When fitting linear regressions models, we always want them to fit as best as possible into the data. Sometimes, we want to transform our variables in order to get the model fit to improve as much as possible. For example, we could apply several transformations (taking logarithms, squared values, and so on) in order to improve the fit.

The acepack package implements the alternating conditional expectation algorithm, which finds the optimal transformations that we need to apply to our data in order to maximize the R2. Another way of looking at this would be: given the data that we have, what would be the best R2 we could get if we found the best possible transformations? In this fashion, we could get a maximum boundary on the best model that we would be able to get, assuming we can only transform the variables to capture the nonlinear relationships.

It is important to realize that whenever we transform the variables, the interpretation of the coefficient changes; so this must be done with some care.