glmnet _1.1-4 is on CRAN now. This version includes cross.validation functions to assist in picking a good value for "lambda" These functions are preliminary, in that they can only handle gaussian or logistic models for binary data. The complete range will appear in the future. For those unfamiliar with glmnet, here is the original blurb: 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 Trevor Hastie