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here is the log from the commit of package octave-forge-nan for 
openSUSE:Factory checked in at 2021-03-17 20:16:43
++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
Comparing /work/SRC/openSUSE:Factory/octave-forge-nan (Old)
 and      /work/SRC/openSUSE:Factory/.octave-forge-nan.new.2401 (New)
++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++

Package is "octave-forge-nan"

Wed Mar 17 20:16:43 2021 rev:12 rq:879588 version:3.5.3

Changes:
--------
--- /work/SRC/openSUSE:Factory/octave-forge-nan/octave-forge-nan.changes        
2020-12-04 21:27:56.902078371 +0100
+++ 
/work/SRC/openSUSE:Factory/.octave-forge-nan.new.2401/octave-forge-nan.changes  
    2021-03-17 20:20:04.859327858 +0100
@@ -1,0 +2,8 @@
+Sat Mar 13 13:56:09 UTC 2021 - Atri Bhattacharya <badshah...@gmail.com>
+
+- Update to version 3.5.3
+  * kappa: Youden index (YI), and SSE as return value
+  * roc: add maxYI as output value
+  * naninsttest: check ttest for NaN handling
+
+-------------------------------------------------------------------

Old:
----
  nan-3.5.2.tar.gz

New:
----
  nan-3.5.3.tar.gz

++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++

Other differences:
------------------
++++++ octave-forge-nan.spec ++++++
--- /var/tmp/diff_new_pack.EhoqwO/_old  2021-03-17 20:20:05.315328482 +0100
+++ /var/tmp/diff_new_pack.EhoqwO/_new  2021-03-17 20:20:05.319328487 +0100
@@ -1,7 +1,7 @@
 #
 # spec file for package octave-forge-nan
 #
-# Copyright (c) 2020 SUSE LLC
+# Copyright (c) 2021 SUSE LLC
 #
 # All modifications and additions to the file contributed by third parties
 # remain the property of their copyright owners, unless otherwise agreed
@@ -18,7 +18,7 @@
 
 %define octpkg  nan
 Name:           octave-forge-%{octpkg}
-Version:        3.5.2
+Version:        3.5.3
 Release:        0
 Summary:        A statistics and machine learning toolbox
 License:        GPL-3.0-or-later

++++++ nan-3.5.2.tar.gz -> nan-3.5.3.tar.gz ++++++
diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn' 
'--exclude=.svnignore' old/nan-3.5.2/DESCRIPTION new/nan-3.5.3/DESCRIPTION
--- old/nan-3.5.2/DESCRIPTION   2020-10-31 17:44:32.000000000 +0100
+++ new/nan-3.5.3/DESCRIPTION   2021-02-15 07:41:05.000000000 +0100
@@ -1,6 +1,6 @@
 Name: NaN
-Version: 3.5.2
-Date: 2020-10-31
+Version: 3.5.3
+Date: 2021-02-15
 Author: Alois Schl??gl <alois.schlo...@gmail.com>
 Maintainer: Alois Schl??gl
 Title: The NaN-toolbox
diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn' 
'--exclude=.svnignore' old/nan-3.5.2/NEWS new/nan-3.5.3/NEWS
--- old/nan-3.5.2/NEWS  2020-10-31 17:44:32.000000000 +0100
+++ new/nan-3.5.3/NEWS  2021-02-15 07:41:05.000000000 +0100
@@ -1,3 +1,9 @@
+2021-02-15: Release of NaN-toolbox 3.5.3
+
+* kappa: Youden index (YI), and SSE as return value
+* roc: add maxYI as output value
+* naninsttest: check ttest for NaN handling
+
 
 2020-10-31: Release of NaN-toolbox 3.5.2
 
diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn' 
'--exclude=.svnignore' old/nan-3.5.2/doc/README.TXT new/nan-3.5.3/doc/README.TXT
--- old/nan-3.5.2/doc/README.TXT        2020-10-31 17:44:32.000000000 +0100
+++ new/nan-3.5.3/doc/README.TXT        2021-02-15 07:41:05.000000000 +0100
@@ -1,6 +1,6 @@
 NaN-Tb: A statistics toolbox 
 ------------------------------------------------------------
-Copyright (C) 2000-2005,2009,2010,2011,2014 Alois Schloegl 
<alois.schlo...@gmail.com>
+Copyright (C) 2000-2021 Alois Schl??gl <alois.schlo...@gmail.com>
 
 
 FEATURES of the NaN-tb:
@@ -18,7 +18,7 @@
  - fixes known bugs
  - compatible with Matlab and Octave
  - easy to use
- - The toolbox is tested with Octave 3.x and Matlab 7.x
+ - The toolbox is tested with Octave 4.4+ and Matlab 7.x
 
 
 Currently are implemented:
@@ -121,7 +121,7 @@
        KAPPA           performance evaluation 
        TRAIN_LDA_SPARSE        utility function 
        FSS             feature subset selection and feature ranking
-       CAT2BIN         converts categorial to binary data
+       CAT2BIN         converts categorical to binary data
        SVMTRAIN_MEX    libSVM-training algorithm 
        ROW_COL_DELETION heuristic to select rows and columns to remove missing 
values
 
@@ -279,8 +279,7 @@
        
   Run NANINSTTEST again to check the stability of the compiled SUMSKIPNAN.  
 
-       $Id$
-       Copyright (C) 2000-2005,2009,2010,2011,2014 by Alois Schloegl 
<alois.schlo...@gmail.com>        
+       Copyright (C) 2000-2021 by Alois Schl??gl <alois.schlo...@gmail.com>
         http://pub.ist.ac.at/~schloegl/matlab/NaN/
 
 
diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn' 
'--exclude=.svnignore' old/nan-3.5.2/inst/cat2bin.m new/nan-3.5.3/inst/cat2bin.m
--- old/nan-3.5.2/inst/cat2bin.m        2020-10-31 17:44:32.000000000 +0100
+++ new/nan-3.5.3/inst/cat2bin.m        2021-02-15 07:41:05.000000000 +0100
@@ -1,12 +1,12 @@
 function [B,BLab]=cat2bin(D, Label, MODE)
-% CAT2BIN converts categorial into binary data 
+% CAT2BIN converts categorical into binary data 
 %   each category of each column in D is converted into a logical column
 % 
 %   B = cat2bin(C); 
 %   [B,BinLabel] = cat2bin(C,Label); 
 %   [B,BinLabel] = cat2bin(C,Label,MODE)
 %
-%  C        categorial data 
+%  C        categorical data 
 %  B        binary data 
 %  Label    description of each column in C
 %  BinLabel description of each column in B
diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn' 
'--exclude=.svnignore' old/nan-3.5.2/inst/kappa.m new/nan-3.5.3/inst/kappa.m
--- old/nan-3.5.2/inst/kappa.m  2020-10-31 17:44:32.000000000 +0100
+++ new/nan-3.5.3/inst/kappa.m  2021-02-15 07:41:05.000000000 +0100
@@ -21,8 +21,8 @@
 % MI   Mutual information or transfer information (in [bits])
 % X    is a struct containing all the fields above
 %       For two classes, a number of additional summary statistics including 
-%         TPR, FPR, FDR, PPV, NPF, F1, dprime, Matthews Correlation 
coefficient (MCC) or 
-%      Phi coefficient (PHI=MCC), Specificity and Sensitivity 
+%         TPR, FPR, FDR, PPV, NPF, F1, dprime, Matthews Correlation 
coefficient (MCC) or
+%      Phi coefficient (PHI=MCC), Specificity and Sensitivity, Youden index 
(YI)
 %       are provided. Note, the positive category must the larger label (in d 
and c), otherwise 
 %       the confusion matrix becomes transposed and the summary statistics are 
messed up. 
 %
@@ -37,7 +37,7 @@
 % [5] http://ourworld.compuserve.com/homepages/jsuebersax/kappa.htm
 % [6] http://en.wikipedia.org/wiki/Receiver_operating_characteristic
 
-%    Copyright (c) 1997-2020 by Alois Schloegl <alois.schlo...@gmail.com>
+%    Copyright (c) 1997-2021 by Alois Schloegl <alois.schlo...@gmail.com>
 %    This function is part of the NaN-toolbox
 %    http://pub.ist.ac.at/~schloegl/matlab/NaN/
 %
@@ -158,11 +158,12 @@
 X.ACC  = p0; 
 X.sACC = SA;
 X.MI   = R;
+X.SSE  = sum(X.data(:))-trace(X.data);
 X.datatype = 'confusion';
 
 if length(H)==2,
        % see http://en.wikipedia.org/wiki/Receiver_operating_characteristic
-       % Note that the confusion matrix used here is has positive values in 
+       % Note that the confusion matrix used here uses more positive values in
        % the 2nd row and column, moreover the true values are indicated by
        % rows (transposed). Thus, in summary H(1,1) and H(2,2) are exchanged 
        % as compared to the wikipedia article.  
@@ -183,6 +184,7 @@
        X.Specificity = 1 - X.FPR;
        X.Precision   = X.PPV;
        X.dprime = norminv(X.TPR) - norminv(X.FDR);
+       X.YI = X.Sensitivity + X.Specificity - 1;  % Youden Index
 
        % statistical significance test of Matthews' correlation coefficient
        NN = sum(H(:));
diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn' 
'--exclude=.svnignore' old/nan-3.5.2/inst/naninsttest.m 
new/nan-3.5.3/inst/naninsttest.m
--- old/nan-3.5.2/inst/naninsttest.m    2020-10-31 17:44:32.000000000 +0100
+++ new/nan-3.5.3/inst/naninsttest.m    2021-02-15 07:41:05.000000000 +0100
@@ -118,8 +118,7 @@
         if exist('nanstd','file'),
                r(33,k)=k*(~isnan(nanstd(0)));
         end;
-        
-        %%% check mex files 
+
         try 
                 histo_mex([1:5]');
                        r(34,k)=0;
@@ -144,6 +143,10 @@
         if ~exist('train','file'),
                 r(38,k)=k;
         end;
+
+        if exist('ttest','file'),
+               r(39,k)=ttest([x,x,x],10);
+        end;
 end;
 
 % check if result is correct
diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn' 
'--exclude=.svnignore' old/nan-3.5.2/inst/roc.m new/nan-3.5.3/inst/roc.m
--- old/nan-3.5.2/inst/roc.m    2020-10-31 17:44:32.000000000 +0100
+++ new/nan-3.5.3/inst/roc.m    2021-02-15 07:41:05.000000000 +0100
@@ -66,7 +66,7 @@
 %       return the optimal threshold for the respective measure.
 %   RES.H_kappa: confusion matrix when Threshold of maximum Kappa is applied.
 %   RES.H_{yi,acc,kappa,mcc,mi,f1,phi}: confusion matrix when threshold of
-%       optimum {...} is applied.
+%       optimum {...} is applied. Its structure is [TN, FN; FP; TP].
 %
 % see also: AUC, PLOT, ROC
 %
@@ -81,7 +81,7 @@
 %     (Eds.) G. Dornhege, J.R. Millan, T. Hinterberger, D.J. McFarland, 
K.-R.M??ller;
 %     Towards Brain-Computer Interfacing, MIT Press, 2007, p.327-342
 
-% Copyright (c) 1997-2019 Alois Schloegl <alois.schlo...@gmail.com>
+% Copyright (c) 1997-2021 Alois Schloegl <alois.schlo...@gmail.com>
 % This is part of the BIOSIG-toolbox http://biosig.sf.net/
 %
 % This library is free software; you can redistribute it and/or
@@ -202,9 +202,9 @@
 %%% compute Cohen's kappa coefficient
 N = size(d,1);
 
-%H = [TP,FN;FP,TN];
-p_i = [TP+FP,FN+TN];%sum(H,1);
-pi_ = [TP+FN,FP+TN];%sum(H,2)';
+% H =[TN, FN; FP, TP]
+p_i = [TP+FP, FN+TN];
+pi_ = [TP+FN, FP+TN];
 pe  = sum(p_i.*pi_,2)/(N*N);  % estimate of change agreement
 kap = (ACC - pe) ./ (1 - pe);
 mcc = (TP .* TN - FN .* FP) ./ sqrt(prod( [p_i, pi_], 2));
@@ -274,7 +274,7 @@
 % Cohen's kappa is best tested for borderline cases, like, few
 % samples only, or when multiple options are possible.
 % others are not that sophisticated.
-[tmp,ix] = max(SEN+SPEC-1);
+[RES.maxYI,ix] = max(SEN+SPEC-1);
 ix = min(ix,length(D));
 RES.THRESHOLD.maxYI   = D(ix);
 RES.THRESHOLD.maxYIix = ix;
diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn' 
'--exclude=.svnignore' old/nan-3.5.2/src/Makefile.in 
new/nan-3.5.3/src/Makefile.in
--- old/nan-3.5.2/src/Makefile.in       2020-10-31 17:44:32.000000000 +0100
+++ new/nan-3.5.3/src/Makefile.in       2021-02-15 07:41:05.000000000 +0100
@@ -125,7 +125,7 @@
 # -DMATLAB_DEFAULT_RELEASE=R2018a: Interleaved Complex
 MATLABMEX += -DMATLAB_DEFAULT_RELEASE=R2017b
 
-CFLAGS := -fexceptions -fPIC -fno-omit-frame-pointer -pthread
+CFLAGS += -fexceptions -fPIC -fno-omit-frame-pointer -pthread
 COPTIMFLAGS := -O -DNDEBUG
 CDEBUGFLAGS := -g
 INCLUDE := -I"$(MWROOT)/extern/include" -I"$(MWROOT)/simulink/include"
diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn' 
'--exclude=.svnignore' old/nan-3.5.2/src/configure.ac 
new/nan-3.5.3/src/configure.ac
--- old/nan-3.5.2/src/configure.ac      2020-10-31 17:44:32.000000000 +0100
+++ new/nan-3.5.3/src/configure.ac      2021-02-15 07:41:05.000000000 +0100
@@ -2,7 +2,7 @@
 # Process this file with autoconf to produce a configure script.
 
 AC_PREREQ([2.69])
-AC_INIT([nan-toolbox], [3.5.0], [alois.schlo...@gmail.com])
+AC_INIT([nan-toolbox], [3.5.3], [alois.schlo...@gmail.com])
 AC_CONFIG_SRCDIR([train.c])
 AC_CONFIG_HEADERS([config.h])
 
diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn' 
'--exclude=.svnignore' old/nan-3.5.2/src/linear.cpp new/nan-3.5.3/src/linear.cpp
--- old/nan-3.5.2/src/linear.cpp        2020-10-31 17:44:32.000000000 +0100
+++ new/nan-3.5.3/src/linear.cpp        2021-02-15 07:41:05.000000000 +0100
@@ -65,7 +65,7 @@
        fputs(s,stdout);
        fflush(stdout);
 }
-static void print_null(const char *s) {}
+static void print_null(const char*) {}
 
 static void (*liblinear_print_string) (const char *) = &print_string_stdout;
 
@@ -3073,7 +3073,7 @@
                free(param->init_sol);
 }
 
-const char *check_parameter(const problem *prob, const parameter *param)
+const char *check_parameter(const problem*, const parameter *param)
 {
        if(param->eps <= 0)
                return "eps <= 0";

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