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\section{User Manual}

This section gives an exhaustive presentation of the objects and functions provided by the $otmixmod$ module, in the alphabetic order.

\subsection{CovarianceModel}

\subsubsection{CovarianceModelImplementation}


\begin{description}
\item[Usage :]  \strut
  \begin{description}
   \item CovarianceModelImplementation ()
  \end{description}
\item[Arguments :] none
\item[Value :]  a CovarianceModelImplementation is the implementation of a CovarianceModel and is one to the following classes:
Gaussian\_p\_L\_I, Gaussian\_p\_Lk\_I, Gaussian\_p\_L\_B, Gaussian\_p\_Lk\_B,
Gaussian\_p\_L\_Bk, Gaussian\_p\_Lk\_Bk, Gaussian\_p\_L\_C, Gaussian\_p\_Lk\_C, Gaussian\_p\_L\_D\_Ak\_D, Gaussian\_p\_Lk D\_Ak\_D, Gaussian\_p\_L\_Dk\_A\_Dk, Gaussian\_p\_Lk\_Dk\_A\_Dk, Gaussian\_p\_L\_Ck, Gaussian\_p\_Lk\_Ck, Gaussian\_pk\_L\_I, Gaussian\_pk\_Lk\_I, Gaussian\_pk\_L\_B,
Gaussian\_pk\_Lk\_B, Gaussian\_pk\_L\_Bk, Gaussian\_pk\_Lk\_Bk, Gaussian\_pk\_L\_C, Gaussian\_pk\_Lk\_C,
Gaussian\_pk\_L\_D\_Ak\_D, Gaussian\_pk\_Lk D\_Ak\_D, Gaussian\_pk\_L\_Dk\_A\_Dk, Gaussian\_pk\_Lk\_Dk\_A\_Dk, Gaussian\_pk\_L\_Ck, Gaussian\_pk\_Lk\_Ck.

\item[Some methods :]  \strut
  \begin{description}
\item $convertToMixmod$
    \begin{description}
    \item[Usage :]  $convertToMixmod()$
    \item[Arguments :] none
    \item[Value :]  a String that is the mixmod name of the covariance model. 
    \end{description}


  \end{description}

\end{description}





\subsection{MixtureFactory}


\begin{description}
\item[Usage :]  \strut
  \begin{description}
  \item $MixtureFactory(atomsNumber)$
  \item $MixtureFactory(atomsNumber, covarianceModel)$
  \end{description}
\item[Arguments :] \strut
  \begin{description}
  \item $atomsNumber$: a UnsignedInteger, the number of atoms to consider in the muxture.
  \item $covarianceModel$: a CovarianceModel, the covariance model assumed for the several atoms of a mixture.
  \end{description}
\item[Some methods :]  \strut

  \begin{description}

  \item $build$
    \begin{description}
    \item[Usage :]  $build(sample)$
    \item[Usage :]  $build(sample, labels, BICLogLikelihood)$
    \item[Arguments :]  \strut
    \begin{description}
      \item $sample$ : a NumericalSample of dimension $n \ge 1$
      \item $labels$ : an Indices that will be filled by the method.
      \item $BICLogLikelihood$ : a NumericalPoint that will be filled by 3 values: the log-likelihood of the estimated model, the corrected log-likelihood taking the number of parameters into account, and the entropy.
    \end{description}
    \item[Value :] a Mixture. 
    \end{description}


  \item $getAtomsNumber$
    \begin{description}
    \item[Usage :] $getAtomsNumber()$
    \item[Arguments :] none
    \item[Value :] an UnsignedInteger, which is the number of atoms  in the mixture factory.
    \end{description}

  \item $getCovarianceModel$
    \begin{description}
    \item[Usage :]  $getCovarianceModel()$
    \item[Arguments :]  none
    \item[Value :]  a CovarianceModel, which is the covariance model assumed for the several atoms of a mixture.
    \end{description}


  \item $setAtomsNumber$
    \begin{description}
    \item[Usage :]  $setAtomsNumber(myAtomNumber)$
    \item[Arguments :]  \strut
    \begin{description}
      \item $myAtomNumber$ : an UnsignedInteger, which is the number of atoms to set in the mixture factory. 
    \end{description}
    \item[Value :]  none
    \end{description}

  \item $setCovarianceModel$
    \begin{description}
    \item[Usage :]  $getCovarianceModel(myCovarianceModel)$
    \item[Arguments :]  \strut
    \begin{description}
      \item $myCovarianceModel$ : a CovarianceModel, which is the covariance model assumed for the several atoms of a mixture.
    \end{description}
    \item[Value :]  none
    \end{description}


  \end{description}

\end{description}