\HeaderA{LassoCMA-methods}{L1 penalized logistic regression}{LassoCMA.Rdash.methods}
\aliasA{LassoCMA,data.frame,missing,formula-method}{LassoCMA-methods}{LassoCMA,data.frame,missing,formula.Rdash.method}
\aliasA{LassoCMA,ExpressionSet,character,missing-method}{LassoCMA-methods}{LassoCMA,ExpressionSet,character,missing.Rdash.method}
\aliasA{LassoCMA,matrix,factor,missing-method}{LassoCMA-methods}{LassoCMA,matrix,factor,missing.Rdash.method}
\aliasA{LassoCMA,matrix,numeric,missing-method}{LassoCMA-methods}{LassoCMA,matrix,numeric,missing.Rdash.method}
\keyword{multivariate}{LassoCMA-methods}
\begin{Description}\relax
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{\LinkA{GeneSelection}{GeneSelection}}
\end{Description}
\begin{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{\LinkA{LassoCMA}{LassoCMA}}.
\end{Section}

