glmnet _1.1-3 is on CRAN now. glmnet fits lasso and elastic net regularization paths for squared error, binomial and multinomial models via coordinate descent. It is extremely fast and can work on large scale problems. See the paper: "Regularized Paths for Generalized Linear Models via Coordinate Descent" by Friedman, Hastie, Tibshirani on my website for details. Glmnet can accommodate sparse data matrices efficiently, and thereby handle even larger problems. For example for a two class logistic model with 11K obs and 750K variables (with > 99% zeros in X matrix), glmnet takes less than two minutes to fit the entire regularization path on a grid of 100 values of the reg. parameter lambda. For a 14-class gene expression dataset (144 obs, 16K vars, not sparse), it takes 15 seconds to fit the path at 100 values of lambda Several minor fixes, as well as two more serious fixes: 1) predict( ...,type="class") was returning flipped labels for a two class logistic model. 2) if a weight argument was supplied to binomial/multinomial model, with some zero weight entries, the program bombed with an unhelpful message. Now it works as expected. Thanks to many users, esp. Tim Hesterberg, for notifying us of the errors. Trevor Hastie