We have submitted glmnet_1.6 to CRAN
This version has an improved convergence criterion, and it also uses
a variable screening algorithm that dramatically reduces the time
to convergence (while still producing the exact solutions).
The speedups in some cases are by a factors of 20 to 50, depending on
the particular problem and loss function.
See our paper http://www-stat.stanford.edu/~tibs/ftp/strong.pdf
"Strong Rules for Discarding Predictors in Lasso-type Problems"
for details of this screening method.
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Trevor Hastie hastie at stanford.edu
Professor, 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
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