. Default is NULL
amplitude of the neighbourhood of the step discontinuities where cumulative probability function is treated as non-continuous.
see quantile
logical variable.
If TRUE (default) the probability or frequency is multiplied by $$\frac{N}{N+1}$$ where \(N\) is the length of data
a character string or NULL containing sample or probability distribution information.
Default is NULL
the normalized variable or its inverse
@note This function makes a Marginal Gaussianization. See the R code for further details