diff --git a/CREClass.m b/CREClass.m
index 42af3ed..9a81f6f 100644
--- a/CREClass.m
+++ b/CREClass.m
@@ -1,4 +1,7 @@
 classdef CREClass < handle
+    % 23 and 24 Nov 2022 I added a new calculation for the RB based on
+    % Shanon entropy given a threshold \lambda. I also think there is a
+    % "mistake" in calculating the RB the classical way. 
     properties
         Data; % the  data set to get the CRE off
         nBin; %number of bins 
@@ -111,7 +114,62 @@ classdef CREClass < handle
                  obj.nBin=numel(obj.Edges)-1;
              end
         end
-        function Calc(obj)
+        function CalcRBShannon(obj)
+            % 23Nov22 Here I will try to implement a (hopefully better)
+            % definition of the relevance boundry. The basic idea is 
+            % assume a dataset X= {x_1 ... x_i} X
+            % set a threshold \lambda NB \lambda \in of the data X
+            %Shanon entropy for this \lambda will be: 
+            % H(\lamda)=p(x<\lambda)log(p(x<lambda))+p(x>=\lambda)log(p(x>=\lambda))
+            % Now the expectation value for H will be :
+            %E(H)=\int p(\lambda)*H(\lambda) d\lambda
+             if isempty(obj.Data)
+                warning('nothing to do');
+                return;
+            end
+            if isempty(obj.Edges);obj.CalcEdges;end
+            [CP,bin]=histcounts(obj.Data(:),transpose(obj.Edges),'Normalization','cdf');
+            dl=transpose(diff(bin)./sum(diff(bin))); %  will capture the case with non-equidistant bins as well as equidistant bins
+            pdf_lambda=histcounts(obj.Data(:),transpose(obj.Edges),'Normalization','pdf');
+            CP=transpose(CP);
+            LogCP=log(CP);
+            LogCP(~isfinite(LogCP))=0; % see comment below
+            FC=1-CP;
+            FC(FC<0)=0; % due to rounding errors, small negative numbers can arrise. These are theoretically not possible. 
+            LogFC=log(FC);
+            LogFC(~isfinite(LogFC))=0;%the log of 0 is -inf. however in Fc.*logFc it should end up as 0. to avoid conflicts removing the -inf
+            H=-1*(CP.*LogCP+FC.*LogFC); %Shannon Entropy when a theshold (lambda) is set to the lower bin edge
+            w=(dl.*pdf_lambda);
+            w=w./sum(w); % for one reason or the other, the sum is not always equal to 1.  
+            EH=w*H; %calculate expectation value for H
+            RB_ind=[find(H>=EH,1,'first') find(H>=EH,1,'last')];
+            obj.RB=obj.Edges(RB_ind)+dl(RB_ind)/2;
+            obj.P_RB=FC(RB_ind);
+            if obj.Monitor
+                lambda=obj.Edges(1:end-1);
+                scatter(w,H);xlabel('weight');ylabel('Shannon Entropy');snapnow
+                plot(lambda,H);xlabel('lambda');ylabel('Shannon Entropy');
+                line(lambda(RB_ind(:)),[EH;EH],'linestyle',':');snapnow
+                histogram(obj.Data(:));snapnow
+                disp('sum of weights')
+                disp(sum(dl.*pdf_lambda))
+                disp('EH')
+                disp(EH)
+                disp('boundry index');
+                disp(RB_ind);
+                disp('boundry lambda value');
+                disp(obj.RB);
+                disp('boundry p-values');
+                disp(FC(RB_ind));
+                disp('fraction inside interval');
+                disp(abs(diff(FC(RB_ind))));
+            end
+        end
+        function Calc(obj) %calculate the CRE as well as a on-tailed definition of the RB. 
+            % RR note: 23Nov22: I am not sure if I still like this RB
+            % definition. I keep having a hard time defending it
+            % theoretically. The code is retained, for now, however for backwards
+            % compatibility. 
             if isempty(obj.Data)
                 warning('nothing to do');
                 return;