Data which is not normal should be transformed so that it becomes normally distributed before applying parametric tests. Some of these transformations include:

lognormal (or log base e or ln) (Y)

1 / Y

square (Y)

1 / square (Y)

square root (Y)

1 / square root (Y)

Box-Cox transformation is a convenient method that detects the deviation and applies the appropriate transformation to data. The package fifer provides a convenient function for this:

**code: **

> library(fifer)

> normalized_vect = boxcoxR(vect)

Where vect is a number series (vector).