Short answer: No.
Longer answer: If you have 50 potential variables and may consider some higher order function (you don't say which) then you have got a preposterously large number of potential models. Even with just 50 variables and a linear fit, you have $2^{50}$ models. If you allow interactions (even just 2-way), that number explodes.
A package such as leaps
may help to find models for further work, but it isn't going to solve your problem for you. Why not?
Each model has to be tested for the assumptions of OLS regression (although you may want to use some other form of regression with fewer assumptions).
leaps
uses a statistic such as Cp or adjr2 to select models. This ignores other reasons to include a variable.
By testing many models, the results will be distorted. Some of this may be gotten around by using training and testing data sets.
As far as I know, leaps
does not deal with the hierarchy problem. (Others will correct me if I am wrong).
You're going to have to do some work before you start the modeling process.