\HeaderA{runHomHMM}{A function to fit unsupervised Hidden Markov model}{runHomHMM}
\aliasA{states.hmm.func}{runHomHMM}{states.hmm.func}
\keyword{models}{runHomHMM}
\begin{Description}\relax
This function fits an unsupervised Hidden Markov model to a given
\code{\LinkA{MAList}{MAList} or \\code\{\LinkA{MAList}{MAList}\}}
\end{Description}
\begin{Usage}
\begin{verbatim}
runHomHMM(input, vr = 0.01,
                maxiter = 100, criteria = "AIC", delta = NA,
                full.output = FALSE, eps = 0.01)
\end{verbatim}
\end{Usage}
\begin{Arguments}
\begin{ldescription}
\item[\code{input}] an object of class \code{\LinkA{MAList}{MAList}} or \code{\LinkA{SegList}{SegList}}
\item[\code{vr}] Gets passed to the function
\code{\LinkA{hidden}{hidden}} as the \code{pshape} arguement.
\item[\code{maxiter}] Gets passed to the function
\code{\LinkA{hidden}{hidden}} as the \code{iterlim}
arguement. 
\item[\code{criteria}] Choice of which selection criteria should be used in
the algorithm.  The choices are either AIC or BIC
\item[\code{delta}] Delta value used of the BIC is selected.  If no value is
entered it defaults to 1.
\item[\code{full.output}] if true the SegList output includes a probability
that a clone is in its assigned state and a smoothed value for the
clone.
\item[\code{eps}] parameter controlling the convergence of the EM
algorithm. 
\end{ldescription}
\end{Arguments}
\begin{SeeAlso}\relax
\code{\LinkA{runDNAcopy}{runDNAcopy}}
\code{\LinkA{runGLAD}{runGLAD}}
\code{\LinkA{SegList}{SegList}}
\end{SeeAlso}

