

    \filetitle{diffloglik}{Approximate gradient and hessian of log-likelihood function}{model/diffloglik}

	\paragraph{Syntax}
 
 \begin{verbatim}
 [MINUSL,GRAD,HESS,V] = diffloglik(M,D,RANGE,LIST,...)
 \end{verbatim}
 \paragraph{Input arguments}
 
 \begin{itemize}
 \item
   \texttt{M} {[} model {]} - Model object whose likelihood function will
   be differentiated.
 \item
   \texttt{D} {[} cell \textbar{} struct {]} - Input data from which
   measurement variables will be taken.
 \item
   \texttt{RANGE} {[} numeric {]} - Date range on which the likelihood
   function will be evaluated.
 \item
   \texttt{LIST} {[} cellstr {]} - List of model parameters with respect
   to which the likelihood function will be differentiated.
 \end{itemize}
 \paragraph{Output arguments}
 
 \begin{itemize}
 \item
   \texttt{MINUSL} {[} numeric {]} - Value of minus the likelihood
   function at the input data.
 \item
   \texttt{GRAD} {[} numeric {]} - Gradient (or score) vector.
 \item
   \texttt{HESS} {[} numeric {]} - Hessian (or information) matrix.
 \item
   \texttt{V} {[} numeric {]} - Estimated variance scale factor if the
   \texttt{'relative='} options is true; otherwise \texttt{v} is 1.
 \end{itemize}
 \paragraph{Options}
 
 \begin{itemize}
 \item
   \texttt{'refresh='} {[} \emph{\texttt{true}} \textbar{} \texttt{false}
   {]} - Refresh dynamic links for each change in a parameter.
 \item
   \texttt{'solve='} {[} \emph{\texttt{true}} \textbar{} \texttt{false}
   {]} - Re-compute solution for each change in a parameter.
 \item
   \texttt{'sstate='} {[} \texttt{true} \textbar{} \emph{\texttt{false}}
   \textbar{} cell {]} - Re-compute steady state in each differentiation
   step; if the model is non-linear, you can pass in a cell array with
   options used in the \texttt{sstate} function.
 \end{itemize}
 See help on \url{model/filter} for other options available.
 
 \paragraph{Description}
 
 \paragraph{Example}


