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 <rdf:Description>
  <dc:title>Lasso and elastic-net regularized generalized linear models</dc:title>
  <dc:subject>CRAN Task View: MachineLearning (http://CRAN.R-project.org/view=MachineLearning)</dc:subject>
  <dc:description>Extremely efficient procedures for fitting the entire
lasso or elastic-net regularization path for linear regression,
logistic and multinomial regression models, poisson regression
and the Cox model. The algorithm uses cyclical coordinate
descent in a pathwise fashion, as described in the paper listed
below.</dc:description>
  <dc:type>Software</dc:type>
  <dc:relation>Depends: Matrix (&gt;= 1.0-6), utils</dc:relation>
  <dc:relation>Suggests: survival</dc:relation>
  <dc:creator>Trevor Hastie &lt;hastie@stanford.edu&gt;</dc:creator>
  <dc:contributor>Jerome Friedman, Trevor Hastie, Rob Tibshirani</dc:contributor>
  <dc:rights>GPL-2</dc:rights>
  <dc:date>2012-04-27</dc:date>
  <dc:format>application/tgz</dc:format>
  <dc:identifier>http://CRAN.R-project.org/package=glmnet</dc:identifier>
 </rdf:Description>
</rdf:RDF>

