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+//schurrc - Schur algorithm.
+//K = SCHURRC(R) computes the reflection coefficients from autocorrelation vector R. If R is a matrix, SCHURRC finds coefficients for each column of R, and returns them in the columns of K.
+//[K,E] = SCHURRC(R) returns the prediction error variance E. If R is a matrix, SCHURRC finds the error for each column of R, and returns them in the rows of E.
+//Modified to match matlab i/p and o/p and handle exceptions
+//Fixed bugs
+//by Debdeep Dey 
+function [k,e] = schurrc(R)
+    narginchk(1,1,argn(2));
+if(type(R)==10) then
+    w=R;
+    [nr,nc]=size(R);
+    if(nr==1 & nc==1) then
+        R=ascii(R);
+        R=matrix(R,length(w));
+    else
+        
+        R=ascii(R);
+        R=matrix(R,size(w));
+    end
+    
+end
+if(type(R) > 1) then
+	error('Input R is not a matrix')
+end
+if (min(size(R)) == 1) then 
+    R = R(:); 
+end 
+[m,n] = size(R);
+// Compute reflection coefficients for each column of the input matrix
+for j = 1:n
+	X = R(:,j).';
+	// Schur's iterative algorithm on a row vector of autocorrelation values
+	U = [0 X(2:m); X(1:m)];
+
+    for i = 2:m,
+        U(2,:) = [0 U(2,1:m-1)];    
+        k(i-1,j) = -U(1,i)/U(2,i); 
+        U = [1 k(i-1,j); conj(k(i-1,j)) 1]*U;
+    end
+   
+	e(j,1) = U(2,$);
+end
+endfunction
+function narginchk(l,h,t)
+    if t<l then
+        error("Too few input arguments");
+    elseif t>l then
+        error("Too many input arguments");
+    end
+endfunction