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+function [X,varargout]= corrmtx(x,m,varargin)
+// Generate data matrix for autocorrelation matrix estimation
+//
+// Calling Sequence
+// X = corrmtx(x,m)
+// [X,R] = corrmtx(x,m)
+// X = corrmtx(x,m,s)
+// [X,R] = corrmtx(x,m,s)
+// 
+// Parameters
+// x: input vector of size N for which correlation matrix of size m is to be calculated
+// m: size of correlation matrix to be computed. Positive integer strictly smaller than the length of the input x
+// X: data matrix as specified according to the input 'method'
+// s: method for type of output matrix X
+//      'autocorrelation': (default) X is the (n + m)-by-(m + 1) rectangular Toeplitz matrix that generates an autocorrelation estimate for the leng    th-n data vector x, derived using prewindowed and postwindowed data, based on an mth-order prediction error model.
+//      'prewindowed': X is the n-by-(m + 1) rectangular Toeplitz matrix that generates an autocorrelation estimate for the length-n data vector x,     derived using prewindowed data, based on an mth-order prediction error model.
+//      'postwindowed': X is the n-by-(m + 1) rectangular Toeplitz matrix that generates an autocorrelation estimate for the length-n data vector x    , derived using postwindowed data, based on an mth-order prediction error model.
+//      'covariance': X is the (n – m)-by-(m + 1) rectangular Toeplitz matrix that generates an autocorrelation estimate for the length-n data vect    or x, derived using nonwindowed data, based on an mth-order prediction error model.
+//      'modified': X is the 2(n – m)-by-(m + 1) modified rectangular Toeplitz matrix that generates an autocorrelation estimate for the length-n d    ata vector x, derived using forward and backward prediction error estimates, based on an mth-order prediction error model.
+// R: (m + 1)-by-(m + 1) autocorrelation matrix estimate calculated as X'*X
+//
+// Description
+// Consider the generic matrix X below
+//     _                _
+//    | x(1) ..........0 |
+//    | :      .       : |
+//    | :       .      : |
+//    | x(m+1).......x(1)|
+//    | :      .       : |
+//    | :       .      : |
+//X = | x(n-m).....x(m+1)|
+//    | :      .       : |
+//    | :       .      : |
+//    | x(n).......x(n-m)|
+//    | :      .       : |
+//    | :       .      : |
+//    |_0 ..........x(n)_|
+// --
+// For different inputs of string s the output would vary ass described below
+// 'autocorrelation' — (default) X = X, above.
+// 'prewindowed' — X is the n-by-(m + 1) submatrix of X whose first row is [x(1) … 0] and whose last row is [x(n) … x(n – m)]
+// 'postwindowed' — X is the n-by-(m + 1) submatrix of X whose first row is [x(m + 1) … x(1)] and whose last row is [0 … x(n)]
+// 'covariance' — X is the (n – m)-by-(m + 1) submatrix of X whose first row is [x(m + 1) … x(1)] and whose last row is [x(n) … x(n – m)]
+// 'modified' — X is the 2(n – m)-by-(m + 1) matrix X_mod shown below
+//         _                _
+//        | x(m+1) ......x(1)|
+//        | :      .       : |
+//        | :       .      : |
+//        | x(n-m).....x(m+1)|
+//        | :      .       : |
+//        | :       .      : |
+//        | x(n).......x(n-m)|
+// X_mod= | x*(1).....x*(m+1)|
+//        | :      .       : |
+//        | :       .      : |
+//        | x*(m+1)...x*(n-m)|
+//        | :      .       : |
+//        | :       .      : |
+//        |_x*(n-m) ...x*(n)_|
+//
+// Examples
+// 
+// 
+//
+// See also
+// peig
+// pmusic
+// rooteig
+// rootmusic
+// xcorr
+//
+// Author:
+// Parthe Pandit
+//
+// Bilbligraphy
+// Marple, S. Lawrence. Digital Spectral Analysis. Englewood Cliffs, NJ: Prentice-Hall, 1987.
+
+
+
+    if(~isvector(x)) then
+        error("Input x must be a length n vector")
+        return
+    elseif (~isscalar(m)) then
+        error("Input m must be scalar")
+        return
+    end
+    
+    if (length(varargin) > 1) then
+        error('Too many input arguments. Third argument must be method for correlation matrix computation')
+        return
+    elseif (length(varargin) < 1)
+        method = 'autocorrelation';
+    elseif (length(varargin) == 1 & type(varargin(1))~=10)
+        disp(type(varargin));
+        error("Input method needs to be string")
+        return
+    else
+        method = varargin(1);
+    end
+    n = length(x);
+    x = matrix(x,1,n);
+    x_padded = [zeros(1,m),x,zeros(1,m)];
+    X = zeros( (n + m),(m + 1) );
+    for i = 1:size(X,1)
+        X(i,:) = x_padded(m+i:-1:i);
+    end
+    
+    select method
+    case "autocorrelation" then
+        X = X;
+    case 'prewindowed' then
+        X = X(1:n,:);
+    case 'postwindowed' then
+        X = X(m+1:$,:);
+    case 'covariance' then
+        X = X(m+1:n,:);
+    case 'modified' then
+        X = [X(m+1:n,:)  ; conj(mtlb_fliplr(X(m+1:n,:)))];
+    else X = X;
+end
+
+    
+    varargout = list(X'*X);
+    
+    
+endfunction