Twenty possible predictor variables in data set. One outcome variable.
Some of the predictor variables are not linear. So a standard linear multiple regression approach probably won't do. (And I do not want to transform the variables using some arbitrary "cubic," "quadratic" approach -- leave the underlying variables as they are.)
Example: Time of day. This oscillates regularly, over 24 hours. (Like a sine wave.)
There is considerable random chaos inherent in some variables.
Example: Highway traffic frequency.
Some curve fitting may be advisable to dampen or smooth out the chaos.
I'm looking for possible statistical packages (and specific procedures) that can accomplish the goal -- determining the two best predictor variables taken together of the twenty, and levels of each, that appear to best predict the outcome variable.
Considering all twenty variables, there are 190 possible combinations. Doing an analysis of each possible pair would take a great deal of effort. Best to find a statistical package that can run tests on each possible combination in one long go.
In summary: Software Package and Specific Procedure please. No need for a bunch of code. (If I want the code I'll ask in a follow-up.)