I'm just getting started in R. I've had good luck finding the right (predictive) correlation weights using orthogonal polynomials in Java. Where to go from here? Quantile regression was introduced in 1978 and Multivariate Adaptive Regression Splines (MARS) was introduced in 1991. What newer/better methods should I be looking at?

(If it helps I'm trying to find non-linear relationships between 10-12 predictors and 1 response variable. I'd stick with orthogonal equations except that the curse of dimensionality means I'm using only 1st and 2nd order polynomials so there's a good chance the fit is off.)

Thanks,

Axl