Usage: 
{mh, clo, cup} = regci(x {,h {,alpha {,K} {,d}}})

Input: 
 x  n x 2, the data. In the first column the
independent, in the second column the
dependent variable.

 h  scalar, bandwidth. If not given, 20% of the
range of x[,1] is used.

 alpha  confidence level, If not given, 0.05 is used.

 K  string, kernel function on [1,1]. If not given,
the Quartic kernel "qua" is used.

 d  scalar, discretization binwidth. d must be smaller
than h. If not given, the minimum of h/3 and
(max(x[,1])min(x[,1]))/100 is used.

Output: 
 mh  m x 2 matrix, the first column is a grid and the
second column contains the regression estimate on
that grid. 
 clo  m x 2 matrix, the first column is a grid and the
second column contains the lower confidence
bounds for that grid. 
 cup  m x 2 matrix, the first column is a grid and the
second column contains the upper confidence
bounds for that grid. 