diff --git a/TheilSen.m b/TheilSen.m
index 5f8dd49f4f526811fc6c32af7e34b89a3937ad4d..512d485d5fec88122a76c885e631441e154f560b 100644
--- a/TheilSen.m
+++ b/TheilSen.m
@@ -1,82 +1,82 @@
-function [b1, b0] = TheilSen(data)
-% Performs Theil-Sen robust linear regression on data.
-%
-% [b1, b0] = TheilSen(data)
-%
-% INPUT
-%   data: A Num_Obs x Num_Dim matrix with Num_Obs observations.
-%         The first Num_Dim - 1 columns are the explanatory variables and the
-%         last column is the response such that
-%         data = [x1, x2, ..., x(Num_Dim - 1), y];
-%
-% OUTPUT
-%   b1: Estimated slope of each explanatory variable with respect to the
-%       response variable. Therefore, b1 will be a vector of Num_Dim - 1 slopes.
-%   b0: Estimated offsets.
-%
-% EXAMPLE
-%   See accompanying file example.m.
-%
-% REFERENCE
-%   - Gilbert, Richard O. (1987), "6.5 Sen's Nonparametric Estimator of Slope",
-%     Statistical Methods for Environmental Pollution Monitoring,
-%     John Wiley and Sons, pp. 217-219, ISBN 978-0-471-28878-7
-%
-% AUTHORS
-%   2014-15 Zachary Danziger
-%   2022 Johannes Keyser
-%
-% LICENSE
-%   BSD 2-clause "simplified" license, see accompanying file license.txt.
-
-sz = size(data);
-
-if length(sz) ~= 2
-    error('Expecting a 2D data matrix Num_Obs x Num_Dim.')
-end
-
-Num_Obs = sz(1);  % number of observations
-Num_Dim = sz(2);  % number of dimensions
-
-if Num_Obs < 2
-    error('Expecting a data matrix Obs x Dim with at least 2 observations.')
-end
-
-if Num_Dim == 2  % normal 2D case
-    C = nan(Num_Obs);
-
-    for i = 1:Num_Obs
-        % accumulate slopes
-        C(i, i:end) = (data(i, 2) - data(i:end, 2)) ./ ...
-                      (data(i, 1) - data(i:end, 1));
-    end
-
-    b1 = median(C(:), 'omitnan');  % calculate slope estimate
-    
-    if nargout == 2
-        % calculate intercept if requested
-        b0 = median(data(:, 2) - b1 * data(:, 1), 'omitnan');
-    end
-    
-else
-    C = nan(Num_Obs, Num_Dim-1, Num_Obs);
-
-    for i = 1:Num_Obs
-        % accumulate slopes
-        C(:, :, i) = bsxfun( @rdivide, data(i, end) - data(:, end), ...
-            bsxfun(@minus, data(i, 1:end-1), data(:, 1:end-1)) );
-    end
-
-    % stack layers of C to 2D
-    Cprm = reshape( permute(C, [1, 3, 2]), [], size(C, 2), 1 );
-    b1 = median(Cprm, 1, 'omitnan');  % calculate slope estimate
-    
-    if nargout == 2
-        % calculate all intercepts if requested
-        b0 = nanmedian( bsxfun(@minus, data(:, end), ...
-                        bsxfun(@times, b1, data(:, 1:end-1))), ...
-                        'omitnan');
-    end
-end
-
-end
\ No newline at end of file
+function [b1, b0] = TheilSen(data)
+% Performs Theil-Sen robust linear regression on data.
+%
+% [b1, b0] = TheilSen(data)
+%
+% INPUT
+%   data: A Num_Obs x Num_Dim matrix with Num_Obs observations.
+%         The first Num_Dim - 1 columns are the explanatory variables and the
+%         last column is the response such that
+%         data = [x1, x2, ..., x(Num_Dim - 1), y];
+%
+% OUTPUT
+%   b1: Estimated slope of each explanatory variable with respect to the
+%       response variable. Therefore, b1 will be a vector of Num_Dim - 1 slopes.
+%   b0: Estimated offsets.
+%
+% EXAMPLE
+%   See accompanying file example.m.
+%
+% REFERENCE
+%   - Gilbert, Richard O. (1987), "6.5 Sen's Nonparametric Estimator of Slope",
+%     Statistical Methods for Environmental Pollution Monitoring,
+%     John Wiley and Sons, pp. 217-219, ISBN 978-0-471-28878-7
+%
+% AUTHORS
+%   2014-15 Zachary Danziger
+%   2022 Johannes Keyser
+%
+% LICENSE
+%   BSD 2-clause "simplified" license, see accompanying file license.txt.
+
+sz = size(data);
+
+if length(sz) ~= 2
+    error('Expecting a 2D data matrix Num_Obs x Num_Dim.')
+end
+
+Num_Obs = sz(1);  % number of observations
+Num_Dim = sz(2);  % number of dimensions
+
+if Num_Obs < 2
+    error('Expecting a data matrix Obs x Dim with at least 2 observations.')
+end
+
+if Num_Dim == 2  % normal 2D case
+    C = nan(Num_Obs);
+
+    for i = 1:Num_Obs
+        % accumulate slopes
+        C(i, i:end) = (data(i, 2) - data(i:end, 2)) ./ ...
+                      (data(i, 1) - data(i:end, 1));
+    end
+
+    b1 = median(C(:), 'omitnan');  % calculate slope estimate
+    
+    if nargout == 2
+        % calculate intercept if requested
+        b0 = median(data(:, 2) - b1 * data(:, 1), 'omitnan');
+    end
+    
+else
+    C = nan(Num_Obs, Num_Dim-1, Num_Obs);
+
+    for i = 1:Num_Obs
+        % accumulate slopes
+        C(:, :, i) = bsxfun( @rdivide, data(i, end) - data(:, end), ...
+            bsxfun(@minus, data(i, 1:end-1), data(:, 1:end-1)) );
+    end
+
+    % stack layers of C to 2D
+    Cprm = reshape( permute(C, [1, 3, 2]), [], size(C, 2), 1 );
+    b1 = median(Cprm, 1, 'omitnan');  % calculate slope estimate
+    
+    if nargout == 2
+        % calculate all intercepts if requested
+        b0 = median( bsxfun(@minus, data(:, end), ...
+                     bsxfun(@times, b1, data(:, 1:end-1))), ...
+                     'omitnan');
+    end
+end
+
+end