%! Computes the likelihood of a stationary state space model, given initial condition for the states (mean), the steady state kalman gain and the steady state inveverted covariance matrix of the prediction errors.
%! @sp 2
%! @strong{Inputs}
%! @sp 1
%! @table @ @var
%! @item Y
%! Matrix (@var{pp}*T) of doubles, data.
%! @item start
%! Integer scalar, first period.
%! @item last
%! Integer scalar, last period (@var{last}-@var{first} has to be inferior to T).
%! @item a
%! Vector (mm*1) of doubles, initial mean of the state vector.
%! @item T
%! Matrix (mm*mm) of doubles, transition matrix of the state equation.
%! @item K
%! Matrix (mm*@var{pp}) of doubles, steady state kalman gain.
%! @item iF
%! Matrix (@var{pp}*@var{pp}) of doubles, inverse of the steady state covariance matrix of the prediction errors.
%! @item dF
%! Double scalar, determinant of the steady state covariance matrix of teh prediction errors.
%! @item Z
%! Matrix (@var{pp}*mm) of doubles or vector of integers, matrix relating the states to the observed variables or vector of indices (depending on the value of @var{Zflag}).
%! @item pp
%! Integer scalar, number of observed variables.
%! @item Zflag
%! Integer scalar, equal to 0 if Z is a vector of indices targeting the obseved variables in the state vector, equal to 1 if Z is a @var{pp}*@var{mm} matrix.
%! @end table
%! @sp 2
%! @strong{Outputs}
%! @sp 1
%! @table @ @var
%! @item LIK
%! Double scalar, value of (minus) the likelihood.
%! @item likk
%! Column vector of doubles, values of the density of each observation.
%! @item a
%! Vector (mm*1) of doubles, mean of the state vector at the end of the (sub)sample.