 Usage:  {filtX,KG,PreviousPs} = kfilter2(y,mu,Sig,H,F,Q,R)  
 
 Input:

  y                      T x m matrix of observed time series, 
                         T is the number of observations, 
                         m is the dimension of time series 
                         
  mu                     n x 1 vector, the mean of the initial state 
                         
  Sig                    n x n covariance matrix of the initial state 
                         
  H                      m x n matrix 
                         
  F                      n x n matrix 
                         
  Q                      m x m variance-covariance matrix 
                         
  R                      n x n variance-covariance matrix 
                         
 Output:

  filtX                  T x n matrix of (Kalman-)filtered states x_{t|t}, 
                         T is the number of observations, 
                         n is the dimension of the states 
                         
  KG                     T x n x m "vector" of Kalman-Gain-matrices for different times 
                         
  PreviousPs             T x n x n "vector" of P_t|t's (Filter-Error-Covariances) 
                         
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(C) MD*TECH Method and Data Technologies, 17.8.2000
