Index: /issm/trunk-jpl/src/m/miscellaneous/normfit_issm.m
===================================================================
--- /issm/trunk-jpl/src/m/miscellaneous/normfit_issm.m	(revision 14324)
+++ /issm/trunk-jpl/src/m/miscellaneous/normfit_issm.m	(revision 14325)
@@ -4,8 +4,20 @@
 function [muhat,sigmahat,muci,sigmaci]=normfit_issm(x,alpha)
 
+	if ~exist('alpha','var')
+		alpha=0.05;
+	end
+
 %  remove any NaN
 
-	x=x(~isnan(x(:)));
-	n=length(x);
+	if find(isnan(x))
+		muhat   =zeros(1,size(x,2));
+		sigmahat=zeros(1,size(x,2));
+		muci    =zeros(2,size(x,2));
+		sigmaci =zeros(2,size(x,2));
+		for j=1:size(x,2)
+			[muhat(j),sigmahat(j),muci(:,j),sigmaci(:,j)]=normfit_issm(x(~isnan(x(:,j)),j),alpha);
+		end
+		return
+	end
 
 %  explicitly calculate the moments
@@ -15,19 +27,26 @@
 
 	if (nargout>2)
-		if ~exist('alpha','var')
-			alpha=0.05;
-		end
 		prob=1.-alpha/2.;
 
+		if (size(x,1) == 1)
+			% operate like matlab normfit, mean, std, etc.
+			n=length(x);
+		else
+			n=size(x,1);
+		end
+
+		muci    =zeros(2,length(muhat   ));
+		sigmaci =zeros(2,length(sigmahat));
+
 		try
-			muci(1,1)   =muhat-tinv(prob,n-1)*sigmahat/sqrt(n);
-			muci(2,1)   =muhat+tinv(prob,n-1)*sigmahat/sqrt(n);
-			sigmaci(1,1)=sigmahat*sqrt((n-1)/chi2inv(prob   ,n-1));
-			sigmaci(2,1)=sigmahat*sqrt((n-1)/chi2inv(1.-prob,n-1));
+			muci(1,:)   =muhat-tinv(prob,n-1)*sigmahat/sqrt(n);
+			muci(2,:)   =muhat+tinv(prob,n-1)*sigmahat/sqrt(n);
+			sigmaci(1,:)=sigmahat*sqrt((n-1)/chi2inv(prob   ,n-1));
+			sigmaci(2,:)=sigmahat*sqrt((n-1)/chi2inv(1.-prob,n-1));
 		catch me
-			muci(1,1)   =muhat;
-			muci(2,1)   =muhat;
-			sigmaci(1,1)=sigmahat;
-			sigmaci(2,1)=sigmahat;
+			muci(1,:)   =muhat;
+			muci(2,:)   =muhat;
+			sigmaci(1,:)=sigmahat;
+			sigmaci(2,:)=sigmahat;
 		end
 	end
Index: /issm/trunk-jpl/src/m/qmu/dakota_out_parse.m
===================================================================
--- /issm/trunk-jpl/src/m/qmu/dakota_out_parse.m	(revision 14324)
+++ /issm/trunk-jpl/src/m/qmu/dakota_out_parse.m	(revision 14325)
@@ -198,5 +198,5 @@
 %  since normfit doesn't have a dim argument, and matlab isvector is true
 %  for a 1xn matrix, handle the case of one row explicitly
-if (min(size(data)) == 1)
+if (size(data,1) > 1)
     %dmean  =mean   (data);
     %dstddev=std    (data,0);
@@ -224,4 +224,5 @@
 
 %  divide the data into structures for consistency
+
 for i=1:length(desc)
     dresp(i).descriptor=char(desc(i));