[R] new version of glmnet

[Trevor Hastie]

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