Add weights parameter as mentioned in section 4.6 "Subsampling Training Data"

First of all, thanks for the great effort- it looks great. The combination of `sparseMatrix` with Rcpp (instead of Rs memory expensive `model.matrix`) looks very promising!

Though, as many times mentioned in the paper, in real world we are facing with very sparse data and very small amount of successes, hence, the data is very unbalanced. The normal logistic regression implementation can't handle this (although generating very high accuracy, no TPs will be found), hence, it is crucial to re-balance the data using some type of weights.

In section 4.6 in the paper, they introduced a pretty straight forward implementation of subsampling correction.