standardize with refit without centering

Thank you so much for the package!

I would like to know if it is possible to provide an option of standardization without centering when using refit method.

The rationale is that the negative and positive values of some predictive variables can have a meaningful signification (i.e. difference of price over a period), and it that case, it is valuable to only scale the variable and not center them as suggested by Andrew Gelman here and here (Gelman, 2008; actually cited in the documentation of the standardize function):

3. subtracting the mean of each input variable and dividing by its standard deviation. (Strictly
   speaking, subtracting the mean is not necessary, but this step allows main effects to be more
   easily interpreted in the presence of interactions.)

We also center each input variable to have a mean of zero so that interactions are more
interpretable. Again, in some applications it can make sense for variables to be centered around
some particular baseline value, but we believe our automatic procedure is better than the current
default of using whatever value happens to be zero on the scale of the data, which all too commonly
results in absurdities such as age = 0 years or party identification = 0 on a 1–7 scale.

In the case where negative and positive values of predictor variables have different meaning, I believe that the centering can change the meaning of the regression coefficients.

I realized that with my own data analysis where a positive coefficient become negative with centering, with the type of explicative variable that I mentionned above.

Start with the standardize function and its refit method, focusing on how centering and scaling are currently applied. Check the existing documentation for standardize and the refit behavior, then define and verify an option that scales without centering while preserving the current default behavior.