\name{LassoCMA-methods}
\docType{methods}
\alias{LassoCMA-methods}
\alias{LassoCMA,matrix,numeric,missing-method}
\alias{LassoCMA,matrix,factor,missing-method}
\alias{LassoCMA,data.frame,missing,formula-method}
\alias{LassoCMA,ExpressionSet,character,missing-method}
\title{L1 penalized logistic regression}
\description{
 The Lasso (Tibshirani, 1996) is one of the most popular
 tools for simultaneous shrinkage and variable selection. Recently,
 Young-Park and Hastie (2007) have developped and algorithm to
 compute the entire solution path of the Lasso for an arbitrary
 generalized linear model, implemented in the package \code{glmpath}.
 The method can be used for variable selection alone, s. \code{\link{GeneSelection}}
}
\section{Methods}{
\describe{

\item{X = "matrix", y = "numeric", f = "missing"}{signature 1}

\item{X = "matrix", y = "factor", f = "missing"}{signature 2}

\item{X = "data.frame", y = "missing", f = "formula"}{signature 3}

\item{X = "ExpressionSet", y = "character", f = "missing"}{signature 4}
}
For references, further argument and output information, consult
\code{\link{LassoCMA}}.
}
\keyword{multivariate}