\HeaderA{ElasticNetCMA-methods}{Classfication and variable selection by the ElasticNet}{ElasticNetCMA.Rdash.methods}
\aliasA{ElasticNetCMA,data.frame,missing,formula-method}{ElasticNetCMA-methods}{ElasticNetCMA,data.frame,missing,formula.Rdash.method}
\aliasA{ElasticNetCMA,ExpressionSet,character,missing-method}{ElasticNetCMA-methods}{ElasticNetCMA,ExpressionSet,character,missing.Rdash.method}
\aliasA{ElasticNetCMA,matrix,factor,missing-method}{ElasticNetCMA-methods}{ElasticNetCMA,matrix,factor,missing.Rdash.method}
\aliasA{ElasticNetCMA,matrix,numeric,missing-method}{ElasticNetCMA-methods}{ElasticNetCMA,matrix,numeric,missing.Rdash.method}
\keyword{multivariate}{ElasticNetCMA-methods}
\begin{Description}\relax
Zou and Hastie (2004) proposed a combined L1/L2 penalty
for regularization and variable selection.
The Elastic Net penalty encourages a grouping
effect, where strongly correlated predictors tend to be in or out of the model together.
The computation is done with the function \code{glmpath} from the package
of the same name.
\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{ElasticNetCMA}{ElasticNetCMA}}
\end{Section}

