ranknormalization
Rank normalization, also known as the rank-based inverse normal transformation or normal scores transformation, is a data preprocessing technique that aims to transform a numeric variable so its distribution resembles a standard normal distribution. It is a nonparametric method that relies on the relative ordering of observations rather than their absolute values and preserves the monotonic relationship with the original data.
Method: for each variable with n observations, compute the ranks r_i (using the average rank for ties).
Properties and notes: the transform is monotone and nonparametric, reducing the impact of skewness and outliers
Applications and limitations: rank normalization is used to satisfy normality or homoscedasticity assumptions in parametric tests