\HeaderA{pnnCMA-methods}{Probabilistic Neural Networks}{pnnCMA.Rdash.methods}
\aliasA{pnnCMA,data.frame,missing,formula-method}{pnnCMA-methods}{pnnCMA,data.frame,missing,formula.Rdash.method}
\aliasA{pnnCMA,ExpressionSet,character,missing-method}{pnnCMA-methods}{pnnCMA,ExpressionSet,character,missing.Rdash.method}
\aliasA{pnnCMA,matrix,factor,missing-method}{pnnCMA-methods}{pnnCMA,matrix,factor,missing.Rdash.method}
\aliasA{pnnCMA,matrix,numeric,missing-method}{pnnCMA-methods}{pnnCMA,matrix,numeric,missing.Rdash.method}
\keyword{multivariate}{pnnCMA-methods}
\begin{Description}\relax
Probabilistic Neural Networks is the term Specht (1990) used
for a Gaussian kernel estimator for the conditional class
densities.
\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{pnnCMA}{pnnCMA}}.
\end{Section}

