New glmnet package on CRAN
glmnet is a package that fits the regularization path for linear, two- and multi-class logistic regression models with "elastic net" regularization (tunable mixture of L1 and L2 penalties). glmnet uses pathwise coordinate descent, and is very fast. Some of the features of glmnet: * by default it computes the path at 100 uniformly spaced (on the log scale) values of the regularization parameter * glmnet appears to be faster than any of the packages that are freely available, in some cases by two orders of magnitude. * recognizes and exploits sparse input matrices (ala Matrix package). Coefficient matrices are output in sparse matrix representation. * penalty is (1-a)*||\beta||_2^2 +a*||beta||_1 where a is between 0 and 1; a=0 is the Lasso penalty, a=1 is the ridge penalty. For many correlated predictors, a=.95 or thereabouts improves the performance of the lasso. * convenient predict, plot, print, and coef methods * variable-wise penalty modulation allows each variable to be penalized by a scalable amount; if zero that variable always enters * glmnet uses a symmetric parametrization for multinomial, with constraints enforced by the penalization. Other families such as poisson might appear in later versions of glmnet. Examples of glmnet speed trials: Newsgroup data: N=11,000, p=4 Million, two class logistic. 100 values along lasso path. Time = 2mins 14 Class cancer data: N=144, p=16K, 14 class multinomial, 100 values along lasso path. Time = 30secs Authors: Jerome Friedman, Trevor Hastie, Rob Tibshirani. See our paper http://www-stat.stanford.edu/~hastie/Papers/glmnet.pdf for implementation details, and comparisons with other related software.
-------------------------------------------------------------------- Trevor Hastie hastie at stanford.edu Professor & Chair, Department of Statistics, Stanford University Phone: (650) 725-2231 (Statistics) Fax: (650) 725-8977 (650) 498-5233 (Biostatistics) Fax: (650) 725-6951 URL: http://www-stat.stanford.edu/~hastie address: room 104, Department of Statistics, Sequoia Hall 390 Serra Mall, Stanford University, CA 94305-4065