diff --git a/ChangeLog.txt b/ChangeLog.txt
index 2ce41df3..a1c0126f 100644
--- a/ChangeLog.txt
+++ b/ChangeLog.txt
@@ -133,7 +133,11 @@ Bug fixes
  - Return parameters in correct order from gp_pak if using hyperhyperpriors
  - Other bug fixes
 
-2013-11-26 Version 4.3.1
+2014-01-01 Version 4.3.2
+
+Big fixes
+ - Changed nested functions to subfunctions for compatibility with Octave 3.8.0
+ - Fixed compilation of C source files with Octave.
 
 Improvements:
  - Updated cpsrf and psrf to follow BDA3: split each chain to two
diff --git a/OCTAVEGPSTUFF_README b/OCTAVEGPSTUFF_README
new file mode 100644
index 00000000..8d7a950e
--- /dev/null
+++ b/OCTAVEGPSTUFF_README
@@ -0,0 +1,3 @@
+This is Octave version of the GPstuff toolbox for matlab. In order to use Octave GPstuff, the following packages are needed: 
+Statistics and gsl. Octave version of GPstuff doesn't have all the functionalities of the matlab-version at the moment, namely 
+piecewise-polynomial covariance functions. Octave GPstuff requires Octave version 3.6.3 or later.
diff --git a/diag/aucs.m b/diag/aucs.m
index ad303a0c..dfe3231b 100644
--- a/diag/aucs.m
+++ b/diag/aucs.m
@@ -23,9 +23,9 @@
 % License.txt, included with the software, for details.
 
 ip=inputParser;
-ip.addRequired('crit',@(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))))
-ip.addRequired('z', @(x) isreal(x) && all(isfinite(x(:))))
-ip.parse(crit,z)
+ip=iparser(ip,'addRequired','crit',@(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))))
+ip=iparser(ip,'addRequired','z', @(x) isreal(x) && all(isfinite(x(:))))
+ip=iparser(ip,'parse',crit,z)
 
 ye=z;
 tps=0;
diff --git a/diag/auct.m b/diag/auct.m
index a29a8b12..0b1d694f 100644
--- a/diag/auct.m
+++ b/diag/auct.m
@@ -21,11 +21,11 @@
 % License.txt, included with the software, for details.
 
 ip=inputParser;
-ip.addRequired('crit',@(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))))
-ip.addRequired('y', @(x) isreal(x) && all(isfinite(x(:))))
-ip.addRequired('z', @(x) isreal(x) && all(isfinite(x(:))))
-ip.addRequired('tt', @(x) isreal(x) && all(isfinite(x(:))))
-ip.parse(crit,y,z,tt)
+ip=iparser(ip,'addRequired','crit',@(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))))
+ip=iparser(ip,'addRequired','y', @(x) isreal(x) && all(isfinite(x(:))))
+ip=iparser(ip,'addRequired','z', @(x) isreal(x) && all(isfinite(x(:))))
+ip=iparser(ip,'addRequired','tt', @(x) isreal(x) && all(isfinite(x(:))))
+ip=iparser(ip,'parse',crit,y,z,tt)
 
 for i=1:size(tt,2)
   comp=bsxfun(@times,bsxfun(@and,y(:,i)<=tt(i),1-z(:,i)),bsxfun(@or,bsxfun(@and,y(:,i)<=tt(i),z(:,i)),y(:,i)>=tt(i))');
diff --git a/diag/diag_install.m b/diag/diag_install.m
index 858e0bd5..7f1b191f 100644
--- a/diag/diag_install.m
+++ b/diag/diag_install.m
@@ -1,7 +1,15 @@
 function diag_install
 
     if ispc   % A windows version of Matlab
+      if ~exist('OCTAVE_VERSION','builtin')        
         mex -O -output bbprctile winCsource\bbprctile.c
+      else       
+        mex --output bbprctile.mex winCsource\bbprctile.c
+      end
     else
+      if ~exist('OCTAVE_VERSION','builtin')
         mex -O -output bbprctile linuxCsource/bbprctile.c
+      else
+        mex --output bbprctile.mex linuxCsource/bbprctile.c
+      end
     end
\ No newline at end of file
diff --git a/diag/ext_auc.m b/diag/ext_auc.m
index 951cb22a..72ac550b 100644
--- a/diag/ext_auc.m
+++ b/diag/ext_auc.m
@@ -20,14 +20,14 @@
 % License.txt, included with the software, for details.
   
   ip=inputParser;
-  ip.addRequired('P',@(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))))
-  ip.addRequired('tt', @(x) isreal(x) && all(isfinite(x(:))))
-  ip.addRequired('t', @(x) isreal(x) && all(isfinite(x(:))))
-  ip.parse(P,tt,t);
+  ip=iparser(ip,'addRequired','P',@(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))));
+  ip=iparser(ip,'addRequired','tt', @(x) isreal(x) && all(isfinite(x(:))));
+  ip=iparser(ip,'addRequired','t', @(x) isreal(x) && all(isfinite(x(:))));
+  ip=iparser(ip,'parse',P,tt,t);
   
   [n,nin]=size(P);
   S=1-P;
-  D=-diff([; ones(n,1) S],1,2);
+  D=-diff([ones(n,1) S],1,2);
   sd=tt(2)-tt(1);
   
   if isempty(find(tt==t,1))
diff --git a/diag/hcs.m b/diag/hcs.m
index febf6842..fd32ce40 100644
--- a/diag/hcs.m
+++ b/diag/hcs.m
@@ -30,12 +30,12 @@
 % License.txt, included with the software, for details.
 
 ip=inputParser;
-ip.addRequired('riskscore',@(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))))
-ip.addRequired('y', @(x) isreal(x) && all(isfinite(x(:))))
-ip.addRequired('z', @(x) isreal(x) && all(isfinite(x(:))))
-ip.addRequired('t', @(x) isreal(x) && isscalar(x) && ~isnan(x))
-ip.addParamValue('rsubstream',0,@(x) isreal(x) && isscalar(x) && isfinite(x) && x>0)
-ip.parse(riskscore,y,z,t,varargin{:})
+ip=iparser(ip,'addRequired','riskscore',@(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))));
+ip=iparser(ip,'addRequired','y', @(x) isreal(x) && all(isfinite(x(:))));
+ip=iparser(ip,'addRequired','z', @(x) isreal(x) && all(isfinite(x(:))));
+ip=iparser(ip,'addRequired','t', @(x) isreal(x) && isscalar(x) && ~isnan(x));
+ip=iparser(ip,'addParamValue','rsubstream',0,@(x) isreal(x) && isscalar(x) && isfinite(x) && x>=0);
+ip=iparser(ip,'parse',riskscore,y,z,t,varargin{:});
 rsubstream=ip.Results.rsubstream;
 
 
diff --git a/diag/hct.m b/diag/hct.m
index 67b83a0f..cfc92ab1 100644
--- a/diag/hct.m
+++ b/diag/hct.m
@@ -20,12 +20,12 @@
 % License.txt, included with the software, for details.
 
 ip=inputParser;
-ip.addRequired('crit',@(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))))
-ip.addRequired('y',@(x) ~isempty(x) && isreal(x))
-ip.addRequired('z', @(x) ~isempty(x) && isreal(x))
-ip.addRequired('tt', @(x) ~isempty(x) && isreal(x))
+ip=iparser(ip,'addRequired','crit',@(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))))
+ip=iparser(ip,'addRequired','y',@(x) ~isempty(x) && isreal(x))
+ip=iparser(ip,'addRequired','z', @(x) ~isempty(x) && isreal(x))
+ip=iparser(ip,'addRequired','tt', @(x) ~isempty(x) && isreal(x))
 
-ip.parse(crit,y,z,tt)
+ip=iparser(ip,'parse',crit,y,z,tt)
 
 if size(y,2) ~= size(z,2)
   error('y and z dimensions must match')   
diff --git a/diag/idis.m b/diag/idis.m
index 6f991edd..6fff1fd9 100644
--- a/diag/idis.m
+++ b/diag/idis.m
@@ -41,31 +41,31 @@
     % model-based estimator
     model_based_estimator = true;
     
-    ip.addRequired('pt', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))))
-    ip.addRequired('pn', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))))
-    ip.addParamValue('rsubstream', 0, @(x) isreal(x) && isscalar(x) && isfinite(x) && x>0)
+    ip=iparser(ip,'addRequired','pt', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))));
+    ip=iparser(ip,'addRequired','pn', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))));
+    ip=iparser(ip,'addParamValue','rsubstream', 0, @(x) isreal(x) && isscalar(x) && isfinite(x) && x>=0);
     if nargin > 2
         % if more than 2 input arguments, there must be four as only a
         % single optional parameter is implemented
         if nargin == 4
-            ip.parse(pt, pn, y, z);
+            ip=iparser(ip,'parse',pt, pn, y, z);
         else
             error('Invalid number of arguments.');
         end
     else
-        ip.parse(pt, pn); 
+        ip=iparser(ip,'parse',pt, pn); 
     end
 else
     % Pencina et al. estimator
     model_based_estimator = false;
     
-    ip.addRequired('pt', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))))
-    ip.addRequired('pn', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))))
-    ip.addRequired('y', @(x) isreal(x) && all(isfinite(x(:))))
-    ip.addRequired('z', @(x) isreal(x) && all(isfinite(x(:))))
-    ip.addRequired('t', @(x) isreal(x) && isscalar(x) && ~isnan(x))
-    ip.addParamValue('rsubstream', 0, @(x) isreal(x) && isscalar(x) && isfinite(x) && x>0)
-    ip.parse(pt, pn, y, z, t, varargin{:})
+    ip=iparser(ip,'addRequired','pt', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))));
+    ip=iparser(ip,'addRequired','pn', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))));
+    ip=iparser(ip,'addRequired','y', @(x) isreal(x) && all(isfinite(x(:))));
+    ip=iparser(ip,'addRequired','z', @(x) isreal(x) && all(isfinite(x(:))));
+    ip=iparser(ip,'addRequired','t', @(x) isreal(x) && isscalar(x) && ~isnan(x));
+    ip=iparser(ip,'addParamValue','rsubstream', 0, @(x) isreal(x) && isscalar(x) && isfinite(x) && x>=0);
+    ip=iparser(ip,'parse',pt, pn, y, z, t, varargin{:});
 end
 rsubstream=ip.Results.rsubstream;
 
diff --git a/diag/rsqr.m b/diag/rsqr.m
index 18307ddb..3317ef34 100644
--- a/diag/rsqr.m
+++ b/diag/rsqr.m
@@ -39,29 +39,29 @@
     % model-based estimator
     model_based_estimator = true;
     
-    ip.addRequired('p', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))))
-    ip.addParamValue('rsubstream', 0, @(x) isreal(x) && isscalar(x) && isfinite(x) && x>0)
+    ip=iparser(ip,'addRequired','p', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))));
+    ip=iparser(ip,'addParamValue','rsubstream', 0, @(x) isreal(x) && isscalar(x) && isfinite(x) && x>=0);
     if nargin > 1
         % if more than 1 input arguments, there must be 3 as only a
         % single optional parameter is implemented
         if nargin == 3
-            ip.parse(p, y, z);
+            ip=iparser(ip,'parse',p, y, z);
         else
             error('Invalid number of arguments.');
         end
     else
-        ip.parse(p);
+        ip=iparser(ip,'parse',p);
     end
 else
     % Pencina et al. estimator
     model_based_estimator = false;
     
-    ip.addRequired('p', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))))
-    ip.addRequired('y', @(x) isreal(x) && all(isfinite(x(:))))
-    ip.addRequired('z', @(x) isreal(x) && all(isfinite(x(:))))
-    ip.addRequired('t', @(x) isreal(x) && isscalar(x) && ~isnan(x))
-    ip.addParamValue('rsubstream', 0, @(x) isreal(x) && isscalar(x) && isfinite(x) && x>0)
-    ip.parse(p, y, z, t, varargin{:})
+    ip=iparser(ip,'addRequired','p', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))));
+    ip=iparser(ip,'addRequired','y', @(x) isreal(x) && all(isfinite(x(:))));
+    ip=iparser(ip,'addRequired','z', @(x) isreal(x) && all(isfinite(x(:))));
+    ip=iparser(ip,'addRequired','t', @(x) isreal(x) && isscalar(x) && ~isnan(x));
+    ip=iparser(ip,'addParamValue','rsubstream', 0, @(x) isreal(x) && isscalar(x) && isfinite(x) && x>=0);
+    ip=iparser(ip,'parse',p, y, z, t, varargin{:});
 end
 rsubstream=ip.Results.rsubstream;
 
diff --git a/dist/dist_install.m b/dist/dist_install.m
index 6052b4d2..175e9bc4 100644
--- a/dist/dist_install.m
+++ b/dist/dist_install.m
@@ -1,6 +1,7 @@
 function dist_install
 
     if ispc   % A windows version of Matlab
+      if ~exist('OCTAVE_VERSION','builtin')
         mex -O -output cond_invgam_invgam1 winCsource\cond_invgam_invgam1.c winCsource\ars.c winCsource\rand.c
         mex -O -output digamma1 winCsource\digamma1.c
         mex -O -output dirrand winCsource\dirrand.c
@@ -11,7 +12,20 @@
         mex -O -output invgamrand1 winCsource\invgamrand1.c winCsource\rand.c
         mex -O -output tanh_f winCsource\tanh_f.c
         mex -O -output trand winCsource\trand.c winCsource\rand.c
+      else
+        mex --output cond_invgam_invgam1.mex winCsource\cond_invgam_invgam1.c winCsource\ars.c winCsource\rand.c
+        mex --output digamma1.mex winCsource\digamma1.c
+        mex --output dirrand.mex winCsource\dirrand.c
+        mex --output exprand.mex winCsource\exprand.c
+        mex --output gamrand.mex winCsource\gamrand.c winCsource\rand.c
+        mex --output gamrand1.mex winCsource\gamrand1.c winCsource\rand.c
+        mex --output invgamrand.mex winCsource\invgamrand.c winCsource\rand.c
+        mex --output invgamrand1.mex winCsource\invgamrand1.c winCsource\rand.c
+        mex --output tanh_f.mex winCsource\tanh_f.c
+        mex --output trand.mex winCsource\trand.c winCsource\rand.c
+      end
     else
+      if ~exist('OCTAVE_VERSION','builtin')        
         mex -O -output cond_invgam_invgam1 linuxCsource/cond_invgam_invgam1.c linuxCsource/ars.c linuxCsource/rand.c;
         mex -O -output digamma1 linuxCsource/digamma1.c;
         mex -O -output dirrand linuxCsource/dirrand.c;
@@ -22,5 +36,17 @@
         mex -O -output invgamrand1 linuxCsource/invgamrand1.c linuxCsource/rand.c;
         mex -O -output tanh_f linuxCsource/tanh_f.c;
         mex -O -output trand linuxCsource/trand.c linuxCsource/rand.c;
+      else
+        mex --output cond_invgam_invgam1.mex linuxCsource/cond_invgam_invgam1.c linuxCsource/ars.c linuxCsource/rand.c;
+        mex --output digamma1.mex linuxCsource/digamma1.c;
+        mex --output dirrand.mex linuxCsource/dirrand.c;
+        mex --output exprand.mex linuxCsource/exprand.c;
+        mex --output gamrand.mex linuxCsource/gamrand.c linuxCsource/rand.c;
+        mex --output gamrand1.mex linuxCsource/gamrand1.c linuxCsource/rand.c;
+        mex --output invgamrand.mex linuxCsource/invgamrand.c linuxCsource/rand.c;
+        mex --output invgamrand1.mex linuxCsource/invgamrand1.c linuxCsource/rand.c;
+        mex --output tanh_f.mex linuxCsource/tanh_f.c;
+        mex --output trand.mex linuxCsource/trand.c linuxCsource/rand.c;
+      end
     end
     
\ No newline at end of file
diff --git a/dist/prior_gamma.m b/dist/prior_gamma.m
index 10bc29d5..a9dd0282 100644
--- a/dist/prior_gamma.m
+++ b/dist/prior_gamma.m
@@ -32,12 +32,12 @@
 
   ip=inputParser;
   ip.FunctionName = 'PRIOR_GAMMA';
-  ip.addOptional('p', [], @isstruct);
-  ip.addParamValue('sh',4, @(x) isscalar(x) && x>0);
-  ip.addParamValue('sh_prior',[], @(x) isstruct(x) || isempty(x));
-  ip.addParamValue('is',1, @(x) isscalar(x) && x>0);
-  ip.addParamValue('is_prior',[], @(x) isstruct(x) || isempty(x));
-  ip.parse(varargin{:});
+  ip=iparser(ip,'addOptional','p', [], @isstruct);
+  ip=iparser(ip,'addParamValue','sh',4, @(x) isscalar(x) && x>0);
+  ip=iparser(ip,'addParamValue','sh_prior',[], @(x) isstruct(x) || isempty(x));
+  ip=iparser(ip,'addParamValue','is',1, @(x) isscalar(x) && x>0);
+  ip=iparser(ip,'addParamValue','is_prior',[], @(x) isstruct(x) || isempty(x));
+  ip=iparser(ip,'parse',varargin{:});
   p=ip.Results.p;
   
   if isempty(p)
diff --git a/dist/prior_gaussian.m b/dist/prior_gaussian.m
index bee3fe76..fae59f02 100644
--- a/dist/prior_gaussian.m
+++ b/dist/prior_gaussian.m
@@ -29,12 +29,12 @@
 
   ip=inputParser;
   ip.FunctionName = 'PRIOR_GAUSSIAN';
-  ip.addOptional('p', [], @isstruct);
-  ip.addParamValue('mu',0, @(x) isscalar(x) && x>0);
-  ip.addParamValue('mu_prior',[], @(x) isstruct(x) || isempty(x));
-  ip.addParamValue('s2',1, @(x) isscalar(x) && x>0);
-  ip.addParamValue('s2_prior',[], @(x) isstruct(x) || isempty(x));
-  ip.parse(varargin{:});
+  ip=iparser(ip,'addOptional','p', [], @isstruct);
+  ip=iparser(ip,'addParamValue','mu',0, @(x) isscalar(x) && x>=0);
+  ip=iparser(ip,'addParamValue','mu_prior',[], @(x) isstruct(x) || isempty(x));
+  ip=iparser(ip,'addParamValue','s2',1, @(x) isscalar(x) && x>0);
+  ip=iparser(ip,'addParamValue','s2_prior',[], @(x) isstruct(x) || isempty(x));
+  ip=iparser(ip,'parse',varargin{:});
   p=ip.Results.p;
   
   if isempty(p)
diff --git a/dist/prior_invgamma.m b/dist/prior_invgamma.m
index 8de7dd52..eee16831 100644
--- a/dist/prior_invgamma.m
+++ b/dist/prior_invgamma.m
@@ -33,12 +33,12 @@
 
   ip=inputParser;
   ip.FunctionName = 'PRIOR_INVGAMMA';
-  ip.addOptional('p', [], @isstruct);
-  ip.addParamValue('sh',4, @(x) isscalar(x) && x>0);
-  ip.addParamValue('sh_prior',[], @(x) isstruct(x) || isempty(x));
-  ip.addParamValue('s',1, @(x) isscalar(x) && x>0);
-  ip.addParamValue('s_prior',[], @(x) isstruct(x) || isempty(x));
-  ip.parse(varargin{:});
+  ip=iparser(ip,'addOptional','p', [], @isstruct);
+  ip=iparser(ip,'addParamValue','sh',4, @(x) isscalar(x) && x>0);
+  ip=iparser(ip,'addParamValue','sh_prior',[], @(x) isstruct(x) || isempty(x));
+  ip=iparser(ip,'addParamValue','s',1, @(x) isscalar(x) && x>0);
+  ip=iparser(ip,'addParamValue','s_prior',[], @(x) isstruct(x) || isempty(x));
+  ip=iparser(ip,'parse',varargin{:});
   p=ip.Results.p;
   
   if isempty(p)
diff --git a/dist/prior_invt.m b/dist/prior_invt.m
index ebd6daa2..9a0d565e 100644
--- a/dist/prior_invt.m
+++ b/dist/prior_invt.m
@@ -36,14 +36,14 @@
 
   ip=inputParser;
   ip.FunctionName = 'PRIOR_INVT';
-  ip.addOptional('p', [], @isstruct);
-  ip.addParamValue('mu',0, @(x) isscalar(x));
-  ip.addParamValue('mu_prior',[], @(x) isstruct(x) || isempty(x));
-  ip.addParamValue('s2',1, @(x) isscalar(x) && x>0);
-  ip.addParamValue('s2_prior',[], @(x) isstruct(x) || isempty(x));
-  ip.addParamValue('nu',4, @(x) isscalar(x) && x>0);
-  ip.addParamValue('nu_prior',[], @(x) isstruct(x) || isempty(x));
-  ip.parse(varargin{:});
+  ip=iparser(ip,'addOptional','p', [], @isstruct);
+  ip=iparser(ip,'addParamValue','mu',0, @(x) isscalar(x));
+  ip=iparser(ip,'addParamValue','mu_prior',[], @(x) isstruct(x) || isempty(x));
+  ip=iparser(ip,'addParamValue','s2',1, @(x) isscalar(x) && x>0);
+  ip=iparser(ip,'addParamValue','s2_prior',[], @(x) isstruct(x) || isempty(x));
+  ip=iparser(ip,'addParamValue','nu',4, @(x) isscalar(x) && x>0);
+  ip=iparser(ip,'addParamValue','nu_prior',[], @(x) isstruct(x) || isempty(x));
+  ip=iparser(ip,'parse',varargin{:});
   p=ip.Results.p;
   
   if isempty(p)
diff --git a/dist/prior_invunif.m b/dist/prior_invunif.m
index 0b6d4064..1db2f983 100644
--- a/dist/prior_invunif.m
+++ b/dist/prior_invunif.m
@@ -17,8 +17,8 @@
 
   ip=inputParser;
   ip.FunctionName = 'PRIOR_INVUNIFORM';
-  ip.addOptional('p', [], @isstruct);
-  ip.parse(varargin{:});
+  ip=iparser(ip,'addOptional','p', [], @isstruct);
+  ip=iparser(ip,'parse',varargin{:});
   p=ip.Results.p;
   
   if isempty(p)
diff --git a/dist/prior_laplace.m b/dist/prior_laplace.m
index 5164770b..116c7e73 100644
--- a/dist/prior_laplace.m
+++ b/dist/prior_laplace.m
@@ -29,12 +29,12 @@
 
   ip=inputParser;
   ip.FunctionName = 'PRIOR_LAPLACE';
-  ip.addOptional('p', [], @isstruct);
-  ip.addParamValue('mu',0, @(x) isscalar(x) && x>0);
-  ip.addParamValue('mu_prior',[], @(x) isstruct(x) || isempty(x));
-  ip.addParamValue('s',1, @(x) isscalar(x) && x>0);
-  ip.addParamValue('s_prior',[], @(x) isstruct(x) || isempty(x));
-  ip.parse(varargin{:});
+  ip=iparser(ip,'addOptional','p', [], @isstruct);
+  ip=iparser(ip,'addParamValue','mu',0, @(x) isscalar(x) && x>0);
+  ip=iparser(ip,'addParamValue','mu_prior',[], @(x) isstruct(x) || isempty(x));
+  ip=iparser(ip,'addParamValue','s',1, @(x) isscalar(x) && x>0);
+  ip=iparser(ip,'addParamValue','s_prior',[], @(x) isstruct(x) || isempty(x));
+  ip=iparser(ip,'parse',varargin{:});
   p=ip.Results.p;
   
   if isempty(p)
diff --git a/dist/prior_loggaussian.m b/dist/prior_loggaussian.m
index 7e42d726..9953c996 100644
--- a/dist/prior_loggaussian.m
+++ b/dist/prior_loggaussian.m
@@ -29,12 +29,12 @@
 
   ip=inputParser;
   ip.FunctionName = 'PRIOR_LOGGAUSSIAN';
-  ip.addOptional('p', [], @isstruct);
-  ip.addParamValue('mu',0, @(x) isscalar(x));
-  ip.addParamValue('mu_prior',[], @(x) isstruct(x) || isempty(x));
-  ip.addParamValue('s2',1, @(x) isscalar(x) && x>0);
-  ip.addParamValue('s2_prior',[], @(x) isstruct(x) || isempty(x));
-  ip.parse(varargin{:});
+  ip=iparser(ip,'addOptional','p', [], @isstruct);
+  ip=iparser(ip,'addParamValue','mu',0, @(x) isscalar(x));
+  ip=iparser(ip,'addParamValue','mu_prior',[], @(x) isstruct(x) || isempty(x));
+  ip=iparser(ip,'addParamValue','s2',1, @(x) isscalar(x) && x>0);
+  ip=iparser(ip,'addParamValue','s2_prior',[], @(x) isstruct(x) || isempty(x));
+  ip=iparser(ip,'parse',varargin{:});
   p=ip.Results.p;
   
   if isempty(p)
diff --git a/dist/prior_loglogunif.m b/dist/prior_loglogunif.m
index f0203826..0d94d469 100644
--- a/dist/prior_loglogunif.m
+++ b/dist/prior_loglogunif.m
@@ -17,8 +17,8 @@
 
   ip=inputParser;
   ip.FunctionName = 'PRIOR_LOGLOGUNIFORM';
-  ip.addOptional('p', [], @isstruct);
-  ip.parse(varargin{:});
+  ip=iparser(ip,'addOptional','p', [], @isstruct);
+  ip=iparser(ip,'parse',varargin{:});
   p=ip.Results.p;
   
   if isempty(p)
diff --git a/dist/prior_logt.m b/dist/prior_logt.m
index 1591081c..88e441a8 100644
--- a/dist/prior_logt.m
+++ b/dist/prior_logt.m
@@ -33,14 +33,14 @@
 
   ip=inputParser;
   ip.FunctionName = 'PRIOR_LOGT';
-  ip.addOptional('p', [], @isstruct);
-  ip.addParamValue('mu',0, @(x) isscalar(x));
-  ip.addParamValue('mu_prior',[], @(x) isstruct(x) || isempty(x));
-  ip.addParamValue('s2',1, @(x) isscalar(x) && x>0);
-  ip.addParamValue('s2_prior',[], @(x) isstruct(x) || isempty(x));
-  ip.addParamValue('nu',4, @(x) isscalar(x) && x>0);
-  ip.addParamValue('nu_prior',[], @(x) isstruct(x) || isempty(x));
-  ip.parse(varargin{:});
+  ip=iparser(ip,'addOptional','p', [], @isstruct);
+  ip=iparser(ip,'addParamValue','mu',0, @(x) isscalar(x));
+  ip=iparser(ip,'addParamValue','mu_prior',[], @(x) isstruct(x) || isempty(x));
+  ip=iparser(ip,'addParamValue','s2',1, @(x) isscalar(x) && x>0);
+  ip=iparser(ip,'addParamValue','s2_prior',[], @(x) isstruct(x) || isempty(x));
+  ip=iparser(ip,'addParamValue','nu',4, @(x) isscalar(x) && x>0);
+  ip=iparser(ip,'addParamValue','nu_prior',[], @(x) isstruct(x) || isempty(x));
+  ip=iparser(ip,'parse',varargin{:});
   p=ip.Results.p;
   
   if isempty(p)
diff --git a/dist/prior_logunif.m b/dist/prior_logunif.m
index 3b714e93..4d4941e4 100644
--- a/dist/prior_logunif.m
+++ b/dist/prior_logunif.m
@@ -18,8 +18,8 @@
 
   ip=inputParser;
   ip.FunctionName = 'PRIOR_LOGUNIFORM';
-  ip.addOptional('p', [], @isstruct);
-  ip.parse(varargin{:});
+  ip=iparser(ip,'addOptional','p', [], @isstruct);
+  ip=iparser(ip,'parse',varargin{:});
   p=ip.Results.p;
   
   if isempty(p)
diff --git a/dist/prior_sinvchi2.m b/dist/prior_sinvchi2.m
index 0f095f4b..5eb527ae 100644
--- a/dist/prior_sinvchi2.m
+++ b/dist/prior_sinvchi2.m
@@ -32,12 +32,12 @@
 
   ip=inputParser;
   ip.FunctionName = 'PRIOR_SINVCHI2';
-  ip.addOptional('p', [], @isstruct);
-  ip.addParamValue('s2',1, @(x) isscalar(x) && x>0);
-  ip.addParamValue('s2_prior',[], @(x) isstruct(x) || isempty(x));
-  ip.addParamValue('nu',4, @(x) isscalar(x) && x>0);
-  ip.addParamValue('nu_prior',[], @(x) isstruct(x) || isempty(x));
-  ip.parse(varargin{:});
+  ip=iparser(ip,'addOptional','p', [], @isstruct);
+  ip=iparser(ip,'addParamValue','s2',1, @(x) isscalar(x) && x>0);
+  ip=iparser(ip,'addParamValue','s2_prior',[], @(x) isstruct(x) || isempty(x));
+  ip=iparser(ip,'addParamValue','nu',4, @(x) isscalar(x) && x>0);
+  ip=iparser(ip,'addParamValue','nu_prior',[], @(x) isstruct(x) || isempty(x));
+  ip=iparser(ip,'parse',varargin{:});
   p=ip.Results.p;
   
   if isempty(p)
diff --git a/dist/prior_sqinvgamma.m b/dist/prior_sqinvgamma.m
index e9f4b7e1..e0859118 100644
--- a/dist/prior_sqinvgamma.m
+++ b/dist/prior_sqinvgamma.m
@@ -32,12 +32,12 @@
 
   ip=inputParser;
   ip.FunctionName = 'PRIOR_SQINVGAMMA';
-  ip.addOptional('p', [], @isstruct);
-  ip.addParamValue('sh',4, @(x) isscalar(x) && x>0);
-  ip.addParamValue('sh_prior',[], @(x) isstruct(x) || isempty(x));
-  ip.addParamValue('is',1, @(x) isscalar(x) && x>0);
-  ip.addParamValue('is_prior',[], @(x) isstruct(x) || isempty(x));
-  ip.parse(varargin{:});
+  ip=iparser(ip,'addOptional','p', [], @isstruct);
+  ip=iparser(ip,'addParamValue','sh',4, @(x) isscalar(x) && x>0);
+  ip=iparser(ip,'addParamValue','sh_prior',[], @(x) isstruct(x) || isempty(x));
+  ip=iparser(ip,'addParamValue','is',1, @(x) isscalar(x) && x>0);
+  ip=iparser(ip,'addParamValue','is_prior',[], @(x) isstruct(x) || isempty(x));
+  ip=iparser(ip,'parse',varargin{:});
   p=ip.Results.p;
   
   if isempty(p)
diff --git a/dist/prior_sqinvlogunif.m b/dist/prior_sqinvlogunif.m
index 85ae983d..a1422930 100644
--- a/dist/prior_sqinvlogunif.m
+++ b/dist/prior_sqinvlogunif.m
@@ -18,8 +18,8 @@
 
   ip=inputParser;
   ip.FunctionName = 'PRIOR_SQINVLOGUNIFORM';
-  ip.addOptional('p', [], @(x) isstruct(x) || isempty(x));
-  ip.parse(varargin{:});
+  ip=iparser(ip,'addOptional','p', [], @(x) isstruct(x) || isempty(x));
+  ip=iparser(ip,'parse',varargin{:});
   p=ip.Results.p;
   
   if isempty(p)
diff --git a/dist/prior_sqinvsinvchi2.m b/dist/prior_sqinvsinvchi2.m
index 66b88563..82a696fd 100644
--- a/dist/prior_sqinvsinvchi2.m
+++ b/dist/prior_sqinvsinvchi2.m
@@ -33,12 +33,12 @@
 
   ip=inputParser;
   ip.FunctionName = 'PRIOR_SQINVSINVCHI2';
-  ip.addOptional('p', [], @isstruct);
-  ip.addParamValue('s2',1, @(x) isscalar(x) && x>0);
-  ip.addParamValue('s2_prior',[], @(x) isstruct(x) || isempty(x));
-  ip.addParamValue('nu',4, @(x) isscalar(x) && x>0);
-  ip.addParamValue('nu_prior',[], @(x) isstruct(x) || isempty(x));
-  ip.parse(varargin{:});
+  ip=iparser(ip,'addOptional','p', [], @isstruct);
+  ip=iparser(ip,'addParamValue','s2',1, @(x) isscalar(x) && x>0);
+  ip=iparser(ip,'addParamValue','s2_prior',[], @(x) isstruct(x) || isempty(x));
+  ip=iparser(ip,'addParamValue','nu',4, @(x) isscalar(x) && x>0);
+  ip=iparser(ip,'addParamValue','nu_prior',[], @(x) isstruct(x) || isempty(x));
+  ip=iparser(ip,'parse',varargin{:});
   p=ip.Results.p;
   
   if isempty(p)
diff --git a/dist/prior_sqinvunif.m b/dist/prior_sqinvunif.m
index 54049454..52c925e2 100644
--- a/dist/prior_sqinvunif.m
+++ b/dist/prior_sqinvunif.m
@@ -17,8 +17,8 @@
 
   ip=inputParser;
   ip.FunctionName = 'PRIOR_SQINVUNIFORM';
-  ip.addOptional('p', [], @isstruct);
-  ip.parse(varargin{:});
+  ip=iparser(ip,'addOptional','p', [], @isstruct);
+  ip=iparser(ip,'parse',varargin{:});
   p=ip.Results.p;
   
   if isempty(p)
diff --git a/dist/prior_sqrtinvt.m b/dist/prior_sqrtinvt.m
index 9875544f..d61a1936 100644
--- a/dist/prior_sqrtinvt.m
+++ b/dist/prior_sqrtinvt.m
@@ -33,14 +33,14 @@
 
   ip=inputParser;
   ip.FunctionName = 'PRIOR_SQRTINVT';
-  ip.addOptional('p', [], @isstruct);
-  ip.addParamValue('mu',0, @(x) isscalar(x));
-  ip.addParamValue('mu_prior',[], @(x) isstruct(x) || isempty(x));
-  ip.addParamValue('s2',1, @(x) isscalar(x) && x>0);
-  ip.addParamValue('s2_prior',[], @(x) isstruct(x) || isempty(x));
-  ip.addParamValue('nu',4, @(x) isscalar(x) && x>0);
-  ip.addParamValue('nu_prior',[], @(x) isstruct(x) || isempty(x));
-  ip.parse(varargin{:});
+  ip=iparser(ip,'addOptional','p', [], @isstruct);
+  ip=iparser(ip,'addParamValue','mu',0, @(x) isscalar(x));
+  ip=iparser(ip,'addParamValue','mu_prior',[], @(x) isstruct(x) || isempty(x));
+  ip=iparser(ip,'addParamValue','s2',1, @(x) isscalar(x) && x>0);
+  ip=iparser(ip,'addParamValue','s2_prior',[], @(x) isstruct(x) || isempty(x));
+  ip=iparser(ip,'addParamValue','nu',4, @(x) isscalar(x) && x>0);
+  ip=iparser(ip,'addParamValue','nu_prior',[], @(x) isstruct(x) || isempty(x));
+  ip=iparser(ip,'parse',varargin{:});
   p=ip.Results.p;
   
   if isempty(p)
diff --git a/dist/prior_sqrtinvunif.m b/dist/prior_sqrtinvunif.m
index 4abbbb0b..e278a3c2 100644
--- a/dist/prior_sqrtinvunif.m
+++ b/dist/prior_sqrtinvunif.m
@@ -18,8 +18,8 @@
 
   ip=inputParser;
   ip.FunctionName = 'PRIOR_INVSQRTUNIFORM';
-  ip.addOptional('p', [], @isstruct);
-  ip.parse(varargin{:});
+  ip=iparser(ip,'addOptional','p', [], @isstruct);
+  ip=iparser(ip,'parse',varargin{:});
   p=ip.Results.p;
   
   if isempty(p)
diff --git a/dist/prior_sqrtt.m b/dist/prior_sqrtt.m
index 737b4d05..800b88d3 100644
--- a/dist/prior_sqrtt.m
+++ b/dist/prior_sqrtt.m
@@ -33,14 +33,14 @@
 
   ip=inputParser;
   ip.FunctionName = 'PRIOR_SQRTT';
-  ip.addOptional('p', [], @isstruct);
-  ip.addParamValue('mu',0, @(x) isscalar(x));
-  ip.addParamValue('mu_prior',[], @(x) isstruct(x) || isempty(x));
-  ip.addParamValue('s2',1, @(x) isscalar(x) && x>0);
-  ip.addParamValue('s2_prior',[], @(x) isstruct(x) || isempty(x));
-  ip.addParamValue('nu',4, @(x) isscalar(x) && x>0);
-  ip.addParamValue('nu_prior',[], @(x) isstruct(x) || isempty(x));
-  ip.parse(varargin{:});
+  ip=iparser(ip,'addOptional','p', [], @isstruct);
+  ip=iparser(ip,'addParamValue','mu',0, @(x) isscalar(x));
+  ip=iparser(ip,'addParamValue','mu_prior',[], @(x) isstruct(x) || isempty(x));
+  ip=iparser(ip,'addParamValue','s2',1, @(x) isscalar(x) && x>0);
+  ip=iparser(ip,'addParamValue','s2_prior',[], @(x) isstruct(x) || isempty(x));
+  ip=iparser(ip,'addParamValue','nu',4, @(x) isscalar(x) && x>0);
+  ip=iparser(ip,'addParamValue','nu_prior',[], @(x) isstruct(x) || isempty(x));
+  ip=iparser(ip,'parse',varargin{:});
   p=ip.Results.p;
   
   if isempty(p)
diff --git a/dist/prior_sqrtunif.m b/dist/prior_sqrtunif.m
index 9147485f..fd49d05f 100644
--- a/dist/prior_sqrtunif.m
+++ b/dist/prior_sqrtunif.m
@@ -18,8 +18,8 @@
 
   ip=inputParser;
   ip.FunctionName = 'PRIOR_SQRTUNIFORM';
-  ip.addOptional('p', [], @isstruct);
-  ip.parse(varargin{:});
+  ip=iparser(ip,'addOptional','p', [], @isstruct);
+  ip=iparser(ip,'parse',varargin{:});
   p=ip.Results.p;
   
   if isempty(p)
diff --git a/dist/prior_t.m b/dist/prior_t.m
index eca0166a..7559ca20 100644
--- a/dist/prior_t.m
+++ b/dist/prior_t.m
@@ -35,14 +35,14 @@
 
   ip=inputParser;
   ip.FunctionName = 'PRIOR_T';
-  ip.addOptional('p', [], @isstruct);
-  ip.addParamValue('mu',0, @(x) isscalar(x));
-  ip.addParamValue('mu_prior',[], @(x) isstruct(x) || isempty(x));
-  ip.addParamValue('s2',1, @(x) isscalar(x) && x>0);
-  ip.addParamValue('s2_prior',[], @(x) isstruct(x) || isempty(x));
-  ip.addParamValue('nu',4, @(x) isscalar(x) && x>0);
-  ip.addParamValue('nu_prior',[], @(x) isstruct(x) || isempty(x));
-  ip.parse(varargin{:});
+  ip=iparser(ip,'addOptional','p', [], @isstruct);
+  ip=iparser(ip,'addParamValue','mu',0, @(x) isscalar(x));
+  ip=iparser(ip,'addParamValue','mu_prior',[], @(x) isstruct(x) || isempty(x));
+  ip=iparser(ip,'addParamValue','s2',1, @(x) isscalar(x) && x>0);
+  ip=iparser(ip,'addParamValue','s2_prior',[], @(x) isstruct(x) || isempty(x));
+  ip=iparser(ip,'addParamValue','nu',4, @(x) isscalar(x) && x>0);
+  ip=iparser(ip,'addParamValue','nu_prior',[], @(x) isstruct(x) || isempty(x));
+  ip=iparser(ip,'parse',varargin{:});
   p=ip.Results.p;
   
   if isempty(p)
diff --git a/dist/prior_unif.m b/dist/prior_unif.m
index 3be9630a..81d6076b 100644
--- a/dist/prior_unif.m
+++ b/dist/prior_unif.m
@@ -16,8 +16,8 @@
 
   ip=inputParser;
   ip.FunctionName = 'PRIOR_UNIFORM';
-  ip.addOptional('p', [], @isstruct);
-  ip.parse(varargin{:});
+  ip=iparser(ip,'addOptional','p', [], @isstruct);
+  ip=iparser(ip,'parse',varargin{:});
   p=ip.Results.p;
   
   if isempty(p)
diff --git a/gp/cf_se_to_ss.m b/gp/cf_se_to_ss.m
index d12b6d64..22bc4c23 100644
--- a/gp/cf_se_to_ss.m
+++ b/gp/cf_se_to_ss.m
@@ -141,8 +141,12 @@
     
     % Solve the corresponding Lyapunov problem
     %   F*Pinf + Pinf*F' + L*Qc*L' = 0
-    Pinf = lyap(F,L*Qc*L');
-
+    if ~exist('OCTAVE_VERSION', 'builtin')
+      Pinf = lyap(F,L*Qc*L');
+    else
+      F=real(F);
+      Pinf = lyap(F',L*Qc*L');
+    end
     % The same thing can be solved as a solution to the
     % algebraic Riccati equation (less stable) 
     %Pinf = are(F',zeros(size(F)),L*Qc*L');
diff --git a/gp/demo_binomial1.m b/gp/demo_binomial1.m
index 67147095..e3b87f92 100644
--- a/gp/demo_binomial1.m
+++ b/gp/demo_binomial1.m
@@ -95,14 +95,14 @@
 color1=ones(1,3)*0.8; color2=ones(1,3)*0.5;
 
 % GP 95% credible interval
-h1=fill([xgrid' fliplr(xgrid')], [(Eyt_la+1.96*sqrt(Varyt_la))' fliplr((Eyt_la-1.96*sqrt(Varyt_la))')], color1, 'edgecolor', color1);
+% h1=fill([xgrid' fliplr(xgrid')], [(Eyt_la+1.96*sqrt(Varyt_la))' fliplr((Eyt_la-1.96*sqrt(Varyt_la))')], color1, 'edgecolor', color1);
 % GP mean
 h2=plot(xgrid, Eyt_la, 'color', color2, 'linewidth', 3);
 % observations
 h3=plot(x, y, 'xk', 'markersize', 10, 'linewidth', 2);
 % true function
 h4=plot(xgrid, 1./(1+exp(-(-1.5.*xgrid.^3+0.5*xgrid.^2+0.75*xgrid)))*100, 'color', 'r', 'linewidth', 2);
-legend([h1 h2 h3 h4], 'GP 95% CI', 'GP mean', 'observations', 'true latent function')
+legend([h2 h3 h4], 'GP mean', 'observations', 'true latent function')
 title('Gaussian process prediction with a squared exponential covariance function')
 
 % To compute predictive densities at the test points xt, the total number
diff --git a/gp/demo_binomial2.m b/gp/demo_binomial2.m
index c768264a..2758699f 100644
--- a/gp/demo_binomial2.m
+++ b/gp/demo_binomial2.m
@@ -117,12 +117,13 @@
 subplot('Position',[0.03 0.7 0.3 0.25])
 hold on
 % GP 95% credible interval
-h2=fill([xgrid' fliplr(xgrid')], [(Eft_la+1.96*sqrt(Varft_la))' fliplr((Eft_la-1.96*sqrt(Varft_la))')], color1, 'edgecolor', color1);
+% h2=fill([xgrid' fliplr(xgrid')], [(Eft+1.96*sqrt(Varft))' fliplr((Eft-1.96*sqrt(Varft))')], color1, 'edgecolor', color1);
 % GP mean
 h1=plot(xgrid, Eft_la, 'color', color2, 'linewidth', 3);
 axis([-1.5 1.5 -14 14])
 title('Laplace+grid approximation')
-legend([h1 h2],'Mean of the latent','95% CI',2)
+legend(h1,'Mean of the latent',2)
+% legend([h1 h2],'Mean of the latent','95% CI',2)
 legend('boxoff')
 line(xlim,[0 0],'Linestyle','--','color','k')
 line([0 0],ylim,'Linestyle','--','color','k')
@@ -132,12 +133,13 @@
 subplot('Position',[0.03 0.38 0.3 0.25])
 hold on
 % GP 95% credible interval
-h2=fill([xgrid' fliplr(xgrid')], [(logitinv(Eft_la+1.96*sqrt(Varft_la)))' fliplr((logitinv(Eft_la-1.96*sqrt(Varft_la)))')], color1, 'edgecolor', color1);
+% h2=fill([xgrid' fliplr(xgrid')], [(logitinv(Eft+1.96*sqrt(Varft)))' fliplr((logitinv(Eft-1.96*sqrt(Varft)))')], color1, 'edgecolor', color1);
 % GP mean
 h1=plot(xgrid, logitinv(Eft_la), 'color', color2, 'linewidth', 3);
 % observations
 h3=plot(x, y./5, 'xr', 'markersize', 10, 'linewidth', 2);
-legend([h1 h2 h3],'Expected prob.','95% CI','Observations',2)
+% legend([h1 h2 h3],'Expected prob.','95% CI','Observations',2)
+legend([h1 h3],'Expected prob.','Observations',2)
 legend('boxoff')
 line(xlim,[.5 .5],'Linestyle','--','color','k')
 line([0 0],ylim,'Linestyle','--','color','k')
@@ -152,10 +154,10 @@
 set(gca,'ytick',[])
 ylim([0 2000])
 xlabel('LD50')
-h1=text(prctile(ld50s,2.5),1850,'2.5%','HorizontalAlignment','center');
-h2=text(prctile(ld50s,50),1850,'50%','HorizontalAlignment','center');
-h3=text(prctile(ld50s,97.5),1850,'97.5%','HorizontalAlignment','center');
-hl=line(repmat(prctile(ld50s,[2.5 50 97.5]),2,1),repmat([0 1800]',1,3),'Color','k');
+% h1=text(prctile(ld50s,2.5),1850,'2.5%','HorizontalAlignment','center');
+% h2=text(prctile(ld50s,50),1850,'50%','HorizontalAlignment','center');
+% h3=text(prctile(ld50s,97.5),1850,'97.5%','HorizontalAlignment','center');
+% hl=line(repmat(prctile(ld50s,[2.5 50 97.5]),2,1),repmat([0 1800]',1,3),'Color','k');
 fprintf('Elapsed time %.0fs\n',toc)
 
 % ------- EP approximation --------
@@ -197,12 +199,13 @@
 subplot('Position',[0.36 0.7 0.3 0.25])
 hold on
 % GP 95% credible interval
-h2=fill([xgrid' fliplr(xgrid')], [(Eft_ep+1.96*sqrt(Varft_ep))' fliplr((Eft_ep-1.96*sqrt(Varft_ep))')], color1, 'edgecolor', color1);
+% h2=fill([xgrid' fliplr(xgrid')], [(Eft+1.96*sqrt(Varft))' fliplr((Eft-1.96*sqrt(Varft))')], color1, 'edgecolor', color1);
 % GP mean
 h1=plot(xgrid, Eft_ep, 'color', color2, 'linewidth', 3);
 axis([-1.5 1.5 -14 14])
 title('EP+grid approximation')
-legend([h1 h2],'Mean of the latent','95% CI',2)
+legend([h1],'Mean of the latent',2)
+% legend([h1 h2],'Mean of the latent','95% CI',2)
 legend('boxoff')
 line(xlim,[0 0],'Linestyle','--','color','k')
 line([0 0],ylim,'Linestyle','--','color','k')
@@ -212,12 +215,13 @@
 subplot('Position',[0.36 0.38 0.3 0.25])
 hold on
 % GP 95% credible interval
-h2=fill([xgrid' fliplr(xgrid')], [(logitinv(Eft_ep+1.96*sqrt(Varft_ep)))' fliplr((logitinv(Eft_ep-1.96*sqrt(Varft_ep)))')], color1, 'edgecolor', color1);
+% h2=fill([xgrid' fliplr(xgrid')], [(logitinv(Eft+1.96*sqrt(Varft)))' fliplr((logitinv(Eft-1.96*sqrt(Varft)))')], color1, 'edgecolor', color1);
 % GP mean
 h1=plot(xgrid, logitinv(Eft_ep), 'color', color2, 'linewidth', 3);
 % observations
 h3=plot(x, y./5, 'xr', 'markersize', 10, 'linewidth', 2);
-legend([h1 h2 h3],'Expected prob.','95% CI','Observations',2)
+legend([h1 h3],'Expected prob.','Observations',2)
+% legend([h1 h2 h3],'Expected prob.','95% CI','Observations',2)
 legend('boxoff')
 line(xlim,[.5 .5],'Linestyle','--','color','k')
 line([0 0],ylim,'Linestyle','--','color','k')
@@ -232,10 +236,10 @@
 set(gca,'ytick',[])
 ylim([0 2000])
 xlabel('LD50')
-h1=text(prctile(ld50s,2.5),1850,'2.5%','HorizontalAlignment','center');
-h2=text(prctile(ld50s,50),1850,'50%','HorizontalAlignment','center');
-h3=text(prctile(ld50s,97.5),1850,'97.5%','HorizontalAlignment','center');
-hl=line(repmat(prctile(ld50s,[2.5 50 97.5]),2,1),repmat([0 1800]',1,3),'Color','k');
+% h1=text(prctile(ld50s,2.5),1850,'2.5%','HorizontalAlignment','center');
+% h2=text(prctile(ld50s,50),1850,'50%','HorizontalAlignment','center');
+% h3=text(prctile(ld50s,97.5),1850,'97.5%','HorizontalAlignment','center');
+% hl=line(repmat(prctile(ld50s,[2.5 50 97.5]),2,1),repmat([0 1800]',1,3),'Color','k');
 fprintf('Elapsed time %.0fs\n',toc)
 
 % ------- MCMC approximation --------
@@ -244,7 +248,7 @@
 
 gp = gp_set(gp, 'latent_method', 'MCMC', 'jitterSigma2', 1e-4);
 
-[rgp,g,opt] = gp_mc(gp, x, y, 'z', N, 'nsamples', 500, 'repeat', 4, 'display', 0);
+[rgp,g,opt] = gp_mc(gp, x, y, 'z', N, 'nsamples', 500, 'repeat', 4, 'display', 10);
 rgp=thin(rgp,101);
 nmc=size(rgp.jitterSigma2,1);
 
@@ -277,12 +281,13 @@
 subplot('Position',[0.69 0.7 0.3 0.25])
 hold on
 % GP 95% credible interval
-h2=fill([xgrid' fliplr(xgrid')], [prctile(fs,97.5,2)' fliplr(prctile(fs,2.5,2)')], color1, 'edgecolor', color1);
+% h2=fill([xgrid' fliplr(xgrid')], [prctile(fs,97.5,2)' fliplr(prctile(fs,2.5,2)')], color1, 'edgecolor', color1);
 % GP mean
 h1=plot(xgrid, Eft_mcmc, 'color', color2, 'linewidth', 3);
 axis([-1.5 1.5 -14 14])
 title('MCMC approximation')
-legend([h1 h2],'Mean of the latent','95% CI',2)
+legend([h1],'Mean of the latent',2)
+% legend([h1 h2],'Mean of the latent','95% CI',2)
 legend('boxoff')
 line(xlim,[0 0],'Linestyle','--','color','k')
 line([0 0],ylim,'Linestyle','--','color','k')
@@ -292,12 +297,13 @@
 subplot('Position',[0.69 0.38 0.3 0.25])
 hold on
 % GP 95% credible interval
-h2=fill([xgrid' fliplr(xgrid')], [(logitinv(prctile(fs,97.5,2)))' fliplr(logitinv(prctile(fs,2.5,2))')], color1, 'edgecolor', color1);
+% h2=fill([xgrid' fliplr(xgrid')], [(logitinv(prctile(fs,97.5,2)))' fliplr(logitinv(prctile(fs,2.5,2))')], color1, 'edgecolor', color1);
 % GP mean
 h1=plot(xgrid, logitinv(Eft_mcmc), 'color', color2, 'linewidth', 3);
 % observations
 h3=plot(x, y./5, 'xr', 'markersize', 10, 'linewidth', 2);
-legend([h1 h2 h3],'Expected prob.','95% CI','Observations',2)
+% legend([h1 h2 h3],'Expected prob.','95% CI','Observations',2)
+legend([h1 h3],'Expected prob.','Observations',2)
 legend('boxoff')
 line(xlim,[.5 .5],'Linestyle','--','color','k')
 line([0 0],ylim,'Linestyle','--','color','k')
@@ -312,8 +318,8 @@
 set(gca,'ytick',[])
 ylim([0 2000])
 xlabel('LD50')
-h1=text(prctile(ld50s,2.5),1850,'2.5%','HorizontalAlignment','center');
-h2=text(prctile(ld50s,50),1850,'50%','HorizontalAlignment','center');
-h3=text(prctile(ld50s,97.5),1850,'97.5%','HorizontalAlignment','center');
-hl=line(repmat(prctile(ld50s,[2.5 50 97.5]),2,1),repmat([0 1800]',1,3),'Color','k');
+% h1=text(prctile(ld50s,2.5),1850,'2.5%','HorizontalAlignment','center');
+% h2=text(prctile(ld50s,50),1850,'50%','HorizontalAlignment','center');
+% h3=text(prctile(ld50s,97.5),1850,'97.5%','HorizontalAlignment','center');
+% hl=line(repmat(prctile(ld50s,[2.5 50 97.5]),2,1),repmat([0 1800]',1,3),'Color','k');
 fprintf('Elapsed time %.0fs\n',toc)
diff --git a/gp/demo_binomial_apc.m b/gp/demo_binomial_apc.m
index 4d2296d5..18372400 100644
--- a/gp/demo_binomial_apc.m
+++ b/gp/demo_binomial_apc.m
@@ -161,8 +161,8 @@
 set(gcf, 'color', 'w'), hold on
 color1=ones(1,3)*0.8; color2=ones(1,3)*0.5;
 % Estimate
-h1=fill([xx1' fliplr(xx1')], [(Eft_1(ind1)+1.96*sqrt(Varft_1(ind1)))' ...
-    fliplr((Eft_1(ind1)-1.96*sqrt(Varft_1(ind1)))')], color1, 'edgecolor', color1);
+% h1=fill([xx1' fliplr(xx1')], [(Eft_1(ind1)+1.96*sqrt(Varft_1(ind1)))' ...
+%     fliplr((Eft_1(ind1)-1.96*sqrt(Varft_1(ind1)))')], color1, 'edgecolor', color1);
 h2=plot(xx1, Eft_1(ind1), 'color', color2, 'linewidth', 3);
 % True function
 h4=plot(xx1, f1o(ind1), 'color', 'r', 'linewidth', 2); hold off
@@ -173,8 +173,8 @@
 % Time period effect
 subplot(3,1,2)
 set(gcf, 'color', 'w'), hold on
-h1=fill([xx2' fliplr(xx2')], [(Eft_2(ind2)+1.96*sqrt(Varft_2(ind2)))' ...
-    fliplr((Eft_2(ind2)-1.96*sqrt(Varft_2(ind2)))')], color1, 'edgecolor', color1);
+% h1=fill([xx2' fliplr(xx2')], [(Eft_2(ind2)+1.96*sqrt(Varft_2(ind2)))' ...
+%     fliplr((Eft_2(ind2)-1.96*sqrt(Varft_2(ind2)))')], color1, 'edgecolor', color1);
 h2=plot(xx2, Eft_2(ind2), 'color', color2, 'linewidth', 3);
 % true function
 h4=plot(xx2, f2o(ind2), 'color', 'r', 'linewidth', 2);
@@ -185,8 +185,8 @@
 % Cohort effect
 subplot(3,1,3)
 set(gcf, 'color', 'w'), hold on
-h1=fill([xx3' fliplr(xx3')], [(Eft_3(ind3)+1.96*sqrt(Varft_3(ind3)))' ...
-    fliplr((Eft_3(ind3)-1.96*sqrt(Varft_3(ind3)))')], color1, 'edgecolor', color1);
+% h1=fill([xx3' fliplr(xx3')], [(Eft_3(ind3)+1.96*sqrt(Varft_3(ind3)))' ...
+%     fliplr((Eft_3(ind3)-1.96*sqrt(Varft_3(ind3)))')], color1, 'edgecolor', color1);
 h2=plot(xx3, Eft_3(ind3), 'color', color2, 'linewidth', 3);
 % true function
 h4=plot(xx3, f3o(ind3), 'color', 'r', 'linewidth', 2);
diff --git a/gp/demo_hierprior.m b/gp/demo_hierprior.m
index 97b064e8..9c464d3a 100644
--- a/gp/demo_hierprior.m
+++ b/gp/demo_hierprior.m
@@ -112,8 +112,13 @@
 end
 gpcf1 = gpcf_matern32('lengthScale', 1, 'magnSigma2', 0.03, ...
   'lengthScale_prior', pl, 'magnSigma2_prior', prior_fixed());
-gpcf2 = gpcf_ppcs3('nin',nin,'lengthScale', 5, 'magnSigma2', 0.05,...
-  'lengthScale_prior', pl, 'magnSigma2_prior', prior_fixed());
+if exist('ldlchol')
+  gpcf2 = gpcf_ppcs3('nin',nin,'lengthScale', 5, 'magnSigma2', 0.05,...
+    'lengthScale_prior', pl, 'magnSigma2_prior', prior_fixed());
+else
+  gpcf2 = gpcf_matern52('lengthScale',5,'magnSigma2', 0.05, ...
+    'lengthScale_prior',pl, 'magnSigma2_prior', prior_fixed());
+end
 
 lik = lik_negbin();
 
@@ -125,8 +130,13 @@
 gp3 = gp_set('type', 'PIC', 'lik', lik, 'cf', gpcf1, 'X_u', Xu, 'Xu_prior', pxu,  ...
            'jitterSigma2', 1e-4, 'infer_params', 'covariance+likelihood+inducing',...
            'tr_index', trindex);
-gp4 = gp_set('type', 'CS+FIC', 'lik', lik, 'cf', {gpcf1 gpcf2}, 'X_u', Xu, 'Xu_prior', pxu,  ...
-           'jitterSigma2', 1e-4, 'infer_params', 'covariance+likelihood+inducing');
+if exist('ldlchol')
+  gp4 = gp_set('type', 'CS+FIC', 'lik', lik, 'cf', {gpcf1 gpcf2}, 'X_u', Xu, 'Xu_prior', pxu,  ...
+    'jitterSigma2', 1e-4, 'infer_params', 'covariance+likelihood+inducing');
+else
+  gp4 = gp_set('type', 'SOR', 'lik', lik, 'cf', {gpcf1 gpcf2}, 'X_u', Xu, 'Xu_prior', pxu,  ...
+    'jitterSigma2', 1e-4, 'infer_params', 'covariance+likelihood+inducing');
+end
 gp4.cf{1}.p.lengthScale=prior_fixed();
 gp4.cf{1}.p.magnSigma2=prior_fixed();
 
@@ -152,9 +162,15 @@
 gp3 = gp_set('type', 'PIC', 'lik', lik, 'cf', gpcf1, 'X_u', Xu, 'Xu_prior', pxu,  ...
            'jitterSigma2', 1e-4, 'infer_params', 'covariance+likelihood+inducing',...
            'tr_index', trindex, 'latent_method', 'EP');
-gp4 = gp_set('type', 'CS+FIC', 'lik', lik, 'cf', {gpcf1 gpcf2}, 'X_u', Xu,  'Xu_prior', pxu, ...
-           'jitterSigma2', 1e-4, 'infer_params', 'covariance+likelihood+inducing', ...
-            'latent_method', 'EP');
+if exist('ldlchol')
+  gp4 = gp_set('type', 'CS+FIC', 'lik', lik, 'cf', {gpcf1 gpcf2}, 'X_u', Xu,  'Xu_prior', pxu, ...
+    'jitterSigma2', 1e-4, 'infer_params', 'covariance+likelihood+inducing', ...
+    'latent_method', 'EP');
+else
+  gp4 = gp_set('type', 'SOR', 'lik', lik, 'cf', {gpcf1 gpcf2}, 'X_u', Xu,  'Xu_prior', pxu, ...
+    'jitterSigma2', 1e-4, 'infer_params', 'covariance+likelihood+inducing', ...
+    'latent_method', 'EP');
+end
 gp4.cf{1}.p.lengthScale=prior_fixed();
 gp4.cf{1}.p.magnSigma2=prior_fixed();
 
diff --git a/gp/demo_improvemarginals.m b/gp/demo_improvemarginals.m
index f13b409e..7a2082cf 100644
--- a/gp/demo_improvemarginals.m
+++ b/gp/demo_improvemarginals.m
@@ -95,9 +95,9 @@
   [pc_ep(:,i), fvec_ep(:,i), p_ep(:,i)] = gp_predcm(gp,x,y,'ind', ind(i), 'fcorr', 'fact'); tt_epfact=toc(start);
   s = plot(fvec_ep(:,i), p_ep(:,i), '-k', fvec_ep(:,i), norm_pdf(fvec_ep(:,i), Eft_ep(ind(i)), sqrt(Varft_ep(ind(i)))),'-m', fvec_ep(:,i), pc_ep(:,i), '-r');
   set(s,'LineWidth',2)
-  set(get(get(b,'Annotation'),'LegendInformation'),...
-    'IconDisplayStyle','off');
-  legend('EP-L', 'EP-G', 'EP-FACT');
+%   set(get(get(b,'Annotation'),'LegendInformation'),...
+%     'IconDisplayStyle','off');
+  legend('MCMC samples', 'EP-L', 'EP-G', 'EP-FACT');
   title('Marginal corrections for probit likelihood (EP)');
 
   subplot(2,1,2)
@@ -153,9 +153,9 @@
   s = plot(fvec_la(:,i), p_la(:,i), '-k', fvec_la(:,i), norm_pdf(fvec_la(:,i), Eft_la(ind(i)), sqrt(Varft_la(ind(i)))), '-m', ...
           fvec_la(:,i), pc_la2(:,i), '-r', fvec_la(:,i), pc_la(:,i), '-b');
   set(s,'LineWidth',2)
-  set(get(get(b,'Annotation'),'LegendInformation'),...
-    'IconDisplayStyle','off');
-  legend('LA-L', 'LA-G', 'LA-FACT', 'LA-CM2');
+%   set(get(get(b,'Annotation'),'LegendInformation'),...
+%     'IconDisplayStyle','off');
+  legend('MCMC samples', 'LA-L', 'LA-G', 'LA-FACT', 'LA-CM2');
   title('Marginal corrections for probit likelihood (Laplace)');
 
   % Predictive corrections
diff --git a/gp/demo_improvemarginals2.m b/gp/demo_improvemarginals2.m
index 427d087d..ef86506c 100644
--- a/gp/demo_improvemarginals2.m
+++ b/gp/demo_improvemarginals2.m
@@ -111,10 +111,14 @@
   set(gca,'ytick',[])
   ylim([0 2500])
   xlabel('LD50')
-  h1=text(prctile(ld50s,2.5),2200,'2.5%','HorizontalAlignment','center');
-  h2=text(prctile(ld50s,50),2350,'50%','HorizontalAlignment','center');
-  h3=text(prctile(ld50s,97.5),2200,'97.5%','HorizontalAlignment','center');
-  hl=line(repmat(prctile(ld50s,[2.5 50 97.5]),2,1),[0 0 0;2150 2300 2150],'Color','k');
+  h1=text(prctile(ld50s',2.5),2200,'2.5%','HorizontalAlignment','center');
+  h2=text(prctile(ld50s',50),2350,'50%','HorizontalAlignment','center');
+  h3=text(prctile(ld50s',97.5),2200,'97.5%','HorizontalAlignment','center');
+%   hl=line(repmat(prctile(ld50s',[2.5 50 97.5]),2,1),[0 0 0;2150 2300 2150],'Color','k');
+  hl=line(repmat(prctile(ld50s',2.5),1,2),[0 2150],'Color','k');
+  hl=line(repmat(prctile(ld50s',50),1,2),[0 2300],'Color','k');
+  hl=line(repmat(prctile(ld50s',97.5),1,2),[0 2150],'Color','k');
+  q2=qqplot(rmc.ld50s);
   drawnow
   
   % ------- Laplace approximation --------
@@ -156,14 +160,19 @@
   set(gca,'ytick',[])
   ylim([0 2500])
   xlabel('LD50')
-  h1=text(prctile(ld50s,2.5),2200,'2.5%','HorizontalAlignment','center');
-  h2=text(prctile(ld50s,50),2350,'50%','HorizontalAlignment','center');
-  h3=text(prctile(ld50s,97.5),2200,'97.5%','HorizontalAlignment','center');
-  hl=line(repmat(prctile(ld50s,[2.5 50 97.5]),2,1),[0 0 0;2150 2300 2150],'Color','k');
+  h1=text(prctile(ld50s',2.5),2200,'2.5%','HorizontalAlignment','center');
+  h2=text(prctile(ld50s',50),2350,'50%','HorizontalAlignment','center');
+  h3=text(prctile(ld50s',97.5),2200,'97.5%','HorizontalAlignment','center');
+  %hl=line(repmat(prctile(ld50s,[2.5 50 97.5]),2,1),[0 0 0;2150 2300 2150],'Color','k');
+  hl=line(repmat(prctile(ld50s',2.5),1,2),[0 2150],'Color','k');
+  hl=line(repmat(prctile(ld50s',50),1,2),[0 2300],'Color','k');
+  hl=line(repmat(prctile(ld50s',97.5),1,2),[0 2150],'Color','k');
   subplot('Position',[0.244 0.060 0.164 0.250]);
-  qqplot(rmc.ld50s,ld50s)
+  qqplot(ld50s);
+  %plot(q2(indn),q1);
   xlim([-.8 1.3])
   ylim([-50 60])
+  ylabel('')
   box on
   drawnow
 
@@ -173,6 +182,8 @@
   % in Gelman et al (2013)
   fprintf('Sampling from the posterior of alpha and beta\n')
   fs = gp_rnd(gpia, x, y, [0 1]', 'z', N, 'nsamp', 10000, 'fcorr', 'cm2');
+  indn=isnan(fs(1,:));
+  fs(:,indn)=[];
   a=fs(1,:);b=fs(2,:)-fs(1,:);
   % compute samples from the LD50 given b>0 (see, Gelman et al (2013))
   ld50s=-a(b>0)./b(b>0);
@@ -199,12 +210,17 @@
   set(gca,'ytick',[])
   ylim([0 2500])
   xlabel('LD50')
-  h1=text(prctile(ld50s,2.5),2200,'2.5%','HorizontalAlignment','center');
-  h2=text(prctile(ld50s,50),2350,'50%','HorizontalAlignment','center');
-  h3=text(prctile(ld50s,97.5),2200,'97.5%','HorizontalAlignment','center');
-  hl=line(repmat(prctile(ld50s,[2.5 50 97.5]),2,1),[0 0 0;2150 2300 2150],'Color','k');
+  h1=text(prctile(ld50s',2.5),2200,'2.5%','HorizontalAlignment','center');
+  h2=text(prctile(ld50s',50),2350,'50%','HorizontalAlignment','center');
+  h3=text(prctile(ld50s',97.5),2200,'97.5%','HorizontalAlignment','center');
+  %hl=line(repmat(prctile(ld50s,[2.5 50 97.5]),2,1),[0 0 0;2150 2300 2150],'Color','k');
+  hl=line(repmat(prctile(ld50s',2.5),1,2),[0 2150],'Color','k');
+  hl=line(repmat(prctile(ld50s',50),1,2),[0 2300],'Color','k');
+  hl=line(repmat(prctile(ld50s',97.5),1,2),[0 2150],'Color','k');
   subplot('Position',[0.438 0.060 0.164 0.250]);
-  qqplot(rmc.ld50s,ld50s)
+  qqplot(ld50s);
+  %q2(indn)=[];
+  %plot(q2,q1);
   xlim([-.8 1.3])
   ylim([-50 60])
   ylabel('')
@@ -253,12 +269,16 @@
   set(gca,'ytick',[])
   ylim([0 2500])
   xlabel('LD50')
-  h1=text(prctile(ld50s,2.5),2200,'2.5%','HorizontalAlignment','center');
-  h2=text(prctile(ld50s,50),2350,'50%','HorizontalAlignment','center');
-  h3=text(prctile(ld50s,97.5),2200,'97.5%','HorizontalAlignment','center');
-  hl=line(repmat(prctile(ld50s,[2.5 50 97.5]),2,1),[0 0 0;2150 2300 2150],'Color','k');
+  h1=text(prctile(ld50s',2.5),2200,'2.5%','HorizontalAlignment','center');
+  h2=text(prctile(ld50s',50),2350,'50%','HorizontalAlignment','center');
+  h3=text(prctile(ld50s',97.5),2200,'97.5%','HorizontalAlignment','center');
+  %hl=line(repmat(prctile(ld50s,[2.5 50 97.5]),2,1),[0 0 0;2150 2300 2150],'Color','k');
+  hl=line(repmat(prctile(ld50s',2.5),1,2),[0 2150],'Color','k');
+  hl=line(repmat(prctile(ld50s',50),1,2),[0 2300],'Color','k');
+  hl=line(repmat(prctile(ld50s',97.5),1,2),[0 2150],'Color','k');
   subplot('Position',[0.632 0.060 0.164 0.250]);
-  qqplot(rmc.ld50s,ld50s)
+  qqplot(ld50s);
+  %plot(q2,q1);
   xlim([-.8 1.3])
   ylim([-50 60])
   ylabel('')
@@ -271,6 +291,8 @@
   % in Gelman et al (2013)
   fprintf('Sampling from the posterior of alpha and beta\n')
   fs = gp_rnd(gpia, x, y, [0 1]', 'z', N, 'zt', [5 5]', 'nsamp', 10000, 'fcorr', 'fact');
+  indn=isnan(fs(1,:));
+  fs(:,indn)=[];
   a=fs(1,:);b=fs(2,:)-fs(1,:);
   % compute samples from the LD50 given b>0 (see, Gelman et al (2013))
   ld50s=-a(b>0)./b(b>0);
@@ -297,12 +319,17 @@
   set(gca,'ytick',[])
   ylim([0 2500])
   xlabel('LD50')
-  h1=text(prctile(ld50s,2.5),2200,'2.5%','HorizontalAlignment','center');
-  h2=text(prctile(ld50s,50),2350,'50%','HorizontalAlignment','center');
-  h3=text(prctile(ld50s,97.5),2200,'97.5%','HorizontalAlignment','center');
-  hl=line(repmat(prctile(ld50s,[2.5 50 97.5]),2,1),[0 0 0;2150 2300 2150],'Color','k');
+  h1=text(prctile(ld50s',2.5),2200,'2.5%','HorizontalAlignment','center');
+  h2=text(prctile(ld50s',50),2350,'50%','HorizontalAlignment','center');
+  h3=text(prctile(ld50s',97.5),2200,'97.5%','HorizontalAlignment','center');
+  %hl=line(repmat(prctile(ld50s,[2.5 50 97.5]),2,1),[0 0 0;2150 2300 2150],'Color','k');
+  hl=line(repmat(prctile(ld50s',2.5),1,2),[0 2150],'Color','k');
+  hl=line(repmat(prctile(ld50s',50),1,2),[0 2300],'Color','k');
+  hl=line(repmat(prctile(ld50s',97.5),1,2),[0 2150],'Color','k');
   subplot('Position',[0.826 0.060 0.164 0.250]);
-  qqplot(rmc.ld50s,ld50s)
+  qqplot(ld50s);
+  %q2(indn)=[];
+  %plot(q2,q1);
   xlim([-.8 1.3])
   ylim([-50 60])
   ylabel('')
diff --git a/gp/demo_inputdependentnoise.m b/gp/demo_inputdependentnoise.m
index 71baf42e..9ae6c167 100644
--- a/gp/demo_inputdependentnoise.m
+++ b/gp/demo_inputdependentnoise.m
@@ -172,18 +172,30 @@
 figure
 subplot(3,1,1),mesh(x1,x2,reshape(f1t,size(x1))),hold on, plot3(xt(:,1),xt(:,2), Ef(1:size(xt,1)), '*')
 title('Input dependent noise model');
-colormap hsv, alpha(.4)
+colormap hsv, 
+if ~exist('OCTAVE_VERSION','builtin')
+  alpha(.4)
+end
 subplot(3,1,2),mesh(x1,x2,reshape(f1t,size(x1))),hold on, plot3(xt(:,1),xt(:,2), Ef2(1:size(xt,1)), '*');
-colormap hsv, alpha(.4)
+colormap hsv, 
+if ~exist('OCTAVE_VERSION','builtin')
+  alpha(.4)
+end
 title('Gaussian noise model');
 subplot(3,1,3),mesh(x1,x2,reshape(f1t,size(x1))),hold on, plot3(xt(:,1),xt(:,2), Ef3(1:size(xt,1)), '*');
-colormap hsv, alpha(.4)
+colormap hsv, 
+if ~exist('OCTAVE_VERSION','builtin')
+  alpha(.4)
+end
 title('Student-t noise model');
 
 figure
 mesh(x1,x2,sigma2.*exp(reshape(f2t,size(x1)))),hold on, plot3(xt(:,1),xt(:,2), s2.*exp(Ef(size(xt,1)+1:end)), '*'); 
 title('Real noise versus predicted noise');
-colormap hsv, alpha(.4)
+colormap hsv, 
+if ~exist('OCTAVE_VERSION','builtin')
+  alpha(.4)
+end
 
 %============================================
 % Demonstration with homoscedastic noise
@@ -274,4 +286,4 @@
 plot(xt, s2.*exp(Ef12), '-b',x, sigma2.*exp(f2), '-k', xt, s2.*exp(Ef12 + 1.96.*sqrt(diag(Varf(nt+1:end, nt+1:end)))), '-r', xt,s2.*exp(Ef12 - 1.96.*sqrt(diag(Varf(nt+1:end, nt+1:end)))), '-r')
 ylim([0 2.5])
 legend('Predicted noise variance', 'Real noise variance','95% CI',2);
-setrandstream(prevstream);
\ No newline at end of file
+setrandstream(prevstream);
diff --git a/gp/demo_inputdependentweibull.m b/gp/demo_inputdependentweibull.m
index e92b1d9b..ca2a0c47 100644
--- a/gp/demo_inputdependentweibull.m
+++ b/gp/demo_inputdependentweibull.m
@@ -45,6 +45,7 @@
 
 xt1=zeros(200,nin); xt1(:,2)=1;
 xt2=zeros(200,nin); xt2(:,2)=-1;
+nt=200;
 
 xt1(:,1)=linspace(min(x(:,1)), max(x(:,1)), 200);
 xt2(:,1)=linspace(min(x(:,1)), max(x(:,1)), 200);
diff --git a/gp/demo_kalman1.m b/gp/demo_kalman1.m
index 18bd3e5e..1ebb6269 100644
--- a/gp/demo_kalman1.m
+++ b/gp/demo_kalman1.m
@@ -77,7 +77,7 @@
   gp = gp_optim(gp, x, y);
   
   % Predict values at test inputs xt
-  [Eft,Varft] = gp_pred(gp, x, y, 'xt', xt);
+  [Eft,Varft] = gp_pred(gp, x, y, xt);
   
   
 %% Compare against full GP solution (table)
diff --git a/gp/demo_kalman2.m b/gp/demo_kalman2.m
index cc898533..26b116e9 100644
--- a/gp/demo_kalman2.m
+++ b/gp/demo_kalman2.m
@@ -89,7 +89,7 @@
                         'lengthScale_prior',pl,'magnSigma2_prior',pl);
   
   % Finally create the GP structure
-  gp = gp_set('lik', lik, 'cf', {gpcf1,gpcf2,gpcf3});
+  gp = gp_set('lik', lik, 'cf', {gpcf1,gpcf2,gpcf3}, 'jitterSigma2',1e-6);
   
   % Set type to KALMAN
   gp = gp_set(gp,'type','KALMAN');
@@ -101,13 +101,13 @@
   opt=optimset('TolFun',1e-4,'TolX',1e-4,'Display','iter');
 
   % Find hyperparameters by optimization (BFGS)
-  gp=gp_optim(gp,x,y,'opt',opt,'optimf',@fminlbfgs);
+  gp=gp_optim(gp,x,y,'opt',opt,'optimf',@fminscg);
   
   % Set the test points
   xt = (x(end):1/12:x(end)+10)';
   
   % Predict values
-  [Eft,Varft] =  gp_pred(gp, x, y,'xt',xt);
+  [Eft,Varft] =  gp_pred(gp, x, y,xt);
   
   % Also predict the latent components separately
   [Eft1, Varft1] = gp_pred(gp, x, y, x, 'predcf', [1 3]);
diff --git a/gp/demo_lgpdens.m b/gp/demo_lgpdens.m
index 9c235729..302afd4b 100644
--- a/gp/demo_lgpdens.m
+++ b/gp/demo_lgpdens.m
@@ -201,8 +201,10 @@
 xt=linspace(-6,6,200)';
 [p,pq]=lgpdens(x,xt,'int_method','grid');
 pla=p;
-line(xt,p,'color','r','marker','none','linewidth',2)
-line(xt,pq,'color','r','marker','none','linewidth',1,'linestyle','--')
+if ~exist('OCTAVE_VERSION', 'builtin')
+  line(xt,p,'color','r','marker','none','linewidth',2)
+  line(xt,pq,'color','r','marker','none','linewidth',1,'linestyle','--')
+end
 xlim([-7 7])
 ylim([0 .5])
 title('t_4 (Laplace)')
@@ -214,19 +216,23 @@
 subplot(2,1,2)
 [p,pq]=lgpdens(x,xt,'latent_method','MCMC');
 pmc=p;
-line(xt,p,'color','r','marker','none','linewidth',2)
-line(xt,pq,'color','r','marker','none','linewidth',1,'linestyle','--')
+if ~exist('OCTAVE_VERSION','builtin')
+  line(xt,p,'color','r','marker','none','linewidth',2)
+  line(xt,pq,'color','r','marker','none','linewidth',1,'linestyle','--')
+end
 xlim([-7 7])
 ylim([0 .5])
 title('t_4 (MCMC)')
 line(xt,p0,'color','k')
 drawnow
 
-[pks] = ksdensity(x,xt);
-
 disp(['Laplace: ' num2str(sum(p0.*log(pla)))])
 disp(['MCMC: ' num2str(sum(p0.*log(pmc)))])
-disp(['ksdensity: ' num2str(sum(p0.*log(pks)))])
+
+if ~exist('OCTAVE_VERSION','builtin')
+  [pks] = ksdensity(x,xt);  
+  disp(['ksdensity: ' num2str(sum(p0.*log(pks)))])
+end
 
 % Set back initial random stream
 setrandstream(prevstream);
\ No newline at end of file
diff --git a/gp/demo_memorysave.m b/gp/demo_memorysave.m
index 8aa877b4..25656dda 100644
--- a/gp/demo_memorysave.m
+++ b/gp/demo_memorysave.m
@@ -167,32 +167,35 @@
 pl = prior_t('s2',10);
 pm = prior_sqrtunif();
 
-gpcf1 = gpcf_ppcs3('nin',nin,'lengthScale', 1, 'magnSigma2', 0.1, 'lengthScale_prior', pl, 'magnSigma2_prior', pm);
 gpcf2 = gpcf_sexp('lengthScale', 5, 'magnSigma2', 0.05, 'lengthScale_prior', pl, 'magnSigma2_prior', pm);
 gpcf3 = gpcf_neuralnetwork('weightSigma2', [1 1], 'biasSigma2', 0.05, 'weightSigma2_prior', pl, 'biasSigma2_prior', pm);
 
 lik = lik_negbin();
-% GP without memory save
-gp = gp_set('type', 'FIC', 'lik', lik, 'cf', gpcf1, 'X_u', Xu, ...
-            'jitterSigma2', 1e-4, 'infer_params', 'covariance+inducing');
-% GP with memory saving option enabled
-gp2 = gp_set('type', 'FIC', 'lik', lik, 'cf', gpcf1, 'X_u', Xu, ...
-            'jitterSigma2', 1e-4, 'infer_params', 'covariance+inducing', 'savememory', 'on');
 opt=optimset('TolFun',1e-2,'TolX',1e-2, 'Display', 'off');
 
-
-fprintf('Spatial process (Laplace), FIC GP, PPCS3 covariance function, without and with memory saving (optimization and prediction)\n');
-
-% Optimization and prediction without memory saving
-tic,gp=gp_optim(gp,x,y,'z',ye,'opt',opt);
-[Ef, Varf] = gp_pred(gp, x, y, x, 'z', ye, 'tstind', [1:n]); toc
-
-% Optimization and prediction with memory saving
-tic,gp2=gp_optim(gp2,x,y,'z',ye,'opt',opt);
-[Ef2, Varf2] = gp_pred(gp2, x, y, x, 'z', ye, 'tstind', [1:n]); toc
-
-% Check that predictions (and optimization) with and without memory saving are same
-assert(all(Ef==Ef2)); assert(all(Varf==Varf2));
+if exist('ldlchol','file')
+  % GP without memory save
+  gp = gp_set('type', 'FIC', 'lik', lik, 'cf', gpcf1, 'X_u', Xu, ...
+    'jitterSigma2', 1e-4, 'infer_params', 'covariance+inducing');
+  % GP with memory saving option enabled
+  gp2 = gp_set('type', 'FIC', 'lik', lik, 'cf', gpcf1, 'X_u', Xu, ...
+    'jitterSigma2', 1e-4, 'infer_params', 'covariance+inducing', 'savememory', 'on');
+  
+  gpcf1 = gpcf_ppcs3('nin',nin,'lengthScale', 1, 'magnSigma2', 0.1, 'lengthScale_prior', pl, 'magnSigma2_prior', pm);
+  
+  fprintf('Spatial process (Laplace), FIC GP, PPCS3 covariance function, without and with memory saving (optimization and prediction)\n');
+  
+  % Optimization and prediction without memory saving
+  tic,gp=gp_optim(gp,x,y,'z',ye,'opt',opt);
+  [Ef, Varf] = gp_pred(gp, x, y, x, 'z', ye, 'tstind', [1:n]); toc
+  
+  % Optimization and prediction with memory saving
+  tic,gp2=gp_optim(gp2,x,y,'z',ye,'opt',opt);
+  [Ef2, Varf2] = gp_pred(gp2, x, y, x, 'z', ye, 'tstind', [1:n]); toc
+  
+  % Check that predictions (and optimization) with and without memory saving are same
+  assert(all(Ef==Ef2)); assert(all(Varf==Varf2));
+end
 
 gp = gp_set('lik', lik, 'cf', gpcf2, 'jitterSigma2', 1e-4, 'latent_method', 'EP');
 gp2 = gp_set('lik', lik, 'cf', gpcf2, 'jitterSigma2', 1e-4, 'latent_method', 'EP', 'savememory', 'on');
diff --git a/gp/demo_monotonic2.m b/gp/demo_monotonic2.m
index 01a30d99..5767292c 100644
--- a/gp/demo_monotonic2.m
+++ b/gp/demo_monotonic2.m
@@ -31,7 +31,14 @@
 % y is the number of insured who died
 S = which('demo_monotonic2');
 L = strrep(S,'demo_monotonic2.m','demodata/broffit.txt');
-d=readtable(L,'Delimiter',';');
+if ~exist('OCTAVE_VERSION','builtin')
+  d=readtable(L,'Delimiter',';');
+else
+  dd=importdata(L);
+  d.age=dd.data(:,1);
+  d.N=dd.data(:,2);
+  d.y=dd.data(:,3);
+end
 x=d.age;
 y=d.y;
 z=d.N.*(sum(d.y)./sum(d.N));
diff --git a/gp/demo_multiclass.m b/gp/demo_multiclass.m
index b01119ac..8e3adade 100644
--- a/gp/demo_multiclass.m
+++ b/gp/demo_multiclass.m
@@ -110,9 +110,9 @@
 plot(x(y(:,2)==1,1),x(y(:,2)==1,2),'x', 'linewidth', 2);
 plot(x(y(:,3)==1,1),x(y(:,3)==1,2),'kd', 'linewidth', 2);
 axis([-0.4 1.4 -0.4 1.4])
-contour(xtg1, xtg2, reshape(exp(pg(:,1)),30,30),'r', 'linewidth', 2)
-contour(xtg1, xtg2, reshape(exp(pg(:,2)),30,30),'b', 'linewidth', 2)
-contour(xtg1, xtg2, reshape(exp(pg(:,3)),30,30),'k', 'linewidth', 2)
+contour(xtg1, xtg2, reshape(exp(pg(:,1)),30,30),0.1:0.1:1,'r', 'linewidth', 2)
+contour(xtg1, xtg2, reshape(exp(pg(:,2)),30,30),0.1:0.1:1,'b', 'linewidth', 2)
+contour(xtg1, xtg2, reshape(exp(pg(:,3)),30,30),0.1:0.1:1,'k', 'linewidth', 2)
 
 % MCMC approach
 
@@ -162,9 +162,9 @@
 plot(x(y(:,2)==1,1),x(y(:,2)==1,2),'x', 'linewidth', 2);
 plot(x(y(:,3)==1,1),x(y(:,3)==1,2),'kd', 'linewidth', 2);
 axis([-0.4 1.4 -0.4 1.4])
-contour(xtg1, xtg2, reshape(pg_mc(:,1),30,30),'r', 'linewidth', 2)
-contour(xtg1, xtg2, reshape(pg_mc(:,2),30,30),'b', 'linewidth', 2)
-contour(xtg1, xtg2, reshape(pg_mc(:,3),30,30),'k', 'linewidth', 2)
+contour(xtg1, xtg2, reshape(pg_mc(:,1),30,30),10,'r', 'linewidth', 2)
+contour(xtg1, xtg2, reshape(pg_mc(:,2),30,30),10,'b', 'linewidth', 2)
+contour(xtg1, xtg2, reshape(pg_mc(:,3),30,30),10,'k', 'linewidth', 2)
 
 
 
@@ -197,7 +197,7 @@
 
 % re-set some of the sampling options
 hmc_opt.repeat=1;
-hmc_opt.steps=4;
+hmc_opt.steps=4;i_d
 hmc_opt.stepadj=0.02;
 latent_opt.repeat = 20;
 hmc2('state', sum(100*clock));
@@ -218,8 +218,8 @@
 plot(x(y(:,2)==1,1),x(y(:,2)==1,2),'x', 'linewidth', 2);
 plot(x(y(:,3)==1,1),x(y(:,3)==1,2),'kd', 'linewidth', 2);
 axis([-0.4 1.4 -0.4 1.4])
-contour(xtg1, xtg2, reshape(pg_mc2(:,1),30,30),'r', 'linewidth', 2)
-contour(xtg1, xtg2, reshape(pg_mc2(:,2),30,30),'b', 'linewidth', 2)
-contour(xtg1, xtg2, reshape(pg_mc2(:,3),30,30),'k', 'linewidth', 2)
+contour(xtg1, xtg2, reshape(pg_mc2(:,1),30,30),10,'r', 'linewidth', 2)
+contour(xtg1, xtg2, reshape(pg_mc2(:,2),30,30),10,'b', 'linewidth', 2)
+contour(xtg1, xtg2, reshape(pg_mc2(:,3),30,30),10,'k', 'linewidth', 2)
 
 
diff --git a/gp/demo_multiclass_nested_ep.m b/gp/demo_multiclass_nested_ep.m
index 755018e8..918f2e80 100644
--- a/gp/demo_multiclass_nested_ep.m
+++ b/gp/demo_multiclass_nested_ep.m
@@ -73,7 +73,7 @@
 gpep = gp_set('lik', lik_multinomprobit, 'cf', {gpcf1}, 'jitterSigma2', 1e-6, 'latent_method', 'EP', 'latent_opt', latent_opt);
 
 % Optimization options
-opt=optimset('TolX',1e-2,'TolFun',1e-2,'display','iter','derivativecheck','off');
+opt=optimset('TolX',1e-2,'TolFun',1e-2,'Display','iter','derivativecheck','off');
 
 disp('Optimize the hyperparameters')
 gpep=gp_optim(gpep,x,y,'opt',opt, 'optimf', @fminlbfgs);
diff --git a/gp/demo_neuralnetcov.m b/gp/demo_neuralnetcov.m
index 90abc7ee..aea2793c 100644
--- a/gp/demo_neuralnetcov.m
+++ b/gp/demo_neuralnetcov.m
@@ -129,23 +129,25 @@
 % Plot the predictions and data
 color1=ones(1,3)*0.8; color2=ones(1,3)*0.5;
 figure, set(gcf, 'color', 'w'), hold on
-h1=fill([xgrid' fliplr(xgrid')], [(Eyt_map+1.96*sqrt(Varyt_map))' fliplr((Eyt_map-1.96*sqrt(Varyt_map))')], color1, 'edgecolor', color1);
+% h1=fill([xgrid' fliplr(xgrid')], [(Eyt_map+1.96*sqrt(Varyt_map))' fliplr((Eyt_map-1.96*sqrt(Varyt_map))')], color1, 'edgecolor', color1);
 % GP mean
 h2=plot(xgrid, Eyt_map, 'color', color2, 'linewidth', 3);
 % observations
 h3=plot(x, y, 'xk', 'markersize', 10, 'linewidth', 2);
 % true function
 h4=plot(xgrid, norm_pdf(4*xgrid), 'color', 'r', 'linewidth', 2);
-legend([h1 h2 h3 h4], 'GP 95% CI', 'GP mean', 'observations', 'true latent function')
+% legend([h1 h2 h3 h4], 'GP 95% CI', 'GP mean', 'observations', 'true latent function')
+legend([h2 h3 h4], 'GP mean', 'observations', 'true latent function')
 title('GP (squared exponential) predictions and the data points');
 
 figure, set(gcf, 'color', 'w'), hold on
-h1=fill([xgrid' fliplr(xgrid')], [(Eyt_map2+1.96*sqrt(Varyt_map2))' fliplr((Eyt_map2-1.96*sqrt(Varyt_map2))')], color1, 'edgecolor', color1);
+% h1=fill([xgrid' fliplr(xgrid')], [(Eyt_map2+1.96*sqrt(Varyt_map2))' fliplr((Eyt_map2-1.96*sqrt(Varyt_map2))')], color1, 'edgecolor', color1);
 % GP mean
 h2=plot(xgrid, Eyt_map2, 'color', color2, 'linewidth', 3);
 % observations
 h3=plot(x, y, 'xk', 'markersize', 10, 'linewidth', 2);
 % true function
 h4=plot(xgrid, norm_pdf(4*xgrid), 'color', 'r', 'linewidth', 2);
-legend([h1 h2 h3 h4], 'GP 95% CI', 'GP mean', 'observations', 'true latent function')
+legend([h2 h3 h4], 'GP mean', 'observations', 'true latent function')
+% legend([h1 h2 h3 h4], 'GP 95% CI', 'GP mean', 'observations', 'true latent function')
 title('GP (neural network) predictions and the data points');
diff --git a/gp/demo_periodic.m b/gp/demo_periodic.m
index 3a125305..b68e0b45 100644
--- a/gp/demo_periodic.m
+++ b/gp/demo_periodic.m
@@ -300,5 +300,6 @@
 plot(xtt(:,1),exp(Eft_full+1.96.*sqrt(Varft_full)).*mean(y),'b--')
 
 legend('Training data', 'Validation data','Predicted mean','95% CI', 'Location', 'NorthWest')
-line(2008,0:80,'LineWidth',2)
+line([2008 2008],[0 80],'LineWidth',2)
+% line(2008,0:80,'LineWidth',2)
 axis tight
diff --git a/gp/demo_regression_additive1.m b/gp/demo_regression_additive1.m
index eddbe23c..8a2a990b 100644
--- a/gp/demo_regression_additive1.m
+++ b/gp/demo_regression_additive1.m
@@ -119,12 +119,12 @@
   warning('GPstuff:SuiteSparseMissing',...
   ['SuiteSparse is not properly installed. (in BECS try ''use suitesparse'')\n' ...
    'Using gpcf_sexp (non-compact support) instead of gpcf_ppcs2 (compact support)']);
-  gpcf2 = gpcf_sexp('lengthScale', 5, 'magnSigma2', 1, 'lengthScale_prior', pl2, 'magnSigma2_prior', pm);
+  gpcf2 = gpcf_sexp('lengthScale', 5, 'magnSigma2', 1, 'lengthScale_prior', pl2, 'magnSigma2_prior', pm2);
 end
 lik = lik_gaussian('sigma2', 0.1, 'sigma2_prior', pn);
 
 % Create the GP structure
-gp = gp_set('lik', lik, 'cf', {gpcf1, gpcf2}, 'jitterSigma2', 1e-9) 
+gp = gp_set('lik', lik, 'cf', {gpcf1, gpcf2}, 'jitterSigma2', 1e-9);
 
 % -----------------------------
 % --- Conduct the inference ---
@@ -179,7 +179,7 @@
 Xu = [min(x):24:max(x)+10]';
 
 % Create the FIC GP structure
-gp_fic = gp_set('type', 'FIC', 'lik', lik, 'cf', {gpcf1,gpcf2}, 'jitterSigma2', 1e-9, 'X_u', Xu)
+gp_fic = gp_set('type', 'FIC', 'lik', lik, 'cf', {gpcf1,gpcf2}, 'jitterSigma2', 1e-9, 'X_u', Xu);
 
 % -----------------------------
 % --- Conduct the inference ---
@@ -227,7 +227,7 @@
     trindex{i} = find(x>edges(i) & x<edges(i+1));
 end
 % Create the FIC GP structure
-gp_pic = gp_set('type', 'PIC', 'lik', lik, 'cf', {gpcf1, gpcf2}, 'jitterSigma2', 1e-6, 'X_u', Xu)
+gp_pic = gp_set('type', 'PIC', 'lik', lik, 'cf', {gpcf1, gpcf2}, 'jitterSigma2', 1e-6, 'X_u', Xu);
 gp_pic = gp_set(gp_pic, 'tr_index', trindex);
 
 % -----------------------------
@@ -278,82 +278,83 @@
 
 % Create the CS+FIC GP structure
 if ~exist('ldlchol')
-  error('GPstuff:SuiteSparseMissing',...
+  warning('GPstuff:SuiteSparseMissing',...
         ['SuiteSparse is not properly installed. (in BECS try ''use suitesparse'')\n' ...
          'Can not use CS+FIC without SuiteSparse']);
+else
+  gp_csfic = gp_set('type','CS+FIC', 'lik', lik, 'cf', {gpcf1, gpcf2}, 'jitterSigma2', 1e-9, 'X_u', Xu);
+  
+  % -----------------------------
+  % --- Conduct the inference ---
+  
+  % --- MAP estimate using modified Newton algorithm ---
+  
+  % Now you can choose, if you want to optimize only parameters or
+  % optimize simultaneously parameters and inducing inputs. Note that
+  % the inducing inputs are not transformed through logarithm when
+  % packed
+  
+  % optimize parameters and inducing inputs
+  %gp_csfic = gp_set(gp_csfic, 'infer_params', 'covariance+likelihood+inducing');
+  % optimize only parameters (default)
+  %gp_csfic = gp_set(gp_csfic, 'infer_params', 'covariance+likelihood');
+  
+  % Set the options for the optimization
+  opt=optimset('TolFun',1e-3,'TolX',1e-3);
+  % Optimize with the scaled conjugate gradient method
+  gp_csfic=gp_optim(gp_csfic,x,y,'opt',opt);
+  
+  % Make the prediction
+  [Eft_csfic, Varft_csfic, lpyt_csfic, Eyt_csfic, Varyt_csfic] = gp_pred(gp_csfic, x, y, x, 'yt', y);
+  
+  % Plot the solution of FIC
+  figure
+  %subplot(4,1,1)
+  hold on
+  plot(x,y,'.', 'MarkerSize',7)
+  plot(x,Eft_csfic,'k', 'LineWidth', 2)
+  plot(x,Eft_csfic-2.*sqrt(Varyt_csfic),'g--', 'LineWidth', 1)
+  plot(gp_csfic.X_u, -30, 'rx', 'MarkerSize', 5, 'LineWidth', 2)
+  plot(x,Eft_csfic+2.*sqrt(Varyt_csfic),'g--', 'LineWidth', 1)
+  axis tight
+  caption2 = sprintf('CS+FIC:  l_1= %.2f, s^2_1 = %.2f, \n l_2= %.2f, s^2_2 = %.2f \n s^2_{noise} = %.2f', gp_csfic.cf{1}.lengthScale, gp_csfic.cf{1}.magnSigma2, gp_csfic.cf{2}.lengthScale, gp_csfic.cf{2}.magnSigma2, gp_csfic.lik.sigma2);
+  title(caption2)
+  legend('Data point', 'predicted mean', '2\sigma error', 'inducing input','Location','Northwest')
+  
+  [Eft, Varft, lpyt, Eyt, Varyt] = gp_pred(gp_csfic, x, y, x, 'yt', y);
+  [Eft1, Varft1] = gp_pred(gp_csfic, x, y, x, 'predcf', 1);
+  [Eft2, Varft2] = gp_pred(gp_csfic, x, y, x, 'predcf', 2);
+  
+  figure
+  set(gcf,'units','centimeters');
+  set(gcf,'DefaultAxesPosition',[0.08  0.13   0.84   0.85]);
+  set(gcf,'DefaultAxesFontSize',16)   %6 8
+  set(gcf,'DefaultTextFontSize',16)   %6 8
+  hold on
+  [AX, H1, H2] = plotyy(x, Eft2, x, Eft1+avgy);
+  set(H2,'LineStyle','--')
+  set(H2, 'LineWidth', 3)
+  set(H1,'LineStyle','-')
+  set(H1, 'LineWidth', 1)
+  
+  set(AX(2), 'XLim', [-1 559])
+  set(AX(1), 'XLim', [-1 559])
+  set(AX(2), 'YLim', [310 380])
+  set(AX(1), 'YLim', [-5 5])
+  set(AX(2), 'XTick' ,[0 276 557])
+  set(AX(2), 'XTicklabel' ,[1958 1981 2004])
+  set(AX(1), 'XTick' ,[0 276 557])
+  set(AX(1), 'XTicklabel' ,[1958 1981 2004])
+  set(AX(2),'YTick',[310 350 380])
+  set(AX(2),'YTicklabel',[310 350 380])
+  set(AX(1),'YTick',[-5 0 5])
+  set(AX(1),'YTicklabel',[-5 0 5])
+  %set(get(AX(2),'Ylabel'),'String','ppmv')
+  %set(get(AX(1),'Ylabel'),'String','ppmv')
+  set(get(AX(2),'Xlabel'),'String','year')
+  set(get(AX(1),'Xlabel'),'String','year')
+  
+  set(gcf,'pos',[5    3   18  10.7])
+  set(gcf,'paperunits',get(gcf,'units'))
+  set(gcf,'paperpos',get(gcf,'pos'))
 end
-gp_csfic = gp_set('type','CS+FIC', 'lik', lik, 'cf', {gpcf1, gpcf2}, 'jitterSigma2', 1e-9, 'X_u', Xu)
-
-% -----------------------------
-% --- Conduct the inference ---
-
-% --- MAP estimate using modified Newton algorithm ---
-
-% Now you can choose, if you want to optimize only parameters or
-% optimize simultaneously parameters and inducing inputs. Note that
-% the inducing inputs are not transformed through logarithm when
-% packed
-
-% optimize parameters and inducing inputs
-%gp_csfic = gp_set(gp_csfic, 'infer_params', 'covariance+likelihood+inducing');  
-% optimize only parameters (default)
-%gp_csfic = gp_set(gp_csfic, 'infer_params', 'covariance+likelihood');           
-
-% Set the options for the optimization
-opt=optimset('TolFun',1e-3,'TolX',1e-3);
-% Optimize with the scaled conjugate gradient method
-gp_csfic=gp_optim(gp_csfic,x,y,'opt',opt);
-
-% Make the prediction
-[Eft_csfic, Varft_csfic, lpyt_csfic, Eyt_csfic, Varyt_csfic] = gp_pred(gp_csfic, x, y, x, 'yt', y);
-
-% Plot the solution of FIC
-figure
-%subplot(4,1,1)
-hold on
-plot(x,y,'.', 'MarkerSize',7)
-plot(x,Eft_csfic,'k', 'LineWidth', 2)
-plot(x,Eft_csfic-2.*sqrt(Varyt_csfic),'g--', 'LineWidth', 1)
-plot(gp_csfic.X_u, -30, 'rx', 'MarkerSize', 5, 'LineWidth', 2)
-plot(x,Eft_csfic+2.*sqrt(Varyt_csfic),'g--', 'LineWidth', 1)
-axis tight
-caption2 = sprintf('CS+FIC:  l_1= %.2f, s^2_1 = %.2f, \n l_2= %.2f, s^2_2 = %.2f \n s^2_{noise} = %.2f', gp_csfic.cf{1}.lengthScale, gp_csfic.cf{1}.magnSigma2, gp_csfic.cf{2}.lengthScale, gp_csfic.cf{2}.magnSigma2, gp_csfic.lik.sigma2);
-title(caption2)
-legend('Data point', 'predicted mean', '2\sigma error', 'inducing input','Location','Northwest')
-
-[Eft, Varft, lpyt, Eyt, Varyt] = gp_pred(gp_csfic, x, y, x, 'yt', y);
-[Eft1, Varft1] = gp_pred(gp_csfic, x, y, x, 'predcf', 1);
-[Eft2, Varft2] = gp_pred(gp_csfic, x, y, x, 'predcf', 2);
-
-figure
-set(gcf,'units','centimeters');
-set(gcf,'DefaultAxesPosition',[0.08  0.13   0.84   0.85]);
-set(gcf,'DefaultAxesFontSize',16)   %6 8
-set(gcf,'DefaultTextFontSize',16)   %6 8
-hold on
-[AX, H1, H2] = plotyy(x, Eft2, x, Eft1+avgy);
-set(H2,'LineStyle','--')
-set(H2, 'LineWidth', 3)
-set(H1,'LineStyle','-')
-set(H1, 'LineWidth', 1)
-
-set(AX(2), 'XLim', [-1 559])
-set(AX(1), 'XLim', [-1 559])
-set(AX(2), 'YLim', [310 380])
-set(AX(1), 'YLim', [-5 5])
-set(AX(2), 'XTick' ,[0 276 557])
-set(AX(2), 'XTicklabel' ,[1958 1981 2004])
-set(AX(1), 'XTick' ,[0 276 557])
-set(AX(1), 'XTicklabel' ,[1958 1981 2004])
-set(AX(2),'YTick',[310 350 380])
-set(AX(2),'YTicklabel',[310 350 380])
-set(AX(1),'YTick',[-5 0 5])
-set(AX(1),'YTicklabel',[-5 0 5])
-%set(get(AX(2),'Ylabel'),'String','ppmv')
-%set(get(AX(1),'Ylabel'),'String','ppmv') 
-set(get(AX(2),'Xlabel'),'String','year')
-set(get(AX(1),'Xlabel'),'String','year') 
-
-set(gcf,'pos',[5    3   18  10.7])
-set(gcf,'paperunits',get(gcf,'units'))
-set(gcf,'paperpos',get(gcf,'pos'))
diff --git a/gp/demo_regression_sparse1.m b/gp/demo_regression_sparse1.m
index 1c293027..60524441 100644
--- a/gp/demo_regression_sparse1.m
+++ b/gp/demo_regression_sparse1.m
@@ -141,7 +141,7 @@
                     'lengthScale_prior', pl, 'magnSigma2_prior', pm);
 end
 
-gp = gp_set('lik', lik, 'cf', gpcf, 'jitterSigma2', 1e-8)
+gp = gp_set('lik', lik, 'cf', gpcf, 'jitterSigma2', 1e-8);
 
 % We have now constructed a GP model with gpcf_ppcs2 covariance
 % function. This is a compact support function which produces
@@ -200,7 +200,7 @@
 [u1,u2]=meshgrid(linspace(-1.8,1.8,6),linspace(-1.8,1.8,6));
 X_u = [u1(:) u2(:)];
 gp_fic = gp_set('type', 'FIC', 'lik', lik, 'cf', gpcf, ...
-                'X_u', X_u, 'jitterSigma2', 1e-4)
+                'X_u', X_u, 'jitterSigma2', 1e-4);
 
 % -----------------------------
 % --- Conduct the inference ---
@@ -300,7 +300,7 @@
                  'lengthScale_prior', pl, 'magnSigma2_prior', pm);
 
 gp_pic = gp_set('type', 'PIC', 'lik', lik, 'cf', gpcf, ...
-                'X_u', X_u, 'tr_index', trindex, 'jitterSigma2', 1e-4)
+                'X_u', X_u, 'tr_index', trindex, 'jitterSigma2', 1e-4);
 
 % -----------------------------
 % --- Conduct the inference ---
@@ -387,7 +387,7 @@
 % packed
 
 % optimize parameters and inducing inputs
-gp_var = gp_set(gp_var, 'infer_params', 'covariance+likelihood+inducing')
+gp_var = gp_set(gp_var, 'infer_params', 'covariance+likelihood+inducing');
 % optimize only parameters
 %gp_var = gp_set(gp_var, 'infer_params', 'covariance+likelihood');           
 
@@ -451,7 +451,7 @@
 [u1,u2]=meshgrid(linspace(-1.8,1.8,6),linspace(-1.8,1.8,6));
 X_u = [u1(:) u2(:)];
 gp_dtc = gp_set('type', 'DTC', 'lik', lik, 'cf', gpcf, ...
-                'X_u', X_u, 'jitterSigma2', 1e-4)
+                'X_u', X_u, 'jitterSigma2', 1e-4);
 
 % -----------------------------
 % --- Conduct the inference ---
diff --git a/gp/demo_spatial1.m b/gp/demo_spatial1.m
index 23e58e6c..638cbf37 100644
--- a/gp/demo_spatial1.m
+++ b/gp/demo_spatial1.m
@@ -236,7 +236,7 @@
 opt.hmc_opt.stepadj=0.03;
 opt.hmc_opt.steps=2;
 
-opt.display = 1;
+opt.display = 0;
 opt.hmc_opt.display = 0;
 opt.latent_opt.display=0;
 
@@ -265,8 +265,8 @@
 axis([0 35 0 60])
 title('relative risk')
 while length(rgp.edata)<500 %
-  [rgp,gp,opt]=gp_mc(gp, x, y, 'record', rgp, 'z', ye, opt, 'display', 0);
-  fprintf('        mean hmcrej: %.2f latrej: %.2f\n', mean(rgp.hmcrejects), mean(rgp.lrejects))
+  [rgp,gp,opt]=gp_mc(gp, x, y, 'record', rgp, 'z', ye, opt);
+  fprintf('%i / 500  mean hmcrej: %.2f latrej: %.2f\n', length(rgp.edata), mean(rgp.hmcrejects), mean(rgp.lrejects))
   set(h1(1),'XData',rgp.cf{1}.lengthScale,'YData',sqrt(rgp.cf{1}.magnSigma2));
   set(h1(2),'XData',rgp.cf{1}.lengthScale(end),'YData',sqrt(rgp.cf{1}.magnSigma2(end)));
   G=repmat(NaN,size(X1));
diff --git a/gp/demo_spatial2.m b/gp/demo_spatial2.m
index 000e296e..b637eae6 100644
--- a/gp/demo_spatial2.m
+++ b/gp/demo_spatial2.m
@@ -69,7 +69,7 @@
 % Create the covariance functions
 pl = prior_t();
 pm = prior_sqrtt('s2', 0.3);
-if ~exist('ldlchol')
+if 1%~exist('ldlchol')
   warning('GPstuff:SuiteSparseMissing',...
     ['SuiteSparse is not properly installed. \n' ...
     'Using gpcf_sexp (non-compact support) instead of gpcf_ppcs2 (compact support)']);
@@ -90,7 +90,7 @@
 gp = gp_set(gp, 'latent_method', 'Laplace');
 
 % Set the options for the scaled conjugate optimization
-opt=optimset('TolFun',1e-2,'TolX',1e-2,'Display','iter');
+opt=optimset('TolFun',1e-4,'TolX',1e-4,'Display','iter');
 % Optimize with the scaled conjugate gradient method
 gp=gp_optim(gp,x,y,'z',ye,'opt',opt);
 
@@ -98,8 +98,8 @@
 figure
 C = gp_trcov(gp,x);
 fprintf('Proportion of non-zeros is %.4f\n',nnz(C) / prod(size(C)))
-p = amd(C);
-spy(C(p,p))
+% p = amd(C);
+% spy(C(p,p))
 
 % Make prediction to the data points
 [Ef, Varf] = gp_pred(gp, x, y, x, 'z', ye);
@@ -149,8 +149,8 @@
 figure
 C = gp_trcov(gp,x);
 fprintf('Proportion of non-zeros is %.4f\n',nnz(C) / prod(size(C)))
-p = amd(C);
-spy(C(p,p))
+% p = amd(C);
+% spy(C(p,p))
 
 % make prediction to the data points
 [Ef, Varf] = gp_pred(gp, x, y, x, 'z', ye);
diff --git a/gp/demo_survival_aft.m b/gp/demo_survival_aft.m
index 07cdd792..e78a72c2 100644
--- a/gp/demo_survival_aft.m
+++ b/gp/demo_survival_aft.m
@@ -49,8 +49,10 @@
 % survival times
 y0=leukemiadata(:,1);
 y=y0;
+% Geometric mean of y0
+gmy0 = exp(1/size(y0,1)*sum(log(y0)));
 % scale survival times (so that constant term for the latent function can be small)
-y=y0/geomean(y0);
+y=y0/gmy0;
 
 ye=1-leukemiadata(:,2); % event indicator, ye = 0 for uncensored event
                         %                  ye = 1 for right censored event
@@ -74,8 +76,8 @@
 
 % Create the likelihood structure
 % log-logistic works best for this data
-lik = lik_loglogistic();
-%lik = lik_loggaussian();
+% lik = lik_loglogistic();
+lik = lik_loggaussian();
 %lik = lik_weibull();
 
 % Create the GP structure
@@ -107,10 +109,10 @@
 [pmu,~,xtc]=gp_cpred(gpia, x, y, x, [i1 i2], 'z', ye, 'target','mu');
 xtc{1}=denormdata(xtc{1},xmean(i2),xstd(i2));
 xtc{2}=denormdata(xtc{2},xmean(i2),xstd(i2));
-h1=semilogy(xtc{1},pmu{1}*geomean(y0),'k--');
+h1=semilogy(xtc{1},pmu{1}*gmy0,'k--');
 set(h1(2),'LineWidth',2)
 hold on
-h2=semilogy(xtc{2},pmu{2}*geomean(y0),'k-');
+h2=semilogy(xtc{2},pmu{2}*gmy0,'k-');
 set(h2(2),'LineWidth',2)
 hold off
 set(gca,'ytick',[10 30 100 300 1000 3000],'yticklabel',{'10' '30' '100' '300' '1000' '3000'},'ylim',[20 2000])
@@ -123,10 +125,10 @@
 [pmu,~,xtc]=gp_cpred(gpia, x, y, x, [i1 i2], 'z', ye, 'target','mu');
 xtc{1}=denormdata(xtc{1},xmean(i2),xstd(i2));
 xtc{2}=denormdata(xtc{2},xmean(i2),xstd(i2));
-h1=semilogy(xtc{1},pmu{1}*geomean(y0),'k--');
+h1=semilogy(xtc{1},pmu{1}*gmy0,'k--');
 set(h1(2),'LineWidth',2)
 hold on
-h2=semilogy(xtc{2},pmu{2}*geomean(y0),'k-');
+h2=semilogy(xtc{2},pmu{2}*gmy0,'k-');
 set(h2(2),'LineWidth',2)
 hold off
 set(gca,'ytick',[10 30 100 300 1000 3000],'yticklabel',{'10' '30' '100' '300' '1000' '3000'},'ylim',[20 2000])
@@ -140,10 +142,10 @@
 [pmu,~,xtc]=gp_cpred(gpia, x, y, x, [i1 i2], 'z', ye, 'target','mu');
 xtc{1}=denormdata(xtc{1},xmean(i2),xstd(i2));
 xtc{2}=denormdata(xtc{2},xmean(i2),xstd(i2));
-h1=semilogy(xtc{1},pmu{1}*geomean(y0),'k--');
+h1=semilogy(xtc{1},pmu{1}*gmy0,'k--');
 set(h1(2),'LineWidth',2)
 hold on
-h2=semilogy(xtc{2},pmu{2}*geomean(y0),'k-');
+h2=semilogy(xtc{2},pmu{2}*gmy0,'k-');
 set(h2(2),'LineWidth',2)
 hold off
 set(gca,'ytick',[10 30 100 300 1000 3000],'yticklabel',{'10' '30' '100' '300' '1000' '3000'},'ylim',[20 2000])
@@ -156,12 +158,12 @@
 i2=3;cla
 [pmu,~,xtc]=gp_cpred(gpia, x, y, x, i2, 'z', ye,'var',[NaN -1 NaN -.368],'target','mu');
 xtc=denormdata(xtc,xmean(i2),xstd(i2));
-h1=semilogy(xtc,pmu*geomean(y0),'k--');
+h1=semilogy(xtc,pmu*gmy0,'k--');
 set(h1(2),'LineWidth',2)
 [pmu,~,xtc]=gp_cpred(gpia, x, y, x, i2, 'z', ye,'var',[NaN -1 NaN 1.56],'target','mu');
 xtc=denormdata(xtc,xmean(i2),xstd(i2));
 hold on
-h2=semilogy(xtc,pmu*geomean(y0),'k-');
+h2=semilogy(xtc,pmu*gmy0,'k-');
 set(h2(2),'LineWidth',2)
 hold off
 set(gca,'ytick',[10 30 100 300 1000 3000],'yticklabel',{'10' '30' '100' '300' '1000' '3000'},'ylim',[20 2000])
diff --git a/gp/gp_avpredcomp.m b/gp/gp_avpredcomp.m
index 3caab923..0f4a7858 100644
--- a/gp/gp_avpredcomp.m
+++ b/gp/gp_avpredcomp.m
@@ -72,18 +72,17 @@
 
 ip=inputParser;
 ip.FunctionName = 'GP_AVPREDCOMP';
-ip.addRequired('gp',@(x) isstruct(x) || iscell(x));
-ip.addRequired('x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))))
-ip.addRequired('y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))))
-ip.addParamValue('z', [], @(x) isreal(x) && all(isfinite(x(:))))
-ip.addParamValue('predcf', [], @(x) isempty(x) || ...
-                 isvector(x) && isreal(x) && all(isfinite(x)&x>0))
-ip.addParamValue('tstind', [], @(x) isempty(x) || iscell(x) ||...
-                 (isvector(x) && isreal(x) && all(isfinite(x)&x>0)))
-ip.addParamValue('nsamp', 500, @(x) isreal(x) && isscalar(x))
-ip.addParamValue('deltadist',[], @(x) isvector(x));
-
-ip.parse(gp, x, y, varargin{:});
+ip=iparser(ip,'addRequired','gp',@(x) isstruct(x) || iscell(x));
+ip=iparser(ip,'addRequired','x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))));
+ip=iparser(ip,'addRequired','y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))));
+ip=iparser(ip,'addParamValue','z', [], @(x) isreal(x) && all(isfinite(x(:))));
+ip=iparser(ip,'addParamValue','predcf', [], @(x) isempty(x) || ...
+                 isvector(x) && isreal(x) && all(isfinite(x)&x>0));
+ip=iparser(ip,'addParamValue','tstind', [], @(x) isempty(x) || iscell(x) ||...
+                 (isvector(x) && isreal(x) && all(isfinite(x)&x>0)));
+ip=iparser(ip,'addParamValue','nsamp', 500, @(x) isreal(x) && isscalar(x));
+ip=iparser(ip,'addParamValue','deltadist',[], @(x) isvector(x));
+ip=iparser(ip,'parse',gp, x, y, varargin{:});
 options=struct();
 options.predcf=ip.Results.predcf;
 options.tstind=ip.Results.tstind;
diff --git a/gp/gp_cpred.m b/gp/gp_cpred.m
index b3ed8912..1d8ac2f3 100644
--- a/gp/gp_cpred.m
+++ b/gp/gp_cpred.m
@@ -37,27 +37,29 @@
 
 ip=inputParser;
 ip.FunctionName = 'GP_CPRED';
-ip.addRequired('gp',@(x) isstruct(x) || iscell(x));
-ip.addRequired('x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))))
-ip.addRequired('y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))))
-ip.addRequired('xt',  @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))))
-ip.addParamValue('yt', [], @(x) isreal(x) && all(isfinite(x(:))))
-ip.addParamValue('zt', [], @(x) isreal(x) && all(isfinite(x(:))))
-ip.addRequired('ind', @(x) ~isempty(x) && isvector(x))
-ip.addParamValue('var',  [], @(x) isreal(x))
-ip.addParamValue('z', [], @(x) isreal(x) && all(isfinite(x(:))))
-ip.addParamValue('predcf', [], @(x) isempty(x) || ...
-                 isvector(x) && isreal(x) && all(isfinite(x)&x>0))
-ip.addParamValue('tstind', [], @(x) isempty(x) || iscell(x) ||...
-                 (isvector(x) && isreal(x) && all(isfinite(x)&x>0)))
-ip.addParamValue('method', 'median', @(x)  ismember(x, {'median', 'mean' 'mode'}))
-ip.addParamValue('plot', 'off', @(x)  ismember(x, {'on', 'off'}))
-ip.addParamValue('tr', 0.25, @(x) isreal(x) && all(isfinite(x(:))))
-ip.addParamValue('target', 'mu', @(x) ismember(x,{'f','mu','cdf'}))
-ip.addParamValue('prct', [5 50 95], @(x) isreal(x) && all(isfinite(x(:))))
-ip.addParamValue('normdata', struct(), @(x) isempty(x) || isstruct(x))
-ip.addParamValue('xlabels', [], @(x) isempty(x) || iscell(x));
-ip.parse(gp, x, y, xt, ind, varargin{:});
+
+ip=iparser(ip,'addRequired','gp',@(x) isstruct(x) || iscell(x));
+ip=iparser(ip,'addRequired','x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))));
+ip=iparser(ip,'addRequired','y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))));
+ip=iparser(ip,'addRequired','xt',  @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))));
+ip=iparser(ip,'addRequired','ind', @(x) ~isempty(x) && isvector(x));
+ip=iparser(ip,'addParamValue','yt', [], @(x) isreal(x) && all(isfinite(x(:))));
+ip=iparser(ip,'addParamValue','var',  [], @(x) isreal(x));
+ip=iparser(ip,'addParamValue','z', [], @(x) isreal(x) && all(isfinite(x(:))));
+ip=iparser(ip,'addParamValue','zt', [], @(x) isreal(x) && all(isfinite(x(:))));
+ip=iparser(ip,'addParamValue','predcf', [], @(x) isempty(x) || ...
+                 isvector(x) && isreal(x) && all(isfinite(x)&x>0));
+ip=iparser(ip,'addParamValue','tstind', [], @(x) isempty(x) || iscell(x) ||...
+                 (isvector(x) && isreal(x) && all(isfinite(x)&x>0)));
+ip=iparser(ip,'addParamValue','method', 'median', @(x)  ismember(x, {'median', 'mean' 'mode'}));
+ip=iparser(ip,'addParamValue','normdata', struct(), @(x) isempty(x) || isstruct(x));
+ip=iparser(ip,'addParamValue','plot', 'off', @(x)  ismember(x, {'on', 'off'}));
+ip=iparser(ip,'addParamValue','tr', 0.25, @(x) isreal(x) && all(isfinite(x(:))));
+ip=iparser(ip,'addParamValue','target', 'mu', @(x) ismember(x,{'f','mu','cdf'}));
+ip=iparser(ip,'addParamValue','prct', [5 50 95], @(x) isreal(x) && all(isfinite(x(:))));
+ip=iparser(ip,'addParamValue','xlabels', [], @(x) isempty(x) || iscell(x));
+ip=iparser(ip,'parse',gp, x, y, xt, ind, varargin{:});
+
 zt=ip.Results.zt;
 options=struct();
 options.predcf=ip.Results.predcf;
@@ -89,13 +91,13 @@
 nd=ip.Results.normdata;
 ipnd=inputParser;
 ipnd.FunctionName = 'normdata';
-ipnd.addParamValue('xmean',zeros(1,size(x,2)),@(x) isempty(x) || (isreal(x) && all(isfinite(x(:)))));
-ipnd.addParamValue('xstd',ones(1,size(x,2)),@(x) isempty(x) || (isreal(x) && all(isfinite(x(:)))));
-ipnd.addParamValue('xlog',zeros(1,size(x,2)),@(x) isempty(x) || (isreal(x) && all(isfinite(x(:)))));
-ipnd.addParamValue('ymean',0,@(x) isempty(x) || (isreal(x) && all(isfinite(x(:)))));
-ipnd.addParamValue('ystd',1,@(x) isempty(x) || (isreal(x) && all(isfinite(x(:)))));
-ipnd.addParamValue('ylog',0,@(x) isempty(x) || (isreal(x) && all(isfinite(x(:)))));
-ipnd.parse(nd);
+ipnd=iparser(ipnd,'addParamValue','xmean',zeros(1,size(x,2)),@(x) isempty(x) || (isreal(x) && all(isfinite(x(:)))));
+ipnd=iparser(ipnd,'addParamValue','xstd',ones(1,size(x,2)),@(x) isempty(x) || (isreal(x) && all(isfinite(x(:)))));
+ipnd=iparser(ipnd,'addParamValue','xlog',zeros(1,size(x,2)),@(x) isempty(x) || (isreal(x) && all(isfinite(x(:)))));
+ipnd=iparser(ipnd,'addParamValue','ymean',0,@(x) isempty(x) || (isreal(x) && all(isfinite(x(:)))));
+ipnd=iparser(ipnd,'addParamValue','ystd',1,@(x) isempty(x) || (isreal(x) && all(isfinite(x(:)))));
+ipnd=iparser(ipnd,'addParamValue','ylog',0,@(x) isempty(x) || (isreal(x) && all(isfinite(x(:)))));
+ipnd=iparser(ipnd,'parse',nd);
 nd=ipnd.Results;
 
 [tmp, nin] = size(x);
@@ -166,7 +168,7 @@
   end
   if isequal(plot_results, 'on')
     if ind>0
-      if ind>=1&numel(nd.xmean)>=ind
+      if ind>=1 && numel(nd.xmean)>=ind
         xtnn=denormdata(xtnn,nd.xmean(ind),nd.xstd(ind));
       end
       deltadist=gp_finddeltadist(gp);
@@ -325,6 +327,7 @@
         case 'f'
           [Ef, Varf] = gp_pred(gp, x, y, xt);
         case 'mu'
+          options
           prctmu = gp_predprctmu(gp, x, y, xt, options, 'prct', 50);
           Ef = prctmu; Varf = [];
         case 'cdf'
diff --git a/gp/gp_dic.m b/gp/gp_dic.m
index 63f131b8..e62f3a30 100644
--- a/gp/gp_dic.m
+++ b/gp/gp_dic.m
@@ -86,13 +86,13 @@
 
   ip=inputParser;
   ip.FunctionName = 'GP_DIC';
-  ip.addRequired('gp',@(x) isstruct(x) || iscell(x));
-  ip.addRequired('x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))))
-  ip.addRequired('y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))))
-  ip.addParamValue('focus', 'all', @(x) ismember(x,{'hyper','all'}))
-  ip.addParamValue('form', 'mean', @(x) ismember(x,{'mean','all','sum','dic'}))
-  ip.addParamValue('z', [], @(x) isreal(x) && all(isfinite(x(:))))
-  ip.parse(gp, x, y, varargin{:});
+  ip=iparser(ip,'addRequired','gp',@(x) isstruct(x) || iscell(x));
+  ip=iparser(ip,'addRequired','x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))));
+  ip=iparser(ip,'addRequired','y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))));
+  ip=iparser(ip,'addParamValue','focus', 'all', @(x) ismember(x,{'hyper','all'}));
+  ip=iparser(ip,'addParamValue','form', 'mean', @(x) ismember(x,{'mean','all','sum','dic'}));
+  ip=iparser(ip,'addParamValue','z', [], @(x) isreal(x) && all(isfinite(x(:))));
+  ip=iparser(ip,'parse',gp, x, y, varargin{:});
   focus=ip.Results.focus;
   form=ip.Results.form;
   % pass these forward
diff --git a/gp/gp_e.m b/gp/gp_e.m
index 003a025e..69986799 100755
--- a/gp/gp_e.m
+++ b/gp/gp_e.m
@@ -66,12 +66,12 @@
 
 ip=inputParser;
 ip.FunctionName = 'GP_E';
-ip.addRequired('w', @(x) isempty(x) || isvector(x) && isreal(x));
-ip.addRequired('gp',@isstruct);
-ip.addRequired('x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))))
-ip.addRequired('y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))))
-ip.addParamValue('z', [], @(x) isreal(x) && all(isfinite(x(:))))
-ip.parse(w, gp, x, y, varargin{:});
+ip=iparser(ip,'addRequired','w', @(x) isempty(x) || isvector(x) && isreal(x));
+ip=iparser(ip,'addRequired','gp',@isstruct);
+ip=iparser(ip,'addRequired','x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))));
+ip=iparser(ip,'addRequired','y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))));
+ip=iparser(ip,'addParamValue','z', [], @(x) isreal(x) && all(isfinite(x(:))));
+ip=iparser(ip,'parse',w, gp, x, y, varargin{:});
 z=ip.Results.z;
 
 gp=gp_unpak(gp, w);
@@ -513,7 +513,7 @@
     %
     %  [3] Simo Sarkka (2006). Recursive Bayesian inference on stochastic
     %      differential equations. Doctoral dissertation, Helsinki 
-    %      University of Technology, Filand.
+    %      University of Technology, Finland.
     %
     
     % Ensure that this is a purely temporal problem
@@ -658,6 +658,7 @@
 end
 
 e = edata + eprior;
+end
 
 function [edata, eprior, e] = set_output_for_notpositivedefinite()
   %instead of stopping to chol error, return NaN
@@ -666,5 +667,4 @@
   e = NaN;
 end
 
-end
 
diff --git a/gp/gp_g.m b/gp/gp_g.m
index e1c683f3..8902ed26 100755
--- a/gp/gp_g.m
+++ b/gp/gp_g.m
@@ -46,12 +46,12 @@
 
 ip=inputParser;
 ip.FunctionName = 'GP_G';
-ip.addRequired('w', @(x) isvector(x) && isreal(x));
-ip.addRequired('gp',@isstruct);
-ip.addRequired('x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))))
-ip.addRequired('y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))))
-ip.addParamValue('z', [], @(x) isreal(x) && all(isfinite(x(:))))
-ip.parse(w, gp, x, y, varargin{:});
+ip=iparser(ip,'addRequired','w', @(x) isvector(x) && isreal(x));
+ip=iparser(ip,'addRequired','gp',@isstruct);
+ip=iparser(ip,'addRequired','x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))));
+ip=iparser(ip,'addRequired','y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))));
+ip=iparser(ip,'addParamValue','z', [], @(x) isreal(x) && all(isfinite(x(:))));
+ip=iparser(ip,'parse',w, gp, x, y, varargin{:});
 z=ip.Results.z;
 if ~all(isfinite(w(:)));
   % instead of stopping to error, return NaN
@@ -221,15 +221,15 @@
         if isfield(gp.lik, 'nondiagW') && ~ismember(gp.lik.type, {'LGP' 'LGPC'})
           % check in which components the covariance function is present
           % for likelihoods with non-diagonal Hessian
-          do = false(nout,1);
+          doo = false(nout,1);
           if multicf
             for z1=1:nout
               if any(gp.comp_cf{z1}==i)
-                do(z1) = true;
+                doo(z1) = true;
               end
             end
           else
-            do = true(nout,1);
+            doo = true(nout,1);
           end         
         end
         
@@ -314,17 +314,17 @@
               % Non-diagonalizable likelihoods
               Bdl=0; Cdl=0;
               if isfield(gp.lik,'xtime');
-                if do(1)
+                if doo(1)
                   Bdl = Bdl + b(1:ntime)'*(DKff*b(1:ntime));
                   Cdl = Cdl + sum(sum(invC(1:ntime,1:ntime).*DKff)); % help arguments
                 end
-                if do(2)
+                if doo(2)
                   Bdl = Bdl + b(ntime+1:end)'*(DKff*b(ntime+1:end));
                   Cdl = Cdl + sum(sum(invC(ntime+1:end,ntime+1:end).*DKff)); % help arguments
                 end
               else
                 for z1=1:nout
-                  if do(z1)
+                  if doo(z1)
                     Bdl = Bdl + b(1+nl(z1):nl(z1+1))'*(DKff*b(1+nl(z1):nl(z1+1)));
                     Cdl = Cdl + sum(sum(invC(:,:,z1).*DKff)); % help arguments
                   end
@@ -1527,7 +1527,7 @@
       P = P - K*S*K';
       
     end
-    
+    gdata=real(gdata);
     % Take all priors into account in the gradient
     gprior = [];
     if ~isempty(strfind(gp.infer_params, 'covariance'))
diff --git a/gp/gp_ia.m b/gp/gp_ia.m
index 84a0963d..779e1cd0 100644
--- a/gp/gp_ia.m
+++ b/gp/gp_ia.m
@@ -93,47 +93,49 @@
 
   ip=inputParser;
   ip.FunctionName = 'GP_IA';
-  ip.addRequired('gp',@(x) isstruct(x) || isempty(x));
-  ip.addRequired('x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))))
-  ip.addRequired('y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))))
-  ip.addOptional('xt',[], @(x) isnumeric(x) && isreal(x) && all(isfinite(x(:))))
-  ip.addParamValue('yt', [], @(x) isreal(x) && all(isfinite(x(:))))
-  ip.addParamValue('z', [], @(x) isreal(x) && all(isfinite(x(:))))
-  ip.addParamValue('int_method', 'CCD', @(x) ischar(x) && ...
-                   ismember(x,{'CCD','grid','is_normal','is_t','hmc'}))
-  ip.addParamValue('predcf', [], @(x) isempty(x) || ...
-                   isvector(x) && isreal(x) && all(isfinite(x)&x>0))
-  ip.addParamValue('tstind', [], @(x) isempty(x) || ...
-                   isvector(x) && isreal(x) && all(isfinite(x)&x>0))
-  ip.addParamValue('rotate', true, @(x) islogical(x) && isscalar(x))
-  ip.addParamValue('autoscale', 'on', @(x) (islogical(x) && isscalar(x))|| ...
-                   ismember(x,{'on' 'off' 'full'}))
-  ip.addParamValue('validate', 1, @(x) ismember(x,[1 2]))
-  ip.addParamValue('threshold', 2.5, @(x) isscalar(x) && isreal(x) && ...
-                   isfinite(x) && x>0)
-  ip.addParamValue('step_size', 1, @(x) isscalar(x) && isreal(x) && ...
-                   isfinite(x) && x>0)
-  ip.addParamValue('t_nu', 4, @(x) isscalar(x) && isreal(x) && ...
-                   isfinite(x) && x>=1)
-  ip.addParamValue('nsamples', 200, @(x) isscalar(x) && isreal(x) && ...
-                   isfinite(x) && x>=1)
-  ip.addParamValue('repeat', 10, @(x) isscalar(x) && isreal(x) && ...
-                   isfinite(x) && x>=1)
-  ip.addParamValue('f0', 1.1, @(x) isscalar(x) && isreal(x) && ...
-                   isfinite(x) && x>0)
-  ip.addParamValue('qmc', true, @(x) islogical(x) && isscalar(x))
-  ip.addParamValue('optimf', @fminscg, @(x) isa(x,'function_handle')||isempty(x))
-  ip.addParamValue('opt_optim', [], @isstruct)
-  ip.addParamValue('opt_hmc', [], @isstruct);
-  ip.addParamValue('persistence_reset', 0, @(x) ~isempty(x) && isreal(x));
-  ip.addParamValue('cache', 'off', @(x) ischar(x) && ismember(x,{'on','off'}));
-  ip.addParamValue('display', 'on', @(x) islogical(x) || isreal(x) || ...
-                   ismember(x,{'on' 'off' 'iter'}))
+
+  ip=iparser(ip,'addRequired','gp',@(x) isstruct(x) || isempty(x));
+  ip=iparser(ip,'addRequired','x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))));
+  ip=iparser(ip,'addRequired','y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))));
+  ip=iparser(ip,'addOptional','xt',[], @(x) isnumeric(x) && isreal(x) && all(isfinite(x(:))));
+  ip=iparser(ip,'addParamValue','yt', [], @(x) isreal(x) && all(isfinite(x(:))));
+  ip=iparser(ip,'addParamValue','z', [], @(x) isreal(x) && all(isfinite(x(:))));
+  ip=iparser(ip,'addParamValue','int_method', 'CCD', @(x) ischar(x) && ...
+                   ismember(x,{'CCD','grid','is_normal','is_t','hmc'}));
+  ip=iparser(ip,'addParamValue','predcf', [], @(x) isempty(x) || ...
+                   isvector(x) && isreal(x) && all(isfinite(x)&x>0));
+  ip=iparser(ip,'addParamValue','tstind', [], @(x) isempty(x) || ...
+                   isvector(x) && isreal(x) && all(isfinite(x)&x>0));
+  ip=iparser(ip,'addParamValue','rotate', true, @(x) islogical(x) && isscalar(x));
+  ip=iparser(ip,'addParamValue','autoscale', 'on', @(x) (islogical(x) && isscalar(x))|| ...
+                   ismember(x,{'on' 'off' 'full'}));
+  ip=iparser(ip,'addParamValue','validate', 1, @(x) ismember(x,[1 2]));
+  ip=iparser(ip,'addParamValue','threshold', 2.5, @(x) isscalar(x) && isreal(x) && ...
+                   isfinite(x) && x>0);
+  ip=iparser(ip,'addParamValue','step_size', 1, @(x) isscalar(x) && isreal(x) && ...
+                   isfinite(x) && x>0);
+  ip=iparser(ip,'addParamValue','t_nu', 4, @(x) isscalar(x) && isreal(x) && ...
+                   isfinite(x) && x>=1);
+  ip=iparser(ip,'addParamValue','nsamples', 40, @(x) isscalar(x) && isreal(x) && ...
+                   isfinite(x) && x>=1);
+  ip=iparser(ip,'addParamValue','repeat', 10, @(x) isscalar(x) && isreal(x) && ...
+                   isfinite(x) && x>=1);
+  ip=iparser(ip,'addParamValue','f0', 1.1, @(x) isscalar(x) && isreal(x) && ...
+                   isfinite(x) && x>0);
+  ip=iparser(ip,'addParamValue','qmc',  true, @(x) islogical(x) && isscalar(x));
+  ip=iparser(ip,'addParamValue','optimf', @fminscg, @(x) isa(x,'function_handle')||isempty(x));
+  ip=iparser(ip,'addParamValue','opt_optim', [], @isstruct);
+  ip=iparser(ip,'addParamValue','opt_hmc', [], @isstruct);
+  ip=iparser(ip,'addParamValue','persistence_reset', 0, @(x) ~isempty(x) && isreal(x));
+  ip=iparser(ip,'addParamValue','cache', 'off', @(x) ischar(x) && ismember(x,{'on','off'}));
+  ip=iparser(ip,'addParamValue','display', 'on', @(x) islogical(x) || isreal(x) || ...
+                   ismember(x,{'on' 'off' 'iter'}));
+
   if numel(varargin)==0 || isnumeric(varargin{1})
     % inputParser should handle this, but it doesn't
-    ip.parse(gp, x, y, varargin{:});
+    ip=iparser(ip,'parse',gp, x, y, varargin{:});
   else
-    ip.parse(gp, x, y, [], varargin{:});
+    ip=iparser(ip,'parse',gp, x, y, [], varargin{:});
   end
   if isempty(gp)
     gp=gp_set();
@@ -206,7 +208,20 @@
   optdefault.GradObj='on';
   optdefault.LargeScale='off';
   optdefault.Display='off';
-  opt_optim=optimset(optdefault,opt_optim);
+  if exist('OCTAVE_VERSION','builtin')
+    % Octave optimset works differently from matlab version
+    if ~isfield(opt_optim, 'GradObj') || isempty(opt_optim.GradObj)
+      opt_optim.GradObj=optdefault.GradObj;
+    end
+    if ~isfield(opt_optim, 'LargeScale') || isempty(opt_optim.LargeScale)
+      opt_optim.LargeScale=optdefault.LargeScale;
+    end
+    if ~isfield(opt_optim, 'Display') || isempty(opt_optim.Display)
+      opt_optim.Display=optdefault.Display;
+    end
+  else
+    opt_optim=optimset(optdefault,opt_optim);
+  end
 
   tall=tic;
   
@@ -258,7 +273,7 @@
       end
       tic
       for i2 = 1:nParam
-        H(:,i2) = hessianMultiplication(w, H(:,i2));
+        H(:,i2) = hessianMultiplication(w, H(:,i2), fh_g, gp, x, y, options);
       end
       et=toc;
       if ismember(opt.display,{'on','iter'}) && et > 1
@@ -269,7 +284,7 @@
           fprintf(' IA-%s: computing Hessian using finite difference\n',int_method);
         end
         tic
-        H = hessian(w);
+        H = hessian(w, fh_e, fh_g, gp, x, y, options);
         et=toc;
         if ismember(opt.display,{'on','iter'}) && et > 1
           fprintf('    Elapsed time %.2f seconds\n',et);
@@ -706,7 +721,7 @@
       end
       tic
       for i2 = 1:nParam
-        H(:,i2) = hessianMultiplication(w, H(:,i2));
+        H(:,i2) = hessianMultiplication(w, H(:,i2), fh_g, gp, x, y, options);
       end
       et=toc;
       if ismember(opt.display,{'on','iter'}) && et > 1
@@ -717,7 +732,7 @@
           fprintf(' IA-%s: computing Hessian using finite difference\n',int_method);
         end
         tic
-        H = hessian(w);
+        H = hessian(w, fh_e, fh_g, gp, x, y, options);
         et=toc;
         if ismember(opt.display,{'on','iter'}) && et > 1
           fprintf('    Elapsed time %.2f seconds\n',et);
@@ -1172,11 +1187,11 @@
     end
   end
 
-  function H = hessian(w0)
+  function H = hessian(w0, fh_e, fh_g, gp, x, y, options)
   % Compute Hessian using finite differences, which is can be slow
   % if number of parameters is high
     
-    m = length(w);
+    m = length(w0);
     e0 = fh_e(w0,gp,x,y,options);
     delta = 1e-4;
     H = -1*ones(m,m);
@@ -1238,10 +1253,9 @@
     end
     
   end
+end
 
-  function vv = hessianMultiplication(w0, v)
-  % Pearlmutter, B. A. (1994). Fast exact multiplication by the
-  % Hessian. Neural computation, 6(1), 147-160.
+  function vv = hessianMultiplication(w0, v, fh_g, gp, x, y, options)
     if size(w0) ~= size(v)
       v = v';
     end
@@ -1250,5 +1264,3 @@
     g1 = fh_g(w0+rr*v, gp, x, y, options);
     vv = (g1 - g2) / (2*rr);
   end
-
-end
diff --git a/gp/gp_install.m b/gp/gp_install.m
index 904facf0..63e22ec2 100644
--- a/gp/gp_install.m
+++ b/gp/gp_install.m
@@ -1,6 +1,6 @@
 function gp_install(suiteSparse)
 %  Matlab function to compile all the c-files to mex in the GPstuff/gp
-%  folder.  The function is called from GPstuff/matlab_install.m but
+%  folder.  The function is called from GPstuff/gpstuff_install.m but
 %  can be run separately also.
 %
 %  If you want to use GPstuff without compactly supported (CS)
@@ -35,9 +35,17 @@ function gp_install(suiteSparse)
 % Compile the 'dist_euclidean' mex-function
 if (~isempty (strfind (computer, '64')))
   % 64-bit MATLAB
-  mex -O -g -largeArrayDims -output private/dist_euclidean linuxCsource/dist_euclidean.c
+  if ~exist('OCTAVE_VERSION','builtin')
+    mex -O -g -largeArrayDims -output private/dist_euclidean linuxCsource/dist_euclidean.c
+  else
+    mex --output private/dist_euclidean.mex linuxCsource/dist_euclidean.c
+  end
 else
-  mex -O -output private/dist_euclidean linuxCsource/dist_euclidean.c
+  if ~exist('OCTAVE_VERSION','builtin')
+    mex -O -output private/dist_euclidean linuxCsource/dist_euclidean.c
+  else
+    mex --output private/dist_euclidean.mex linuxCsource/dist_euclidean.c
+  end
 end
 
 if nargin<1 || isempty(suiteSparse)
@@ -48,9 +56,21 @@ function gp_install(suiteSparse)
     % Compile the 'trcov' mex-function
     if (~isempty (strfind (computer, '64')))
       % 64-bit MATLAB
-      mex -O -g -largeArrayDims -output private/trcov linuxCsource/trcov.c
+      if ~exist('OCTAVE_VERSION', 'builtin')
+        mex -O -g -largeArrayDims -output private/trcov linuxCsource/trcov.c
+        mex -O -g -largeArrayDims -output private/dist_euclidean linuxCsource/dist_euclidean.c
+      else
+        mex --output private/trcov.mex linuxCsource/trcov.c
+        mex --output private/dist_euclidean.mex linuxCsource/dist_euclidean.c
+      end
     else
-      mex -O -output private/trcov linuxCsource/trcov.c
+      if ~exist('OCTAVE_VERSION', 'builtin')
+        mex -O -output private/trcov linuxCsource/trcov.c
+        mex -O -output private/dist_euclidean linuxCsource/dist_euclidean.c
+      else
+        mex --output private/trcov.mex linuxCsource/trcov.c
+        mex --output private/dist_euclidean.mex linuxCsource/dist_euclidean.c
+      end
     end
     
     fprintf ('\n GP package succesfully compiled ') ;
@@ -145,7 +165,6 @@ function gp_install(suiteSparse)
 
     % This is exceedingly ugly.  The MATLAB mex command needs to be told where to
     % fine the LAPACK and BLAS libraries, which is a real portability nightmare.
-
     if (pc)
         if (v < 6.5)
             % MATLAB 6.1 and earlier: use the version supplied here
@@ -264,7 +283,7 @@ function gp_install(suiteSparse)
         'Lib/subdomains', ...
         'Lib/timing', ...
         'Lib/util' } ;
-
+    
     for i = 1:length (metis_src)
         metis_src {i} = [metis_path '/' metis_src{i}] ;
     end
@@ -339,12 +358,18 @@ function gp_install(suiteSparse)
     if (have_metis)
         source = [metis_src source] ;
     end
-
-    source = strrep(source, '../../', suiteSparse);
-    source = strrep(source, '../', cholmod_path);
+    if exist('OCTAVE_VERSION', 'builtin')
+      for i1=1:length(source)
+        source{i1} = strrep(source{i1}, '../../', suiteSparse);
+        source{i1} = strrep(source{i1}, '../', cholmod_path);
+      end
+    else
+      source = strrep(source, '../../', suiteSparse);
+      source = strrep(source, '../', cholmod_path);
+    end
 
     kk = 0 ;
-
+    
     for f = source
         ff = strrep (f {1}, '/', filesep) ;
         slash = strfind (ff, filesep) ;
@@ -355,15 +380,23 @@ function gp_install(suiteSparse)
         end
         o = ff (slash:end) ;
         obj = [obj  ' ' o obj_extension] ;					    %#ok
-        s = sprintf ('mex %s -DDLONG -O %s -c %s.c', d, include, ff) ;
+        if ~exist('OCTAVE_VERSION','builtin')
+          s = sprintf ('mex %s -DDLONG -O %s -c %s.c', d, include, ff) ;
+        else
+          s = sprintf ('mex %s -DDLONG %s -c %s.c', d, include, ff) ;
+        end
         kk = do_cmd (s, kk, details) ;
     end
-
+    
     if pc
         % compile mexFunctions
         mex_src =  'winCsource\spinv';
         outpath = 'private\spinv';
-        s = sprintf ('mex %s -DDLONG -O %s -output %s %s.c', d, include, outpath, mex_src) ;
+        if ~exist('OCTAVE_VERSION','builtin')
+          s = sprintf ('mex %s -DDLONG -O %s -output %s %s.c', d, include, outpath, mex_src) ;
+        else
+          s = sprintf ('mex %s -DDLONG %s -output %s %s.c', d, include, outpath, mex_src) ;
+        end
         s = [s obj];
         s = [s ' '];
         s = [s lapack];
@@ -372,7 +405,11 @@ function gp_install(suiteSparse)
         %mex_src = 'linuxCsource/ldlrowupdate';
         mex_src = 'winCsource\ldlrowupdate';
         outpath = 'private\ldlrowupdate';
-        s = sprintf ('mex %s -DDLONG -O %s -output %s %s.c', d, include, outpath, mex_src) ;
+        if ~exist('OCTAVE_VERSION','builtin')
+          s = sprintf ('mex %s -DDLONG -O %s -output %s %s.c', d, include, outpath, mex_src) ;
+        else
+          s = sprintf ('mex %s -DDLONG %s -output %s %s.c', d, include, outpath, mex_src) ;
+        end
         s = [s obj];
         s = [s ' '];
         s = [s lapack];
@@ -381,7 +418,11 @@ function gp_install(suiteSparse)
         % compile mexFunctions
         mex_src =  'linuxCsource/spinv';
         outpath = 'private/spinv';
-        s = sprintf ('mex %s -DDLONG -O %s -output %s %s.c', d, include, outpath, mex_src) ;
+        if ~exist('OCTAVE_VERSION','builtin')
+          s = sprintf ('mex %s -DDLONG -O %s -output %s %s.c', d, include, outpath, mex_src) ;
+        else
+          s = sprintf ('mex %s -DDLONG %s -output %s %s.c', d, include, outpath, mex_src) ;
+        end
         s = [s obj];
         s = [s ' '];
         s = [s lapack];
@@ -390,15 +431,19 @@ function gp_install(suiteSparse)
         %mex_src = 'linuxCsource/ldlrowupdate';
         mex_src = 'linuxCsource/ldlrowupdate';
         outpath = 'private/ldlrowupdate';
-        s = sprintf ('mex %s -DDLONG -O %s -output %s %s.c', d, include, outpath, mex_src) ;
+        if ~exist('OCTAVE_VERSION','builtin')
+          s = sprintf ('mex %s -DDLONG -O %s -output %s %s.c', d, include, outpath, mex_src) ;
+        else
+          s = sprintf ('mex %s -DDLONG %s -output %s %s.c', d, include, outpath, mex_src) ;
+        end
         s = [s obj];
         s = [s ' '];
         s = [s lapack];
         kk = do_cmd (s, kk, details) ;
     end
-
     % clean up
     s = ['delete ' obj] ;
+    
     do_cmd (s, kk, details) ;
     fprintf ('\nGP package succesfully compiled \n') ;
 end
@@ -415,7 +460,18 @@ function gp_install(suiteSparse)
     kk = kk + 1 ;
     fprintf ('.') ;
 end
-eval (s) ;
+if ~exist('OCTAVE_VERSION','builtin')
+  eval (s) ;
+else
+  tmp=regexp(s,'\S+','match');
+  if strcmp(tmp{1},'delete')
+    for i=1:length(tmp)-1
+      eval([tmp{1} ' ' tmp{i+1}]);
+    end
+  else
+    eval (s) ;
+  end
+end
 
 %-------------------------------------------------------------------------------
 function v = getversion
diff --git a/gp/gp_jpred.m b/gp/gp_jpred.m
index 4026dd38..a1f05a2d 100644
--- a/gp/gp_jpred.m
+++ b/gp/gp_jpred.m
@@ -109,22 +109,20 @@
 
 ip=inputParser;
 ip.FunctionName = 'GP_JPRED';
-ip.addRequired('gp',@isstruct);
-ip.addRequired('x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))))
-ip.addRequired('y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))))
-ip.addOptional('xt', [], @(x) isempty(x) || (isreal(x) && all(isfinite(x(:)))))
-ip.addParamValue('yt', [], @(x) isreal(x) && all(isfinite(x(:))))
-ip.addParamValue('predcf', [], @(x) isempty(x) || ...
-                 isvector(x) && isreal(x) && all(isfinite(x)&x>0))
-ip.addParamValue('tstind', [], @(x) isempty(x) || iscell(x) ||...
-                 (isvector(x) && isreal(x) && all(isfinite(x)&x>0)))
-ip.addParamValue('fcorr', 'off', @(x) ismember(x, {'off', 'fact', 'cm2', 'on'}))
-
+ip=iparser(ip,'addRequired','gp',@isstruct);
+ip=iparser(ip,'addRequired','x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))));
+ip=iparser(ip,'addRequired','y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))));
+ip=iparser(ip,'addOptional','xt', [], @(x) isempty(x) || (isreal(x) && all(isfinite(x(:)))));
+ip=iparser(ip,'addParamValue','yt', [], @(x) isreal(x) && all(isfinite(x(:))));
+ip=iparser(ip,'addParamValue','predcf', [], @(x) isempty(x) || ...
+                 isvector(x) && isreal(x) && all(isfinite(x)&x>0));
+ip=iparser(ip,'addParamValue','tstind', [], @(x) isempty(x) || iscell(x) ||...
+                 (isvector(x) && isreal(x) && all(isfinite(x)&x>0)));
+ip=iparser(ip,'addParamValue','fcorr', 'off', @(x) ismember(x, {'off', 'fact', 'cm2', 'on'}));
 % Add z and zt for compatibility although not used
-ip.addParamValue('z', [], @(x) isreal(x) && all(isfinite(x(:))))
-ip.addParamValue('zt', [], @(x) isreal(x) && all(isfinite(x(:))))
-
-ip.parse(gp, x, y, varargin{:});
+ip=iparser(ip,'addParamValue','z', [], @(x) isreal(x) && all(isfinite(x(:))))
+ip=iparser(ip,'addParamValue','zt', [], @(x) isreal(x) && all(isfinite(x(:))))
+ip=iparser(ip,'parse',gp, x, y, varargin{:});
 xt=ip.Results.xt;
 yt=ip.Results.yt;
 predcf=ip.Results.predcf;
diff --git a/gp/gp_kfcv.m b/gp/gp_kfcv.m
index f1346534..820181f0 100644
--- a/gp/gp_kfcv.m
+++ b/gp/gp_kfcv.m
@@ -204,27 +204,27 @@
 
   ip=inputParser;
   ip.FunctionName = 'GP_KFCV';
-  ip.addRequired('gp',@(x) isstruct(x) || iscell(x));
-  ip.addRequired('x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))))
-  ip.addRequired('y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))))
-  ip.addParamValue('z', [], @(x) isreal(x) && all(isfinite(x(:))))
-  ip.addParamValue('yt', [], @(x) isreal(x) && all(isfinite(x(:))))
-  ip.addParamValue('inf_method', 'MAP', @(x) ...
-                   ismember(x,{'MAP' 'LOO' 'KFCV' 'WAIC' 'WAICV' 'WAICG' 'MCMC' 'IA' 'fixed'}))
-  ip.addParamValue('optimf', @fminscg, @(x) isa(x,'function_handle'))
-  ip.addParamValue('opt', struct(), @isstruct)
-  ip.addParamValue('pred','f+lp+y',@ischar)
-  ip.addParamValue('k', 10, @(x) isreal(x) && isscalar(x) && isfinite(x) && x>0)
-  ip.addParamValue('rstream', 1, @(x) isreal(x) && isscalar(x) && isfinite(x) && x>0)
-  ip.addParamValue('trindex', [], @(x) isempty(x) || iscell(x))
-  ip.addParamValue('tstindex', [], @(x) isempty(x) || iscell(x))
-  ip.addParamValue('display', 'on', @(x) islogical(x) || ...
-                   ismember(x,{'on' 'off' 'iter' 'fold'}))
-  ip.addParamValue('save_results', false, @(x) islogical(x))
-  ip.addParamValue('folder', [], @(x) ischar(x) )
-  ip.addParamValue('fcorr', 'off', @(x) ismember(x, {'off', 'fact', 'cm2', 'on', 'lr'}));
-  ip.addParamValue('joint', 'off', @(x) ismember(x, {'off', 'on'}));
-  ip.parse(gp, x, y, varargin{:});
+  ip=iparser(ip,'addRequired','gp',@(x) isstruct(x) || iscell(x));
+  ip=iparser(ip,'addRequired','x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))));
+  ip=iparser(ip,'addRequired','y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))));
+  ip=iparser(ip,'addParamValue','z', [], @(x) isreal(x) && all(isfinite(x(:))));
+  ip=iparser(ip,'addParamValue','yt', [], @(x) isreal(x) && all(isfinite(x(:))));
+  ip=iparser(ip,'addParamValue','inf_method', 'MAP', @(x) ...
+                   ismember(x,{'MAP' 'LOO' 'KFCV' 'WAIC' 'WAICV' 'WAICG' 'MCMC' 'IA' 'fixed'}));
+  ip=iparser(ip,'addParamValue','optimf', @fminscg, @(x) isa(x,'function_handle'));
+  ip=iparser(ip,'addParamValue','opt', struct(), @isstruct);
+  ip=iparser(ip,'addParamValue','pred','f+lp+y',@ischar);
+  ip=iparser(ip,'addParamValue','k', 10, @(x) isreal(x) && isscalar(x) && isfinite(x) && x>0);
+  ip=iparser(ip,'addParamValue','rstream', 1, @(x) isreal(x) && isscalar(x) && isfinite(x) && x>0);
+  ip=iparser(ip,'addParamValue','trindex', [], @(x) isempty(x) || iscell(x));
+  ip=iparser(ip,'addParamValue','tstindex', [], @(x) isempty(x) || iscell(x));
+  ip=iparser(ip,'addParamValue','display', 'on', @(x) islogical(x) || ...
+                   ismember(x,{'on' 'off' 'iter' 'fold'}));
+  ip=iparser(ip,'addParamValue','save_results', false, @(x) islogical(x));
+  ip=iparser(ip,'addParamValue','folder', [], @(x) ischar(x) );
+  ip=iparser(ip,'addParamValue','fcorr', 'off', @(x) ismember(x, {'off', 'fact', 'cm2', 'on', 'lr'}));
+  ip=iparser(ip,'addParamValue','joint', 'off', @(x) ismember(x, {'off', 'on'}));
+  ip=iparser(ip,'parse',gp, x, y, varargin{:});
   z=ip.Results.z;
   yt=ip.Results.yt;
   inf_method=ip.Results.inf_method;
diff --git a/gp/gp_kfcv_cdf.m b/gp/gp_kfcv_cdf.m
index 4fb2900b..c636cb42 100644
--- a/gp/gp_kfcv_cdf.m
+++ b/gp/gp_kfcv_cdf.m
@@ -63,22 +63,22 @@
 
 ip=inputParser;
 ip.FunctionName = 'GP_KFCV';
-ip.addRequired('gp',@(x) isstruct(x) || iscell(x));
-ip.addRequired('x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))))
-ip.addRequired('y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))))
-ip.addParamValue('z', [], @(x) isreal(x) && all(isfinite(x(:))))
-ip.addParamValue('yt', [], @(x) isreal(x) && all(isfinite(x(:))))
-ip.addParamValue('inf_method', 'MAP', @(x) ...
-  ismember(x,{'MAP' 'LOO' 'KFCV' 'WAIC' 'WAICV' 'WAICG' 'MCMC' 'IA' 'fixed'}))
-ip.addParamValue('optimf', @fminscg, @(x) isa(x,'function_handle'))
-ip.addParamValue('opt', struct(), @isstruct)
-ip.addParamValue('k', 10, @(x) isreal(x) && isscalar(x) && isfinite(x) && x>0)
-ip.addParamValue('rstream', 1, @(x) isreal(x) && isscalar(x) && isfinite(x) && x>0)
-ip.addParamValue('trindex', [], @(x) isempty(x) || iscell(x))
-ip.addParamValue('tstindex', [], @(x) isempty(x) || iscell(x))
-ip.addParamValue('display', 'on', @(x) islogical(x) || ...
-  ismember(x,{'iter' 'fold'}))
-ip.parse(gp, x, y, varargin{:});
+ip=iparser(ip,'addRequired','gp',@(x) isstruct(x) || iscell(x));
+ip=iparser(ip,'addRequired','x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))));
+ip=iparser(ip,'addRequired','y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))));
+ip=iparser(ip,'addParamValue','z', [], @(x) isreal(x) && all(isfinite(x(:))));
+ip=iparser(ip,'addParamValue','yt', [], @(x) isreal(x) && all(isfinite(x(:))));
+ip=iparser(ip,'addParamValue','inf_method', 'MAP', @(x) ...
+  ismember(x,{'MAP' 'LOO' 'KFCV' 'WAIC' 'WAICV' 'WAICG' 'MCMC' 'IA' 'fixed'}));
+ip=iparser(ip,'addParamValue','optimf', @fminscg, @(x) isa(x,'function_handle'));
+ip=iparser(ip,'addParamValue','opt', struct(), @isstruct);
+ip=iparser(ip,'addParamValue','k', 10, @(x) isreal(x) && isscalar(x) && isfinite(x) && x>0);
+ip=iparser(ip,'addParamValue','rstream', 1, @(x) isreal(x) && isscalar(x) && isfinite(x) && x>0);
+ip=iparser(ip,'addParamValue','trindex', [], @(x) isempty(x) || iscell(x));
+ip=iparser(ip,'addParamValue','tstindex', [], @(x) isempty(x) || iscell(x));
+ip=iparser(ip,'addParamValue','display', 'on', @(x) islogical(x) || ...
+  ismember(x,{'on' 'iter' 'fold'}));
+ip=iparser(ip,'parse',gp, x, y, varargin{:});
 z=ip.Results.z;
 yt=ip.Results.yt;
 inf_method=ip.Results.inf_method;
@@ -91,6 +91,7 @@
 display = ip.Results.display;
 if isequal(display,'fold');display='iter';end
 
+
 [n,nin] = size(x);
 
 gp_orig = gp;
diff --git a/gp/gp_kfcve.m b/gp/gp_kfcve.m
index da491395..e7b39eee 100644
--- a/gp/gp_kfcve.m
+++ b/gp/gp_kfcve.m
@@ -38,17 +38,17 @@
 
 ip=inputParser;
 ip.FunctionName = 'GPEP_KFCVE';
-ip.addRequired('w', @(x) isempty(x) || ...
+ip=iparser(ip,'addRequired','w', @(x) isempty(x) || ...
                isvector(x) && isreal(x) && all(isfinite(x)));
-ip.addRequired('gp', @(x) isstruct(x) || iscell(x));
-ip.addRequired('x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))))
-ip.addRequired('y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))))
-ip.addParamValue('z', [], @(x) isreal(x) && all(isfinite(x(:))))
-ip.addParamValue('k', 10, @(x) isreal(x) && isscalar(x) && isfinite(x) && x>0)
-ip.addParamValue('rstream', 1, @(x) isreal(x) && isscalar(x) && isfinite(x) && x>0)
-ip.addParamValue('trindex', [], @(x) ~isempty(x) || iscell(x))
-ip.addParamValue('tstindex', [], @(x) ~isempty(x) || iscell(x))
-ip.parse(w, gp, x, y, varargin{:});
+ip=iparser(ip,'addRequired','gp', @(x) isstruct(x) || iscell(x));
+ip=iparser(ip,'addRequired','x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))));
+ip=iparser(ip,'addRequired','y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))));
+ip=iparser(ip,'addParamValue','z', [], @(x) isreal(x) && all(isfinite(x(:))));
+ip=iparser(ip,'addParamValue','k', 10, @(x) isreal(x) && isscalar(x) && isfinite(x) && x>0);
+ip=iparser(ip,'addParamValue','rstream', 1, @(x) isreal(x) && isscalar(x) && isfinite(x) && x>0);
+ip=iparser(ip,'addParamValue','trindex', [], @(x) ~isempty(x) || iscell(x));
+ip=iparser(ip,'addParamValue','tstindex', [], @(x) ~isempty(x) || iscell(x));
+ip=iparser(ip,'parse',w, gp, x, y, varargin{:});
 k=ip.Results.k;
 rstream=ip.Results.rstream;
 trindex=ip.Results.trindex;
diff --git a/gp/gp_looe.m b/gp/gp_looe.m
index 4b827394..31d40702 100644
--- a/gp/gp_looe.m
+++ b/gp/gp_looe.m
@@ -28,11 +28,11 @@
 % Nothing to parse, but check the arguments anyway
 ip=inputParser;
 ip.FunctionName = 'GP_LOOE';
-ip.addRequired('w', @(x) isvector(x) && isreal(x) && all(isfinite(x)));
-ip.addRequired('gp',@isstruct);
-ip.addRequired('x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))))
-ip.addRequired('y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))))
-ip.parse(w, gp, x, y);
+ip=iparser(ip,'addRequired','w', @(x) isvector(x) && isreal(x) && all(isfinite(x)));
+ip=iparser(ip,'addRequired','gp',@isstruct);
+ip=iparser(ip,'addRequired','x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))));
+ip=iparser(ip,'addRequired','y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))));
+ip=iparser(ip,'parse',w, gp, x, y);
 
 if isfield(gp,'mean') & ~isempty(gp.mean.meanFuncs)
   error('GP_LOOE: Mean functions not yet supported');
diff --git a/gp/gp_looeg.m b/gp/gp_looeg.m
index e45f1977..62908df4 100644
--- a/gp/gp_looeg.m
+++ b/gp/gp_looeg.m
@@ -37,11 +37,11 @@
 % Nothing to parse, but check the arguments anyway
 ip=inputParser;
 ip.FunctionName = 'GP_LOOEG';
-ip.addRequired('w', @(x) isvector(x) && isreal(x) && all(isfinite(x)));
-ip.addRequired('gp',@isstruct);
-ip.addRequired('x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))))
-ip.addRequired('y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))))
-ip.parse(w, gp, x, y);
+ip=iparser(ip,'addRequired','w', @(x) isvector(x) && isreal(x) && all(isfinite(x)));
+ip=iparser(ip,'addRequired','gp',@isstruct);
+ip=iparser(ip,'addRequired','x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))));
+ip=iparser(ip,'addRequired','y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))));
+ip=iparser(ip,'parse',w, gp, x, y);
 
 if isfield(gp,'mean') & ~isempty(gp.mean.meanFuncs)
   error('GP_LOOEG: Mean functions not yet supported');
diff --git a/gp/gp_loog.m b/gp/gp_loog.m
index 28d45c4b..b9bf93f7 100644
--- a/gp/gp_loog.m
+++ b/gp/gp_loog.m
@@ -27,11 +27,11 @@
 % Nothing to parse, but check the arguments anyway
 ip=inputParser;
 ip.FunctionName = 'GP_LOOG';
-ip.addRequired('w', @(x) isvector(x) && isreal(x) && all(isfinite(x)));
-ip.addRequired('gp',@isstruct);
-ip.addRequired('x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))))
-ip.addRequired('y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))))
-ip.parse(w, gp, x, y);
+ip=iparser(ip,'addRequired','w', @(x) isvector(x) && isreal(x) && all(isfinite(x)));
+ip=iparser(ip,'addRequired','gp',@isstruct);
+ip=iparser(ip,'addRequired','x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))));
+ip=iparser(ip,'addRequired','y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))));
+ip=iparser(ip,'parse',w, gp, x, y);
 
 if isfield(gp,'mean') & ~isempty(gp.mean.meanFuncs)
   error('GP_LOOE: Mean functions not yet supported');
diff --git a/gp/gp_loopred.m b/gp/gp_loopred.m
index 308774c7..abc36dc9 100644
--- a/gp/gp_loopred.m
+++ b/gp/gp_loopred.m
@@ -94,10 +94,10 @@
 % Nothing to parse, but check the arguments anyway
 ip=inputParser;
 ip.FunctionName = 'GP_LOOPRED';
-ip.addRequired('gp',@isstruct);
-ip.addRequired('x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))))
-ip.addRequired('y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))))
-ip.parse(gp, x, y);
+ip=iparser(ip,'addRequired','gp',@isstruct);
+ip=iparser(ip,'addRequired','x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))));
+ip=iparser(ip,'addRequired','y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))));
+ip=iparser(ip,'parse',gp, x, y);
 
 if isfield(gp,'meanf') && ~isempty(gp.meanf)
   error('GP_LOOPRED: Mean functions not yet supported');
diff --git a/gp/gp_mc.m b/gp/gp_mc.m
index a2bde520..b7a01d98 100755
--- a/gp/gp_mc.m
+++ b/gp/gp_mc.m
@@ -79,23 +79,23 @@
 
   ip=inputParser;
   ip.FunctionName = 'GP_MC';
-  ip.addRequired('gp',@isstruct);
-  ip.addRequired('x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))))
-  ip.addRequired('y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))))
-  ip.addParamValue('z', [], @(x) isreal(x) && all(isfinite(x(:))))
-  ip.addParamValue('nsamples', 1, @(x) isreal(x) && all(isfinite(x(:))))
-  ip.addParamValue('repeat', 1, @(x) isreal(x) && all(isfinite(x(:))))
-  ip.addParamValue('display', 1, @(x) isreal(x) && all(isfinite(x(:))))
-  ip.addParamValue('record',[], @(x) isstruct(x) || isempty(x));
-  ip.addParamValue('hmc_opt', [], @(x) isstruct(x) || isempty(x));
-  ip.addParamValue('sls_opt', [], @(x) isstruct(x) || isempty(x));
-  ip.addParamValue('ssls_opt', [], @(x) isstruct(x) || isempty(x));
-  ip.addParamValue('latent_opt', [], @(x) isstruct(x) || isempty(x));
-  ip.addParamValue('lik_hmc_opt', [], @(x) isstruct(x) || isempty(x));
-  ip.addParamValue('lik_sls_opt', [], @(x) isstruct(x) || isempty(x));
-  ip.addParamValue('lik_gibbs_opt', [], @(x) isstruct(x) || isempty(x));
-  ip.addParamValue('persistence_reset', 0, @(x) ~isempty(x) && isreal(x));
-  ip.parse(gp, x, y, varargin{:});
+  ip=iparser(ip,'addRequired','gp',@isstruct);
+  ip=iparser(ip,'addRequired','x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))));
+  ip=iparser(ip,'addRequired','y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))));
+  ip=iparser(ip,'addParamValue','z', [], @(x) isreal(x) && all(isfinite(x(:))));
+  ip=iparser(ip,'addParamValue','nsamples', 1, @(x) isreal(x) && all(isfinite(x(:))));
+  ip=iparser(ip,'addParamValue','repeat', 1, @(x) isreal(x) && all(isfinite(x(:))));
+  ip=iparser(ip,'addParamValue','display', 1, @(x) isreal(x) && all(isfinite(x(:))));
+  ip=iparser(ip,'addParamValue','record',[], @(x) isstruct(x) || isempty(x));
+  ip=iparser(ip,'addParamValue','hmc_opt', [], @(x) isstruct(x) || isempty(x));
+  ip=iparser(ip,'addParamValue','sls_opt', [], @(x) isstruct(x) || isempty(x));
+  ip=iparser(ip,'addParamValue','ssls_opt', [], @(x) isstruct(x) || isempty(x));
+  ip=iparser(ip,'addParamValue','latent_opt', [], @(x) isstruct(x) || isempty(x));
+  ip=iparser(ip,'addParamValue','lik_hmc_opt', [], @(x) isstruct(x) || isempty(x));
+  ip=iparser(ip,'addParamValue','lik_sls_opt', [], @(x) isstruct(x) || isempty(x));
+  ip=iparser(ip,'addParamValue','lik_gibbs_opt', [], @(x) isstruct(x) || isempty(x));
+  ip=iparser(ip,'addParamValue','persistence_reset', 0, @(x) ~isempty(x) && isreal(x));
+  ip=iparser(ip,'parse',gp, x, y, varargin{:});
   z=ip.Results.z;
   opt.nsamples=ip.Results.nsamples;
   opt.repeat=ip.Results.repeat;
@@ -198,7 +198,7 @@
   % Initialize record
   if isempty(record)
     % No old record
-    record=recappend();
+    [record,ri,lrej,indrej,hmcrej,lik_hmcrej]=recappend([],gp, x, y, z, opt);
   else
     ri=size(record.etr,1);
   end
@@ -471,7 +471,7 @@
     
     % --- Set record -------    
     ri=ri+1;
-    record=recappend(record);
+    record=recappend(record, gp, x, y, z, opt, ri, hmcrej, lik_hmcrej, lrej);
     
     % Display some statistics  THIS COULD BE DONE NICER ALSO...
     if opt.display && rem(ri,opt.display)==0
@@ -495,9 +495,10 @@
       fprintf('\n');
     end
   end
-  
+end
+
 %------------------------
-function record = recappend(record)
+function [record,ri,lrej,indrej,hmcrej,lik_hmcrej] = recappend(record, gp, x, y, z, opt, ri, hmcrej, lik_hmcrej, lrej)
 % RECAPPEND - Record append
 %          Description
 %          RECORD = RECAPPEND(RECORD, RI, GP, P, T, PP, TT, REJS, U) takes
@@ -508,7 +509,7 @@
   
   ncf = length(gp.cf);
   
-  if nargin == 0   % Initialize record structure
+  if isempty(record)   % Initialize record structure
     record=gp;
     record.type = gp.type;
     record.lik = gp.lik;
@@ -715,4 +716,3 @@
   end
 
 end
-end
diff --git a/gp/gp_monotonic.m b/gp/gp_monotonic.m
index 95bf901a..584c8ffa 100644
--- a/gp/gp_monotonic.m
+++ b/gp/gp_monotonic.m
@@ -66,21 +66,21 @@
 % parse inputs
 ip=inputParser;
 ip.FunctionName = 'GP_MONOTONIC';
-ip.addRequired('gp',@isstruct);
-ip.addOptional('x', [], @(x) isnumeric(x) && isreal(x) && all(isfinite(x(:))))
-ip.addOptional('y', [], @(x) isnumeric(x) && isreal(x) && all(isfinite(x(:))))
-ip.addParamValue('z', [], @(x) isnumeric(x) && isreal(x) && all(isfinite(x(:))))
-ip.addParamValue('nu', 1e-6, @(x) isreal(x) && isscalar(x) && (x>0))
-ip.addParamValue('nv', [], @(x) isreal(x) && isscalar(x))
-ip.addParamValue('init', 'sample', @(x) ismember(x, {'sample', 'kmeans'}));
-ip.addParamValue('xv', [], @(x) isnumeric(x) && isreal(x) && all(isfinite(x(:))))
-ip.addParamValue('force', true, @(x) islogical(x));
-ip.addParamValue('display', true, @(x) islogical(x));
-ip.addParamValue('optimf', @fminscg, @(x) isa(x,'function_handle'))
-ip.addParamValue('opt', [], @isstruct)
-ip.addParamValue('optimize', 'off', @(x) ismember(x, {'on', 'off'}));
-ip.addParamValue('nvd', [], @(x) isreal(x));
-ip.parse(gp, varargin{:});
+ip=iparser(ip,'addRequired','gp',@isstruct);
+ip=iparser(ip,'addOptional','x', [], @(x) isnumeric(x) && isreal(x) && all(isfinite(x(:))));
+ip=iparser(ip,'addOptional','y', [], @(x) isnumeric(x) && isreal(x) && all(isfinite(x(:))));
+ip=iparser(ip,'addParamValue','z', [], @(x) isnumeric(x) && isreal(x) && all(isfinite(x(:))));
+ip=iparser(ip,'addParamValue','nu', 1e-6, @(x) isreal(x) && isscalar(x) && (x>0));
+ip=iparser(ip,'addParamValue','nv', [], @(x) isreal(x) && isscalar(x));
+ip=iparser(ip,'addParamValue','init', 'sample', @(x) ismember(x, {'sample', 'kmeans'}));
+ip=iparser(ip,'addParamValue','xv', [], @(x) isnumeric(x) && isreal(x) && all(isfinite(x(:))));
+ip=iparser(ip,'addParamValue','force', true, @(x) islogical(x));
+ip=iparser(ip,'addParamValue','display', true, @(x) islogical(x));
+ip=iparser(ip,'addParamValue','optimf', @fminscg, @(x) isa(x,'function_handle'));
+ip=iparser(ip,'addParamValue','opt', [], @isstruct);
+ip=iparser(ip,'addParamValue','optimize', 'off', @(x) ismember(x, {'on', 'off'}));
+ip=iparser(ip,'addParamValue','nvd', [], @(x) isreal(x));
+ip=iparser(ip,'parse',gp, varargin{:});
 x=ip.Results.x;
 y=ip.Results.y;
 z=ip.Results.z;
@@ -143,14 +143,10 @@
 end
 gp.latent_opt.init_prev='off';
 gp.latent_opt.maxiter=100;
-gpep_e('clearcache',gp);
 if isequal(optimize, 'on')
   % Optimize the parameters
   gp=gp_optim(gp,x,y,'opt',opt, 'z', z, 'optimf', optimf);
 end
-
-
-
 if force
     % Predict gradients at the training points 
     n=size(x,1);
diff --git a/gp/gp_optim.m b/gp/gp_optim.m
index 827b934e..21725ba3 100644
--- a/gp/gp_optim.m
+++ b/gp/gp_optim.m
@@ -38,15 +38,15 @@
 
 ip=inputParser;
 ip.FunctionName = 'GP_OPTIM';
-ip.addRequired('gp',@(x) isstruct(x) || isempty(x));
-ip.addRequired('x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))))
-ip.addRequired('y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))))
-ip.addParamValue('z', [], @(x) isreal(x) && all(isfinite(x(:))))
-ip.addParamValue('optimf', @fminscg, @(x) isa(x,'function_handle'))
-ip.addParamValue('opt', [], @isstruct)
-ip.addParamValue('loss', 'e', @(x) ismember(lower(x),{'e', 'loo', 'kfcv', 'waic' 'waic' 'waicv' 'waicg'}))
-ip.addParamValue('k', 10, @(x) isreal(x) && isscalar(x) && isfinite(x) && x>0)
-ip.parse(gp, x, y, varargin{:});
+ip=iparser(ip,'addRequired','gp',@(x) isstruct(x) || isempty(x));
+ip=iparser(ip,'addRequired','x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))));
+ip=iparser(ip,'addRequired','y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))));
+ip=iparser(ip,'addParamValue','z', [], @(x) isreal(x) && all(isfinite(x(:))));
+ip=iparser(ip,'addParamValue','optimf', @fminscg, @(x) isa(x,'function_handle'));
+ip=iparser(ip,'addParamValue','opt', [], @isstruct);
+ip=iparser(ip,'addParamValue','loss', 'e', @(x) ismember(lower(x),{'e', 'loo', 'kfcv', 'waic' 'waic' 'waicv' 'waicg'}));
+ip=iparser(ip,'addParamValue','k', 10, @(x) isreal(x) && isscalar(x) && isfinite(x) && x>0);
+ip=iparser(ip,'parse',gp, x, y, varargin{:});
 if isempty(gp)
   gp=gp_set();
 end
@@ -150,7 +150,18 @@
 
 function opt=setOpt(optdefault, opt)
   % Set default options
-  opttmp=optimset(optdefault,opt);
+  if ~exist('OCTAVE_VERSION','builtin')
+    opttmp=optimset(optdefault,opt);
+  else
+    opttmp=opt;
+    names=fieldnames(optdefault);
+    for i=1:length(names)
+      if ~isfield(opt,names{i})
+        opttmp.(names{i})=optdefault.(names{i});
+      end
+    end
+    
+  end
   
   % Set some additional options for @fminscg
   if isfield(opt,'lambda')
diff --git a/gp/gp_peff.m b/gp/gp_peff.m
index 6b5446fd..25a36473 100644
--- a/gp/gp_peff.m
+++ b/gp/gp_peff.m
@@ -53,11 +53,11 @@
 
   ip=inputParser;
   ip.FunctionName = 'GP_PEFF';
-  ip.addRequired('gp',@isstruct);
-  ip.addRequired('x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))))
-  ip.addRequired('y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))))
-  ip.addParamValue('z', [], @(x) isreal(x) && all(isfinite(x(:))))
-  ip.parse(gp, x, y, varargin{:});
+  ip=iparser(ip,'addRequired','gp',@isstruct);
+  ip=iparser(ip,'addRequired','x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))));
+  ip=iparser(ip,'addRequired','y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))));
+  ip=iparser(ip,'addParamValue','z', [], @(x) isreal(x) && all(isfinite(x(:))));
+  ip=iparser(ip,'parse',gp, x, y, varargin{:});
   z=ip.Results.z;
   
   tn = size(x,1);
diff --git a/gp/gp_plot.m b/gp/gp_plot.m
index ddfdce92..0c29b185 100644
--- a/gp/gp_plot.m
+++ b/gp/gp_plot.m
@@ -81,26 +81,27 @@
 
 ip=inputParser;
 ip.FunctionName = 'GP_PLOT';
-ip.addRequired('gp',@(x) isstruct(x) || iscell(x));
-ip.addRequired('x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))))
-ip.addRequired('y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))))
-ip.addOptional('xt', [], @(x) isempty(x) || (isreal(x) && all(isfinite(x(:)))))
-ip.addParamValue('zt', [], @(x) isreal(x) && all(isfinite(x(:))))
-ip.addParamValue('z', [], @(x) isreal(x) && all(isfinite(x(:))))
-ip.addParamValue('xlabels', [], @(x) isempty(x) || iscell(x));
-ip.addParamValue('predcf', [], @(x) isempty(x) || ...
-                 isvector(x) && isreal(x) && all(isfinite(x)&x>=0))
-ip.addParamValue('tstind', [], @(x) isempty(x) || iscell(x) ||...
-                 (isvector(x) && isreal(x) && all(isfinite(x)&x>0)))
-ip.addParamValue('fcorr', 'off', @(x) ismember(x, {'off', 'fact', 'cm2', 'on'}));
-ip.addParamValue('tr', 0.25, @(x) isreal(x) && all(isfinite(x(:))))
-ip.addParamValue('target', 'mu', @(x) ismember(x,{'f','mu'}))
-ip.addParamValue('normdata', struct(), @(x) isempty(x) || isstruct(x))
+
+ip=iparser(ip,'addRequired','gp',@(x) isstruct(x) || iscell(x));
+ip=iparser(ip,'addRequired','x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))));
+ip=iparser(ip,'addRequired','y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))));
+ip=iparser(ip,'addOptional','xt', [], @(x) isempty(x) || (isreal(x) && all(isfinite(x(:)))));
+ip=iparser(ip,'addParamValue','zt', [], @(x) isreal(x) && all(isfinite(x(:))));
+ip=iparser(ip,'addParamValue','z', [], @(x) isreal(x) && all(isfinite(x(:))));
+ip=iparser(ip,'addParamValue','predcf', [], @(x) isempty(x) || ...
+                 isvector(x) && isreal(x) && all(isfinite(x)&x>=0));
+ip=iparser(ip,'addParamValue','tstind', [], @(x) isempty(x) || iscell(x) ||...
+                 (isvector(x) && isreal(x) && all(isfinite(x)&x>0)));
+ip=iparser(ip,'addParamValue','fcorr', 'off', @(x) ismember(x, {'off', 'fact', 'cm2', 'on'}));
+ip=iparser(ip,'addParamValue','tr', 0.25, @(x) isreal(x) && all(isfinite(x(:))));
+ip=iparser(ip,'addParamValue','target', 'mu', @(x) ismember(x,{'f','mu'}));
+ip=iparser(ip,'addParamValue','normdata', struct(), @(x) isempty(x) || isstruct(x));
+
 if numel(varargin)==0 || isnumeric(varargin{1})
   % inputParser should handle this, but it doesn't
-  ip.parse(gp, x, y, varargin{:});
+  ip=iparser(ip,'parse',gp, x, y, varargin{:});
 else
-  ip.parse(gp, x, y, [], varargin{:});
+  ip=iparser(ip,'parse',gp, x, y, [], varargin{:});
 end
 xt=ip.Results.xt;
 zt=ip.Results.zt;
@@ -146,13 +147,13 @@
 nd=ip.Results.normdata;
 ipnd=inputParser;
 ipnd.FunctionName = 'normdata';
-ipnd.addParamValue('xmean',0,@(x) isempty(x) || (isreal(x) && all(isfinite(x(:)))));
-ipnd.addParamValue('xstd',1,@(x) isempty(x) || (isreal(x) && all(isfinite(x(:)))));
-ipnd.addParamValue('xlog',0,@(x) isempty(x) || (isreal(x) && all(isfinite(x(:)))));
-ipnd.addParamValue('ymean',0,@(x) isempty(x) || (isreal(x) && all(isfinite(x(:)))));
-ipnd.addParamValue('ystd',1,@(x) isempty(x) || (isreal(x) && all(isfinite(x(:)))));
-ipnd.addParamValue('ylog',0,@(x) isempty(x) || (isreal(x) && all(isfinite(x(:)))));
-ipnd.parse(nd);
+ipnd=iparser(ipnd,'addParamValue','xmean',0,@(x) isempty(x) || (isreal(x) && all(isfinite(x(:)))));
+ipnd=iparser(ipnd,'addParamValue','xstd',1,@(x) isempty(x) || (isreal(x) && all(isfinite(x(:)))));
+ipnd=iparser(ipnd,'addParamValue','xlog',0,@(x) isempty(x) || (isreal(x) && all(isfinite(x(:)))));
+ipnd=iparser(ipnd,'addParamValue','ymean',0,@(x) isempty(x) || (isreal(x) && all(isfinite(x(:)))));
+ipnd=iparser(ipnd,'addParamValue','ystd',1,@(x) isempty(x) || (isreal(x) && all(isfinite(x(:)))));
+ipnd=iparser(ipnd,'addParamValue','ylog',0,@(x) isempty(x) || (isreal(x) && all(isfinite(x(:)))));
+ipnd=iparser(ipnd,'parse',nd);
 nd=ipnd.Results;
 
 if iscell(gp)
diff --git a/gp/gp_pred.m b/gp/gp_pred.m
index f86bb52c..4404ff17 100644
--- a/gp/gp_pred.m
+++ b/gp/gp_pred.m
@@ -90,23 +90,23 @@
 
 ip=inputParser;
 ip.FunctionName = 'GP_PRED';
-ip.addRequired('gp',@(x) isstruct(x) || iscell(x));
-ip.addRequired('x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))))
-ip.addRequired('y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))))
-ip.addOptional('xt', [], @(x) isempty(x) || (isreal(x) && all(isfinite(x(:)))))
-ip.addParamValue('yt', [], @(x) isreal(x) && all(isfinite(x(:))))
-ip.addParamValue('zt', [], @(x) isreal(x) && all(isfinite(x(:))))
-ip.addParamValue('z', [], @(x) isreal(x) && all(isfinite(x(:))))
-ip.addParamValue('predcf', [], @(x) isempty(x) || ...
-                 isvector(x) && isreal(x) && all(isfinite(x)&x>=0))
-ip.addParamValue('tstind', [], @(x) isempty(x) || iscell(x) ||...
-                 (isvector(x) && isreal(x) && all(isfinite(x)&x>0)))
-ip.addParamValue('fcorr', 'off', @(x) ismember(x, {'off', 'fact', 'cm2', 'on', 'lr'}));
+ip=iparser(ip,'addRequired','gp',@(x) isstruct(x) || iscell(x));
+ip=iparser(ip,'addRequired','x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))));
+ip=iparser(ip,'addRequired','y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))));
+ip=iparser(ip,'addOptional','xt', [], @(x) isempty(x) || (isreal(x) && all(isfinite(x(:)))));
+ip=iparser(ip,'addParamValue','yt', [], @(x) isreal(x) && all(isfinite(x(:))));
+ip=iparser(ip,'addParamValue','z', [], @(x) isreal(x) && all(isfinite(x(:))));
+ip=iparser(ip,'addParamValue','zt', [], @(x) isreal(x) && all(isfinite(x(:))));
+ip=iparser(ip,'addParamValue','predcf', [], @(x) isempty(x) || ...
+                 isvector(x) && isreal(x) && all(isfinite(x)&x>=0));
+ip=iparser(ip,'addParamValue','tstind', [], @(x) isempty(x) || iscell(x) ||...
+                 (isvector(x) && isreal(x) && all(isfinite(x)&x>0)));
+ip=iparser(ip,'addParamValue','fcorr', 'off', @(x) ismember(x, {'off', 'fact', 'cm2', 'on', 'lr'}));
 if numel(varargin)==0 || isnumeric(varargin{1})
   % inputParser should handle this, but it doesn't
-  ip.parse(gp, x, y, varargin{:});
+  ip=iparser(ip,'parse',gp, x, y, varargin{:});
 else
-  ip.parse(gp, x, y, [], varargin{:});
+  ip=iparser(ip,'parse',gp, x, y, [], varargin{:});
 end
 xt=ip.Results.xt;
 yt=ip.Results.yt;
diff --git a/gp/gp_predcdf.m b/gp/gp_predcdf.m
index b94d5fd1..00aa2907 100644
--- a/gp/gp_predcdf.m
+++ b/gp/gp_predcdf.m
@@ -34,24 +34,24 @@
 
   ip=inputParser;
   ip.FunctionName = 'GP_PREDCDF';
-  ip.addRequired('gp',@(x) isstruct(x) || iscell(x));
-  ip.addRequired('x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))))
-  ip.addRequired('y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))))
-  ip.addOptional('xt', [], @(x) isempty(x) || (isreal(x) && all(isfinite(x(:)))))
-  ip.addParamValue('yt', [], @(x) isreal(x) && all(isfinite(x(:))))
-  ip.addParamValue('z', [], @(x) isreal(x) && all(isfinite(x(:))))
-  ip.addParamValue('zt', [], @(x) isreal(x) && all(isfinite(x(:))))
-  ip.addParamValue('prct', [5 50 95], @(x) isreal(x) && all(isfinite(x(:))))
-  ip.addParamValue('nsamp', 5000, @(x) isreal(x) && all(isfinite(x(:))))
-  ip.addParamValue('predcf', [], @(x) isempty(x) || ...
-                   isvector(x) && isreal(x) && all(isfinite(x)&x>0))
-  ip.addParamValue('tstind', [], @(x) isempty(x) || iscell(x) ||...
-                   (isvector(x) && isreal(x) && all(isfinite(x)&x>0)))
+  ip=iparser(ip,'addRequired','gp',@(x) isstruct(x) || iscell(x));
+  ip=iparser(ip,'addRequired','x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))));
+  ip=iparser(ip,'addRequired','y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))));
+  ip=iparser(ip,'addOptional','xt', [], @(x) isempty(x) || (isreal(x) && all(isfinite(x(:)))));
+  ip=iparser(ip,'addParamValue','yt', [], @(x) isreal(x) && all(isfinite(x(:))));
+  ip=iparser(ip,'addParamValue','z', [], @(x) isreal(x) && all(isfinite(x(:))));
+  ip=iparser(ip,'addParamValue','zt', [], @(x) isreal(x) && all(isfinite(x(:))));
+  ip=iparser(ip,'addParamValue','prct', [5 50 95], @(x) isreal(x) && all(isfinite(x(:))));
+  ip=iparser(ip,'addParamValue','nsamp', 5000, @(x) isreal(x) && all(isfinite(x(:))));
+  ip=iparser(ip,'addParamValue','predcf', [], @(x) isempty(x) || ...
+                   isvector(x) && isreal(x) && all(isfinite(x)&x>0));
+  ip=iparser(ip,'addParamValue','tstind', [], @(x) isempty(x) || iscell(x) ||...
+                   (isvector(x) && isreal(x) && all(isfinite(x)&x>0)));
   if numel(varargin)==0 || isnumeric(varargin{1})
     % inputParser should handle this, but it doesn't
-    ip.parse(gp, x, y, varargin{:});
+    ip=iparser(ip,'parse',gp, x, y, varargin{:});
   else
-    ip.parse(gp, x, y, [], varargin{:});
+    ip=iparser(ip,'parse',gp, x, y, [], varargin{:});
   end
   xt=ip.Results.xt;
   yt=ip.Results.yt;
diff --git a/gp/gp_predcm.m b/gp/gp_predcm.m
index e9c955c0..5477db68 100644
--- a/gp/gp_predcm.m
+++ b/gp/gp_predcm.m
@@ -43,21 +43,20 @@
 % License.txt, included with the software, for details.
 
 ip=inputParser;
-ip.addRequired('gp',@isstruct);
-ip.addRequired('x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))))
-ip.addRequired('y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))))
-%ip.addRequired('fvec',  @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))))
-ip.addOptional('xt', [], @(x) isreal(x) && all(isfinite(x(:))))
-ip.addParamValue('z', [], @(x) isreal(x) && all(isfinite(x(:))))
-ip.addParamValue('ind', 1, @(x) isreal(x) && all(isfinite(x(:))))
-ip.addParamValue('ng', 50, @(x) isreal(x) && all(isfinite(x(:))) && x > 1)
-ip.addParamValue('fcorr', 'on', @(x) ismember(x, {'fact', 'cm2', 'on','lr'}))
-ip.addParamValue('tstind', [], @(x) isempty(x) || iscell(x) ||...
-                   (isvector(x) && isreal(x) && all(isfinite(x)&x>0)))
+ip=iparser(ip,'addRequired','gp',@isstruct);
+ip=iparser(ip,'addRequired','x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))));
+ip=iparser(ip,'addRequired','y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))));
+ip=iparser(ip,'addOptional','xt', [], @(x) isreal(x) && all(isfinite(x(:))));
+ip=iparser(ip,'addParamValue','z', [], @(x) isreal(x) && all(isfinite(x(:))));
+ip=iparser(ip,'addParamValue','ind', 1, @(x) isreal(x) && all(isfinite(x(:))));
+ip=iparser(ip,'addParamValue','ng', 50, @(x) isreal(x) && all(isfinite(x(:))) && x > 1);
+ip=iparser(ip,'addParamValue','fcorr', 'on', @(x) ismember(x, {'fact', 'cm2', 'on','lr'}));
+ip=iparser(ip,'addParamValue','tstind', [], @(x) isempty(x) || iscell(x) ||...
+                   (isvector(x) && isreal(x) && all(isfinite(x)&x>0)));
 if rem(size(varargin,2), 2) == 0
-  ip.parse(gp, x, y, [],varargin{:});
+  ip=iparser(ip,'parse',gp, x, y, [],varargin{:});
 else
-  ip.parse(gp, x, y, varargin{:});
+  ip=iparser(ip,'parse',gp, x, y, varargin{:});
 end
 tstind = ip.Results.tstind;
 z = ip.Results.z;
diff --git a/gp/gp_predprctmu.m b/gp/gp_predprctmu.m
index 84e7a355..742c46c7 100644
--- a/gp/gp_predprctmu.m
+++ b/gp/gp_predprctmu.m
@@ -42,23 +42,23 @@
 
   ip=inputParser;
   ip.FunctionName = 'GP_PREDPRCTMU';
-  ip.addRequired('gp',@(x) isstruct(x) || iscell(x));
-  ip.addRequired('x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))))
-  ip.addRequired('y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))))
-  ip.addOptional('xt', [], @(x) isempty(x) || (isreal(x) && all(isfinite(x(:)))))
-  ip.addParamValue('z', [], @(x) isreal(x) && all(isfinite(x(:))))
-  ip.addParamValue('zt', [], @(x) isreal(x) && all(isfinite(x(:))))
-  ip.addParamValue('prct', [5 50 95], @(x) isreal(x) && all(isfinite(x(:))))
-  ip.addParamValue('nsamp', 5000, @(x) isreal(x) && all(isfinite(x(:))))
-  ip.addParamValue('predcf', [], @(x) isempty(x) || ...
-                   isvector(x) && isreal(x) && all(isfinite(x)&x>0))
-  ip.addParamValue('tstind', [], @(x) isempty(x) || iscell(x) ||...
-                   (isvector(x) && isreal(x) && all(isfinite(x)&x>0)))
+  ip=iparser(ip,'addRequired','gp',@(x) isstruct(x) || iscell(x));
+  ip=iparser(ip,'addRequired','x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))));
+  ip=iparser(ip,'addRequired','y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))));
+  ip=iparser(ip,'addOptional','xt', [], @(x) isempty(x) || (isreal(x) && all(isfinite(x(:)))));
+  ip=iparser(ip,'addParamValue','z', [], @(x) isempty(x) || (isreal(x) && all(isfinite(x(:)))));
+  ip=iparser(ip,'addParamValue','zt', [], @(x) isempty(x) || (isreal(x) && all(isfinite(x(:)))));
+  ip=iparser(ip,'addParamValue','prct', [5 50 95], @(x) isreal(x) && all(isfinite(x(:))));
+  ip=iparser(ip,'addParamValue','nsamp', 5000, @(x) isreal(x) && all(isfinite(x(:))));
+  ip=iparser(ip,'addParamValue','predcf', [], @(x) isempty(x) || ...
+                   isvector(x) && isreal(x) && all(isfinite(x)&x>0));
+  ip=iparser(ip,'addParamValue','tstind', [], @(x) isempty(x) || iscell(x) ||...
+                   (isvector(x) && isreal(x) && all(isfinite(x)&x>0)));
   if numel(varargin)==0 || isnumeric(varargin{1})
     % inputParser should handle this, but it doesn't
-    ip.parse(gp, x, y, varargin{:});
+    ip=iparser(ip,'parse',gp, x, y, varargin{:});
   else
-    ip.parse(gp, x, y, [], varargin{:});
+    ip=iparser(ip,'parse',gp, x, y, [], varargin{:});
   end
   xt=ip.Results.xt;
   z = ip.Results.z;
diff --git a/gp/gp_predprcty.m b/gp/gp_predprcty.m
index 901d0b81..f1f0dc70 100644
--- a/gp/gp_predprcty.m
+++ b/gp/gp_predprcty.m
@@ -40,23 +40,23 @@
 
   ip=inputParser;
   ip.FunctionName = 'GP_PREDPRCTY';
-  ip.addRequired('gp',@(x) isstruct(x) || iscell(x));
-  ip.addRequired('x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))))
-  ip.addRequired('y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))))
-  ip.addOptional('xt', [], @(x) isempty(x) || (isreal(x) && all(isfinite(x(:)))))
-  ip.addParamValue('z', [], @(x) isreal(x) && all(isfinite(x(:))))
-  ip.addParamValue('zt', [], @(x) isreal(x) && all(isfinite(x(:))))
-  ip.addParamValue('prct', [5 50 95], @(x) isreal(x) && all(isfinite(x(:))))
-  ip.addParamValue('nsamp', 5000, @(x) isreal(x) && all(isfinite(x(:))))
-  ip.addParamValue('predcf', [], @(x) isempty(x) || ...
-                   isvector(x) && isreal(x) && all(isfinite(x)&x>0))
-  ip.addParamValue('tstind', [], @(x) isempty(x) || iscell(x) ||...
-                   (isvector(x) && isreal(x) && all(isfinite(x)&x>0)))
+  ip=iparser(ip,'addRequired','gp',@(x) isstruct(x) || iscell(x));
+  ip=iparser(ip,'addRequired','x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))));
+  ip=iparser(ip,'addRequired','y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))));
+  ip=iparser(ip,'addOptional','xt', [], @(x) isempty(x) || (isreal(x) && all(isfinite(x(:)))));
+  ip=iparser(ip,'addParamValue','z', [], @(x) isreal(x) && all(isfinite(x(:))));
+  ip=iparser(ip,'addParamValue','zt', [], @(x) isreal(x) && all(isfinite(x(:))));
+  ip=iparser(ip,'addParamValue','prct', [5 50 95], @(x) isreal(x) && all(isfinite(x(:))));
+  ip=iparser(ip,'addParamValue','nsamp', 5000, @(x) isreal(x) && all(isfinite(x(:))));
+  ip=iparser(ip,'addParamValue','predcf', [], @(x) isempty(x) || ...
+                   isvector(x) && isreal(x) && all(isfinite(x)&x>0));
+  ip=iparser(ip,'addParamValue','tstind', [], @(x) isempty(x) || iscell(x) ||...
+                   (isvector(x) && isreal(x) && all(isfinite(x)&x>0)));
   if numel(varargin)==0 || isnumeric(varargin{1})
     % inputParser should handle this, but it doesn't
-    ip.parse(gp, x, y, varargin{:});
+    ip=iparser(ip,'parse',gp, x, y, varargin{:});
   else
-    ip.parse(gp, x, y, [], varargin{:});
+    ip=iparser(ip,'parse',gp, x, y, [], varargin{:});
   end
   xt=ip.Results.xt;
   z = ip.Results.z;
diff --git a/gp/gp_rnd.m b/gp/gp_rnd.m
index 34d3cc1b..b8a7d7ab 100644
--- a/gp/gp_rnd.m
+++ b/gp/gp_rnd.m
@@ -65,28 +65,31 @@
 
 ip=inputParser;
 ip.FunctionName = 'GP_RND';
-ip.addRequired('gp',@(x) isstruct(x) || iscell(x));
-ip.addRequired('x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))))
-ip.addRequired('y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))))
-ip.addOptional('xt', [], @(x) isempty(x) || (isreal(x) && all(isfinite(x(:)))))
-ip.addParamValue('z', [], @(x) isreal(x) && all(isfinite(x(:))))
-ip.addParamValue('zt', [], @(x) isreal(x) && all(isfinite(x(:))))
-ip.addParamValue('predcf', [], @(x) isempty(x) || ...
-                 isvector(x) && isreal(x) && all(isfinite(x)&x>0))
-ip.addParamValue('tstind', [], @(x) isempty(x) || iscell(x) ||...
-                 (isvector(x) && isreal(x) && all(isfinite(x)&x>0)))
-ip.addParamValue('nsamp', 1, @(x) isreal(x) && isscalar(x))
-ip.addParamValue('fcorr', 'off', @(x) ismember(x, {'fact','cm2','off','on','lr'}))
-ip.addParamValue('splitnormal', 'on', @(x) (islogical(x) && isscalar(x))|| ...
-                 ismember(x,{'on' 'off' 'full'}))
-ip.addParamValue('n_scale',50, @(x) isnumeric(x) && x>=0);
+
+ip=iparser(ip,'addRequired','gp',@(x) isstruct(x) || iscell(x));
+ip=iparser(ip,'addRequired','x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))));
+ip=iparser(ip,'addRequired','y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))));
+ip=iparser(ip,'addRequired','xt', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))));
+ip=iparser(ip,'addParamValue','z', [], @(x) isreal(x) && all(isfinite(x(:))));
+ip=iparser(ip,'addParamValue','zt', [], @(x) isreal(x) && all(isfinite(x(:))));
+ip=iparser(ip,'addParamValue','predcf', [], @(x) isempty(x) || ...
+                 isvector(x) && isreal(x) && all(isfinite(x)&x>0));
+ip=iparser(ip,'addParamValue','tstind', [], @(x) isempty(x) || iscell(x) ||...
+                 (isvector(x) && isreal(x) && all(isfinite(x)&x>0)));
+ip=iparser(ip,'addParamValue','nsamp', 1, @(x) isreal(x) && isscalar(x));
+ip=iparser(ip,'addParamValue','fcorr', 'off', @(x) ismember(x, {'fact','cm2','off','on','lr'}));
+ip=iparser(ip,'addParamValue','splitnormal', 'on', @(x) (islogical(x) && isscalar(x))|| ...
+                 ismember(x,{'on' 'off' 'full'}));
+ip=iparser(ip,'addParamValue','n_scale',50, @(x) isnumeric(x) && x>=0);
+
 if numel(varargin)==0 || isnumeric(varargin{1})
   % inputParser should handle this, but it doesn't
-  ip.parse(gp, x, y, varargin{:});
+  ip=iparser(ip,'parse',gp, x, y, varargin{:});
 else
-  ip.parse(gp, x, y, [], varargin{:});
+  ip=iparser(ip,'parse',gp, x, y, [], varargin{:});
 end
 xt=ip.Results.xt;
+%ip=iparser(ip,'parse',gp, x, y, xt, varargin{:});
 z=ip.Results.z;
 zt=ip.Results.zt;
 tn = size(x,1);
diff --git a/gp/gp_set.m b/gp/gp_set.m
index 1917b012..add3a394 100644
--- a/gp/gp_set.m
+++ b/gp/gp_set.m
@@ -202,29 +202,28 @@
 
   ip=inputParser;
   ip.FunctionName = 'GP_SET';
-  ip.addOptional('gp', [], @isstruct);
-  ip.addParamValue('cf',[], @(x) isempty(x) || isstruct(x) || iscell(x));
-  ip.addParamValue('meanf',[], @(x) isempty(x) || isstruct(x) || iscell(x));
-  ip.addParamValue('type','FULL', ...
+  ip=iparser(ip,'addOptional','gp', [], @isstruct);
+  ip=iparser(ip,'addParamValue','cf',[], @(x) isempty(x) || isstruct(x) || iscell(x));
+  ip=iparser(ip,'addParamValue','meanf',[], @(x) isempty(x) || isstruct(x) || iscell(x));
+  ip=iparser(ip,'addParamValue','type','FULL', ...
                    @(x) ismember(x,{'FULL' 'FIC' 'PIC' 'PIC_BLOCK' 'VAR' ...
-                      'DTC' 'SOR' 'CS+FIC','KALMAN'}));
-  ip.addParamValue('lik',lik_gaussian(), @(x) isstruct(x));
-  ip.addParamValue('jitterSigma2',1e-6, @(x) isscalar(x) && x>=0);
-  ip.addParamValue('infer_params','covariance+likelihood', @(x) ischar(x));
-  ip.addParamValue('latent_method','Laplace', @(x) ischar(x) || iscell(x));
-  ip.addParamValue('latent_opt',struct(), @isstruct);
-  ip.addParamValue('X_u',[],  @(x) isreal(x) && all(isfinite(x(:))));
-  ip.addParamValue('Xu_prior',prior_unif,  @(x) isstruct(x) || isempty(x) || ...
-                   iscell(x));
-  ip.addParamValue('tr_index', [], @(x) ~isempty(x) || iscell(x))    
-  ip.addParamValue('comp_cf', [], @(x) iscell(x))    
-  ip.addParamValue('derivobs','off', @(x) islogical(x) || isscalar(x) || ...
+                      'DTC' 'SOR' 'CS+FIC' 'KALMAN'}));
+  ip=iparser(ip,'addParamValue','lik',lik_gaussian(), @(x) isstruct(x));
+  ip=iparser(ip,'addParamValue','jitterSigma2',1e-6, @(x) isscalar(x) && x>=0);
+  ip=iparser(ip,'addParamValue','infer_params','covariance+likelihood', @(x) ischar(x));
+  ip=iparser(ip,'addParamValue','latent_method','Laplace', @(x) ischar(x) || iscell(x));
+  ip=iparser(ip,'addParamValue','latent_opt',struct(), @isstruct);
+  ip=iparser(ip,'addParamValue','X_u',[],  @(x) isreal(x) && all(isfinite(x(:))));
+  ip=iparser(ip,'addParamValue','Xu_prior',prior_unif,  @(x) iscell(x) || isstruct(x) || isempty(x));
+  ip=iparser(ip,'addParamValue','tr_index', [], @(x) ~isempty(x) || iscell(x));    
+  ip=iparser(ip,'addParamValue','comp_cf', [], @(x) iscell(x));    
+  ip=iparser(ip,'addParamValue','derivobs','off', @(x) islogical(x) || isscalar(x) || ...
                    (ischar(x) && ismember(x,{'on' 'off'})));
-  ip.addParamValue('savememory','off', @(x) islogical(x) || isscalar(x) || ...
+  ip=iparser(ip,'addParamValue','savememory','off', @(x) islogical(x) || isscalar(x) || ...
                    (ischar(x) && ismember(x,{'on' 'off'})));
-%   ip.addParamValue('optim_method', [], @(x) isreal(x) && (x==1 || x==2) &&  ...
+%   ip=iparser(ip,'addParamValue','optim_method', [], @(x) isreal(x) && (x==1 || x==2) &&  ...
 %                     isfinite(x))
-  ip.parse(varargin{:});
+  ip=iparser(ip,'parse',varargin{:});
   gp=ip.Results.gp;
 
 
@@ -401,7 +400,7 @@
           % Remove traces of other latent methods
           if isfield(gp,'latent_opt'); gp=rmfield(gp,'latent_opt'); end
           if isfield(gp,'fh') && isfield(gp.fh,'ne')
-            gp.fh=rmfield(gp.fh,{'ne' 'e' 'g' 'pred' 'jpred' 'looe' 'loog'}); 
+            gp.fh=rmfield(gp.fh,{'ne' 'e' 'g' 'pred' 'jpred' 'looe'}); 
           end
           % Set latent method
           gp.latent_method=latent_method;
@@ -452,9 +451,9 @@
           % Handle latent_opt
           ipmc=inputParser;
           ipmc.FunctionName = 'GP_SET - latent method MCMC options';
-          ipmc.addParamValue('method',@esls, @(x) isa(x,'function_handle'));
-          ipmc.addParamValue('f',[],  @(x) isreal(x) && all(isfinite(x(:))));
-          ipmc.parse(latent_opt);
+          ipmc=iparser(ipmc,'addParamValue','method',@esls, @(x) isa(x,'function_handle'));
+          ipmc=iparser(ipmc,'addParamValue','f',[],  @(x) isreal(x) && all(isfinite(x(:))));
+          ipmc=iparser(ipmc,'parse',latent_opt);
           if init || ~ismember('method',ipmc.UsingDefaults) || ~isfield(gp.fh,'mc')
             gp.fh.mc = ipmc.Results.method;
           end
@@ -465,47 +464,47 @@
           % Handle latent_opt
           ipep=inputParser;
           ipep.FunctionName = 'GP_SET - latent method EP options';
-          ipep.addParamValue('optim_method',[], @(x) ischar(x));
-          ipep.addParamValue('maxiter',20, @(x) isreal(x) && isscalar(x) && isfinite(x) && x>0);
-          ipep.addParamValue('display', 'off', @(x) ischar(x) && ismember(x,{'off', 'final', 'iter'}))
-          ipep.addParamValue('parallel','on', @(x) ischar(x) && ismember(x,{'off', 'on'}));    % default on
-          ipep.addParamValue('init_prev', 'on', @(x) ischar(x) && ismember(x,{'off', 'on'}));    % default on
+          ipep=iparser(ipep,'addParamValue','optim_method',[], @(x) ischar(x));
+          ipep=iparser(ipep,'addParamValue','maxiter',20, @(x) isreal(x) && isscalar(x) && isfinite(x) && x>0);
+          ipep=iparser(ipep,'addParamValue','display', 'off', @(x) ischar(x) && ismember(x,{'off', 'final', 'iter'}));
+          ipep=iparser(ipep,'addParamValue','parallel','on', @(x) ischar(x) && ismember(x,{'off', 'on'}));    % default on
+          ipep=iparser(ipep,'addParamValue','init_prev', 'on', @(x) ischar(x) && ismember(x,{'off', 'on'}));    % default on
           % Following option is only for basic-EP
           % all changes in the log predictive densities and the log marginal
           % likelihood are smaller than tol.
-          ipep.addParamValue('tol',1e-4, @(x) isreal(x) && isscalar(x) && isfinite(x) && x>0);
+          ipep=iparser(ipep,'addParamValue','tol',1e-4, @(x) isreal(x) && isscalar(x) && isfinite(x) && x>0);
           % Following options are only for robust-EP
           % max number of initial parallel iterations
-          ipep.addParamValue('ninit', 10, @(x) isreal(x) && (x==1 || rem(1,x)==1) && isfinite(x))
+          ipep=iparser(ipep,'addParamValue','ninit', 10, @(x) isreal(x) && (x==1 || rem(1,x)==1) && isfinite(x));
           % max number of inner loop iterations in the double-loop algorithm
-          ipep.addParamValue('max_ninner', 4, @(x) isreal(x) && (x==1 || rem(1,x)==1) && isfinite(x))
+          ipep=iparser(ipep,'addParamValue','max_ninner', 4, @(x) isreal(x) && (x==1 || rem(1,x)==1) && isfinite(x));
           % converge tolerance with respect to the maximum change in E[f] and Var[f]
-          ipep.addParamValue('tolStop', 1e-4, @(x) isreal(x) && isfinite(x))
+          ipep=iparser(ipep,'addParamValue','tolStop', 1e-4, @(x) isreal(x) && isfinite(x));
           % tolerance for the EP site updates
-          ipep.addParamValue('tolUpdate', 1e-6, @(x) isreal(x) && isfinite(x))
+          ipep=iparser(ipep,'addParamValue','tolUpdate', 1e-6, @(x) isreal(x) && isfinite(x));
           % inner loop energy tolerance
-          ipep.addParamValue('tolInner', 1e-3, @(x) isreal(x) && isfinite(x))
+          ipep=iparser(ipep,'addParamValue','tolInner', 1e-3, @(x) isreal(x) && isfinite(x));
           % minimum gradient (g) decrease in the search direction, abs(g_new)<tolGrad*abs(g)
-          ipep.addParamValue('tolGrad', 0.9, @(x) isreal(x) && isfinite(x))
+          ipep=iparser(ipep,'addParamValue','tolGrad', 0.9, @(x) isreal(x) && isfinite(x));
           % limit for the cavity variance Vc, Vc < Vc_lim*diag(K)
-          ipep.addParamValue('cavity_var_lim', 2, @(x) isreal(x) && isfinite(x))
+          ipep=iparser(ipep,'addParamValue','cavity_var_lim', 2, @(x) isreal(x) && isfinite(x));
           % the intial damping factor
-          ipep.addParamValue('df', 0.8, @(x) isreal(x) && isfinite(x))
+          ipep=iparser(ipep,'addParamValue','df', 0.8, @(x) isreal(x) && isfinite(x));
           % the intial damping factor related to input dependent
           % noise/magnitude
-          ipep.addParamValue('df2', 0.8, @(x) isreal(x) && isfinite(x))
+          ipep=iparser(ipep,'addParamValue','df2', 0.8, @(x) isreal(x) && isfinite(x))
           % the initial fraction parameter
-          ipep.addParamValue('eta', 1, @(x) isreal(x) && isfinite(x))
+          ipep=iparser(ipep,'addParamValue','eta', 1, @(x) isreal(x) && isfinite(x));
           % the secondary fraction parameter          
-          ipep.addParamValue('eta2', .5, @(x) isreal(x) && isfinite(x))
+          ipep=iparser(ipep,'addParamValue','eta2', .5, @(x) isreal(x) && isfinite(x));
           % update mode in double-loop iterations
-          ipep.addParamValue('up_mode', 'ep', @(x) ischar(x) && ismember(x,{'ep' 'grad'}))
+          ipep=iparser(ipep,'addParamValue','up_mode', 'ep', @(x) ischar(x) && ismember(x,{'ep' 'grad'}));
           % step size limit (1 suitable for ep updates)
-          ipep.addParamValue('df_lim', 1, @(x) isreal(x) && isfinite(x))
+          ipep=iparser(ipep,'addParamValue','df_lim', 1, @(x) isreal(x) && isfinite(x));
           % whether to do one inner loop-iteration per site ('on') or until
           % convergence ('off') in nester ep
-          ipep.addParamValue('incremental', 'on', @(x) ischar(x) && ismember(x,{'off', 'on'}));    % default on                    
-          ipep.parse(latent_opt);
+          ipep=iparser(ipep,'addParamValue','incremental', 'on', @(x) ischar(x) && ismember(x,{'off', 'on'}));    % default on                    
+          ipep=iparser(ipep,'parse',latent_opt);
           optim_method = ipep.Results.optim_method;
           if ~isempty(optim_method)
             gp.latent_opt.optim_method=optim_method;
@@ -603,10 +602,10 @@
           % Handle latent_opt
           ipla=inputParser;
           ipla.FunctionName = 'GP_SET - latent method Laplace options';
-          ipla.addParamValue('optim_method',[], @(x) ischar(x));
-          ipla.addParamValue('tol',1e-4, @(x) isreal(x) && isscalar(x) && isfinite(x) && x>0);
-          ipla.addParamValue('maxiter', 40, @(x) isreal(x) && isscalar(x) && isfinite(x) && x>0);
-          ipla.parse(latent_opt);
+          ipla=iparser(ipla,'addParamValue','optim_method',[], @(x) ischar(x));
+          ipla=iparser(ipla,'addParamValue','tol',1e-4, @(x) isreal(x) && isscalar(x) && isfinite(x) && x>0);
+          ipla=iparser(ipla,'addParamValue','maxiter', 40, @(x) isreal(x) && isscalar(x) && isfinite(x) && x>0);
+          ipla=iparser(ipla,'parse',latent_opt);
           optim_method=ipla.Results.optim_method;
           if ~isempty(optim_method)
             gp.latent_opt.optim_method=optim_method;
diff --git a/gp/gp_waic.m b/gp/gp_waic.m
index e140fc21..b79e8f80 100644
--- a/gp/gp_waic.m
+++ b/gp/gp_waic.m
@@ -61,13 +61,13 @@
 
   ip=inputParser;
   ip.FunctionName = 'GP_WAIC';
-  ip.addRequired('gp',@(x) isstruct(x) || iscell(x));
-  ip.addRequired('x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))))
-  ip.addRequired('y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))))
-  ip.addParamValue('method', 'V', @(x) ismember(x,{'V' 'G'}))
-  ip.addParamValue('form', 'mean', @(x) ismember(x,{'mean','all','sum','dic'}))
-  ip.addParamValue('z', [], @(x) isreal(x) && all(isfinite(x(:))))
-  ip.parse(gp, x, y, varargin{:});
+  ip=iparser(ip,'addRequired','gp',@(x) isstruct(x) || iscell(x));
+  ip=iparser(ip,'addRequired','x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))));
+  ip=iparser(ip,'addRequired','y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))));
+  ip=iparser(ip,'addParamValue','method', 'V', @(x) ismember(x,{'V' 'G'}));
+  ip=iparser(ip,'addParamValue','form', 'mean', @(x) ismember(x,{'mean','all','sum','dic'}));
+  ip=iparser(ip,'addParamValue','z', [], @(x) isreal(x) && all(isfinite(x(:))));
+  ip=iparser(ip,'parse',gp, x, y, varargin{:});
   method=ip.Results.method;
   form=ip.Results.form;
   % pass these forward
@@ -160,10 +160,10 @@
             if ~isequal(gp.lik.type, 'Coxph')
               fmin = mean(Ef(i,:) - 9*sqrt(Varf(i,:)));
               fmax = mean(Ef(i,:) + 9*sqrt(Varf(i,:)));
-              Elog(i) = quadgk(@(f) mean(multi_npdf(f,Ef(i,:),(Varf(i,:))) ...
-                .*llvec(gp_array, y(i), f, z1).^2), fmin, fmax);
-              Elog2(i) = quadgk(@(f) mean(multi_npdf(f,Ef(i,:),(Varf(i,:))) ...
-                .*llvec(gp_array, y(i), f, z1)), fmin, fmax);
+              Elog(i) = quadgk(@(f) reshape(mean(multi_npdf(f(:),Ef(i,:),(Varf(i,:))) ...
+                .*llvec(gp_array, y(i), f(:), z1).^2),size(f,1),size(f,2)), fmin, fmax);
+              Elog2(i) = quadgk(@(f) reshape(mean(multi_npdf(f(:),Ef(i,:),(Varf(i,:))) ...
+                .*llvec(gp_array, y(i), f(:), z1)),size(f,1),size(f,2)), fmin, fmax);
             else
               ntime = size(gp.lik.xtime,1);
               for i2=1:nsamples
@@ -192,8 +192,8 @@
           for i=1:tn
             fmin = mean(Ef(i,:) - 9*sqrt(Varf(i,:)));
             fmax = mean(Ef(i,:) + 9*sqrt(Varf(i,:)));
-            GUt(i) = quadgk(@(f) mean(multi_npdf(f,Ef(i,:),(Varf(i,:))) ...
-                                      .*bsxfun(@minus,-bsxfun(@rdivide,(repmat((y(i)-f),nsamples,1)).^2,(2.*sigma2(i,:))'), 0.5*log(2*pi*sigma2(i,:))')), fmin, fmax);
+            GUt(i) = quadgk(@(f) reshape(mean(multi_npdf(f(:),Ef(i,:),(Varf(i,:))) ...
+                                      .*bsxfun(@minus,-bsxfun(@rdivide,(repmat((y(i)-f(:)'),nsamples,1)).^2,(2.*sigma2(i,:))'), 0.5*log(2*pi*sigma2(i,:))')),size(f,1),size(f,2)), fmin, fmax);
           end
           waic = BUt-2*(BUt-GUt);
         else
@@ -206,8 +206,8 @@
             end
             fmin = mean(Ef(i,:) - 9*sqrt(Varf(i,:)));
             fmax = mean(Ef(i,:) + 9*sqrt(Varf(i,:)));
-            GUt(i) = quadgk(@(f) mean(multi_npdf(f,Ef(i,:),(Varf(i,:))) ...
-                                      .*llvec(gp_array, y(i), f, z1)), fmin, fmax);
+            GUt(i) = quadgk(@(f) reshape(mean(multi_npdf(f(:),Ef(i,:),(Varf(i,:))) ...
+                                      .*llvec(gp_array, y(i), f(:), z1)), size(f,1),size(f,2)), fmin, fmax);
           end
           waic = BUt-2*(BUt-GUt);
         end
@@ -413,10 +413,10 @@
           end
           fmin = sum(weight.*Ef(i,:) - 9*weight.*sqrt(Varf(i,:)));
           fmax = sum(weight.*Ef(i,:) + 9*weight.*sqrt(Varf(i,:)));
-          Elog(i) = quadgk(@(f) sum(bsxfun(@times, multi_npdf(f,Ef(i,:),(Varf(i,:))),weight') ...
-                                    .*llvec(gp, y(i), f, z1).^2), fmin, fmax);
-          Elog2(i) = quadgk(@(f) sum(bsxfun(@times, multi_npdf(f,Ef(i,:),(Varf(i,:))),weight') ...
-                                     .*llvec(gp, y(i), f, z1)), fmin, fmax);
+          Elog(i) = quadgk(@(f) reshape(sum(bsxfun(@times, multi_npdf(f,Ef(i,:),(Varf(i,:))),weight') ...
+                                    .*llvec(gp, y(i), f, z1).^2),size(f,1),size(f,2)), fmin, fmax);
+          Elog2(i) = quadgk(@(f) reshape(sum(bsxfun(@times, multi_npdf(f,Ef(i,:),(Varf(i,:))),weight') ...
+                                     .*llvec(gp, y(i), f, z1)),size(f,1),size(f,2)), fmin, fmax);
         end
         Elog2 = Elog2.^2;
         Vn = (Elog-Elog2);
@@ -449,8 +449,8 @@
           end
           fmin = sum(weight.*Ef(i,:) - 9*weight.*sqrt(Varf(i,:)));
           fmax = sum(weight.*Ef(i,:) + 9*weight.*sqrt(Varf(i,:)));
-          GUt(i) = quadgk(@(f) sum(bsxfun(@times, multi_npdf(f,Ef(i,:),(Varf(i,:))),weight') ...
-                                   .*llvec(gp, y(i), f, z1)), fmin, fmax);
+          GUt(i) = quadgk(@(f) reshape(sum(bsxfun(@times, multi_npdf(f,Ef(i,:),(Varf(i,:))),weight') ...
+                                   .*llvec(gp, y(i), f, z1)),size(f,1),size(f,2)), fmin, fmax);
         end
         waic = BUt-2*(BUt-GUt);
 
diff --git a/gp/gpcf_cat.m b/gp/gpcf_cat.m
index 44f98e94..9b4070ac 100644
--- a/gp/gpcf_cat.m
+++ b/gp/gpcf_cat.m
@@ -30,10 +30,10 @@
 
   ip=inputParser;
   ip.FunctionName = 'GPCF_CAT';
-  ip.addOptional('gpcf', [], @isstruct);
-  ip.addParamValue('selectedVariables',[], ...
+  ip=iparser(ip,'addOptional','gpcf', [], @isstruct);
+  ip=iparser(ip,'addParamValue','selectedVariables',[], ...
                    @(x) isempty(x) || (isvector(x) && all(x>0)));
-  ip.parse(varargin{:});
+  ip=iparser(ip,'parse',varargin{:});
   gpcf=ip.Results.gpcf;
 
   if isempty(gpcf)
diff --git a/gp/gpcf_constant.m b/gp/gpcf_constant.m
index 52026e84..8eab62a1 100644
--- a/gp/gpcf_constant.m
+++ b/gp/gpcf_constant.m
@@ -32,10 +32,10 @@
 
   ip=inputParser;
   ip.FunctionName = 'GPCF_CONSTANT';
-  ip.addOptional('gpcf', [], @isstruct);
-  ip.addParamValue('constSigma2',.1, @(x) isscalar(x) && x>0);
-  ip.addParamValue('constSigma2_prior',prior_sqrtt(), @(x) isstruct(x) || isempty(x));
-  ip.parse(varargin{:});
+  ip=iparser(ip,'addOptional','gpcf', [], @isstruct);
+  ip=iparser(ip,'addParamValue','constSigma2',0.1, @(x) isscalar(x) && x>0);
+  ip=iparser(ip,'addParamValue','constSigma2_prior',prior_sqrtt(), @(x) isstruct(x) || isempty(x));
+  ip=iparser(ip,'parse',varargin{:});
   gpcf=ip.Results.gpcf;
 
   if isempty(gpcf)
diff --git a/gp/gpcf_exp.m b/gp/gpcf_exp.m
index 0a159178..a0dd244b 100755
--- a/gp/gpcf_exp.m
+++ b/gp/gpcf_exp.m
@@ -47,17 +47,17 @@
   
   ip=inputParser;
   ip.FunctionName = 'GPCF_EXP';
-  ip.addOptional('gpcf', [], @isstruct);
-  ip.addParamValue('magnSigma2',0.1, @(x) isscalar(x) && x>0);
-  ip.addParamValue('lengthScale',1, @(x) isvector(x) && all(x>0));
-  ip.addParamValue('metric',[], @isstruct);
-  ip.addParamValue('magnSigma2_prior', prior_logunif(), ...
+  ip=iparser(ip,'addOptional','gpcf', [], @isstruct);
+  ip=iparser(ip,'addParamValue','magnSigma2',0.1, @(x) isscalar(x) && x>0);
+  ip=iparser(ip,'addParamValue','lengthScale',1, @(x) isvector(x) && all(x>0));
+  ip=iparser(ip,'addParamValue','metric',[], @isstruct);
+  ip=iparser(ip,'addParamValue','magnSigma2_prior', prior_logunif(), ...
                    @(x) isstruct(x) || isempty(x));
-  ip.addParamValue('lengthScale_prior',prior_t(), ...
+  ip=iparser(ip,'addParamValue','lengthScale_prior',prior_t(), ...
                    @(x) isstruct(x) || isempty(x));
-  ip.addParamValue('selectedVariables',[], @(x) isempty(x) || ...
+  ip=iparser(ip,'addParamValue','selectedVariables',[], @(x) isempty(x) || ...
                    (isvector(x) && all(x>0)));
-  ip.parse(varargin{:});
+  ip=iparser(ip,'parse',varargin{:});
   gpcf=ip.Results.gpcf;
   
   if isempty(gpcf)
diff --git a/gp/gpcf_linear.m b/gp/gpcf_linear.m
index 4cf60925..223f57b1 100644
--- a/gp/gpcf_linear.m
+++ b/gp/gpcf_linear.m
@@ -38,11 +38,11 @@
 
   ip=inputParser;
   ip.FunctionName = 'GPCF_LINEAR';
-  ip.addOptional('gpcf', [], @isstruct);
-  ip.addParamValue('coeffSigma2',10, @(x) isvector(x) && all(x>0));
-  ip.addParamValue('coeffSigma2_prior',prior_logunif, @(x) isstruct(x) || isempty(x));
-  ip.addParamValue('selectedVariables',[], @(x) isvector(x) && all(x>0));
-  ip.parse(varargin{:});
+  ip=iparser(ip,'addOptional','gpcf', [], @isstruct);
+  ip=iparser(ip,'addParamValue','coeffSigma2',10, @(x) isvector(x) && all(x>0));
+  ip=iparser(ip,'addParamValue','coeffSigma2_prior',prior_logunif, @(x) isstruct(x) || isempty(x));
+  ip=iparser(ip,'addParamValue','selectedVariables',[], @(x) isvector(x) && all(x>0));
+  ip=iparser(ip,'parse',varargin{:});
   gpcf=ip.Results.gpcf;
 
   if isempty(gpcf)
@@ -973,4 +973,4 @@
   dQc   = dQc(:,:,ind);
   dPinf = dPinf(:,:,ind);
   
-end
\ No newline at end of file
+end
diff --git a/gp/gpcf_mask.m b/gp/gpcf_mask.m
index 3eb275e2..0348c9f9 100644
--- a/gp/gpcf_mask.m
+++ b/gp/gpcf_mask.m
@@ -30,10 +30,10 @@
 
   ip=inputParser;
   ip.FunctionName = 'GPCF_MASK';
-  ip.addOptional('gpcf', [], @isstruct);
-  ip.addParamValue('selectedVariables',[], ...
+  ip=iparser(ip,'addOptional','gpcf', [], @isstruct);
+  ip=iparser(ip,'addParamValue','selectedVariables',[], ...
                    @(x) isempty(x) || (isvector(x) && all(x>0)));
-  ip.parse(varargin{:});
+  ip=iparser(ip,'parse',varargin{:});
   gpcf=ip.Results.gpcf;
 
   if isempty(gpcf)
diff --git a/gp/gpcf_matern32.m b/gp/gpcf_matern32.m
index 4aae46f8..7625a69e 100755
--- a/gp/gpcf_matern32.m
+++ b/gp/gpcf_matern32.m
@@ -47,17 +47,17 @@
   
   ip=inputParser;
   ip.FunctionName = 'GPCF_MATERN32';
-  ip.addOptional('gpcf', [], @isstruct);
-  ip.addParamValue('magnSigma2',0.1, @(x) isscalar(x) && x>0);
-  ip.addParamValue('lengthScale',1, @(x) isvector(x) && all(x>0));
-  ip.addParamValue('metric',[], @isstruct);
-  ip.addParamValue('magnSigma2_prior', prior_logunif(), ...
+  ip=iparser(ip,'addOptional','gpcf', [], @isstruct);
+  ip=iparser(ip,'addParamValue','magnSigma2',0.1, @(x) isscalar(x) && x>0);
+  ip=iparser(ip,'addParamValue','lengthScale',1, @(x) isvector(x) && all(x>0));
+  ip=iparser(ip,'addParamValue','metric',[], @isstruct);
+  ip=iparser(ip,'addParamValue','magnSigma2_prior', prior_logunif(), ...
                    @(x) isstruct(x) || isempty(x));
-  ip.addParamValue('lengthScale_prior',prior_t(), ...
+  ip=iparser(ip,'addParamValue','lengthScale_prior',prior_t(), ...
                    @(x) isstruct(x) || isempty(x));
-  ip.addParamValue('selectedVariables',[], @(x) isempty(x) || ...
+  ip=iparser(ip,'addParamValue','selectedVariables',[], @(x) isempty(x) || ...
                    (isvector(x) && all(x>0)));
-  ip.parse(varargin{:});
+  ip=iparser(ip,'parse',varargin{:});
   gpcf=ip.Results.gpcf;
   
   if isempty(gpcf)
diff --git a/gp/gpcf_matern52.m b/gp/gpcf_matern52.m
index 1d0836ab..1bb4f3d8 100755
--- a/gp/gpcf_matern52.m
+++ b/gp/gpcf_matern52.m
@@ -47,17 +47,17 @@
   
   ip=inputParser;
   ip.FunctionName = 'GPCF_MATERN52';
-  ip.addOptional('gpcf', [], @isstruct);
-  ip.addParamValue('magnSigma2',0.1, @(x) isscalar(x) && x>0);
-  ip.addParamValue('lengthScale',1, @(x) isvector(x) && all(x>0));
-  ip.addParamValue('metric',[], @isstruct);
-  ip.addParamValue('magnSigma2_prior', prior_logunif(), ...
+  ip=iparser(ip,'addOptional','gpcf', [], @isstruct);
+  ip=iparser(ip,'addParamValue','magnSigma2',0.1, @(x) isscalar(x) && x>0);
+  ip=iparser(ip,'addParamValue','lengthScale',1, @(x) isvector(x) && all(x>0));
+  ip=iparser(ip,'addParamValue','metric',[], @isstruct);
+  ip=iparser(ip,'addParamValue','magnSigma2_prior', prior_logunif(), ...
                    @(x) isstruct(x) || isempty(x));
-  ip.addParamValue('lengthScale_prior',prior_t(), ...
+  ip=iparser(ip,'addParamValue','lengthScale_prior',prior_t(), ...
                    @(x) isstruct(x) || isempty(x));
-  ip.addParamValue('selectedVariables',[], @(x) isempty(x) || ...
+  ip=iparser(ip,'addParamValue','selectedVariables',[], @(x) isempty(x) || ...
                    (isvector(x) && all(x>0)));
-  ip.parse(varargin{:});
+  ip=iparser(ip,'parse',varargin{:});
   gpcf=ip.Results.gpcf;
   
   if isempty(gpcf)
diff --git a/gp/gpcf_neuralnetwork.m b/gp/gpcf_neuralnetwork.m
index 02aa6610..7b3cb66f 100755
--- a/gp/gpcf_neuralnetwork.m
+++ b/gp/gpcf_neuralnetwork.m
@@ -39,13 +39,13 @@
 
   ip=inputParser;
   ip.FunctionName = 'GPCF_NEURALNETWORK';
-  ip.addOptional('gpcf', [], @isstruct);
-  ip.addParamValue('biasSigma2',0.1, @(x) isscalar(x) && x>0);
-  ip.addParamValue('weightSigma2',10, @(x) isvector(x) && all(x>0));
-  ip.addParamValue('biasSigma2_prior',prior_logunif, @(x) isstruct(x) || isempty(x));
-  ip.addParamValue('weightSigma2_prior',prior_logunif, @(x) isstruct(x) || isempty(x));
-  ip.addParamValue('selectedVariables',[], @(x) isvector(x) && all(x>0));
-  ip.parse(varargin{:});
+  ip=iparser(ip,'addOptional','gpcf', [], @isstruct);
+  ip=iparser(ip,'addParamValue','biasSigma2',0.1, @(x) isscalar(x) && x>0);
+  ip=iparser(ip,'addParamValue','weightSigma2',10, @(x) isvector(x) && all(x>0));
+  ip=iparser(ip,'addParamValue','biasSigma2_prior',prior_logunif, @(x) isstruct(x) || isempty(x));
+  ip=iparser(ip,'addParamValue','weightSigma2_prior',prior_logunif, @(x) isstruct(x) || isempty(x));
+  ip=iparser(ip,'addParamValue','selectedVariables',[], @(x) isvector(x) && all(x>0));
+  ip=iparser(ip,'parse',varargin{:});
   gpcf=ip.Results.gpcf;
   
   if isempty(gpcf)
diff --git a/gp/gpcf_noise.m b/gp/gpcf_noise.m
index b5162b0c..17b71c48 100644
--- a/gp/gpcf_noise.m
+++ b/gp/gpcf_noise.m
@@ -32,10 +32,10 @@
 
   ip=inputParser;
   ip.FunctionName = 'GPCF_NOISE';
-  ip.addOptional('gpcf', [], @isstruct);
-  ip.addParamValue('noiseSigma2',0.1, @(x) isscalar(x) && x>0);
-  ip.addParamValue('noiseSigma2_prior',prior_logunif, @(x) isstruct(x) || isempty(x));
-  ip.parse(varargin{:});
+  ip=iparser(ip,'addOptional','gpcf', [], @isstruct);
+  ip=iparser(ip,'addParamValue','noiseSigma2',0.1, @(x) isscalar(x) && x>0);
+  ip=iparser(ip,'addParamValue','noiseSigma2_prior',prior_logunif, @(x) isstruct(x) || isempty(x));
+  ip=iparser(ip,'parse',varargin{:});
   gpcf=ip.Results.gpcf;
   
   if isempty(gpcf)
@@ -427,4 +427,4 @@
   dQc   = dQc(:,:,ind);
   dPinf = dPinf(:,:,ind);
   
-end
\ No newline at end of file
+end
diff --git a/gp/gpcf_periodic.m b/gp/gpcf_periodic.m
index f6ab1d86..46f35918 100644
--- a/gp/gpcf_periodic.m
+++ b/gp/gpcf_periodic.m
@@ -47,6 +47,7 @@
 %  See also
 %    GP_SET, GPCF_*, PRIOR_*
 %
+  
 % Copyright (c) 2009-2010 Heikki Peura
 % Copyright (c) 2010 Aki Vehtari
 % Copyright (c) 2014 Arno Solin and Jukka Koskenranta
@@ -57,22 +58,22 @@
 
   ip=inputParser;
   ip.FunctionName = 'GPCF_PERIODIC';
-  ip.addOptional('gpcf', [], @isstruct);
-  ip.addParamValue('magnSigma2',0.1, @(x) isscalar(x) && x>0);
-  ip.addParamValue('lengthScale',10, @(x) isvector(x) && all(x>0));
-  ip.addParamValue('period',1, @(x) isscalar(x) && x>0);
-  ip.addParamValue('lengthScale_sexp',10, @(x) isvector(x) && all(x>0));
-  ip.addParamValue('decay',0, @(x) isscalar(x) && (x==0||x==1));
-  ip.addParamValue('kalman_deg',[], @(x) isscalar(x) && mod(x,1)==0);
-  ip.addParamValue('kalman_deg_sexp',[], @(x) isscalar(x) && mod(x,1)==0);
-  ip.addParamValue('kalman_valid',[], @(x) isscalar(x) && (x==0||x==1));
-  ip.addParamValue('magnSigma2_prior',prior_logunif, @(x) isstruct(x) || isempty(x));
-  ip.addParamValue('lengthScale_prior',prior_t, @(x) isstruct(x) || isempty(x));
-  ip.addParamValue('lengthScale_sexp_prior',[], @(x) isstruct(x) || isempty(x));
-  ip.addParamValue('period_prior',[], @(x) isstruct(x) || isempty(x));
-  ip.addParamValue('selectedVariables',[], @(x) isempty(x) || ...
+  ip=iparser(ip,'addOptional','gpcf', [], @isstruct);
+  ip=iparser(ip,'addParamValue','magnSigma2',0.1, @(x) isscalar(x) && x>0);
+  ip=iparser(ip,'addParamValue','lengthScale',10, @(x) isvector(x) && all(x>0));
+  ip=iparser(ip,'addParamValue','period',1, @(x) isscalar(x) && x>0);
+  ip=iparser(ip,'addParamValue','lengthScale_sexp',10, @(x) isvector(x) && all(x>0));
+  ip=iparser(ip,'addParamValue','decay',0, @(x) isscalar(x) && (x==0||x==1));
+  ip=iparser(ip,'addParamValue','kalman_deg',[], @(x) isscalar(x) && mod(x,1)==0);
+  ip=iparser(ip,'addParamValue','kalman_deg_sexp',[], @(x) isscalar(x) && mod(x,1)==0);
+  ip=iparser(ip,'addParamValue','kalman_valid',[], @(x) isscalar(x) && (x==0||x==1));
+  ip=iparser(ip,'addParamValue','magnSigma2_prior',prior_logunif, @(x) isstruct(x) || isempty(x));
+  ip=iparser(ip,'addParamValue','lengthScale_prior',prior_t, @(x) isstruct(x) || isempty(x));
+  ip=iparser(ip,'addParamValue','lengthScale_sexp_prior',[], @(x) isstruct(x) || isempty(x));
+  ip=iparser(ip,'addParamValue','period_prior',[], @(x) isstruct(x) || isempty(x));
+  ip=iparser(ip,'addParamValue','selectedVariables',[], @(x) isempty(x) || ...
                    (isvector(x) && all(x>0)));
-  ip.parse(varargin{:});
+  ip=iparser(ip,'parse',varargin{:});
   gpcf=ip.Results.gpcf;
   
   if isempty(gpcf)
@@ -142,7 +143,6 @@
     gpcf.fh.cfg = @gpcf_periodic_cfg;
     gpcf.fh.ginput = @gpcf_periodic_ginput;
     gpcf.fh.cov = @gpcf_periodic_cov;
-    gpcf.fh.covvec = @gpcf_periodic_covvec;
     gpcf.fh.trcov  = @gpcf_periodic_trcov;
     gpcf.fh.trvar  = @gpcf_periodic_trvar;
     gpcf.fh.recappend = @gpcf_periodic_recappend;
@@ -1125,13 +1125,6 @@
 %      df(t)/dt = F f(t) + L w(t),
 %    where w(t) is a white noise process. The observation model now 
 %    corresponds to y_k = H f(t_k) + r_k, where r_k ~ N(0,sigma2).
-%
-%  References:
-%    Arno Solin and Simo Sarkka (2014). Explicit link between periodic 
-%    covariance functions and state space models. Accepted for 
-%    publication in Proceedings of the Seventeenth International 
-%    Conference on Artifcial Intelligence and Statistics (AISTATS 2014).
-%
 
   % Check arguments
   if nargin < 2, x = []; end
diff --git a/gp/gpcf_ppcs0.m b/gp/gpcf_ppcs0.m
index 5ac8f467..e0a4aa71 100644
--- a/gp/gpcf_ppcs0.m
+++ b/gp/gpcf_ppcs0.m
@@ -62,19 +62,19 @@
   
   ip=inputParser;
   ip.FunctionName = 'GPCF_PPCS0';
-  ip.addOptional('gpcf', [], @isstruct);
-  ip.addParamValue('nin',[], @(x) isscalar(x) && x>0 && mod(x,1)==0);
-  ip.addParamValue('magnSigma2',0.1, @(x) isscalar(x) && x>0);
-  ip.addParamValue('lengthScale',1, @(x) isvector(x) && all(x>0));
-  ip.addParamValue('l_nin',[], @(x) isscalar(x) && x>0 && mod(x,1)==0);
-  ip.addParamValue('metric',[], @isstruct);
-  ip.addParamValue('magnSigma2_prior', prior_logunif(), ...
+  ip=iparser(ip,'addOptional','gpcf', [], @isstruct);
+  ip=iparser(ip,'addParamValue','nin',[], @(x) isscalar(x) && x>0 && mod(x,1)==0);
+  ip=iparser(ip,'addParamValue','magnSigma2',0.1, @(x) isscalar(x) && x>0);
+  ip=iparser(ip,'addParamValue','lengthScale',1, @(x) isvector(x) && all(x>0));
+  ip=iparser(ip,'addParamValue','l_nin',[], @(x) isscalar(x) && x>0 && mod(x,1)==0);
+  ip=iparser(ip,'addParamValue','metric',[], @isstruct);
+  ip=iparser(ip,'addParamValue','magnSigma2_prior', prior_logunif(), ...
                    @(x) isstruct(x) || isempty(x));
-  ip.addParamValue('lengthScale_prior',prior_t(), ...
+  ip=iparser(ip,'addParamValue','lengthScale_prior',prior_t(), ...
                    @(x) isstruct(x) || isempty(x));
-  ip.addParamValue('selectedVariables',[], @(x) isempty(x) || ...
+  ip=iparser(ip,'addParamValue','selectedVariables',[], @(x) isempty(x) || ...
                    (isvector(x) && all(x>0)));
-  ip.parse(varargin{:});
+  ip=iparser(ip,'parse',varargin{:});
   gpcf=ip.Results.gpcf;
 
   if isempty(gpcf)
diff --git a/gp/gpcf_ppcs1.m b/gp/gpcf_ppcs1.m
index 5e7661fa..f07511c1 100644
--- a/gp/gpcf_ppcs1.m
+++ b/gp/gpcf_ppcs1.m
@@ -62,19 +62,19 @@
   
   ip=inputParser;
   ip.FunctionName = 'GPCF_PPCS1';
-  ip.addOptional('gpcf', [], @isstruct);
-  ip.addParamValue('nin',[], @(x) isscalar(x) && x>0 && mod(x,1)==0);
-  ip.addParamValue('magnSigma2',0.1, @(x) isscalar(x) && x>0);
-  ip.addParamValue('lengthScale',1, @(x) isvector(x) && all(x>0));
-  ip.addParamValue('l_nin',[], @(x) isscalar(x) && x>0 && mod(x,1)==0);
-  ip.addParamValue('metric',[], @isstruct);
-  ip.addParamValue('magnSigma2_prior', prior_logunif(), ...
+  ip=iparser(ip,'addOptional','gpcf', [], @isstruct);
+  ip=iparser(ip,'addParamValue','nin',[], @(x) isscalar(x) && x>0 && mod(x,1)==0);
+  ip=iparser(ip,'addParamValue','magnSigma2',0.1, @(x) isscalar(x) && x>0);
+  ip=iparser(ip,'addParamValue','lengthScale',1, @(x) isvector(x) && all(x>0));
+  ip=iparser(ip,'addParamValue','l_nin',[], @(x) isscalar(x) && x>0 && mod(x,1)==0);
+  ip=iparser(ip,'addParamValue','metric',[], @isstruct);
+  ip=iparser(ip,'addParamValue','magnSigma2_prior', prior_logunif(), ...
                    @(x) isstruct(x) || isempty(x));
-  ip.addParamValue('lengthScale_prior',prior_t(), ...
+  ip=iparser(ip,'addParamValue','lengthScale_prior',prior_t(), ...
                    @(x) isstruct(x) || isempty(x));
-  ip.addParamValue('selectedVariables',[], @(x) isempty(x) || ...
+  ip=iparser(ip,'addParamValue','selectedVariables',[], @(x) isempty(x) || ...
                    (isvector(x) && all(x>0)));
-  ip.parse(varargin{:});
+  ip=iparser(ip,'parse',varargin{:});
   gpcf=ip.Results.gpcf;
 
   if isempty(gpcf)
diff --git a/gp/gpcf_ppcs2.m b/gp/gpcf_ppcs2.m
index aea2528d..bc610b4e 100755
--- a/gp/gpcf_ppcs2.m
+++ b/gp/gpcf_ppcs2.m
@@ -62,19 +62,19 @@
   
   ip=inputParser;
   ip.FunctionName = 'GPCF_PPCS2';
-  ip.addOptional('gpcf', [], @isstruct);
-  ip.addParamValue('nin',[], @(x) isscalar(x) && x>0 && mod(x,1)==0);
-  ip.addParamValue('magnSigma2',0.1, @(x) isscalar(x) && x>0);
-  ip.addParamValue('lengthScale',1, @(x) isvector(x) && all(x>0));
-  ip.addParamValue('l_nin',[], @(x) isscalar(x) && x>0 && mod(x,1)==0);
-  ip.addParamValue('metric',[], @isstruct);
-  ip.addParamValue('magnSigma2_prior', prior_logunif(), ...
+  ip=iparser(ip,'addOptional','gpcf', [], @isstruct);
+  ip=iparser(ip,'addParamValue','nin',[], @(x) isscalar(x) && x>0 && mod(x,1)==0);
+  ip=iparser(ip,'addParamValue','magnSigma2',0.1, @(x) isscalar(x) && x>0);
+  ip=iparser(ip,'addParamValue','lengthScale',1, @(x) isvector(x) && all(x>0));
+  ip=iparser(ip,'addParamValue','l_nin',[], @(x) isscalar(x) && x>0 && mod(x,1)==0);
+  ip=iparser(ip,'addParamValue','metric',[], @isstruct);
+  ip=iparser(ip,'addParamValue','magnSigma2_prior', prior_logunif(), ...
                    @(x) isstruct(x) || isempty(x));
-  ip.addParamValue('lengthScale_prior',prior_t(), ...
+  ip=iparser(ip,'addParamValue','lengthScale_prior',prior_t(), ...
                    @(x) isstruct(x) || isempty(x));
-  ip.addParamValue('selectedVariables',[], @(x) isempty(x) || ...
+  ip=iparser(ip,'addParamValue','selectedVariables',[], @(x) isempty(x) || ...
                    (isvector(x) && all(x>0)));
-  ip.parse(varargin{:});
+  ip=iparser(ip,'parse',varargin{:});
   gpcf=ip.Results.gpcf;
 
   if isempty(gpcf)
diff --git a/gp/gpcf_ppcs3.m b/gp/gpcf_ppcs3.m
index 75cd2b2f..fa108a69 100644
--- a/gp/gpcf_ppcs3.m
+++ b/gp/gpcf_ppcs3.m
@@ -63,19 +63,19 @@
   
   ip=inputParser;
   ip.FunctionName = 'GPCF_PPCS3';
-  ip.addOptional('gpcf', [], @isstruct);
-  ip.addParamValue('nin',[], @(x) isscalar(x) && x>0 && mod(x,1)==0);
-  ip.addParamValue('magnSigma2',0.1, @(x) isscalar(x) && x>0);
-  ip.addParamValue('lengthScale',1, @(x) isvector(x) && all(x>0));
-  ip.addParamValue('l_nin',[], @(x) isscalar(x) && x>0 && mod(x,1)==0);
-  ip.addParamValue('metric',[], @isstruct);
-  ip.addParamValue('magnSigma2_prior', prior_logunif(), ...
+  ip=iparser(ip,'addOptional','gpcf', [], @isstruct);
+  ip=iparser(ip,'addParamValue','nin',[], @(x) isscalar(x) && x>0 && mod(x,1)==0);
+  ip=iparser(ip,'addParamValue','magnSigma2',0.1, @(x) isscalar(x) && x>0);
+  ip=iparser(ip,'addParamValue','lengthScale',1, @(x) isvector(x) && all(x>0));
+  ip=iparser(ip,'addParamValue','l_nin',[], @(x) isscalar(x) && x>0 && mod(x,1)==0);
+  ip=iparser(ip,'addParamValue','metric',[], @isstruct);
+  ip=iparser(ip,'addParamValue','magnSigma2_prior', prior_logunif(), ...
                    @(x) isstruct(x) || isempty(x));
-  ip.addParamValue('lengthScale_prior',prior_t(), ...
+  ip=iparser(ip,'addParamValue','lengthScale_prior',prior_t(), ...
                    @(x) isstruct(x) || isempty(x));
-  ip.addParamValue('selectedVariables',[], @(x) isempty(x) || ...
+  ip=iparser(ip,'addParamValue','selectedVariables',[], @(x) isempty(x) || ...
                    (isvector(x) && all(x>0)));
-  ip.parse(varargin{:});
+  ip=iparser(ip,'parse',varargin{:});
   gpcf=ip.Results.gpcf;
 
   if isempty(gpcf)
diff --git a/gp/gpcf_prod.m b/gp/gpcf_prod.m
index 8f044e0f..2bf96f07 100644
--- a/gp/gpcf_prod.m
+++ b/gp/gpcf_prod.m
@@ -19,9 +19,9 @@
 
   ip=inputParser;
   ip.FunctionName = 'GPCF_PROD';
-  ip.addOptional('gpcf', [], @isstruct);
-  ip.addParamValue('cf',[], @iscell);
-  ip.parse(varargin{:});
+  ip=iparser(ip,'addOptional','gpcf', [], @isstruct);
+  ip=iparser(ip,'addParamValue','cf',[], @iscell);
+  ip=iparser(ip,'parse',varargin{:});
   gpcf=ip.Results.gpcf;
 
   if isempty(gpcf)
diff --git a/gp/gpcf_rq.m b/gp/gpcf_rq.m
index c1807629..b11fd575 100644
--- a/gp/gpcf_rq.m
+++ b/gp/gpcf_rq.m
@@ -51,22 +51,22 @@
   
   ip=inputParser;
   ip.FunctionName = 'GPCF_RQ';
-  ip.addOptional('gpcf', [], @isstruct);
-  ip.addParamValue('magnSigma2',0.1, @(x) isscalar(x) && x>0);
-  ip.addParamValue('lengthScale',1, @(x) isvector(x) && all(x>0));
-  ip.addParamValue('alpha',20, @(x) isscalar(x) && x>0);
-  ip.addParamValue('metric',[], @isstruct);
-  ip.addParamValue('magnSigma2_prior', prior_logunif(), ...
+  ip=iparser(ip,'addOptional','gpcf', [], @isstruct);
+  ip=iparser(ip,'addParamValue','magnSigma2',0.1, @(x) isscalar(x) && x>0);
+  ip=iparser(ip,'addParamValue','lengthScale',1, @(x) isvector(x) && all(x>0));
+  ip=iparser(ip,'addParamValue','alpha',20, @(x) isscalar(x) && x>0);
+  ip=iparser(ip,'addParamValue','metric',[], @isstruct);
+  ip=iparser(ip,'addParamValue','magnSigma2_prior', prior_logunif(), ...
                    @(x) isstruct(x) || isempty(x));
-  ip.addParamValue('lengthScale_prior',prior_t(), ...
+  ip=iparser(ip,'addParamValue','lengthScale_prior',prior_t(), ...
                    @(x) isstruct(x) || isempty(x));
-  ip.addParamValue('alpha_prior', prior_unif(), ...
+  ip=iparser(ip,'addParamValue','alpha_prior', prior_unif(), ...
                    @(x) isstruct(x) || isempty(x));
-  ip.addParamValue('selectedVariables',[], @(x) isempty(x) || ...
+  ip=iparser(ip,'addParamValue','selectedVariables',[], @(x) isempty(x) || ...
                    (isvector(x) && all(x>0)));
-  ip.addParamValue('kalman_rq_deg',6, @(x) isscalar(x) && mod(x,1)==0);
-  ip.addParamValue('kalman_se_deg',6, @(x) isscalar(x) && mod(x,1)==0);
-  ip.parse(varargin{:});
+  ip=iparser(ip,'addParamValue','kalman_rq_deg',6, @(x) isscalar(x) && mod(x,1)==0);
+  ip=iparser(ip,'addParamValue','kalman_se_deg',6, @(x) isscalar(x) && mod(x,1)==0);
+  ip=iparser(ip,'parse',varargin{:});
   gpcf=ip.Results.gpcf;
 
   if isempty(gpcf)
@@ -1014,4 +1014,4 @@
   dQc   = dQc(:,:,ind);
   dPinf = dPinf(:,:,ind);
   
-end
\ No newline at end of file
+end
diff --git a/gp/gpcf_scaled.m b/gp/gpcf_scaled.m
index 6b6de898..ce59aa9a 100644
--- a/gp/gpcf_scaled.m
+++ b/gp/gpcf_scaled.m
@@ -29,10 +29,10 @@
 
   ip=inputParser;
   ip.FunctionName = 'GPCF_SCALED';
-  ip.addOptional('gpcf', [], @isstruct);
-  ip.addParamValue('cf',[], @isstruct);
-  ip.addParamValue('scaler',1, @(x) isscalar(x) && x>0);
-  ip.parse(varargin{:});
+  ip=iparser(ip,'addOptional','gpcf', [], @isstruct);
+  ip=iparser(ip,'addParamValue','cf',[], @isstruct);
+  ip=iparser(ip,'addParamValue','scaler',1, @(x) isscalar(x) && x>0);
+  ip=iparser(ip,'parse',varargin{:});
   gpcf=ip.Results.gpcf;
 
   if isempty(gpcf)
diff --git a/gp/gpcf_sexp.m b/gp/gpcf_sexp.m
index f5889e01..4d1f8fad 100755
--- a/gp/gpcf_sexp.m
+++ b/gp/gpcf_sexp.m
@@ -44,18 +44,18 @@
   % inputParser checks the arguments and assigns some default values
   ip=inputParser;
   ip.FunctionName = 'GPCF_SEXP';
-  ip.addOptional('gpcf', [], @isstruct);
-  ip.addParamValue('magnSigma2',0.1, @(x) isscalar(x) && x>0);
-  ip.addParamValue('lengthScale',1, @(x) isvector(x) && all(x>0));
-  ip.addParamValue('kalman_deg',[], @(x) isscalar(x) && mod(x,1)==0);
-  ip.addParamValue('metric',[], @isstruct);
-  ip.addParamValue('magnSigma2_prior', prior_logunif(), ...
+  ip=iparser(ip,'addOptional','gpcf', [], @isstruct);
+  ip=iparser(ip,'addParamValue','magnSigma2',0.1, @(x) isscalar(x) && x>0);
+  ip=iparser(ip,'addParamValue','lengthScale',1, @(x) isvector(x) && all(x>0));
+  ip=iparser(ip,'addParamValue','metric',[], @isstruct);
+  ip=iparser(ip,'addParamValue','kalman_deg',[], @(x) isscalar(x) && mod(x,1)==0);
+  ip=iparser(ip,'addParamValue','magnSigma2_prior', prior_logunif(), ...
                    @(x) isstruct(x) || isempty(x));
-  ip.addParamValue('lengthScale_prior',prior_t(), ...
+  ip=iparser(ip,'addParamValue','lengthScale_prior',prior_t(), ...
                    @(x) isstruct(x) || isempty(x));
-  ip.addParamValue('selectedVariables',[], @(x) isempty(x) || ...
+  ip=iparser(ip,'addParamValue','selectedVariables',[], @(x) isempty(x) || ...
                    (isvector(x) && all(x>0)));
-  ip.parse(varargin{:});
+  ip=iparser(ip,'parse',varargin{:});
   gpcf=ip.Results.gpcf;
 
   if isempty(gpcf)
diff --git a/gp/gpcf_squared.m b/gp/gpcf_squared.m
index 712385e7..471e3395 100644
--- a/gp/gpcf_squared.m
+++ b/gp/gpcf_squared.m
@@ -41,12 +41,12 @@
 
   ip=inputParser;
   ip.FunctionName = 'GPCF_SQUARED';
-  ip.addOptional('gpcf', [], @isstruct);
-  ip.addParamValue('interactions', 'off', @(x) ismember(x,{'on' 'off'}))
-  ip.addParamValue('coeffSigma2',10, @(x) isvector(x) && all(x>0));
-  ip.addParamValue('coeffSigma2_prior',prior_logunif, @(x) isstruct(x) || isempty(x));
-  ip.addParamValue('selectedVariables',[], @(x) isvector(x) && all(x>0));
-  ip.parse(varargin{:});
+  ip=iparser(ip,'addOptional', 'gpcf', [], @isstruct);
+  ip=iparser(ip,'addParamValue', 'interactions', 'off', @(x) ismember(x,{'on' 'off'}));
+  ip=iparser(ip,'addParamValue', 'coeffSigma2',10, @(x) isvector(x) && all(x>0));
+  ip=iparser(ip,'addParamValue', 'coeffSigma2_prior',prior_logunif, @(x) isstruct(x) || isempty(x));
+  ip=iparser(ip,'addParamValue', 'selectedVariables',[], @(x) isvector(x) && all(x>0));
+  ip=iparser(ip,'parse',w, gp, x, y, varargin{:});
   gpcf=ip.Results.gpcf;
 
   if isempty(gpcf)
diff --git a/gp/gpcf_sum.m b/gp/gpcf_sum.m
index 78c47974..99e9c23c 100644
--- a/gp/gpcf_sum.m
+++ b/gp/gpcf_sum.m
@@ -19,9 +19,9 @@
 
   ip=inputParser;
   ip.FunctionName = 'GPCF_SUM';
-  ip.addOptional('gpcf', [], @isstruct);
-  ip.addParamValue('cf',[], @iscell);
-  ip.parse(varargin{:});
+  ip=iparser(ip,'addOptional','gpcf', [], @isstruct);
+  ip=iparser(ip,'addParamValue','cf',[], @iscell);
+  ip=iparser(ip,'parse',varargin{:});
   gpcf=ip.Results.gpcf;
 
   if isempty(gpcf)
diff --git a/gp/gpep_e.m b/gp/gpep_e.m
index 1d3acb87..d852dbce 100644
--- a/gp/gpep_e.m
+++ b/gp/gpep_e.m
@@ -69,23 +69,23 @@
 % parse inputs
 ip=inputParser;
 ip.FunctionName = 'GPEP_E';
-ip.addRequired('w', @(x) ...
+ip=iparser(ip,'addRequired','w', @(x) ...
   isempty(x) || ...
   (ischar(x) && ismember(x, {'init' 'clearcache'})) || ...
   (isvector(x) && isreal(x) && all(isfinite(x))) || ...
   all(isnan(x)));
-ip.addRequired('gp',@isstruct);
-ip.addOptional('x', [], @(x) isnumeric(x) && isreal(x) && all(isfinite(x(:))))
-ip.addOptional('y', [], @(x) isnumeric(x) && isreal(x) && all(isfinite(x(:))))
-ip.addParamValue('z', [], @(x) isnumeric(x) && isreal(x) && all(isfinite(x(:))))
-ip.parse(w, gp, varargin{:});
+ip=iparser(ip,'addRequired','gp',@isstruct);
+ip=iparser(ip,'addOptional','x', [], @(x) isnumeric(x) && isreal(x) && all(isfinite(x(:))));
+ip=iparser(ip,'addOptional','y', [], @(x) isnumeric(x) && isreal(x) && all(isfinite(x(:))));
+ip=iparser(ip,'addParamValue','z', [], @(x) isnumeric(x) && isreal(x) && all(isfinite(x(:))));
+ip=iparser(ip,'parse',w, gp, varargin{:});
 x=ip.Results.x;
 y=ip.Results.y;
 z=ip.Results.z;
 
 if strcmp(w, 'init')
   % intialize cache
-  ch = [];
+  %ch = [];
   
   % return function handle to the nested function ep_algorithm
   % this way each gp has its own peristent memory for EP
@@ -108,34 +108,25 @@
   % call ep_algorithm using the function handle to the nested function
   % this way each gp has its own peristent memory for EP
   [e, edata, eprior, param] = gp.fh.ne(w, gp, x, y, z);
+end
 end
 
-  function [e, edata, eprior, param] = ep_algorithm(w, gp, x, y, z)
-    
-    if strcmp(w, 'clearcache')
-      ch=[];
-      return
-    end
-    if isempty(z)
-      datahash=hash_sha512([x y]);
-    else
-      datahash=hash_sha512([x y z]);
-    end
+  function [e, edata, eprior, param] = ep_algorithm(w, gp, x, y, z)    
     
     if isfield(gp.lik,'nondiagW') % non-diagonal W
       
-      if ~isempty(ch) && all(size(w)==size(ch.w)) && all(abs(w-ch.w)<1e-8) && isequal(datahash,ch.datahash)
-        % The covariance function parameters or data haven't changed
-        % so we can return the energy and the site parameters that are saved
-        e = ch.e;
-        edata = ch.edata;
-        eprior = ch.eprior;
-        param.tautilde = ch.tautilde;
-        param.nutilde = ch.nutilde;
-        param.BKnu=ch.BKnu;
-        param.B=ch.B;
-        param.cholP=ch.cholP;
-        param.invPBKnu=ch.invPBKnu;
+      if 0%~isempty(ch) && all(size(w)==size(ch.w)) && all(abs(w-ch.w)<1e-8) && isequal(datahash,ch.datahash)
+%         % The covariance function parameters or data haven't changed
+%         % so we can return the energy and the site parameters that are saved
+%         e = ch.e;
+%         edata = ch.edata;
+%         eprior = ch.eprior;
+%         param.tautilde = ch.tautilde;
+%         param.nutilde = ch.nutilde;
+%         param.BKnu=ch.BKnu;
+%         param.B=ch.B;
+%         param.cholP=ch.cholP;
+%         param.invPBKnu=ch.invPBKnu;
         
       else
         
@@ -512,54 +503,55 @@
         param.cholP=cholP;
         param.invPBKnu=invPBKnu;
         
-        % store values to the cache
-        ch.w = w;
-        ch.e = e;
-        ch.edata = edata;
-        ch.eprior = eprior;
-        ch.tautilde = tautilde;
-        ch.nutilde = nutilde;
-        ch.BKnu=BKnu;
-        ch.B=B;
-        ch.cholP=cholP;
-        ch.invPBKnu=invPBKnu;
-        
-        ch.datahash=datahash;
+%         % store values to the cache
+%         ch.w = w;
+%         ch.e = e;
+%         ch.edata = edata;
+%         ch.eprior = eprior;
+%         ch.tautilde = tautilde;
+%         ch.nutilde = nutilde;
+%         ch.BKnu=BKnu;
+%         ch.B=B;
+%         ch.cholP=cholP;
+%         ch.invPBKnu=invPBKnu;
+%         
+%         ch.datahash=datahash;
       end
       
     else % isfield(gp.lik,'nondiagW') % diagonal W
       
-      if ~isempty(ch) && all(size(w)==size(ch.w)) && all(abs(w-ch.w)<1e-8) && isequal(datahash,ch.datahash)
-        % The covariance function parameters or data haven't changed
-        % so we can return the energy and the site parameters that are saved
-        e = ch.e;
-        edata = ch.edata;
-        eprior = ch.eprior;
-        param.tautilde = ch.tautilde;
-        param.nutilde = ch.nutilde;
-        param.L = ch.L;
-        param.La2 = ch.La2;
-        param.b = ch.b;
-        param.muvec_i = ch.muvec_i;
-        param.sigm2vec_i = ch.sigm2vec_i;
-        param.logZ_i = ch.logZ_i;
-        param.eta = ch.eta;
-        if  isfield(gp.lik, 'int_likparam')
-          if ~(isfield(gp.lik,'joint_mean_magnitude') && gp.lik.joint_mean_magnitude)
-            if isfield(gp.lik, 'int_likparam') && gp.lik.int_likparam
-              param.mf2=ch.mf2;
-              param.Sigm2=ch.La2;
-            end
-            if isfield(gp.lik, 'int_magnitude') && gp.lik.int_magnitude
-              param.mf3=ch.mf3;
-              param.La3=ch.La3;
-            end
-          else
-            param.mf=ch.mf;
-            param.Sigm=ch.Sigm;
-            param.C=ch.C;
-          end
-        end
+
+      if 0%~isempty(ch) && all(size(w)==size(ch.w)) && all(abs(w-ch.w)<1e-8) && isequal(datahash,ch.datahash)
+%        % The covariance function parameters or data haven't changed
+%        % so we can return the energy and the site parameters that are saved
+%        e = ch.e;
+%        edata = ch.edata;
+%        eprior = ch.eprior;
+%        param.tautilde = ch.tautilde;
+%        param.nutilde = ch.nutilde;
+%        param.L = ch.L;
+%        param.La2 = ch.La2;
+%        param.b = ch.b;
+%        param.muvec_i = ch.muvec_i;
+%        param.sigm2vec_i = ch.sigm2vec_i;
+%        param.logZ_i = ch.logZ_i;
+%        param.eta = ch.eta;
+%        if  isfield(gp.lik, 'int_likparam')
+%          if ~(isfield(gp.lik,'joint_mean_magnitude') && gp.lik.joint_mean_magnitude)
+%            if isfield(gp.lik, 'int_likparam') && gp.lik.int_likparam
+%              param.mf2=ch.mf2;
+%              param.Sigm2=ch.La2;
+%            end
+%            if isfield(gp.lik, 'int_magnitude') && gp.lik.int_magnitude
+%              param.mf3=ch.mf3;
+%              param.La3=ch.La3;
+%            end
+%          else
+%            param.mf=ch.mf;
+%            param.Sigm=ch.Sigm;
+%            param.C=ch.C;
+%          end
+%        end
       else
         
         switch gp.latent_opt.optim_method
@@ -2085,9 +2077,9 @@
                       C=C(size(x2,1)+1:end,size(x2,1)+1:end);
                     end
                     
-                    if isequal(gp.latent_opt.init_prev, 'on') && iter==1 && ~isempty(ch) && all(size(w)==size(ch.w)) && all(abs(w-ch.w)<1) && isequal(datahash,ch.datahash)
-                      tautilde=ch.tautilde;
-                      nutilde=ch.nutilde;
+                    if 0%isequal(gp.latent_opt.init_prev, 'on') && iter==1 && ~isempty(ch) && all(size(w)==size(ch.w)) && all(abs(w-ch.w)<1) && isequal(datahash,ch.datahash)
+%                       tautilde=ch.tautilde;
+%                       nutilde=ch.nutilde;
                     else
                       if isequal(gp.latent_opt.parallel,'on')
                         % parallel-EP
@@ -2117,7 +2109,7 @@
                           [logM0, muhat, sigm2hat] = gp.lik.fh.tiltedMoments(gp.lik, y, 1:n, sigm2vec_i, muvec_i, z);
                         end
                         if any(isnan(logM0))
-                          [e, edata, eprior, param, ch] = set_output_for_notpositivedefinite();
+                          [e, edata, eprior, param] = set_output_for_notpositivedefinite();
                           return
                         end
                         % update site parameters
@@ -2175,7 +2167,7 @@
                           % marginal moments
                           [logM0(i1), muhat(i1), sigm2hat(i1)] = gp.lik.fh.tiltedMoments(gp.lik, y, i1, sigm2_i, mu_i, z);
                           if isnan(logM0(i1))
-                            [e, edata, eprior, param, ch] = set_output_for_notpositivedefinite();
+                            [e, edata, eprior, param] = set_output_for_notpositivedefinite();
                             return
                           end
                           % update site parameters
@@ -2227,7 +2219,7 @@
                       B(1:size(B,1)+1:end)=B(1:size(B,1)+1:end)+1;
                       [L,notpositivedefinite] = chol(B,'lower');
                       if notpositivedefinite
-                        [e, edata, eprior, param, ch] = set_output_for_notpositivedefinite();
+                        [e, edata, eprior, param] = set_output_for_notpositivedefinite();
                         return
                       end
                       %V=(L\Stildesqr)*C;
@@ -2292,13 +2284,13 @@
                       % L to return, without the hBh term
                       [L,notpositivedefinite]=chol(B,'lower');
                       if notpositivedefinite
-                        [e, edata, eprior, param, ch] = set_output_for_notpositivedefinite();
+                        [e, edata, eprior, param] = set_output_for_notpositivedefinite();
                         return
                       end
                       % L for the calculation with mean term
                       [L_m,notpositivedefinite]=chol(B_h,'lower');
                       if notpositivedefinite
-                        [e, edata, eprior, param, ch] = set_output_for_notpositivedefinite();
+                        [e, edata, eprior, param] = set_output_for_notpositivedefinite();
                         return
                       end
                       
@@ -2379,7 +2371,7 @@
                       % compute moments of tilted distributions
                       [logM0, muhat, sigm2hat] = gp.lik.fh.tiltedMoments(gp.lik, y, 1:n, sigm2vec_i, muvec_i, z);
                       if any(isnan(logM0))
-                        [e, edata, eprior, param, ch] = set_output_for_notpositivedefinite();
+                        [e, edata, eprior, param] = set_output_for_notpositivedefinite();
                         return
                       end
                       % update site parameters
@@ -2441,7 +2433,7 @@
                     B = ssmult(sqrtS,KsqrtS) + Inn;
                     [VD, notpositivedefinite] = ldlchol(B);
                     if notpositivedefinite
-                      [e, edata, eprior, param, ch] = set_output_for_notpositivedefinite();
+                      [e, edata, eprior, param] = set_output_for_notpositivedefinite();
                       return
                     end
                     Knutilde = K*nutilde;
@@ -2485,7 +2477,7 @@
                   end
                   [L, notpositivedefinite] = ldlchol(B);
                   if notpositivedefinite
-                    [e, edata, eprior, param, ch] = set_output_for_notpositivedefinite();
+                    [e, edata, eprior, param] = set_output_for_notpositivedefinite();
                     return
                   end
                   
@@ -2494,7 +2486,7 @@
                 if isfield(gp, 'lik_mono')
                   [La2,notpositivedefinite]=chol(Sigm);
                   if notpositivedefinite
-                    [e, edata, eprior, param, ch] = set_output_for_notpositivedefinite();
+                    [e, edata, eprior, param] = set_output_for_notpositivedefinite();
                     return
                   end
 %                   iter
@@ -2524,7 +2516,7 @@
                 K_uu = (K_uu+K_uu')./2;     % ensure the symmetry of K_uu
                 [Luu, notpositivedefinite] = chol(K_uu, 'lower');
                 if notpositivedefinite
-                  [e, edata, eprior, param, ch] = set_output_for_notpositivedefinite();
+                  [e, edata, eprior, param] = set_output_for_notpositivedefinite();
                   return
                 end
                 % Evaluate the Lambda (La)
@@ -2542,7 +2534,7 @@
                 A = K_uu+K_fu'*iLaKfu;  A = (A+A')./2;     % Ensure symmetry
                 [A, notpositivedefinite] = chol(A);
                 if notpositivedefinite
-                  [e, edata, eprior, param, ch] = set_output_for_notpositivedefinite();
+                  [e, edata, eprior, param] = set_output_for_notpositivedefinite();
                   return
                 end
                 L = iLaKfu/A;
@@ -2551,7 +2543,7 @@
                 
                 [R0, notpositivedefinite] = chol(inv(K_uu));
                 if notpositivedefinite
-                  [e, edata, eprior, param, ch] = set_output_for_notpositivedefinite();
+                  [e, edata, eprior, param] = set_output_for_notpositivedefinite();
                   return
                 end
                 R = R0;
@@ -2580,7 +2572,7 @@
                     % compute moments of tilted distributions
                     [logM0, muhat, sigm2hat] = gp.lik.fh.tiltedMoments(gp.lik, y, 1:n, sigm2vec_i, muvec_i, z);
                     if any(isnan(logM0))
-                      [e, edata, eprior, param, ch] = set_output_for_notpositivedefinite();
+                      [e, edata, eprior, param] = set_output_for_notpositivedefinite();
                       return
                     end
                     % update site parameters
@@ -2668,7 +2660,7 @@
                   AA = K_uu + (SsqrtKfu'./repmat(D',m,1))*SsqrtKfu; AA = (AA+AA')/2;
                   [AA, notpositivedefinite] = chol(AA,'lower');
                   if notpositivedefinite
-                    [e, edata, eprior, param, ch] = set_output_for_notpositivedefinite();
+                    [e, edata, eprior, param] = set_output_for_notpositivedefinite();
                     return
                   end
                   term41 = - 0.5*sum(log(1+tautilde.*sigm2vec_i)) - sum(log(diag(Luu))) + sum(log(diag(AA))) + 0.5.*sum(log(D));
@@ -2720,7 +2712,7 @@
                 K_uu = (K_uu+K_uu')./2;     % ensure the symmetry of K_uu
                 [Luu, notpositivedefinite] = chol(K_uu, 'lower');
                 if notpositivedefinite
-                  [e, edata, eprior, param, ch] = set_output_for_notpositivedefinite();
+                  [e, edata, eprior, param] = set_output_for_notpositivedefinite();
                   return
                 end
                 % Evaluate the Lambda (La)
@@ -2736,7 +2728,7 @@
                   Labl{i} = Cbl_ff - Qbl_ff;
                   [Llabl, notpositivedefinite] = chol(Labl{i});
                   if notpositivedefinite
-                    [e, edata, eprior, param, ch] = set_output_for_notpositivedefinite();
+                    [e, edata, eprior, param] = set_output_for_notpositivedefinite();
                     return
                   end
                   iLaKfu(ind{i},:) = Llabl\(Llabl'\K_fu(ind{i},:));
@@ -2745,7 +2737,7 @@
                 A = (A+A')./2;     % Ensure symmetry
                 [A, notpositivedefinite] = chol(A);
                 if notpositivedefinite
-                  [e, edata, eprior, param, ch] = set_output_for_notpositivedefinite();
+                  [e, edata, eprior, param] = set_output_for_notpositivedefinite();
                   return
                 end
                 L = iLaKfu/A;
@@ -2753,7 +2745,7 @@
                 
                 [R0, notpositivedefinite] = chol(inv(K_uu));
                 if notpositivedefinite
-                  [e, edata, eprior, param, ch] = set_output_for_notpositivedefinite();
+                  [e, edata, eprior, param] = set_output_for_notpositivedefinite();
                   return
                 end
                 R = R0;
@@ -2787,7 +2779,7 @@
                     % compute moments of tilted distributions
                     [logM0, muhat, sigm2hat] = gp.lik.fh.tiltedMoments(gp.lik, y, 1:n, sigm2vec_i, muvec_i, z);
                     if any(isnan(logM0))
-                      [e, edata, eprior, param, ch] = set_output_for_notpositivedefinite();
+                      [e, edata, eprior, param] = set_output_for_notpositivedefinite();
                       return
                     end
                     % update site parameters
@@ -2856,7 +2848,7 @@
                     Dhat = sdtautilde*Labl{i}*sdtautilde + eye(size(Labl{i}));
                     [Ldhat{i}, notpositivedefinite] = chol(Dhat);
                     if notpositivedefinite
-                      [e, edata, eprior, param, ch] = set_output_for_notpositivedefinite();
+                      [e, edata, eprior, param] = set_output_for_notpositivedefinite();
                       return
                     end
                     D{i} = Labl{i} - Labl{i}*sdtautilde*(Ldhat{i}\(Ldhat{i}'\sdtautilde*Labl{i}));
@@ -2894,7 +2886,7 @@
                   AA = K_uu + SsqrtKfu'*iDSsqrtKfu; AA = (AA+AA')/2;
                   [AA, notpositivedefinite] = chol(AA,'lower');
                   if notpositivedefinite
-                    [e, edata, eprior, param, ch] = set_output_for_notpositivedefinite();
+                    [e, edata, eprior, param] = set_output_for_notpositivedefinite();
                     return
                   end
                   term41 = term41 - 0.5*sum(log(1+tautilde.*sigm2vec_i)) - sum(log(diag(Luu))) + sum(log(diag(AA)));
@@ -2956,7 +2948,7 @@
                 K_uu = (K_uu+K_uu')./2;            % ensure the symmetry of K_uu
                 [Luu, notpositivedefinite] = chol(K_uu, 'lower');
                 if notpositivedefinite
-                  [e, edata, eprior, param, ch] = set_output_for_notpositivedefinite();
+                  [e, edata, eprior, param] = set_output_for_notpositivedefinite();
                   return
                 end
                 
@@ -2987,14 +2979,14 @@
                 
                 [VD, notpositivedefinite] = ldlchol(La);
                 if notpositivedefinite
-                  [e, edata, eprior, param, ch] = set_output_for_notpositivedefinite();
+                  [e, edata, eprior, param] = set_output_for_notpositivedefinite();
                   return
                 end
                 iLaKfu = ldlsolve(VD,K_fu);
                 A = K_uu+K_fu'*iLaKfu; A = (A+A')./2;     % Ensure symmetry
                 [A, notpositivedefinite] = chol(A);
                 if notpositivedefinite
-                  [e, edata, eprior, param, ch] = set_output_for_notpositivedefinite();
+                  [e, edata, eprior, param] = set_output_for_notpositivedefinite();
                   return
                 end
                 L = iLaKfu/A;
@@ -3005,7 +2997,7 @@
                 sqrtS = sparse(1:n,1:n,0,n,n);
                 [R0, notpositivedefinite] = chol(inv(K_uu));
                 if notpositivedefinite
-                  [e, edata, eprior, param, ch] = set_output_for_notpositivedefinite();
+                  [e, edata, eprior, param] = set_output_for_notpositivedefinite();
                   return
                 end
                 R = R0;
@@ -3018,7 +3010,7 @@
                 LasqrtS = La*sqrtS;
                 [VD, notpositivedefinite] = ldlchol(Inn);
                 if notpositivedefinite
-                  [e, edata, eprior, param, ch] = set_output_for_notpositivedefinite();
+                  [e, edata, eprior, param] = set_output_for_notpositivedefinite();
                   return
                 end
                 
@@ -3042,7 +3034,7 @@
                     % compute moments of tilted distributions
                     [logM0, muhat, sigm2hat] = gp.lik.fh.tiltedMoments(gp.lik, y, 1:n, sigm2vec_i, muvec_i, z);
                     if any(isnan(logM0))
-                      [e, edata, eprior, param, ch] = set_output_for_notpositivedefinite();
+                      [e, edata, eprior, param] = set_output_for_notpositivedefinite();
                       return
                     end
                     % update site parameters
@@ -3116,7 +3108,7 @@
                   LasqrtS = ssmult(La,sqrtS);
                   [VD, notpositivedefinite] = ldlchol(D2);
                   if notpositivedefinite
-                    [e, edata, eprior, param, ch] = set_output_for_notpositivedefinite();
+                    [e, edata, eprior, param] = set_output_for_notpositivedefinite();
                     return
                   end
                   
@@ -3138,7 +3130,7 @@
                   AA = K_uu + SsqrtKfu'*iDSsqrtKfu; AA = (AA+AA')/2;
                   [AA, notpositivedefinite] = chol(AA,'lower');
                   if notpositivedefinite
-                    [e, edata, eprior, param, ch] = set_output_for_notpositivedefinite();
+                    [e, edata, eprior, param] = set_output_for_notpositivedefinite();
                     return
                   end
                   term41 = - 0.5*sum(log(1+tautilde.*sigm2vec_i)) - sum(log(diag(Luu))) + sum(log(diag(AA))) + 0.5*sum(log(diag(VD)));
@@ -3204,7 +3196,7 @@
                 K_uu = (K_uu+K_uu')./2;     % ensure the symmetry of K_uu
                 [Luu, notpositivedefinite] = chol(K_uu, 'lower');
                 if notpositivedefinite
-                  [e, edata, eprior, param, ch] = set_output_for_notpositivedefinite();
+                  [e, edata, eprior, param] = set_output_for_notpositivedefinite();
                   return
                 end
                 % Evaluate the Lambda (La)
@@ -3240,7 +3232,7 @@
                     % compute moments of tilted distributions
                     [logM0, muhat, sigm2hat] = gp.lik.fh.tiltedMoments(gp.lik, y, 1:n, sigm2vec_i, muvec_i, z);
                     if any(isnan(logM0))
-                      [e, edata, eprior, param, ch] = set_output_for_notpositivedefinite();
+                      [e, edata, eprior, param] = set_output_for_notpositivedefinite();
                       return
                     end
                     % update site parameters
@@ -3302,7 +3294,7 @@
                   AA = eye(m,m) + SsqrtPhi'*SsqrtPhi; AA = (AA+AA')/2;
                   [AA, notpositivedefinite] = chol(AA,'lower');
                   if notpositivedefinite
-                    [e, edata, eprior, param, ch] = set_output_for_notpositivedefinite();
+                    [e, edata, eprior, param] = set_output_for_notpositivedefinite();
                     return
                   end
                   term41 = - 0.5*sum(log(1+tautilde.*sigm2vec_i)) + sum(log(diag(AA)));
@@ -3446,7 +3438,7 @@
                 [Kv_ff, Cv_ff] = gp_trvar(gp,x);
                 [Luu, notpositivedefinite] = chol(K_uu, 'lower');
                 if notpositivedefinite
-                  [e, edata, eprior, param, ch] = set_output_for_notpositivedefinite();
+                  [e, edata, eprior, param] = set_output_for_notpositivedefinite();
                   return
                 end
                 B=Luu\(K_fu');
@@ -4178,19 +4170,20 @@
         end
         param.mf=mf;
         
-        % store values to the cache
-        ch=param;
-        ch.w = w;
-        ch.e = e;
-        ch.edata = edata;
-        ch.eprior = eprior;
-        ch.datahash = datahash;
+%         % store values to the cache
+%         ch=param;
+%         ch.w = w;
+%         ch.e = e;
+%         ch.edata = edata;
+%         ch.eprior = eprior;
+%         ch.datahash = datahash;
         
       end
     end
-  end
+  end  
 
-  function [e, edata, eprior, param, ch] = set_output_for_notpositivedefinite()
+
+  function [e, edata, eprior, param] = set_output_for_notpositivedefinite()
     % Instead of stopping to chol error, return NaN
     e = NaN;
     edata = NaN;
@@ -4207,15 +4200,14 @@
     param.mf3=NaN;
     param.La3=NaN;
     param.mf2=NaN;
-    ch=param;
-    ch.e = e;
-    ch.edata = edata;
-    ch.eprior = eprior;
-    ch.datahash = NaN;
-    ch.w = NaN;
+%     ch=param;
+%     ch.e = e;
+%     ch.edata = edata;
+%     ch.eprior = eprior;
+%     ch.datahash = NaN;
+%     ch.w = NaN;
   end
 
-end
 
 function [m_q,S_q,lnZ_q,L1,L2]=evaluate_q(nu_q,tau_q,K,display)
 
diff --git a/gp/gpep_g.m b/gp/gpep_g.m
index 8927361b..2f7377d8 100644
--- a/gp/gpep_g.m
+++ b/gp/gpep_g.m
@@ -31,15 +31,13 @@
 
   ip=inputParser;
   ip.FunctionName = 'GPEP_G';
-  ip.addRequired('w', @(x) isvector(x) && isreal(x) && all(isfinite(x)));
-  ip.addRequired('gp',@isstruct);
-  ip.addRequired('x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))))
-  ip.addRequired('y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))))
-  ip.addParamValue('z', [], @(x) isreal(x) && all(isfinite(x(:))))
-  ip.addOptional('method', '1', @(x) ismember(x,{'1','2'}))
-  ip.parse(w, gp, x, y, varargin{:});
+  ip=iparser(ip,'addRequired','w', @(x) isvector(x) && isreal(x) && all(isfinite(x)));
+  ip=iparser(ip,'addRequired','gp',@isstruct);
+  ip=iparser(ip,'addRequired','x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))));
+  ip=iparser(ip,'addRequired','y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))));
+  ip=iparser(ip,'addParamValue','z', [], @(x) isreal(x) && all(isfinite(x(:))));
+  ip=iparser(ip,'parse',w, gp, x, y, varargin{:});
   z=ip.Results.z;
-  method = ip.Results.method;
 
   gp=gp_unpak(gp, w);       % unpak the parameters
   [tmp,tmp,hier]=gp_pak(gp);% Hierarchy of the parameters
@@ -148,15 +146,15 @@
             gprior_cf = -feval(gpcf.fh.lpg, gpcf);
 
             % check in which components the covariance function is present
-            do = false(nout,1);
+            doo = false(nout,1);
             if multicf
               for z1=1:nout
                 if any(gp.comp_cf{z1}==i)
-                  do(z1) = true;
+                  doo(z1) = true;
                 end
               end
             else
-              do = true(nout,1);
+              doo = true(nout,1);
             end
 
             for i2 = 1:length(DKff)
@@ -165,7 +163,7 @@
               Cdl=0;
               DKffb=zeros(n*nout,1);
               for z1=1:nout
-                if do(z1)
+                if doo(z1)
                   DKffb((1:n)+(z1-1)*n)=DKff{i2}*b((1:n)+(z1-1)*n);
                   Cdl = Cdl + sum(sum( M(:,:,z1) .* DKff{i2} ));
                 end
@@ -1563,4 +1561,4 @@
 
   end
 
-end
\ No newline at end of file
+end
diff --git a/gp/gpep_jpred.m b/gp/gpep_jpred.m
index e55aa880..02010dbb 100644
--- a/gp/gpep_jpred.m
+++ b/gp/gpep_jpred.m
@@ -84,23 +84,23 @@
 
   ip=inputParser;
   ip.FunctionName = 'GPEP_JPRED';
-  ip.addRequired('gp', @isstruct);
-  ip.addRequired('x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))))
-  ip.addRequired('y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))))
-  ip.addOptional('xt', [], @(x) isempty(x) || (isreal(x) && all(isfinite(x(:)))))
-  ip.addParamValue('yt', [], @(x) isreal(x) && all(isfinite(x(:))))
-  ip.addParamValue('z', [], @(x) isreal(x) && all(isfinite(x(:))))
-  ip.addParamValue('zt', [], @(x) isreal(x) && all(isfinite(x(:))))
-  ip.addParamValue('predcf', [], @(x) isempty(x) || ...
-                   isvector(x) && isreal(x) && all(isfinite(x)&x>0))
-  ip.addParamValue('tstind', [], @(x) isempty(x) || iscell(x) ||...
-                   (isvector(x) && isreal(x) && all(isfinite(x)&x>0)))
-  ip.addParamValue('fcorr', 'off', @(x) ismember(x, {'off', 'fact', 'cm2', 'on'}))
+  ip=iparser(ip,'addRequired','gp', @isstruct);
+  ip=iparser(ip,'addRequired','x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))));
+  ip=iparser(ip,'addRequired','y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))));
+  ip=iparser(ip,'addOptional','xt', [], @(x) isempty(x) || (isreal(x) && all(isfinite(x(:)))));
+  ip=iparser(ip,'addParamValue','yt', [], @(x) isreal(x) && all(isfinite(x(:))));
+  ip=iparser(ip,'addParamValue','z', [], @(x) isreal(x) && all(isfinite(x(:))));
+  ip=iparser(ip,'addParamValue','zt', [], @(x) isreal(x) && all(isfinite(x(:))));
+  ip=iparser(ip,'addParamValue','predcf', [], @(x) isempty(x) || ...
+                   isvector(x) && isreal(x) && all(isfinite(x)&x>0));
+  ip=iparser(ip,'addParamValue','tstind', [], @(x) isempty(x) || iscell(x) ||...
+                   (isvector(x) && isreal(x) && all(isfinite(x)&x>0)));
+  ip=iparser(ip,'addParamValue','fcorr', 'off', @(x) ismember(x, {'off', 'fact', 'cm2', 'on'}));
   if numel(varargin)==0 || isnumeric(varargin{1})
     % inputParser should handle this, but it doesn't
-    ip.parse(gp, x, y, varargin{:});
+    ip=iparser(ip,'parse',gp, x, y, varargin{:});
   else
-    ip.parse(gp, x, y, [], varargin{:});
+    ip=iparser(ip,'parse',gp, x, y, [], varargin{:});
   end
   xt=ip.Results.xt;
   yt=ip.Results.yt;
diff --git a/gp/gpep_looe.m b/gp/gpep_looe.m
index 2d9b58a0..8a71c967 100644
--- a/gp/gpep_looe.m
+++ b/gp/gpep_looe.m
@@ -41,13 +41,13 @@
 
 ip=inputParser;
 ip.FunctionName = 'GPEP_LOOE';
-ip.addRequired('w', @(x) isempty(x) || ...
+ip=iparser(ip,'addRequired','w', @(x) isempty(x) || ...
                isvector(x) && isreal(x) && all(isfinite(x)));
-ip.addRequired('gp', @(x) isstruct(x) || iscell(x));
-ip.addRequired('x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))))
-ip.addRequired('y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))))
-ip.addParamValue('z', [], @(x) isreal(x) && all(isfinite(x(:))))
-ip.parse(w, gp, x, y, varargin{:});
+ip=iparser(ip,'addRequired','gp', @(x) isstruct(x) || iscell(x));
+ip=iparser(ip,'addRequired','x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))));
+ip=iparser(ip,'addRequired','y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))));
+ip=iparser(ip,'addParamValue','z', [], @(x) isreal(x) && all(isfinite(x(:))));
+ip=iparser(ip,'parse',w, gp, x, y, varargin{:});
 z=ip.Results.z;
 
 gp=gp_unpak(gp, w);
diff --git a/gp/gpep_loog.m b/gp/gpep_loog.m
index 705e3d08..70a291a4 100644
--- a/gp/gpep_loog.m
+++ b/gp/gpep_loog.m
@@ -27,13 +27,13 @@
 
 ip=inputParser;
 ip.FunctionName = 'GPEP_LOOG';
-ip.addRequired('w', @(x) isempty(x) || ...
+ip=iparser(ip,'addRequired','w', @(x) isempty(x) || ...
   isvector(x) && isreal(x) && all(isfinite(x)));
-ip.addRequired('gp', @(x) isstruct(x) || iscell(x));
-ip.addRequired('x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))))
-ip.addRequired('y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))))
-ip.addParamValue('z', [], @(x) isreal(x) && all(isfinite(x(:))))
-ip.parse(w, gp, x, y, varargin{:});
+ip=iparser(ip,'addRequired','gp', @(x) isstruct(x) || iscell(x));
+ip=iparser(ip,'addRequired','x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))));
+ip=iparser(ip,'addRequired','y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))));
+ip=iparser(ip,'addParamValue','z', [], @(x) isreal(x) && all(isfinite(x(:))));
+ip=iparser(ip,'parse',w, gp, x, y, varargin{:});
 z=ip.Results.z;
 
 if isfield(gp,'mean') & ~isempty(gp.mean.meanFuncs)
diff --git a/gp/gpep_loopred.m b/gp/gpep_loopred.m
index ae3f9223..e89362d7 100644
--- a/gp/gpep_loopred.m
+++ b/gp/gpep_loopred.m
@@ -41,11 +41,11 @@
 
   ip=inputParser;
   ip.FunctionName = 'GPEP_LOOPRED';
-  ip.addRequired('gp', @(x) isstruct(x));
-  ip.addRequired('x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))))
-  ip.addRequired('y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))))
-  ip.addParamValue('z', [], @(x) isreal(x) && all(isfinite(x(:))))
-  ip.parse(gp, x, y, varargin{:});
+  ip=iparser(ip,'addRequired','gp', @(x) isstruct(x));
+  ip=iparser(ip,'addRequired','x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))));
+  ip=iparser(ip,'addRequired','y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))));
+  ip=iparser(ip,'addParamValue','z', [], @(x) isreal(x) && all(isfinite(x(:))));
+  ip=iparser(ip,'parse',gp, x, y, varargin{:});
   z=ip.Results.z;
 
   [tmp,tmp,tmp,param] = gpep_e(gp_pak(gp), gp, x, y, 'z', z);
diff --git a/gp/gpep_pred.m b/gp/gpep_pred.m
index 8d76255c..e9d5248a 100644
--- a/gp/gpep_pred.m
+++ b/gp/gpep_pred.m
@@ -81,62 +81,61 @@
 % License (version 3 or later); please refer to the file
 % License.txt, included with the software, for details.
 
-  ip=inputParser;
-  ip.FunctionName = 'GPEP_PRED';
-  ip.addRequired('gp', @isstruct);
-  ip.addRequired('x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))))
-  ip.addRequired('y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))))
-  ip.addOptional('xt', [], @(x) isempty(x) || (isreal(x) && all(isfinite(x(:)))))
-  ip.addParamValue('yt', [], @(x) isreal(x) && all(isfinite(x(:))))
-  ip.addParamValue('z', [], @(x) isreal(x) && all(isfinite(x(:))))
-  ip.addParamValue('zt', [], @(x) isreal(x) && all(isfinite(x(:))))
-  ip.addParamValue('predcf', [], @(x) isempty(x) || ...
-                   isvector(x) && isreal(x) && all(isfinite(x)&x>0))
-  ip.addParamValue('tstind', [], @(x) isempty(x) || iscell(x) ||...
-                   (isvector(x) && isreal(x) && all(isfinite(x)&x>0)))
-  ip.addParamValue('fcorr', 'off', @(x) ismember(x, {'off', ...
-                   'cm2', 'fact', 'on','lr'}))
-  if numel(varargin)==0 || isnumeric(varargin{1})
-    % inputParser should handle this, but it doesn't
-    ip.parse(gp, x, y, varargin{:});
-  else
-    ip.parse(gp, x, y, [], varargin{:});
-  end
-  xt=ip.Results.xt;
-  yt=ip.Results.yt;
-  z=ip.Results.z;
-  zt=ip.Results.zt;
-  predcf=ip.Results.predcf;
-  tstind=ip.Results.tstind;
-  fcorr=ip.Results.fcorr;
-  if isempty(xt)
-    xt=x;
-    if isempty(tstind)
-      if iscell(gp)
-        gptype=gp{1}.type;
-      else
-        gptype=gp.type;
-      end
-      switch gptype
-        case {'FULL' 'VAR' 'DTC' 'SOR'}
-          tstind = [];
-        case {'FIC' 'CS+FIC'}
-          tstind = 1:size(x,1);
-        case 'PIC'
-          if iscell(gp)
-            tstind = gp{1}.tr_index;
-          else
-            tstind = gp.tr_index;
-          end
-      end
-    end
-    if isempty(yt)
-      yt=y;
+ip=inputParser;
+ip.FunctionName = 'GPEP_PRED';
+ip=iparser(ip,'addRequired','gp', @isstruct);
+ip=iparser(ip,'addRequired','x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))));
+ip=iparser(ip,'addRequired','y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))));
+ip=iparser(ip,'addOptional','xt', [], @(x) isempty(x) || (isreal(x) && all(isfinite(x(:)))));
+ip=iparser(ip,'addParamValue','yt', [], @(x) isreal(x) && all(isfinite(x(:))));
+ip=iparser(ip,'addParamValue','z', [], @(x) isreal(x) && all(isfinite(x(:))));
+ip=iparser(ip,'addParamValue','zt', [], @(x) isreal(x) && all(isfinite(x(:))));
+ip=iparser(ip,'addParamValue','predcf', [], @(x) isempty(x) || ...
+                 isvector(x) && isreal(x) && all(isfinite(x)&x>0));
+ip=iparser(ip,'addParamValue','tstind', [], @(x) isempty(x) || iscell(x) ||...
+                 (isvector(x) && isreal(x) && all(isfinite(x)&x>0)));
+ip=iparser(ip,'addParamValue','fcorr', 'off', @(x) ismember(x, {'off', 'fact', 'cm2', 'on','lr'}));
+if numel(varargin)==0 || isnumeric(varargin{1})
+  % inputParser should handle this, but it doesn't
+  ip=iparser(ip,'parse',gp, x, y, varargin{:});
+else
+  ip=iparser(ip,'parse',gp, x, y, [], varargin{:});
+end
+xt=ip.Results.xt;
+yt=ip.Results.yt;
+z=ip.Results.z;
+zt=ip.Results.zt;
+predcf=ip.Results.predcf;
+tstind=ip.Results.tstind;
+fcorr=ip.Results.fcorr;
+if isempty(xt)
+  xt=x;
+  if isempty(tstind)
+    if iscell(gp)
+      gptype=gp{1}.type;
+    else
+      gptype=gp.type;
     end
-    if isempty(zt)
-      zt=z;
+    switch gptype
+      case {'FULL' 'VAR' 'DTC' 'SOR'}
+        tstind = [];
+      case {'FIC' 'CS+FIC'}
+        tstind = 1:size(x,1);
+      case 'PIC'
+        if iscell(gp)
+          tstind = gp{1}.tr_index;
+        else
+          tstind = gp.tr_index;
+        end
     end
   end
+  if isempty(yt)
+    yt=y;
+  end
+  if isempty(zt)
+    zt=z;
+  end
+end
 
   [tn, tnin] = size(x);
   [n, nout] = size(y);
@@ -835,6 +834,7 @@
   end
 end
 
+
 function [m,S]=pred_var(tau_q,K,A,b)
 
 % helper function for determining
diff --git a/gp/gpep_predgrad.m b/gp/gpep_predgrad.m
index 9faccca5..407dea3e 100644
--- a/gp/gpep_predgrad.m
+++ b/gp/gpep_predgrad.m
@@ -67,24 +67,24 @@
 % License.txt, included with the software, for details.
 
   ip=inputParser;
-  ip.FunctionName = 'GPEP_PRED';
-  ip.addRequired('gp', @isstruct);
-  ip.addRequired('x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))))
-  ip.addRequired('y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))))
-  ip.addOptional('xt', [], @(x) isempty(x) || (isreal(x) && all(isfinite(x(:)))))
-  ip.addParamValue('yt', [], @(x) isreal(x) && all(isfinite(x(:))))
-  ip.addParamValue('z', [], @(x) isreal(x) && all(isfinite(x(:))))
-  ip.addParamValue('zt', [], @(x) isreal(x) && all(isfinite(x(:))))
-  ip.addParamValue('predcf', [], @(x) isempty(x) || ...
-                   isvector(x) && isreal(x) && all(isfinite(x)&x>0))
-  ip.addParamValue('tstind', [], @(x) isempty(x) || iscell(x) ||...
-                   (isvector(x) && isreal(x) && all(isfinite(x)&x>0)))
-  ip.addParamValue('fcorr', 'off', @(x) ismember(x, {'off', 'fact', 'cm2', 'on'}))
+  ip.FunctionName = 'GPEP_PREDGRAD';
+  ip=iparser(ip,'addRequired','gp', @isstruct);
+  ip=iparser(ip,'addRequired','x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))));
+  ip=iparser(ip,'addRequired','y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))));
+  ip=iparser(ip,'addOptional','xt', [], @(x) isempty(x) || (isreal(x) && all(isfinite(x(:)))));
+  ip=iparser(ip,'addParamValue','yt', [], @(x) isreal(x) && all(isfinite(x(:))));
+  ip=iparser(ip,'addParamValue','z', [], @(x) isreal(x) && all(isfinite(x(:))));
+  ip=iparser(ip,'addParamValue','zt', [], @(x) isreal(x) && all(isfinite(x(:))));
+  ip=iparser(ip,'addParamValue','predcf', [], @(x) isempty(x) || ...
+                   isvector(x) && isreal(x) && all(isfinite(x)&x>0));
+  ip=iparser(ip,'addParamValue','tstind', [], @(x) isempty(x) || iscell(x) ||...
+                   (isvector(x) && isreal(x) && all(isfinite(x)&x>0)));
+  ip=iparser(ip,'addParamValue','fcorr', 'off', @(x) ismember(x, {'off', 'fact', 'cm2', 'on'}));
   if numel(varargin)==0 || isnumeric(varargin{1})
     % inputParser should handle this, but it doesn't
-    ip.parse(gp, x, y, varargin{:});
+    ip=iparser(ip,'parse',gp, x, y, varargin{:});
   else
-    ip.parse(gp, x, y, [], varargin{:});
+    ip=iparser(ip,'parse',gp, x, y, [], varargin{:});
   end
   xt=ip.Results.xt;
   yt=ip.Results.yt;
diff --git a/gp/gpia_jpred.m b/gp/gpia_jpred.m
index 468f83ba..7abfa43d 100644
--- a/gp/gpia_jpred.m
+++ b/gp/gpia_jpred.m
@@ -80,22 +80,22 @@
   
   ip=inputParser;
   ip.FunctionName = 'GPIA_JPRED';
-  ip.addRequired('gp_array', @iscell);
-  ip.addRequired('x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))))
-  ip.addRequired('y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))))
-  ip.addOptional('xt', [], @(x) isempty(x) || (isreal(x) && all(isfinite(x(:)))))
-  ip.addParamValue('yt', [], @(x) isreal(x) && all(isfinite(x(:))))
-  ip.addParamValue('z', [], @(x) isreal(x) && all(isfinite(x(:))))
-  ip.addParamValue('zt', [], @(x) isreal(x) && all(isfinite(x(:))))
-  ip.addParamValue('predcf', [], @(x) isempty(x) || ...
-                   isvector(x) && isreal(x) && all(isfinite(x)&x>0))
-  ip.addParamValue('tstind', [], @(x) isempty(x) || iscell(x) ||...
-                   (isvector(x) && isreal(x) && all(isfinite(x)&x>0)))
+  ip=iparser(ip,'addRequired','gp_array', @iscell);
+  ip=iparser(ip,'addRequired','x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))));
+  ip=iparser(ip,'addRequired','y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))));
+  ip=iparser(ip,'addOptional','xt', [], @(x) isempty(x) || (isreal(x) && all(isfinite(x(:)))));
+  ip=iparser(ip,'addParamValue','yt', [], @(x) isreal(x) && all(isfinite(x(:))));
+  ip=iparser(ip,'addParamValue','z', [], @(x) isreal(x) && all(isfinite(x(:))));
+  ip=iparser(ip,'addParamValue','zt', [], @(x) isreal(x) && all(isfinite(x(:))));
+  ip=iparser(ip,'addParamValue','predcf', [], @(x) isempty(x) || ...
+                   isvector(x) && isreal(x) && all(isfinite(x)&x>0));
+  ip=iparser(ip,'addParamValue','tstind', [], @(x) isempty(x) || iscell(x) ||...
+                   (isvector(x) && isreal(x) && all(isfinite(x)&x>0)));
   if numel(varargin)==0 || isnumeric(varargin{1})
     % inputParser should handle this, but it doesn't
-    ip.parse(gp_array, x, y, varargin{:});
+    ip=iparser(ip,'parse',gp_array, x, y, varargin{:});
   else
-    ip.parse(gp_array, x, y, [], varargin{:});
+    ip=iparser(ip,'parse',gp_array, x, y, [], varargin{:});
   end
   xt=ip.Results.xt;
   yt=ip.Results.yt;
diff --git a/gp/gpia_jpreds.m b/gp/gpia_jpreds.m
index c345a159..8a36039e 100644
--- a/gp/gpia_jpreds.m
+++ b/gp/gpia_jpreds.m
@@ -69,22 +69,22 @@
   
   ip=inputParser;
   ip.FunctionName = 'GPIA_JPREDS';
-  ip.addRequired('gp_array', @iscell);
-  ip.addRequired('x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))))
-  ip.addRequired('y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))))
-  ip.addOptional('xt', [], @(x) isempty(x) || (isreal(x) && all(isfinite(x(:)))))
-  ip.addParamValue('yt', [], @(x) isreal(x) && all(isfinite(x(:))))
-  ip.addParamValue('z', [], @(x) isreal(x) && all(isfinite(x(:))))
-  ip.addParamValue('zt', [], @(x) isreal(x) && all(isfinite(x(:))))
-  ip.addParamValue('predcf', [], @(x) isempty(x) || ...
-                   isvector(x) && isreal(x) && all(isfinite(x)&x>0))
-  ip.addParamValue('tstind', [], @(x) isempty(x) || iscell(x) ||...
-                   (isvector(x) && isreal(x) && all(isfinite(x)&x>0)))
+  ip=iparser(ip,'addRequired','gp_array', @iscell);
+  ip=iparser(ip,'addRequired','x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))));
+  ip=iparser(ip,'addRequired','y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))));
+  ip=iparser(ip,'addOptional','xt', [], @(x) isempty(x) || (isreal(x) && all(isfinite(x(:)))));
+  ip=iparser(ip,'addParamValue','yt', [], @(x) isreal(x) && all(isfinite(x(:))));
+  ip=iparser(ip,'addParamValue','z', [], @(x) isreal(x) && all(isfinite(x(:))));
+  ip=iparser(ip,'addParamValue','zt', [], @(x) isreal(x) && all(isfinite(x(:))));
+  ip=iparser(ip,'addParamValue','predcf', [], @(x) isempty(x) || ...
+                   isvector(x) && isreal(x) && all(isfinite(x)&x>0));
+  ip=iparser(ip,'addParamValue','tstind', [], @(x) isempty(x) || iscell(x) ||...
+                   (isvector(x) && isreal(x) && all(isfinite(x)&x>0)));
   if numel(varargin)==0 || isnumeric(varargin{1})
     % inputParser should handle this, but it doesn't
-    ip.parse(gp_array, x, y, varargin{:});
+    ip=iparser(ip,'parse',gp_array, x, y, varargin{:});
   else
-    ip.parse(gp_array, x, y, [], varargin{:});
+    ip=iparser(ip,'parse',gp_array, x, y, [], varargin{:});
   end
   xt=ip.Results.xt;
   yt=ip.Results.yt;
diff --git a/gp/gpia_loopred.m b/gp/gpia_loopred.m
index f2880780..5a0beffd 100644
--- a/gp/gpia_loopred.m
+++ b/gp/gpia_loopred.m
@@ -47,12 +47,12 @@
 
   ip=inputParser;
   ip.FunctionName = 'GPIA_LOOPRED';
-  ip.addRequired('gp_array',@iscell);
-  ip.addRequired('x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))))
-  ip.addRequired('y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))))
-  ip.addParamValue('z', [], @(x) isreal(x) && all(isfinite(x(:))))
-  ip.addParamValue('is', 'on', @(x) ismember(x,{'on' 'off'}))
-  ip.parse(gp_array, x, y, varargin{:});
+  ip=iparser(ip,'addRequired','gp_array',@iscell);
+  ip=iparser(ip,'addRequired','x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))));
+  ip=iparser(ip,'addRequired','y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))));
+  ip=iparser(ip,'addParamValue','z', [], @(x) isreal(x) && all(isfinite(x(:))));
+  ip=iparser(ip,'addParamValue','is', 'on', @(x) ismember(x,{'on' 'off'}));
+  ip=iparser(ip,'parse',gp_array, x, y, varargin{:});
   z=ip.Results.z;
   is=ip.Results.is;
 
diff --git a/gp/gpia_pred.m b/gp/gpia_pred.m
index bac7ef7d..d432879d 100644
--- a/gp/gpia_pred.m
+++ b/gp/gpia_pred.m
@@ -83,23 +83,23 @@
 
   ip=inputParser;
   ip.FunctionName = 'GPIA_PRED';
-  ip.addRequired('gp_array', @iscell);
-  ip.addRequired('x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))))
-  ip.addRequired('y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))))
-  ip.addOptional('xt', [], @(x) isempty(x) || (isreal(x) && all(isfinite(x(:)))))
-  ip.addParamValue('yt', [], @(x) isreal(x) && all(isfinite(x(:))))
-  ip.addParamValue('z', [], @(x) isreal(x) && all(isfinite(x(:))))
-  ip.addParamValue('zt', [], @(x) isreal(x) && all(isfinite(x(:))))
-  ip.addParamValue('predcf', [], @(x) isempty(x) || ...
-                   isvector(x) && isreal(x) && all(isfinite(x)&x>0))
-  ip.addParamValue('tstind', [], @(x) isempty(x) || iscell(x) ||...
-                   (isvector(x) && isreal(x) && all(isfinite(x)&x>0)))
-  ip.addParamValue('fcorr', 'off', @(x) ismember(x, {'off', 'fact', 'cm2', 'on'}));
+  ip=iparser(ip,'addRequired','gp_array', @iscell);
+  ip=iparser(ip,'addRequired','x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))));
+  ip=iparser(ip,'addRequired','y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))));
+  ip=iparser(ip,'addOptional','xt', [], @(x) isempty(x) || (isreal(x) && all(isfinite(x(:)))));
+  ip=iparser(ip,'addParamValue','yt', [], @(x) isreal(x) && all(isfinite(x(:))));
+  ip=iparser(ip,'addParamValue','z', [], @(x) isreal(x) && all(isfinite(x(:))));
+  ip=iparser(ip,'addParamValue','zt', [], @(x) isreal(x) && all(isfinite(x(:))));
+  ip=iparser(ip,'addParamValue','predcf', [], @(x) isempty(x) || ...
+                   isvector(x) && isreal(x) && all(isfinite(x)&x>0));
+  ip=iparser(ip,'addParamValue','tstind', [], @(x) isempty(x) || iscell(x) ||...
+                   (isvector(x) && isreal(x) && all(isfinite(x)&x>0)));
+  ip=iparser(ip,'addParamValue','fcorr', 'off', @(x) ismember(x, {'off', 'fact', 'cm2', 'on'}));
   if numel(varargin)==0 || isnumeric(varargin{1})
     % inputParser should handle this, but it doesn't
-    ip.parse(gp_array, x, y, varargin{:});
+    ip=iparser(ip,'parse',gp_array, x, y, varargin{:});
   else
-    ip.parse(gp_array, x, y, [], varargin{:});
+    ip=iparser(ip,'parse',gp_array, x, y, [], varargin{:});
   end
   xt=ip.Results.xt;
   yt=ip.Results.yt;
diff --git a/gp/gpla_e.m b/gp/gpla_e.m
index 3c7d5719..46ffe3ec 100644
--- a/gp/gpla_e.m
+++ b/gp/gpla_e.m
@@ -67,16 +67,16 @@
   % parse inputs
   ip=inputParser;
   ip.FunctionName = 'GPLA_E';
-  ip.addRequired('w', @(x) ...
-                 isempty(x) || ...
-                 (ischar(x) && strcmp(w, 'init')) || ...
-                 isvector(x) && isreal(x) && all(isfinite(x)) ...
-                 || all(isnan(x)));
-  ip.addRequired('gp',@isstruct);
-  ip.addOptional('x', @(x) isnumeric(x) && isreal(x) && all(isfinite(x(:))))
-  ip.addOptional('y', @(x) isnumeric(x) && isreal(x) && all(isfinite(x(:))))
-  ip.addParamValue('z', [], @(x) isnumeric(x) && isreal(x) && all(isfinite(x(:))))
-  ip.parse(w, gp, varargin{:});
+  ip=iparser(ip,'addRequired','w', @(x) ...
+    isempty(x) || ...
+    (ischar(x) && strcmp(w, 'init')) || ...
+    isvector(x) && isreal(x) && all(isfinite(x)) ...
+    || all(isnan(x)));
+  ip=iparser(ip,'addRequired','gp',@isstruct);
+  ip=iparser(ip,'addOptional','x', @(x) isnumeric(x) && isreal(x) && all(isfinite(x(:))));
+  ip=iparser(ip,'addOptional','y', @(x) isnumeric(x) && isreal(x) && all(isfinite(x(:))));
+  ip=iparser(ip,'addParamValue','z', [], @(x) isnumeric(x) && isreal(x) && all(isfinite(x(:))));
+  ip=iparser(ip,'parse',w, gp, varargin{:});
   x=ip.Results.x;
   y=ip.Results.y;
   z=ip.Results.z;
@@ -94,7 +94,7 @@
     gp.fh.pred=@gpla_pred;
     gp.fh.jpred=@gpla_jpred;
     gp.fh.looe=@gpla_looe;
-    gp.fh.loog=@gpla_loog;
+%     gp.fh.loog=@gpla_loog;
     gp.fh.loopred=@gpla_loopred;
     e = gp;
     % remove clutter from the nested workspace
@@ -109,33 +109,22 @@
     [e, edata, eprior, param] = gp.fh.ne(w, gp, x, y, z);
   end
 
+end
   function [e, edata, eprior, param] = laplace_algorithm(w, gp, x, y, z)
       
-  if strcmp(w, 'clearcache')
-      ch=[];
-      return
-  end
-  % code for the Laplace algorithm
-
-  % check whether saved values can be used
-    if isempty(z)
-      datahash=hash_sha512([x y]);
-    else
-      datahash=hash_sha512([x y z]);
-    end
-    if ~isempty(ch) && all(size(w)==size(ch.w)) && all(abs(w-ch.w)<1e-8) && ...
-          isequal(datahash,ch.datahash)
-      % The covariance function parameters or data haven't changed so we
-      % can return the energy and the site parameters that are
-      % saved in the cache
-      e = ch.e;
-      edata = ch.edata;
-      eprior = ch.eprior;
-      param.f = ch.f;
-      param.L = ch.L;
-      param.La2 = ch.La2;
-      param.a = ch.a;
-      param.p = ch.p;
+    if 0%~isempty(ch) && all(size(w)==size(ch.w)) && all(abs(w-ch.w)<1e-8) && ...
+%           isequal(datahash,ch.datahash)
+%       % The covariance function parameters or data haven't changed so we
+%       % can return the energy and the site parameters that are
+%       % saved in the cache
+%       e = ch.e;
+%       edata = ch.edata;
+%       eprior = ch.eprior;
+%       param.f = ch.f;
+%       param.L = ch.L;
+%       param.La2 = ch.La2;
+%       param.a = ch.a;
+%       param.p = ch.p;
     else
       % The parameters or data have changed since
       % the last call for gpla_e. In this case we need to
@@ -212,7 +201,7 @@
                     [L, notpositivedefinite] = chol(L);
                   end
                   if notpositivedefinite
-                    [edata,e,eprior,param,ch] = set_output_for_notpositivedefinite();
+                    [edata,e,eprior,param] = set_output_for_notpositivedefinite();
                     return
                   end
                   if ~isfield(gp,'meanf')
@@ -226,7 +215,7 @@
                     a = b - sW.*(L\(L'\(sW.*(K*b))));
                   end
                   if any(isnan(a))
-                    [edata,e,eprior,param,ch] = set_output_for_notpositivedefinite();
+                    [edata,e,eprior,param] = set_output_for_notpositivedefinite();
                     return
                   end
                   f = K*a;
@@ -315,7 +304,7 @@
                     [L, notpositivedefinite] = chol(L);
                   end
                   if notpositivedefinite
-                    [edata,e,eprior,param,ch] = set_output_for_notpositivedefinite();
+                    [edata,e,eprior,param] = set_output_for_notpositivedefinite();
                     return
                   end
                   b = W.*f+dlp;
@@ -357,14 +346,14 @@
                 if issparse(K)
                   [LD,notpositivedefinite] = ldlchol(K);
                   if notpositivedefinite
-                    [edata,e,eprior,param,ch] = set_output_for_notpositivedefinite();
+                    [edata,e,eprior,param] = set_output_for_notpositivedefinite();
                     return
                   end
                   fhm = @(W, f, varargin) (ldlsolve(LD,f) + repmat(W,1,size(f,2)).*f);  % W*f; %
                 else
                   [LD,notpositivedefinite] = chol(K);
                   if notpositivedefinite
-                    [edata,e,eprior,param,ch] = set_output_for_notpositivedefinite();
+                    [edata,e,eprior,param] = set_output_for_notpositivedefinite();
                     return
                   end
                   fhm = @(W, f, varargin) (LD\(LD'\f) + repmat(W,1,size(f,2)).*f);  % W*f; %
@@ -403,7 +392,7 @@
               case 'lik_specific'
                 [f, a] = gp.lik.fh.optimizef(gp, y, K);
                 if isnan(f)
-                  [edata,e,eprior,param,ch] = set_output_for_notpositivedefinite();
+                  [edata,e,eprior,param] = set_output_for_notpositivedefinite();
                   return
                 end
               otherwise
@@ -436,7 +425,7 @@
                 edata = logZ + sum(log(diag(L))); % 0.5*log(det(eye(size(K)) + K*W)) ; %
               end
               if notpositivedefinite
-                [edata,e,eprior,param,ch] = set_output_for_notpositivedefinite();
+                [edata,e,eprior,param] = set_output_for_notpositivedefinite();
                 return
               end
             else
@@ -452,7 +441,7 @@
               
               [L, notpositivedefinite] = chol(K);
               if notpositivedefinite
-                [edata,e,eprior,param,ch] = set_output_for_notpositivedefinite();
+                [edata,e,eprior,param] = set_output_for_notpositivedefinite();
                 return
               end
               L1 = L;
@@ -469,7 +458,7 @@
                   if ~isfield(gp.lik.fh,'upfact')
                     % log-concave likelihood, this should not happen
                     % let's just return NaN
-                    [edata,e,eprior,param,ch] = set_output_for_notpositivedefinite();
+                    [edata,e,eprior,param] = set_output_for_notpositivedefinite();
                     return
                   end
                   
@@ -487,7 +476,7 @@
                 if upfact > 0
                   [L,notpositivedefinite] = cholupdate(L, l.*sqrt(upfact), '-');
                   if notpositivedefinite
-                    [edata,e,eprior,param,ch] = set_output_for_notpositivedefinite();
+                    [edata,e,eprior,param] = set_output_for_notpositivedefinite();
                     return
                   end
                 else
@@ -712,7 +701,7 @@
                     Ltmp(1:(size(Dt,1)+1):end)=Ltmp(1:(size(Dt,1)+1):end)+1;
                     [L,notpositivedefinite] = chol(Ltmp,'lower');
                     if notpositivedefinite
-                      [edata,e,eprior,param,ch] = set_output_for_notpositivedefinite();
+                      [edata,e,eprior,param] = set_output_for_notpositivedefinite();
                       return
                     end
                     
@@ -727,13 +716,13 @@
                     if size(x,2)==1
                       gge=zeros(2*n,1);
                       gge(1:n)=b;
-                      q=ifft(fftKcirc.*fft(gge'));
+                      q=real(ifft(fftKcirc.*fft(gge')));
                       Kg=q(1:n)';
                     elseif size(x,2)==2
                       gge=zeros(2*n2,2*n1);
                       gge(1:n2,1:n1)=reshape(b,n2,n1);
                       
-                      q=ifft2(fftKcirc.*fft2(gge));
+                      q=real(ifft2(fftKcirc.*fft2(gge)));
                       q=q(1:n2,1:n1);
                       Kg=q(:);
                     else
@@ -751,8 +740,8 @@
                     else
                       [iSg,~]=pcg(@(z) mvm_fft(g2,ny,fftKcirc,[],[],z), v, gp.latent_opt.pcg_tol);
                     end
+                    iSg=real(iSg);
                     a=b-sqrt(ny)*(g2sq.*iSg  - g2*(g2'*(iSg./g2sq)));
-                    
                   else
                     if strcmpi(gp.lik.type,'LGPC')
                       R=zeros(n);
@@ -769,7 +758,7 @@
                       [L,notpositivedefinite] = chol(RKR,'lower');
                       
                       if notpositivedefinite
-                        [edata,e,eprior,param,ch] = set_output_for_notpositivedefinite();
+                        [edata,e,eprior,param] = set_output_for_notpositivedefinite();
                         return
                       end
                       
@@ -786,7 +775,7 @@
                       [L,notpositivedefinite] = chol(RKR,'lower');
                       
                       if notpositivedefinite
-                        [edata,e,eprior,param,ch] = set_output_for_notpositivedefinite();
+                        [edata,e,eprior,param] = set_output_for_notpositivedefinite();
                         return
                       end
                       
@@ -818,7 +807,7 @@
                     if size(x,2)==1
                       a2=zeros(2*n,1);
                       a2(1:n)=a;
-                      f2=ifft(fftKcirc.*fft(a2'));
+                      f2=real(ifft(fftKcirc.*fft(a2')));
                       f=f2(1:n)';
                       if isfield(gp,'meanf')
                         f=f+H'*(B_m*(H*a));
@@ -827,7 +816,7 @@
                       a2=zeros(2*n2,2*n1);
                       a2(1:n2,1:n1)=reshape(a,n2,n1);
                       
-                      f2=ifft2(fftKcirc.*fft2(a2));
+                      f2=real(ifft2(fftKcirc.*fft2(a2)));
                       f2=f2(1:n2,1:n1);
                       f=f2(:);
                       if isfield(gp,'meanf')
@@ -871,7 +860,7 @@
                       if size(x,2)==1
                         a2=zeros(2*n,1);
                         a2(1:n)=a;
-                        f2=ifft(fftKcirc.*fft(a2'));
+                        f2=real(ifft(fftKcirc.*fft(a2')));
                         f=f2(1:n)';
                         
                         if isfield(gp,'meanf')
@@ -880,7 +869,7 @@
                       elseif size(x,2)==2
                         a2=zeros(2*n2,2*n1);
                         a2(1:n2,1:n1)=reshape(a,n2,n1);
-                        f2=ifft2(fftKcirc.*fft2(a2));
+                        f2=real(ifft2(fftKcirc.*fft2(a2)));
                         f2=f2(1:n2,1:n1);
                         f=f2(:);
                         
@@ -953,7 +942,7 @@
                   Ltmp(1:(size(Dt,1)+1):end)=Ltmp(1:(size(Dt,1)+1):end)+1;
                   [L,notpositivedefinite]=chol(Ltmp,'lower');
                   if notpositivedefinite
-                    [edata,e,eprior,param,ch] = set_output_for_notpositivedefinite();
+                    [edata,e,eprior,param] = set_output_for_notpositivedefinite();
                     return
                   end
                   
@@ -974,7 +963,7 @@
                   RKR(1:(n+1):end)=RKR(1:(n+1):end)+1;
                   [L,notpositivedefinite] = chol(RKR,'lower');
                   if notpositivedefinite
-                    [edata,e,eprior,param,ch] = set_output_for_notpositivedefinite();
+                    [edata,e,eprior,param] = set_output_for_notpositivedefinite();
                     return
                   end
                   edata = logZ + sum(log(diag(L)));
@@ -1004,7 +993,7 @@
                   RKR(1:(n+1):end)=RKR(1:(n+1):end)+1;
                   [L,notpositivedefinite] = chol(RKR,'lower');
                   if notpositivedefinite
-                    [edata,e,eprior,param,ch] = set_output_for_notpositivedefinite();
+                    [edata,e,eprior,param] = set_output_for_notpositivedefinite();
                     return
                   end
                   edata = logZ + sum(log(diag(L)));
@@ -1066,7 +1055,7 @@
                     Lc(1:n+1:end)=Lc(1:n+1:end)+1;
                     [Lc,notpositivedefinite]=chol(Lc);
                     if notpositivedefinite
-                      [edata,e,eprior,param,ch] = set_output_for_notpositivedefinite();
+                      [edata,e,eprior,param] = set_output_for_notpositivedefinite();
                       return
                     end
                     L(:,:,i1)=Lc;
@@ -1078,7 +1067,7 @@
                   end
                   [M, notpositivedefinite]=chol(sum(RER,3));
                   if notpositivedefinite
-                    [edata,e,eprior,param,ch] = set_output_for_notpositivedefinite();
+                    [edata,e,eprior,param] = set_output_for_notpositivedefinite();
                     return
                   end
                   
@@ -1127,7 +1116,7 @@
                   Lc(1:n+1:end)=Lc(1:n+1:end)+1;
                   [Lc, notpositivedefinite]=chol(Lc);
                   if notpositivedefinite
-                    [edata,e,eprior,param,ch] = set_output_for_notpositivedefinite();
+                    [edata,e,eprior,param] = set_output_for_notpositivedefinite();
                     return
                   end
                   L(:,:,i1)=Lc;
@@ -1144,7 +1133,7 @@
                 end
                 [M, notpositivedefinite]=chol(sum(RER,3));
                 if notpositivedefinite
-                  [edata,e,eprior,param,ch] = set_output_for_notpositivedefinite();
+                  [edata,e,eprior,param] = set_output_for_notpositivedefinite();
                   return
                 end
                 
@@ -1341,8 +1330,12 @@
                 B=WK;
                 B(1:sum(nl)+1:end) = B(1:sum(nl)+1:end) + 1;
                 
-                [Ll,Lu]=lu(B);
-                edata = logZ + 0.5*det(Ll)*prod(sign(diag(Lu))).*sum(log(abs(diag(Lu))));
+                [Ll,Lu,p]=lu(B);
+                if sum(diag(p))==size(p,1)
+                  edata = logZ + 0.5*prod(sign(diag(Lu))).*sum(log(abs(diag(Lu))));
+                else
+                  edata = logZ + 0.5*det(p)*prod(sign(diag(Lu))).*sum(log(abs(diag(Lu))));
+                end
                 
                 % Return help parameters for gradient and prediction
                 % calculations
@@ -1369,7 +1362,7 @@
           K_uu = gp_trcov(gp, u);    % u x u, noiseles covariance K_uu
           [Luu, notpositivedefinite] = chol(K_uu, 'lower');
           if notpositivedefinite
-            [edata,e,eprior,param,ch] = set_output_for_notpositivedefinite();
+            [edata,e,eprior,param] = set_output_for_notpositivedefinite();
             return
           end
           % Evaluate the Lambda (La)
@@ -1382,7 +1375,7 @@
           A = K_uu+K_fu'*iLaKfu;  A = (A+A')./2;     % Ensure symmetry
           [A, notpositivedefinite] = chol(A);
           if notpositivedefinite
-            [edata,e,eprior,param,ch] = set_output_for_notpositivedefinite();
+            [edata,e,eprior,param] = set_output_for_notpositivedefinite();
             return
           end
           L = iLaKfu/A;
@@ -1455,7 +1448,7 @@
             case 'lik_specific'
               [f, a] = gp.lik.fh.optimizef(gp, y, K_uu, Lav, K_fu);
               if isnan(f)
-                [edata,e,eprior,param,ch] = set_output_for_notpositivedefinite();
+                [edata,e,eprior,param] = set_output_for_notpositivedefinite();
                 return
               end
             otherwise 
@@ -1477,7 +1470,7 @@
             A = K_uu + sWKfu'*(repmat(Lah,1,m).\sWKfu);   A = (A+A')./2;
             [A, notpositivedefinite] = chol(A);
             if notpositivedefinite
-              [edata,e,eprior,param,ch] = set_output_for_notpositivedefinite();
+              [edata,e,eprior,param] = set_output_for_notpositivedefinite();
               return
             end
             edata = sum(log(Lah)) - 2*sum(log(diag(Luu))) + 2*sum(log(diag(A)));
@@ -1491,7 +1484,7 @@
             
             [L, notpositivedefinite] = chol(K);
             if notpositivedefinite
-              [edata,e,eprior,param,ch] = set_output_for_notpositivedefinite();
+              [edata,e,eprior,param] = set_output_for_notpositivedefinite();
               return
             end
             L1 = L;
@@ -1543,7 +1536,7 @@
           K_uu = (K_uu+K_uu')./2;     % ensure the symmetry of K_uu
           [Luu, notpositivedefinite] = chol(K_uu, 'lower');
           if notpositivedefinite
-            [edata,e,eprior,param,ch] = set_output_for_notpositivedefinite();
+            [edata,e,eprior,param] = set_output_for_notpositivedefinite();
             return
           end
           % Evaluate the Lambda (La)
@@ -1559,7 +1552,7 @@
             Labl{i} = Cbl_ff - Qbl_ff;
             [LLabl{i}, notpositivedefinite] = chol(Labl{i});
             if notpositivedefinite
-              [edata,e,eprior,param,ch] = set_output_for_notpositivedefinite();
+              [edata,e,eprior,param] = set_output_for_notpositivedefinite();
               return
             end
             iLaKfu(ind{i},:) = LLabl{i}\(LLabl{i}'\K_fu(ind{i},:));
@@ -1568,7 +1561,7 @@
           A = (A+A')./2;     % Ensure symmetry
           [A, notpositivedefinite] = chol(A);
           if notpositivedefinite
-            [edata,e,eprior,param,ch] = set_output_for_notpositivedefinite();
+            [edata,e,eprior,param] = set_output_for_notpositivedefinite();
             return
           end
           L = iLaKfu/A;
@@ -1611,7 +1604,7 @@
                   Lah{i} = eye(size(Labl{i})) + diag(sW(ind{i}))*Labl{i}*diag(sW(ind{i}));
                   [LLah{i}, notpositivedefinite] = chol(Lah{i});
                   if notpositivedefinite
-                    [edata,e,eprior,param,ch] = set_output_for_notpositivedefinite();
+                    [edata,e,eprior,param] = set_output_for_notpositivedefinite();
                     return
                   end
                   V2(ind{i},:) = LLah{i}\(LLah{i}'\V(ind{i},:));
@@ -1620,7 +1613,7 @@
                 A = K_uu + V'*V2;   A = (A+A')./2;
                 [A, notpositivedefinite] = chol(A);
                 if notpositivedefinite
-                  [edata,e,eprior,param,ch] = set_output_for_notpositivedefinite();
+                  [edata,e,eprior,param] = set_output_for_notpositivedefinite();
                   return
                 end
                 Lb = V2/A;
@@ -1670,7 +1663,7 @@
             Lahat = eye(size(Labl{i})) + diag(sqrtW(ind{i}))*Labl{i}*diag(sqrtW(ind{i}));
             [LLahat, notpositivedefinite] = chol(Lahat);
             if notpositivedefinite
-              [edata,e,eprior,param,ch] = set_output_for_notpositivedefinite();
+              [edata,e,eprior,param] = set_output_for_notpositivedefinite();
               return
             end
             iLahatWKfu(ind{i},:) = LLahat\(LLahat'\WKfu(ind{i},:));
@@ -1679,7 +1672,7 @@
           A = K_uu + WKfu'*iLahatWKfu;   A = (A+A')./2;
           [A, notpositivedefinite] = chol(A);
           if notpositivedefinite
-            [edata,e,eprior,param,ch] = set_output_for_notpositivedefinite();
+            [edata,e,eprior,param] = set_output_for_notpositivedefinite();
             return
           end
           edata =  edata - 2*sum(log(diag(Luu))) + 2*sum(log(diag(A)));
@@ -1718,7 +1711,7 @@
           K_uu = (K_uu+K_uu')./2;     % ensure the symmetry of K_uu
           [Luu, notpositivedefinite] = chol(K_uu, 'lower');
           if notpositivedefinite
-            [edata,e,eprior,param,ch] = set_output_for_notpositivedefinite();
+            [edata,e,eprior,param] = set_output_for_notpositivedefinite();
             return
           end
 
@@ -1747,7 +1740,7 @@
           B = B(:,p);
           [VD, notpositivedefinite] = ldlchol(La);
           if notpositivedefinite
-            [edata,e,eprior,param,ch] = set_output_for_notpositivedefinite();
+            [edata,e,eprior,param] = set_output_for_notpositivedefinite();
             return
           end
           
@@ -1757,7 +1750,7 @@
           A = K_uu+K_fu'*iLaKfu;  A = (A+A')./2;     % Ensure symmetry
           [A, notpositivedefinite] = chol(A);
           if notpositivedefinite
-            [edata,e,eprior,param,ch] = set_output_for_notpositivedefinite();
+            [edata,e,eprior,param] = set_output_for_notpositivedefinite();
             return
           end
           L = iLaKfu/A;
@@ -1801,7 +1794,7 @@
                 Lah = I + sqrtW*La*sqrtW; 
                 [VDh, notpositivedefinite] = ldlchol(Lah);
                 if notpositivedefinite
-                  [edata,e,eprior,param,ch] = set_output_for_notpositivedefinite();
+                  [edata,e,eprior,param] = set_output_for_notpositivedefinite();
                   return
                 end
                 V = repmat(sW,1,m).*K_fu;
@@ -1809,7 +1802,7 @@
                 A = K_uu + V'*Vt;   A = (A+A')./2;
                 [A, notpositivedefinite] = chol(A);
                 if notpositivedefinite
-                  [edata,e,eprior,param,ch] = set_output_for_notpositivedefinite();
+                  [edata,e,eprior,param] = set_output_for_notpositivedefinite();
                   return
                 end
                 Lb = Vt/A;
@@ -1847,13 +1840,13 @@
           Lahat = sparse(1:n,1:n,1,n,n) + sqrtW*La*sqrtW;
           [LDh, notpositivedefinite] = ldlchol(Lahat);
           if notpositivedefinite
-            [edata,e,eprior,param,ch] = set_output_for_notpositivedefinite();
+            [edata,e,eprior,param] = set_output_for_notpositivedefinite();
             return
           end
           A = K_uu + WKfu'*ldlsolve(LDh,WKfu);   A = (A+A')./2;
           [A, notpositivedefinite] = chol(A);
           if notpositivedefinite
-            [edata,e,eprior,param,ch] = set_output_for_notpositivedefinite();
+            [edata,e,eprior,param] = set_output_for_notpositivedefinite();
             return
           end
           edata = sum(log(diag(LDh))) - 2*sum(log(diag(Luu))) + 2*sum(log(diag(A)));
@@ -1885,7 +1878,7 @@
           K_uu = gp_trcov(gp, u);    % u x u, noiseles covariance K_uu
           [Luu, notpositivedefinite] = chol(K_uu, 'lower');
           if notpositivedefinite
-            [edata,e,eprior,param,ch] = set_output_for_notpositivedefinite();
+            [edata,e,eprior,param] = set_output_for_notpositivedefinite();
             return
           end
           % Evaluate the Lambda (La)
@@ -1937,7 +1930,7 @@
                 A = K_uu + sWKfu'*sWKfu;   A = (A+A')./2;
                 [A, notpositivedefinite]=chol(A);
                 if notpositivedefinite
-                  [edata,e,eprior,param,ch] = set_output_for_notpositivedefinite();
+                  [edata,e,eprior,param] = set_output_for_notpositivedefinite();
                   return
                 end
                 Lb = sWKfu/A;
@@ -1987,7 +1980,7 @@
             A = K_uu + sWKfu'*sWKfu;   A = (A+A')./2;
             [A, notpositivedefinite] = chol(A);
             if notpositivedefinite
-              [edata,e,eprior,param,ch] = set_output_for_notpositivedefinite();
+              [edata,e,eprior,param] = set_output_for_notpositivedefinite();
               return
             end
             edata = -sum(log(diag(Luu))) + sum(log(diag(A)));
@@ -2008,7 +2001,7 @@
 %             
 %             [L, notpositivedefinite] = chol(K);
 %             if notpositivedefinite
-%               [edata,e,eprior,param,ch] = set_output_for_notpositivedefinite();
+%               [edata,e,eprior,param] = set_output_for_notpositivedefinite();
 %               return
 %             end
 %             L1 = L;
@@ -2101,14 +2094,14 @@
       param.a = a;
       param.p=p;
     
-      % store values to the cache
-      ch = param;
-      ch.w = w;
-      ch.e = e;
-      ch.edata = edata;
-      ch.eprior = eprior;
-      ch.n = size(x,1);
-      ch.datahash=datahash;
+%       % store values to the cache
+%       ch = param;
+%       ch.w = w;
+%       ch.e = e;
+%       ch.edata = edata;
+%       ch.eprior = eprior;
+%       ch.n = size(x,1);
+%       ch.datahash=datahash;
     end
     
 %    assert(isreal(edata))
@@ -2118,7 +2111,9 @@
 % ==============================================================
 % Begin of the nested functions
 % ==============================================================
-%        
+%      
+end
+
 function [e, g, h] = egh(f, varargin)
   ikf = iKf(f');
   e = 0.5*f*ikf - gp.lik.fh.ll(gp.lik, y, f', z);
@@ -2138,8 +2133,8 @@
       ikf = ldlsolve(VD,f) - L*(L'*f);
   end
 end
-end
-function [edata,e,eprior,param,ch] = set_output_for_notpositivedefinite()
+
+function [edata,e,eprior,param] = set_output_for_notpositivedefinite()
   % Instead of stopping to chol error, return NaN
   edata=NaN;
   e=NaN;
@@ -2149,14 +2144,13 @@
   param.a=NaN;
   param.La2=NaN;
   param.p=NaN;
-  w=NaN;
-  datahash = NaN;
-  ch=param;
-  ch.e = e;
-  ch.edata = edata;
-  ch.eprior = eprior;
-  ch.datahash=datahash;
-  ch.w = NaN;
+%   w=NaN;
+%   datahash = NaN;
+%   ch=param;
+%   ch.e = e;
+%   ch.edata = edata;
+%   ch.eprior = eprior;
+%   ch.datahash=datahash;
+%   ch.w = NaN;
 end
 
-end
diff --git a/gp/gpla_g.m b/gp/gpla_g.m
index 4af802a6..90ee01e9 100644
--- a/gp/gpla_g.m
+++ b/gp/gpla_g.m
@@ -31,12 +31,12 @@
 
   ip=inputParser;
   ip.FunctionName = 'GPLA_G';
-  ip.addRequired('w', @(x) isvector(x) && isreal(x) && all(isfinite(x)));
-  ip.addRequired('gp',@isstruct);
-  ip.addRequired('x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))))
-  ip.addRequired('y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))))
-  ip.addParamValue('z', [], @(x) isreal(x) && all(isfinite(x(:))))
-  ip.parse(w, gp, x, y, varargin{:});
+  ip=iparser(ip,'addRequired','w', @(x) isvector(x) && isreal(x) && all(isfinite(x)));
+  ip=iparser(ip,'addRequired','gp',@isstruct);
+  ip=iparser(ip,'addRequired','x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))));
+  ip=iparser(ip,'addRequired','y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))));
+  ip=iparser(ip,'addParamValue','z', [], @(x) isreal(x) && all(isfinite(x(:))));
+  ip=iparser(ip,'parse',w, gp, x, y, varargin{:});
   z=ip.Results.z;
 
   gp = gp_unpak(gp, w);       % unpak the parameters
@@ -712,15 +712,15 @@
             DKffba=zeros(n*nout,1);
             
             % check in which components the covariance function is present
-            do = false(nout,1);
+            doo = false(nout,1);
             if multicf
               for z1=1:nout
                 if any(gp.comp_cf{z1}==i)
-                  do(z1) = true;
+                  doo(z1) = true;
                 end
               end
             else
-              do = true(nout,1);
+              doo = true(nout,1);
             end
             
             i1=0;
@@ -752,7 +752,7 @@
               % Derivative of explicit terms
               trace_sum_tmp=0;
               for z1=1:nout
-                if do(z1)
+                if doo(z1)
                   DKffba((1:n)+(z1-1)*n)=DKffb*a((1:n)+(z1-1)*n);
                   trace_sum_tmp = trace_sum_tmp + sum(sum( inv_iWK(:,:,z1) .* DKffb ));
                 end
@@ -762,7 +762,7 @@
               
               % Derivative of f w.r.t. theta
               for z1=1:nout
-                if do(z1)
+                if doo(z1)
                   DKllg((1:n)+(z1-1)*n)=DKffb*llg((1:n)+(z1-1)*n);
                   EDKllg((1:n)+(z1-1)*n)=E(:,:,z1)*DKllg((1:n)+(z1-1)*n);
                 end
@@ -928,10 +928,10 @@
               gpcf = gp.cf{i};
               
               % check in which components the covariance function is present
-              do = false(nlp,1);
+              doo = false(nlp,1);
               for z1=1:nlp
                 if any(gp.comp_cf{z1}==i)
-                  do(z1) = true;
+                  doo(z1) = true;
                 end
               end
               
@@ -988,7 +988,7 @@
                 if ~isfield(gp,'meanf')
                   dKnl = zeros(sum(nl));
                   if isfield(gp.lik,'xtime')
-                    if ~isempty(intersect(gp.comp_cf{1},i)) %do(indnl)
+                    if ~isempty(intersect(gp.comp_cf{1},i)) %doo(indnl)
                       if savememory
                         DKff=gpcf.fh.cfg(gpcf,xtime,[],[],i2);
                       else
@@ -1002,7 +1002,7 @@
                       else
                         DKff=DKffc{i2};
                       end
-                      %if do(indnl)
+                      %if doo(indnl)
                       dKnl(ntime+(1:n),ntime+(1:n)) = DKff;
                       %end
                     end
@@ -1013,7 +1013,7 @@
                       DKff=DKffc{i2};
                     end
                     for indnl=1:nlp
-                      if do(indnl)
+                      if doo(indnl)
                         dKnl((1:n)+(indnl-1)*n,(1:n)+(indnl-1)*n) = DKff;
                       end
                     end
diff --git a/gp/gpla_jpred.m b/gp/gpla_jpred.m
index ff9890ad..ac747489 100644
--- a/gp/gpla_jpred.m
+++ b/gp/gpla_jpred.m
@@ -86,23 +86,23 @@
 
   ip=inputParser;
   ip.FunctionName = 'GPLA_JPRED';
-  ip.addRequired('gp', @isstruct);
-  ip.addRequired('x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))))
-  ip.addRequired('y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))))
-  ip.addOptional('xt', [], @(x) isempty(x) || (isreal(x) && all(isfinite(x(:)))))
-  ip.addParamValue('yt', [], @(x) isreal(x) && all(isfinite(x(:))))
-  ip.addParamValue('z', [], @(x) isreal(x) && all(isfinite(x(:))))
-  ip.addParamValue('zt', [], @(x) isreal(x) && all(isfinite(x(:))))
-  ip.addParamValue('predcf', [], @(x) isempty(x) || ...
-                   isvector(x) && isreal(x) && all(isfinite(x)&x>0))
-  ip.addParamValue('tstind', [], @(x) isempty(x) || iscell(x) ||...
-                   (isvector(x) && isreal(x) && all(isfinite(x)&x>0)))
-  ip.addParamValue('fcorr', 'off', @(x) ismember(x, {'off', 'fact', 'cm2', 'lr', 'on'}))
+  ip=iparser(ip,'addRequired','gp', @isstruct);
+  ip=iparser(ip,'addRequired','x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))));
+  ip=iparser(ip,'addRequired','y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))));
+  ip=iparser(ip,'addOptional','xt', [], @(x) isempty(x) || (isreal(x) && all(isfinite(x(:)))));
+  ip=iparser(ip,'addParamValue','yt', [], @(x) isreal(x) && all(isfinite(x(:))));
+  ip=iparser(ip,'addParamValue','z', [], @(x) isreal(x) && all(isfinite(x(:))));
+  ip=iparser(ip,'addParamValue','zt', [], @(x) isreal(x) && all(isfinite(x(:))));
+  ip=iparser(ip,'addParamValue','predcf', [], @(x) isempty(x) || ...
+                   isvector(x) && isreal(x) && all(isfinite(x)&x>0));
+  ip=iparser(ip,'addParamValue','tstind', [], @(x) isempty(x) || iscell(x) ||...
+                   (isvector(x) && isreal(x) && all(isfinite(x)&x>0)));
+  ip=iparser(ip,'addParamValue','fcorr', 'off', @(x) ismember(x, {'off', 'fact', 'cm2', 'on','lr'}))
   if numel(varargin)==0 || isnumeric(varargin{1})
     % inputParser should handle this, but it doesn't
-    ip.parse(gp, x, y, varargin{:});
+    ip=iparser(ip,'parse',gp, x, y, varargin{:});
   else
-    ip.parse(gp, x, y, [], varargin{:});
+    ip=iparser(ip,'parse',gp, x, y, [], varargin{:});
   end
   xt=ip.Results.xt;
   yt=ip.Results.yt;
diff --git a/gp/gpla_looe.m b/gp/gpla_looe.m
index 38b665ec..7077a4df 100644
--- a/gp/gpla_looe.m
+++ b/gp/gpla_looe.m
@@ -33,13 +33,13 @@
 
 ip=inputParser;
 ip.FunctionName = 'GPLA_LOOE';
-ip.addRequired('w', @(x) isempty(x) || ...
+ip=iparser(ip,'addRequired','w', @(x) isempty(x) || ...
                isvector(x) && isreal(x) && all(isfinite(x)));
-ip.addRequired('gp', @(x) isstruct(x) || iscell(x));
-ip.addRequired('x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))))
-ip.addRequired('y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))))
-ip.addParamValue('z', [], @(x) isreal(x) && all(isfinite(x(:))))
-ip.parse(w, gp, x, y, varargin{:});
+ip=iparser(ip,'addRequired','gp', @(x) isstruct(x) || iscell(x));
+ip=iparser(ip,'addRequired','x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))));
+ip=iparser(ip,'addRequired','y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))));
+ip=iparser(ip,'addParamValue','z', [], @(x) isreal(x) && all(isfinite(x(:))));
+ip=iparser(ip,'parse',w, gp, x, y, varargin{:});
 z=ip.Results.z;
 
 gp=gp_unpak(gp, w);
diff --git a/gp/gpla_loopred.m b/gp/gpla_loopred.m
index 350ebb79..288131ce 100644
--- a/gp/gpla_loopred.m
+++ b/gp/gpla_loopred.m
@@ -36,13 +36,15 @@
 
   ip=inputParser;
   ip.FunctionName = 'GPLA_LOOPRED';
-  ip.addRequired('gp', @(x) isstruct(x));
-  ip.addRequired('x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))))
-  ip.addRequired('y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))))
-  ip.addParamValue('z', [], @(x) isreal(x) && all(isfinite(x(:))))
-  ip.addParamValue('method', 'cavity', @(x) ismember(x, {'lrs' 'cavity' 'inla'}))
-  ip.addParamValue('fcorr', 'off', @(x) ismember(x, {'off', 'fact', 'cm2', 'on'}));
-  ip.parse(gp, x, y, varargin{:});
+
+  ip=iparser(ip,'addRequired','gp', @(x) isstruct(x));
+  ip=iparser(ip,'addRequired','x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))));
+  ip=iparser(ip,'addRequired','y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))));
+  ip=iparser(ip,'addParamValue','z', [], @(x) isreal(x) && all(isfinite(x(:))));
+  ip=iparser(ip,'addParamValue','method', 'lrs', @(x) ismember(x, {'lrs' 'cavity' 'inla'}));
+  ip=iparser(ip,'addParamValue','fcorr', 'off', @(x) ismember(x, {'off', 'fact', 'cm2', 'on'}));
+  ip=iparser(ip,'parse',gp, x, y, varargin{:});
+
   z=ip.Results.z;
   method = ip.Results.method;
   fcorr=ip.Results.fcorr;
diff --git a/gp/gpla_pred.m b/gp/gpla_pred.m
index 6452b9e0..ba445372 100644
--- a/gp/gpla_pred.m
+++ b/gp/gpla_pred.m
@@ -83,24 +83,24 @@
 
   ip=inputParser;
   ip.FunctionName = 'GPLA_PRED';
-  ip.addRequired('gp', @isstruct);
-  ip.addRequired('x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))))
-  ip.addRequired('y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))))
-  ip.addOptional('xt', [], @(x) isempty(x) || (isreal(x) && all(isfinite(x(:)))))
-  ip.addParamValue('yt', [], @(x) isreal(x) && all(isfinite(x(:))))
-  ip.addParamValue('z', [], @(x) isreal(x) && all(isfinite(x(:))))
-  ip.addParamValue('zt', [], @(x) isreal(x) && all(isfinite(x(:))))
-  ip.addParamValue('predcf', [], @(x) isempty(x) || ...
-                   isvector(x) && isreal(x) && all(isfinite(x)&x>0))
-  ip.addParamValue('tstind', [], @(x) isempty(x) || iscell(x) ||...
-                   (isvector(x) && isreal(x) && all(isfinite(x)&x>0)))
-  ip.addParamValue('fcorr', 'off', @(x) ismember(x, {'off', ...
-                   'cm2', 'fact', 'on','lr'}))
+  ip=iparser(ip,'addRequired','gp', @isstruct);
+  ip=iparser(ip,'addRequired','x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))));
+  ip=iparser(ip,'addRequired','y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))));
+  ip=iparser(ip,'addOptional','xt', [], @(x) isempty(x) || (isreal(x) && all(isfinite(x(:)))));
+  ip=iparser(ip,'addParamValue','yt', [], @(x) isreal(x) && all(isfinite(x(:))));
+  ip=iparser(ip,'addParamValue','z', [], @(x) isreal(x) && all(isfinite(x(:))));
+  ip=iparser(ip,'addParamValue','zt', [], @(x) isreal(x) && all(isfinite(x(:))));
+  ip=iparser(ip,'addParamValue','predcf', [], @(x) isempty(x) || ...
+                   isvector(x) && isreal(x) && all(isfinite(x)&x>0));
+  ip=iparser(ip,'addParamValue','tstind', [], @(x) isempty(x) || iscell(x) ||...
+                   (isvector(x) && isreal(x) && all(isfinite(x)&x>0)));
+  ip=iparser(ip,'addParamValue','fcorr', 'off', @(x) ismember(x, {'off', ...
+                   'cm2', 'fact', 'on','lr'}));
   if numel(varargin)==0 || isnumeric(varargin{1})
     % inputParser should handle this, but it doesn't
-    ip.parse(gp, x, y, varargin{:});
+    ip=iparser(ip,'parse',gp, x, y, varargin{:});
   else
-    ip.parse(gp, x, y, [], varargin{:});
+    ip=iparser(ip,'parse',gp, x, y, [], varargin{:});
   end
   xt=ip.Results.xt;
   yt=ip.Results.yt;
diff --git a/gp/gpmc_jpreds.m b/gp/gpmc_jpreds.m
index de7f48f9..a36111b9 100644
--- a/gp/gpmc_jpreds.m
+++ b/gp/gpmc_jpreds.m
@@ -103,22 +103,22 @@
   
   ip=inputParser;
   ip.FunctionName = 'GPMC_JPREDS';
-  ip.addRequired('gp',@isstruct);
-  ip.addRequired('x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))))
-  ip.addRequired('y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))))
-  ip.addOptional('xt', [], @(x) isempty(x) || (isreal(x) && all(isfinite(x(:)))))
-  ip.addParamValue('yt', [], @(x) isreal(x) && all(isfinite(x(:))))
-  ip.addParamValue('zt', [], @(x) isreal(x) && all(isfinite(x(:))))
-  ip.addParamValue('z', [], @(x) isreal(x) && all(isfinite(x(:))))
-  ip.addParamValue('predcf', [], @(x) isempty(x) || ...
-                   isvector(x) && isreal(x) && all(isfinite(x)&x>0))
-  ip.addParamValue('tstind', [], @(x) isempty(x) || iscell(x) ||...
-                   (isvector(x) && isreal(x) && all(isfinite(x)&x>0)))
+  ip=iparser(ip,'addRequired','gp',@isstruct);
+  ip=iparser(ip,'addRequired','x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))));
+  ip=iparser(ip,'addRequired','y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))));
+  ip=iparser(ip,'addOptional','xt', [], @(x) isempty(x) || (isreal(x) && all(isfinite(x(:)))));
+  ip=iparser(ip,'addParamValue','yt', [], @(x) isreal(x) && all(isfinite(x(:))));
+  ip=iparser(ip,'addParamValue','zt', [], @(x) isreal(x) && all(isfinite(x(:))));
+  ip=iparser(ip,'addParamValue','z', [], @(x) isreal(x) && all(isfinite(x(:))));
+  ip=iparser(ip,'addParamValue','predcf', [], @(x) isempty(x) || ...
+                   isvector(x) && isreal(x) && all(isfinite(x)&x>0));
+  ip=iparser(ip,'addParamValue','tstind', [], @(x) isempty(x) || iscell(x) ||...
+                   (isvector(x) && isreal(x) && all(isfinite(x)&x>0)));
   if numel(varargin)==0 || isnumeric(varargin{1})
     % inputParser should handle this, but it doesn't
-    ip.parse(gp, x, y, varargin{:});
+    ip=iparser(ip,'parse',gp, x, y, varargin{:});
   else
-    ip.parse(gp, x, y, [], varargin{:});
+    ip=iparser(ip,'parse',gp, x, y, [], varargin{:});
   end
   xt=ip.Results.xt;
   yt=ip.Results.yt;
diff --git a/gp/gpmc_loopred.m b/gp/gpmc_loopred.m
index bf1085bf..22446bdb 100644
--- a/gp/gpmc_loopred.m
+++ b/gp/gpmc_loopred.m
@@ -65,12 +65,14 @@
 
 ip=inputParser;
 ip.FunctionName = 'GPMC_LOOPRED';
-ip.addRequired('gp',@isstruct);
-ip.addRequired('x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))))
-ip.addRequired('y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))))
-ip.addParamValue('z', [], @(x) isreal(x) && all(isfinite(x(:))))
-ip.addParamValue('is', 'on', @(x) ismember(x,{'on' 'off' 'psis' 'is'}))
-ip.parse(gp, x, y, varargin{:});
+
+ip=iparser(ip,'addRequired','gp',@isstruct);
+ip=iparser(ip,'addRequired','x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))));
+ip=iparser(ip,'addRequired','y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))));
+ip=iparser(ip,'addParamValue','z', [], @(x) isreal(x) && all(isfinite(x(:))));
+ip=iparser(ip,'addParamValue','is', 'on', @(x) ismember(x,{'on' 'off'}));
+ip=iparser(ip,'parse',gp, x, y, varargin{:});
+
 z=ip.Results.z;
 is=ip.Results.is;
 
diff --git a/gp/gpmc_loopreds.m b/gp/gpmc_loopreds.m
index 65d15fa5..efd0d77a 100644
--- a/gp/gpmc_loopreds.m
+++ b/gp/gpmc_loopreds.m
@@ -38,11 +38,11 @@
 % Nothing to parse, but check the arguments anyway
 ip=inputParser;
 ip.FunctionName = 'GPMC_LOOPREDS';
-ip.addRequired('gp',@isstruct);
-ip.addRequired('x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))))
-ip.addRequired('y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))))
-ip.addParamValue('z', [], @(x) isreal(x) && all(isfinite(x(:))))
-ip.parse(gp, x, y, varargin{:});
+ip=iparser(ip,'addRequired','gp',@isstruct);
+ip=iparser(ip,'addRequired','x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))));
+ip=iparser(ip,'addRequired','y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))));
+ip=iparser(ip,'addParamValue','z', [], @(x) isreal(x) && all(isfinite(x(:))));
+ip=iparser(ip,'parse',gp, x, y, varargin{:});
 z=ip.Results.z;
 
 if isfield(gp,'meanf') & ~isempty(gp.meanf)
diff --git a/gp/gpmc_preds.m b/gp/gpmc_preds.m
index 012581d1..17be1771 100644
--- a/gp/gpmc_preds.m
+++ b/gp/gpmc_preds.m
@@ -101,23 +101,23 @@
   
   ip=inputParser;
   ip.FunctionName = 'GPMC_PREDS';
-  ip.addRequired('gp',@isstruct);
-  ip.addRequired('x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))))
-  ip.addRequired('y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))))
-  ip.addOptional('xt', [], @(x) isempty(x) || (isreal(x) && all(isfinite(x(:)))))
-  ip.addParamValue('yt', [], @(x) isreal(x) && all(isfinite(x(:))))
-  ip.addParamValue('zt', [], @(x) isreal(x) && all(isfinite(x(:))))
-  ip.addParamValue('z', [], @(x) isreal(x) && all(isfinite(x(:))))
-  ip.addParamValue('predcf', [], @(x) isempty(x) || ...
-                   isvector(x) && isreal(x) && all(isfinite(x)&x>0))
-  ip.addParamValue('tstind', [], @(x) isempty(x) || iscell(x) ||...
-                   (isvector(x) && isreal(x) && all(isfinite(x)&x>0)))
-  ip.addParamValue('fcorr', 'off', @(x) ismember(x, {'off', 'fact', 'cm2', 'on'}));
+  ip=iparser(ip,'addRequired','gp',@isstruct);
+  ip=iparser(ip,'addRequired','x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))));
+  ip=iparser(ip,'addRequired','y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))));
+  ip=iparser(ip,'addOptional','xt', [], @(x) isempty(x) || (isreal(x) && all(isfinite(x(:)))));
+  ip=iparser(ip,'addParamValue','yt', [], @(x) isreal(x) && all(isfinite(x(:))));
+  ip=iparser(ip,'addParamValue','zt', [], @(x) isreal(x) && all(isfinite(x(:))));
+  ip=iparser(ip,'addParamValue','z', [], @(x) isreal(x) && all(isfinite(x(:))));
+  ip=iparser(ip,'addParamValue','predcf', [], @(x) isempty(x) || ...
+                   isvector(x) && isreal(x) && all(isfinite(x)&x>0));
+  ip=iparser(ip,'addParamValue','tstind', [], @(x) isempty(x) || iscell(x) ||...
+                   (isvector(x) && isreal(x) && all(isfinite(x)&x>0)));
+  ip=iparser(ip,'addParamValue','fcorr', 'off', @(x) ismember(x, {'off', 'fact', 'cm2', 'on'}));
   if numel(varargin)==0 || isnumeric(varargin{1})
     % inputParser should handle this, but it doesn't
-    ip.parse(gp, x, y, varargin{:});
+    ip=iparser(ip,'parse',gp, x, y, varargin{:});
   else
-    ip.parse(gp, x, y, [], varargin{:});
+    ip=iparser(ip,'parse',gp, x, y, [], varargin{:});
   end
   xt=ip.Results.xt;
   yt=ip.Results.yt;
diff --git a/gp/gpmf_constant.m b/gp/gpmf_constant.m
index f1bb32b1..3a967187 100644
--- a/gp/gpmf_constant.m
+++ b/gp/gpmf_constant.m
@@ -38,13 +38,13 @@
 
   ip=inputParser;
   ip.FunctionName = 'GPMF_CONSTANT';
-  ip.addOptional('gpmf', [], @isstruct);
-  ip.addParamValue('constant',1, @(x) isvector(x));
-  ip.addParamValue('prior_mean',0, @(x) isvector(x));
-  ip.addParamValue('prior_cov',100, @(x) isvector(x));
-  ip.addParamValue('mean_prior', [], @isstruct);
-  ip.addParamValue('cov_prior', [], @isstruct);
-  ip.parse(varargin{:});
+  ip=iparser(ip,'addOptional','gpmf', [], @isstruct);
+  ip=iparser(ip,'addParamValue','constant',1, @(x) isvector(x));
+  ip=iparser(ip,'addParamValue','prior_mean',0, @(x) isvector(x));
+  ip=iparser(ip,'addParamValue','prior_cov',100, @(x) isvector(x));
+  ip=iparser(ip,'addParamValue','mean_prior', [], @isstruct);
+  ip=iparser(ip,'addParamValue','cov_prior', [], @isstruct);
+  ip=iparser(ip,'parse',varargin{:});
   gpmf=ip.Results.gpmf;
   
   if isempty(gpmf)
diff --git a/gp/gpmf_linear.m b/gp/gpmf_linear.m
index a370e413..4cb0cdef 100644
--- a/gp/gpmf_linear.m
+++ b/gp/gpmf_linear.m
@@ -37,13 +37,13 @@
 
   ip=inputParser;
   ip.FunctionName = 'GPMF_LINEAR';
-  ip.addOptional('gpmf', [], @isstruct);
-  ip.addParamValue('selectedVariables',[], @(x) isvector(x) && all(x>0));
-  ip.addParamValue('prior_mean',0, @(x) isvector(x));
-  ip.addParamValue('prior_cov',100, @(x) isvector(x));
-  ip.addParamValue('mean_prior', [], @isstruct);
-  ip.addParamValue('cov_prior', [], @isstruct);
-  ip.parse(varargin{:});
+  ip=iparser(ip,'addOptional','gpmf', [], @isstruct);
+  ip=iparser(ip,'addParamValue','selectedVariables',[], @(x) isvector(x) && all(x>0));
+  ip=iparser(ip,'addParamValue','prior_mean',0, @(x) isvector(x));
+  ip=iparser(ip,'addParamValue','prior_cov',100, @(x) isvector(x));
+  ip=iparser(ip,'addParamValue','mean_prior', [], @isstruct);
+  ip=iparser(ip,'addParamValue','cov_prior', [], @isstruct);
+  ip=iparser(ip,'parse',varargin{:});
   gpmf=ip.Results.gpmf;
   
   if isempty(gpmf)
diff --git a/gp/gpmf_squared.m b/gp/gpmf_squared.m
index c6032be4..36e23aff 100644
--- a/gp/gpmf_squared.m
+++ b/gp/gpmf_squared.m
@@ -33,14 +33,16 @@
 
   ip=inputParser;
   ip.FunctionName = 'GPMF_SQUARED';
-  ip.addOptional('gpmf', [], @isstruct);
-  ip.addParamValue('selectedVariables',[], @(x) isvector(x) && all(x>0));
-  ip.addParamValue('interactions', 'off', @(x) ismember(x,{'on' 'off'}))
-  ip.addParamValue('prior_mean',0, @(x) isvector(x));
-  ip.addParamValue('prior_cov',100, @(x) isvector(x));
-  ip.addParamValue('mean_prior', [], @isstruct);
-  ip.addParamValue('cov_prior', [], @isstruct);
-  ip.parse(varargin{:});
+
+  ip=iparser(ip,'addOptional','gpmf', [], @isstruct);
+  ip=iparser(ip,'addParamValue','selectedVariables',[], @(x) isvector(x) && all(x>0));
+  ip=iparser(ip,'addParamValue','interactions', 'off', @(x) islogical(x) || ismember(x,{'on' 'off'}));
+  ip=iparser(ip,'addParamValue','prior_mean',0, @(x) isvector(x));
+  ip=iparser(ip,'addParamValue','prior_cov',100, @(x) isvector(x));
+  ip=iparser(ip,'addParamValue','mean_prior', [], @isstruct);
+  ip=iparser(ip,'addParamValue','cov_prior', [], @isstruct);
+  ip=iparser(ip,'parse',varargin{:});
+
   gpmf=ip.Results.gpmf;
   
   if isempty(gpmf)
diff --git a/gp/lgcp.m b/gp/lgcp.m
index a8fea6f4..7cca3354 100644
--- a/gp/lgcp.m
+++ b/gp/lgcp.m
@@ -33,16 +33,16 @@
 
   ip=inputParser;
   ip.FunctionName = 'LGCP';
-  ip.addRequired('x', @(x) isnumeric(x) && size(x,2)==1 || size(x,2)==2);
-  ip.addOptional('xt',NaN, @(x) isnumeric(x) && size(x,2)==1 || size(x,2)==2);
-  ip.addParamValue('gridn',[], @(x) isscalar(x) && x>0 && mod(x,1)==0);
-  ip.addParamValue('range',[], @(x) isreal(x)&&(length(x)==2||length(x)==4));
-  ip.addParamValue('gpcf',@gpcf_sexp,@(x) ischar(x) || isa(x,'function_handle'));
-  ip.addParamValue('latent_method','EP', @(x) ismember(x,{'EP' 'Laplace'}))
-  ip.addParamValue('int_method','mode', @(x) ismember(x,{'mode' 'CCD', 'grid'}))
-  ip.addParamValue('normalize',false, @islogical);
+  ip=iparser(ip,'addRequired','x', @(x) isnumeric(x) && size(x,2)==1 || size(x,2)==2);
+  ip=iparser(ip,'addOptional','xt',NaN, @(x) isnumeric(x) && size(x,2)==1 || size(x,2)==2);
+  ip=iparser(ip,'addParamValue','gridn',[], @(x) isscalar(x) && x>0 && mod(x,1)==0);
+  ip=iparser(ip,'addParamValue','range',[], @(x) isreal(x)&&(length(x)==2||length(x)==4));
+  ip=iparser(ip,'addParamValue','gpcf',@gpcf_sexp,@(x) ischar(x) || isa(x,'function_handle'));
+  ip=iparser(ip,'addParamValue','latent_method','EP', @(x) ismember(x,{'EP' 'Laplace'}));
+  ip=iparser(ip,'addParamValue','int_method','mode', @(x) ismember(x,{'mode' 'CCD', 'grid'}));
+  ip=iparser(ip,'addParamValue','normalize',false, @islogical);
   
-  ip.parse(x,varargin{:});
+  ip=iparser(ip,'parse',x,varargin{:});
   x=ip.Results.x;
   xt=ip.Results.xt;
   gridn=ip.Results.gridn;
diff --git a/gp/lgpdens.m b/gp/lgpdens.m
index 1a505a83..93a07ab8 100644
--- a/gp/lgpdens.m
+++ b/gp/lgpdens.m
@@ -69,30 +69,30 @@
 
   ip=inputParser;
   ip.FunctionName = 'LGPDENS';
-  ip.addRequired('x', @(x) isnumeric(x) ...
+  ip=iparser(ip,'addRequired','x', @(x) isnumeric(x) ...
                  && (size(x,2)==1 || size(x,2)==2));
-  ip.addOptional('xt',NaN, @(x) isnumeric(x) && size(x,2)==1 || size(x,2)==2);
-  ip.addParamValue('gridn',[], @(x) isnumeric(x));
-  ip.addParamValue('range',[], @(x) isempty(x)||isreal(x)&&(length(x)==2||length(x)==4));
-  ip.addParamValue('gpcf',@gpcf_sexp,@(x) ischar(x) || isa(x,'function_handle'));
-  ip.addParamValue('latent_method','Laplace', @(x) ismember(x,{'EP' 'Laplace' 'MCMC'}))
-  ip.addParamValue('int_method','mode', @(x) ismember(x,{'mode' 'CCD', 'grid'}))
-  ip.addParamValue('normalize',false, @islogical);
-  ip.addParamValue('display', 'off', @(x) islogical(x) || ...
-                   ismember(x,{'on' 'off' 'iter'}))
-  ip.addParamValue('speedup',[], @(x) ismember(x,{'on' 'off'}));
-  ip.addParamValue('rej_sampling','on', @(x) ismember(x,{'on' 'off'}));
-  ip.addParamValue('imp_sampling','on', @(x) ismember(x,{'on' 'off' 'is' 'psis'}));
-  ip.addParamValue('cond_dens',[], @(x) ismember(x,{'on' 'off'}));
-  ip.addParamValue('basis','gaussian', @(x) ismember(x,{'gaussian' 'exp' 'off'}));
-  ip.addParamValue('bounded',[0 0], @(x) isnumeric(x) && min(size(x))==1 && max(size(x))==2);
-  
+  ip=iparser(ip,'addOptional','xt',NaN, @(x) isnumeric(x) && size(x,2)==1 || size(x,2)==2);
+  ip=iparser(ip,'addParamValue','gridn',[], @(x) isnumeric(x));
+  ip=iparser(ip,'addParamValue','range',[], @(x) isempty(x)||isreal(x)&&(length(x)==2||length(x)==4));
+  ip=iparser(ip,'addParamValue','gpcf',@gpcf_sexp,@(x) ischar(x) || isa(x,'function_handle'));
+  ip=iparser(ip,'addParamValue','latent_method','Laplace', @(x) ismember(x,{'EP' 'Laplace' 'MCMC'}));
+  ip=iparser(ip,'addParamValue','int_method','mode', @(x) ismember(x,{'mode' 'CCD', 'grid'}));
+  ip=iparser(ip,'addParamValue','normalize',false, @islogical);
+  ip=iparser(ip,'addParamValue','display', 'off', @(x) islogical(x) || ...
+                   ismember(x,{'on' 'off' 'iter'}));
+  ip=iparser(ip,'addParamValue','speedup',[], @(x) ismember(x,{'on' 'off'}));
+  ip=iparser(ip,'addParamValue','rej_sampling','on', @(x) ismember(x,{'on' 'off'}));
+  ip=iparser(ip,'addParamValue','imp_sampling','on', @(x) ismember(x,{'on' 'off'}));
+  ip=iparser(ip,'addParamValue','cond_dens',[], @(x) ismember(x,{'on' 'off'}));
+  ip=iparser(ip,'addParamValue','basis','gaussian', @(x) ismember(x,{'gaussian' 'exp' 'off'}));
+  ip=iparser(ip,'addParamValue','bounded',[0 0], @(x) isnumeric(x) && min(size(x))==1 && max(size(x))==2);
+
   % additional undocumented parameters for importance sampling
-  ip.addParamValue('n_is',8000, @(x) isnumeric(x) && x>=0);
-  ip.addParamValue('n_scale',50, @(x) isnumeric(x) && x>=0);
-  ip.addParamValue('contSplit','on', @(x) ismember(x,{'on' 'off'}));
+  ip=iparser(ip,'addParamValue','n_is',8000, @(x) isnumeric(x) && x>=0);
+  ip=iparser(ip,'addParamValue','n_scale',50, @(x) isnumeric(x) && x>=0);
+  ip=iparser(ip,'addParamValue','contSplit','on', @(x) ismember(x,{'on' 'off'}));
+  ip=iparser(ip,'parse',x,varargin{:});
 
-  ip.parse(x,varargin{:});
   x=ip.Results.x;
   xt=ip.Results.xt;
   gridn=ip.Results.gridn;
@@ -319,7 +319,11 @@
           % Monte Carlo integration. Econometrica 57:1317-1339.
 
           nParam = size(Ef,1);
-          [V, D] = svd(Covf);
+          if exist('OCTAVE_VERSION','builtin')
+            [V,D,tmp]=svd(Covf);
+          else
+            [V, D] = svd(Covf);
+          end
           eig = diag(real(D));
           T = real(V) * sqrt(real(D));
           gpis=gp_set(gp,'latent_method','MCMC');
@@ -670,7 +674,11 @@
             clear Covft
           end
           nParam = size(Ef,1);
-          [V, D] = svd(Covf);
+          if exist('OCTAVE_VERSION','builtin')
+            [V,D,tmp]=svd(Covf);
+          else
+            [V, D] = svd(Covf);
+          end
           T = real(V) * sqrt(real(D));
           eig = diag(real(D));
           gpis=gp_set(gp,'latent_method','MCMC');
diff --git a/gp/lgpdens_cum.m b/gp/lgpdens_cum.m
index 0787089d..1c20a7f5 100644
--- a/gp/lgpdens_cum.m
+++ b/gp/lgpdens_cum.m
@@ -13,10 +13,10 @@
 % License.txt, included with the software, for details.
 
 ip=inputParser;
-ip.addRequired('bb',@(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))))
-ip.addRequired('x1',@(x) ~isempty(x) && isreal(x))
-ip.addRequired('x2', @(x) ~isempty(x) && isreal(x))
-ip.parse(bb,x1,x2)
+ip=iparser(ip,'addRequired','bb',@(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))));
+ip=iparser(ip,'addRequired','x1',@(x) ~isempty(x) && isreal(x));
+ip=iparser(ip,'addRequired','x2', @(x) ~isempty(x) && isreal(x));
+ip=iparser(ip,'parse',bb,x1,x2);
 
  [p,pq,xt]=lgpdens(bb);
  I1=min(find(xt>x1));
diff --git a/gp/lik_binomial.m b/gp/lik_binomial.m
index 37fb097c..c1b391a6 100644
--- a/gp/lik_binomial.m
+++ b/gp/lik_binomial.m
@@ -29,8 +29,8 @@
 
   ip=inputParser;
   ip.FunctionName = 'LIK_BINOMIAL';
-  ip.addOptional('lik', [], @isstruct);
-  ip.parse(varargin{:});
+  ip=iparser(ip,'addOptional','lik', [], @isstruct);
+  ip=iparser(ip,'parse',varargin{:});
   lik=ip.Results.lik;
 
   if isempty(lik)
@@ -237,7 +237,7 @@
 %
 %  See also
 %    GPEP_E
-  
+
 %  if isempty(z)
 %    error(['lik_binomial -> lik_binomial_tiltedMoments: missing z!'... 
 %           'Binomial likelihood needs the expected number of               '...
@@ -411,7 +411,7 @@
 %   ldconst = log(factorial(N)/(factorial(yy)*factorial(N-yy))-log(sigm2_i)/2 -log(2*pi)/2;
   
  % Create function handle for the function to be integrated
-  df = @binomial_norm;
+  df = @(f) binomial_norm(f, ldconst, yy, N, myy_i, sigm2_i);
  % use log to avoid underflow, and derivates for faster search
   ld = @log_binomial_norm;
   ldg = @log_binomial_norm_g;
@@ -441,8 +441,8 @@
   niter=3;       % number of Newton iterations
   mindelta=1e-6; % tolerance in stopping Newton iterations
   for ni=1:niter
-      g = ldg(modef);
-      h = ldg2(modef);
+      g = ldg(modef, ldconst, yy, N, myy_i, sigm2_i);
+      h = ldg2(modef, ldconst, yy, N, myy_i, sigm2_i);
       delta=-g/h;
       modef=modef+delta;
       if abs(delta)<mindelta
@@ -453,36 +453,36 @@
     % Newton algorithm didn't work properly so do binary search
     modef=myy_i;
     a=modef-5.*sqrt(sigm2_i); b=modef+5.*sqrt(sigm2_i); delta=1;
-    while ldg(a)<0
+    while ldg(a, ldconst, yy, N, myy_i, sigm2_i)<0
       a=a-5.*sqrt(sigm2_i);
     end
-    while ldg(b)>0
+    while ldg(b, ldconst, yy, N, myy_i, sigm2_i)>0
       b=b+5.*sqrt(sigm2_i);
     end
     while delta > 0.1
       modef=(a+b)/2;
-      if ldg(modef) > 0
+      if ldg(modef, ldconst, yy, N, myy_i, sigm2_i) > 0
         a=modef;
       else
         b=modef;
       end
       delta=b-a;
     end
-    h=ldg2(modef);
+    h=ldg2(modef, ldconst, yy, N, myy_i, sigm2_i);
   end
   % integrand limits based on Gaussian approximation at mode
   modes=sqrt(-1/h);
   minf=modef-4*modes;
   maxf=modef+4*modes;
-  modeld=ld(modef);
+  modeld=ld(modef, ldconst, yy, N, myy_i, sigm2_i);
   iter=0;
   % check that density at end points is low enough
   lddiff=12; % min difference in log-density between mode and end-points
-  minld=ld(minf);
+  minld=ld(minf, ldconst, yy, N, myy_i, sigm2_i);
   step=1;
   while minld>(modeld-lddiff)
     minf=minf-step*modes;
-    minld=ld(minf);
+    minld=ld(minf, ldconst, yy, N, myy_i, sigm2_i);
     iter=iter+1;
     step=step*2;
     if iter>100
@@ -491,12 +491,12 @@
              'even after looking hard!'])
     end
   end
-  maxld=ld(maxf);
+  maxld=ld(maxf, ldconst, yy, N, myy_i, sigm2_i);
   iter=0;
   step=1;
   while maxld>(modeld-lddiff)
     maxf=maxf+step*modes;
-    maxld=ld(maxf);
+    maxld=ld(maxf, ldconst, yy, N, myy_i, sigm2_i);
     iter=iter+1;
     step=step*2;
     if iter>100
@@ -505,9 +505,9 @@
              'even after looking hard!'])
     end
   end
+end
   
-  
-  function integrand = binomial_norm(f)
+  function integrand = binomial_norm(f, ldconst, yy, N, myy_i, sigm2_i)
   % Logit * Gaussian
     integrand = exp(ldconst + yy*log(1./(1.+exp(-f)))+(N-yy)*log(1-1./(1.+exp(-f)))...
                    - 0.5 * (f-myy_i).^2./sigm2_i);
@@ -517,7 +517,7 @@
     integrand(isnan(integrand)|isinf(integrand))=0;
   end
   
-  function log_int = log_binomial_norm(f)
+  function log_int = log_binomial_norm(f, ldconst, yy, N, myy_i, sigm2_i)
   % log(Binomial * Gaussian)
   % log_binomial_norm is used to avoid underflow when searching
   % integration interval
@@ -530,7 +530,7 @@
     log_int(isnan(log_int)|isinf(log_int))=-Inf;
   end
   
-  function g = log_binomial_norm_g(f)
+  function g = log_binomial_norm_g(f, ldconst, yy, N, myy_i, sigm2_i)
   % d/df log(Binomial * Gaussian)
   % derivative of log_binomial_norm
     g = -(f-myy_i)./sigm2_i - exp(-f).*(N-yy)./((1+exp(-f)).^2.*(1-1./(1+exp(-f)))) ...
@@ -539,7 +539,7 @@
 %         + (myy_i - f)./sigm2_i;
   end
   
-  function g2 = log_binomial_norm_g2(f)
+  function g2 = log_binomial_norm_g2(f, ldconst, yy, N, myy_i, sigm2_i)
   % d^2/df^2 log(Binomial * Gaussian)
   % second derivate of log_binomial_norm
     g2 = - (1+exp(2.*f)+exp(f).*(2+N*sigm2_i)./((1+exp(f))^2*sigm2_i));
@@ -548,7 +548,7 @@
 %          -1/sigm2_i;
   end
   
-end
+% end
 
 function p = lik_binomial_invlink(lik, f, z)
 %LIK_BINOMIAL_INVLINK  Returns values of inverse link function
@@ -592,7 +592,7 @@
     reclik.fh.invlink = @lik_binomial_invlink;
     reclik.fh.predprcty = @lik_binomial_predprcty;
     reclik.fh.predy = @lik_binomial_predy;
-    reclik.fh.recappend = @likelih_binomial_recappend;
+    reclik.fh.recappend = @lik_binomial_recappend;
     return
   end
 
diff --git a/gp/lik_coxph.m b/gp/lik_coxph.m
index 981ccb20..718d67a3 100644
--- a/gp/lik_coxph.m
+++ b/gp/lik_coxph.m
@@ -48,11 +48,11 @@
 
   ip=inputParser;
   ip.FunctionName = 'LIK_COXPH';
-  ip.addOptional('lik', [], @isstruct);
-  ip.addParamValue('S', linspace(0,1.001,50), @(x) isvector(x));
-  ip.addParamValue('stratificationVariables', [], @(x) isvector(x) && all(rem(x,1)==0));
-  ip.addParamValue('removeStratificationVariables', [], @(x) ismember(x, {'on' 'off'}));
-  ip.parse(varargin{:});
+  ip=iparser(ip,'addOptional','lik', [], @isstruct);
+  ip=iparser(ip,'addParamValue','S', linspace(0,1.001,50), @(x) isvector(x));
+  ip=iparser(ip,'addParamValue','stratificationVariables', [], @(x) isvector(x) && all(rem(x,1)==0));
+  ip=iparser(ip,'addParamValue','removeStratificationVariables', [], @(x) ismember(x, {'on' 'off'}));
+  ip=iparser(ip,'parse',varargin{:});
   lik=ip.Results.lik;
   
   if isempty(lik)
@@ -93,12 +93,13 @@
     lik.fh.llg = @lik_coxph_llg;    
     lik.fh.llg2 = @lik_coxph_llg2;
     lik.fh.llg3 = @lik_coxph_llg3;
-    lik.fh.tiltedMoments = @lik_coxph_tiltedMoments;
+%     lik.fh.tiltedMoments = @lik_coxph_tiltedMoments;
     lik.fh.predy = @lik_coxph_predy;
     lik.fh.invlink = @lik_coxph_invlink;
     lik.fh.recappend = @lik_coxph_recappend;
     lik.fh.predcdf= @lik_coxph_predcdf;
   end
+end
 
   function [w,s,h] = lik_coxph_pak(lik)
   %LIK_COXPH_PAK  Combine likelihood parameters into one vector.
@@ -1364,7 +1365,7 @@
       reclik.fh.llg = @lik_coxph_llg;    
       reclik.fh.llg2 = @lik_coxph_llg2;
       reclik.fh.llg3 = @lik_coxph_llg3;
-      reclik.fh.tiltedMoments = @lik_coxph_tiltedMoments;
+%       reclik.fh.tiltedMoments = @lik_coxph_tiltedMoments;
       reclik.fh.predy = @lik_coxph_predy;
       reclik.fh.invlink = @lik_coxph_invlink;
       reclik.fh.recappend = @lik_coxph_recappend;
@@ -1383,6 +1384,3 @@
     end
   end
 
-
-end
-
diff --git a/gp/lik_gaussian.m b/gp/lik_gaussian.m
index 2c2f909c..2b63af03 100644
--- a/gp/lik_gaussian.m
+++ b/gp/lik_gaussian.m
@@ -48,11 +48,11 @@
 
   ip=inputParser;
   ip.FunctionName = 'LIK_GAUSSIAN';
-  ip.addOptional('lik', [], @(x) isstruct(x) || isempty(x));
-  ip.addParamValue('sigma2',0.1, @(x) isscalar(x) && x>=0);
-  ip.addParamValue('sigma2_prior',prior_logunif(), @(x) isstruct(x) || isempty(x));
-  ip.addParamValue('n',[], @(x) isreal(x) && all(x>0));
-  ip.parse(varargin{:});
+  ip=iparser(ip,'addOptional','lik', [], @(x) isstruct(x) || isempty(x));
+  ip=iparser(ip,'addParamValue','sigma2',0.1, @(x) isscalar(x) && x>=0);
+  ip=iparser(ip,'addParamValue','sigma2_prior',prior_logunif(), @(x) isstruct(x) || isempty(x));
+  ip=iparser(ip,'addParamValue','n',[], @(x) isreal(x) && all(x>0));
+  ip=iparser(ip,'parse',varargin{:});
   lik=ip.Results.lik;
 
   if isempty(lik)
diff --git a/gp/lik_gaussiansmt.m b/gp/lik_gaussiansmt.m
index 91a2f1f7..93f5708d 100644
--- a/gp/lik_gaussiansmt.m
+++ b/gp/lik_gaussiansmt.m
@@ -58,17 +58,17 @@
 
   ip=inputParser;
   ip.FunctionName = 'LIK_GAUSSIANSMT';
-  ip.addOptional('lik', [], @isstruct);
-  ip.addParamValue('ndata',[], @(x) isscalar(x) && x>0 && mod(x,1)==0);
-  ip.addParamValue('sigma2',[], @(x) isvector(x) && all(x>0));
-  ip.addParamValue('U',[], @isvector);
-  ip.addParamValue('tau2',0.1, @isscalar);
-  ip.addParamValue('alpha',0.5, @isscalar);
-  ip.addParamValue('nu',4, @isscalar);
-  ip.addParamValue('nu_prior',[], @(x) isstruct(x) || isempty(x));
-  ip.addParamValue('censored',[], @(x) isstruct);
-  ip.addParamValue('gibbs','on', @(x) ismember(x,{'on' 'off'}));
-  ip.parse(varargin{:});
+  ip=iparser(ip,'addOptional','lik', [], @isstruct);
+  ip=iparser(ip,'addParamValue','ndata',[], @(x) isscalar(x) && x>0 && mod(x,1)==0);
+  ip=iparser(ip,'addParamValue','sigma2',[], @(x) isvector(x) && all(x>0));
+  ip=iparser(ip,'addParamValue','U',[], @isvector);
+  ip=iparser(ip,'addParamValue','tau2',0.1, @isscalar);
+  ip=iparser(ip,'addParamValue','alpha',0.5, @isscalar);
+  ip=iparser(ip,'addParamValue','nu',4, @isscalar);
+  ip=iparser(ip,'addParamValue','nu_prior',[], @(x) isstruct(x) || isempty(x));
+  ip=iparser(ip,'addParamValue','censored',[], @(x) isstruct);
+  ip=iparser(ip,'addParamValue','gibbs','on', @(x) ismember(x,{'on' 'off'}));
+  ip=iparser(ip,'parse',varargin{:});
   lik=ip.Results.lik;
 
   if isempty(lik)
@@ -333,7 +333,6 @@
     reccf.fh.lp = @lik_gaussiansmt_lp;
     reccf.fh.lpg = @lik_gaussiansmt_lpg;
     reccf.fh.cfg = @lik_gaussiansmt_cfg;
-    reccf.fh.cov = @lik_gaussiansmt_cov;
     reccf.fh.trcov  = @lik_gaussiansmt_trcov;
     reccf.fh.trvar  = @lik_gaussiansmt_trvar;
     reccf.fh.gibbs = @lik_gaussiansmt_gibbs;
diff --git a/gp/lik_inputdependentnoise.m b/gp/lik_inputdependentnoise.m
index 44507414..94141797 100644
--- a/gp/lik_inputdependentnoise.m
+++ b/gp/lik_inputdependentnoise.m
@@ -42,10 +42,10 @@
 
   ip=inputParser;
   ip.FunctionName = 'LIK_INPUTDEPENDENTNOISE';
-  ip.addOptional('lik', [], @isstruct);
-  ip.addParamValue('sigma2',0.1, @(x) isscalar(x) && x>0);
-  ip.addParamValue('sigma2_prior',prior_logunif(), @(x) isstruct(x) || isempty(x));
-  ip.parse(varargin{:});
+  ip=iparser(ip,'addOptional','lik', [], @isstruct);
+  ip=iparser(ip,'addParamValue','sigma2',0.1, @(x) isscalar(x) && x>0);
+  ip=iparser(ip,'addParamValue','sigma2_prior',prior_logunif(), @(x) isstruct(x) || isempty(x));
+  ip=iparser(ip,'parse',varargin{:});
   lik=ip.Results.lik;
   
   if isempty(lik)
@@ -86,6 +86,7 @@
     lik.fh.predprcty = @lik_inputdependentnoise_predprcty;
     lik.fh.recappend = @lik_inputdependentnoise_recappend;
   end
+end
 
   function [w,s,h] = lik_inputdependentnoise_pak(lik)
   %LIK_INPUTDEPENDENTNOISE_PAK  Combine likelihood parameters into one vector.
@@ -519,4 +520,3 @@
       reclik.p.sigma2 = feval(lik.p.sigma2.fh.recappend, reclik.p.sigma2, ri, lik.p.sigma2);
     end
   end
-end
diff --git a/gp/lik_inputdependentweibull.m b/gp/lik_inputdependentweibull.m
index bc7b185d..c0250806 100644
--- a/gp/lik_inputdependentweibull.m
+++ b/gp/lik_inputdependentweibull.m
@@ -52,10 +52,10 @@
 
   ip=inputParser;
   ip.FunctionName = 'LIK_INPUTDEPENDENTWEIBULL';
-  ip.addOptional('lik', [], @isstruct);
-  ip.addParamValue('shape',1, @(x) isscalar(x) && x>0);
-  ip.addParamValue('shape_prior',prior_logunif(), @(x) isstruct(x) || isempty(x));
-  ip.parse(varargin{:});
+  ip=iparser(ip,'addOptional','lik', [], @isstruct);
+  ip=iparser(ip,'addParamValue','shape',1, @(x) isscalar(x) && x>0);
+  ip=iparser(ip,'addParamValue','shape_prior',prior_logunif(), @(x) isstruct(x) || isempty(x));
+  ip=iparser(ip,'parse',varargin{:});
   lik=ip.Results.lik;
   
   if isempty(lik)
diff --git a/gp/lik_lgp.m b/gp/lik_lgp.m
index 66cdd8b7..a4306b41 100644
--- a/gp/lik_lgp.m
+++ b/gp/lik_lgp.m
@@ -27,8 +27,8 @@
 
   ip=inputParser;
   ip.FunctionName = 'LIK_LGP';
-  ip.addOptional('lik', [], @isstruct);
-  ip.parse(varargin{:});
+  ip=iparser(ip,'addOptional','lik', [], @isstruct);
+  ip=iparser(ip,'parse',varargin{:});
   lik=ip.Results.lik;
 
   if isempty(lik)
diff --git a/gp/lik_lgpc.m b/gp/lik_lgpc.m
index eac675a5..e6c55a69 100644
--- a/gp/lik_lgpc.m
+++ b/gp/lik_lgpc.m
@@ -24,8 +24,8 @@
 
   ip=inputParser;
   ip.FunctionName = 'LIK_LGPC';
-  ip.addOptional('lik', [], @isstruct);
-  ip.parse(varargin{:});
+  ip=iparser(ip,'addOptional','lik', [], @isstruct);
+  ip=iparser(ip,'parse',varargin{:});
   lik=ip.Results.lik;
 
   if isempty(lik)
@@ -249,10 +249,9 @@
 %    GPLA_PRED, GPEP_PRED, GPMC_PRED
 
   error('Not implemented')
-  
 end  
 
-function mu = lik_lpgpc_invlink(lik, f, z)
+function mu = lik_lgpc_invlink(lik, f, z)
 %LIK_LPGPC_INVLINK  Returns values of inverse link function
 %             
 %  Description 
diff --git a/gp/lik_loggaussian.m b/gp/lik_loggaussian.m
index 950480c2..76047eb6 100644
--- a/gp/lik_loggaussian.m
+++ b/gp/lik_loggaussian.m
@@ -48,10 +48,10 @@
 
   ip=inputParser;
   ip.FunctionName = 'LIK_LOGGAUSSIAN';
-  ip.addOptional('lik', [], @isstruct);
-  ip.addParamValue('sigma2',1, @(x) isscalar(x) && x>0);
-  ip.addParamValue('sigma2_prior',prior_logunif(), @(x) isstruct(x) || isempty(x));
-  ip.parse(varargin{:});
+  ip=iparser(ip,'addOptional','lik', [], @isstruct);
+  ip=iparser(ip,'addParamValue','sigma2',1, @(x) isscalar(x) && x>0);
+  ip=iparser(ip,'addParamValue','sigma2_prior',prior_logunif(), @(x) isstruct(x) || isempty(x));
+  ip=iparser(ip,'parse',varargin{:});
   lik=ip.Results.lik;
   
   if isempty(lik)
@@ -540,10 +540,11 @@
     m_0=norm_cdf(zi);
 %     [m_0, fhncnt] = quadgk(tf, minf, maxf);
   end
-  [g_i, fhncnt] = quadgk(@(f) td(f).*tf(f)./m_0, minf, maxf);
+  [g_i, fhncnt] = quadgk(@(f) td(f, yy, yc, s2).*tf(f)./m_0, minf, maxf);
   g_i = g_i.*s2;
+end
 
-  function g = deriv(f)
+  function g = deriv(f, yy, yc, s2)
     if yc==0
       r=log(yy)-f;
       g = 1./(1-norm_cdf(r/sqrt(s2))) ...
@@ -553,7 +554,6 @@
       g = -1./(2.*s2) + r.^2./(2.*s2^2);
     end
   end
-end
 
 function [lpy, Ey, Vary] = lik_loggaussian_predy(lik, Ef, Varf, yt, zt)
 %LIK_LOGGAUSSIAN_PREDY  Returns the predictive mean, variance and density of y
@@ -655,7 +655,7 @@
             - log(sigm2_i)/2 - log(2*pi)/2;
   
   % Create function handle for the function to be integrated
-  df = @loggaussian_norm;
+  df = @(f) loggaussian_norm(f, ldconst, yy, yc, s2, myy_i, sigm2_i);
   % use log to avoid underflow, and derivates for faster search
   ld = @log_loggaussian_norm;
   ldg = @log_loggaussian_norm_g;
@@ -684,8 +684,8 @@
   niter=4;       % number of Newton iterations
   mindelta=1e-6; % tolerance in stopping Newton iterations
   for ni=1:niter
-    g=ldg(modef);
-    h=ldg2(modef);
+    g=ldg(modef, ldconst, yy, yc, s2, myy_i, sigm2_i);
+    h=ldg2(modef, ldconst, yy, yc, s2, myy_i, sigm2_i);
     delta=-g/h;
     modef=modef+delta;
     if abs(delta)<mindelta||isnan(modef)
@@ -701,15 +701,15 @@
   % integrand limits based on Gaussian approximation at mode
   minf=modef-8*modes;
   maxf=modef+8*modes;
-  modeld=ld(modef);
+  modeld=ld(modef, ldconst, yy, yc, s2, myy_i, sigm2_i);
   iter=0;
   % check that density at end points is low enough
   lddiff=20; % min difference in log-density between mode and end-points
-  minld=ld(minf);
+  minld=ld(minf, ldconst, yy, yc, s2, myy_i, sigm2_i);
   step=1;
   while minld>(modeld-lddiff)
     minf=minf-step*modes;
-    minld=ld(minf);
+    minld=ld(minf, ldconst, yy, yc, s2, myy_i, sigm2_i);
     iter=iter+1;
     step=step*2;
     if iter>100
@@ -718,12 +718,12 @@
              'even after looking hard!'])
     end
   end
-  maxld=ld(maxf);
+  maxld=ld(maxf, ldconst, yy, yc, s2, myy_i, sigm2_i);
   iter=0;
   step=1;
   while maxld>(modeld-lddiff)
     maxf=maxf+step*modes;
-    maxld=ld(maxf);
+    maxld=ld(maxf, ldconst, yy, yc, s2, myy_i, sigm2_i);
     iter=iter+1;
     step=step*2;
     if iter>100
@@ -733,7 +733,8 @@
     end
   end
   
-  function integrand = loggaussian_norm(f)
+end
+  function integrand = loggaussian_norm(f, ldconst, yy, yc, s2, myy_i, sigm2_i)
   % loggaussian * Gaussian
     if yc
       % observed
@@ -747,7 +748,7 @@
                     -0.5*(f-myy_i).^2./sigm2_i);
   end
 
-  function log_int = log_loggaussian_norm(f)
+  function log_int = log_loggaussian_norm(f, ldconst, yy, yc, s2, myy_i, sigm2_i)
   % log(loggaussian * Gaussian)
   % log_loggaussian_norm is used to avoid underflow when searching
   % integration interval
@@ -763,7 +764,7 @@
               -0.5*(f-myy_i).^2./sigm2_i;
   end
 
-  function g = log_loggaussian_norm_g(f)
+  function g = log_loggaussian_norm_g(f, ldconst, yy, yc, s2, myy_i, sigm2_i)
   % d/df log(loggaussian * Gaussian)
   % derivative of log_loggaussian_norm
     if yc
@@ -777,7 +778,7 @@
         + (myy_i - f)./sigm2_i;
   end
 
-  function g2 = log_loggaussian_norm_g2(f)
+  function g2 = log_loggaussian_norm_g2(f, ldconst, yy, yc, s2, myy_i, sigm2_i)
   % d^2/df^2 log(loggaussian * Gaussian)
   % second derivate of log_loggaussian_norm
     if yc
@@ -792,7 +793,6 @@
               -1/sigm2_i;
   end
 
-end
 
 function cdf = lik_loggaussian_predcdf(lik, Ef, Varf, yt)
 %LIK_LOGGAUSSIAN_PREDCDF  Returns the predictive cdf evaluated at yt 
@@ -871,7 +871,6 @@
     reclik.fh.tiltedMoments = @lik_loggaussian_tiltedMoments;
     reclik.fh.invlink = @lik_loggaussian_invlink;
     reclik.fh.predy = @lik_loggaussian_predy;
-    reclik.fh.predcdf = @lik_loggaussian_predcdf;
     reclik.fh.recappend = @lik_loggaussian_recappend;
     reclik.p=[];
     reclik.p.sigma2=[];
diff --git a/gp/lik_logit.m b/gp/lik_logit.m
index 101e8c02..94f7138e 100755
--- a/gp/lik_logit.m
+++ b/gp/lik_logit.m
@@ -26,9 +26,9 @@
 
   ip=inputParser;
   ip.FunctionName = 'LIK_LOGIT';
-  ip.addOptional('lik', [], @isstruct);
-  ip.addParamValue('moment_method','gh', @(x) ismember(x,{'gh' 'agk'}));
-  ip.parse(varargin{:});
+  ip=iparser(ip,'addOptional','lik', [], @isstruct);
+  ip=iparser(ip,'addParamValue','moment_method','gh', @(x) ismember(x,{'gh' 'agk'}));
+  ip=iparser(ip,'parse',varargin{:});
   lik=ip.Results.lik;
 
   if isempty(lik)
@@ -405,7 +405,7 @@
   ldconst = -log(sigm2_i)/2 -log(2*pi)/2;
   
   % Create function handle for the function to be integrated
-  df = @logit_norm;
+  df = @(f) logit_norm(f, ldconst, yy, myy_i, sigm2_i);
   % use log to avoid underflow, and derivates for faster search
   ld = @log_logit_norm;
   ldg = @log_logit_norm_g;
@@ -428,8 +428,8 @@
   niter=2;       % number of Newton iterations
   mindelta=1e-6; % tolerance in stopping Newton iterations
   for ni=1:niter
-    g=ldg(modef);
-    h=ldg2(modef);
+    g=ldg(modef, ldconst, yy, myy_i, sigm2_i);
+    h=ldg2(modef, ldconst, yy, myy_i, sigm2_i);
     delta=-g/h;
     modef=modef+delta;
     if abs(delta)<mindelta
@@ -440,7 +440,7 @@
   modes=sqrt(-1/h);
   minf=modef-8*modes;
   maxf=modef+8*modes;
-  modeld=ld(modef);
+  modeld=ld(modef, ldconst, yy, myy_i, sigm2_i);
   if isinf(modeld) || isnan(modeld)
     minf=NaN;maxf=NaN;
     return
@@ -448,11 +448,11 @@
   iter=0;
   % check that density at end points is low enough
   lddiff=20; % min difference in log-density between mode and end-points
-  minld=ld(minf);
+  minld=ld(minf, ldconst, yy, myy_i, sigm2_i);
   step=1;
   while minld>(modeld-lddiff)
     minf=minf-step*modes;
-    minld=ld(minf);
+    minld=ld(minf, ldconst, yy, myy_i, sigm2_i);
     iter=iter+1;
     step=step*2;
     if iter>100
@@ -461,12 +461,12 @@
              'even after looking hard!'])
     end
   end
-  maxld=ld(maxf);
+  maxld=ld(maxf, ldconst, yy, myy_i, sigm2_i);
   iter=0;
   step=1;
   while maxld>(modeld-lddiff)
     maxf=maxf+step*modes;
-    maxld=ld(maxf);
+    maxld=ld(maxf, ldconst, yy, myy_i, sigm2_i);
     iter=iter+1;
     step=step*2;
     if iter>100
@@ -475,15 +475,15 @@
              'even after looking hard!'])
     end
   end
-  
-  function integrand = logit_norm(f)
+end
+  function integrand = logit_norm(f, ldconst, yy, myy_i, sigm2_i)
   % Logit * Gaussian
     integrand = exp(ldconst ...
                     -log(1+exp(-yy.*f)) ...
                     -0.5*(f-myy_i).^2./sigm2_i);
   end
   
-  function log_int = log_logit_norm(f)
+  function log_int = log_logit_norm(f, ldconst, yy, myy_i, sigm2_i)
   % log(Logit * Gaussian)
   % log_logit_norm is used to avoid underflow when searching
   % integration interval
@@ -492,23 +492,21 @@
               -0.5*(f-myy_i).^2./sigm2_i;
   end
   
-  function g = log_logit_norm_g(f)
+  function g = log_logit_norm_g(f, ldconst, yy, myy_i, sigm2_i)
   % d/df log(Logit * Gaussian)
   % derivative of log_logit_norm
     g = yy./(exp(f*yy)+1)...
         + (myy_i - f)./sigm2_i;
   end
   
-  function g2 = log_logit_norm_g2(f)
+  function g2 = log_logit_norm_g2(f, ldconst, yy, myy_i, sigm2_i)
   % d^2/df^2 log(Logit * Gaussian)
   % second derivate of log_logit_norm
     a=exp(f*yy);
     g2 = -a*(yy./(a+1)).^2 ...
          -1/sigm2_i;
   end
-  
-end
-
+ 
 function p = lik_logit_invlink(lik, f, z)
 %LIK_LOGIT_INVLINK  Returns values of inverse link function
 %             
diff --git a/gp/lik_loglogistic.m b/gp/lik_loglogistic.m
index 8de87cf8..7cd98c46 100644
--- a/gp/lik_loglogistic.m
+++ b/gp/lik_loglogistic.m
@@ -48,10 +48,10 @@
 
   ip=inputParser;
   ip.FunctionName = 'LIK_LOGLOGISTIC';
-  ip.addOptional('lik', [], @isstruct);
-  ip.addParamValue('shape',1, @(x) isscalar(x) && x>0);
-  ip.addParamValue('shape_prior',prior_logunif(), @(x) isstruct(x) || isempty(x));
-  ip.parse(varargin{:});
+  ip=iparser(ip,'addOptional','lik', [], @isstruct);
+  ip=iparser(ip,'addParamValue','shape',1, @(x) isscalar(x) && x>0);
+  ip=iparser(ip,'addParamValue','shape_prior',prior_logunif(), @(x) isstruct(x) || isempty(x));
+  ip=iparser(ip,'parse',varargin{:});
   lik=ip.Results.lik;
   
   if isempty(lik)
@@ -489,14 +489,15 @@
   % and useful integration limits
   [tf,minf,maxf]=init_loglogistic_norm(yy,myy_i,sigm2_i,yc,r);
   % additionally get function handle for the derivative
-  td = @deriv;
+  td = @(f) deriv(f, yc, yy, r);
   
   % Integrate with quadgk
   [m_0, fhncnt] = quadgk(tf, minf, maxf);
   [g_i, fhncnt] = quadgk(@(f) td(f).*tf(f)./m_0, minf, maxf);
   g_i = g_i.*r;
+end
 
-  function g = deriv(f)
+  function g = deriv(f, yc, yy, r)
     m = yy./exp(f);
     if yc==0
       g = -1./(1+m.^r).*m.^r.*log(m);
@@ -504,7 +505,6 @@
       g = (1/r+log(m)) - 2./(1+m.^r).*m.^r.*log(m);
     end
   end
-end
 
 function [lpy, Ey, Vary] = lik_loglogistic_predy(lik, Ef, Varf, yt, zt)
 %LIK_LOGLOGISTIC_PREDY  Returns the predictive mean, variance and density of y
@@ -587,7 +587,7 @@
             - log(sigm2_i)/2 - log(2*pi)/2;
   
   % Create function handle for the function to be integrated
-  df = @loglogistic_norm;
+  df = @(f) loglogistic_norm(f, ldconst, yc, r, yy, myy_i, sigm2_i);
   % use log to avoid underflow, and derivates for faster search
   ld = @log_loglogistic_norm;
   ldg = @log_loglogistic_norm_g;
@@ -617,8 +617,8 @@
   niter=4;       % number of Newton iterations
   mindelta=1e-6; % tolerance in stopping Newton iterations
   for ni=1:niter
-    g=ldg(modef);
-    h=ldg2(modef);
+    g=ldg(modef, ldconst, yc, r, yy, myy_i, sigm2_i);
+    h=ldg2(modef, ldconst, yc, r, yy, myy_i, sigm2_i);
     delta=-g/h;
     modef=modef+delta;
     if abs(delta)<mindelta
@@ -634,15 +634,15 @@
   % integrand limits based on Gaussian approximation at mode
   minf=modef-8*modes;
   maxf=modef+8*modes;
-  modeld=ld(modef);
+  modeld=ld(modef, ldconst, yc, r, yy, myy_i, sigm2_i);
   iter=0;
   % check that density at end points is low enough
   lddiff=20; % min difference in log-density between mode and end-points
-  minld=ld(minf);
+  minld=ld(minf, ldconst, yc, r, yy, myy_i, sigm2_i);
   step=1;
   while minld>(modeld-lddiff)
     minf=minf-step*modes;
-    minld=ld(minf);
+    minld=ld(minf, ldconst, yc, r, yy, myy_i, sigm2_i);
     iter=iter+1;
     step=step*2;
     if iter>100
@@ -651,12 +651,12 @@
              'even after looking hard!'])
     end
   end
-  maxld=ld(maxf);
+  maxld=ld(maxf, ldconst, yc, r, yy, myy_i, sigm2_i);
   iter=0;
   step=1;
   while maxld>(modeld-lddiff)
     maxf=maxf+step*modes;
-    maxld=ld(maxf);
+    maxld=ld(maxf, ldconst, yc, r, yy, myy_i, sigm2_i);
     iter=iter+1;
     step=step*2;
     if iter>100
@@ -665,8 +665,9 @@
              'even after looking hard!'])
     end
   end
+end
   
-  function integrand = loglogistic_norm(f)
+  function integrand = loglogistic_norm(f, ldconst, yc, r, yy, myy_i, sigm2_i)
   % loglogistic * Gaussian
     if yc
       % observed
@@ -680,7 +681,7 @@
                     -0.5*(f-myy_i).^2./sigm2_i);
   end
 
-  function log_int = log_loglogistic_norm(f)
+  function log_int = log_loglogistic_norm(f, ldconst, yc, r, yy, myy_i, sigm2_i)
   % log(loglogistic * Gaussian)
   % log_loglogistic_norm is used to avoid underflow when searching
   % integration interval
@@ -695,8 +696,8 @@
               + lik ...
               -0.5*(f-myy_i).^2./sigm2_i;
   end
-  
-  function g = log_loglogistic_norm_g(f)
+
+  function g = log_loglogistic_norm_g(f, ldconst, yc, r, yy, myy_i, sigm2_i)
   % d/df log(loglogistic * Gaussian)
   % derivative of log_loglogistic_norm
     if yc
@@ -710,7 +711,7 @@
         + (myy_i - f)./sigm2_i;
   end
 
-  function g2 = log_loglogistic_norm_g2(f)
+  function g2 = log_loglogistic_norm_g2(f, ldconst, yc, r, yy, myy_i, sigm2_i)
   % d^2/df^2 log(loglogistic * Gaussian)
   % second derivate of log_loglogistic_norm
     if yc
@@ -724,7 +725,6 @@
               -1/sigm2_i;
   end
 
-end
 
 function cdf = lik_loglogistic_predcdf(lik, Ef, Varf, yt)
 %LIK_LOGLOGISTIC_PREDCDF  Returns the predictive cdf evaluated at yt 
@@ -802,7 +802,6 @@
     reclik.fh.tiltedMoments = @lik_loglogistic_tiltedMoments;
     reclik.fh.invlink = @lik_loglogistic_invlink;
     reclik.fh.predy = @lik_loglogistic_predy;
-    reclik.fh.predcdf=@lik_loglogistic_predcdf;
     reclik.fh.recappend = @lik_loglogistic_recappend;
     reclik.p=[];
     reclik.p.shape=[];
diff --git a/gp/lik_multinom.m b/gp/lik_multinom.m
index eb38a37c..9ad7a645 100644
--- a/gp/lik_multinom.m
+++ b/gp/lik_multinom.m
@@ -27,8 +27,8 @@
 
   ip=inputParser;
   ip.FunctionName = 'LIK_MULTINOM';
-  ip.addOptional('lik', [], @isstruct);
-  ip.parse(varargin{:});
+  ip=iparser(ip,'addOptional','lik', [], @isstruct);
+  ip=iparser(ip,'parse',varargin{:});
   lik=ip.Results.lik;
 
   if isempty(lik)
diff --git a/gp/lik_multinomprobit.m b/gp/lik_multinomprobit.m
index 1c953a86..d4150404 100644
--- a/gp/lik_multinomprobit.m
+++ b/gp/lik_multinomprobit.m
@@ -22,8 +22,8 @@
 
   ip=inputParser;
   ip.FunctionName = 'LIK_MULTINOMPROBIT';
-  ip.addOptional('lik', [], @isstruct);
-  ip.parse(varargin{:});
+  ip=iparser(ip,'addOptional','lik', [], @isstruct);
+  ip=iparser(ip,'parse', varargin{:});
   lik=ip.Results.lik;
 
   if isempty(lik)
@@ -50,6 +50,7 @@
     lik.fh.recappend = @lik_multinomprobit_recappend;
   end
   
+end
 
   function [w,s,h] = lik_multinomprobit_pak(lik)
   %LIK_MULTINOMPROBIT_PAK  Combine likelihood parameters into one vector.
@@ -416,4 +417,3 @@
     end
     
   end
-end
diff --git a/gp/lik_negbin.m b/gp/lik_negbin.m
index 8f34854a..9ad06716 100644
--- a/gp/lik_negbin.m
+++ b/gp/lik_negbin.m
@@ -49,11 +49,11 @@
   % inputParser checks the arguments and assigns some default values
   ip=inputParser;
   ip.FunctionName = 'LIK_NEGBIN';
-  ip.addOptional('lik', [], @isstruct);
-  ip.addParamValue('disper',10, @(x) isscalar(x) && x>0);
-  ip.addParamValue('disper_prior',prior_gamma('sh',2,'is',.1), ...
-                   @(x) isstruct(x) || isempty(x));
-  ip.parse(varargin{:});
+  ip=iparser(ip,'addOptional','lik', [], @isstruct);
+  ip=iparser(ip,'addParamValue','disper',10, @(x) isscalar(x) && x>0);
+  ip=iparser(ip,'addParamValue','disper_prior',prior_gamma('sh',2,'is',.1), ...
+    @(x) isstruct(x) || isempty(x));
+  ip=iparser(ip,'parse',varargin{:});
   lik=ip.Results.lik;
   
   if isempty(lik)
@@ -431,14 +431,14 @@
   % and useful integration limits
   [tf,minf,maxf]=init_negbin_norm(yy,myy_i,sigm2_i,avgE,r);
   % additionally get function handle for the derivative
-  td = @deriv;
+  td = @(f) deriv(f, r, avgE, yy);
   
   % Integrate with quadgk
   [m_0, fhncnt] = quadgk(tf, minf, maxf);
   [g_i, fhncnt] = quadgk(@(f) td(f).*tf(f)./m_0, minf, maxf);
   g_i = g_i.*r;
-
-  function g = deriv(f)
+end
+  function g = deriv(f, r, avgE, yy)
     mu = avgE.*exp(f);
     % Derivative using the psi function
     g = 1 + log(r./(r+mu)) - (r+yy)./(r+mu) + psi(r + yy) - psi(r);
@@ -449,7 +449,6 @@
     %        g = g + 1 ./ (i2 + r);
     %      end
   end
-end
 
 function [lpy, Ey, Vary] = lik_negbin_predy(lik, Ef, Varf, yt, zt)
 %LIK_NEGBIN_PREDY  Returns the predictive mean, variance and density of y
@@ -585,7 +584,7 @@
   ldconst = -gammaln(r)-gammaln(yy+1)+gammaln(r+yy)...
             - log(sigm2_i)/2 - log(2*pi)/2;
   % Create function handle for the function to be integrated
-  df = @negbin_norm;
+  df = @(f) negbin_norm(f, yy, ldconst, r, avgE, myy_i, sigm2_i);
   % use log to avoid underflow, and derivates for faster search
   ld = @log_negbin_norm;
   ldg = @log_negbin_norm_g;
@@ -610,8 +609,8 @@
   niter=4;       % number of Newton iterations
   mindelta=1e-6; % tolerance in stopping Newton iterations
   for ni=1:niter
-    g=ldg(modef);
-    h=ldg2(modef);
+    g=ldg(modef, yy, ldconst, r, avgE, myy_i, sigm2_i);
+    h=ldg2(modef, yy, ldconst, r, avgE, myy_i, sigm2_i);
     delta=-g/h;
     modef=modef+delta;
     if abs(delta)<mindelta
@@ -622,15 +621,15 @@
   modes=sqrt(-1/h);
   minf=modef-8*modes;
   maxf=modef+8*modes;
-  modeld=ld(modef);
+  modeld=ld(modef, yy, ldconst, r, avgE, myy_i, sigm2_i);
   iter=0;
   % check that density at end points is low enough
   lddiff=20; % min difference in log-density between mode and end-points
-  minld=ld(minf);
+  minld=ld(minf, yy, ldconst, r, avgE, myy_i, sigm2_i);
   step=1;
   while minld>(modeld-lddiff)
     minf=minf-step*modes;
-    minld=ld(minf);
+    minld=ld(minf, yy, ldconst, r, avgE, myy_i, sigm2_i);
     iter=iter+1;
     step=step*2;
     if iter>100
@@ -639,12 +638,12 @@
              'even after looking hard!'])
     end
   end
-  maxld=ld(maxf);
+  maxld=ld(maxf, yy, ldconst, r, avgE, myy_i, sigm2_i);
   iter=0;
   step=1;
   while maxld>(modeld-lddiff)
     maxf=maxf+step*modes;
-    maxld=ld(maxf);
+    maxld=ld(maxf, yy, ldconst, r, avgE, myy_i, sigm2_i);
     iter=iter+1;
     step=step*2;
     if iter>100
@@ -653,8 +652,8 @@
              'even after looking hard!'])
     end
   end
-  
-  function integrand = negbin_norm(f)
+end
+  function integrand = negbin_norm(f, yy, ldconst, r, avgE, myy_i, sigm2_i)
   % Negative-binomial * Gaussian
     mu = avgE.*exp(f);
     integrand = exp(ldconst ...
@@ -662,7 +661,7 @@
                     -0.5*(f-myy_i).^2./sigm2_i);
   end
   
-  function log_int = log_negbin_norm(f)
+  function log_int = log_negbin_norm(f, yy, ldconst, r, avgE, myy_i, sigm2_i)
   % log(Negative-binomial * Gaussian)
   % log_negbin_norm is used to avoid underflow when searching
   % integration interval
@@ -672,7 +671,7 @@
               -0.5*(f-myy_i).^2./sigm2_i;
   end
   
-  function g = log_negbin_norm_g(f)
+  function g = log_negbin_norm_g(f, yy, ldconst, r, avgE, myy_i, sigm2_i)
   % d/df log(Negative-binomial * Gaussian)
   % derivative of log_negbin_norm
     mu = avgE.*exp(f);
@@ -680,15 +679,13 @@
         + (myy_i - f)./sigm2_i;
   end
   
-  function g2 = log_negbin_norm_g2(f)
+  function g2 = log_negbin_norm_g2(f, yy, ldconst, r, avgE, myy_i, sigm2_i)
   % d^2/df^2 log(Negative-binomial * Gaussian)
   % second derivate of log_negbin_norm
     mu = avgE.*exp(f);
     g2 = -(r*(r + yy))/(mu + r)^2.*mu ...
          -1/sigm2_i;
   end
-  
-end
 
 function mu = lik_negbin_invlink(lik, f, z)
 %LIK_NEGBIN_INVLINK  Returns values of inverse link function
diff --git a/gp/lik_negbinztr.m b/gp/lik_negbinztr.m
index d748d81c..9503ea7c 100644
--- a/gp/lik_negbinztr.m
+++ b/gp/lik_negbinztr.m
@@ -51,10 +51,10 @@
 
   ip=inputParser;
   ip.FunctionName = 'LIK_NEGBINZTR';
-  ip.addOptional('lik', [], @isstruct);
-  ip.addParamValue('disper',10, @(x) isscalar(x) && x>0);
-  ip.addParamValue('disper_prior',prior_logunif(), @(x) isstruct(x) || isempty(x));
-  ip.parse(varargin{:});
+  ip=iparser(ip,'addOptional','lik', [], @isstruct);
+  ip=iparser(ip,'addParamValue','disper',10, @(x) isscalar(x) && x>0);
+  ip=iparser(ip,'addParamValue','disper_prior',prior_logunif(), @(x) isstruct(x) || isempty(x));
+  ip=iparser(ip,'parse',varargin{:});
   lik=ip.Results.lik;
   
   if isempty(lik)
@@ -519,16 +519,9 @@
   
   % Integrate with quadgk
   [m_0, fhncnt] = quadgk(tf, minf, maxf);
-  [g_i, fhncnt] = quadgk(@(f) td(f).*tf(f)./m_0, minf, maxf);
+  [g_i, fhncnt] = quadgk(@(f) td(f,avgE,r,yy).*tf(f)./m_0, minf, maxf);
   g_i = g_i.*r;
 
-  function g = deriv(f)
-    mu = avgE.*exp(f);
-    % Derivative using the psi function
-    g = 1 + log(r./(r+mu)) - (r+yy)./(r+mu) + psi(r + yy) - psi(r);
-    lp0=r.*(log(r) - log(r+mu));
-    g = g -(1./(1 - exp(-lp0)).*(log(r./(mu + r)) - r./(mu + r) + 1));
-  end
 end
 
 function [g_i] = lik_negbinztr_siteDeriv2(lik, y, i1, sigm2_i, myy_i, z, eta, lnZhat)
@@ -566,21 +559,13 @@
   % and useful integration limits
   [tf,minf,maxf]=init_negbinztr_norm(yy,myy_i,sigm2_i,avgE,r, eta);
   % additionally get function handle for the derivative
-  td = @deriv;
+  td = @(f) deriv(f, avgE, r, yy);
   
   % Integrate with quadgk
   [m_0, fhncnt] = quadgk(tf, minf, maxf);
   [g_i, fhncnt] = quadgk(@(f) td(f).*tf(f)./m_0, minf, maxf);
   g_i = g_i.*r;
 
-  function g = deriv(f)
-    mu = avgE.*exp(f);
-    % Derivative using the psi function
-    g = 1 + log(r./(r+mu)) - (r+yy)./(r+mu) + psi(r + yy) - psi(r);
-    lp0=r.*(log(r) - log(r+mu));
-    g = g -(1./(1 - exp(-lp0)).*(log(r./(mu + r)) - r./(mu + r) + 1));
-%     g = eta.*g;
-  end
 end
 
 function upfact = lik_negbinztr_upfact(gp, y, mu, ll, z)
@@ -699,7 +684,7 @@
   ldconst = eta*(-gammaln(r)-gammaln(yy+1)+gammaln(r+yy))...
             - log(sigm2_i)/2 - log(2*pi)/2;
   % Create function handle for the function to be integrated
-  df = @negbinztr_norm;
+  df = @(f) negbinztr_norm(f, ldconst,avgE,r,yy,myy_i,sigm2_i, eta);
   % use log to avoid underflow, and derivates for faster search
   ld = @log_negbinztr_norm;
   ldg = @log_negbinztr_norm_g;
@@ -724,8 +709,8 @@
   niter=4;       % number of Newton iterations
   mindelta=1e-6; % tolerance in stopping Newton iterations
   for ni=1:niter
-    g=ldg(modef);
-    h=ldg2(modef);
+    g=ldg(modef,ldconst,avgE,r,yy,myy_i,sigm2_i,eta);
+    h=ldg2(modef,ldconst,avgE,r,yy,myy_i,sigm2_i,eta);
     delta=-g/h;
     modef=modef+delta;
     if abs(delta)<mindelta
@@ -736,20 +721,20 @@
   modes=sqrt(-1/h);
   minf=modef-8*modes;
   maxf=modef+8*modes;
-  modeld=ld(modef);
+  modeld=ld(modef,ldconst,avgE,r,yy,myy_i,sigm2_i,eta);
   iter=0;
   % check that density at end points is low enough
   lddiff=20; % min difference in log-density between mode and end-points
-  minld=ld(minf);
+  minld=ld(minf,ldconst,avgE,r,yy,myy_i,sigm2_i,eta);
   step=1;
   while minld<(modeld-lddiff) && minf<modef;
     % sometimes minf is too small
     minf=minf+step*modes;
-    minld=ld(minf);
+    minld=ld(minf,ldconst,avgE,r,yy,myy_i,sigm2_i,eta);
   end
   while minld>(modeld-lddiff)
     minf=minf-step*modes;
-    minld=ld(minf);
+    minld=ld(minf,ldconst,avgE,r,yy,myy_i,sigm2_i,eta);
     iter=iter+1;
     step=step*2;
     if iter>100
@@ -758,12 +743,12 @@
              'even after looking hard!'])
     end
   end
-  maxld=ld(maxf);
+  maxld=ld(maxf,ldconst,avgE,r,yy,myy_i,sigm2_i,eta);
   iter=0;
   step=1;
   while maxld>(modeld-lddiff)
     maxf=maxf+step*modes;
-    maxld=ld(maxf);
+    maxld=ld(maxf,ldconst,avgE,r,yy,myy_i,sigm2_i,eta);
     iter=iter+1;
     step=step*2;
     if iter>100
@@ -772,8 +757,9 @@
              'even after looking hard!'])
     end
   end
+end
   
-  function integrand = negbinztr_norm(f)
+  function integrand = negbinztr_norm(f,ldconst,avgE,r,yy,myy_i,sigm2_i,eta)
   % Negative-binomial * Gaussian
     mu = avgE.*exp(f);
     lp0=r.*(log(r) - log(r+mu));
@@ -794,7 +780,7 @@
     end
   end
   
-  function log_int = log_negbinztr_norm(f)
+  function log_int = log_negbinztr_norm(f,ldconst,avgE,r,yy,myy_i,sigm2_i,eta)
   % log(Negative-binomial * Gaussian)
   % log_negbinztr_norm is used to avoid underflow when searching
   % integration interval
@@ -817,7 +803,7 @@
     end
   end
   
-  function g = log_negbinztr_norm_g(f)
+  function g = log_negbinztr_norm_g(f,ldconst,avgE,r,yy,myy_i,sigm2_i,eta)
   % d/df log(Negative-binomial * Gaussian)
   % derivative of log_negbinztr_norm
     mu = avgE.*exp(f);
@@ -833,7 +819,7 @@
     end
   end
   
-  function g2 = log_negbinztr_norm_g2(f)
+  function g2 = log_negbinztr_norm_g2(f,ldconst,avgE,r,yy,myy_i,sigm2_i,eta)
   % d^2/df^2 log(Negative-binomial * Gaussian)
   % second derivate of log_negbinztr_norm
     mu = avgE.*exp(f);
@@ -848,8 +834,6 @@
            + eta*(r^2 + r^2*exp(-lp0)*(mu - 1))/((mu + r)^2*(exp(-lp0) - 1)^2)*mu;
     end
   end
-  
-end
 
 function prctys = lik_negbinztr_predprcty(lik, Ef, Varf, zt, prcty)
 %LIK_BINOMIAL_PREDPRCTY  Returns the percentiled of predictive density of y
@@ -907,22 +891,22 @@
       prctys(i1,i2)=a;
     end
   end
-
-  function expll = llvec(lik,yt,f,z)
-    % Compute vector of likelihoods of single predictions
-    n = length(yt);
-    if n>0
-      for i=1:n
-        expll(i) = exp(lik.fh.ll(lik, yt(i), f, z));
-      end
-    else
-      expll = 0;
+end
+function expll = llvec(lik,yt,f,z)
+  % Compute vector of likelihoods of single predictions
+  n = length(yt);
+  if n>0
+    for i=1:n
+      expll(i) = exp(lik.fh.ll(lik, yt(i), f, z));
     end
+  else
+    expll = 0;
   end
-
 end
 
 
+
+
 function mu = lik_negbinztr_invlink(lik, f, z)
 %LIK_NEGBINZTR_INVLINK  Returns values of inverse link function
 %             
@@ -992,3 +976,11 @@
     end
   end
 end
+
+function g = deriv(f,avgE,r,yy)
+  mu = avgE.*exp(f);
+  % Derivative using the psi function
+  g = 1 + log(r./(r+mu)) - (r+yy)./(r+mu) + psi(r + yy) - psi(r);
+  lp0=r.*(log(r) - log(r+mu));
+  g = g -(1./(1 - exp(-lp0)).*(log(r./(mu + r)) - r./(mu + r) + 1));
+end
diff --git a/gp/lik_poisson.m b/gp/lik_poisson.m
index 8768f44f..655121ec 100644
--- a/gp/lik_poisson.m
+++ b/gp/lik_poisson.m
@@ -30,8 +30,8 @@
 
   ip=inputParser;
   ip.FunctionName = 'LIK_POISSON';
-  ip.addOptional('lik', [], @isstruct);
-  ip.parse(varargin{:});
+  ip=iparser(ip,'addOptional','lik', [], @isstruct);
+  ip=iparser(ip,'parse',varargin{:});
   lik=ip.Results.lik;
 
   if isempty(lik)
@@ -393,7 +393,7 @@
   ldconst = -gammaln(yy+1) - log(sigm2_i)/2 - log(2*pi)/2;
   
   % Create function handle for the function to be integrated
-  df = @poisson_norm;
+  df = @(f) poisson_norm(f,avgE,ldconst,yy,myy_i,sigm2_i);
   % use log to avoid underflow, and derivates for faster search
   ld = @log_poisson_norm;
   ldg = @log_poisson_norm_g;
@@ -418,8 +418,8 @@
   niter=3;       % number of Newton iterations
   mindelta=1e-6; % tolerance in stopping Newton iterations
   for ni=1:niter
-    g=ldg(modef);
-    h=ldg2(modef);
+    g=ldg(modef,avgE,ldconst,yy,myy_i,sigm2_i);
+    h=ldg2(modef,avgE,ldconst,yy,myy_i,sigm2_i);
     delta=-g/h;
     modef=modef+delta;
     if abs(delta)<mindelta
@@ -430,15 +430,15 @@
   modes=sqrt(-1/h);
   minf=modef-8*modes;
   maxf=modef+8*modes;
-  modeld=ld(modef);
+  modeld=ld(modef,avgE,ldconst,yy,myy_i,sigm2_i);
   iter=0;
   % check that density at end points is low enough
   lddiff=20; % min difference in log-density between mode and end-points
-  minld=ld(minf);
+  minld=ld(minf,avgE,ldconst,yy,myy_i,sigm2_i);
   step=1;
   while minld>(modeld-lddiff)
     minf=minf-step*modes;
-    minld=ld(minf);
+    minld=ld(minf,avgE,ldconst,yy,myy_i,sigm2_i);
     iter=iter+1;
     step=step*2;
     if iter>100
@@ -447,12 +447,12 @@
              'even after looking hard!'])
     end
   end
-  maxld=ld(maxf);
+  maxld=ld(maxf,avgE,ldconst,yy,myy_i,sigm2_i);
   iter=0;
   step=1;
   while maxld>(modeld-lddiff)
     maxf=maxf+step*modes;
-    maxld=ld(maxf);
+    maxld=ld(maxf,avgE,ldconst,yy,myy_i,sigm2_i);
     iter=iter+1;
     step=step*2;
     if iter>100
@@ -461,8 +461,9 @@
              'even after looking hard!'])
     end
   end
+end
 
-  function integrand = poisson_norm(f)
+  function integrand = poisson_norm(f,avgE,ldconst,yy,myy_i,sigm2_i)
   % Poisson * Gaussian
     mu = avgE.*exp(f);
     integrand = exp(ldconst ...
@@ -470,7 +471,7 @@
                     -0.5*(f-myy_i).^2./sigm2_i);
   end
 
-  function log_int = log_poisson_norm(f)
+  function log_int = log_poisson_norm(f,avgE,ldconst,yy,myy_i,sigm2_i)
   % log(Poisson * Gaussian)
   % log_poisson_norm is used to avoid underflow when searching
   % integration interval
@@ -480,7 +481,7 @@
               -0.5*(f-myy_i).^2./sigm2_i;
   end
 
-  function g = log_poisson_norm_g(f)
+  function g = log_poisson_norm_g(f,avgE,ldconst,yy,myy_i,sigm2_i)
   % d/df log(Poisson * Gaussian)
   % derivative of log_poisson_norm
     mu = avgE.*exp(f);
@@ -488,7 +489,7 @@
         + (myy_i - f)./sigm2_i;
   end
 
-  function g2 = log_poisson_norm_g2(f)
+  function g2 = log_poisson_norm_g2(f,avgE,ldconst,yy,myy_i,sigm2_i)
   % d^2/df^2 log(Poisson * Gaussian)
   % second derivate of log_poisson_norm
     mu = avgE.*exp(f);
@@ -496,7 +497,6 @@
          -1/sigm2_i;
   end
 
-end
 
 function mu = lik_poisson_invlink(lik, f, z)
 %LIK_POISSON_INVLINK  Returns values of inverse link function
diff --git a/gp/lik_probit.m b/gp/lik_probit.m
index c3e1fd1a..eeb4b9e1 100644
--- a/gp/lik_probit.m
+++ b/gp/lik_probit.m
@@ -24,8 +24,8 @@
 
   ip=inputParser;
   ip.FunctionName = 'LIK_PROBIT';
-  ip.addOptional('lik', [], @isstruct);
-  ip.parse(varargin{:});
+  ip=iparser(ip,'addOptional','lik', [], @isstruct);
+  ip=iparser(ip,'parse',varargin{:});
   lik=ip.Results.lik;
 
   if isempty(lik)
diff --git a/gp/lik_qgp.m b/gp/lik_qgp.m
index fae85335..4b221152 100644
--- a/gp/lik_qgp.m
+++ b/gp/lik_qgp.m
@@ -50,11 +50,11 @@
 
   ip=inputParser;
   ip.FunctionName = 'LIK_QGP';
-  ip.addOptional('lik', [], @isstruct);
-  ip.addParamValue('sigma2',0.1, @(x) isscalar(x) && x>0);
-  ip.addParamValue('sigma2_prior',prior_logunif(), @(x) isstruct(x) || isempty(x));
-  ip.addParamValue('quantile',0.5, @(x) isscalar(x) && x>0 && x<1);
-  ip.parse(varargin{:});
+  ip=iparser(ip,'addOptional','lik', [], @isstruct);
+  ip=iparser(ip,'addParamValue','sigma2',0.1, @(x) isscalar(x) && x>0);
+  ip=iparser(ip,'addParamValue','sigma2_prior',prior_logunif(), @(x) isstruct(x) || isempty(x));
+  ip=iparser(ip,'addParamValue','quantile',0.5, @(x) isscalar(x) && x>0 && x<1);
+  ip=iparser(ip,'parse',varargin{:});
   lik=ip.Results.lik;
 
   if isempty(lik)
@@ -408,15 +408,15 @@
   
   % Integrate with quadgk
   [m_0, fhncnt] = quadgk(tf, minf, maxf);
-  [g_i, fhncnt] = quadgk(@(f) td(f).*tf(f)./m_0, minf, maxf);
+  [g_i, fhncnt] = quadgk(@(f) td(f, yy, sigma2, tau).*tf(f)./m_0, minf, maxf);
   g_i = g_i.*sigma2;
+end
 
-  function g = deriv(f)
+  function g = deriv(f, yy, sigma2, tau)
 
     g = -1/(2.*sigma2) + (yy-f)./(2.*sigma2^(3/2)).*(tau-(yy<=f));
     
   end
-end
 
 function [lpy, Ey, Vary] = lik_qgp_predy(lik, Ef, Varf, yt, zt)
 %LIK_QGP_PREDY  Returns the predictive mean, variance and density of y
@@ -492,7 +492,7 @@
   ldconst = log(tau*(1-tau)/sigma) ...
             - log(sigm2_i)/2 - log(2*pi)/2;
   % Create function handle for the function to be integrated
-  df = @qgp_norm;
+  df = @(f) qgp_norm(f, ldconst, yy, sigma2, tau, myy_i, sigm2_i);
   % use log to avoid underflow, and derivates for faster search
   ld = @log_qgp_norm;
   ldg = @log_qgp_norm_g;
@@ -515,17 +515,17 @@
   niter=8;       % number of Newton iterations 
   
   minf=modef-6*sigm2_i;
-  while ldg(minf) < 0
+  while ldg(minf, ldconst, yy, sigma2, tau, myy_i, sigm2_i) < 0
     minf=minf-2*sigm2_i;
   end
   maxf=modef+6*sigm2_i;
-  while ldg(maxf) > 0
+  while ldg(maxf, ldconst, yy, sigma2, tau, myy_i, sigm2_i) > 0
     maxf=maxf+2*sigm2_i;
   end
   for ni=1:niter
 %     h=ldg2(modef);
     modef=0.5*(minf+maxf);
-    if ldg(modef) < 0
+    if ldg(modef, ldconst, yy, sigma2, tau, myy_i, sigm2_i) < 0
       maxf=modef;
     else
       minf=modef;
@@ -534,15 +534,15 @@
   % integrand limits based on Gaussian approximation at mode
   minf=modef-6*sqrt(sigm2_i);
   maxf=modef+6*sqrt(sigm2_i);
-  modeld=ld(modef);
+  modeld=ld(modef, ldconst, yy, sigma2, tau, myy_i, sigm2_i);
   iter=0;
   % check that density at end points is low enough
   lddiff=20; % min difference in log-density between mode and end-points
-  minld=ld(minf);
+  minld=ld(minf, ldconst, yy, sigma2, tau, myy_i, sigm2_i);
   step=1;
   while minld>(modeld-lddiff)
     minf=minf-step*sqrt(sigm2_i);
-    minld=ld(minf);
+    minld=ld(minf, ldconst, yy, sigma2, tau, myy_i, sigm2_i);
     iter=iter+1;
     step=step*2;
     if iter>100
@@ -551,12 +551,12 @@
              'even after looking hard!'])
     end
   end
-  maxld=ld(maxf);
+  maxld=ld(maxf, ldconst, yy, sigma2, tau, myy_i, sigm2_i);
   iter=0;
   step=1;
   while maxld>(modeld-lddiff)
     maxf=maxf+step*sqrt(sigm2_i);
-    maxld=ld(maxf);
+    maxld=ld(maxf, ldconst, yy, sigma2, tau, myy_i, sigm2_i);
     iter=iter+1;
     step=step*2;
     if iter>100
@@ -565,15 +565,15 @@
              'even after looking hard!'])
     end
   end
-  
-  function integrand = qgp_norm(f)
+end
+  function integrand = qgp_norm(f, ldconst, yy, sigma2, tau, myy_i, sigm2_i)
   % Quantile-GP * Gaussian
     integrand = exp(ldconst ...
                     -(yy-f)./sqrt(sigma2).*(tau-(yy<=f)) ...
                     -0.5*(f-myy_i).^2./sigm2_i);
   end
   
-  function log_int = log_qgp_norm(f)
+  function log_int = log_qgp_norm(f, ldconst, yy, sigma2, tau, myy_i, sigm2_i)
   % log(Quantile-GP * Gaussian)
   % log_qgp_norm is used to avoid underflow when searching
   % integration interval
@@ -582,16 +582,13 @@
               -0.5*(f-myy_i).^2./sigm2_i;
   end
   
-  function g = log_qgp_norm_g(f)
+  function g = log_qgp_norm_g(f, ldconst, yy, sigma2, tau, myy_i, sigm2_i)
   % d/df log(Quantile-GP * Gaussian)
   % derivative of log_qgp_norm
     g = (tau-(yy<=f))/sqrt(sigma2) ...
         + (myy_i - f)./sigm2_i;
   end
   
-  
-end
-
 function mu = lik_qgp_invlink(lik, f, z)
 %LIK_QGP_INVLINK  Returns values of inverse link function
 %             
diff --git a/gp/lik_softmax.m b/gp/lik_softmax.m
index 9ff68496..06853cf5 100644
--- a/gp/lik_softmax.m
+++ b/gp/lik_softmax.m
@@ -26,8 +26,8 @@
 
   ip=inputParser;
   ip.FunctionName = 'LIK_SOFTMAX';
-  ip.addOptional('lik', [], @isstruct);
-  ip.parse(varargin{:});
+  ip=iparser(ip,'addOptional','lik', [], @isstruct);
+  ip=iparser(ip,'parse',varargin{:});
   lik=ip.Results.lik;
 
   if isempty(lik)
diff --git a/gp/lik_t.m b/gp/lik_t.m
index dab5c77d..758b06f0 100644
--- a/gp/lik_t.m
+++ b/gp/lik_t.m
@@ -42,12 +42,12 @@
 
   ip=inputParser;
   ip.FunctionName = 'LIK_T';
-  ip.addOptional('lik', [], @isstruct);
-  ip.addParamValue('sigma2',0.1, @(x) isscalar(x) && x>0);
-  ip.addParamValue('sigma2_prior',prior_logunif(), @(x) isstruct(x) || isempty(x));
-  ip.addParamValue('nu',4, @(x) isscalar(x) && x>0);
-  ip.addParamValue('nu_prior',prior_fixed, @(x) isstruct(x) || isempty(x));
-  ip.parse(varargin{:});
+  ip=iparser(ip,'addOptional','lik', [], @isstruct);
+  ip=iparser(ip,'addParamValue','sigma2',0.1, @(x) isscalar(x) && x>0);
+  ip=iparser(ip,'addParamValue','sigma2_prior',prior_logunif(), @(x) isstruct(x) || isempty(x));
+  ip=iparser(ip,'addParamValue','nu',4, @(x) isscalar(x) && x>0);
+  ip=iparser(ip,'addParamValue','nu_prior',prior_fixed, @(x) isstruct(x) || isempty(x));
+  ip=iparser(ip,'parse',varargin{:});
   lik=ip.Results.lik;
   
   if isempty(lik)
@@ -390,8 +390,6 @@
 %    GPEP_E
 
   
-  zm = @zeroth_moment;
-  
   tol = 1e-8;
   nu = lik.nu;
   sigma2 = lik.sigma2;
@@ -401,6 +399,7 @@
   for i=i1
 
     yy = y(i);
+    zm = @(f) zeroth_moment(f, yy, nu, sigma2, myy_i(i), sigm2_i(i));
     
     % Set the limits for integration and integrate with quad
     % -----------------------------------------------------
@@ -492,15 +491,15 @@
 %
 %  See also
 %    GPEP_G
-
-  zm = @zeroth_moment;
-  znu = @deriv_nu;
-  zsigma2 = @deriv_sigma2;
   
   tol = 1e-8;
   yy = y(i1);
   nu = lik.nu;
   sigma2 = lik.sigma2;
+  
+  zm = @(f) zeroth_moment(f, yy, nu, sigma2, myy_i, sigm2_i);
+  znu = @(f) deriv_nu(f, yy, nu, sigma2, myy_i, sigm2_i);
+  zsigma2 = @(f) deriv_sigma2(f, yy, nu, sigma2, myy_i, sigm2_i);
 
   % Set the limits for integration and integrate with quad
   mean_app = myy_i;
@@ -560,24 +559,6 @@
     [g_i(2), fhncnt] = quadgk(@(f) znu(f).*zm(f) , lambdaconf(1), lambdaconf(2));
     g_i(2) = g_i(2)/m_0.*nu.*log(nu);
   end
-  
-  function integrand = zeroth_moment(f)
-    r = yy-f;
-    term = gammaln((nu + 1) / 2) - gammaln(nu/2) -log(nu.*pi.*sigma2)/2;
-    integrand = exp(term + log(1 + r.^2./nu./sigma2) .* (-(nu+1)/2));
-    integrand = integrand.*exp(- 0.5 * (f-myy_i).^2./sigm2_i - log(sigm2_i)/2 - log(2*pi)/2);
-  end        
-
-  function g = deriv_nu(f)
-    r = yy-f;
-    temp = 1 + r.^2./nu./sigma2;
-    g = psi((nu+1)/2)./2 - psi(nu/2)./2 - 1./(2.*nu) - log(temp)./2 + (nu+1)./(2.*temp).*(r./nu).^2./sigma2;
-  end
-
-  function g = deriv_sigma2(f)
-    r = yy-f;
-    g  = -1/sigma2/2 + (nu+1)./2.*r.^2./(nu.*sigma2.^2 + r.^2.*sigma2);
-  end
 
 end
 
@@ -725,13 +706,13 @@
 %     lnZhat = log(m_0) -C;
 %   end
   
-  function lpdf = lpt(f,C)
-    % logarithm of the tilted distribution
-    r = yy-f;
-    lpdf = gammaln((nu + 1) / 2) - gammaln(nu/2) -log(nu.*pi.*sigma2)/2;
-    lpdf = lpdf + log(1 + r.^2./nu./sigma2) .* (-(nu+1)/2);
-    lpdf = lpdf*eta - (0.5/sigm2_i) * (f-myy_i).^2 + (C-log(2*pi*sigm2_i)/2);
-  end
+%   function lpdf = lpt(f,C)
+%     % logarithm of the tilted distribution
+%     r = yy-f;
+%     lpdf = gammaln((nu + 1) / 2) - gammaln(nu/2) -log(nu.*pi.*sigma2)/2;
+%     lpdf = lpdf + log(1 + r.^2./nu./sigma2) .* (-(nu+1)/2);
+%     lpdf = lpdf*eta - (0.5/sigm2_i) * (f-myy_i).^2 + (C-log(2*pi*sigm2_i)/2);
+%   end
 end
 
 function [g_i] = lik_t_siteDeriv2(likelih, y, yi, sigm2_i, myy_i, z, eta, lnZhat)
@@ -804,8 +785,8 @@
       end
       
       np=numel(fvec);
-      logpt = lpt(fvec,0);
-      lpt_max = max([logpt lpt([myy_i mg],0)]);
+      logpt = lpt(fvec,0,yy,nu,sigma2,myy_i,sigm2_i,eta);
+      lpt_max = max([logpt lpt([myy_i mg],0,nu,sigma2,myy_i,sigm2_i,eta)]);
       lambdaconf=[fvec(1), fvec(end)];
       for i1=2:np-1
         if logpt(i1) < lpt_max+log(1e-7) %(exp(logpt(i1))/exp(lpt_max) < 1e-7)
@@ -831,12 +812,12 @@
         lambdaconf=[min(mg-6*sg,myy_i-6*sigm_i),myy_i+6*sigm_i];
         fvec=linspace(mg,myy_i,np);
       end
-      lpt_max=max(lpt(fvec,0));
+      lpt_max=max(lpt(fvec,0,yy,nu,sigma2,myy_i,sigm2_i,eta));
     end
     C=log(1)-lpt_max; % scale the log-density for the quadrature tolerance
   else
     lambdaconf=[mg-6*sg,mg+6*sg];
-    C=log(1)-lpt(mg,0);
+    C=log(1)-lpt(mg,0,yy,nu,sigma2,myy_i,sigm2_i,eta);
   end
   
   if nu>nu_lim
@@ -860,47 +841,17 @@
     % Use the normalization determined in the lik_t_tiltedMoments2
     m_0=exp(lnZhat+C);
     
-    zm=@(f) deriv_sigma2(f).*exp(lpt(f,C))*sigma2;
+    zm=@(f) deriv_sigma2(f,yy,nu,sigma2).*exp(lpt(f,C,yy,nu,sigma2,myy_i,sigm2_i,eta))*sigma2;
     [g_i(1), fhncnt] = quadgk( zm, lambdaconf(1), lambdaconf(2),'AbsTol',ATOL,'RelTol',RTOL);
     g_i(1) = g_i(1)/m_0;
     
     if (isfield(likelih,'p') && ~isempty(likelih.p.nu))
-      zm=@(f) deriv_nu(f).*exp(lpt(f,C));
+      zm=@(f) deriv_nu(f,yy,nu,sigma2).*exp(lpt(f,C,yy,nu,sigma2,myy_i,sigm2_i,eta));
       [g_i(2), fhncnt] = quadgk( zm, lambdaconf(1), lambdaconf(2),'AbsTol',ATOL,'RelTol',RTOL);
       g_i(2) = g_i(2)/m_0.*nu.*log(nu);
     end
     
   end
-  
-  function lpdf = lpt(f,C)
-    % logarithm of the tilted distribution
-    r = yy-f;
-    lpdf = gammaln((nu + 1) / 2) - gammaln(nu/2) -log(nu.*pi.*sigma2)/2;
-    lpdf = lpdf + log(1 + r.^2./nu./sigma2) .* (-(nu+1)/2);
-    lpdf = lpdf*eta - (0.5/sigm2_i) * (f-myy_i).^2 + (C-log(2*pi*sigm2_i)/2);
-  end
-
-  function g = deriv_nu(f)
-    % derivative of the log-likelihood wrt nu
-    r = yy-f;
-    temp = r.^2 ./(nu*sigma2);
-    g = psi((nu+1)/2) - psi(nu/2) - 1/nu;
-    g = g + (1+1/nu).*temp./(1+temp);
-    
-    % for small values use a more accurate method for log(1+x)
-    ii = temp<1e3;
-    g(ii) = g(ii)  - log1p(temp(ii));
-    g(~ii) = g(~ii) - log(1+temp(~ii));
-    g = g*0.5;
-    
-  end
-
-  function g = deriv_sigma2(f)
-    % derivative of the log-likelihood wrt sigma2
-    r = yy-f;
-    temp = r.^2 /sigma2;
-    g  = -1/sigma2/2 + ((1+1/nu)/2) * temp ./ (1 + temp/nu) /sigma2;
-  end
 
 end
 
@@ -1197,3 +1148,53 @@
   end
 
 end
+
+
+function integrand = zeroth_moment(f, yy, nu, sigma2, myy_i, sigm2_i)
+r = yy-f;
+term = gammaln((nu + 1) / 2) - gammaln(nu/2) -log(nu.*pi.*sigma2)/2;
+integrand = exp(term + log(1 + r.^2./nu./sigma2) .* (-(nu+1)/2));
+integrand = integrand.*exp(- 0.5 * (f-myy_i).^2./sigm2_i - log(sigm2_i)/2 - log(2*pi)/2); %
+end
+
+
+function lpdf = lpt(f,C,yy,nu,sigma2,myy_i,sigm2_i,eta)
+% logarithm of the tilted distribution
+r = yy-f;
+lpdf = gammaln((nu + 1) / 2) - gammaln(nu/2) -log(nu.*pi.*sigma2)/2;
+lpdf = lpdf + log(1 + r.^2./nu./sigma2) .* (-(nu+1)/2);
+lpdf = lpdf*eta - (0.5/sigm2_i) * (f-myy_i).^2 + (C-log(2*pi*sigm2_i)/2);
+end
+
+function g = deriv_nu(f,yy,nu,sigma2)
+% derivative of the log-likelihood wrt nu
+r = yy-f;
+temp = r.^2 ./(nu*sigma2);
+g = psi((nu+1)/2) - psi(nu/2) - 1/nu;
+g = g + (1+1/nu).*temp./(1+temp);
+
+% for small values use a more accurate method for log(1+x)
+ii = temp<1e3;
+g(ii) = g(ii)  - log1p(temp(ii));
+g(~ii) = g(~ii) - log(1+temp(~ii));
+g = g*0.5;
+
+end
+
+function g = deriv_sigma2(f,yy,nu,sigma2)
+% derivative of the log-likelihood wrt sigma2
+r = yy-f;
+temp = r.^2 /sigma2;
+g  = -1/sigma2/2 + ((1+1/nu)/2) * temp ./ (1 + temp/nu) /sigma2;
+end
+
+function g = deriv_nu2(f, yy, nu, sigma2, myy_i, sigm2_i)
+r = yy-f;
+temp = 1 + r.^2./nu./sigma2;
+g = psi((nu+1)/2)./2 - psi(nu/2)./2 - 1./(2.*nu) - log(temp)./2 + (nu+1)./(2.*temp).*(r./nu).^2./sigma2;
+end
+
+function g = deriv_sigma22(f, yy, nu, sigma2, myy_i, sigm2_i)
+r = yy-f;
+g  = -1/sigma2/2 + (nu+1)./2.*r.^2./(nu.*sigma2.^2 + r.^2.*sigma2);
+end
diff --git a/gp/lik_weibull.m b/gp/lik_weibull.m
index a7d2c992..3819e759 100644
--- a/gp/lik_weibull.m
+++ b/gp/lik_weibull.m
@@ -48,10 +48,10 @@
 
   ip=inputParser;
   ip.FunctionName = 'LIK_WEIBULL';
-  ip.addOptional('lik', [], @isstruct);
-  ip.addParamValue('shape',1, @(x) isscalar(x) && x>0);
-  ip.addParamValue('shape_prior',prior_logunif(), @(x) isstruct(x) || isempty(x));
-  ip.parse(varargin{:});
+  ip=iparser(ip,'addOptional','lik', [], @isstruct);
+  ip=iparser(ip,'addParamValue','shape',1, @(x) isscalar(x) && x>0);
+  ip=iparser(ip,'addParamValue','shape_prior',prior_logunif(), @(x) isstruct(x) || isempty(x));
+  ip=iparser(ip,'parse',varargin{:});
   lik=ip.Results.lik;
   
   if isempty(lik)
@@ -438,21 +438,21 @@
   % and useful integration limits
   [tf,minf,maxf]=init_weibull_norm(yy,myy_i,sigm2_i,yc,r);
   % additionally get function handle for the derivative
-  td = @deriv;
+  td = @(f) deriv(f, yc, r, yy);
   
   % Integrate with quadgk
   [m_0, fhncnt] = quadgk(tf, minf, maxf);
   [g_i, fhncnt] = quadgk(@(f) td(f).*tf(f)./m_0, minf, maxf);
   g_i = g_i.*r;
+end
 
-  function g = deriv(f)
+  function g = deriv(f, yc, r, yy)
     if yc==0     
       g = -exp(-f).*yy.^r.*log(yy);
     else
       g = (1./r + log(yy)) - exp(-f).*yy.^r.*log(yy);
     end
   end
-end
 
 function [lpy, Ey, Vary] = lik_weibull_predy(lik, Ef, Varf, yt, zt)
 %LIK_WEIBULL_PREDY  Returns the predictive mean, variance and density of y
@@ -555,7 +555,7 @@
             - log(sigm2_i)/2 - log(2*pi)/2;
   
   % Create function handle for the function to be integrated
-  df = @weibull_norm;
+  df = @(f) weibull_norm(f, ldconst, yc, yy, r, myy_i, sigm2_i);
   % use log to avoid underflow, and derivates for faster search
   ld = @log_weibull_norm;
   ldg = @log_weibull_norm_g;
@@ -585,8 +585,8 @@
   niter=4;       % number of Newton iterations
   mindelta=1e-6; % tolerance in stopping Newton iterations
   for ni=1:niter
-    g=ldg(modef);
-    h=ldg2(modef);
+    g=ldg(modef, ldconst, yc, yy, r, myy_i, sigm2_i);
+    h=ldg2(modef, ldconst, yc, yy, r, myy_i, sigm2_i);
     delta=-g/h;
     modef=modef+delta;
     if abs(delta)<mindelta
@@ -602,15 +602,15 @@
   % integrand limits based on Gaussian approximation at mode
   minf=modef-8*modes;
   maxf=modef+8*modes;
-  modeld=ld(modef);
+  modeld=ld(modef, ldconst, yc, yy, r, myy_i, sigm2_i);
   iter=0;
   % check that density at end points is low enough
   lddiff=20; % min difference in log-density between mode and end-points
-  minld=ld(minf);
+  minld=ld(minf, ldconst, yc, yy, r, myy_i, sigm2_i);
   step=1;
   while minld>(modeld-lddiff)
     minf=minf-step*modes;
-    minld=ld(minf);
+    minld=ld(minf, ldconst, yc, yy, r, myy_i, sigm2_i);
     iter=iter+1;
     step=step*2;
     if iter>100
@@ -619,12 +619,12 @@
              'even after looking hard!'])
     end
   end
-  maxld=ld(maxf);
+  maxld=ld(maxf, ldconst, yc, yy, r, myy_i, sigm2_i);
   iter=0;
   step=1;
   while maxld>(modeld-lddiff)
     maxf=maxf+step*modes;
-    maxld=ld(maxf);
+    maxld=ld(maxf, ldconst, yc, yy, r, myy_i, sigm2_i);
     iter=iter+1;
     step=step*2;
     if iter>100
@@ -633,8 +633,9 @@
              'even after looking hard!'])
     end
   end
-  
-  function integrand = weibull_norm(f)
+end
+
+  function integrand = weibull_norm(f, ldconst, yc, yy, r, myy_i, sigm2_i)
   % Weibull * Gaussian
     if yc
       % observed
@@ -648,7 +649,7 @@
                     -0.5*(f-myy_i).^2./sigm2_i);
   end
 
-  function log_int = log_weibull_norm(f)
+  function log_int = log_weibull_norm(f, ldconst, yc, yy, r, myy_i, sigm2_i)
   % log(Weibull * Gaussian)
   % log_weibull_norm is used to avoid underflow when searching
   % integration interval
@@ -664,7 +665,7 @@
               -0.5*(f-myy_i).^2./sigm2_i;
   end
 
-  function g = log_weibull_norm_g(f)
+  function g = log_weibull_norm_g(f, ldconst, yc, yy, r, myy_i, sigm2_i)
   % d/df log(Weibull * Gaussian)
   % derivative of log_weibull_norm
     if yc
@@ -678,14 +679,13 @@
         + (myy_i - f)./sigm2_i;
   end
 
-  function g2 = log_weibull_norm_g2(f)
+  function g2 = log_weibull_norm_g2(f, ldconst, yc, yy, r, myy_i, sigm2_i)
   % d^2/df^2 log(Weibull * Gaussian)
   % second derivate of log_weibull_norm
     g2 = -exp(-f).*yy.^r ...
          -1/sigm2_i;
   end
 
-end
 
 function cdf = lik_weibull_predcdf(lik, Ef, Varf, yt)
 %LIK_WEIBULL_PREDCDF  Returns the predictive cdf evaluated at yt 
diff --git a/gp/lik_zinegbin.m b/gp/lik_zinegbin.m
index 010e399e..24c5f1c4 100644
--- a/gp/lik_zinegbin.m
+++ b/gp/lik_zinegbin.m
@@ -61,10 +61,10 @@
 
   ip=inputParser;
   ip.FunctionName = 'LIK_ZINEGBIN';
-  ip.addOptional('lik', [], @isstruct);
-  ip.addParamValue('disper',10, @(x) isscalar(x) && x>0);
-  ip.addParamValue('disper_prior',prior_logunif(), @(x) isstruct(x) || isempty(x));
-  ip.parse(varargin{:});
+  ip=iparser(ip,'addOptional','lik', [], @isstruct);
+  ip=iparser(ip,'addParamValue','disper',10, @(x) isscalar(x) && x>0);
+  ip=iparser(ip,'addParamValue','disper_prior',prior_logunif(), @(x) isstruct(x) || isempty(x));
+  ip=iparser(ip,'parse',varargin{:});
   lik=ip.Results.lik;
   
   if isempty(lik)
@@ -104,6 +104,7 @@
     lik.fh.invlink = @lik_zinegbin_invlink;
     lik.fh.recappend = @lik_zinegbin_recappend;
   end
+end
 
   function [w,s,h] = lik_zinegbin_pak(lik)
   %LIK_ZINEGBIN_PAK  Combine likelihood parameters into one vector.
@@ -609,6 +610,5 @@
         reclik.p.disper = feval(lik.p.disper.fh.recappend, reclik.p.disper, ri, lik.p.disper);
     end
   end
-end
 
 
diff --git a/gp/linuxCsource/dist_euclidean.c b/gp/linuxCsource/dist_euclidean.c
index 77912945..df3213e7 100644
--- a/gp/linuxCsource/dist_euclidean.c
+++ b/gp/linuxCsource/dist_euclidean.c
@@ -24,7 +24,12 @@
 #include <stdlib.h>
 #include <math.h>
 #include "mex.h"
+#ifndef HAVE_OCTAVE
 #include "matrix.h"
+#endif
+#ifndef true
+#define true 1
+#endif
 #define max(a,b) (((a) > (b)) ? (a) : (b))
 
 void mexFunction(const int nlhs, mxArray *plhs[],
diff --git a/gp/linuxCsource/ldlrowmodify.c b/gp/linuxCsource/ldlrowmodify.c
index e85e0eb8..23bb6274 100644
--- a/gp/linuxCsource/ldlrowmodify.c
+++ b/gp/linuxCsource/ldlrowmodify.c
@@ -31,7 +31,9 @@
 #include <stdlib.h>
 #include <math.h>
 #include "mex.h"
+#ifndef HAVE_OCTAVE
 #include "matrix.h"
+#endif
 #define max(a,b) (((a) > (b)) ? (a) : (b))
 #define min(a,b) (((a) < (b)) ? (a) : (b))
 
diff --git a/gp/linuxCsource/trcov.c b/gp/linuxCsource/trcov.c
index 611d2e59..4fb410b3 100644
--- a/gp/linuxCsource/trcov.c
+++ b/gp/linuxCsource/trcov.c
@@ -24,7 +24,9 @@
 #include <stdlib.h>
 #include <math.h>
 #include "mex.h"
+#ifndef HAVE_OCTAVE
 #include "matrix.h"
+#endif
 #define max(a, b) (((a) > (b)) ? (a) : (b))
 #define PI (3.141592653589793)
 void cumsum2(mwIndex *p, mwIndex *c, mwIndex n);
diff --git a/gp/metric_distancematrix.m b/gp/metric_distancematrix.m
index d2b928fa..742a5ef3 100644
--- a/gp/metric_distancematrix.m
+++ b/gp/metric_distancematrix.m
@@ -38,19 +38,19 @@
 
     ip=inputParser;
     ip.FunctionName = 'METRIC_DISTANCEMATRIX';
-    ip.addOptional('metric', [], @isstruct);
-%     ip.addParamValue('nin', [], @(x) isreal(x));
-    ip.addParamValue('components',[], @(x) isempty(x) || iscell(x));
-    ip.addParamValue('lengthScales',[] , @(x) isvector(x));
-    ip.addParamValue('Kstarstar',[], @(x) ismatrix(x));
-    ip.addParamValue('lengthScales_prior',prior_unif, ...
+    ip=iparser(ip,'addOptional','metric', [], @isstruct);
+%     ip=iparser(ip,'addParamValue','nin', [], @(x) isreal(x));
+    ip=iparser(ip,'addParamValue','components',[], @(x) isempty(x) || iscell(x));
+    ip=iparser(ip,'addParamValue','lengthScales',[] , @(x) isvector(x));
+    ip=iparser(ip,'addParamValue','Kstarstar',[], @(x) ismatrix(x));
+    ip=iparser(ip,'addParamValue','lengthScales_prior',prior_unif, ...
                    @(x) isstruct(x) || isempty(x));
-%     ip.addParamValue('Kff', [], @(x) ismatrix(x));
-%     ip.addParamValue('Kfu', [], @(x) ismatrix(x));
-%     ip.addParamValue('Kffstar', [], @(x) ismatrix(x));
-%     ip.addParamValue('Kfstaru', [], @(x) ismatrix(x));
-%     ip.addParamValue('X_u', [], @(x) ismatrix(x));
-    ip.parse(varargin{:});
+%     ip=iparser(ip,'addParamValue','Kff', [], @(x) ismatrix(x));
+%     ip=iparser(ip,'addParamValue','Kfu', [], @(x) ismatrix(x));
+%     ip=iparser(ip,'addParamValue','Kffstar', [], @(x) ismatrix(x));
+%     ip=iparser(ip,'addParamValue','Kfstaru', [], @(x) ismatrix(x));
+%     ip=iparser(ip,'addParamValue','X_u', [], @(x) ismatrix(x));
+    ip=iparser(ip,'parse',varargin{:});
     metric=ip.Results.metric;
     
     if isempty(metric)
diff --git a/gp/metric_euclidean.m b/gp/metric_euclidean.m
index 55d0bbae..178ca076 100644
--- a/gp/metric_euclidean.m
+++ b/gp/metric_euclidean.m
@@ -47,13 +47,13 @@
 
   ip=inputParser;
   ip.FunctionName = 'METRIC_EUCLIDEAN';
-  ip.addOptional('metric', [], @isstruct);
-  ip.addParamValue('components',[], @(x) isempty(x) || iscell(x));
-  ip.addParamValue('deltadist',[], @(x) isvector(x) || isempty(x));
-  ip.addParamValue('lengthScale',[], @(x) isvector(x) && all(x>0));
-  ip.addParamValue('lengthScale_prior',prior_unif, ...
+  ip=iparser(ip,'addOptional','metric', [], @isstruct);
+  ip=iparser(ip,'addParamValue','components',[], @(x) isempty(x) || iscell(x));
+  ip=iparser(ip,'addParamValue','deltadist',[], @(x) isvector(x) || isempty(x));
+  ip=iparser(ip,'addParamValue','lengthScale',[], @(x) isvector(x) && all(x>0));
+  ip=iparser(ip,'addParamValue','lengthScale_prior',prior_unif, ...
                    @(x) isstruct(x) || isempty(x));
-  ip.parse(varargin{:});
+  ip=iparser(ip,'parse',varargin{:});
   metric=ip.Results.metric;
 
   if isempty(metric)
diff --git a/gp/passgp.m b/gp/passgp.m
index 6f309a9f..7a417416 100644
--- a/gp/passgp.m
+++ b/gp/passgp.m
@@ -49,22 +49,22 @@
 
   ip=inputParser;
   ip.FunctionName = 'PASSGP';
-  ip.addRequired('gp',@(x) isstruct(x) || iscell(x));
-  ip.addRequired('x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))))
-  ip.addRequired('y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))))
-  ip.addParamValue('npass', 3, @(x) isscalar(x) && x > 0)
-  ip.addParamValue('ninit', 100, @(x) isscalar(x) && x > 0)
-  ip.addParamValue('nsub', 10, @(x) isscalar(x) && x > 0)
-  ip.addParamValue('pinc', 0.5, @(x) isscalar(x) && x > 0)
-  ip.addParamValue('pdel', 0.99, @(x) isscalar(x) && x > 0)
-  ip.addParamValue('pexc', 0.1, @(x) isscalar(x) && x > 0)
-  ip.addParamValue('opt', [], @(x) isstruct(x))
-  ip.addParamValue('inf_method', 'MAP', @(x) ismember(x, {'MAP', 'MCMC', 'IA'}));
-  ip.addParamValue('fixed', 'off', @(x) ismember(x,{'on','off'}))
-  ip.addParamValue('display', 'off', @(x) ismember(x,{'on','off','iter'}))
-  ip.addParamValue('optimn', 1', @(x) isscalar(x) && x > 0 && rem(10*x,2)==0)
-  ip.addParamValue('z', [], @(x) isreal(x) && all(isfinite(x(:))))
-  ip.parse(gp, x, y, varargin{:});
+  ip=iparser(ip,'addRequired','gp',@(x) isstruct(x) || iscell(x));
+  ip=iparser(ip,'addRequired','x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))));
+  ip=iparser(ip,'addRequired','y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))));
+  ip=iparser(ip,'addParamValue','npass', 3, @(x) isscalar(x) && x > 0);
+  ip=iparser(ip,'addParamValue','ninit', 100, @(x) isscalar(x) && x > 0);
+  ip=iparser(ip,'addParamValue','nsub', 10, @(x) isscalar(x) && x > 0);
+  ip=iparser(ip,'addParamValue','pinc', 0.5, @(x) isscalar(x) && x > 0);
+  ip=iparser(ip,'addParamValue','pdel', 0.99, @(x) isscalar(x) && x > 0);
+  ip=iparser(ip,'addParamValue','pexc', 0.1, @(x) isscalar(x) && x > 0);
+  ip=iparser(ip,'addParamValue','opt', [], @(x) isstruct(x));
+  ip=iparser(ip,'addParamValue','inf_method', 'MAP', @(x) ismember(x, {'MAP', 'MCMC', 'IA'}));
+  ip=iparser(ip,'addParamValue','fixed', 'off', @(x) ismember(x,{'on','off'}));
+  ip=iparser(ip,'addParamValue','display', 'off', @(x) ismember(x,{'on','off'}));
+  ip=iparser(ip,'addParamValue','optimn', 1', @(x) isscalar(x) && x > 0 && rem(10*x,2)==0);
+  ip=iparser(ip,'addParamValue','z', [], @(x) isreal(x) && all(isfinite(x(:))));
+  ip=iparser(ip,'parse',gp, x, y, varargin{:});
   opt=ip.Results.opt;
   fixed=ip.Results.fixed;
   options.z = ip.Results.z;
diff --git a/gp/pred_coxph.m b/gp/pred_coxph.m
index c2daa0db..0d588cbd 100644
--- a/gp/pred_coxph.m
+++ b/gp/pred_coxph.m
@@ -14,18 +14,18 @@
 
 ip=inputParser;
 ip.FunctionName = 'PRED_COXPH';
-ip.addRequired('gp',@(x) isstruct(x) || iscell(x));
-ip.addRequired('x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))))
-ip.addRequired('y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))))
-ip.addRequired('xt',  @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))))
-ip.addParamValue('yt', [], @(x) isreal(x) && all(isfinite(x(:))))
-ip.addParamValue('z', [], @(x) isreal(x) && all(isfinite(x(:))))
-ip.addParamValue('zt', [], @(x) isreal(x) && all(isfinite(x(:))))
-ip.addParamValue('predcf', [], @(x) isempty(x) || ...
-                 isvector(x) && isreal(x) && all(isfinite(x)&x>0))
-ip.addParamValue('tstind', [], @(x) isempty(x) || iscell(x) ||...
-                 (isvector(x) && isreal(x) && all(isfinite(x)&x>0)))
-ip.parse(gp, x, y, xt, varargin{:});
+ip=iparser(ip,'addRequired','gp',@(x) isstruct(x) || iscell(x));
+ip=iparser(ip,'addRequired','x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))));
+ip=iparser(ip,'addRequired','y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))));
+ip=iparser(ip,'addRequired','xt',  @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:))));
+ip=iparser(ip,'addParamValue','yt', [], @(x) isreal(x) && all(isfinite(x(:))));
+ip=iparser(ip,'addParamValue','z', [], @(x) isreal(x) && all(isfinite(x(:))));
+ip=iparser(ip,'addParamValue','zt', [], @(x) isreal(x) && all(isfinite(x(:))));
+ip=iparser(ip,'addParamValue','predcf', [], @(x) isempty(x) || ...
+                 isvector(x) && isreal(x) && all(isfinite(x)&x>0));
+ip=iparser(ip,'addParamValue','tstind', [], @(x) isempty(x) || iscell(x) ||...
+                 (isvector(x) && isreal(x) && all(isfinite(x)&x>0)));
+ip=iparser(ip,'parse',gp, x, y, xt, varargin{:});
 
 if iscell(gp)
   gp0=gp{1};
diff --git a/gp/scaled_hmc.m b/gp/scaled_hmc.m
index 39998ec1..8ab2cc5b 100644
--- a/gp/scaled_hmc.m
+++ b/gp/scaled_hmc.m
@@ -107,21 +107,34 @@
   % Transform the latent values
   switch gp.type
     case 'FULL'
-      getL(f, gp, x, y, u, z);             % Evaluate the help matrix for transformation
+      output=getL(f, gp, x, y, u, z);             % Evaluate the help matrix for transformation
+      L2=output.L2;      
       w = (L2\f)';                   % Rotate f towards prior
     case 'FIC'
-      getL(f, gp, x, y, u, z);          % Evaluate the help matrix for transformation
+      output=getL(f, gp, x, y, u, z);          % Evaluate the help matrix for transformation
+      Lp=output.Lp;
+      J=output.J;
+      U=output.U;
+      iJUU=output.iJUU;
       fs = f./Lp;                    % Rotate f towards prior
       w = fs + U*((J*U'-U')*fs);     
     case {'PIC' 'PIC_BLOCK'}
-      getL(f, gp, x, y, u, z);          % Evaluate the help matrix for transformation
+      output=getL(f, gp, x, y, u, z);          % Evaluate the help matrix for transformation
+      Lp=output.Lp;
+      U=output.U;
+      J=output.J;
+      iJUU=output.iJUU;
       fs=zeros(size(f));             % Rotate f towards prior
       for i=1:length(ind)
         fs(ind{i}) = Lp{i}\f(ind{i});
       end
       w = fs + U*((J*U'-U')*fs);
     case {'CS+FIC'}
-      getL(f, gp, x, y, u, z);          % Evaluate the help matrix for transformation
+      output=getL(f, gp, x, y, u, z);          % Evaluate the help matrix for transformation
+      Lp=output.Lp;
+      U=output.U;
+      J=output.J;
+      iJUU=output.iJUU;
       fs = Lp\f;                        % Rotate f towards prior
       w = fs + U*((J*U'-U')*fs);
     otherwise 
@@ -134,7 +147,7 @@
   hmc2('state',latent_rstate)
   rej = 0;
   for li=1:opt.repeat 
-    [w, energ, diagn] = hmc2(@f_e, w, opt, @f_g, gp, x, y, u, z);
+    [w, energ, diagn] = hmc2(@f_e, w, opt, @f_g, gp, x, y, u, z, output);
     w = w(end,:);
     % Find an optimal scaling during the first half of repeat
     if li<opt.repeat/2
@@ -172,16 +185,18 @@
   diagn.opt = opt;
   diagn.rej = rej;
   diagn.lvs = opt.stepadj;
-
-  function [g, gdata, gprior] = f_g(w, gp, x, y, u, z)
-  %F_G     Evaluate gradient function for transformed GP latent values 
-  %               
+end
+  function [g, gdata, gprior] = f_g(w, gp, x, y, u, z, output)
+    %F_G     Evaluate gradient function for transformed GP latent values
+    %
     
-  % Force f and E to be a column vector
+    % Force f and E to be a column vector
     w=w(:);
-    
+    mincut=-300;
     switch gp.type
       case 'FULL'
+        L2=output.L2;
+        Linv=output.Linv;        
         f = L2*w;
         f = max(f,mincut);
         gdata = - gp.lik.fh.llg(gp.lik, y, f, 'latent', z);
@@ -198,6 +213,11 @@
         
         g = (L2'*(gdata + gprior))';
       case 'FIC'
+        Lp=output.Lp;
+        U=output.U;
+        iJUU=output.iJUU;
+        Lav=output.Lav;
+        iLaKfuic=output.iLaKfuic;
         %        w(w<eps)=0;
         f = Lp.*(w + U*(iJUU*w));
         gdata = - gp.lik.fh.llg(gp.lik, y, f, 'latent', z);
@@ -208,6 +228,11 @@
         g = g + U*(iJUU*g);
         g = g';
       case {'PIC' 'PIC_BLOCK'}
+        Lp=output.Lp;
+        U=output.U;
+        iJUU=output.iJUU;
+        Lav=output.Lav;
+        iLaKfuic=output.iLaKfuic;
         w2= w + U*(iJUU*w);
         for i=1:length(ind)
           f(ind{i}) = Lp{i}*w2(ind{i});
@@ -226,6 +251,11 @@
         g = g + g*U*(iJUU);
         %g = g';
       case {'CS+FIC'}
+        Lp=output.Lp;
+        U=output.U;
+        iJUU=output.iJUU;
+        Lav=output.Lav;
+        iLaKfuic=output.iLaKfuic;
         w2= w + U*(iJUU*w);
         f = Lp*w2;
         f = max(f,mincut);
@@ -239,13 +269,16 @@
     end
   end
 
-  function [e, edata, eprior] = f_e(w, gp, x, y, u, z)
-  % F_E     Evaluate energy function for transformed GP latent values 
+  function [e, edata, eprior] = f_e(w, gp, x, y, u, z, output)
+    % F_E     Evaluate energy function for transformed GP latent values
     
-  % force f and E to be a column vector
+    % force f and E to be a column vector
     w=w(:);
+    mincut=-300;
     switch gp.type
       case 'FULL'
+        L2=output.L2;
+        Linv=output.Linv;
         f = L2*w;
         f = max(f,mincut);
         
@@ -260,11 +293,19 @@
         
         eprior=.5*sum(B.^2);
       case 'FIC' 
+        Lp=output.Lp;
+        U=output.U;
+        iJUU=output.iJUU;
+        Lav=output.Lav;
+        iLaKfuic=output.iLaKfuic;
         f = Lp.*(w + U*(iJUU*w));
         f = max(f,mincut);                
         B = f'*iLaKfuic;  % 1 x u
         eprior = 0.5*sum(f.^2./Lav)-0.5*sum(B.^2);
       case {'PIC' 'PIC_BLOCK'}
+        Lp=output.Lp;
+        U=output.U;
+        iJUU=output.iJUU;
         w2= w + U*(iJUU*w);
         for i=1:length(ind)
           f(ind{i}) = Lp{i}*w2(ind{i});
@@ -276,6 +317,10 @@
           eprior = eprior + 0.5*f(ind{i})'/Labl{i}*f(ind{i});
         end
       case {'CS+FIC'}
+        iJUU=output.iJUU;
+        U=output.U;
+        Labl=output.Labl;
+        iLaKfuic=output.iLaKfuic;
         w2= w + U*(iJUU*w);
         f = Lp*w2;
         f = max(f,mincut);
@@ -287,8 +332,9 @@
     e=edata + eprior;
   end
 
-  function getL(w, gp, x, y, u, z)
+  function output=getL(w, gp, x, y, u, z)
   % GETL        Evaluate the transformation matrix (or matrices)
+    n=size(y,1);
     
     if ~isfield(gp.lik, 'nondiagW')
       % Likelihoods with diagonal Hessian
@@ -312,6 +358,8 @@ function getL(w, gp, x, y, u, z)
           
           L2 = C/chol(diag(1./E) + C);
           L2 = chol(C - L2*L2')';
+          output.L2=L2;
+          output.Linv=Linv;
         case 'FIC'
           [Kv_ff, Cv_ff] = gp_trvar(gp, x);  % f x 1  vector
           K_fu = gp_cov(gp, x, u);           % f x u
@@ -348,6 +396,12 @@ function getL(w, gp, x, y, u, z)
           % J = diag(sqrt(2/(1+diag(S))));
           iJUU = J\U'-U';
           iJUU(abs(iJUU)<eps)=0;
+          output.Lav=Lav;
+          output.iLaKfuic=iLaKfuic;
+          output.Lp=Lp;
+          output.U=U;
+          output.J=J;
+          output.iJUU=iJUU;
         case {'PIC' 'PIC_BLOCK'}
           [Kv_ff, Cv_ff] = gp_trvar(gp, x);  % f x 1  vector
           K_fu = gp_cov(gp, x, u);           % f x u
@@ -391,6 +445,13 @@ function getL(w, gp, x, y, u, z)
           % J = diag(sqrt(2./(1+diag(S))));
           iJUU = J\U'-U';
           iJUU(abs(iJUU)<eps)=0;
+          
+          output.Labl=Labl;
+          output.iLaKfuic=iLaKfuic;
+          output.Lp=Lp;
+          output.U=U;
+          output.J=J;
+          output.iJUU=iJUU;
         case 'CS+FIC'
           
           % Evaluate the FIC part of the prior covariance
@@ -451,9 +512,22 @@ function getL(w, gp, x, y, u, z)
           
           iJUU = J\U'-U';
           iJUU(abs(iJUU)<eps)=0;
+          
+          output.Lav=Lav;
+          output.Labl=Labl;
+          output.iLaKfuic=iLaKfuic;
+          output.Lp=Lp;
+          output.U=U;
+          output.J=J;
+          output.iJUU=iJUU;
       end
     else
       % Likelihoods with non-diagonal Hessian
+      if isfield(gp, 'comp_cf' ) && ~isempty(gp.comp_cf)
+        nout=length(gp.comp_cf);
+      else
+        nout=size(y,2);
+      end
       switch gp.lik.type
         case {'LGP' 'LGPC'}
           K = gp_trcov(gp, x);
@@ -613,6 +687,8 @@ function getL(w, gp, x, y, u, z)
           % L2tmp=chol(inv(inv(K)+W),'lower');
           
       end
+      output.L2=L2;
+      output.Linv=Linv;
     end      
   end
-end
+
diff --git a/gp/surrogate_sls.m b/gp/surrogate_sls.m
index 81f8c27c..d64fa6fc 100644
--- a/gp/surrogate_sls.m
+++ b/gp/surrogate_sls.m
@@ -748,9 +748,11 @@
 %   [tmp,tmp, m2(ii,:)] = gp.lik.fh.tiltedMoments(gp.lik, yy, ii, C(ii,ii), 0, z);
 % end
 % S = diag(1./(1./m2 - 1./diag(C)));
-S = 10*eye(size(K));
+S = 10*eye(size(C));
 if isempty(eta) || isempty(g)
-  g = mvnrnd(f,S)';
+  LS=chol(S,'lower');
+  g = f + LS*randn(size(f,1),1);
+  %   g = mvnrnd(f,S)';
 end
 R = S-S*((S+K)\S);
 R = (R+R')./2;
diff --git a/matlab_install.m b/gpstuff_install.m
similarity index 92%
rename from matlab_install.m
rename to gpstuff_install.m
index 802dc9ac..1ccc809f 100644
--- a/matlab_install.m
+++ b/gpstuff_install.m
@@ -1,11 +1,12 @@
-function matlab_install(SuiteSparse_path)
-%  Matlab function to compile all the c-files to mex in the GPstuff toolbox.
+function gpstuff_install(SuiteSparse_path)
+%  Matlab/Octave function to compile all the c-files to mex in the GPstuff toolbox.
 %
 %  Some of the sparse GP functionalities in the toolbox require 
 %  SuiteSparse toolbox by Tim Davis:
 %    http://www.cise.ufl.edu/research/sparse/SuiteSparse/current/SuiteSparse/
 %
-%  This package includes the SuiteSparse version 3.4. 
+%  This package includes the SuiteSparse version 3.4. Note that Octave
+%  GPstuff doesn't work with SuiteSparse at the moment.
 % 
 %  * To install without SuiteSparse run matlab_install
 %  * To install with SuiteSparse run matlab_install('SuiteSparseOn')
@@ -18,9 +19,7 @@ function matlab_install(SuiteSparse_path)
 % This software is distributed under the GNU General Public 
 % License (version 3 or later); please refer to the file 
 % License.txt, included with the software, for details.
-
-
-    
+      
     if nargin < 1
         SuiteSparse_path = [];
         fprintf('\n The path to the SuiteSparse package is not provided. \n')
@@ -74,7 +73,6 @@ function matlab_install(SuiteSparse_path)
     S{4} = [PP '/mc']; 
     S{5} = [PP '/misc']; 
     S{6} = [PP '/optim']; 
-    S{7} = [PP '/tests']; 
     
     fprintf ('\n The following paths have been added.  You may wish to add them\n') ;
     fprintf ('permanently, using the MATLAB pathtool command or copying the below\n') ;
diff --git a/inputparser/@inputParser/addOptional.m b/inputparser/@inputParser/addOptional.m
new file mode 100644
index 00000000..b2863524
--- /dev/null
+++ b/inputparser/@inputParser/addOptional.m
@@ -0,0 +1,63 @@
+## Copyright (C) 2011 Carnë Draug <carandraug+dev@gmail.com>
+##
+## This program is free software; you can redistribute it and/or modify it under
+## the terms of the GNU General Public License as published by the Free Software
+## Foundation; either version 3 of the License, or (at your option) any later
+## version.
+##
+## This program is distributed in the hope that it will be useful, but WITHOUT
+## ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or
+## FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more
+## details.
+##
+## You should have received a copy of the GNU General Public License along with
+## this program; if not, see <http://www.gnu.org/licenses/>.
+
+## -*- texinfo -*-
+## @deftypefn {Function File} {@var{parser} =} addOptional (@var{parser}, @var{argname}, @var{default})
+## @deftypefnx {Function File} {@var{parser} =} @var{parser}.addOptional (@var{argname}, @var{default})
+## @deftypefnx {Function File} {@var{parser} =} addOptional (@var{parser}, @var{argname}, @var{default}, @var{validator})
+## @deftypefnx {Function File} {@var{parser} =} @var{parser}.addOptional (@var{argname}, @var{default}, @var{validator})
+## Add new optional argument to the object @var{parser} of the class inputParser
+## to implement an ordered arguments type of API 
+##
+## @var{argname} must be a string with the name of the new argument. The order
+## in which new arguments are added with @command{addOptional}, represents the
+## expected order of arguments.
+##
+## @var{default} will be the value used when the argument is not specified.
+##
+## @var{validator} is an optional anonymous function to validate the given values
+## for the argument with name @var{argname}. Alternatively, a function name
+## can be used.
+##
+## See @command{help inputParser} for examples.
+##
+## @emph{Note}: if a string argument does not validate, it will be considered a
+## ParamValue key. If an optional argument is not given a validator, anything
+## will be valid, and so any string will be considered will be the value of the
+## optional argument (in @sc{matlab}, if no validator is given and argument is
+## a string it will also be considered a ParamValue key).
+##
+## @seealso{inputParser, @@inputParser/addParamValue, @@inputParser/addSwitch,
+## @@inputParser/addParamValue, @@inputParser/addRequired, @@inputParser/parse}
+## @end deftypefn
+
+function inPar = addOptional (inPar, name, def, val)
+
+  ## check @inputParser/subsref for the actual code
+  if (nargin == 3)
+    inPar = subsref (inPar, substruct(
+                                      '.' , 'addOptional',
+                                      '()', {name, def}
+                                      ));
+  elseif (nargin == 4)
+    inPar = subsref (inPar, substruct(
+                                      '.' , 'addOptional',
+                                      '()', {name, def, val}
+                                      ));
+  else
+    print_usage;
+  endif
+
+endfunction
diff --git a/inputparser/@inputParser/addParamValue.m b/inputparser/@inputParser/addParamValue.m
new file mode 100644
index 00000000..95934f0b
--- /dev/null
+++ b/inputparser/@inputParser/addParamValue.m
@@ -0,0 +1,55 @@
+## Copyright (C) 2011 Carnë Draug <carandraug+dev@gmail.com>
+##
+## This program is free software; you can redistribute it and/or modify it under
+## the terms of the GNU General Public License as published by the Free Software
+## Foundation; either version 3 of the License, or (at your option) any later
+## version.
+##
+## This program is distributed in the hope that it will be useful, but WITHOUT
+## ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or
+## FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more
+## details.
+##
+## You should have received a copy of the GNU General Public License along with
+## this program; if not, see <http://www.gnu.org/licenses/>.
+
+## -*- texinfo -*-
+## @deftypefn {Function File} {@var{parser} =} addParamValue (@var{parser}, @var{argname}, @var{default})
+## @deftypefnx {Function File} {@var{parser} =} @var{parser}.addParamValue (@var{argname}, @var{default})
+## @deftypefnx {Function File} {@var{parser} =} addParamValue (@var{parser}, @var{argname}, @var{default}, @var{validator})
+## @deftypefnx {Function File} {@var{parser} =} @var{parser}.addParamValue (@var{argname}, @var{default}, @var{validator})
+## Add new parameter to the object @var{parser} of the class inputParser to implement
+## a name/value pair type of API.
+##
+## @var{argname} must be a string with the name of the new parameter.
+##
+## @var{default} will be the value used when the parameter is not specified.
+##
+## @var{validator} is an optional function handle to validate the given values
+## for the parameter with name @var{argname}. Alternatively, a function name
+## can be used.
+##
+## See @command{help inputParser} for examples.
+##
+## @seealso{inputParser, @@inputParser/addOptional, @@inputParser/addSwitch,
+## @@inputParser/addParamValue, @@inputParser/addRequired, @@inputParser/parse}
+## @end deftypefn
+
+function inPar = addParamValue (inPar, name, def, val)
+
+  ## check @inputParser/subsref for the actual code
+  if (nargin == 3)
+    inPar = subsref (inPar, substruct(
+                                      '.' , 'addParamValue',
+                                      '()', {name, def}
+                                      ));
+  elseif (nargin == 4)
+    inPar = subsref (inPar, substruct(
+                                      '.' , 'addParamValue',
+                                      '()', {name, def, val}
+                                      ));
+  else
+    print_usage;
+  endif
+
+endfunction
diff --git a/inputparser/@inputParser/addRequired.m b/inputparser/@inputParser/addRequired.m
new file mode 100644
index 00000000..bd8e7e5e
--- /dev/null
+++ b/inputparser/@inputParser/addRequired.m
@@ -0,0 +1,60 @@
+## Copyright (C) 2011 Carnë Draug <carandraug+dev@gmail.com>
+##
+## This program is free software; you can redistribute it and/or modify it under
+## the terms of the GNU General Public License as published by the Free Software
+## Foundation; either version 3 of the License, or (at your option) any later
+## version.
+##
+## This program is distributed in the hope that it will be useful, but WITHOUT
+## ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or
+## FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more
+## details.
+##
+## You should have received a copy of the GNU General Public License along with
+## this program; if not, see <http://www.gnu.org/licenses/>.
+
+## -*- texinfo -*-
+## @deftypefn {Function File} {@var{parser} =} addRequired (@var{parser}, @var{argname})
+## @deftypefnx {Function File} {@var{parser} =} @var{parser}.addRequired (@var{argname})
+## @deftypefnx {Function File} {@var{parser} =} addRequired (@var{parser}, @var{argname}, @var{validator})
+## @deftypefnx {Function File} {@var{parser} =} @var{parser}.addRequired (@var{argname}, @var{validator})
+## Add new mandatory argument to the object @var{parser} of inputParser class.
+##
+## This method belongs to the inputParser class and implements an ordered
+## arguments type of API.
+##
+## @var{argname} must be a string with the name of the new argument. The order
+## in which new arguments are added with @command{addrequired}, represents the
+## expected order of arguments.
+##
+## @var{validator} is an optional function handle to validate the given values
+## for the argument with name @var{argname}. Alternatively, a function name
+## can be used.
+##
+## See @command{help inputParser} for examples.
+##
+## @emph{Note}: this can be used together with the other type of arguments but
+## it must be the first (see @command{@@inputParser}).
+##
+## @seealso{inputParser, @@inputParser/addOptional, @@inputParser/addParamValue
+## @@inputParser/addParamValue, @@inputParser/addSwitch, @@inputParser/parse}
+## @end deftypefn
+
+function inPar = addRequired (inPar, name, val)
+
+  ## check @inputParser/subsref for the actual code
+  if (nargin == 2)
+    inPar = subsref (inPar, substruct(
+                                      '.' , 'addRequired',
+                                      '()', {name}
+                                      ));
+  elseif (nargin == 3)
+    inPar = subsref (inPar, substruct(
+                                      '.' , 'addRequired',
+                                      '()', {name, val}
+                                      ));
+  else
+    print_usage;
+  endif
+
+endfunction
diff --git a/inputparser/@inputParser/addSwitch.m b/inputparser/@inputParser/addSwitch.m
new file mode 100644
index 00000000..518ff80e
--- /dev/null
+++ b/inputparser/@inputParser/addSwitch.m
@@ -0,0 +1,48 @@
+## Copyright (C) 2011-2012 Carnë Draug <carandraug+dev@gmail.com>
+##
+## This program is free software; you can redistribute it and/or modify it under
+## the terms of the GNU General Public License as published by the Free Software
+## Foundation; either version 3 of the License, or (at your option) any later
+## version.
+##
+## This program is distributed in the hope that it will be useful, but WITHOUT
+## ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or
+## FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more
+## details.
+##
+## You should have received a copy of the GNU General Public License along with
+## this program; if not, see <http://www.gnu.org/licenses/>.
+
+## -*- texinfo -*-
+## @deftypefn {Function File} {@var{parser} =} addSwitch (@var{parser}, @var{argname})
+## @deftypefnx {Function File} {@var{parser} =} @var{parser}.addSwitch (@var{argname})
+## Add new switch type of argument to the object @var{parser} of inputParser class.
+##
+## This method belongs to the inputParser class and implements a switch
+## arguments type of API.
+##
+## @var{argname} must be a string with the name of the new argument. Arguments
+## of this type can be specified at the end, after @code{Required} and @code{Optional},
+## and mixed between the @code{ParamValue}. They default to false. If one of the
+## arguments supplied is a string like @var{argname}, then after parsing the value
+## of @var{parse}.Results.@var{argname} will be true.
+##
+## See @command{help inputParser} for examples.
+##
+## @seealso{inputParser, @@inputParser/addOptional, @@inputParser/addParamValue
+## @@inputParser/addParamValue, @@inputParser/addRequired, @@inputParser/parse}
+## @end deftypefn
+
+function inPar = addSwitch (inPar, name)
+
+  ## check @inputParser/subsref for the actual code
+  if (nargin == 2)
+    inPar = subsref (inPar, substruct(
+                                      '.' , 'addSwitch',
+                                      '()', {name}
+                                      ));
+  else
+    print_usage;
+  endif
+
+endfunction
diff --git a/inputparser/@inputParser/createCopy.m b/inputparser/@inputParser/createCopy.m
new file mode 100644
index 00000000..86e897a7
--- /dev/null
+++ b/inputparser/@inputParser/createCopy.m
@@ -0,0 +1,38 @@
+## Copyright (C) 2011 Carnë Draug <carandraug+dev@gmail.com>
+##
+## This program is free software; you can redistribute it and/or modify it under
+## the terms of the GNU General Public License as published by the Free Software
+## Foundation; either version 3 of the License, or (at your option) any later
+## version.
+##
+## This program is distributed in the hope that it will be useful, but WITHOUT
+## ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or
+## FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more
+## details.
+##
+## You should have received a copy of the GNU General Public License along with
+## this program; if not, see <http://www.gnu.org/licenses/>.
+
+## -*- texinfo -*-
+## @deftypefn {Function File} {@var{new_parser} =} createCopy (@var{parser})
+## Creates a copy @var{new_parser} of the object @var{parser} of the inputParser
+## class.
+##
+## @seealso{inputParser, @@inputParser/addOptional, @@inputParser/addParamValue
+## @@inputParser/addParamValue, @@inputParser/addRequired, @@inputParser/addSwitch,
+## @@inputParser/parse}
+## @end deftypefn
+
+function outPar = createCopy (inPar)
+
+  if ( nargin != 1 )
+    print_usage;
+  elseif ( !isa (inPar, 'inputParser') )
+    error ("object must be of the inputParser class but '%s' was used", class (inPar) );
+  endif
+
+  ## yes, it's a ridiculous function but exists for MatLab compatibility. In there
+  ## the inputParser class is a 'handle class' and this would just return a reference
+  outPar = inPar;
+
+endfunction
diff --git a/inputparser/@inputParser/display.m b/inputparser/@inputParser/display.m
new file mode 100644
index 00000000..230b8d0b
--- /dev/null
+++ b/inputparser/@inputParser/display.m
@@ -0,0 +1,58 @@
+## Copyright (C) 2011-2012 Carnë Draug <carandraug+dev@gmail.com>
+##
+## This program is free software; you can redistribute it and/or modify it under
+## the terms of the GNU General Public License as published by the Free Software
+## Foundation; either version 3 of the License, or (at your option) any later
+## version.
+##
+## This program is distributed in the hope that it will be useful, but WITHOUT
+## ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or
+## FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more
+## details.
+##
+## You should have received a copy of the GNU General Public License along with
+## this program; if not, see <http://www.gnu.org/licenses/>.
+
+function display (inPar)
+
+  if (inPar.FunctionName)
+    name = inPar.FunctionName(1:end-3);
+  else
+    name = "";
+  endif
+
+  required    = arg_list (inPar.Required);
+  optional    = arg_list (inPar.Optional);
+  paramvalue  = arg_list (inPar.ParamValue);
+  switches    = arg_list (inPar.Switch);
+
+  printf ("Input Parser object with:\n");
+  printf ("CaseSensitive: %s\n", binstr (inPar.CaseSensitive));
+  printf ("StructExpand : %s\n", binstr (inPar.StructExpand));
+  printf ("KeepUnmatched: %s\n", binstr (inPar.KeepUnmatched));
+  printf ("FunctionName : '%s'\n", name);
+  printf ("\n");
+  printf ("Required arguments  : %s\n", required);
+  printf ("Optional arguments  : %s\n", optional);
+  printf ("ParamValue arguments: %s\n", paramvalue);
+  printf ("Switch arguments    : %s\n", switches);
+
+endfunction
+
+function [str] = binstr (bin)
+  if (bin)
+    str = "true";
+  else
+    str = "false";
+  endif
+endfunction
+
+function [str] = arg_list (args)
+  str = strcat ("'", fieldnames (args), {"', "});
+  if (!isempty (str))
+    str = cstrcat (str{:});
+    str = str(1:end-2);     # remove the last comma and space
+  else
+    str = "";
+  endif
+endfunction
diff --git a/inputparser/@inputParser/inputParser.m b/inputparser/@inputParser/inputParser.m
new file mode 100644
index 00000000..19642795
--- /dev/null
+++ b/inputparser/@inputParser/inputParser.m
@@ -0,0 +1,228 @@
+## Copyright (C) 2011-2012 Carnë Draug <carandraug+dev@gmail.com>
+##
+## This program is free software; you can redistribute it and/or modify it under
+## the terms of the GNU General Public License as published by the Free Software
+## Foundation; either version 3 of the License, or (at your option) any later
+## version.
+##
+## This program is distributed in the hope that it will be useful, but WITHOUT
+## ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or
+## FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more
+## details.
+##
+## You should have received a copy of the GNU General Public License along with
+## this program; if not, see <http://www.gnu.org/licenses/>.
+##
+## Changelog:
+##   12.03.2013, Ville Tolvanen - Added option to give inputparser optional
+##      parameter & value pairs in structure. 
+
+## -*- texinfo -*-
+## @deftypefn {Function File} {@var{parser} =} inputParser ()
+## Create object @var{parser} of the inputParser class.
+##
+## This class is designed to allow easy parsing of function arguments. This class
+## supports four types of arguments:
+##
+## @enumerate
+## @item mandatory (see @command{@@inputParser/addRequired});
+## @item optional (see @command{@@inputParser/addOptional});
+## @item named (see @command{@@inputParser/addParamValue});
+## @item switch (see @command{@@inputParser/addSwitch}).
+## @end enumerate
+##
+## After defining the function API with this methods, the supplied arguments can
+## be parsed with the @command{@@inputParser/parse} method and the parsing results
+## accessed with the @command{Results} accessor.
+##
+## @deftypefnx {Accessor method} parser.Parameters
+## Return list of parameters name already defined.
+##
+## @deftypefnx {Accessor method} parser.Results
+## Return structure with argument names as fieldnames and corresponding values.
+##
+## @deftypefnx {Accessor method} parser.Unmatched
+## Return structure similar to @command{Results} for unmatched parameters. See
+## the @command{KeepUnmatched} property.
+##
+## @deftypefnx {Accessor method} parser.UsingDefaults
+## Return cell array with the names of arguments that are using default values.
+##
+## @deftypefnx {Class property} parser.CaseSensitive = @var{boolean}
+## Set whether matching of argument names should be case sensitive. Defaults to false.
+##
+## @deftypefnx {Class property} parser.FunctionName = @var{name}
+## Set function name to be used on error messages. Defauls to empty string.
+##
+## @deftypefnx {Class property} parser.KeepUnmatched = @var{boolean}
+## Set whether an error should be given for non-defined arguments. Defaults to
+## false. If set to true, the extra arguments can be accessed through
+## @code{Unmatched} after the @code{parse} method. Note that since @command{Switch}
+## and @command{ParamValue} arguments can be mixed, it is not possible to know
+## the unmatched type. If argument is found unmatched it is assumed to be of the
+## @command{ParamValue} type and it is expected to be followed by a value.
+##
+## @deftypefnx {Class property} parser.StructExpand = @var{boolean}
+## Set whether a structure can be passed to the function instead of parameter
+## value pairs. Defaults to true. Not implemented yet.
+##
+## The following example shows how to use this class:
+##
+## @example
+## @group
+## function check (pack, path, mat, varargin)
+##     p = inputParser;                             # create object
+##     p.FunctionName = "check";                    # set function name
+##     p = p.addRequired ("pack", @@ischar);         # create mandatory argument
+##
+##     p = p.addOptional ("path", pwd(), @@ischar);  # create optional argument
+##
+##     ## one can create a function handle to anonymous functions for validators
+##     val_mat = @@(x)isvector(x) && all( x <= 1) && all(x >= 0);
+##     p = p.addOptional ("mat", [0 0], @@val_mat);
+##
+##     ## create two ParamValue type of arguments
+##     val_type = @@(x) ischar(x) && any(strcmp(x, @{"linear", "quadratic"@});
+##     p = p.addParamValue ("type", "linear", @@val_type);
+##     val_verb = @@(x) ischar(x) && any(strcmp(x, @{"low", "medium", "high"@});
+##     p = p.addParamValue ("tolerance", "low", @@val_verb);
+##
+##     ## create a switch type of argument
+##     p = p.addSwitch ("verbose");
+##
+##     p = p.parse (pack, path, mat, varargin@{:@});
+##
+##     ## the rest of the function can access the input by accessing p.Results
+##     ## for example, to access the value of tolerance, use p.Results.tolerance
+## endfunction
+##
+## check ("mech");            # valid, will use defaults for other arguments
+## check ();                  # error since at least one argument is mandatory
+## check (1);                 # error since !ischar
+## check ("mech", "~/dev");   # valid, will use defaults for other arguments
+##
+## check ("mech", "~/dev", [0 1 0 0], "type", "linear");  # valid
+##
+## ## the following is also valid. Note how the Switch type of argument can be
+## ## mixed into or before the ParamValue (but still after Optional)
+## check ("mech", "~/dev", [0 1 0 0], "verbose", "tolerance", "high");
+##
+## ## the following returns an error since not all optional arguments, `path' and
+## ## `mat', were given before the named argument `type'.
+## check ("mech", "~/dev", "type", "linear");
+## @end group
+## @end example
+##
+## @emph{Note 1}: a function can have any mixture of the four API types but they
+## must appear in a specific order. @command{Required} arguments must be the very
+## first which can be followed by @command{Optional} arguments. Only the
+## @command{ParamValue} and @command{Switch} arguments can be mixed together but
+## must be at the end.
+##
+## @emph{Note 2}: if both @command{Optional} and @command{ParamValue} arguments
+## are mixed in a function API, once a string Optional argument fails to validate
+## against, it will be considered the end of @command{Optional} arguments and the
+## first key for a @command{ParamValue} and @command{Switch} arguments.
+##
+## @seealso{@@inputParser/addOptional, @@inputParser/addSwitch,
+## @@inputParser/addParamValue, @@inputParser/addRequired,
+## @@inputParser/createCopy, @@inputParser/parse}
+## @end deftypefn
+
+function inPar = inputParser
+
+  if (nargin != 0)
+    print_usage;
+  endif
+
+  inPar = struct;
+
+  ## these are not to be accessed by users. Each will have a field whose names
+  ## are the argnames which will also be a struct with fieldnames 'validator'
+  ## and 'default'
+  inPar.ParamValue    = struct;
+  inPar.Optional      = struct;
+  inPar.Required      = struct;
+  inPar.Switch        = struct;
+
+  ## this will be filled when the methodd parse is used and will be a struct whose
+  ## fieldnames are the argnames that return their value
+  inPar.Results       = struct;
+
+  ## an 1xN cell array with argnames. It is read only by the user and its order
+  ## showws the order that they were added to the object (which is the order they
+  ## will be expected)
+  inPar.Parameters    = {};
+
+  inPar.CaseSensitive = false;
+  inPar.FunctionName  = '';      # name of the function for the error message
+  inPar.KeepUnmatched = false;
+  inPar.StructExpand  = true;
+  inPar.Unmatched     = struct;
+  inPar.UsingDefaults = {};
+
+  inPar = class (inPar, 'inputParser');
+
+endfunction
+
+%!shared p, out
+%! p = inputParser;
+%! p = p.addRequired   ("req1", @(x) ischar (x));
+%! p = p.addOptional   ("op1", "val", @(x) ischar (x) && any (strcmp (x, {"val", "foo"})));
+%! p = p.addOptional   ("op2", 78, @(x) (x) > 50);
+%! p = p.addSwitch     ("verbose");
+%! p = p.addParamValue ("line", "tree", @(x) ischar (x) && any (strcmp (x, {"tree", "circle"})));
+%! ## check normal use, only required are given
+%! out = p.parse ("file");
+%!assert ({out.Results.req1, out.Results.op1, out.Results.op2, out.Results.verbose, out.Results.line}, 
+%!        {"file"          , "val"          , 78             , false              , "tree"});
+%!assert (out.UsingDefaults, {"op1", "op2", "verbose", "line"});
+%! ## check normal use, but give values different than defaults
+%! out = p.parse ("file", "foo", 80, "line", "circle", "verbose");
+%!assert ({out.Results.req1, out.Results.op1, out.Results.op2, out.Results.verbose, out.Results.line}, 
+%!        {"file"          , "foo"          , 80             , true              , "circle"});
+%! ## check optional is skipped and considered ParamValue if unvalidated string
+%! out = p.parse ("file", "line", "circle");
+%!assert ({out.Results.req1, out.Results.op1, out.Results.op2, out.Results.verbose, out.Results.line}, 
+%!        {"file"          , "val"          , 78             , false              , "circle"});
+%! ## check case insensitivity
+%! out = p.parse ("file", "foo", 80, "LiNE", "circle", "vERbOSe");
+%!assert ({out.Results.req1, out.Results.op1, out.Results.op2, out.Results.verbose, out.Results.line}, 
+%!        {"file"          , "foo"          , 80             , true              , "circle"});
+%! ## check KeepUnmatched
+%! p.KeepUnmatched = true;
+%! out = p.parse ("file", "foo", 80, "line", "circle", "verbose", "extra", 50);
+%!assert (out.Unmatched.extra, 50)
+%! ## check error when missing required
+%!error(p.parse())
+%! ## check error when given required do not validate
+%!error(p.parse(50))
+%! ## check error when given optional do not validate
+%!error(p.parse("file", "no-val"))
+%! ## check error when given ParamValue do not validate
+%!error(p.parse("file", "foo", 51, "line", "round"))
+
+## check alternative method (obj), ...) API
+%!shared p, out
+%! p = inputParser;
+%! p = addRequired   (p, "req1", @(x) ischar (x));
+%! p = addOptional   (p, "op1", "val", @(x) ischar (x) && any (strcmp (x, {"val", "foo"})));
+%! p = addOptional   (p, "op2", 78, @(x) (x) > 50);
+%! p = addSwitch     (p, "verbose");
+%! p = addParamValue (p, "line", "tree", @(x) ischar (x) && any (strcmp (x, {"tree", "circle"})));
+%! ## check normal use, only required are given
+%! out = parse (p, "file");
+%!assert ({out.Results.req1, out.Results.op1, out.Results.op2, out.Results.verbose, out.Results.line}, 
+%!        {"file"          , "val"          , 78             , false              , "tree"});
+%!assert (out.UsingDefaults, {"op1", "op2", "verbose", "line"});
+%! ## check normal use, but give values different than defaults
+%! out = parse (p, "file", "foo", 80, "line", "circle", "verbose");
+%!assert ({out.Results.req1, out.Results.op1, out.Results.op2, out.Results.verbose, out.Results.line}, 
+%!        {"file"          , "foo"          , 80             , true              , "circle"});
+
+## if we were matlab compatible...
+%!shared p, out
+%! p = inputParser;
+%! p = p.addOptional   ("op1", "val");
+%! p = p.addParamValue ("line", "tree");
+%!xtest assert (getfield (p.parse("line", "circle"), "Results"), struct ("op1", "val", "line", "circle"));
diff --git a/inputparser/@inputParser/parse.m b/inputparser/@inputParser/parse.m
new file mode 100644
index 00000000..2a7c407e
--- /dev/null
+++ b/inputparser/@inputParser/parse.m
@@ -0,0 +1,37 @@
+## Copyright (C) 2011 Carnë Draug <carandraug+dev@gmail.com>
+##
+## This program is free software; you can redistribute it and/or modify it under
+## the terms of the GNU General Public License as published by the Free Software
+## Foundation; either version 3 of the License, or (at your option) any later
+## version.
+##
+## This program is distributed in the hope that it will be useful, but WITHOUT
+## ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or
+## FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more
+## details.
+##
+## You should have received a copy of the GNU General Public License along with
+## this program; if not, see <http://www.gnu.org/licenses/>.
+
+## -*- texinfo -*-
+## @deftypefn {Function File} {@var{parser} =} parse (@var{parser}, @var{varargin})
+## @deftypefnx {Function File} {@var{parser} =} @var{parser}.parse (@var{varargin})
+## Parses and validates list of arguments according to object @var{parser} of the
+## class inputParser.
+##
+## After parsing, the results can be accessed with the @command{Results}
+## accessor. See @command{help inputParser} for a more complete description.
+##
+## @seealso{inputParser, @@inputParser/addOptional, @@inputParser/addParamValue
+## @@inputParser/addParamValue, @@inputParser/addRequired, @@inputParser/addSwitch}
+## @end deftypefn
+
+function inPar = parse (inPar, varargin)
+
+  ## check @inputParser/subsref for the actual code
+  inPar = subsref (inPar, substruct(
+                                    '.' , 'parse',
+                                    '()', varargin
+                                    ));
+
+endfunction
diff --git a/inputparser/@inputParser/subsasgn.m b/inputparser/@inputParser/subsasgn.m
new file mode 100644
index 00000000..01250e33
--- /dev/null
+++ b/inputparser/@inputParser/subsasgn.m
@@ -0,0 +1,37 @@
+## Copyright (C) 2011 Carnë Draug <carandraug+dev@gmail.com>
+##
+## This program is free software; you can redistribute it and/or modify it under
+## the terms of the GNU General Public License as published by the Free Software
+## Foundation; either version 3 of the License, or (at your option) any later
+## version.
+##
+## This program is distributed in the hope that it will be useful, but WITHOUT
+## ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or
+## FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more
+## details.
+##
+## You should have received a copy of the GNU General Public License along with
+## this program; if not, see <http://www.gnu.org/licenses/>.
+
+function inPar = subsasgn (inPar, idx, rhs)
+
+  if ( idx.type != '.' )
+    error ("invalid index for class %s", class (inPar) );
+  endif
+
+  switch idx.subs
+    case {'CaseSensitive', 'KeepUnmatched', 'StructExpand'}
+      if ( !islogical (rhs) )
+        error("Property '%s' of the class inputParser must be logical", idx.subs)
+      endif
+      inPar.(idx.subs) = rhs;
+    case 'FunctionName'
+      if ( !ischar (rhs) )
+        error("Property 'FunctionName' of the class inputParser can only be set to a string")
+      endif
+      inPar.(idx.subs) = sprintf("%s : ", rhs);
+    otherwise
+      error ("invalid index for assignment of class %s", class (inPar) );
+  endswitch
+
+endfunction
diff --git a/inputparser/@inputParser/subsref.m b/inputparser/@inputParser/subsref.m
new file mode 100644
index 00000000..aa35d57c
--- /dev/null
+++ b/inputparser/@inputParser/subsref.m
@@ -0,0 +1,355 @@
+## Copyright (C) 2011-2012 Carnë Draug <carandraug+dev@gmail.com>
+##
+## This program is free software; you can redistribute it and/or modify it under
+## the terms of the GNU General Public License as published by the Free Software
+## Foundation; either version 3 of the License, or (at your option) any later
+## version.
+##
+## This program is distributed in the hope that it will be useful, but WITHOUT
+## ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or
+## FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more
+## details.
+##
+## You should have received a copy of the GNU General Public License along with
+## this program; if not, see <http://www.gnu.org/licenses/>.
+##
+## Changelog:
+##   12.03.2013, Ville Tolvanen - Added option to give inputparser optional
+##      parameter & value pairs in structure. 
+
+
+function inPar = subsref (inPar, idx)
+
+  if ( !isa (inPar, 'inputParser') )
+    error ("object must be of the inputParser class but '%s' was used", class (inPar) );
+  elseif ( idx(1).type != '.' )
+    error ("invalid index for class %s", class (inPar) );
+  endif
+
+  ## the following at the end may allow to use the obj.method notation one day
+  ## jwe is very against this ugly hack
+  ## what would happen if the user has the obj inside a struct? Bad things!
+#  ori = inputname(1);
+#  assignin('caller', ori, inPar);
+
+  method = idx(1).subs;
+
+  switch method
+  case 'Results'
+    inPar = retrieve_results (inPar, idx)
+  case 'Parameters'
+    inPar = inPar.Parameters;
+  case 'parse'
+    inPar = parse_args (inPar, idx);
+  case 'Unmatched'
+  case 'UsingDefaults'
+  case {'addOptional', 'addParamValue', 'addRequired', 'addSwitch'}
+    inPar = check_methods (inPar, idx);
+  otherwise
+    error ("invalid index for reference of class %s", class (inPar) );
+  endswitch
+
+  ## TODO we should make inPar an object of the inputParser class again. At
+  ## least after running parse it becomes just a structure again. While that is
+  ## bad, at least allows for easy access to the Results and Unmatched fields
+  ## without extra coding.
+#  inPar = class (inPar, 'inputParser');
+
+endfunction
+
+function out = retrieve_results (inPar, idx)
+
+  if ( numel(idx) != 2 || idx(2).type != '.' )
+    print_usage ("@inputParser/Results");
+  endif
+
+  out = inPar.Results.(idx(2).subs);
+
+endfunction
+
+
+## when parsing options, here's the principle: Required options have to be the
+## first ones. They are followed by Optional if any. In the end come the
+## ParamValue mixed with Switch. Any other order makes no sense
+function inPar = parse_args (inPar, idx)
+  
+  ## syntax is inPar.parse (arguments)
+  if ( numel(idx) != 2 || idx(2).type != '()' )
+    print_usage ("@inputParser/parse");
+  endif
+
+  ## this makes it easier to read but may be memory instensive
+  args = idx(2).subs;
+  if ~isempty(args) && isstruct(args{1}) && isempty(fieldnames(args{1}))
+    args={};
+  end
+
+  ## make copy of ordered list of Parameters to keep the original intact and readable
+  inPar.copy = inPar.Parameters;
+  if ( numel (fieldnames (inPar.Required)) > numel (args) )
+    error("%sNot enough arguments", inPar.FunctionName);
+  endif
+
+  ## we take names out of 'copy' and values out of 'args', evaluate them and
+  ## store them into 'Results'
+  for i = 1 : numel (fieldnames (inPar.Required))
+    [name, inPar.copy] = shift (inPar.copy);
+    [value, args]      = shift (args);
+    if ( !feval (inPar.Required.(name).validator, value) )
+      error_invalid (inPar.FunctionName, name, inPar.Required.(name).validator);
+    endif
+    inPar.Results.(name) = value;
+  endfor
+
+  ## loop a maximum #times of the number of Optional, similarly to the required
+  ## loop. Once ran out of 'args', move their name into usingDefaults, place
+  ## their default values into 'Results', and break
+
+  ## because if an argument is string and does not validate, should be considered
+  ## a ParamValue key
+  found_possible_key = false;
+
+  for i = 1 : numel (fieldnames (inPar.Optional))
+    if ( !numel (args) || found_possible_key)
+      ## loops the number of Optional options minus the number of them already processed
+      for n = 1 : (numel (fieldnames (inPar.Optional)) - i + 1 )
+        [name, inPar.copy]   = shift (inPar.copy);
+        inPar.UsingDefaults  = push (inPar.UsingDefaults, name);
+        inPar.Results.(name) = inPar.Optional.(name).default;
+      endfor
+      break
+    endif
+    [name, inPar.copy] = shift (inPar.copy);
+    [value, args]      = shift (args);
+    if ( !feval (inPar.Optional.(name).validator, value) )
+      if (ischar (value) ) || (isstruct(value) && ~isempty(value))
+        ## maybe the other optional are not defined, this can be Paramvalue
+        ## place this one on defaults and go back to the top with note to clean loop
+        inPar.UsingDefaults  = push (inPar.UsingDefaults, name);
+        inPar.Results.(name) = inPar.Optional.(name).default;
+        found_possible_key   = true;
+        args = unshift (args, value);
+        continue
+      else
+        error_invalid (inPar.FunctionName, name, inPar.Optional.(name).validator);
+      endif
+    else
+      inPar.Results.(name) = value;
+    endif
+  endfor
+
+  ## loop a maximum #times of the number of ParamValue, taking pairs of keys and
+  ## values out 'args'. We no longer expect an order so we need the index in
+  ## 'copy' to remove it from there. Once ran out of 'args', move their name
+  ## into usingDefaults, place their default values into 'Results', and break
+  for i = 1 : (numel (fieldnames (inPar.ParamValue)) + numel (fieldnames (inPar.Switch)))
+    if numel(args)==1 && isstruct(args{1}) %&& isequal(fieldnames(args{1})(:), inPar.copy(:))
+      ## input parser given structure containing ParamValues, modify args
+      tmpargs=args{1};
+      args={};
+      names=fieldnames(tmpargs);
+      for j = 1 : numel(names)
+        args{1,end+1} = names{j};
+        args{1,end+1} = getfield(tmpargs, names{j});
+      endfor
+    endif
+    if ( numel (args) < 2)
+      ## loops the number of times left in 'copy' since these are the last type
+      for n = 1 : numel (inPar.copy)
+        [name, inPar.copy]   = shift (inPar.copy);
+        inPar.UsingDefaults  = push (inPar.UsingDefaults, name);
+        if (isfield (inPar.ParamValue, name))
+          inPar.Results.(name) = inPar.ParamValue.(name).default;
+        else
+          inPar.Results.(name) = inPar.Switch.(name).default;
+        endif
+      endfor
+      break
+    endif
+    [key, args] = shift (args);
+    if ( !ischar (key) )
+      %keyboard
+      error("%sParameter/Switch names must be strings", inPar.FunctionName);
+    endif
+    if (inPar.CaseSensitive)
+      index = find( strcmp(inPar.copy, key));
+    else
+      index = find( strcmpi(inPar.copy, key));
+    endif
+    ## we can't use isfield here to support case insensitive
+    if (any (strcmpi (fieldnames (inPar.Switch), key)))
+      value  = true;
+      method = "Switch";
+    else
+      ## then it must be a ParamValue (even if unmatched), shift its value
+      if (numel (args) < 1 )
+        error ("%sparameter '%s' does not have a value", inPar.FunctionName, key);
+      endif
+      [value, args] = shift (args);
+      method = "ParamValue";
+    endif
+
+    ## empty index means no match so either return error or move them into 'Unmatched'
+    if (!isempty (index))
+      [name, inPar.copy] = shift (inPar.copy, index);
+      if ( !feval (inPar.(method).(name).validator, value))
+        error_invalid (inPar.FunctionName, key, inPar.(method).(name).validator);
+      endif
+      ## we use the name shifted from 'copy' instead of the key from 'args' in case
+      ## the key is in the wrong case
+      inPar.Results.(name) = value;
+    elseif (isempty (index) && inPar.KeepUnmatched )
+      inPar.Unmatched.(key) = value;
+      i = i - 1; # this time didn't count, go back one
+    else
+      error ("%sargument '%s' did not match any valid parameter of the parser", inPar.FunctionName, key);
+    endif
+  endfor
+
+  ## if there's leftovers they must be unmatched. Note that some unmatched can
+  ## have already been processed in the ParamValue loop
+  if ( numel (args) && inPar.KeepUnmatched )
+    for i = 1 : ( numel(args) / 2 )
+      [key, args]           = shift (args);
+      [value, args]         = shift (args);
+      inPar.Unmatched.(key) = value;
+    endfor
+  elseif ( numel (args) ) && ~isempty(all(struct2cell(args{:})))
+    error("%sfound unmatched parameters at end of arguments list", inPar.FunctionName);
+  endif
+
+  ## remove copied field, keep it clean
+  inPar = rmfield (inPar, 'copy');
+
+endfunction
+
+
+function inPar = check_methods (inPar, idx)
+
+  ## this makes it easier to read but is more memory intensive?
+  method  = idx(1).subs;
+  args    = idx(2).subs;
+  func    = sprintf ("@inputParser/%s", method);
+
+  if ( idx(2).type != '()' )
+    print_usage (func);
+  endif
+  def_val      = @() true;
+  [name, args] = shift (args);
+  ## a validator is optional but that complicates handling all the parsing with
+  ## few functions and conditions. If not specified @() true will always return
+  ## true. Simply using true is not enough because if the argument is zero it
+  ## return false and if it's too large, takes up memory
+  switch method
+  case {'addOptional', 'addParamValue'}
+    if     ( numel (args) == 1 )
+      args{2} = def_val;
+    elseif ( numel (args) == 2 )
+      args{2} = validate_validator (args{2});
+    else
+      print_usage(func);
+    endif
+    [def, val] = args{:};
+  case {'addRequired'}
+    if     ( numel (args) == 0 )
+      val = def_val;
+    elseif ( numel (args) == 1 )
+      val = validate_validator (args{1});
+    else
+      print_usage(func);
+    endif
+    def = false;
+  case {'addSwitch'}
+    if ( numel (args) == 0 )
+      val = def_val;
+      def = false;
+    else
+      print_usage(func);
+    endif
+  otherwise
+    error ("invalid index for reference of class %s", class (inPar) );
+  endswitch
+
+  inPar = validate_args (method(4:end), inPar, name, val, def);
+
+endfunction
+
+## because we are nice we also support using the name of a function and not only
+## a function handle
+function val = validate_validator (val)
+  if ( ischar (val) )
+    val = str2func (val);
+  elseif ( !isa (val, 'function_handle') )
+    error ("validator must be a function handle or the name of a valid function");
+  end
+endfunction
+
+## to have a single function that handles them all, something must be done with
+## def value, because addRequire does not have those. That's why the order of
+## the last two args is different from the rest and why 'def' has a default value
+function inPar = validate_args (method, inPar, name, val, def = false)
+
+  if ( !strcmp (class (inPar), 'inputParser') )
+    error ("object must be of the inputParser class but '%s' was used", class (inPar) );
+  elseif ( !isvarname (name) )
+    error ("invalid variable name in argname");
+  endif
+
+  ## because the order arguments are specified are the order they are expected,
+  ## can't have ParamValue/Switch before Optional, and Optional before Required
+  n_optional  = numel (fieldnames (inPar.Optional));
+  n_params    = numel (fieldnames (inPar.ParamValue));
+  n_switch    = numel (fieldnames (inPar.Switch));
+  if     ( strcmp (method, 'Required') && ( n_optional || n_params || n_switch) )
+    error ("Can't specify 'Required' arguments after Optional, ParamValue or Switch");
+  elseif ( strcmp (method, 'Optional') && ( n_params || n_switch) )
+    error ("Can't specify 'Optional' arguments after ParamValue or Switch");
+  endif
+
+  ## even if CaseSensitive is turned on, we still shouldn't have two args with
+  ## the same. What if they decide to change in the middle of specifying them?
+  if ( any (strcmpi (inPar.Parameters, name)) )
+    error ("argname '%s' has already been specified", name);
+  else
+    inPar.Parameters = push (inPar.Parameters, name);
+    inPar.(method).(name).default = def;
+    inPar.(method).(name).validator = val;
+  endif
+
+  ## make sure that the given default value is actually valid
+  ## TODO make sure that when using the default, it's only validated once
+  ## allow empty default as in Matlab
+  if ( isa (val, 'function_handle') && !strcmpi (method, 'Required') && !isempty(def) && !feval (val, def) )
+    error ("default value for '%s' failed validation with '%s'", name, func2str (val) );
+  endif
+
+endfunction
+
+## this is just for consistency of error message
+function error_invalid (prefix, name, val)
+  error("%sargument '%s' failed validation %s", prefix, name, func2str (val));
+endfunction
+
+################################################################################
+## very auxiliary functions
+################################################################################
+
+function [out, in] = shift (in, idx = 1)
+  out     = in{idx};
+  in(idx) = [];
+endfunction
+
+function [in] = unshift (in, add)
+  if ( !iscell (add) )
+    add = {add};
+  endif
+  in (numel(add) + 1 : end + numel(add)) = in;
+  in (1:numel(add)) = add;
+endfunction
+
+function [in] = push (in, add)
+  if ( !iscell (add) )
+    add = {add};
+  endif
+  in( end+1 : end+numel(add) ) = add;
+endfunction
diff --git a/inputparser/@inputParser/subsref.m~ b/inputparser/@inputParser/subsref.m~
new file mode 100644
index 00000000..86bd7612
--- /dev/null
+++ b/inputparser/@inputParser/subsref.m~
@@ -0,0 +1,336 @@
+## Copyright (C) 2011-2012 Carnë Draug <carandraug+dev@gmail.com>
+##
+## This program is free software; you can redistribute it and/or modify it under
+## the terms of the GNU General Public License as published by the Free Software
+## Foundation; either version 3 of the License, or (at your option) any later
+## version.
+##
+## This program is distributed in the hope that it will be useful, but WITHOUT
+## ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or
+## FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more
+## details.
+##
+## You should have received a copy of the GNU General Public License along with
+## this program; if not, see <http://www.gnu.org/licenses/>.
+
+function inPar = subsref (inPar, idx)
+
+  if ( !isa (inPar, 'inputParser') )
+    error ("object must be of the inputParser class but '%s' was used", class (inPar) );
+  elseif ( idx(1).type != '.' )
+    error ("invalid index for class %s", class (inPar) );
+  endif
+
+  ## the following at the end may allow to use the obj.method notation one day
+  ## jwe is very against this ugly hack
+  ## what would happen if the user has the obj inside a struct? Bad things!
+#  ori = inputname(1);
+#  assignin('caller', ori, inPar);
+
+  method = idx(1).subs;
+
+  switch method
+  case 'Results'
+    inPar = retrieve_results (inPar, idx)
+  case 'Parameters'
+    inPar = inPar.Parameters;
+  case 'parse'
+    inPar = parse_args (inPar, idx);
+  case 'Unmatched'
+  case 'UsingDefaults'
+  case {'addOptional', 'addParamValue', 'addRequired', 'addSwitch'}
+    inPar = check_methods (inPar, idx);
+  otherwise
+    error ("invalid index for reference of class %s", class (inPar) );
+  endswitch
+
+  ## TODO we should make inPar an object of the inputParser class again. At
+  ## least after running parse it becomes just a structure again. While that is
+  ## bad, at least allows for easy access to the Results and Unmatched fields
+  ## without extra coding.
+#  inPar = class (inPar, 'inputParser');
+
+endfunction
+
+function out = retrieve_results (inPar, idx)
+
+  if ( numel(idx) != 2 || idx(2).type != '.' )
+    print_usage ("@inputParser/Results");
+  endif
+
+  out = inPar.Results.(idx(2).subs);
+
+endfunction
+
+
+## when parsing options, here's the principle: Required options have to be the
+## first ones. They are followed by Optional if any. In the end come the
+## ParamValue mixed with Switch. Any other order makes no sense
+function inPar = parse_args (inPar, idx)
+
+  ## syntax is inPar.parse (arguments)
+  if ( numel(idx) != 2 || idx(2).type != '()' )
+    print_usage ("@inputParser/parse");
+  endif
+
+  ## this makes it easier to read but may be memory instensive
+  args = idx(2).subs;
+
+  ## make copy of ordered list of Parameters to keep the original intact and readable
+  inPar.copy = inPar.Parameters;
+
+  if ( numel (fieldnames (inPar.Required)) > numel (args) )
+    error("%sNot enough arguments", inPar.FunctionName);
+  endif
+
+  ## we take names out of 'copy' and values out of 'args', evaluate them and
+  ## store them into 'Results'
+  for i = 1 : numel (fieldnames (inPar.Required))
+    [name, inPar.copy] = shift (inPar.copy);
+    [value, args]      = shift (args);
+    if ( !feval (inPar.Required.(name).validator, value) )
+      error_invalid (inPar.FunctionName, name, inPar.Required.(name).validator);
+    endif
+    inPar.Results.(name) = value;
+  endfor
+
+  ## loop a maximum #times of the number of Optional, similarly to the required
+  ## loop. Once ran out of 'args', move their name into usingDefaults, place
+  ## their default values into 'Results', and break
+
+  ## because if an argument is string and does not validate, should be considered
+  ## a ParamValue key
+  found_possible_key = false;
+
+  for i = 1 : numel (fieldnames (inPar.Optional))
+    if ( !numel (args) || found_possible_key)
+      ## loops the number of Optional options minus the number of them already processed
+      for n = 1 : (numel (fieldnames (inPar.Optional)) - i + 1 )
+        [name, inPar.copy]   = shift (inPar.copy);
+        inPar.UsingDefaults  = push (inPar.UsingDefaults, name);
+        inPar.Results.(name) = inPar.Optional.(name).default;
+      endfor
+      break
+    endif
+    [name, inPar.copy] = shift (inPar.copy);
+    [value, args]      = shift (args);
+    if ( !feval (inPar.Optional.(name).validator, value) )
+      if (ischar (value) )
+        ## maybe the other optional are not defined, this can be Paramvalue
+        ## place this one on defaults and go back to the top with note to clean loop
+        inPar.UsingDefaults  = push (inPar.UsingDefaults, name);
+        inPar.Results.(name) = inPar.Optional.(name).default;
+        found_possible_key   = true;
+        args = unshift (args, value);
+        continue
+      else
+        error_invalid (inPar.FunctionName, name, inPar.Optional.(name).validator);
+      endif
+    else
+      inPar.Results.(name) = value;
+    endif
+  endfor
+
+  ## loop a maximum #times of the number of ParamValue, taking pairs of keys and
+  ## values out 'args'. We no longer expect an order so we need the index in
+  ## 'copy' to remove it from there. Once ran out of 'args', move their name
+  ## into usingDefaults, place their default values into 'Results', and break
+  for i = 1 : (numel (fieldnames (inPar.ParamValue)) + numel (fieldnames (inPar.Switch)))
+    if ( !numel (args) )
+      ## loops the number of times left in 'copy' since these are the last type
+      for n = 1 : numel (inPar.copy)
+        [name, inPar.copy]   = shift (inPar.copy);
+        inPar.UsingDefaults  = push (inPar.UsingDefaults, name);
+        if (isfield (inPar.ParamValue, name))
+          inPar.Results.(name) = inPar.ParamValue.(name).default;
+        else
+          inPar.Results.(name) = inPar.Switch.(name).default;
+        endif
+      endfor
+      break
+    endif
+    [key, args] = shift (args);
+    if ( !ischar (key) )
+      error("%sParameter/Switch names must be strings", inPar.FunctionName);
+    endif
+    if (inPar.CaseSensitive)
+      index = find( strcmp(inPar.copy, key));
+    else
+      index = find( strcmpi(inPar.copy, key));
+    endif
+    ## we can't use isfield here to support case insensitive
+    if (any (strcmpi (fieldnames (inPar.Switch), key)))
+      value  = true;
+      method = "Switch";
+    else
+      ## then it must be a ParamValue (even if unmatched), shift its value
+      if (numel (args) < 1)
+        error ("%sparameter '%s' does not have a value", inPar.FunctionName, key);
+      endif
+      [value, args] = shift (args);
+      method = "ParamValue";
+    endif
+
+    ## empty index means no match so either return error or move them into 'Unmatched'
+    if (!isempty (index))
+      [name, inPar.copy] = shift (inPar.copy, index);
+      if ( !feval (inPar.(method).(name).validator, value))
+        error_invalid (inPar.FunctionName, key, inPar.(method).(name).validator);
+      endif
+      ## we use the name shifted from 'copy' instead of the key from 'args' in case
+      ## the key is in the wrong case
+      inPar.Results.(name) = value;
+    elseif (isempty (index) && inPar.KeepUnmatched )
+      inPar.Unmatched.(key) = value;
+      i = i - 1; # this time didn't count, go back one
+    else
+      error ("%sargument '%s' did not match any valid parameter of the parser", inPar.FunctionName, key);
+    endif
+  endfor
+
+  ## if there's leftovers they must be unmatched. Note that some unmatched can
+  ## have already been processed in the ParamValue loop
+  if ( numel (args) && inPar.KeepUnmatched )
+    for i = 1 : ( numel(args) / 2 )
+      [key, args]           = shift (args);
+      [value, args]         = shift (args);
+      inPar.Unmatched.(key) = value;
+    endfor
+  elseif ( numel (args) )
+    error("%sfound unmatched parameters at end of arguments list", inPar.FunctionName);
+  endif
+
+  ## remove copied field, keep it clean
+  inPar = rmfield (inPar, 'copy');
+
+endfunction
+
+
+function inPar = check_methods (inPar, idx)
+
+  ## this makes it easier to read but is more memory intensive?
+  method  = idx(1).subs;
+  args    = idx(2).subs;
+  func    = sprintf ("@inputParser/%s", method);
+
+  if ( idx(2).type != '()' )
+    print_usage (func);
+  endif
+  def_val      = @() true;
+  [name, args] = shift (args);
+  ## a validator is optional but that complicates handling all the parsing with
+  ## few functions and conditions. If not specified @() true will always return
+  ## true. Simply using true is not enough because if the argument is zero it
+  ## return false and if it's too large, takes up memory
+  switch method
+  case {'addOptional', 'addParamValue'}
+    if     ( numel (args) == 1 )
+      args{2} = def_val;
+    elseif ( numel (args) == 2 )
+      args{2} = validate_validator (args{2});
+    else
+      print_usage(func);
+    endif
+    [def, val] = args{:};
+  case {'addRequired'}
+    if     ( numel (args) == 0 )
+      val = def_val;
+    elseif ( numel (args) == 1 )
+      val = validate_validator (args{1});
+    else
+      print_usage(func);
+    endif
+    def = false;
+  case {'addSwitch'}
+    if ( numel (args) == 0 )
+      val = def_val;
+      def = false;
+    else
+      print_usage(func);
+    endif
+  otherwise
+    error ("invalid index for reference of class %s", class (inPar) );
+  endswitch
+
+  inPar = validate_args (method(4:end), inPar, name, val, def);
+
+endfunction
+
+## because we are nice we also support using the name of a function and not only
+## a function handle
+function val = validate_validator (val)
+  if ( ischar (val) )
+    val = str2func (val);
+  elseif ( !isa (val, 'function_handle') )
+    error ("validator must be a function handle or the name of a valid function");
+  end
+endfunction
+
+## to have a single function that handles them all, something must be done with
+## def value, because addRequire does not have those. That's why the order of
+## the last two args is different from the rest and why 'def' has a default value
+function inPar = validate_args (method, inPar, name, val, def = false)
+
+  if ( !strcmp (class (inPar), 'inputParser') )
+    error ("object must be of the inputParser class but '%s' was used", class (inPar) );
+  elseif ( !isvarname (name) )
+    error ("invalid variable name in argname");
+  endif
+
+  ## because the order arguments are specified are the order they are expected,
+  ## can't have ParamValue/Switch before Optional, and Optional before Required
+  n_optional  = numel (fieldnames (inPar.Optional));
+  n_params    = numel (fieldnames (inPar.ParamValue));
+  n_switch    = numel (fieldnames (inPar.Switch));
+  if     ( strcmp (method, 'Required') && ( n_optional || n_params || n_switch) )
+    error ("Can't specify 'Required' arguments after Optional, ParamValue or Switch");
+  elseif ( strcmp (method, 'Optional') && ( n_params || n_switch) )
+    error ("Can't specify 'Optional' arguments after ParamValue or Switch");
+  endif
+
+  ## even if CaseSensitive is turned on, we still shouldn't have two args with
+  ## the same. What if they decide to change in the middle of specifying them?
+  if ( any (strcmpi (inPar.Parameters, name)) )
+    error ("argname '%s' has already been specified", name);
+  else
+    inPar.Parameters = push (inPar.Parameters, name);
+    inPar.(method).(name).default = def;
+    inPar.(method).(name).validator = val;
+  endif
+
+  ## make sure that the given default value is actually valid
+  ## TODO make sure that when using the default, it's only validated once
+  if ( isa (val, 'function_handle') && !strcmpi (method, 'Required') && !feval (val, def) )
+    error ("default value for '%s' failed validation with '%s'", name, func2str (val) );
+  endif
+
+endfunction
+
+## this is just for consistency of error message
+function error_invalid (prefix, name, val)
+  error("%sargument '%s' failed validation %s", prefix, name, func2str (val));
+endfunction
+
+################################################################################
+## very auxiliary functions
+################################################################################
+
+function [out, in] = shift (in, idx = 1)
+  out     = in{idx};
+  in(idx) = [];
+endfunction
+
+function [in] = unshift (in, add)
+  if ( !iscell (add) )
+    add = {add};
+  endif
+  in (numel(add) + 1 : end + numel(add)) = in;
+  in (1:numel(add)) = add;
+endfunction
+
+function [in] = push (in, add)
+  if ( !iscell (add) )
+    add = {add};
+  endif
+  in( end+1 : end+numel(add) ) = add;
+endfunction
diff --git a/inputparser/iparser.m b/inputparser/iparser.m
new file mode 100644
index 00000000..baa48e57
--- /dev/null
+++ b/inputparser/iparser.m
@@ -0,0 +1,10 @@
+function ip = iparser(ip, action, varargin)
+
+if ~exist('OCTAVE_VERSION', 'builtin')
+  ip.(sprintf('%s', action))(varargin{:})
+else
+  ip=ip.(sprintf('%s', action))(varargin{:});
+end
+
+end
+
diff --git a/mc/hmc2.m b/mc/hmc2.m
index b6f44f31..d8c34c0a 100755
--- a/mc/hmc2.m
+++ b/mc/hmc2.m
@@ -329,7 +329,9 @@
 
 global HMC_MOM
 state.stream=setrandstream();
-state.streamstate = state.stream.State;
+if ~exist('OCTAVE_VERSION','builtin')
+  state.streamstate = state.stream.State;
+end
 state.mom = HMC_MOM;
 return
 
@@ -347,12 +349,14 @@ function set_state(f, x)
   if ~isstruct(x)
     error('Second argument to hmc must be number or state structure');
   end
-  if (~isfield(x, 'stream') | ~isfield(x, 'streamstate') ...
+  if (~isfield(x, 'stream') | (~exist('OCTAVE_VERSION','builtin') && ~isfield(x, 'streamstate')) ...
       | ~isfield(x, 'mom'))
     error('Second argument to hmc must contain correct fields')
   end
   setrandstream(x.stream);
-  x.State=x.streamstate;
+  if ~exist('OCTAVE_VERSION','builtin')
+    x.State=x.streamstate;
+  end
   HMC_MOM = x.mom;
 end
 return
diff --git a/mc/hmc2_opt.m b/mc/hmc2_opt.m
index 49115420..5274848d 100755
--- a/mc/hmc2_opt.m
+++ b/mc/hmc2_opt.m
@@ -54,19 +54,19 @@
 if ~isfield(opt,'checkgrad')
   opt.checkgrad=0;
 end
-if ~isfield(opt,'steps') | opt.steps < 1
+if ~isfield(opt,'steps') || opt.steps < 1
   opt.steps=10;
 end
-if ~isfield(opt,'nsamples') | opt.nsamples < 1
+if ~isfield(opt,'nsamples') || opt.nsamples < 1
   opt.nsamples=1;
 end
-if ~isfield(opt,'nomit') | opt.nomit < 0
+if ~isfield(opt,'nomit') || opt.nomit < 0
   opt.nomit=0;
 end
 if ~isfield(opt,'persistence')
   opt.persistence=0;
 end
-if ~isfield(opt,'decay') | opt.decay < 0 | opt.decay > 1
+if ~isfield(opt,'decay') || opt.decay < 0 || opt.decay > 1
   opt.decay=0.9;
 end
 if ~isfield(opt,'stepadj')
@@ -75,7 +75,7 @@
 if ~isfield(opt,'stepsf')
   opt.stepsf=[];
 end
-if ~isfield(opt,'window') | opt.window < 0
+if ~isfield(opt,'window') || opt.window < 0
   opt.window=1;
 end
 if opt.window > opt.steps
diff --git a/mc/mc_install.m b/mc/mc_install.m
index 64193724..ef0ffd4f 100644
--- a/mc/mc_install.m
+++ b/mc/mc_install.m
@@ -2,15 +2,31 @@
 
     
     if ispc   % A windows version of Matlab
+      if ~exist('OCTAVE_VERSION','builtin')        
         mex -O -output bbmean winCSource\bbmean.c
         mex -O -output resampres winCsource\resampres.c winCsource\binsgeq.c
         mex -O -output resampsim winCsource\resampsim.c winCsource\binsgeq.c
         mex -O -output resampstr winCsource\resampstr.c winCsource\binsgeq.c
         mex -O -output resampdet winCsource\resampdet.c winCsource\binsgeq.c        
+      else
+        mex --output bbmean.mex winCSource\bbmean.c
+        mex --output resampres.mex winCsource\resampres.c winCsource\binsgeq.c
+        mex --output resampsim.mex winCsource\resampsim.c winCsource\binsgeq.c
+        mex --output resampstr.mex winCsource\resampstr.c winCsource\binsgeq.c
+        mex --output resampdet.mex winCsource\resampdet.c winCsource\binsgeq.c 
+      end
     else
+      if ~exist('OCTAVE_VERSION','builtin')        
         mex -O -output bbmean linuxCsource/bbmean.c
         mex -O -output resampres linuxCsource/resampres.c linuxCsource/binsgeq.c
         mex -O -output resampsim linuxCsource/resampsim.c linuxCsource/binsgeq.c
         mex -O -output resampstr linuxCsource/resampstr.c linuxCsource/binsgeq.c
         mex -O -output resampdet linuxCsource/resampdet.c linuxCsource/binsgeq.c
+      else
+        mex --output bbmean.mex linuxCsource/bbmean.c
+        mex --output resampres.mex linuxCsource/resampres.c linuxCsource/binsgeq.c
+        mex --output resampsim.mex linuxCsource/resampsim.c linuxCsource/binsgeq.c
+        mex --output resampstr.mex linuxCsource/resampstr.c linuxCsource/binsgeq.c
+        mex --output resampdet.mex linuxCsource/resampdet.c linuxCsource/binsgeq.c
+      end
     end
diff --git a/misc/fixFunctionHandles.m b/misc/fixFunctionHandles.m
new file mode 100644
index 00000000..942ea692
--- /dev/null
+++ b/misc/fixFunctionHandles.m
@@ -0,0 +1,73 @@
+function gpo = fixFunctionHandles(gpo)
+%FIXFUNCTIONHANDLES
+%
+%  Syntax:
+%    gpo = fixFunctionHandles(gpo);
+%  
+%  Description:
+%    Fix the function handles that are broken when  
+%	 when octatve saves and loads gp structures
+%
+% Copyright (c) 2016 Markus Paasiniemi
+%
+% This software is distributed under the GNU General Public
+% License (version 3 or later); please refer to the file
+% License.txt, included with the software, for details.
+
+% list all subfields of the struct gpo
+gpFields = fieldnamesRec(gpo); 
+gpFields = cellstr(horzcat(repmat(['gpo.'],length(gpFields),1),char(gpFields)));
+
+%Fix all function handles for all execpt *_rec
+for k = 1:length(gpFields)
+    
+    %check if gpFields{k} is a function/subfunction handle
+    if strcmp(typeinfo(eval(gpFields{k})),'function handle')
+    	
+    	spl2 = strsplit(functions(eval(gpFields{k})).file,{'\' '/','.'});
+        fn = func2str(eval(gpFields{k}));
+        sfn = strsplit(gpFields{k},'.'){end};
+        
+        %check if gpFields{k} is a function handle	
+        if strcmp(spl2{end-1},fn) 
+        	eval(strcat(gpFields{k},'=@',fn,';'));
+        
+        elseif strcmp(strsplit(gpFields{k},'.'){end},'ne')
+        	eval(strcat(gpFields{k},'=',spl2{end-1},'("init",gpo).fh.ne;'));
+        
+        %gpFields{k} is a subfunction handle
+        elseif isfield(eval(spl2{end-1}).fh,sfn) 
+				eval(strcat(gpFields{k},'=',spl2{end-1},'.fh.',sfn,';'));            
+		
+		%gpFields{k} is a subfunction handle of type *_rec
+		else
+			eval(strcat(gpFields{k},'=',spl2{end-1},'.fh.recappend(',spl2{end-1},',',spl2{end-1},').fh.',sfn,';'));
+		end
+	end
+end
+
+
+% Helper function to recurse through 
+% a struct and find all subfields
+    function fNames = fieldnamesRec(s)
+        
+        fNames = fieldnames(s);
+        for i = 1:length(fNames)
+            
+            if isstruct(s.(fNames{i}))
+                newFields = fieldnamesRec(s.(fNames{i}));
+                fNames(end+1:end+length(newFields)) = cellstr(horzcat(repmat([fNames{i} '.'], length(newFields), 1),char(newFields)));
+            
+            elseif iscell(s.(fNames{i}))
+                for j = 1:length(s.(fNames{i}))
+                
+                    if isstruct(s.(fNames{i}){j})
+                        newFields = fieldnamesRec(s.(fNames{i}){j});
+                        fNames(end+1:end+length(newFields)) = cellstr(horzcat(repmat([fNames{i} '{' num2str(j) '}.'],length(newFields), 1),char(newFields)));
+                    end
+                end
+            end
+        end
+    end
+
+end
\ No newline at end of file
diff --git a/misc/psislw.m b/misc/psislw.m
index 982f9858..24b47317 100644
--- a/misc/psislw.m
+++ b/misc/psislw.m
@@ -80,3 +80,24 @@
 %     warning('Following indeces have estimated tail index k>1');
 %     disp(ksi)
 % end
+
+end
+
+function x = gpinv(p,k,sigma)
+x = NaN(size(p));
+if sigma <= 0
+  return
+end
+ok = (p>0) & (p<1);
+if abs(k) < eps
+  x(ok) = -log1p(-p(ok));
+else
+  x(ok) = expm1(-k * log1p(-p(ok))) ./ k;
+end
+x = sigma*x;
+if ~all(ok)
+  x(p==0) = 0;
+  x(p==1 & k>=0) = Inf;
+  x(p==1 & k<0) = -sigma/k;
+end
+end
diff --git a/misc/setrandstream.m b/misc/setrandstream.m
index 859ffb9a..05d9fe04 100644
--- a/misc/setrandstream.m
+++ b/misc/setrandstream.m
@@ -35,45 +35,64 @@
 % License (version 3 or later); please refer to the file
 % License.txt, included with the software, for details.
   
-if nargin>=1 && ~isnumeric(seed) 
-  % First argument is random stream object
-  stream=seed;
-  if str2double(regexprep(version('-release'), '[a-c]', '')) < 2012
-    prevstream = RandStream.setDefaultStream(stream);
+p=which('randn');
+p=strfind(p, 'RcppOctave');
+% Check if RcppOctave is active as the seed initialization does not work
+% in RcppOctave
+if isempty(p)
+  if nargin>=1 && ~isnumeric(seed)
+    % First argument is random stream object
+    stream=seed;
+    if str2double(regexprep(version('-release'), '[a-c]', '')) < 2012
+      prevstream = RandStream.setDefaultStream(stream);
+    else
+      prevstream=rng(stream);
+    end
   else
-    prevstream=rng(stream);
-  end
-else
-  if nargin<2
-    if nargin<1
-      % Get current random stream
-      if str2double(regexprep(version('-release'), '[a-c]', '')) < 2012
-        prevstream = RandStream.getDefaultStream();
+    if nargin<2
+      if nargin<1
+        % Get current random stream
+        if exist('OCTAVE_VERSION', 'builtin')
+          prevstream(1) = randn('seed');
+          prevstream(2) = rand('seed');
+        elseif str2double(regexprep(version('-release'), '[a-c]', '')) < 2012
+          prevstream = RandStream.getDefaultStream();
+        else
+          prevstream = rng;
+        end
+        return
       else
-        prevstream = rng;
+        % If stream is not provided, use Mersenne Twister
+        stream='mt19937ar';
       end
-      return
-    else
-      % If stream is not provided, use Mersenne Twister
-      stream='mt19937ar';
     end
-  end
-  if isempty(seed)
-    % Default seed
-    seed=0;
-  end
-  if str2double(regexprep(version('-release'), '[a-c]', '')) < 2012
-    if ischar(stream)
-      stream = RandStream(stream,'Seed',seed);
+    if isempty(seed)
+      % Default seed
+      seed=0;
     end
-    prevstream = RandStream.setDefaultStream(stream);
-  else
-    if ischar(stream)
-      prevstream = rng(seed,stream);
+    if exist('OCTAVE_VERSION', 'builtin')
+      prevstream(1) = randn('seed');
+      prevstream(2) = rand('seed');
+      if length(seed)==1
+        seed(2)=seed(1);
+      end
+      randn('seed', seed(1));
+      rand('seed', seed(2));
+    elseif str2double(regexprep(version('-release'), '[a-c]', '')) < 2012
+      if ischar(stream)
+        stream = RandStream(stream,'Seed',seed);
+      end
+      prevstream = RandStream.setDefaultStream(stream);
     else
-      prevstream=rng(stream);
+      if ischar(stream)
+        prevstream = rng(seed,stream);
+      else
+        prevstream=rng(stream);
+      end
     end
   end
+else
+  prevstream=[];
 end
 
 end
diff --git a/octave_compat/crosstab.m b/octave_compat/crosstab.m
new file mode 100644
index 00000000..085a4ed3
--- /dev/null
+++ b/octave_compat/crosstab.m
@@ -0,0 +1,14 @@
+function y = crosstab(x)
+% Count numer of occurences of elements of x in x
+
+ux=unique(x);
+y=zeros(size(ux));
+for i=1:length(ux)
+  y(i) = sum(x==ux(i));
+end
+if size(y,1)==1
+  y=y';
+end
+
+end
+
diff --git a/octave_compat/fcnchk.m b/octave_compat/fcnchk.m
new file mode 100644
index 00000000..ffcb2a08
--- /dev/null
+++ b/octave_compat/fcnchk.m
@@ -0,0 +1,5 @@
+function [f,m] = fcnchk(x,n)
+f=x;
+m='';
+end
+
diff --git a/octave_compat/mnpdf.m b/octave_compat/mnpdf.m
new file mode 100644
index 00000000..fb1920e4
--- /dev/null
+++ b/octave_compat/mnpdf.m
@@ -0,0 +1,134 @@
+## Copyright (C) 2012  Arno Onken
+##
+## This program is free software: you can redistribute it and/or modify
+## it under the terms of the GNU General Public License as published by
+## the Free Software Foundation, either version 3 of the License, or
+## (at your option) any later version.
+##
+## This program is distributed in the hope that it will be useful,
+## but WITHOUT ANY WARRANTY; without even the implied warranty of
+## MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+## GNU General Public License for more details.
+##
+## You should have received a copy of the GNU General Public License
+## along with this program.  If not, see <http://www.gnu.org/licenses/>.
+
+## -*- texinfo -*-
+## @deftypefn {Function File} {@var{y} =} mnpdf (@var{x}, @var{p})
+## Compute the probability density function of the multinomial distribution.
+##
+## @subheading Arguments
+##
+## @itemize @bullet
+## @item
+## @var{x} is vector with a single sample of a multinomial distribution with
+## parameter @var{p} or a matrix of random samples from multinomial
+## distributions. In the latter case, each row of @var{x} is a sample from a
+## multinomial distribution with the corresponding row of @var{p} being its
+## parameter.
+##
+## @item
+## @var{p} is a vector with the probabilities of the categories or a matrix
+## with each row containing the probabilities of a multinomial sample.
+## @end itemize
+##
+## @subheading Return values
+##
+## @itemize @bullet
+## @item
+## @var{y} is a vector of probabilites of the random samples @var{x} from the
+## multinomial distribution with corresponding parameter @var{p}. The parameter
+## @var{n} of the multinomial distribution is the sum of the elements of each
+## row of @var{x}. The length of @var{y} is the number of columns of @var{x}.
+## If a row of @var{p} does not sum to @code{1}, then the corresponding element
+## of @var{y} will be @code{NaN}.
+## @end itemize
+##
+## @subheading Examples
+##
+## @example
+## @group
+## x = [1, 4, 2];
+## p = [0.2, 0.5, 0.3];
+## y = mnpdf (x, p);
+## @end group
+##
+## @group
+## x = [1, 4, 2; 1, 0, 9];
+## p = [0.2, 0.5, 0.3; 0.1, 0.1, 0.8];
+## y = mnpdf (x, p);
+## @end group
+## @end example
+##
+## @subheading References
+##
+## @enumerate
+## @item
+## Wendy L. Martinez and Angel R. Martinez. @cite{Computational Statistics
+## Handbook with MATLAB}. Appendix E, pages 547-557, Chapman & Hall/CRC, 2001.
+##
+## @item
+## Merran Evans, Nicholas Hastings and Brian Peacock. @cite{Statistical
+## Distributions}. pages 134-136, Wiley, New York, third edition, 2000.
+## @end enumerate
+## @end deftypefn
+
+## Author: Arno Onken <asnelt@asnelt.org>
+## Description: PDF of the multinomial distribution
+
+function y = mnpdf (x, p)
+
+  # Check arguments
+  if (nargin != 2)
+    print_usage ();
+  endif
+
+  if (! ismatrix (x) || any (x(:) < 0 | round (x(:) != x(:))))
+    error ("mnpdf: x must be a matrix of non-negative integer values");
+  endif
+  if (! ismatrix (p) || any (p(:) < 0))
+    error ("mnpdf: p must be a non-empty matrix with rows of probabilities");
+  endif
+
+  # Adjust input sizes
+  if (! isvector (x) || ! isvector (p))
+    if (isvector (x))
+      x = x(:)';
+    endif
+    if (isvector (p))
+      p = p(:)';
+    endif
+    if (size (x, 1) == 1 && size (p, 1) > 1)
+      x = repmat (x, size (p, 1), 1);
+    elseif (size (x, 1) > 1 && size (p, 1) == 1)
+      p = repmat (p, size (x, 1), 1);
+    endif
+  endif
+  # Continue argument check
+  if (any (size (x) != size (p)))
+    error ("mnpdf: x and p must have compatible sizes");
+  endif
+
+  # Count total number of elements of each multinomial sample
+  n = sum (x, 2);
+  # Compute probability density function of the multinomial distribution
+  t = x .* log (p);
+  t(x == 0) = 0;
+  y = exp (gammaln (n+1) - sum (gammaln (x+1), 2) + sum (t, 2));
+  # Set invalid rows to NaN
+  k = (abs (sum (p, 2) - 1) > 1e-6);
+  y(k) = NaN;
+
+endfunction
+
+%!test
+%! x = [1, 4, 2];
+%! p = [0.2, 0.5, 0.3];
+%! y = mnpdf (x, p);
+%! assert (y, 0.11812, 0.001);
+
+%!test
+%! x = [1, 4, 2; 1, 0, 9];
+%! p = [0.2, 0.5, 0.3; 0.1, 0.1, 0.8];
+%! y = mnpdf (x, p);
+%! assert (y, [0.11812; 0.13422], 0.001);
diff --git a/octave_compat/mnrnd.m b/octave_compat/mnrnd.m
new file mode 100644
index 00000000..1332faff
--- /dev/null
+++ b/octave_compat/mnrnd.m
@@ -0,0 +1,184 @@
+## Copyright (C) 2012  Arno Onken
+##
+## This program is free software: you can redistribute it and/or modify
+## it under the terms of the GNU General Public License as published by
+## the Free Software Foundation, either version 3 of the License, or
+## (at your option) any later version.
+##
+## This program is distributed in the hope that it will be useful,
+## but WITHOUT ANY WARRANTY; without even the implied warranty of
+## MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+## GNU General Public License for more details.
+##
+## You should have received a copy of the GNU General Public License
+## along with this program.  If not, see <http://www.gnu.org/licenses/>.
+
+## -*- texinfo -*-
+## @deftypefn {Function File} {@var{x} =} mnrnd (@var{n}, @var{p})
+## @deftypefnx {Function File} {@var{x} =} mnrnd (@var{n}, @var{p}, @var{s})
+## Generate random samples from the multinomial distribution.
+##
+## @subheading Arguments
+##
+## @itemize @bullet
+## @item
+## @var{n} is the first parameter of the multinomial distribution. @var{n} can
+## be scalar or a vector containing the number of trials of each multinomial
+## sample. The elements of @var{n} must be non-negative integers.
+##
+## @item
+## @var{p} is the second parameter of the multinomial distribution. @var{p} can
+## be a vector with the probabilities of the categories or a matrix with each
+## row containing the probabilities of a multinomial sample. If @var{p} has
+## more than one row and @var{n} is non-scalar, then the number of rows of
+## @var{p} must match the number of elements of @var{n}.
+##
+## @item
+## @var{s} is the number of multinomial samples to be generated. @var{s} must
+## be a non-negative integer. If @var{s} is specified, then @var{n} must be
+## scalar and @var{p} must be a vector.
+## @end itemize
+##
+## @subheading Return values
+##
+## @itemize @bullet
+## @item
+## @var{x} is a matrix of random samples from the multinomial distribution with
+## corresponding parameters @var{n} and @var{p}. Each row corresponds to one
+## multinomial sample. The number of columns, therefore, corresponds to the
+## number of columns of @var{p}. If @var{s} is not specified, then the number
+## of rows of @var{x} is the maximum of the number of elements of @var{n} and
+## the number of rows of @var{p}. If a row of @var{p} does not sum to @code{1},
+## then the corresponding row of @var{x} will contain only @code{NaN} values.
+## @end itemize
+##
+## @subheading Examples
+##
+## @example
+## @group
+## n = 10;
+## p = [0.2, 0.5, 0.3];
+## x = mnrnd (n, p);
+## @end group
+##
+## @group
+## n = 10 * ones (3, 1);
+## p = [0.2, 0.5, 0.3];
+## x = mnrnd (n, p);
+## @end group
+##
+## @group
+## n = (1:2)';
+## p = [0.2, 0.5, 0.3; 0.1, 0.1, 0.8];
+## x = mnrnd (n, p);
+## @end group
+## @end example
+##
+## @subheading References
+##
+## @enumerate
+## @item
+## Wendy L. Martinez and Angel R. Martinez. @cite{Computational Statistics
+## Handbook with MATLAB}. Appendix E, pages 547-557, Chapman & Hall/CRC, 2001.
+##
+## @item
+## Merran Evans, Nicholas Hastings and Brian Peacock. @cite{Statistical
+## Distributions}. pages 134-136, Wiley, New York, third edition, 2000.
+## @end enumerate
+## @end deftypefn
+
+## Author: Arno Onken <asnelt@asnelt.org>
+## Description: Random samples from the multinomial distribution
+
+function x = mnrnd (n, p, s)
+
+  # Check arguments
+  if (nargin == 3)
+    if (! isscalar (n) || n < 0 || round (n) != n)
+      error ("mnrnd: n must be a non-negative integer");
+    endif
+    if (! isvector (p) || any (p < 0 | p > 1))
+      error ("mnrnd: p must be a vector of probabilities");
+    endif
+    if (! isscalar (s) || s < 0 || round (s) != s)
+      error ("mnrnd: s must be a non-negative integer");
+    endif
+  elseif (nargin == 2)
+    if (isvector (p) && size (p, 1) > 1)
+      p = p';
+    endif
+    if (! isvector (n) || any (n < 0 | round (n) != n) || size (n, 2) > 1)
+      error ("mnrnd: n must be a non-negative integer column vector");
+    endif
+    if (! ismatrix (p) || isempty (p) || any (p < 0 | p > 1))
+      error ("mnrnd: p must be a non-empty matrix with rows of probabilities");
+    endif
+    if (! isscalar (n) && size (p, 1) > 1 && length (n) != size (p, 1))
+      error ("mnrnd: the length of n must match the number of rows of p");
+    endif
+  else
+    print_usage ();
+  endif
+
+  # Adjust input sizes
+  if (nargin == 3)
+    n = n * ones (s, 1);
+    p = repmat (p(:)', s, 1);
+  elseif (nargin == 2)
+    if (isscalar (n) && size (p, 1) > 1)
+      n = n * ones (size (p, 1), 1);
+    elseif (size (p, 1) == 1)
+      p = repmat (p, length (n), 1);
+    endif
+  endif
+  sz = size (p);
+
+  # Upper bounds of categories
+  ub = cumsum (p, 2);
+  # Make sure that the greatest upper bound is 1
+  gub = ub(:, end);
+  ub(:, end) = 1;
+  # Lower bounds of categories
+  lb = [zeros(sz(1), 1) ub(:, 1:(end-1))];
+
+  # Draw multinomial samples
+  x = zeros (sz);
+  for i = 1:sz(1)
+    # Draw uniform random numbers
+    r = repmat (rand (n(i), 1), 1, sz(2));
+    # Compare the random numbers of r to the cumulated probabilities of p and
+    # count the number of samples for each category
+    x(i, :) =  sum (r <= repmat (ub(i, :), n(i), 1) & r > repmat (lb(i, :), n(i), 1), 1);
+  endfor
+  # Set invalid rows to NaN
+  k = (abs (gub - 1) > 1e-6);
+  x(k, :) = NaN;
+
+endfunction
+
+%!test
+%! n = 10;
+%! p = [0.2, 0.5, 0.3];
+%! x = mnrnd (n, p);
+%! assert (size (x), size (p));
+%! assert (all (x >= 0));
+%! assert (all (round (x) == x));
+%! assert (sum (x) == n);
+
+%!test
+%! n = 10 * ones (3, 1);
+%! p = [0.2, 0.5, 0.3];
+%! x = mnrnd (n, p);
+%! assert (size (x), [length(n), length(p)]);
+%! assert (all (x >= 0));
+%! assert (all (round (x) == x));
+%! assert (all (sum (x, 2) == n));
+
+%!test
+%! n = (1:2)';
+%! p = [0.2, 0.5, 0.3; 0.1, 0.1, 0.8];
+%! x = mnrnd (n, p);
+%! assert (size (x), size (p));
+%! assert (all (x >= 0));
+%! assert (all (round (x) == x));
+%! assert (all (sum (x, 2) == n));
diff --git a/octave_compat/nanmean.m b/octave_compat/nanmean.m
new file mode 100644
index 00000000..ed679507
--- /dev/null
+++ b/octave_compat/nanmean.m
@@ -0,0 +1,8 @@
+function m = nanmean(x)
+
+nnanx=~isnan(x);
+x(~nnanx)=0;
+m=sum(x)./sum(nnanx);
+m(isinf(m))=NaN;
+end
+
diff --git a/octave_compat/nanstd.m b/octave_compat/nanstd.m
new file mode 100644
index 00000000..735caedf
--- /dev/null
+++ b/octave_compat/nanstd.m
@@ -0,0 +1,10 @@
+function sd = nanstd(x)
+
+nanm=nanmean(x);
+nnanx=~isnan(x);
+x=bsxfun(@minus, x, nanm).^2;
+x(~nnanx)=0;
+sd=sqrt(sum(x)./(sum(nnanx)-1));
+sd(isinf(sd))=NaN;
+end
+
diff --git a/optim/fminscg.m b/optim/fminscg.m
index 19db555c..afd8917d 100644
--- a/optim/fminscg.m
+++ b/optim/fminscg.m
@@ -103,11 +103,16 @@
   otherwise
     display=2;
 end
-maxiter = optimget(opt,'MaxIter',defaultopt,'fast');
-tolfun = optimget(opt,'TolFun',defaultopt,'fast');
-tolx = optimget(opt,'TolX',defaultopt,'fast');
-lambda = optimget(opt,'lambda', defaultopt, 'fast');
-lambdalim = optimget(opt, 'lambdalim', defaultopt, 'fast');
+maxiter = optimget(opt,'MaxIter',400);
+tolfun = optimget(opt,'TolFun',1e-6);
+tolx = optimget(opt,'TolX',1e-6);
+if ~exist('OCTAVE_VERSION', 'builtin')
+  lambda = optimget(opt,'lambda', defaultopt, 'fast');
+  lambdalim = optimget(opt, 'lambdalim', defaultopt, 'fast');
+else
+  lambda = optimget(opt,'lambda', 10);
+  lambdalim = optimget(opt, 'lambdalim', 1e20);
+end
 GradConstr = optimget(opt, 'GradConstr', defaultopt, 'fast');
 
 nparams = length(x);
diff --git a/startup.m b/startup.m
index 4f45d547..e14b9753 100644
--- a/startup.m
+++ b/startup.m
@@ -3,9 +3,9 @@
 F = mfilename;
 S = which(F);
 if exist('OCTAVE_VERSION', 'builtin')
-  subfolders={'diag' 'dist' 'gp' 'mc' 'misc' 'optim' 'tests', 'octave_compat', 'inputparser'};
+  subfolders={'diag' 'dist' 'gp' 'mc' 'misc' 'optim' 'test_gpstuff' 'octave_compat', 'inputparser'};
 else
-  subfolders={'diag' 'dist' 'gp' 'mc' 'misc' 'optim' 'tests'};
+  subfolders={'diag' 'dist' 'gp' 'mc' 'misc' 'optim' 'test_gpstuff' 'inputparser'};
 end
 for sf=subfolders
   addpath(strrep(S,[F '.m'],sf{:}))
@@ -25,4 +25,4 @@
 
 % If using Octave version of GPstuff, also add the following
 %addpath([gpstuffroot 'inputparser'])
-%addpath([gpstuffroot 'octave_compat'])
\ No newline at end of file
+%addpath([gpstuffroot 'octave_compat'])
diff --git a/test_gpstuff/README.txt b/test_gpstuff/README.txt
new file mode 100644
index 00000000..c9963ad8
--- /dev/null
+++ b/test_gpstuff/README.txt
@@ -0,0 +1,4 @@
+This folder has tests for testing the GPstuff package with both octave and 
+matlab. The tests can be run by running the script run_tests. The script runs 
+specific demos to see that they run properly. Furthermore, the results from the
+demos are compared to old results to check if the values have changed.
\ No newline at end of file
diff --git a/tests/realValues/binomial1.mat b/test_gpstuff/matlab/realValues_binomial1.mat
similarity index 100%
rename from tests/realValues/binomial1.mat
rename to test_gpstuff/matlab/realValues_binomial1.mat
diff --git a/tests/realValues/classific.mat b/test_gpstuff/matlab/realValues_classific.mat
similarity index 100%
rename from tests/realValues/classific.mat
rename to test_gpstuff/matlab/realValues_classific.mat
diff --git a/tests/realValues/derivativeobs.mat b/test_gpstuff/matlab/realValues_derivativeobs.mat
similarity index 100%
rename from tests/realValues/derivativeobs.mat
rename to test_gpstuff/matlab/realValues_derivativeobs.mat
diff --git a/tests/realValues/multinom.mat b/test_gpstuff/matlab/realValues_multinom.mat
similarity index 100%
rename from tests/realValues/multinom.mat
rename to test_gpstuff/matlab/realValues_multinom.mat
diff --git a/tests/realValues/neuralnetcov.mat b/test_gpstuff/matlab/realValues_neuralnetcov.mat
similarity index 100%
rename from tests/realValues/neuralnetcov.mat
rename to test_gpstuff/matlab/realValues_neuralnetcov.mat
diff --git a/tests/realValues/periodic.mat b/test_gpstuff/matlab/realValues_periodic.mat
similarity index 100%
rename from tests/realValues/periodic.mat
rename to test_gpstuff/matlab/realValues_periodic.mat
diff --git a/tests/realValues/regression1.mat b/test_gpstuff/matlab/realValues_regression1.mat
similarity index 100%
rename from tests/realValues/regression1.mat
rename to test_gpstuff/matlab/realValues_regression1.mat
diff --git a/tests/realValues/regression_additive1.mat b/test_gpstuff/matlab/realValues_regression_additive1.mat
similarity index 100%
rename from tests/realValues/regression_additive1.mat
rename to test_gpstuff/matlab/realValues_regression_additive1.mat
diff --git a/tests/realValues/regression_hier.mat b/test_gpstuff/matlab/realValues_regression_hier.mat
similarity index 100%
rename from tests/realValues/regression_hier.mat
rename to test_gpstuff/matlab/realValues_regression_hier.mat
diff --git a/tests/realValues/regression_sparse1.mat b/test_gpstuff/matlab/realValues_regression_sparse1.mat
similarity index 100%
rename from tests/realValues/regression_sparse1.mat
rename to test_gpstuff/matlab/realValues_regression_sparse1.mat
diff --git a/tests/realValues/survival_aft.mat b/test_gpstuff/matlab/realValues_survival_aft.mat
similarity index 100%
rename from tests/realValues/survival_aft.mat
rename to test_gpstuff/matlab/realValues_survival_aft.mat
diff --git a/test_gpstuff/octave/realValues_binomial1.mat b/test_gpstuff/octave/realValues_binomial1.mat
new file mode 100644
index 00000000..2e04b934
--- /dev/null
+++ b/test_gpstuff/octave/realValues_binomial1.mat
@@ -0,0 +1,1219 @@
+# Created by Octave 3.8.1, Tue Jun 07 13:29:24 2016 EEST <siivole1@cs-014>
+# name: Eyt_la
+# type: matrix
+# rows: 400
+# columns: 1
+ 44.20206073880297
+ 46.4479408626664
+ 58.32213514257558
+ 44.82633934735057
+ 70.72275172672887
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diff --git a/test_gpstuff/octave/realValues_classific.mat b/test_gpstuff/octave/realValues_classific.mat
new file mode 100644
index 00000000..4655960a
--- /dev/null
+++ b/test_gpstuff/octave/realValues_classific.mat
@@ -0,0 +1,2437 @@
+# Created by Octave 3.8.1, Tue Jun 07 13:32:48 2016 EEST <siivole1@cs-014>
+# name: Efs_mc
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diff --git a/test_gpstuff/octave/realValues_derivativeobs.mat b/test_gpstuff/octave/realValues_derivativeobs.mat
new file mode 100644
index 00000000..e8a81522
--- /dev/null
+++ b/test_gpstuff/octave/realValues_derivativeobs.mat
@@ -0,0 +1,509 @@
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diff --git a/test_gpstuff/octave/realValues_multinom.mat b/test_gpstuff/octave/realValues_multinom.mat
new file mode 100644
index 00000000..bf3dd146
--- /dev/null
+++ b/test_gpstuff/octave/realValues_multinom.mat
@@ -0,0 +1,735 @@
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diff --git a/test_gpstuff/octave/realValues_neuralnetcov.mat b/test_gpstuff/octave/realValues_neuralnetcov.mat
new file mode 100644
index 00000000..bf82036a
--- /dev/null
+++ b/test_gpstuff/octave/realValues_neuralnetcov.mat
@@ -0,0 +1,825 @@
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diff --git a/test_gpstuff/octave/realValues_periodic.mat b/test_gpstuff/octave/realValues_periodic.mat
new file mode 100644
index 00000000..d49ab197
--- /dev/null
+++ b/test_gpstuff/octave/realValues_periodic.mat
@@ -0,0 +1,2941 @@
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diff --git a/test_gpstuff/octave/realValues_regression1.mat b/test_gpstuff/octave/realValues_regression1.mat
new file mode 100644
index 00000000..bda08257
--- /dev/null
+++ b/test_gpstuff/octave/realValues_regression1.mat
@@ -0,0 +1,8251 @@
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diff --git a/test_gpstuff/octave/realValues_regression_additive1.mat b/test_gpstuff/octave/realValues_regression_additive1.mat
new file mode 100644
index 00000000..c6a8fcf0
--- /dev/null
+++ b/test_gpstuff/octave/realValues_regression_additive1.mat
@@ -0,0 +1,3401 @@
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diff --git a/test_gpstuff/octave/realValues_regression_hier.mat b/test_gpstuff/octave/realValues_regression_hier.mat
new file mode 100644
index 00000000..87deddee
--- /dev/null
+++ b/test_gpstuff/octave/realValues_regression_hier.mat
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diff --git a/test_gpstuff/octave/realValues_regression_sparse1.mat b/test_gpstuff/octave/realValues_regression_sparse1.mat
new file mode 100644
index 00000000..ac2907f4
--- /dev/null
+++ b/test_gpstuff/octave/realValues_regression_sparse1.mat
@@ -0,0 +1,6876 @@
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diff --git a/test_gpstuff/octave/realValues_survival_aft.mat b/test_gpstuff/octave/realValues_survival_aft.mat
new file mode 100644
index 00000000..b80bdd5f
--- /dev/null
+++ b/test_gpstuff/octave/realValues_survival_aft.mat
@@ -0,0 +1,463 @@
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+ 0.2244152413418602 0.3937165576314025 0.706590300742107
+ 0.220849499053247 0.3890070372229811 0.6946018480637115
+ 0.2190209063105227 0.386208842681069 0.6893737794067717
+ 0.2157181837159798 0.3809093360072174 0.6804683778816185
+ 0.2144419842404895 0.3789559489600989 0.6782160386068221
+ 0.2121649304769752 0.3756410011427954 0.6747286403218031
+ 0.2105700290209236 0.3730751912230704 0.6699579498697994
+ 0.2059423719869333 0.3655972016862803 0.6575356987812927
+ 0.2045929034121997 0.3634706890569879 0.6545184763278944
+ 0.2024650356724561 0.3609891965506989 0.6503019217018221
+ 0.1989605059632063 0.3563010070337208 0.6409356515053333
+ 0.1971949392914402 0.3538186863776232 0.6363507463892472
+ 0.1954102376793428 0.3510610961000229 0.6319376451603952
+ 0.1937923259775574 0.3486098135450991 0.6282442325752851
+ 0.1907942677247498 0.3443717698906602 0.6186418839303252
+ 0.1871804400339603 0.3402240874209242 0.611002636306158
+ 0.184848118681844 0.3363652077249641 0.6027139011217288
+ 0.1844169510585499 0.3357450903074846 0.6009468039553174
+ 0.1818470347314569 0.3312992646471651 0.590818422924398
+ 0.1791206262676577 0.3271906143623599 0.5833869452369553
+ 0.1764658110888669 0.323019319980421 0.5766088599495089
+ 0.1720976037336975 0.3168795173138669 0.5665349244921062
+ 0.1693899335847118 0.3129186152171803 0.56048513263392
+ 0.1682613753460016 0.3110207093788716 0.557732296591003
+ 0.1644346158997692 0.305162657541258 0.548494871054497
+ 0.163290692968431 0.3033403805669924 0.5450855680193073
+ 0.1599785173723458 0.2975727567093527 0.5394919205645232
+ 0.1586845786158986 0.2956372722222183 0.538136573251073
+ 0.1546801773979968 0.2904274838422028 0.5305288688581123
+ 0.1457699068920425 0.2774239666306157 0.5100744551560523
+ 0.1426399861631496 0.2727472681448925 0.5047840646795736
+ 0.1417393675644046 0.2710357665880898 0.5028363016837131
+ 0.1377253970264976 0.2655638736953657 0.4938292922496813
+ 0.1368854230642513 0.2643579730406098 0.4921196415754291
+ 0.1357240410773413 0.2625896996724912 0.4906564315341115
+ 0.1345558008094009 0.2611335270928304 0.4879996335130547
+ 0.1262384912326139 0.2490075176408559 0.4722243355409557
+ 0.1239872916580645 0.2449392753248641 0.4665318733506076
+ 0.1224279527932122 0.2420418818939499 0.463207244455682
+ 0.118991600495888 0.2361172250357779 0.453431524293656
+ 0.1179100090565159 0.2346947658114108 0.4521213840339449
+ 0.1150097149544691 0.2304026254815247 0.4470041251060027
+ 0.113361934795479 0.2281680883683862 0.4438634644802883
+ 0.1085554991079411 0.2218625387207401 0.4336261789433838
+ 0.1080057579207626 0.2208472629805547 0.4308599785205322
+ 0.1048915696656245 0.2168594332927274 0.424969540858226
+ 0.08595802133865832 0.1885824031848514 0.3874310075043566
+ 0.08421630758011329 0.1865808857230728 0.3847828917827194
+ 0.08374477316003004 0.1859016430209318 0.3836956275118782
+ 0.07919435038501893 0.1794439742893525 0.3748636264900498
+ 0.07694692986479643 0.1756253433650087 0.3695477151225088
+ 0.07592651266281938 0.1737887427430411 0.3679493511709871
+ 0.07071835237906722 0.1654284715601424 0.3572988835268792
+ 0.06767134338337338 0.1616101538378398 0.3539113633494149
+ 0.0658653055228533 0.1594901236680711 0.351755205195146
+ 0.06294247284733875 0.1551543912900627 0.3470275012697289
+ 0.06043861786002882 0.1513004060223987 0.3413396680809865
+ 0.05609819107237627 0.1450528535057662 0.3354989337224782
+ 0.05227337922269128 0.1401112624206844 0.3325358403034011
+ 0.05096116304432959 0.1385805198907011 0.3323655284416129
+ 0.05084307080388557 0.1382170866752817 0.3321989470839741
+ 0.0424668484480567 0.1251468261645505 0.3208173139525312
+ 0.03803070838174732 0.1196997855810452 0.3207148384351949
+ 0.03753429033217082 0.118837722543698 0.3201837501789481
+
+
diff --git a/test_gpstuff/run_tests.m b/test_gpstuff/run_tests.m
new file mode 100644
index 00000000..1a5d6333
--- /dev/null
+++ b/test_gpstuff/run_tests.m
@@ -0,0 +1,59 @@
+% Run selected demos, compare results from demos to old results and save
+% any error messages.
+
+demos={'demo_binomial1', ...
+  'demo_classific', 'demo_derivativeobs',  ...
+  'demo_multinom', 'demo_neuralnetcov', ...
+  'demo_periodic', 'demo_regression1', 'demo_regression_additive1', ...
+  'demo_regression_hier', 'demo_regression_sparse1', 'demo_survival_aft'};
+
+invalid_demos={};
+iter=0;
+failed=0;
+path=strrep(which('run_tests'), 'run_tests.m', '');
+for ii=1:length(demos)
+  iter=iter+1;
+  setrandstream(0);  
+  if exist('OCTAVE_VERSION','builtin')
+    values.real=load([path 'octave/realValues_' strrep(demos{ii}, 'demo_', '')]);
+  else
+    values.real=load([path 'matlab/realValues_' strrep(demos{ii}, 'demo_', '')]);
+  end
+  field=fieldnames(values.real);
+  fprintf('\nRunning demo: %s\n\n', demos{ii});
+  try
+    eval(demos{ii});
+    for j=1:length(field)
+      iter=iter+1;
+      if mean(mean((abs(getfield(values.real, field{j}) - eval(field{j})))./getfield(values.real, field{j})))>1e-2
+        failed=failed+1;
+        invalid_demos(failed).name=demos{ii};
+        invalid_demos(failed).paramName=field{j};
+        invalid_demos(failed).paramValue_old=getfield(values.real, field{j});
+        invalid_demos(failed).paramValue_new=eval(field{j});
+        invalid_demos(failed).error='New values dont match with old saved values with relative tolerance of 1e-2.';
+      end
+    end
+  catch err
+    % Error while running a demo
+    iter=iter+length(field);
+    failed=failed+1;
+    invalid_demos(failed).name=demos{ii};
+    invalid_demos(failed).paramName='';
+    invalid_demos(failed).paramValue_old=[];
+    invalid_demos(failed).paramValue_new=[];
+    % Save error structure to field error.
+    invalid_demos(failed).error=err;    
+    for j=1:length(field)
+      failed=failed+1;
+      invalid_demos(failed).name=demos{ii};
+      invalid_demos(failed).paramName=field{j};
+      invalid_demos(failed).paramValue_old=getfield(values.real, field{j});
+      invalid_demos(failed).paramValue_new=[];
+      invalid_demos(failed).error='Error while runnig the demo.';
+    end
+  end
+  close all;
+end
+invalid_demos
+fprintf('Failed %d of %d tests. Check struct invalid_demos for further details.\n',failed, iter);
\ No newline at end of file
diff --git a/test_gpstuff/update_test_references.m b/test_gpstuff/update_test_references.m
new file mode 100644
index 00000000..c1093c3c
--- /dev/null
+++ b/test_gpstuff/update_test_references.m
@@ -0,0 +1,34 @@
+% Run selected demos, compare results from demos to old results and save
+% any error messages.
+
+demos={'demo_binomial1', ...
+  'demo_classific', 'demo_derivativeobs',  ...
+  'demo_multinom', 'demo_neuralnetcov', ...
+  'demo_periodic', 'demo_regression1', 'demo_regression_additive1', ...
+  'demo_regression_hier', 'demo_regression_sparse1', 'demo_survival_aft'};
+
+iter=0;
+failed=0;
+path=strrep(which('run_tests'), 'run_tests.m', '');
+for ii=1:length(demos)
+  iter=iter+1;
+  setrandstream(0);  
+  if exist('OCTAVE_VERSION','builtin')
+    values.real=load([path 'octave/realValues_' strrep(demos{ii}, 'demo_', '')]);
+  else
+    values.real=load([path 'matlab/realValues_' strrep(demos{ii}, 'demo_', '')]);
+  end
+  field=fieldnames(values.real);
+  fprintf('\nRunning demo: %s\n\n', demos{ii});
+  try
+    eval(demos{ii});
+    delete([path 'realValues_' strrep(demos{ii}, 'demo_', '') '.mat'])
+    for jj=1:length(field)
+      save('-append', [path 'realValues_' strrep(demos{ii}, 'demo_', '') '.mat'], field{jj})
+    end
+  catch err
+    fprintf('Failed %s\n s%', demos{ii}, err)
+  end
+  close all;
+end
+fprintf('Results saved to %s.\n', path);
diff --git a/tests/Readme.txt b/tests/Readme.txt
deleted file mode 100644
index 69ceb3ad..00000000
--- a/tests/Readme.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-This directory contains unit test for GPstuff. The tests can be run using
-the built-in unit test framewoork (Matlab 2013b or greater) or using the
-xUnit package by Steve Eddins.
-
-Test structure in every test is basically the same: Run demo, save values
-from demo, compare those saved test values to previously saved "correct"
-values. Users can run these tests to determine if some calculations within
-demos don't give correct answers.
-
-Tests are named test_<demo_name>.m (e.g. test_binomial.m is a corresponding
-test for demo_binomial1.m) and they can include multiple test cases each.
-Individual tests can be run with command "runtests <test_file_name>".
-Function runtestset can be used to run all the tests or a predetermined set
-of tests.
-
-When a test is run, the results of the demo are saved into the folder
-testValues. The saved components include figures, selected workspace
-variables and the log file of the command line output. Folder realValues
-contain similar precomputed files for each demo.
-
-For more information to the MATLAB unit testing framework, see Matlab
-documentation.
-
-For more information to the xUnit Test Framework package, visit 
-http://www.mathworks.com/matlabcentral/fx_files/22846/11/content/matlab_xunit/doc/xunit_product_page.html
-
-Real values from revision 990.
diff --git a/tests/realValues/binomial1.txt b/tests/realValues/binomial1.txt
deleted file mode 100644
index 15e0ee95..00000000
--- a/tests/realValues/binomial1.txt
+++ /dev/null
@@ -1,7 +0,0 @@
- TolFun reached. Func-count 37. Final f(x)=169.569. Elapsed time 1.02
-
- gp hyperparameters: 
- 
-   -0.7902   -1.0035
-
-Demo completed in 0.088 minutes
diff --git a/tests/realValues/binomial1_fig1.fig b/tests/realValues/binomial1_fig1.fig
deleted file mode 100644
index 65e70317..00000000
Binary files a/tests/realValues/binomial1_fig1.fig and /dev/null differ
diff --git a/tests/realValues/binomial1_fig2.fig b/tests/realValues/binomial1_fig2.fig
deleted file mode 100644
index 4e4facf7..00000000
Binary files a/tests/realValues/binomial1_fig2.fig and /dev/null differ
diff --git a/tests/realValues/binomial1_fig3.fig b/tests/realValues/binomial1_fig3.fig
deleted file mode 100644
index 33e82424..00000000
Binary files a/tests/realValues/binomial1_fig3.fig and /dev/null differ
diff --git a/tests/realValues/binomial2.mat b/tests/realValues/binomial2.mat
deleted file mode 100644
index d8cf88bb..00000000
Binary files a/tests/realValues/binomial2.mat and /dev/null differ
diff --git a/tests/realValues/binomial2.txt b/tests/realValues/binomial2.txt
deleted file mode 100644
index ae204201..00000000
--- a/tests/realValues/binomial2.txt
+++ /dev/null
@@ -1,19 +0,0 @@
-Laplace+grid approximation  IA-grid: finding the mode
- IA-grid: computing Hessian using multiplication
- IA-grid: evaluating density in a grid
- IA-grid: evaluated density at 30 points
-Elapsed time 2s
-EP+grid approximation       IA-grid: finding the mode
- IA-grid: computing Hessian using multiplication
- IA-grid: evaluating density in a grid
- IA-grid: evaluated density at 30 points
-    Elapsed time 1.03 seconds
- IA-grid: Total elapsed time 1.23 seconds
-Elapsed time 5s
-MCMC approximation         Elapsed time 194s
-
- gp hyperparameters: 
- 
-    4.3811    2.3256
-
-Demo completed in 3.367 minutes
diff --git a/tests/realValues/binomial2_fig1.fig b/tests/realValues/binomial2_fig1.fig
deleted file mode 100644
index 0e394f63..00000000
Binary files a/tests/realValues/binomial2_fig1.fig and /dev/null differ
diff --git a/tests/realValues/binomial_apc.mat b/tests/realValues/binomial_apc.mat
deleted file mode 100644
index 51042db6..00000000
Binary files a/tests/realValues/binomial_apc.mat and /dev/null differ
diff --git a/tests/realValues/binomial_apc.txt b/tests/realValues/binomial_apc.txt
deleted file mode 100644
index 42f51b54..00000000
--- a/tests/realValues/binomial_apc.txt
+++ /dev/null
@@ -1,59 +0,0 @@
- Iteration  Func-count   Grad-count         f(x)         Step-size
-     0           1           1             1958.28    
-     1           2           2             1911.95      0.00221514
-     2           6           6             1877.23           0.001
-     3           9           9                1847            0.01
-     4          12          12             1845.58       0.0163264
-     5          14          14             1787.72             0.5
-     6          18          18             1781.43      0.00238076
-     7          22          22             1771.39       0.0225751
-     8          23          23             1730.37               1
-     9          24          24             1723.09               1
-    10          25          25             1720.62               1
-    11          26          26             1720.31               1
-    12          27          27              1719.5               1
-    13          28          28             1719.15               1
-    14          29          29             1718.86               1
-    15          30          30             1718.68               1
-    16          31          31             1718.47               1
-    17          32          32             1718.41               1
-    18          33          33             1718.28               1
-    19          34          34             1718.15               1
-    20          35          35             1717.93               1
-    21          36          36             1717.68               1
-    22          37          37              1717.1               1
-    23          38          38             1716.73               1
-    24          40          40             1716.48             0.1
-    25          42          42             1716.22             0.1
-    26          44          44             1715.82        0.357093
-    27          45          45             1715.53               1
-    28          46          46             1715.25               1
-    29          47          47                1715               1
-    30          48          48             1714.92               1
-    31          49          49             1714.84               1
-    32          51          51             1714.71        0.230448
-    33          53          53             1714.63             0.1
-    34          54          54              1714.5               1
-    35          59          59             1713.39        0.344853
-    36          62          62             1713.36       0.0398992
-    37          64          64                1713        0.465217
-    38          65          65             1712.46               1
-    39          66          66             1711.82               1
-    40          67          67             1711.67               1
-    41          68          68             1711.67               1
-    42          69          69             1711.67               1
-    43          70          70             1711.67               1
- Optimizer Results
-  Algorithm Used: Broyden-Fletcher-Goldfarb-Shanno (BFGS)
-  Exit message : Change in x was smaller than the specified tolerance TolX.
-  Iterations : 44
-  Function Count : 71
-  Minimum found : 1711.6691
-  Intern Time : 0.22471 seconds
-  Total Time : 8.3825 seconds
-
- gp hyperparameters: 
- 
-    0.7965    2.4325   -1.1382    1.1745   -3.6053    1.8489   -0.1535    1.9498    1.9306
-
-Demo completed in 0.217 minutes
diff --git a/tests/realValues/binomial_apc_fig1.fig b/tests/realValues/binomial_apc_fig1.fig
deleted file mode 100644
index a5345e8c..00000000
Binary files a/tests/realValues/binomial_apc_fig1.fig and /dev/null differ
diff --git a/tests/realValues/binomial_apc_fig2.fig b/tests/realValues/binomial_apc_fig2.fig
deleted file mode 100644
index 42462601..00000000
Binary files a/tests/realValues/binomial_apc_fig2.fig and /dev/null differ
diff --git a/tests/realValues/classific.txt b/tests/realValues/classific.txt
deleted file mode 100644
index 33ecb53b..00000000
--- a/tests/realValues/classific.txt
+++ /dev/null
@@ -1,28 +0,0 @@
-Probit model with Laplace integration over the latent values and
-MAP estimate for the parameters
- TolFun reached. Func-count 27. Final f(x)=81.5289. Elapsed time 1.10
-Probit model with EP integration over the latent values and
-MAP estimate for the parameters
- TolFun reached. Func-count 7. Final f(x)=81.748. Elapsed time 0.30
-Probit model with MCMC integration over the latent values and
-the parameters
- Using SSLS sampler for hyperparameters and ESLS for latent values
- cycle  etr      lslsn
-   20  -943.088  2.0e+00  
-   40  -933.602  7.0e+00  
-   60  -975.302  7.0e+00  
-   80  -978.888  6.0e+00  
-  100  -1009.051  1.1e+01  
-  120  -1048.534  7.0e+00  
-  140  -1027.652  6.0e+00  
-  160  -1001.384  6.0e+00  
-  180  -1016.275  2.0e+00  
-  200  -1090.211  4.0e+00  
-  220  -1088.827  0.0e+00  
-Compare MCMC, Laplace and EP results for two latent variables
-
- gp hyperparameters: 
- 
-    3.2607   -0.7237    0.0613
-
-Demo completed in 0.966 minutes
diff --git a/tests/realValues/classific_fig1.fig b/tests/realValues/classific_fig1.fig
deleted file mode 100644
index ea435fbe..00000000
Binary files a/tests/realValues/classific_fig1.fig and /dev/null differ
diff --git a/tests/realValues/classific_fig2.fig b/tests/realValues/classific_fig2.fig
deleted file mode 100644
index 0bbbfe99..00000000
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diff --git a/tests/realValues/classific_fig3.fig b/tests/realValues/classific_fig3.fig
deleted file mode 100644
index a31c543a..00000000
Binary files a/tests/realValues/classific_fig3.fig and /dev/null differ
diff --git a/tests/realValues/classific_fig4.fig b/tests/realValues/classific_fig4.fig
deleted file mode 100644
index b79c3b69..00000000
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diff --git a/tests/realValues/classific_fig5.fig b/tests/realValues/classific_fig5.fig
deleted file mode 100644
index faaeadea..00000000
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diff --git a/tests/realValues/classific_fig6.fig b/tests/realValues/classific_fig6.fig
deleted file mode 100644
index 0fc74f1e..00000000
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diff --git a/tests/realValues/classific_fig7.fig b/tests/realValues/classific_fig7.fig
deleted file mode 100644
index 756036e4..00000000
Binary files a/tests/realValues/classific_fig7.fig and /dev/null differ
diff --git a/tests/realValues/derivativeobs.txt b/tests/realValues/derivativeobs.txt
deleted file mode 100644
index ea980cbf..00000000
--- a/tests/realValues/derivativeobs.txt
+++ /dev/null
@@ -1,25 +0,0 @@
-GP model without derivative obs
-Checking gradient ...
-
-   analytic   diffs     delta
-
-    2.2001    2.2001    0.0000
-   -1.0624   -1.0624   -0.0000
-
- TolFun reached. Func-count 17. Final f(x)=5.67863. Elapsed time 0.12
-GP model with derivative obs
-Checking gradient ...
-
-   analytic   diffs     delta
-
-    3.6684    3.6684    0.0000
-  -10.3567  -10.3567   -0.0000
-    1.1596    1.1596    0.0000
-
- TolX reached. Func-count 19. Final f(x)=3.15085. Elapsed time 0.17
-
- gp hyperparameters: 
- 
-   -1.6957   -0.3805   -4.1351
-
-Demo completed in 0.012 minutes
diff --git a/tests/realValues/derivativeobs_fig1.fig b/tests/realValues/derivativeobs_fig1.fig
deleted file mode 100644
index 28c9b7ff..00000000
Binary files a/tests/realValues/derivativeobs_fig1.fig and /dev/null differ
diff --git a/tests/realValues/epinf.mat b/tests/realValues/epinf.mat
deleted file mode 100644
index 0ba33a00..00000000
Binary files a/tests/realValues/epinf.mat and /dev/null differ
diff --git a/tests/realValues/epinf.txt b/tests/realValues/epinf.txt
deleted file mode 100644
index 5e67d3d8..00000000
--- a/tests/realValues/epinf.txt
+++ /dev/null
@@ -1,35 +0,0 @@
-Optimise the input-dependent model
-  Iteration  Func-count      f(x)      Lambda
-      0          1          783.75    
-      1          3         783.714       10
-      2          5         783.705        5
-      3          7         783.705      2.5
-      4          8         783.705       10
-      5         10         783.703       10
-      6         12         783.703        5
-      7         13         783.703       20
- TolFun reached. Func-count 13. Final f(x)=783.703. Elapsed time 4600.79
-mlpd input-dependent: -0.99
-Optimise the normal gaussian model
-  Iteration  Func-count      f(x)      Lambda
-      0          1          2984.2    
-      1          3         1408.89       10
-      2          5         937.085        5
-      3          7         837.594      2.5
-      4          9         828.249     1.25
-      5         11         827.869    0.625
-      6         13         827.834    0.312
-      7         15         827.818    0.156
-      8         17         827.811   0.0781
-      9         19         827.796   0.0391
-     10         21         827.795   0.0195
-     11         23         827.795   0.00977
-     12         25         827.795   0.00488
- TolFun reached. Func-count 25. Final f(x)=827.795. Elapsed time 1.47
-mlpd gaussian: -1.12
-
- gp hyperparameters: 
- 
-    0.1823   -2.4816   -0.3657   -1.3494    1.0286   -0.2708
-
-Demo completed in 76.727 minutes
diff --git a/tests/realValues/epinf_fig1.fig b/tests/realValues/epinf_fig1.fig
deleted file mode 100644
index 3aab0be0..00000000
Binary files a/tests/realValues/epinf_fig1.fig and /dev/null differ
diff --git a/tests/realValues/epinf_fig2.fig b/tests/realValues/epinf_fig2.fig
deleted file mode 100644
index fa5e25b8..00000000
Binary files a/tests/realValues/epinf_fig2.fig and /dev/null differ
diff --git a/tests/realValues/hierprior.mat b/tests/realValues/hierprior.mat
deleted file mode 100644
index 57d117f5..00000000
Binary files a/tests/realValues/hierprior.mat and /dev/null differ
diff --git a/tests/realValues/hierprior.txt b/tests/realValues/hierprior.txt
deleted file mode 100644
index 9be1279f..00000000
--- a/tests/realValues/hierprior.txt
+++ /dev/null
@@ -1,583 +0,0 @@
-
-w =
-
-   -2.3026         0         0         0    2.3026         0   -2.3026         0
-
-
-s = 
-
-    'log(sexp.magnSigma2)'
-    'prior-t.mu'
-    'log(sexp.lengthScale)'
-    'prior-t.mu'
-    'log(linear.coeffSigma2)'
-    'prior-t.mu'
-    'log(gaussian.sigma2)'
-    't.mu'
-
-  Iteration  Func-count      f(x)      Lambda
-      0          1          77.436    
-      1          3         62.2251       10
-      2          5         48.7816        5
-      3          7         44.4582      2.5
-      4          9         40.3733     1.25
-      5         11         37.7161    0.625
-      6         13         37.1274    0.312
-      7         15         36.8407    0.156
-      8         17         36.6905   0.0781
-      9         19         36.5944   0.0391
-     10         21         36.4577   0.0195
-     11         23         36.4165   0.00977
-     12         25         36.4114   0.00488
-     13         27         36.4061   0.00244
-     14         29         36.3983   0.00122
-     15         31         36.3891   0.00061
-     16         33         36.3852   0.000305
-     17         35         36.3845   0.000153
-     18         37         36.3834   7.63e-05
-     19         39         36.3833   3.81e-05
-  Iteration  Func-count      f(x)    Lambda
-     20         41         36.3832   1.91e-05
-     21         43         36.3832   9.54e-06
-     22         45         36.3832   4.77e-06
-     23         47         36.3832   2.38e-06
- TolFun reached. Func-count 47. Final f(x)=36.3832. Elapsed time 0.69
-Checking gradient ...
-
-   analytic   diffs     delta
-
-   -0.0026   -0.0026    0.0000
-    0.0016    0.0016   -0.0000
-   -0.0004   -0.0004   -0.0000
-   -0.0017   -0.0017   -0.0000
-    0.0000    0.0000    0.0000
-   -0.0008   -0.0008    0.0000
-   -0.0041   -0.0041   -0.0000
-    0.0000    0.0000    0.0000
-
-  Iteration  Func-count      f(x)      Lambda
-      0          1          93.314    
-      1          3         77.6036       10
-      2          5         59.7768        5
-      3          7         49.7327      2.5
-      4          9         45.2948     1.25
-      5         11         42.1805     1.25
-      6         13          39.769    0.625
-      7         15         39.1496    0.312
-      8         17         38.1454    0.156
-      9         19          36.954   0.0781
-     10         21         36.4547   0.0391
-     11         23         36.1068   0.0391
-     12         25         35.9828   0.0195
-     13         27         35.9296   0.00977
-     14         29         35.8335   0.00488
-     15         31         35.7956   0.00244
-     16         33         35.7757   0.00122
-     17         35         35.7302   0.00061
-     18         37         35.7072   0.000305
-     19         39         35.6887   0.000153
-  Iteration  Func-count      f(x)    Lambda
-     20         41         35.6841   7.63e-05
-     21         43         35.6691   3.81e-05
-     22         45         35.6528   1.91e-05
-     23         47         35.6523   9.54e-06
-     24         49         35.6498   4.77e-06
-     25         51         35.6483   2.38e-06
-     26         53          35.648   1.19e-06
-     27         55         35.6464   5.96e-07
-     28         57         35.6462   2.98e-07
-     29         59         35.6458   1.49e-07
-     30         61         35.6438   7.45e-08
-     31         63         35.6435   3.73e-08
-     32         65         35.6415   1.86e-08
-     33         67         35.6415   9.31e-09
-     34         68         35.6415   3.73e-08
-     35         69         35.6415   1.49e-07
-     36         70         35.6415   5.96e-07
-     37         71         35.6415   2.38e-06
-     38         72         35.6415   9.54e-06
-     39         73         35.6415   3.81e-05
-  Iteration  Func-count      f(x)    Lambda
-     40         74         35.6415   0.000153
-     41         75         35.6415   0.00061
-     42         76         35.6415   0.00244
-     43         77         35.6415   0.00977
-     44         78         35.6415   0.0391
-     45         79         35.6251    0.156
-     46         81          35.625   0.0781
-     47         83          35.625    0.585
-     48         84         35.6038     2.34
-     49         86         35.6038     6.33
-     50         87         35.5681     25.3
-     51         89          34.811     27.7
-     52         91         34.3255     13.9
-     53         93         34.3255     6.93
-     54         94         33.3519     27.7
-     55         96         33.1555     13.9
-     56         98         33.0861     6.93
-     57        100         33.0672     3.46
-     58        102         33.0522     1.73
-     59        104         33.0429    0.866
-  Iteration  Func-count      f(x)    Lambda
-     60        106         33.0282    0.433
-     61        108         33.0108    0.216
-     62        110         32.9964    0.108
-     63        112         32.9924   0.0541
-     64        114         32.9841   0.0271
-     65        116         32.9807   0.0135
-     66        118         32.9761   0.00676
-     67        120         32.9761   0.00676
-     68        122         32.9719   0.00338
-     69        124         32.9706   0.00169
-     70        126         32.9681   0.000845
-     71        128         32.9674   0.000423
-     72        130         32.9629   0.000211
-     73        132         32.9624   0.000106
-     74        134         32.9599   5.28e-05
-     75        136         32.9577   2.64e-05
-     76        138          32.957   1.32e-05
-     77        140         32.9562   6.6e-06
-     78        142         32.9552   3.3e-06
-     79        144          32.955   1.65e-06
-  Iteration  Func-count      f(x)    Lambda
-     80        146         32.9537   8.26e-07
-     81        148          32.953   4.13e-07
-     82        150         32.9511   2.06e-07
-     83        152         32.9509   1.03e-07
-     84        154         32.9499   5.16e-08
-     85        156         32.9497   2.58e-08
-     86        158         32.9496   1.29e-08
-     87        160         32.9493   6.45e-09
-     88        162         32.9491   3.23e-09
-     89        164          32.949   1.61e-09
-     90        166         32.9489   8.06e-10
-     91        168         32.9489   4.03e-10
-     92        170         32.9489   2.02e-10
-     93        172         32.9489   1.01e-10
-     94        174         32.9488   5.04e-11
-     95        176         32.9488   2.52e-11
- TolFun reached. Func-count 176. Final f(x)=32.9488. Elapsed time 2.98
-Checking gradient ...
-
-   analytic   diffs     delta
-
-    0.0043   -0.0574    0.0618
-   -0.0006   -0.0006    0.0000
-   -0.0335    0.0518   -0.0852
-    0.0001    0.0001    0.0000
-    0.0017    0.0794   -0.0777
-   -0.0002   -0.0002   -0.0000
-    0.0056   -0.1566    0.1622
-   -0.0000   -0.0000    0.0000
-    0.0107   -0.0431    0.0538
-   -0.0083   -0.1051    0.0968
-   -0.0046   -0.0231    0.0185
-    0.0004   -0.0933    0.0937
-    0.0004   -0.2254    0.2258
-    0.0007   -0.0042    0.0049
-   -0.0025   -0.0359    0.0334
-   -0.0016    0.0204   -0.0220
-   -0.0011   -0.0367    0.0356
-   -0.0004    0.0602   -0.0606
-   -0.0066   -0.0066   -0.0000
-
-  Iteration  Func-count      f(x)      Lambda
-      0          1          93.397    
-      1          3         78.2287       10
-      2          5         64.8018        5
-      3          7         58.5794      2.5
-      4          9         54.6623     1.25
-      5         11         54.6623    0.625
-      6         12         54.6623      2.5
-      7         13         51.3705       10
-      8         15         50.8605        5
-      9         17         50.6164      2.5
-     10         19         49.1723     1.25
-     11         21         48.5081    0.625
-     12         23         48.0247    0.312
-     13         25          47.655    0.156
-     14         27         47.5183   0.0781
-     15         29         47.4572   0.0391
-     16         31         47.4044   0.0195
-     17         33         47.3788   0.00977
-     18         35         47.3314   0.00488
-     19         37         47.3164   0.00244
-  Iteration  Func-count      f(x)    Lambda
-     20         39         47.3093   0.00122
-     21         41         47.2816   0.00061
-     22         43         47.2798   0.000305
-     23         45         47.2714   0.000153
-     24         47         47.2677   7.63e-05
-     25         49         47.2643   3.81e-05
-     26         51         47.2429   1.91e-05
-     27         53         47.2368   9.54e-06
-     28         55          47.234   4.77e-06
-     29         57         47.2163   2.38e-06
-     30         59         47.1508   1.19e-06
-     31         61         47.1295   5.96e-07
-     32         63         47.1024   2.98e-07
-     33         65         47.0685   1.49e-07
-     34         67         47.0551   1.49e-07
-     35         69         47.0229   7.45e-08
-     36         71         46.9535   3.73e-08
-     37         73         46.9347   1.86e-08
-     38         75          46.928   9.31e-09
-     39         77         46.9252   4.66e-09
-  Iteration  Func-count      f(x)    Lambda
-     40         79         46.9227   2.33e-09
-     41         81         46.9215   1.16e-09
-     42         83         46.9183   5.82e-10
-     43         85         46.9147   2.91e-10
-     44         87         46.9146   1.46e-10
-     45         89         46.9137   7.28e-11
-     46         91         46.9135   3.64e-11
-     47         93         46.9134   1.82e-11
-     48         95         46.9128   9.09e-12
-     49         97         46.9125   4.55e-12
-     50         99         46.9122   2.27e-12
-     51        101          46.912   1.14e-12
-     52        103          46.912   5.68e-13
-     53        105         46.9119   2.84e-13
-     54        107         46.9118   1.42e-13
-     55        109         46.9118   7.11e-14
-     56        111         46.9118   3.55e-14
-     57        113         46.9118   1.78e-14
- TolFun reached. Func-count 113. Final f(x)=46.9118. Elapsed time 1.90
-Checking gradient ...
-
-   analytic   diffs     delta
-
-   -0.0039    0.0072   -0.0111
-    0.0021    0.0021   -0.0000
-    0.0331   -0.0090    0.0422
-    0.0000    0.0000    0.0000
-    0.0004   -0.0009    0.0013
-    0.0017    0.0017   -0.0000
-   -0.0006   -0.0281    0.0275
-   -0.0000   -0.0000    0.0000
-   -0.0003   -0.0018    0.0015
-   -0.0003   -0.0027    0.0024
-   -0.0000    0.0150   -0.0150
-   -0.0005   -0.0006    0.0000
-   -0.0020   -0.0929    0.0909
-   -0.0022   -0.0135    0.0114
-    0.0006    0.0001    0.0005
-   -0.0002   -0.0004    0.0002
-    0.0009    0.0019   -0.0009
-    0.0017    0.0017   -0.0000
-   -0.0012   -0.0012   -0.0000
-
-  Iteration  Func-count      f(x)      Lambda
-      0          1          93.377    
-      1          3          77.902       10
-      2          5          63.145        5
-      3          7          56.938      2.5
-      4          9         51.3383     1.25
-      5         11         46.6458    0.625
-      6         13         46.6458    0.312
-      7         14         46.6458     1.25
-      8         15         46.3136        5
-      9         17         46.2949       20
-     10         19         44.5981       10
-     11         21         43.7977        5
-     12         23         43.6585      2.5
-     13         25         43.6585     1.25
-     14         26           42.84        5
-     15         28         42.5202      2.5
-     16         30         42.4726     1.25
-     17         32         42.1871    0.625
-     18         34          42.122    0.312
-     19         36         42.0868    0.156
-  Iteration  Func-count      f(x)    Lambda
-     20         38          42.032   0.0781
-     21         40         41.9998   0.0391
-     22         42         41.9998   0.0195
-     23         43         41.9998   0.0781
-     24         44         41.9998    0.312
-     25         45         41.9587     1.25
-     26         47         41.9085     1.25
-     27         49         41.8434    0.625
-     28         51         41.8262    0.312
-     29         53         41.8022    0.156
-     30         55         41.7995   0.0781
-     31         57         41.7975   0.0391
-     32         59         41.7927   0.0195
-     33         61         41.7902   0.00977
-     34         63         41.7869   0.00488
-     35         65         41.7859   0.00244
-     36         67         41.7832   0.00122
-     37         69          41.782   0.00061
-     38         71         41.7807   0.000305
-     39         73         41.7801   0.000153
-  Iteration  Func-count      f(x)    Lambda
-     40         75         41.7792   7.63e-05
-     41         77         41.7781   3.81e-05
-     42         79         41.7777   1.91e-05
-     43         81         41.7774   9.54e-06
-     44         83         41.7773   4.77e-06
-     45         85         41.7772   2.38e-06
-     46         87         41.7768   1.19e-06
-     47         89         41.7761   5.96e-07
-     48         91         41.7761   2.98e-07
-     49         93         41.7759   1.49e-07
-     50         95         41.7759   7.45e-08
-     51         97         41.7758   3.73e-08
- TolFun reached. Func-count 97. Final f(x)=41.7758. Elapsed time 1.54
-Checking gradient ...
-
-   analytic   diffs     delta
-
-   -0.0071   -0.1225    0.1154
-   -0.0033   -0.0033   -0.0000
-   -0.0477   -0.0638    0.0160
-    0.0011    0.0011   -0.0000
-   -0.0017    0.0868   -0.0885
-   -0.0039   -0.0039   -0.0000
-   -0.0100   -0.1310    0.1210
-    0.0000    0.0000   -0.0000
-    0.0032    0.0482   -0.0450
-    0.0067    0.0217   -0.0150
-    0.0023   -0.0444    0.0468
-    0.0022    0.0070   -0.0048
-    0.0027    0.0358   -0.0331
-    0.0027    0.0486   -0.0459
-   -0.0008   -0.0135    0.0126
-   -0.0017   -0.0038    0.0021
-    0.0004   -0.0042    0.0046
-    0.0015    0.0102   -0.0087
-    0.0028    0.0028   -0.0000
-
-Laplace 
- 
-Full GP:
- 
-Checking gradient ...
-
-   analytic   diffs     delta
-
-   -2.2030   -2.2030    0.0000
-    4.2467    4.2467    0.0000
-  -13.4153  -13.4153   -0.0000
-
-FIC:
- 
-Checking gradient ...
-
-   analytic   diffs     delta
-
-   -1.0235   -1.0180   -0.0055
-    4.2467    4.2467    0.0000
-  -12.9344  -12.9344   -0.0000
-    0.3752    0.3752   -0.0000
-    0.0957    0.0957   -0.0000
-    0.7028    0.7028    0.0000
-    0.2347    0.2251    0.0096
-    0.3373    0.3465   -0.0092
-    0.1788    0.1788   -0.0000
-    0.3103    0.3083    0.0020
-    0.1460    0.1441    0.0019
-    0.1498    0.1498    0.0000
-    0.2502    0.2501    0.0001
-    0.0953    0.0953    0.0000
-    0.1337    0.1337    0.0000
-    0.3556    0.3556    0.0001
-    0.2201    0.2215   -0.0015
-    0.0271    0.0271   -0.0000
-    0.1324    0.1317    0.0007
-    0.1099    0.1109   -0.0010
-   -0.0019   -0.0019    0.0000
-    0.1841    0.1841   -0.0000
-    0.0958    0.0958   -0.0000
-   -0.0179   -0.0179   -0.0000
-    0.1801    0.1801   -0.0000
-    0.0825    0.0825    0.0000
-   -0.0277   -0.0277   -0.0000
-
-PIC:
- 
-Checking gradient ...
-
-   analytic   diffs     delta
-
-   -2.4573   -2.4562   -0.0011
-    4.2467    4.2467   -0.0000
-  -13.5125  -13.5125   -0.0000
-    0.3753    0.3753    0.0000
-    0.0958    0.0958   -0.0000
-    0.7028    0.7028    0.0000
-    0.3135    0.3095    0.0039
-    0.1868    0.1907   -0.0039
-    0.1788    0.1788    0.0000
-    0.2314    0.2299    0.0015
-    0.0945    0.0930    0.0015
-    0.1498    0.1498   -0.0000
-    0.2515    0.2514    0.0000
-    0.0963    0.0963    0.0000
-    0.1337    0.1337    0.0000
-    0.2581    0.2574    0.0007
-    0.1981    0.2001   -0.0020
-    0.0271    0.0271   -0.0000
-    0.1377    0.1372    0.0005
-    0.1372    0.1381   -0.0009
-   -0.0019   -0.0019   -0.0000
-    0.1852    0.1852   -0.0000
-    0.0952    0.0952   -0.0000
-   -0.0179   -0.0179   -0.0000
-    0.1800    0.1800   -0.0000
-    0.0824    0.0824    0.0000
-   -0.0277   -0.0277   -0.0000
-
-CS+FIC:
- 
-Checking gradient ...
-
-   analytic   diffs     delta
-
-   -0.1610   -0.1610   -0.0000
-    3.3462    3.3462   -0.0000
-  -13.6002  -13.6002   -0.0000
-    0.3752    0.3752    0.0000
-    0.0957    0.0957   -0.0000
-    0.7028    0.7028    0.0000
-    0.2911    0.2890    0.0021
-    0.2082    0.2115   -0.0033
-    0.1788    0.1788   -0.0000
-    0.2977    0.2960    0.0017
-    0.1296    0.1273    0.0023
-    0.1498    0.1498    0.0000
-    0.2506    0.2505    0.0001
-    0.0956    0.0956    0.0000
-    0.1337    0.1337    0.0000
-    0.2405    0.2394    0.0011
-    0.1809    0.1820   -0.0010
-    0.0271    0.0271   -0.0000
-    0.1458    0.1449    0.0009
-    0.1182    0.1195   -0.0014
-   -0.0019   -0.0019    0.0000
-    0.1823    0.1823   -0.0000
-    0.0959    0.0959   -0.0000
-   -0.0179   -0.0179   -0.0000
-    0.1801    0.1801   -0.0000
-    0.0825    0.0825    0.0000
-   -0.0277   -0.0277   -0.0000
-
-EP 
- 
-Full GP:
- 
-Checking gradient ...
-
-   analytic   diffs     delta
-
-   -2.1972   -2.1972    0.0000
-    4.2467    4.2467    0.0000
-  -13.4149  -13.4149   -0.0000
-
-FIC:
- 
-Checking gradient ...
-
-   analytic   diffs     delta
-
-   -1.0170   -1.0169   -0.0000
-    4.2467    4.2467    0.0000
-  -12.9336  -12.9336   -0.0000
-    0.3752    0.3752    0.0000
-    0.0957    0.0957    0.0000
-    0.7028    0.7028    0.0000
-    0.2254    0.2254    0.0000
-    0.3465    0.3465   -0.0000
-    0.1788    0.1788   -0.0000
-    0.3083    0.3083   -0.0000
-    0.1441    0.1441   -0.0000
-    0.1498    0.1498    0.0000
-    0.2501    0.2501    0.0000
-    0.0953    0.0953    0.0000
-    0.1337    0.1337    0.0000
-    0.3548    0.3549   -0.0000
-    0.2221    0.2221   -0.0000
-    0.0271    0.0271   -0.0000
-    0.1317    0.1317    0.0000
-    0.1109    0.1109    0.0000
-   -0.0019   -0.0019    0.0000
-    0.1841    0.1841   -0.0000
-    0.0958    0.0958   -0.0000
-   -0.0179   -0.0179   -0.0000
-    0.1801    0.1801    0.0000
-    0.0825    0.0825   -0.0000
-   -0.0277   -0.0277   -0.0000
-
-PIC:
- 
-Checking gradient ...
-
-   analytic   diffs     delta
-
-   -2.4510   -2.4510    0.0000
-    4.2467    4.2467   -0.0000
-  -13.5119  -13.5119    0.0000
-    0.3753    0.3753   -0.0000
-    0.0958    0.0958   -0.0000
-    0.7028    0.7028    0.0000
-    0.3096    0.3096   -0.0000
-    0.1907    0.1907   -0.0000
-    0.1788    0.1788    0.0000
-    0.2299    0.2299    0.0000
-    0.0930    0.0930    0.0000
-    0.1498    0.1498   -0.0000
-    0.2514    0.2514    0.0000
-    0.0963    0.0963   -0.0000
-    0.1337    0.1337    0.0000
-    0.2574    0.2574   -0.0000
-    0.2002    0.2002   -0.0000
-    0.0271    0.0271   -0.0000
-    0.1372    0.1372    0.0000
-    0.1381    0.1381   -0.0000
-   -0.0019   -0.0019   -0.0000
-    0.1852    0.1852   -0.0000
-    0.0952    0.0952    0.0000
-   -0.0179   -0.0179   -0.0000
-    0.1800    0.1800   -0.0000
-    0.0824    0.0824   -0.0000
-   -0.0277   -0.0277   -0.0000
-
-CS+FIC:
- 
-Checking gradient ...
-
-   analytic   diffs     delta
-
-   -0.1264   -0.1264   -0.0000
-    3.3462    3.3462   -0.0000
-  -13.5887  -13.5887   -0.0000
-    0.3752    0.3752    0.0000
-    0.0957    0.0957   -0.0000
-    0.7028    0.7028    0.0000
-    0.2893    0.2893   -0.0000
-    0.2115    0.2115   -0.0000
-    0.1788    0.1788    0.0000
-    0.2960    0.2960   -0.0000
-    0.1273    0.1273   -0.0000
-    0.1498    0.1498   -0.0000
-    0.2505    0.2505    0.0000
-    0.0956    0.0956    0.0000
-    0.1337    0.1337    0.0000
-    0.2395    0.2395   -0.0000
-    0.1822    0.1822   -0.0000
-    0.0271    0.0271   -0.0000
-    0.1449    0.1449    0.0000
-    0.1195    0.1195   -0.0000
-   -0.0019   -0.0019   -0.0000
-    0.1823    0.1823   -0.0000
-    0.0959    0.0959   -0.0000
-   -0.0179   -0.0179   -0.0000
-    0.1801    0.1801   -0.0000
-    0.0825    0.0825    0.0000
-   -0.0277   -0.0277   -0.0000
-
-
- gp hyperparameters: 
- 
-         0    2.3026    2.3026
-
-Demo completed in 2.753 minutes
diff --git a/tests/realValues/hierprior_fig1.fig b/tests/realValues/hierprior_fig1.fig
deleted file mode 100644
index 87fcdc82..00000000
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diff --git a/tests/realValues/hurdle.mat b/tests/realValues/hurdle.mat
deleted file mode 100644
index 433e0ea8..00000000
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diff --git a/tests/realValues/hurdle.txt b/tests/realValues/hurdle.txt
deleted file mode 100644
index 4fa486e4..00000000
--- a/tests/realValues/hurdle.txt
+++ /dev/null
@@ -1,37 +0,0 @@
- Iteration  Func-count   Grad-count         f(x)         Step-size
-     0           1           1             308.325    
-     1           2           2             250.194       0.0270693
-     2           4           4              217.36        0.343244
-     3           5           5             213.499               1
-     4           6           6             212.393               1
-     5           7           7             208.388               1
-     6           9           9             207.639             0.1
-     7          12          12             207.121        0.199533
-     8          13          13             207.077               1
-     9          14          14             207.056               1
- Optimizer Results
-  Algorithm Used: Broyden-Fletcher-Goldfarb-Shanno (BFGS)
-  Exit message : Change in the objective function value was less than TolFun.
-  Iterations : 10
-  Function Count : 15
-  Minimum found : 207.0551
-  Intern Time : 0.012945 seconds
-  Total Time : 21.6357 seconds
- Iteration  Func-count   Grad-count         f(x)         Step-size
-     0           1           1             2750.29    
-     1           2           2             2642.11      0.00651754
-     2           4           4             2634.45             0.1
-     3           5           5             2604.95               1
-     4           7           7             2603.24        0.426833
-     5           8           8             2602.72               1
-     6           9           9             2602.69               1
-     7          10          10             2602.69               1
- Optimizer Results
-  Algorithm Used: Broyden-Fletcher-Goldfarb-Shanno (BFGS)
-  Exit message : Change in x was smaller than the specified tolerance TolX.
-  Iterations : 8
-  Function Count : 11
-  Minimum found : 2602.6849
-  Intern Time : 0.008021 seconds
-  Total Time : 116.946 seconds
-Demo completed in 2.323 minutes
diff --git a/tests/realValues/hurdle_fig1.fig b/tests/realValues/hurdle_fig1.fig
deleted file mode 100644
index d58bec58..00000000
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diff --git a/tests/realValues/improvemarginals.mat b/tests/realValues/improvemarginals.mat
deleted file mode 100644
index 12bb6bd7..00000000
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diff --git a/tests/realValues/improvemarginals.txt b/tests/realValues/improvemarginals.txt
deleted file mode 100644
index a9d1aaaf..00000000
--- a/tests/realValues/improvemarginals.txt
+++ /dev/null
@@ -1,9 +0,0 @@
-Time elapsed for marginal corrections with EP-FACT: 0.2 s and for predictions 0.1 s
-Time elapsed for marginal corrections with LA-CM2: 0.2 s and for predictions 0.1 s
-Time elapsed for marginal corrections with LA-FACT: 0.1 s and for predictions 0.1 s
-
- gp hyperparameters: 
- 
-    2.3026   -0.1054   -0.1054
-
-Demo completed in 0.029 minutes
diff --git a/tests/realValues/improvemarginals_fig1.fig b/tests/realValues/improvemarginals_fig1.fig
deleted file mode 100644
index 8264f11e..00000000
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diff --git a/tests/realValues/improvemarginals_fig2.fig b/tests/realValues/improvemarginals_fig2.fig
deleted file mode 100644
index 6d7960a1..00000000
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diff --git a/tests/realValues/kalman1.mat b/tests/realValues/kalman1.mat
deleted file mode 100644
index d48a9d43..00000000
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diff --git a/tests/realValues/kalman1.txt b/tests/realValues/kalman1.txt
deleted file mode 100644
index 08ba5d8a..00000000
--- a/tests/realValues/kalman1.txt
+++ /dev/null
@@ -1,14 +0,0 @@
- TolFun reached. Func-count 27. Final f(x)=-10.5562. Elapsed time 0.16
- TolFun reached. Func-count 27. Final f(x)=-10.5562. Elapsed time 1.36
-
-   Parameter |     FULL |   KALMAN 
------------------------------------
-  magnSigma2 |   0.0944 |   0.0944 
- lengthScale |   0.7018 |   0.7018 
-      sigma2 |   0.0083 |   0.0083 
-
- gp hyperparameters: 
- 
-   -2.3606   -0.3541   -4.7891
-
-Demo completed in 0.036 minutes
diff --git a/tests/realValues/kalman1_fig1.fig b/tests/realValues/kalman1_fig1.fig
deleted file mode 100644
index 480d71b0..00000000
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diff --git a/tests/realValues/kalman1_fig2.fig b/tests/realValues/kalman1_fig2.fig
deleted file mode 100644
index c373be74..00000000
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diff --git a/tests/realValues/kalman2.mat b/tests/realValues/kalman2.mat
deleted file mode 100644
index e977f07c..00000000
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diff --git a/tests/realValues/kalman2.txt b/tests/realValues/kalman2.txt
deleted file mode 100644
index f34192eb..00000000
--- a/tests/realValues/kalman2.txt
+++ /dev/null
@@ -1,35 +0,0 @@
- Iteration  Func-count   Grad-count         f(x)         Step-size
-     0           1           1              621.31    
-     1           2           2             419.233      0.00442489
-     2           7           7             304.067        0.011875
-     3          10          10             224.226         0.23565
-     4          14          14             181.318         0.00625
-     5          18          18             162.607       0.0519188
-     6          20          20             153.013         0.40472
-     7          23          23             142.123        0.124208
-     8          25          25             137.704        0.436747
-     9          27          27             134.033        0.472882
-    10          29          29             132.053        0.228239
-    11          31          31             131.375        0.412029
-    12          32          32             130.964               1
-    13          33          33             130.872               1
-    14          34          34             130.833               1
-    15          35          35             130.827               1
-    16          36          36             130.827               1
-    17          37          37             130.827               1
-    18          38          38             130.826               1
-    19          39          39             130.826               1
- Optimizer Results
-  Algorithm Used: Broyden-Fletcher-Goldfarb-Shanno (BFGS)
-  Exit message : Change in x was smaller than the specified tolerance TolX.
-  Iterations : 20
-  Function Count : 40
-  Minimum found : 130.8265
-  Intern Time : 0.23871 seconds
-  Total Time : 349.7141 seconds
-
- gp hyperparameters: 
- 
-    7.4345    3.2174    1.9601    0.3056    4.9391   -1.6257   -0.7279   -3.1561
-
-Demo completed in 5.920 minutes
diff --git a/tests/realValues/kalman2_fig1.fig b/tests/realValues/kalman2_fig1.fig
deleted file mode 100644
index 95019019..00000000
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diff --git a/tests/realValues/kalman2_fig2.fig b/tests/realValues/kalman2_fig2.fig
deleted file mode 100644
index f0ea95e6..00000000
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diff --git a/tests/realValues/kalman2_fig3.fig b/tests/realValues/kalman2_fig3.fig
deleted file mode 100644
index 1ee1f8d3..00000000
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diff --git a/tests/realValues/lgcp.mat b/tests/realValues/lgcp.mat
deleted file mode 100644
index 3111c4ff..00000000
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diff --git a/tests/realValues/lgcp.txt b/tests/realValues/lgcp.txt
deleted file mode 100644
index 3315abad..00000000
--- a/tests/realValues/lgcp.txt
+++ /dev/null
@@ -1,4 +0,0 @@
-Coal disaster data with EP integration over the latent values
-Redwood data with Laplace integration over the latent
-values and MAP estimate for the parameters
-Demo completed in 0.094 minutes
diff --git a/tests/realValues/lgcp_fig1.fig b/tests/realValues/lgcp_fig1.fig
deleted file mode 100644
index a06721f1..00000000
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diff --git a/tests/realValues/lgcp_fig2.fig b/tests/realValues/lgcp_fig2.fig
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index 7386cb73..00000000
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diff --git a/tests/realValues/loopred.mat b/tests/realValues/loopred.mat
deleted file mode 100644
index 77a23346..00000000
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diff --git a/tests/realValues/loopred.txt b/tests/realValues/loopred.txt
deleted file mode 100644
index 68c0cf40..00000000
--- a/tests/realValues/loopred.txt
+++ /dev/null
@@ -1,214 +0,0 @@
-Probit model with Laplace integration over the latent values and
-MAP estimate for the parameters
- TolFun reached. Func-count 27. Final f(x)=81.5289. Elapsed time 0.88
-Evaluating the CV utility. The inference method is MAP.
- The CV-fold number: 1/100 
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-Probit model with EP integration over the latent values and
-MAP estimate for the parameters
- TolFun reached. Func-count 7. Final f(x)=81.748. Elapsed time 0.31
-Evaluating the CV utility. The inference method is MAP.
- The CV-fold number: 1/100 
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-
- gp hyperparameters: 
- 
-    3.2607   -0.7237    0.0613
-
-Demo completed in 0.977 minutes
diff --git a/tests/realValues/loopred_fig1.fig b/tests/realValues/loopred_fig1.fig
deleted file mode 100644
index f57b234c..00000000
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diff --git a/tests/realValues/loopred_fig2.fig b/tests/realValues/loopred_fig2.fig
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index 4591505c..00000000
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diff --git a/tests/realValues/memorysave.txt b/tests/realValues/memorysave.txt
deleted file mode 100644
index db1ab382..00000000
--- a/tests/realValues/memorysave.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification (EP), Neuralnetwork covariance function without and with memory saving (optimization and prediction)
-Elapsed time is 6.419354 seconds.
-Elapsed time is 6.433990 seconds.
-Classification (Laplace), Squared-Exponential covariance function without and with memory saving (optimization and prediction)
-Elapsed time is 1.118030 seconds.
-Elapsed time is 1.522736 seconds.
-Regression, FIC GP, Matern-3/2 covariance function, without and with memory saving (optimization and prediction)
-Elapsed time is 0.295367 seconds.
-Elapsed time is 0.314384 seconds.
-Regression, VAR GP, Squared-Exponential covariance function, without and with memory saving (optimization and prediction)
-Elapsed time is 2.041667 seconds.
-Elapsed time is 1.953337 seconds.
-Spatial process (Laplace), FIC GP, PPCS3 covariance function, without and with memory saving (optimization and prediction)
-Elapsed time is 0.911109 seconds.
-Elapsed time is 1.163756 seconds.
-Spatial process (EP), FIC GP, Squared-Exponential covariance function, without and with memory saving (optimization and prediction)
-Elapsed time is 11.673574 seconds.
-Elapsed time is 11.344609 seconds.
-Spatial process (Laplace), FIC GP, Neuralnetwork covariance function, without and with memory saving (optimization and prediction)
-Elapsed time is 0.872615 seconds.
-Elapsed time is 1.700451 seconds.
-
- gp hyperparameters: 
- 
-    0.2956    1.5729   -2.9479    4.1251
-
-Demo completed in 0.806 minutes
diff --git a/tests/realValues/modelassesment1.mat b/tests/realValues/modelassesment1.mat
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index 2d312822..00000000
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diff --git a/tests/realValues/modelassesment1.txt b/tests/realValues/modelassesment1.txt
deleted file mode 100644
index 820342c2..00000000
--- a/tests/realValues/modelassesment1.txt
+++ /dev/null
@@ -1,567 +0,0 @@
-Full GP model with Gaussian noise model
-MAP estimate for the parameters
- Optimizer Results
-  Algorithm Used: Broyden-Fletcher-Goldfarb-Shanno (BFGS)
-  Exit message : Change in x was smaller than the specified tolerance TolX.
-  Iterations : 16
-  Function Count : 24
-  Minimum found : 44.1644
-  Intern Time : 0.014187 seconds
-  Total Time : 0.67327 seconds
-MAP estimate for the parameters - k-fold-CV
-Evaluating the CV utility. The inference method is MAP.
- The CV-fold number: 1/10 
- The CV-fold number: 2/10 
- The CV-fold number: 3/10 
- The CV-fold number: 4/10 
- The CV-fold number: 5/10 
- The CV-fold number: 6/10 
- The CV-fold number: 7/10 
- The CV-fold number: 8/10 
- The CV-fold number: 9/10 
- The CV-fold number: 10/10 
-MAP estimate for the parameters - LOO-CV
-MCMC integration over the parameters
- Using SLS sampler for hyperparameters
- cycle  etr      slsrej  
-   20  46.162  sls  
-   40  44.536  sls  
-   60  46.492  sls  
-   80  44.724  sls  
-  100  46.073  sls  
-  120  48.356  sls  
-  140  45.043  sls  
-  160  44.583  sls  
-  180  45.860  sls  
-  200  45.606  sls  
-  220  45.049  sls  
-MCMC integration over the parameters - k-fold-CV
-Evaluating the CV utility. The inference method is MCMC.
- The CV-fold number: 1/10 
- cycle  etr      slsrej  
-   20  47.365  sls  
-   40  47.871  sls  
- The CV-fold number: 2/10 
- cycle  etr      slsrej  
-   20  48.040  sls  
-   40  47.595  sls  
- The CV-fold number: 3/10 
- cycle  etr      slsrej  
-   20  47.035  sls  
-   40  47.781  sls  
- The CV-fold number: 4/10 
- cycle  etr      slsrej  
-   20  46.956  sls  
-   40  44.509  sls  
- The CV-fold number: 5/10 
- cycle  etr      slsrej  
-   20  48.573  sls  
-   40  46.945  sls  
- The CV-fold number: 6/10 
- cycle  etr      slsrej  
-   20  50.441  sls  
-   40  50.100  sls  
- The CV-fold number: 7/10 
- cycle  etr      slsrej  
-   20  47.685  sls  
-   40  46.701  sls  
- The CV-fold number: 8/10 
- cycle  etr      slsrej  
-   20  43.340  sls  
-   40  41.618  sls  
- The CV-fold number: 9/10 
- cycle  etr      slsrej  
-   20  47.305  sls  
-   40  47.582  sls  
- The CV-fold number: 10/10 
- cycle  etr      slsrej  
-   20  47.354  sls  
-   40  46.455  sls  
-MCMC integration over the parameters - LOO-CV
-Grid integration over the parameters
- IA-grid: finding the mode
- IA-grid: computing Hessian using multiplication
- IA-grid: evaluating density in a grid
- IA-grid: evaluated density at 335 points
-    Elapsed time 1.52 seconds
- IA-grid: Total elapsed time 1.72 seconds
-Grid integration over the parameters - k-fold-CV
-Evaluating the CV utility. The inference method is IA.
- The CV-fold number: 1/10 
- IA-grid: finding the mode
- IA-grid: computing Hessian using multiplication
- IA-grid: evaluating density in a grid
- IA-grid: evaluated density at 326 points
-    Elapsed time 1.34 seconds
- IA-grid: Total elapsed time 1.81 seconds
- The CV-fold number: 2/10 
- IA-grid: finding the mode
- IA-grid: computing Hessian using multiplication
- IA-grid: evaluating density in a grid
- IA-grid: evaluated density at 333 points
-    Elapsed time 1.36 seconds
- IA-grid: Total elapsed time 1.74 seconds
- The CV-fold number: 3/10 
- IA-grid: finding the mode
- IA-grid: computing Hessian using multiplication
- IA-grid: evaluating density in a grid
- IA-grid: evaluated density at 372 points
-    Elapsed time 1.52 seconds
- IA-grid: Total elapsed time 1.85 seconds
- The CV-fold number: 4/10 
- IA-grid: finding the mode
- IA-grid: computing Hessian using multiplication
- IA-grid: evaluating density in a grid
- IA-grid: evaluated density at 342 points
-    Elapsed time 1.40 seconds
- IA-grid: Total elapsed time 1.92 seconds
- The CV-fold number: 5/10 
- IA-grid: finding the mode
- IA-grid: computing Hessian using multiplication
- IA-grid: evaluating density in a grid
- IA-grid: evaluated density at 357 points
-    Elapsed time 1.46 seconds
- IA-grid: Total elapsed time 1.80 seconds
- The CV-fold number: 6/10 
- IA-grid: finding the mode
- IA-grid: computing Hessian using multiplication
- IA-grid: evaluating density in a grid
- IA-grid: evaluated density at 314 points
-    Elapsed time 1.28 seconds
- IA-grid: Total elapsed time 1.86 seconds
- The CV-fold number: 7/10 
- IA-grid: finding the mode
- IA-grid: computing Hessian using multiplication
- IA-grid: evaluating density in a grid
- IA-grid: evaluated density at 354 points
-    Elapsed time 1.44 seconds
- IA-grid: Total elapsed time 1.93 seconds
- The CV-fold number: 8/10 
- IA-grid: finding the mode
- IA-grid: computing Hessian using multiplication
- IA-grid: evaluating density in a grid
- IA-grid: evaluated density at 363 points
-    Elapsed time 1.48 seconds
- IA-grid: Total elapsed time 2.08 seconds
- The CV-fold number: 9/10 
- IA-grid: finding the mode
- IA-grid: computing Hessian using multiplication
- IA-grid: evaluating density in a grid
- IA-grid: evaluated density at 351 points
-    Elapsed time 1.46 seconds
- IA-grid: Total elapsed time 1.74 seconds
- The CV-fold number: 10/10 
- IA-grid: finding the mode
- IA-grid: computing Hessian using multiplication
- IA-grid: evaluating density in a grid
- IA-grid: evaluated density at 341 points
-    Elapsed time 1.45 seconds
- IA-grid: Total elapsed time 1.82 seconds
-Grid integration over the parameters - LOO-CV
-GP with FIC sparse approximation
-
-gp_fic = 
-
-            type: 'FIC'
-             lik: [1x1 struct]
-              cf: {[1x1 struct]}
-    infer_params: 'covariance+likelihood'
-    jitterSigma2: 1.0000e-06
-             X_u: [36x2 double]
-            nind: 36
-               p: [1x1 struct]
-              fh: [1x1 struct]
-
-MAP estimate for the parameters
- Optimizer Results
-  Algorithm Used: Broyden-Fletcher-Goldfarb-Shanno (BFGS)
-  Exit message : Change in x was smaller than the specified tolerance TolX.
-  Iterations : 19
-  Function Count : 30
-  Minimum found : 42.5028
-  Intern Time : 0.013008 seconds
-  Total Time : 0.34139 seconds
-
-n =
-
-   225
-
-MAP estimate for the parameters - k-fold-CV
-Evaluating the CV utility. The inference method is MAP.
- The CV-fold number: 1/10 
- The CV-fold number: 2/10 
- The CV-fold number: 3/10 
- The CV-fold number: 4/10 
- The CV-fold number: 5/10 
- The CV-fold number: 6/10 
- The CV-fold number: 7/10 
- The CV-fold number: 8/10 
- The CV-fold number: 9/10 
- The CV-fold number: 10/10 
-MAP estimate for the parameters - LOO-CV
-MCMC integration over the parameters
- Using SLS sampler for hyperparameters
- cycle  etr      slsrej  
-   20  43.432  sls  
-   40  47.122  sls  
-   60  43.719  sls  
-   80  46.022  sls  
-  100  43.813  sls  
-  120  43.664  sls  
-  140  45.968  sls  
-  160  43.173  sls  
-  180  45.430  sls  
-  200  43.797  sls  
-  220  45.146  sls  
-MCMC integration over the parameters - k-fold-CV
-Evaluating the CV utility. The inference method is MCMC.
- The CV-fold number: 1/10 
- Using SLS sampler for hyperparameters
- cycle  etr      slsrej  
-   20  47.086  sls  
-   40  46.972  sls  
- The CV-fold number: 2/10 
- Using SLS sampler for hyperparameters
- cycle  etr      slsrej  
-   20  46.050  sls  
-   40  46.532  sls  
- The CV-fold number: 3/10 
- Using SLS sampler for hyperparameters
- cycle  etr      slsrej  
-   20  46.107  sls  
-   40  47.328  sls  
- The CV-fold number: 4/10 
- Using SLS sampler for hyperparameters
- cycle  etr      slsrej  
-   20  48.334  sls  
-   40  42.812  sls  
- The CV-fold number: 5/10 
- Using SLS sampler for hyperparameters
- cycle  etr      slsrej  
-   20  46.991  sls  
-   40  45.679  sls  
- The CV-fold number: 6/10 
- Using SLS sampler for hyperparameters
- cycle  etr      slsrej  
-   20  46.842  sls  
-   40  46.800  sls  
- The CV-fold number: 7/10 
- Using SLS sampler for hyperparameters
- cycle  etr      slsrej  
-   20  46.764  sls  
-   40  46.945  sls  
- The CV-fold number: 8/10 
- Using SLS sampler for hyperparameters
- cycle  etr      slsrej  
-   20  39.821  sls  
-   40  41.284  sls  
- The CV-fold number: 9/10 
- Using SLS sampler for hyperparameters
- cycle  etr      slsrej  
-   20  47.632  sls  
-   40  47.333  sls  
- The CV-fold number: 10/10 
- Using SLS sampler for hyperparameters
- cycle  etr      slsrej  
-   20  45.015  sls  
-   40  44.994  sls  
-MCMC integration over the parameters - LOO-CV
-Grid integration over the parameters
- IA-grid: finding the mode
- IA-grid: computing Hessian using multiplication
- IA-grid: evaluating density in a grid
- IA-grid: evaluated density at 348 points
-    Elapsed time 1.30 seconds
- IA-grid: Total elapsed time 1.43 seconds
-Grid integration over the parameters - k-fold-CV
-Evaluating the CV utility. The inference method is IA.
- The CV-fold number: 1/10 
- IA-grid: finding the mode
- IA-grid: computing Hessian using multiplication
- IA-grid: evaluating density in a grid
- IA-grid: evaluated density at 296 points
-    Elapsed time 1.10 seconds
- IA-grid: Total elapsed time 1.78 seconds
- The CV-fold number: 2/10 
- IA-grid: finding the mode
- IA-grid: computing Hessian using multiplication
- IA-grid: evaluating density in a grid
- IA-grid: evaluated density at 341 points
-    Elapsed time 1.25 seconds
- IA-grid: Total elapsed time 1.66 seconds
- The CV-fold number: 3/10 
- IA-grid: finding the mode
- IA-grid: computing Hessian using multiplication
- IA-grid: evaluating density in a grid
- IA-grid: evaluated density at 385 points
-    Elapsed time 1.38 seconds
- IA-grid: Total elapsed time 1.81 seconds
- The CV-fold number: 4/10 
- IA-grid: finding the mode
- IA-grid: computing Hessian using multiplication
- IA-grid: evaluating density in a grid
- IA-grid: evaluated density at 312 points
-    Elapsed time 1.12 seconds
- IA-grid: Total elapsed time 1.68 seconds
- The CV-fold number: 5/10 
- IA-grid: finding the mode
- IA-grid: computing Hessian using multiplication
- IA-grid: evaluating density in a grid
- IA-grid: evaluated density at 348 points
-    Elapsed time 1.24 seconds
- IA-grid: Total elapsed time 1.54 seconds
- The CV-fold number: 6/10 
- IA-grid: finding the mode
- IA-grid: computing Hessian using multiplication
- IA-grid: evaluating density in a grid
- IA-grid: evaluated density at 350 points
-    Elapsed time 1.25 seconds
- IA-grid: Total elapsed time 1.66 seconds
- The CV-fold number: 7/10 
- IA-grid: finding the mode
- IA-grid: computing Hessian using multiplication
- IA-grid: evaluating density in a grid
- IA-grid: evaluated density at 336 points
-    Elapsed time 1.21 seconds
- IA-grid: Total elapsed time 2.23 seconds
- The CV-fold number: 8/10 
- IA-grid: finding the mode
- IA-grid: computing Hessian using multiplication
- IA-grid: evaluating density in a grid
- IA-grid: evaluated density at 353 points
-    Elapsed time 1.28 seconds
- IA-grid: Total elapsed time 1.82 seconds
- The CV-fold number: 9/10 
- IA-grid: finding the mode
- IA-grid: computing Hessian using multiplication
- IA-grid: evaluating density in a grid
- IA-grid: evaluated density at 337 points
-    Elapsed time 1.23 seconds
- IA-grid: Total elapsed time 1.55 seconds
- The CV-fold number: 10/10 
- IA-grid: finding the mode
- IA-grid: computing Hessian using multiplication
- IA-grid: evaluating density in a grid
- IA-grid: evaluated density at 324 points
-    Elapsed time 1.17 seconds
- IA-grid: Total elapsed time 1.56 seconds
-Grid integration over the parameters - LOO-CV
-GP with PIC sparse approximation
-MAP estimate for the parameters
- Optimizer Results
-  Algorithm Used: Broyden-Fletcher-Goldfarb-Shanno (BFGS)
-  Exit message : Change in x was smaller than the specified tolerance TolX.
-  Iterations : 14
-  Function Count : 22
-  Minimum found : 41.3886
-  Intern Time : 0.009816 seconds
-  Total Time : 0.70206 seconds
-MAP estimate for the parameters - k-fold-CV
-Evaluating the CV utility. The inference method is MAP.
- The CV-fold number: 1/10 
- The CV-fold number: 2/10 
- The CV-fold number: 3/10 
- The CV-fold number: 4/10 
- The CV-fold number: 5/10 
- The CV-fold number: 6/10 
- The CV-fold number: 7/10 
- The CV-fold number: 8/10 
- The CV-fold number: 9/10 
- The CV-fold number: 10/10 
-MAP estimate for the parameters - LOO-CV
-MCMC integration over the parameters
- Using SLS sampler for hyperparameters
- cycle  etr      slsrej  
-   20  43.063  sls  
-   40  43.080  sls  
-   60  42.595  sls  
-   80  42.580  sls  
-  100  42.209  sls  
-  120  42.570  sls  
-  140  41.681  sls  
-  160  41.755  sls  
-  180  42.976  sls  
-  200  45.913  sls  
-  220  43.838  sls  
-MCMC integration over the parameters - k-fold-CV
-Evaluating the CV utility. The inference method is MCMC.
- The CV-fold number: 1/10 
- Using SLS sampler for hyperparameters
- cycle  etr      slsrej  
-   20  44.577  sls  
-   40  44.882  sls  
- The CV-fold number: 2/10 
- Using SLS sampler for hyperparameters
- cycle  etr      slsrej  
-   20  45.445  sls  
-   40  47.048  sls  
- The CV-fold number: 3/10 
- Using SLS sampler for hyperparameters
- cycle  etr      slsrej  
-   20  49.586  sls  
-   40  45.713  sls  
- The CV-fold number: 4/10 
- Using SLS sampler for hyperparameters
- cycle  etr      slsrej  
-   20  42.448  sls  
-   40  44.822  sls  
- The CV-fold number: 5/10 
- Using SLS sampler for hyperparameters
- cycle  etr      slsrej  
-   20  45.504  sls  
-   40  43.310  sls  
- The CV-fold number: 6/10 
- Using SLS sampler for hyperparameters
- cycle  etr      slsrej  
-   20  46.215  sls  
-   40  44.736  sls  
- The CV-fold number: 7/10 
- Using SLS sampler for hyperparameters
- cycle  etr      slsrej  
-   20  45.338  sls  
-   40  45.546  sls  
- The CV-fold number: 8/10 
- Using SLS sampler for hyperparameters
- cycle  etr      slsrej  
-   20  41.780  sls  
-   40  41.196  sls  
- The CV-fold number: 9/10 
- Using SLS sampler for hyperparameters
- cycle  etr      slsrej  
-   20  46.193  sls  
-   40  46.335  sls  
- The CV-fold number: 10/10 
- Using SLS sampler for hyperparameters
- cycle  etr      slsrej  
-   20  43.116  sls  
-   40  43.045  sls  
-MCMC integration over the parameters - LOO-CV
-Grid integration over the parameters
- IA-grid: finding the mode
- IA-grid: computing Hessian using multiplication
- IA-grid: evaluating density in a grid
- IA-grid: evaluated density at 338 points
-    Elapsed time 3.18 seconds
- IA-grid: Total elapsed time 3.55 seconds
-Grid integration over the parameters - k-fold-CV
-Evaluating the CV utility. The inference method is IA.
- The CV-fold number: 1/10 
- IA-grid: finding the mode
- IA-grid: computing Hessian using multiplication
- IA-grid: evaluating density in a grid
- IA-grid: evaluated density at 347 points
-    Elapsed time 3.18 seconds
- IA-grid: Total elapsed time 4.11 seconds
- The CV-fold number: 2/10 
- IA-grid: finding the mode
- IA-grid: computing Hessian using multiplication
- IA-grid: evaluating density in a grid
- IA-grid: evaluated density at 337 points
-    Elapsed time 3.10 seconds
- IA-grid: Total elapsed time 4.11 seconds
- The CV-fold number: 3/10 
- IA-grid: finding the mode
- IA-grid: computing Hessian using multiplication
- IA-grid: evaluating density in a grid
- IA-grid: evaluated density at 355 points
-    Elapsed time 3.26 seconds
- IA-grid: Total elapsed time 4.30 seconds
- The CV-fold number: 4/10 
- IA-grid: finding the mode
-    Elapsed time 1.04 seconds
- IA-grid: computing Hessian using multiplication
- IA-grid: evaluating density in a grid
- IA-grid: evaluated density at 348 points
-    Elapsed time 3.20 seconds
- IA-grid: Total elapsed time 4.47 seconds
- The CV-fold number: 5/10 
- IA-grid: finding the mode
-    Elapsed time 1.03 seconds
- IA-grid: computing Hessian using multiplication
- IA-grid: evaluating density in a grid
- IA-grid: evaluated density at 352 points
-    Elapsed time 3.20 seconds
- IA-grid: Total elapsed time 4.45 seconds
- The CV-fold number: 6/10 
- IA-grid: finding the mode
- IA-grid: computing Hessian using multiplication
- IA-grid: evaluating density in a grid
- IA-grid: evaluated density at 336 points
-    Elapsed time 3.06 seconds
- IA-grid: Total elapsed time 3.88 seconds
- The CV-fold number: 7/10 
- IA-grid: finding the mode
-    Elapsed time 1.53 seconds
- IA-grid: computing Hessian using multiplication
- IA-grid: evaluating density in a grid
- IA-grid: evaluated density at 347 points
-    Elapsed time 3.17 seconds
- IA-grid: Total elapsed time 4.92 seconds
- The CV-fold number: 8/10 
- IA-grid: finding the mode
-    Elapsed time 1.03 seconds
- IA-grid: computing Hessian using multiplication
- IA-grid: evaluating density in a grid
- IA-grid: evaluated density at 342 points
-    Elapsed time 3.13 seconds
- IA-grid: Total elapsed time 4.39 seconds
- The CV-fold number: 9/10 
- IA-grid: finding the mode
-    Elapsed time 1.25 seconds
- IA-grid: computing Hessian using multiplication
- IA-grid: evaluating density in a grid
- IA-grid: evaluated density at 335 points
-    Elapsed time 3.06 seconds
- IA-grid: Total elapsed time 4.53 seconds
- The CV-fold number: 10/10 
- IA-grid: finding the mode
- IA-grid: computing Hessian using multiplication
- IA-grid: evaluating density in a grid
- IA-grid: evaluated density at 344 points
-    Elapsed time 3.15 seconds
- IA-grid: Total elapsed time 4.20 seconds
-Grid integration over the parameters - LOO-CV
-Summary of the results
-
-S =
-
-        full_MAP  full_MCMC  full_IA  FIC_MAP  FIC_MCMC  FIC_IA  PIC_MAP  PIC_MCMC  PIC_IA
- CV-mlpd   0.05      0.06      0.05      0.06     0.06      0.06     0.07     0.08     0.08
- CV-rmse   0.24      0.24      0.24      0.23     0.23      0.23     0.23     0.23     0.23
- LOO-mlpd  0.06      0.05      0.05      0.06     0.05      0.05     0.07     0.06     0.06
- LOO-rmse  0.24      0.24      0.24      0.23     0.23      0.23     0.23     0.23     0.23
- 
- WAIC_V    0.07      0.06      0.07      0.11    -1.06     -0.11     0.09     0.08     0.08
- WAIC_G    0.10      0.09      0.09      0.17    -0.45      0.42     0.12     0.12     0.12
- 
- DIC_h      NaN     -0.20     -0.20       NaN    -0.19     -0.19      NaN    -0.19    -0.18
- DIC_a      NaN      0.05      0.07       NaN     0.12      0.12      NaN     0.09     0.09
- DIC_l     0.07      0.00      0.00      0.11     0.00      0.00     0.09    
- peff_h     NaN      3.47      3.25       NaN     4.20      3.21      NaN     3.64     3.34
- peff_a     NaN     43.12     39.79       NaN    81.90     82.93      NaN    46.67    47.16
- peff_l   37.44      0.00      0.00     76.09     0.00      0.00    45.15    
- peff_l2  37.61      0.00      0.00     76.28     0.00      0.00    45.22    
- 
- 
- The notation is as follows:
- CV-mlpd  = mean log predictive density from the 10-fold CV. 
- CV-rmse  = root mean squared error from the 10-fold LOO-CV. 
- LOO-mlpd = mean log predictive density from the LOO-CV. 
- LOO-rmse = root mean squared error from the 10-fold CV. 
- WAIC_V   = WAIC via variance method 
- WAIC_G   = WAIC via Gibbs training utility method 
- DIC_h    = DIC with focus on parameters. 
- DIC_a    = DIC with focus on parameters and latent variables (all). 
- DIC_l    = DIC with focus on latent variables. 
- peff_h   = effective number of parameters (latent variables marginalized). 
- peff_a   = effective number of parameters and latent variables. 
- peff_l   = effective number of latent variables evaluated with gp_peff. 
- peff_l2  = effective number of latent variables evaluated with gp_dic. 
- 
-
-
- gp hyperparameters: 
- 
-    1.0742   -0.1662   -0.2199   -3.1540
-
-Demo completed in 12.993 minutes
diff --git a/tests/realValues/modelassesment2.mat b/tests/realValues/modelassesment2.mat
deleted file mode 100644
index b008b78e..00000000
Binary files a/tests/realValues/modelassesment2.mat and /dev/null differ
diff --git a/tests/realValues/modelassesment2.txt b/tests/realValues/modelassesment2.txt
deleted file mode 100644
index 9b6af42f..00000000
--- a/tests/realValues/modelassesment2.txt
+++ /dev/null
@@ -1,481 +0,0 @@
-Data analysis with probit likelihood
-Probit with Laplace integration over the latent values 
-and MAP estimate for the parameters                    
- Optimizer Results
-  Algorithm Used: Broyden-Fletcher-Goldfarb-Shanno (BFGS)
-  Exit message : Change in the objective function value was less than TolFun.
-  Iterations : 9
-  Function Count : 13
-  Minimum found : 78.8894
-  Intern Time : 0.007441 seconds
-  Total Time : 0.54661 seconds
-Evaluating the CV utility. The inference method is MAP.
- The CV-fold number: 1/10 
- The CV-fold number: 2/10 
- The CV-fold number: 3/10 
- The CV-fold number: 4/10 
- The CV-fold number: 5/10 
- The CV-fold number: 6/10 
- The CV-fold number: 7/10 
- The CV-fold number: 8/10 
- The CV-fold number: 9/10 
- The CV-fold number: 10/10 
-Probit with EP integration over the latent values and MAP 
-estimate for the parameters                               
- Optimizer Results
-  Algorithm Used: Broyden-Fletcher-Goldfarb-Shanno (BFGS)
-  Exit message : Change in the objective function value was less than TolFun.
-  Iterations : 4
-  Function Count : 8
-  Minimum found : 79.1395
-  Intern Time : 0.004267 seconds
-  Total Time : 0.50173 seconds
-Evaluating the CV utility. The inference method is MAP.
- The CV-fold number: 1/10 
- The CV-fold number: 2/10 
- The CV-fold number: 3/10 
- The CV-fold number: 4/10 
- The CV-fold number: 5/10 
- The CV-fold number: 6/10 
- The CV-fold number: 7/10 
- The CV-fold number: 8/10 
- The CV-fold number: 9/10 
- The CV-fold number: 10/10 
-Probit with MCMC integration over the latent values and 
-the parameters                                          
- Using SSLS sampler for hyperparameters and ESLS for latent values
- cycle  etr      lslsn
-   20  -588.426  9.0e+00  
-   40  -587.970  1.0e+01  
-   60  -626.360  1.5e+01  
-   80  -626.707  4.0e+00  
-  100  -617.044  0.0e+00  
-  120  -630.091  2.0e+01  
-  140  -615.030  5.0e+00  
-  160  -653.144  6.0e+00  
-  180  -608.386  1.8e+01  
-  200  -635.256  1.3e+01  
-  220  -590.438  2.0e+00  
-Evaluating the CV utility. The inference method is MCMC.
- The CV-fold number: 1/10 
- Using SSLS sampler for hyperparameters and ESLS for latent values
- cycle  etr      lslsn
-   20  -547.984  7.0e+00  
-   40  -540.295  8.0e+00  
- The CV-fold number: 2/10 
- Using SSLS sampler for hyperparameters and ESLS for latent values
- cycle  etr      lslsn
-   20  -535.925  1.0e+01  
-   40  -505.911  0.0e+00  
- The CV-fold number: 3/10 
- Using SSLS sampler for hyperparameters and ESLS for latent values
- cycle  etr      lslsn
-   20  -526.682  9.0e+00  
-   40  -472.494  5.0e+00  
- The CV-fold number: 4/10 
- Using SSLS sampler for hyperparameters and ESLS for latent values
- cycle  etr      lslsn
-   20  -530.221  1.5e+01  
-   40  -562.331  1.3e+01  
- The CV-fold number: 5/10 
- Using SSLS sampler for hyperparameters and ESLS for latent values
- cycle  etr      lslsn
-   20  -549.694  8.0e+00  
-   40  -521.740  9.0e+00  
- The CV-fold number: 6/10 
- Using SSLS sampler for hyperparameters and ESLS for latent values
- cycle  etr      lslsn
-   20  -481.416  5.0e+00  
-   40  -495.256  2.0e+00  
- The CV-fold number: 7/10 
- Using SSLS sampler for hyperparameters and ESLS for latent values
- cycle  etr      lslsn
-   20  -523.814  1.2e+01  
-   40  -486.134  8.0e+00  
- The CV-fold number: 8/10 
- Using SSLS sampler for hyperparameters and ESLS for latent values
- cycle  etr      lslsn
-   20  -550.736  1.3e+01  
-   40  -551.778  1.3e+01  
- The CV-fold number: 9/10 
- Using SSLS sampler for hyperparameters and ESLS for latent values
- cycle  etr      lslsn
-   20  -551.138  8.0e+00  
-   40  -615.547  8.0e+00  
- The CV-fold number: 10/10 
- Using SSLS sampler for hyperparameters and ESLS for latent values
- cycle  etr      lslsn
-   20  -480.774  0.0e+00  
-   40  -418.991  7.0e+00  
-[Warning: For 1 data point the effective sample size in IS is less than n/10] 
-[> In <a href="matlab: opentoline('/proj/bayes/tsivula/gpstuff/gp/gpmc_loopred.m',113,1)">gpmc_loopred at 113</a>
-  In <a href="matlab: opentoline('/proj/bayes/tsivula/gpstuff/gp/gp_loopred.m',85,1)">gp_loopred at 85</a>
-  In <a href="matlab: opentoline('/proj/bayes/tsivula/gpstuff/gp/demo_modelassesment2.m',137,1)">demo_modelassesment2 at 137</a>
-  In <a href="matlab: opentoline('/proj/matlab/matlab2013b/toolbox/matlab/lang/run.m',63,1)">run at 63</a>
-  In <a href="matlab: opentoline('/proj/bayes/tsivula/gpstuff/xunit/rundemo.m',111,1)">rundemo at 111</a>
-  In <a href="matlab: opentoline('/proj/bayes/tsivula/gpstuff/xunit/test_modelassesment2.m',44,1)">test_modelassesment2>testRunDemo at 44</a>
-  In <a href="matlab: opentoline('/proj/matlab/matlab2013b/toolbox/matlab/testframework/+matlab/+unittest/FunctionTestCase.m',90,1)">FunctionTestCase>FunctionTestCase.test at 90</a>
-  In <a href="matlab: opentoline('/proj/matlab/matlab2013b/toolbox/matlab/testframework/+matlab/+unittest/TestRunner.m',684,1)">TestRunner>TestRunner.runMethod at 684</a>
-  In <a href="matlab: opentoline('/proj/matlab/matlab2013b/toolbox/matlab/testframework/+matlab/+unittest/+plugins/TestRunnerPlugin.m',99,1)">TestRunnerPlugin>TestRunnerPlugin.runMethod at 99</a>
-  In <a href="matlab: opentoline('/proj/matlab/matlab2013b/toolbox/matlab/testframework/+matlab/+unittest/+plugins/TestRunnerPlugin.m',99,1)">TestRunnerPlugin>TestRunnerPlugin.runMethod at 99</a>
-  In <a href="matlab: opentoline('/proj/matlab/matlab2013b/toolbox/matlab/testframework/+matlab/+unittest/TestRunner.m',538,1)">TestRunner>TestRunner.runMethodOnPlugins at 538</a>
-  In <a href="matlab: opentoline('/proj/matlab/matlab2013b/toolbox/matlab/testframework/+matlab/+unittest/TestRunner.m',555,1)">TestRunner>TestRunner.runMethodsOnTestContent at 555</a>
-  In <a href="matlab: opentoline('/proj/matlab/matlab2013b/toolbox/matlab/testframework/+matlab/+unittest/TestRunner.m',753,1)">TestRunner>TestRunner.runTestMethod at 753</a>
-  In <a href="matlab: opentoline('/proj/matlab/matlab2013b/toolbox/matlab/testframework/+matlab/+unittest/+plugins/TestRunnerPlugin.m',95,1)">TestRunnerPlugin>TestRunnerPlugin.runTestMethod at 95</a>
-  In <a href="matlab: opentoline('/proj/matlab/matlab2013b/toolbox/matlab/testframework/+matlab/+unittest/+plugins/TestRunnerPlugin.m',95,1)">TestRunnerPlugin>TestRunnerPlugin.runTestMethod at 95</a>
-  In <a href="matlab: opentoline('/proj/matlab/matlab2013b/toolbox/matlab/testframework/+matlab/+unittest/TestRunner.m',538,1)">TestRunner>TestRunner.runMethodOnPlugins at 538</a>
-  In <a href="matlab: opentoline('/proj/matlab/matlab2013b/toolbox/matlab/testframework/+matlab/+unittest/TestRunner.m',655,1)">TestRunner>TestRunner.runTestSuite at 655</a>
-  In <a href="matlab: opentoline('/proj/matlab/matlab2013b/toolbox/matlab/testframework/+matlab/+unittest/+plugins/TestRunnerPlugin.m',54,1)">TestRunnerPlugin>TestRunnerPlugin.runTestSuite at 54</a>
-  In <a href="matlab: opentoline('/proj/matlab/matlab2013b/toolbox/matlab/testframework/+matlab/+unittest/+plugins/TestRunnerPlugin.m',54,1)">TestRunnerPlugin>TestRunnerPlugin.runTestSuite at 54</a>
-  In <a href="matlab: opentoline('/proj/matlab/matlab2013b/toolbox/matlab/testframework/+matlab/+unittest/+plugins/FailureDiagnosticsPlugin.m',77,1)">FailureDiagnosticsPlugin>FailureDiagnosticsPlugin.runTestSuite at 77</a>
-  In <a href="matlab: opentoline('/proj/matlab/matlab2013b/toolbox/matlab/testframework/+matlab/+unittest/TestRunner.m',538,1)">TestRunner>TestRunner.runMethodOnPlugins at 538</a>
-  In <a href="matlab: opentoline('/proj/matlab/matlab2013b/toolbox/matlab/testframework/+matlab/+unittest/TestRunner.m',175,1)">TestRunner>TestRunner.run at 175</a>
-  In <a href="matlab: opentoline('/proj/matlab/matlab2013b/toolbox/matlab/testframework/+matlab/+unittest/TestSuite.m',364,1)">TestSuite>TestSuite.run at 364</a>
-  In <a href="matlab: opentoline('/proj/matlab/matlab2013b/toolbox/matlab/testframework/runtests.m',44,1)">runtests at 44</a>
-  In <a href="matlab: opentoline('/proj/bayes/tsivula/gpstuff/xunit/runtestset.m',94,1)">runtestset at 94</a>] 
-Probit with EP integration over the latent values and 
-grid integration over the parameters                  
- Optimizer Results
-  Algorithm Used: Broyden-Fletcher-Goldfarb-Shanno (BFGS)
-  Exit message : Change in x was smaller than the specified tolerance TolX.
-  Iterations : 8
-  Function Count : 11
-  Minimum found : 79.1395
-  Intern Time : 0.006339 seconds
-  Total Time : 0.6401 seconds
- IA-grid: finding the mode
- IA-grid: computing Hessian using multiplication
- IA-grid: evaluating density in a grid
- IA-grid: evaluated density at 32 points
-    Elapsed time 1.72 seconds
- IA-grid: Total elapsed time 2.02 seconds
-Evaluating the CV utility. The inference method is IA.
- The CV-fold number: 1/10 
- IA-grid: finding the mode
- IA-grid: computing Hessian using multiplication
- IA-grid: evaluating density in a grid
- IA-grid: evaluated density at 28 points
-    Elapsed time 1.15 seconds
- IA-grid: Total elapsed time 2.03 seconds
- The CV-fold number: 2/10 
- IA-grid: finding the mode
-    Elapsed time 1.18 seconds
- IA-grid: computing Hessian using multiplication
- IA-grid: evaluating density in a grid
- IA-grid: evaluated density at 32 points
-    Elapsed time 1.37 seconds
- IA-grid: Total elapsed time 2.72 seconds
- The CV-fold number: 3/10 
- IA-grid: finding the mode
- IA-grid: computing Hessian using multiplication
- IA-grid: evaluating density in a grid
- IA-grid: evaluated density at 32 points
-    Elapsed time 1.51 seconds
- IA-grid: Total elapsed time 2.50 seconds
- The CV-fold number: 4/10 
- IA-grid: finding the mode
-    Elapsed time 1.07 seconds
- IA-grid: computing Hessian using multiplication
- IA-grid: evaluating density in a grid
- IA-grid: evaluated density at 32 points
-    Elapsed time 1.45 seconds
- IA-grid: Total elapsed time 2.70 seconds
- The CV-fold number: 5/10 
- IA-grid: finding the mode
- IA-grid: computing Hessian using multiplication
- IA-grid: evaluating density in a grid
- IA-grid: evaluated density at 32 points
-    Elapsed time 1.49 seconds
- IA-grid: Total elapsed time 2.10 seconds
- The CV-fold number: 6/10 
- IA-grid: finding the mode
-    Elapsed time 1.03 seconds
- IA-grid: computing Hessian using multiplication
- IA-grid: evaluating density in a grid
- IA-grid: evaluated density at 32 points
-    Elapsed time 1.45 seconds
- IA-grid: Total elapsed time 2.66 seconds
- The CV-fold number: 7/10 
- IA-grid: finding the mode
- IA-grid: computing Hessian using multiplication
- IA-grid: evaluating density in a grid
- IA-grid: evaluated density at 32 points
-    Elapsed time 1.34 seconds
- IA-grid: Total elapsed time 2.15 seconds
- The CV-fold number: 8/10 
- IA-grid: finding the mode
- IA-grid: computing Hessian using multiplication
- IA-grid: evaluating density in a grid
- IA-grid: evaluated density at 32 points
-    Elapsed time 1.40 seconds
- IA-grid: Total elapsed time 2.44 seconds
- The CV-fold number: 9/10 
- IA-grid: finding the mode
-    Elapsed time 1.07 seconds
- IA-grid: computing Hessian using multiplication
- IA-grid: evaluating density in a grid
- IA-grid: evaluated density at 28 points
-    Elapsed time 1.28 seconds
- IA-grid: Total elapsed time 2.53 seconds
- The CV-fold number: 10/10 
- IA-grid: finding the mode
- IA-grid: computing Hessian using multiplication
- IA-grid: evaluating density in a grid
- IA-grid: evaluated density at 32 points
-    Elapsed time 1.34 seconds
- IA-grid: Total elapsed time 2.30 seconds
-Data analysis with logit likelihood
-Logit with Laplace integration over the latent values and 
-MAP estimate for the parameters                           
- Optimizer Results
-  Algorithm Used: Broyden-Fletcher-Goldfarb-Shanno (BFGS)
-  Exit message : Change in the objective function value was less than TolFun.
-  Iterations : 12
-  Function Count : 15
-  Minimum found : 79.3867
-  Intern Time : 0.008351 seconds
-  Total Time : 0.48857 seconds
-Evaluating the CV utility. The inference method is MAP.
- The CV-fold number: 1/10 
- The CV-fold number: 2/10 
- The CV-fold number: 3/10 
- The CV-fold number: 4/10 
- The CV-fold number: 5/10 
- The CV-fold number: 6/10 
- The CV-fold number: 7/10 
- The CV-fold number: 8/10 
- The CV-fold number: 9/10 
- The CV-fold number: 10/10 
-Logit with EP integration over the latent values and MAP
-estimate for the parameters                             
- Optimizer Results
-  Algorithm Used: Broyden-Fletcher-Goldfarb-Shanno (BFGS)
-  Exit message : Change in the objective function value was less than TolFun.
-  Iterations : 4
-  Function Count : 9
-  Minimum found : 79.5156
-  Intern Time : 0.004829 seconds
-  Total Time : 8.2143 seconds
-Evaluating the CV utility. The inference method is MAP.
- The CV-fold number: 1/10 
- The CV-fold number: 2/10 
- The CV-fold number: 3/10 
- The CV-fold number: 4/10 
- The CV-fold number: 5/10 
- The CV-fold number: 6/10 
- The CV-fold number: 7/10 
- The CV-fold number: 8/10 
- The CV-fold number: 9/10 
- The CV-fold number: 10/10 
-Logit with MCMC integration over the latent values and 
-the parameters                                         
- Using SSLS sampler for hyperparameters and ESLS for latent values
- cycle  etr      lslsn
-   20  -533.055  1.0e+01  
-   40  -518.968  6.0e+00  
-   60  -472.492  3.0e+00  
-   80  -478.123  0.0e+00  
-  100  -599.868  5.0e+00  
-  120  -577.869  1.0e+01  
-  140  -535.928  1.3e+01  
-  160  -555.210  8.0e+00  
-  180  -578.161  7.0e+00  
-  200  -580.977  7.0e+00  
-Evaluating the CV utility. The inference method is MCMC.
- The CV-fold number: 1/10 
- Using SSLS sampler for hyperparameters and ESLS for latent values
- cycle  etr      lslsn
-   20  -501.194  8.0e+00  
-   40  -474.223  5.0e+00  
- The CV-fold number: 2/10 
- Using SSLS sampler for hyperparameters and ESLS for latent values
- cycle  etr      lslsn
-   20  -534.713  1.3e+01  
-   40  -528.955  1.3e+01  
- The CV-fold number: 3/10 
- Using SSLS sampler for hyperparameters and ESLS for latent values
- cycle  etr      lslsn
-   20  -450.989  7.0e+00  
-   40  -500.478  7.0e+00  
- The CV-fold number: 4/10 
- Using SSLS sampler for hyperparameters and ESLS for latent values
- cycle  etr      lslsn
-   20  -503.487  4.0e+00  
-   40  -510.591  0.0e+00  
- The CV-fold number: 5/10 
- Using SSLS sampler for hyperparameters and ESLS for latent values
- cycle  etr      lslsn
-   20  -388.814  5.0e+00  
-   40  -437.343  9.0e+00  
- The CV-fold number: 6/10 
- Using SSLS sampler for hyperparameters and ESLS for latent values
- cycle  etr      lslsn
-   20  -460.051  1.1e+01  
-   40  -470.438  1.0e+01  
- The CV-fold number: 7/10 
- Using SSLS sampler for hyperparameters and ESLS for latent values
- cycle  etr      lslsn
-   20  -438.615  3.0e+00  
-   40  -499.552  6.0e+00  
- The CV-fold number: 8/10 
- Using SSLS sampler for hyperparameters and ESLS for latent values
- cycle  etr      lslsn
-   20  -495.175  1.0e+01  
-   40  -504.217  1.0e+01  
- The CV-fold number: 9/10 
- Using SSLS sampler for hyperparameters and ESLS for latent values
- cycle  etr      lslsn
-   20  -513.708  2.0e+00  
-   40  -522.626  8.0e+00  
- The CV-fold number: 10/10 
- Using SSLS sampler for hyperparameters and ESLS for latent values
- cycle  etr      lslsn
-   20  -519.795  5.0e+00  
-   40  -486.984  7.0e+00  
-Logit with EP integration over the latent values and grid 
-integration over the parameters                           
- Optimizer Results
-  Algorithm Used: Broyden-Fletcher-Goldfarb-Shanno (BFGS)
-  Exit message : Change in x was smaller than the specified tolerance TolX.
-  Iterations : 5
-  Function Count : 9
-  Minimum found : 79.5156
-  Intern Time : 0.005054 seconds
-  Total Time : 9.4608 seconds
- IA-grid: finding the mode
- IA-grid: computing Hessian using multiplication
-    Elapsed time 2.05 seconds
- IA-grid: evaluating density in a grid
- IA-grid: evaluated density at 32 points
-    Elapsed time 47.04 seconds
- IA-grid: Total elapsed time 49.57 seconds
-Evaluating the CV utility. The inference method is IA.
- The CV-fold number: 1/10 
- IA-grid: finding the mode
-    Elapsed time 13.49 seconds
- IA-grid: computing Hessian using multiplication
-    Elapsed time 1.93 seconds
- IA-grid: evaluating density in a grid
- IA-grid: evaluated density at 28 points
-    Elapsed time 34.38 seconds
- IA-grid: Total elapsed time 49.80 seconds
- The CV-fold number: 2/10 
- IA-grid: finding the mode
-    Elapsed time 8.59 seconds
- IA-grid: computing Hessian using multiplication
-    Elapsed time 1.92 seconds
- IA-grid: evaluating density in a grid
- IA-grid: evaluated density at 32 points
-    Elapsed time 40.96 seconds
- IA-grid: Total elapsed time 51.48 seconds
- The CV-fold number: 3/10 
- IA-grid: finding the mode
-    Elapsed time 9.60 seconds
- IA-grid: computing Hessian using multiplication
-    Elapsed time 1.84 seconds
- IA-grid: evaluating density in a grid
- IA-grid: evaluated density at 32 points
-    Elapsed time 42.37 seconds
- IA-grid: Total elapsed time 53.81 seconds
- The CV-fold number: 4/10 
- IA-grid: finding the mode
-    Elapsed time 10.00 seconds
- IA-grid: computing Hessian using multiplication
-    Elapsed time 1.83 seconds
- IA-grid: evaluating density in a grid
- IA-grid: evaluated density at 32 points
-    Elapsed time 40.80 seconds
- IA-grid: Total elapsed time 52.63 seconds
- The CV-fold number: 5/10 
- IA-grid: finding the mode
-    Elapsed time 6.13 seconds
- IA-grid: computing Hessian using multiplication
-    Elapsed time 1.84 seconds
- IA-grid: evaluating density in a grid
- IA-grid: evaluated density at 32 points
-    Elapsed time 42.12 seconds
- IA-grid: Total elapsed time 50.09 seconds
- The CV-fold number: 6/10 
- IA-grid: finding the mode
-    Elapsed time 11.59 seconds
- IA-grid: computing Hessian using multiplication
-    Elapsed time 1.75 seconds
- IA-grid: evaluating density in a grid
- IA-grid: evaluated density at 28 points
-    Elapsed time 35.16 seconds
- IA-grid: Total elapsed time 48.50 seconds
- The CV-fold number: 7/10 
- IA-grid: finding the mode
-    Elapsed time 9.40 seconds
- IA-grid: computing Hessian using multiplication
-    Elapsed time 1.74 seconds
- IA-grid: evaluating density in a grid
- IA-grid: evaluated density at 31 points
-    Elapsed time 39.63 seconds
- IA-grid: Total elapsed time 50.78 seconds
- The CV-fold number: 8/10 
- IA-grid: finding the mode
-    Elapsed time 12.50 seconds
- IA-grid: computing Hessian using multiplication
-    Elapsed time 1.93 seconds
- IA-grid: evaluating density in a grid
- IA-grid: evaluated density at 32 points
-    Elapsed time 41.01 seconds
- IA-grid: Total elapsed time 55.45 seconds
- The CV-fold number: 9/10 
- IA-grid: finding the mode
-    Elapsed time 14.53 seconds
- IA-grid: computing Hessian using multiplication
-    Elapsed time 1.93 seconds
- IA-grid: evaluating density in a grid
- IA-grid: evaluated density at 28 points
-    Elapsed time 35.96 seconds
- IA-grid: Total elapsed time 52.43 seconds
- The CV-fold number: 10/10 
- IA-grid: finding the mode
-    Elapsed time 10.44 seconds
- IA-grid: computing Hessian using multiplication
-    Elapsed time 1.75 seconds
- IA-grid: evaluating density in a grid
- IA-grid: evaluated density at 32 points
-    Elapsed time 40.17 seconds
- IA-grid: Total elapsed time 52.37 seconds
-Summary of the results
-
-S =
-
-         pr_Laplace  pr_EP  pr_MCMC  pr_IA  lo_Laplace  lo_EP  lo_MCMC  lo_IA
- CV-mlpd   -0.29     -0.28   -0.29   -0.28    -0.29     -0.28   -0.28   -0.28
- LOO-mlpd  -0.28     -0.27   -0.28   -0.28    -0.28     -0.28   -0.28   -0.28
- 
- WAIC      -0.31     -0.28   -0.30   -0.28    -0.29     -0.28   -0.28   -0.28
- 
- DIC_h       NaN       NaN   -0.33   -0.33      NaN       NaN   -0.33   -0.33
- DIC_a       NaN       NaN   -0.28   -0.27      NaN       NaN   -0.28   -0.28
- DIC_l     -0.30     -0.28     NaN     NaN    -0.30     -0.28     NaN     NaN
- peff_h      NaN       NaN    1.45    2.40      NaN       NaN    2.96    2.42
- peff_a      NaN       NaN    7.90    6.20      NaN       NaN    9.71    9.20
- peff_l     8.72      9.19     NaN     NaN     8.84      9.18     NaN     NaN
- peff_l2   13.14      8.48     NaN     NaN    12.19      8.41     NaN     NaN
- 
- The notation is as follows:
- pr_*     = probit likelihood and inference method
- lo_*     = logit likelihood and inference method
- CV-mlpd  = mean log predictive density from the 10-fold CV. 
- LOO-mlpd = mean log predictive density from the 10-fold CV. 
- WAIC     = Widely applicable information criterion. 
- DIC_h    = DIC with focus on parameters. 
- DIC_a    = DIC with focus on parameters and laten variables (all). 
- DIC_l    = DIC with focus on latent variables. 
- peff_h   = effective number of parameters (latent variables marginalized). 
- peff_a   = effective number of parameters and latent variables. 
- peff_l   = effective number of latent variables evaluated with gp_peff. 
- peff_l2  = effective number of latent variables evaluated with gp_dic. 
- 
-
-
- gp hyperparameters: 
- 
-    3.8250   -0.8579   -0.1429
-
-Demo completed in 46.102 minutes
diff --git a/tests/realValues/monotonic2.mat b/tests/realValues/monotonic2.mat
deleted file mode 100644
index 066552a8..00000000
Binary files a/tests/realValues/monotonic2.mat and /dev/null differ
diff --git a/tests/realValues/monotonic2.txt b/tests/realValues/monotonic2.txt
deleted file mode 100644
index f912b144..00000000
--- a/tests/realValues/monotonic2.txt
+++ /dev/null
@@ -1,61 +0,0 @@
- IA-CCD: finding the mode
-    Elapsed time 1.34 seconds
- IA-CCD: computing Hessian using multiplication
- IA-CCD: 9 points for 2 parameters
- IA-CCD: autoscaling in 4 directions
- IA-CCD: scaling minmax [0.89 1.10]
- IA-CCD: evaluating density at 9 points
- IA-CCD: Total elapsed time 2.85 seconds
- Iteration  Func-count   Grad-count         f(x)         Step-size
-     0           1           1             102.417    
-     1           2           2             89.3513       0.0650335
-     2           4           4             87.5645        0.148177
-     3           5           5             86.4853               1
-     4           6           6             85.9438               1
-     5           8           8              84.971        0.430378
- Optimizer Results
-  Algorithm Used: Broyden-Fletcher-Goldfarb-Shanno (BFGS)
-  Exit message : Change in x was smaller than the specified tolerance TolX.
-  Iterations : 6
-  Function Count : 10
-  Minimum found : 84.9383
-  Intern Time : 0.006423 seconds
-  Total Time : 1.715 seconds
- IA-CCD: finding the mode
-    Elapsed time 2.74 seconds
- IA-CCD: computing Hessian using multiplication
- IA-CCD: 9 points for 2 parameters
- IA-CCD: autoscaling in 4 directions
- IA-CCD: scaling minmax [0.90 1.35]
- IA-CCD: evaluating density at 9 points
-    Elapsed time 1.29 seconds
- IA-CCD: Total elapsed time 5.31 seconds
- IA-CCD: finding the mode
- IA-CCD: computing Hessian using multiplication
- IA-CCD: 9 points for 2 parameters
- IA-CCD: autoscaling in 4 directions
- IA-CCD: scaling minmax [1.02 1.44]
- IA-CCD: evaluating density at 9 points
-    Elapsed time 1.02 seconds
- IA-CCD: Total elapsed time 2.67 seconds
- Iteration  Func-count   Grad-count         f(x)         Step-size
-     0           1           1             84.3228    
-     1           3           3             84.0392        0.112757
-     2           4           4             83.9396               1
- Optimizer Results
-  Algorithm Used: Broyden-Fletcher-Goldfarb-Shanno (BFGS)
-  Exit message : Change in the objective function value was less than TolFun.
-  Iterations : 3
-  Function Count : 5
-  Minimum found : 83.9237
-  Intern Time : 0.003398 seconds
-  Total Time : 0.6747 seconds
- IA-CCD: finding the mode
-    Elapsed time 2.32 seconds
- IA-CCD: computing Hessian using multiplication
- IA-CCD: 9 points for 2 parameters
- IA-CCD: autoscaling in 4 directions
- IA-CCD: scaling minmax [1.02 1.32]
- IA-CCD: evaluating density at 9 points
- IA-CCD: Total elapsed time 4.41 seconds
-Demo completed in 0.439 minutes
diff --git a/tests/realValues/monotonic2_fig1.fig b/tests/realValues/monotonic2_fig1.fig
deleted file mode 100644
index e1709614..00000000
Binary files a/tests/realValues/monotonic2_fig1.fig and /dev/null differ
diff --git a/tests/realValues/multiclass.mat b/tests/realValues/multiclass.mat
deleted file mode 100644
index 667ccb47..00000000
Binary files a/tests/realValues/multiclass.mat and /dev/null differ
diff --git a/tests/realValues/multiclass.txt b/tests/realValues/multiclass.txt
deleted file mode 100644
index ac205194..00000000
--- a/tests/realValues/multiclass.txt
+++ /dev/null
@@ -1,665 +0,0 @@
-Softmax model with Laplace integration over the latent
-values and MAP estimate for the parameters
- Iteration  Func-count   Grad-count         f(x)         Step-size
-     0           1           1             277.318    
-     1           2           2             192.117       0.0228515
-     2           4           4             187.479        0.174366
-     3           7           7             178.445        0.038357
-     4           9           9             177.153        0.428397
-     5          11          11              174.94         0.55422
-     6          13          13             174.885        0.207464
-     7          14          14             174.863               1
-     8          15          15             174.862               1
- Optimizer Results
-  Algorithm Used: Broyden-Fletcher-Goldfarb-Shanno (BFGS)
-  Exit message : Change in the objective function value was less than TolFun.
-  Iterations : 9
-  Function Count : 16
-  Minimum found : 174.8621
-  Intern Time : 0.053621 seconds
-  Total Time : 30.1563 seconds
-
-missed =
-
-    0.1333
-
-
-ans =
-
-   3.3233e+05
-
-
-ans =
-
-   1.0e+04 *
-
-   -1.1812    5.6124    4.4558    1.5772    1.5647
-
-Checking gradient ...
-
-   analytic   diffs     delta
-
-   1.0e+05 *
-
-   -0.3752   -0.3752   -0.0000
-    0.5975    0.5976   -0.0000
-    1.2293    1.2294   -0.0000
-    1.5432    1.5432   -0.0000
-    0.6947    0.6947   -0.0000
-
- Using HMC sampler for hyperparameters and MCMC for latent values
- cycle  etr      hrej     lrej     lvScale    
-    2  -3601.918   0.0e+00  6.4e-01  0.038151
- Using HMC sampler for hyperparameters and MCMC for latent values
- cycle  etr      hrej     lrej     lvScale    
-    3  -4195.235   0.0e+00  4.0e-01  0.044182
-    4  -4173.255   0.0e+00  8.0e-01  0.069478
-    5  -4141.263   0.0e+00  6.0e-01  0.053155
-    6  -4116.405   0.0e+00  6.0e-01  0.072371
-    7  -4087.959   0.0e+00  6.0e-01  0.041504
-    8  -4050.433   0.0e+00  8.0e-01  0.056509
-    9  -3998.193   0.0e+00  1.0e+00  0.076937
-   10  -3976.434   0.0e+00  8.0e-01  0.067985
-   11  -3943.680   0.0e+00  4.0e-01  0.069386
-   12  -3912.732   0.0e+00  8.0e-01  0.070815
-   13  -3877.543   1.0e+00  6.0e-01  0.083477
-   14  -3854.397   0.0e+00  2.0e-01  0.063865
-   15  -3835.217   0.0e+00  6.0e-01  0.056433
-   16  -3818.584   1.0e+00  4.0e-01  0.076835
-   17  -3801.007   0.0e+00  4.0e-01  0.078418
-   18  -3807.111   0.0e+00  8.0e-01  0.069293
-   19  -3784.737   0.0e+00  1.0e+00  0.081683
-   20  -3773.910   0.0e+00  8.0e-01  0.054106
-   21  -3744.992   0.0e+00  6.0e-01  0.085084
-   22  -3738.260   0.0e+00  8.0e-01  0.056358
-   23  -3743.156   0.0e+00  4.0e-01  0.057519
-   24  -3742.546   0.0e+00  4.0e-01  0.078313
-   25  -3765.457   0.0e+00  2.0e-01  0.051874
-   26  -3763.581   0.0e+00  8.0e-01  0.061149
-   27  -3755.346   0.0e+00  8.0e-01  0.062409
-   28  -3745.480   0.0e+00  1.0e+00  0.084970
-   29  -3732.728   0.0e+00  8.0e-01  0.075083
-   30  -3731.040   1.0e+00  8.0e-01  0.057443
-   31  -3734.529   0.0e+00  6.0e-01  0.067713
-   32  -3739.670   0.0e+00  8.0e-01  0.069109
-   33  -3763.025   0.0e+00  6.0e-01  0.094092
-   34  -3757.489   0.0e+00  6.0e-01  0.083144
-   35  -3769.894   0.0e+00  8.0e-01  0.073469
-   36  -3775.090   0.0e+00  8.0e-01  0.056208
-   37  -3774.249   0.0e+00  6.0e-01  0.066258
-   38  -3774.156   0.0e+00  8.0e-01  0.078105
-   39  -3780.886   0.0e+00  1.0e+00  0.069017
-   40  -3779.080   0.0e+00  4.0e-01  0.060986
-   41  -3794.636   0.0e+00  1.0e+00  0.071890
-   42  -3808.517   0.0e+00  8.0e-01  0.073371
-   43  -3809.519   1.0e+00  6.0e-01  0.056133
-   44  -3821.706   0.0e+00  8.0e-01  0.076426
-   45  -3813.244   0.0e+00  4.0e-01  0.067533
-   46  -3803.672   0.0e+00  1.0e+00  0.068925
-   47  -3808.233   0.0e+00  6.0e-01  0.070345
-   48  -3791.349   1.0e+00  8.0e-01  0.082922
-   49  -3784.581   0.0e+00  6.0e-01  0.063440
-   50  -3777.135   0.0e+00  4.0e-01  0.042022
-   51  -3792.102   0.0e+00  8.0e-01  0.076324
-   52  -3805.075   0.0e+00  8.0e-01  0.103916
-   53  -3799.061   0.0e+00  8.0e-01  0.068833
-   54  -3807.984   0.0e+00  2.0e-01  0.070251
-   55  -3798.667   0.0e+00  8.0e-01  0.095648
-   56  -3807.055   0.0e+00  8.0e-01  0.063356
-   57  -3785.899   0.0e+00  8.0e-01  0.086260
-   58  -3796.989   1.0e+00  4.0e-01  0.065994
-   59  -3784.183   0.0e+00  1.0e+00  0.067353
-   60  -3782.214   0.0e+00  4.0e-01  0.059516
-   61  -3773.576   0.0e+00  6.0e-01  0.070157
-   62  -3790.747   1.0e+00  6.0e-01  0.061994
-   63  -3790.818   0.0e+00  8.0e-01  0.054780
-   64  -3820.407   0.0e+00  8.0e-01  0.074584
-   65  -3816.671   0.0e+00  8.0e-01  0.087920
-   66  -3813.713   0.0e+00  8.0e-01  0.058237
-   67  -3811.637   0.0e+00  6.0e-01  0.068649
-   68  -3834.506   0.0e+00  8.0e-01  0.070064
-   69  -3830.291   0.0e+00  8.0e-01  0.046409
-   70  -3815.373   0.0e+00  6.0e-01  0.072981
-   71  -3790.707   0.0e+00  6.0e-01  0.064489
-   72  -3797.375   0.0e+00  4.0e-01  0.076020
-   73  -3788.317   0.0e+00  6.0e-01  0.067174
-   74  -3793.363   0.0e+00  1.0e+00  0.068558
-   75  -3798.745   0.0e+00  8.0e-01  0.045412
-   76  -3815.207   0.0e+00  4.0e-01  0.040128
-   77  -3813.056   0.0e+00  6.0e-01  0.035459
-   78  -3801.258   0.0e+00  8.0e-01  0.055760
-   79  -3805.214   0.0e+00  8.0e-01  0.056909
-   80  -3816.489   0.0e+00  8.0e-01  0.050287
-   81  -3811.881   0.0e+00  4.0e-01  0.038473
-   82  -3815.111   0.0e+00  4.0e-01  0.069877
-   83  -3798.296   0.0e+00  6.0e-01  0.053460
-   84  -3789.290   0.0e+00  6.0e-01  0.054562
-   85  -3799.642   0.0e+00  4.0e-01  0.041743
-   86  -3797.829   0.0e+00  1.0e+00  0.075817
-   87  -3805.328   0.0e+00  2.0e-01  0.043481
-   88  -3793.960   0.0e+00  4.0e-01  0.033265
-   89  -3773.501   0.0e+00  6.0e-01  0.060419
-   90  -3789.466   0.0e+00  6.0e-01  0.061664
-   91  -3782.442   0.0e+00  6.0e-01  0.062935
-   92  -3782.395   0.0e+00  4.0e-01  0.048149
-   93  -3784.553   0.0e+00  8.0e-01  0.075716
-   94  -3808.720   0.0e+00  4.0e-01  0.037595
-   95  -3800.279   1.0e+00  1.0e+00  0.059121
-   96  -3799.708   0.0e+00  6.0e-01  0.060339
-   97  -3800.343   0.0e+00  8.0e-01  0.053318
-   98  -3812.644   0.0e+00  8.0e-01  0.054416
-   99  -3794.699   0.0e+00  8.0e-01  0.055538
-  100  -3801.718   0.0e+00  8.0e-01  0.065468
-  101  -3766.419   0.0e+00  4.0e-01  0.050086
-  102  -3766.488   0.0e+00  4.0e-01  0.078763
-  103  -3751.015   0.0e+00  6.0e-01  0.060258
-  104  -3759.311   0.0e+00  6.0e-01  0.046101
-  105  -3759.972   0.0e+00  1.0e+00  0.096712
-  106  -3773.921   0.0e+00  4.0e-01  0.048020
-  107  -3765.351   0.0e+00  8.0e-01  0.065380
-  108  -3747.822   0.0e+00  8.0e-01  0.057773
-  109  -3774.499   0.0e+00  1.0e+00  0.090850
-  110  -3782.396   0.0e+00  8.0e-01  0.052102
-  111  -3818.952   0.0e+00  1.0e+00  0.061418
-  112  -3825.824   0.0e+00  6.0e-01  0.046988
-  113  -3814.132   0.0e+00  6.0e-01  0.085344
-  114  -3820.902   0.0e+00  8.0e-01  0.056531
-  115  -3814.556   0.0e+00  8.0e-01  0.057696
-  116  -3814.463   0.0e+00  1.0e+00  0.068012
-  117  -3825.162   0.0e+00  8.0e-01  0.069413
-  118  -3806.801   0.0e+00  6.0e-01  0.053105
-  119  -3789.657   0.0e+00  4.0e-01  0.040628
-  120  -3766.190   0.0e+00  6.0e-01  0.055316
-  121  -3773.364   0.0e+00  4.0e-01  0.036641
-  122  -3760.463   0.0e+00  6.0e-01  0.076865
-  123  -3779.984   0.0e+00  8.0e-01  0.050914
-  124  -3784.808   0.0e+00  6.0e-01  0.033725
-  125  -3778.953   0.0e+00  2.0e-01  0.025802
-  126  -3765.729   1.0e+00  6.0e-01  0.035129
-  127  -3768.953   0.0e+00  2.0e-01  0.041410
-  128  -3785.662   1.0e+00  8.0e-01  0.048814
-  129  -3793.821   0.0e+00  6.0e-01  0.043134
-  130  -3795.783   0.0e+00  8.0e-01  0.050847
-  131  -3794.770   0.0e+00  1.0e+00  0.069228
-  132  -3797.170   0.0e+00  1.0e+00  0.045856
-  133  -3798.982   0.0e+00  4.0e-01  0.062433
-  134  -3789.888   1.0e+00  8.0e-01  0.041355
-  135  -3798.331   1.0e+00  8.0e-01  0.042207
-  136  -3792.892   0.0e+00  6.0e-01  0.049754
-  137  -3785.129   0.0e+00  4.0e-01  0.050779
-  138  -3784.428   0.0e+00  8.0e-01  0.051825
-  139  -3780.444   0.0e+00  8.0e-01  0.061091
-  140  -3789.482   0.0e+00  6.0e-01  0.030334
-  141  -3793.330   0.0e+00  4.0e-01  0.041300
-  142  -3806.120   0.0e+00  6.0e-01  0.075013
-  143  -3794.395   0.0e+00  6.0e-01  0.057389
-  144  -3780.075   0.0e+00  8.0e-01  0.078136
-  145  -3775.411   0.0e+00  2.0e-01  0.059778
-  146  -3770.718   0.0e+00  8.0e-01  0.061010
-  147  -3787.710   0.0e+00  6.0e-01  0.046676
-  148  -3784.415   0.0e+00  6.0e-01  0.055022
-  149  -3778.897   0.0e+00  4.0e-01  0.064859
-  150  -3774.164   0.0e+00  8.0e-01  0.088307
-  151  -3769.296   1.0e+00  6.0e-01  0.067560
-  152  -3783.333   0.0e+00  1.0e+00  0.059699
-  153  -3784.360   0.0e+00  8.0e-01  0.060929
-  154  -3772.686   0.0e+00  6.0e-01  0.053839
-  155  -3778.163   0.0e+00  6.0e-01  0.063465
-  156  -3787.678   0.0e+00  6.0e-01  0.064773
-  157  -3797.304   0.0e+00  8.0e-01  0.042905
-  158  -3776.033   0.0e+00  8.0e-01  0.058415
-  159  -3761.853   0.0e+00  4.0e-01  0.068860
-  160  -3761.480   0.0e+00  8.0e-01  0.052682
-  161  -3772.897   0.0e+00  1.0e+00  0.046552
-  162  -3761.781   1.0e+00  4.0e-01  0.054875
-  163  -3750.158   0.0e+00  1.0e+00  0.056006
-  164  -3753.373   0.0e+00  4.0e-01  0.049489
-  165  -3763.750   1.0e+00  4.0e-01  0.050509
-  166  -3784.475   0.0e+00  1.0e+00  0.079427
-  167  -3785.106   0.0e+00  1.0e+00  0.070185
-  168  -3775.811   0.0e+00  6.0e-01  0.082734
-  169  -3758.508   1.0e+00  6.0e-01  0.073107
-  170  -3749.692   0.0e+00  4.0e-01  0.074614
-  171  -3748.731   0.0e+00  8.0e-01  0.076151
-  172  -3745.757   0.0e+00  4.0e-01  0.067290
-  173  -3730.261   0.0e+00  8.0e-01  0.068677
-  174  -3727.500   0.0e+00  8.0e-01  0.070092
-  175  -3720.234   0.0e+00  1.0e+00  0.053624
-  176  -3713.491   0.0e+00  8.0e-01  0.063212
-  177  -3714.120   0.0e+00  4.0e-01  0.055857
-  178  -3739.274   0.0e+00  4.0e-01  0.065844
-  179  -3727.604   0.0e+00  6.0e-01  0.050374
-  180  -3724.040   0.0e+00  4.0e-01  0.059381
-  181  -3726.512   0.0e+00  8.0e-01  0.080848
-  182  -3746.341   0.0e+00  6.0e-01  0.071441
-  183  -3743.999   0.0e+00  4.0e-01  0.054656
-  184  -3762.334   0.0e+00  4.0e-01  0.055782
-  185  -3775.652   0.0e+00  6.0e-01  0.056932
-  186  -3757.765   1.0e+00  4.0e-01  0.058105
-  187  -3768.766   0.0e+00  1.0e+00  0.091372
-  188  -3750.685   0.0e+00  8.0e-01  0.093255
-  189  -3740.558   0.0e+00  6.0e-01  0.082404
-  190  -3756.157   1.0e+00  4.0e-01  0.084102
-  191  -3762.877   0.0e+00  6.0e-01  0.055708
-  192  -3773.125   0.0e+00  8.0e-01  0.056856
-  193  -3769.778   0.0e+00  6.0e-01  0.067021
-  194  -3780.267   0.0e+00  2.0e-01  0.051275
-  195  -3783.277   0.0e+00  8.0e-01  0.052332
-  196  -3774.568   0.0e+00  6.0e-01  0.061689
-  197  -3780.674   0.0e+00  2.0e-01  0.054510
-  198  -3763.258   1.0e+00  1.0e+00  0.085720
-  199  -3753.619   0.0e+00  4.0e-01  0.056780
-  200  -3710.222   0.0e+00  8.0e-01  0.057950
-  201  -3730.524   0.0e+00  6.0e-01  0.038385
-  202  -3726.483   0.0e+00  6.0e-01  0.039176
-  203  -3733.478   0.0e+00  1.0e+00  0.061606
-  204  -3743.554   0.0e+00  4.0e-01  0.054438
-  205  -3754.502   0.0e+00  8.0e-01  0.098875
-  206  -3769.876   1.0e+00  6.0e-01  0.075645
-  207  -3774.106   0.0e+00  4.0e-01  0.057873
-  208  -3746.711   0.0e+00  8.0e-01  0.068220
-  209  -3763.920   0.0e+00  6.0e-01  0.080418
-  210  -3757.036   0.0e+00  4.0e-01  0.061524
-  211  -3781.399   0.0e+00  8.0e-01  0.062792
-  212  -3773.995   0.0e+00  1.0e+00  0.074019
-  213  -3773.308   0.0e+00  6.0e-01  0.049029
-  214  -3763.448   0.0e+00  8.0e-01  0.043324
-  215  -3753.225   0.0e+00  4.0e-01  0.068129
-  216  -3747.969   0.0e+00  6.0e-01  0.080311
-  217  -3730.204   0.0e+00  6.0e-01  0.070966
-  218  -3724.264   0.0e+00  8.0e-01  0.072428
-  219  -3716.910   0.0e+00  4.0e-01  0.055412
-  220  -3701.976   0.0e+00  1.0e+00  0.065319
-  221  -3696.320   0.0e+00  1.0e+00  0.057719
-  222  -3709.295   0.0e+00  6.0e-01  0.068039
-  223  -3684.540   1.0e+00  8.0e-01  0.092635
-  224  -3678.245   0.0e+00  8.0e-01  0.070871
-  225  -3689.371   0.0e+00  4.0e-01  0.083543
-  226  -3675.508   0.0e+00  4.0e-01  0.047911
-  227  -3664.092   0.0e+00  8.0e-01  0.065232
-  228  -3687.212   1.0e+00  6.0e-01  0.066576
-  229  -3675.789   0.0e+00  2.0e-01  0.050934
-  230  -3691.580   0.0e+00  6.0e-01  0.092512
-  231  -3705.979   0.0e+00  1.0e+00  0.081747
-  232  -3680.725   0.0e+00  6.0e-01  0.046882
-  233  -3685.298   0.0e+00  8.0e-01  0.073724
-  234  -3709.674   0.0e+00  0.0e+00  0.056403
-  235  -3687.350   0.0e+00  4.0e-01  0.049840
-  236  -3709.087   0.0e+00  8.0e-01  0.078375
-  237  -3710.757   0.0e+00  8.0e-01  0.079990
-  238  -3715.134   0.0e+00  2.0e-01  0.070682
-  239  -3744.154   0.0e+00  1.0e+00  0.062458
-  240  -3748.320   0.0e+00  1.0e+00  0.085037
-  241  -3729.778   0.0e+00  6.0e-01  0.075142
-  242  -3725.670   0.0e+00  1.0e+00  0.088578
-  243  -3720.673   0.0e+00  1.0e+00  0.050799
-  244  -3747.327   0.0e+00  1.0e+00  0.051846
-  245  -3737.666   0.0e+00  8.0e-01  0.081530
-  246  -3732.087   0.0e+00  8.0e-01  0.072043
-  247  -3716.902   0.0e+00  1.0e+00  0.055117
-  248  -3737.774   0.0e+00  1.0e+00  0.086674
-  249  -3730.718   0.0e+00  6.0e-01  0.088460
-  250  -3727.279   0.0e+00  4.0e-01  0.043923
-  251  -3718.843   0.0e+00  6.0e-01  0.059802
-  252  -3718.690   0.0e+00  4.0e-01  0.061034
-  253  -3709.168   0.0e+00  8.0e-01  0.046694
-  254  -3711.905   0.0e+00  6.0e-01  0.063575
-  255  -3717.334   0.0e+00  1.0e+00  0.064885
-  256  -3735.511   0.0e+00  6.0e-01  0.076486
-  257  -3719.203   0.0e+00  4.0e-01  0.058516
-  258  -3721.657   0.0e+00  2.0e-01  0.079671
-  259  -3723.383   1.0e+00  6.0e-01  0.045691
-  260  -3732.024   1.0e+00  8.0e-01  0.062209
-  261  -3746.038   0.0e+00  8.0e-01  0.073331
-  262  -3735.285   0.0e+00  6.0e-01  0.042055
-  263  -3728.452   0.0e+00  6.0e-01  0.057259
-  264  -3738.065   1.0e+00  4.0e-01  0.058438
-  265  -3731.135   0.0e+00  2.0e-01  0.051639
-  266  -3734.995   0.0e+00  4.0e-01  0.045630
-  267  -3751.828   1.0e+00  8.0e-01  0.053788
-  268  -3755.531   0.0e+00  8.0e-01  0.063406
-  269  -3759.178   0.0e+00  8.0e-01  0.048509
-  270  -3745.865   0.0e+00  1.0e+00  0.066046
-  271  -3769.565   0.0e+00  6.0e-01  0.058361
-  272  -3771.036   0.0e+00  4.0e-01  0.059563
-  273  -3770.653   0.0e+00  6.0e-01  0.060790
-  274  -3782.947   0.0e+00  8.0e-01  0.062043
-  275  -3768.867   0.0e+00  8.0e-01  0.073136
-  276  -3772.262   1.0e+00  1.0e+00  0.074643
-  277  -3757.198   0.0e+00  1.0e+00  0.076181
-  278  -3741.587   0.0e+00  6.0e-01  0.067317
-  279  -3729.355   0.0e+00  1.0e+00  0.105859
-  280  -3755.000   0.0e+00  8.0e-01  0.070119
-  281  -3726.823   0.0e+00  2.0e-01  0.061960
-  282  -3736.325   0.0e+00  8.0e-01  0.054751
-  283  -3733.783   0.0e+00  1.0e+00  0.086098
-  284  -3745.705   0.0e+00  4.0e-01  0.049377
-  285  -3733.087   0.0e+00  6.0e-01  0.067227
-  286  -3737.383   0.0e+00  1.0e+00  0.068612
-  287  -3743.256   0.0e+00  4.0e-01  0.052492
-  288  -3730.573   0.0e+00  8.0e-01  0.046384
-  289  -3718.471   0.0e+00  4.0e-01  0.072941
-  290  -3727.431   0.0e+00  4.0e-01  0.055804
-  291  -3694.668   0.0e+00  6.0e-01  0.075978
-  292  -3693.530   0.0e+00  4.0e-01  0.067137
-  293  -3703.467   0.0e+00  8.0e-01  0.051364
-  294  -3705.682   0.0e+00  8.0e-01  0.069933
-  295  -3710.475   0.0e+00  6.0e-01  0.053502
-  296  -3714.367   0.0e+00  6.0e-01  0.047277
-  297  -3719.820   1.0e+00  6.0e-01  0.064368
-  298  -3715.425   0.0e+00  8.0e-01  0.065694
-  299  -3721.159   0.0e+00  4.0e-01  0.050260
-  300  -3718.133   0.0e+00  6.0e-01  0.059246
-  301  -3704.743   0.0e+00  2.0e-01  0.045327
-  302  -3706.917   0.0e+00  6.0e-01  0.071278
-  303  -3698.103   0.0e+00  8.0e-01  0.084023
-  304  -3699.137   0.0e+00  4.0e-01  0.064282
-  305  -3701.228   1.0e+00  2.0e-01  0.049180
-  306  -3717.713   0.0e+00  1.0e+00  0.066959
-  307  -3714.611   0.0e+00  8.0e-01  0.091165
-  308  -3705.346   0.0e+00  8.0e-01  0.069746
-  309  -3707.682   0.0e+00  6.0e-01  0.061631
-  310  -3720.721   0.0e+00  1.0e+00  0.083911
-  311  -3705.195   0.0e+00  4.0e-01  0.055581
-  312  -3692.410   0.0e+00  1.0e+00  0.065519
-  313  -3693.175   0.0e+00  1.0e+00  0.057896
-  314  -3678.808   1.0e+00  8.0e-01  0.068247
-  315  -3684.662   0.0e+00  6.0e-01  0.052213
-  316  -3705.135   0.0e+00  8.0e-01  0.053289
-  317  -3698.372   0.0e+00  1.0e+00  0.047088
-  318  -3709.428   1.0e+00  8.0e-01  0.048058
-  319  -3707.251   0.0e+00  6.0e-01  0.075574
-  320  -3716.650   0.0e+00  1.0e+00  0.057818
-  321  -3724.419   0.0e+00  6.0e-01  0.059010
-  322  -3716.526   0.0e+00  8.0e-01  0.060226
-  323  -3724.378   0.0e+00  8.0e-01  0.039893
-  324  -3718.451   0.0e+00  6.0e-01  0.047025
-  325  -3722.991   0.0e+00  6.0e-01  0.041553
-  326  -3723.741   0.0e+00  8.0e-01  0.065345
-  327  -3718.498   0.0e+00  4.0e-01  0.057741
-  328  -3713.086   0.0e+00  6.0e-01  0.051023
-  329  -3709.218   0.0e+00  8.0e-01  0.033797
-  330  -3695.001   0.0e+00  8.0e-01  0.046015
-  331  -3689.727   0.0e+00  6.0e-01  0.030479
-  332  -3692.385   0.0e+00  8.0e-01  0.047930
-  333  -3700.441   0.0e+00  8.0e-01  0.065258
-  334  -3686.540   0.0e+00  6.0e-01  0.049926
-  335  -3689.740   0.0e+00  6.0e-01  0.058853
-  336  -3685.474   0.0e+00  1.0e+00  0.052004
-  337  -3680.613   0.0e+00  4.0e-01  0.045953
-  338  -3696.918   0.0e+00  6.0e-01  0.054170
-  339  -3688.954   0.0e+00  1.0e+00  0.047866
-  340  -3693.716   0.0e+00  6.0e-01  0.042297
-  341  -3701.336   0.0e+00  8.0e-01  0.057588
-  342  -3702.666   0.0e+00  4.0e-01  0.067884
-  343  -3696.179   0.0e+00  6.0e-01  0.044966
-  344  -3686.677   0.0e+00  0.0e+00  0.053005
-  345  -3692.984   0.0e+00  4.0e-01  0.046838
-  346  -3701.219   0.0e+00  8.0e-01  0.085071
-  347  -3705.684   0.0e+00  4.0e-01  0.031664
-  348  -3691.495   0.0e+00  6.0e-01  0.037325
-  349  -3704.778   1.0e+00  8.0e-01  0.067794
-  350  -3711.365   0.0e+00  8.0e-01  0.044906
-  351  -3701.497   0.0e+00  4.0e-01  0.039680
-  352  -3692.845   0.0e+00  6.0e-01  0.054025
-  353  -3695.949   0.0e+00  6.0e-01  0.047739
-  354  -3704.371   0.0e+00  6.0e-01  0.056275
-  355  -3698.152   0.0e+00  6.0e-01  0.066336
-  356  -3707.626   0.0e+00  6.0e-01  0.058618
-  357  -3728.164   0.0e+00  1.0e+00  0.059825
-  358  -3719.003   0.0e+00  8.0e-01  0.052864
-  359  -3703.139   0.0e+00  6.0e-01  0.071975
-  360  -3692.106   0.0e+00  4.0e-01  0.047675
-  361  -3695.802   0.0e+00  4.0e-01  0.064911
-  362  -3688.800   0.0e+00  4.0e-01  0.049660
-  363  -3706.053   0.0e+00  8.0e-01  0.078093
-  364  -3710.709   0.0e+00  8.0e-01  0.069006
-  365  -3732.794   0.0e+00  1.0e+00  0.081344
-  366  -3729.170   0.0e+00  8.0e-01  0.053881
-  367  -3716.301   0.0e+00  8.0e-01  0.063515
-  368  -3710.745   0.0e+00  8.0e-01  0.086477
-  369  -3694.249   0.0e+00  1.0e+00  0.057281
-  370  -3700.308   0.0e+00  4.0e-01  0.058461
-  371  -3724.359   0.0e+00  8.0e-01  0.051659
-  372  -3729.625   0.0e+00  8.0e-01  0.070334
-  373  -3721.202   0.0e+00  4.0e-01  0.046588
-  374  -3730.963   0.0e+00  2.0e-01  0.041167
-  375  -3739.605   0.0e+00  6.0e-01  0.074772
-  376  -3748.069   0.0e+00  6.0e-01  0.076313
-  377  -3726.154   0.0e+00  6.0e-01  0.050549
-  378  -3735.244   0.0e+00  8.0e-01  0.051590
-  379  -3741.248   0.0e+00  8.0e-01  0.070241
-  380  -3738.338   0.0e+00  6.0e-01  0.053738
-  381  -3736.404   0.0e+00  8.0e-01  0.084505
-  382  -3736.662   0.0e+00  8.0e-01  0.074672
-  383  -3710.237   0.0e+00  6.0e-01  0.101667
-  384  -3725.762   0.0e+00  2.0e-01  0.058306
-  385  -3725.313   0.0e+00  1.0e+00  0.079384
-  386  -3728.950   0.0e+00  6.0e-01  0.039417
-  387  -3745.684   0.0e+00  8.0e-01  0.071592
-  388  -3729.012   0.0e+00  1.0e+00  0.063262
-  389  -3732.725   0.0e+00  4.0e-01  0.055901
-  390  -3760.881   0.0e+00  8.0e-01  0.065896
-  391  -3770.414   0.0e+00  8.0e-01  0.067253
-  392  -3769.848   0.0e+00  4.0e-01  0.059428
-  393  -3773.187   0.0e+00  1.0e+00  0.070053
-  394  -3752.826   0.0e+00  1.0e+00  0.046402
-  395  -3750.152   0.0e+00  4.0e-01  0.047359
-  396  -3732.863   0.0e+00  2.0e-01  0.055826
-  397  -3737.672   0.0e+00  1.0e+00  0.087789
-  398  -3752.304   0.0e+00  4.0e-01  0.058150
-  399  -3750.609   0.0e+00  4.0e-01  0.038518
-  400  -3760.889   0.0e+00  8.0e-01  0.060572
-  401  -3748.131   0.0e+00  4.0e-01  0.053523
-  402  -3751.729   0.0e+00  8.0e-01  0.072873
- Using HMC sampler for hyperparameters and MCMC for latent values
- cycle  etr      hrej     lrej     lvScale    
-    2  -3546.859   0.0e+00  6.0e-02  0.125687
- Using HMC sampler for hyperparameters and MCMC for latent values
- cycle  etr      hrej     lrej     lvScale    
-    3  -3553.563   0.0e+00  0.0e+00  0.179264
-    4  -3572.433   0.0e+00  0.0e+00  0.214237
-    5  -3523.501   0.0e+00  1.0e-01  0.256033
-    6  -3619.135   0.0e+00  2.5e-01  0.305983
-    7  -3566.772   0.0e+00  1.0e-01  0.179326
-    8  -3604.777   1.0e+00  5.0e-02  0.214311
-    9  -3564.579   0.0e+00  5.0e-02  0.179357
-   10  -3569.154   0.0e+00  0.0e+00  0.214348
-   11  -3546.679   0.0e+00  1.0e-01  0.256165
-   12  -3623.375   0.0e+00  1.0e-01  0.214385
-   13  -3616.679   0.0e+00  2.0e-01  0.125643
-   14  -3625.434   0.0e+00  5.0e-02  0.105151
-   15  -3641.577   0.0e+00  0.0e+00  0.125665
-   16  -3587.926   0.0e+00  5.0e-02  0.150181
-   17  -3620.766   0.0e+00  0.0e+00  0.179480
-   18  -3600.002   1.0e+00  5.0e-02  0.214496
-   19  -3649.535   0.0e+00  1.0e-01  0.125708
-   20  -3630.994   0.0e+00  5.0e-02  0.150233
-   21  -3630.374   0.0e+00  0.0e+00  0.179542
-   22  -3619.010   0.0e+00  0.0e+00  0.214570
-   23  -3555.384   0.0e+00  5.0e-02  0.179573
-   24  -3587.871   0.0e+00  1.0e-01  0.214607
-   25  -3589.657   0.0e+00  5.0e-02  0.179604
-   26  -3604.577   0.0e+00  0.0e+00  0.214644
-   27  -3595.302   0.0e+00  5.0e-02  0.179635
-   28  -3605.356   0.0e+00  1.0e-01  0.150337
-   29  -3596.330   1.0e+00  2.0e-01  0.125817
-   30  -3552.534   0.0e+00  5.0e-02  0.105296
-   31  -3544.548   0.0e+00  0.0e+00  0.125838
-   32  -3598.823   1.0e+00  0.0e+00  0.150388
-   33  -3611.053   0.0e+00  0.0e+00  0.179728
-   34  -3592.953   0.0e+00  1.0e-01  0.214792
-   35  -3640.496   0.0e+00  0.0e+00  0.256696
-   36  -3608.115   0.0e+00  1.5e-01  0.306776
-   37  -3608.110   1.0e+00  0.0e+00  0.366625
-   38  -3606.910   0.0e+00  2.5e-01  0.306828
-   39  -3584.905   0.0e+00  5.0e-02  0.256785
-   40  -3578.951   0.0e+00  1.0e-01  0.214903
-   41  -3629.357   0.0e+00  5.0e-02  0.179852
-   42  -3622.591   1.0e+00  0.0e+00  0.214940
-   43  -3617.690   0.0e+00  1.0e-01  0.256873
-   44  -3671.473   0.0e+00  5.0e-02  0.214977
-   45  -3544.613   0.0e+00  1.5e-01  0.125990
-   46  -3543.886   1.0e+00  5.0e-02  0.150570
-   47  -3578.844   1.0e+00  0.0e+00  0.179945
-   48  -3586.392   1.0e+00  0.0e+00  0.215051
-   49  -3568.503   1.0e+00  1.5e-01  0.257006
-   50  -3583.069   0.0e+00  1.0e-01  0.215088
-   51  -3593.919   0.0e+00  5.0e-02  0.180007
-   52  -3560.095   0.0e+00  0.0e+00  0.215125
-   53  -3619.776   0.0e+00  5.0e-02  0.257095
-   54  -3611.814   0.0e+00  2.0e-01  0.307252
-   55  -3618.396   0.0e+00  5.0e-02  0.367195
-   56  -3606.316   0.0e+00  1.5e-01  0.438831
-   57  -3583.648   0.0e+00  5.0e-02  0.367258
-   58  -3581.269   0.0e+00  1.0e-01  0.215237
-   59  -3619.048   0.0e+00  0.0e+00  0.257228
-   60  -3596.755   0.0e+00  1.0e-01  0.215274
-   61  -3612.793   0.0e+00  1.5e-01  0.180163
-   62  -3628.661   0.0e+00  0.0e+00  0.215311
-   63  -3583.840   0.0e+00  5.0e-02  0.180194
-   64  -3575.540   0.0e+00  0.0e+00  0.215348
-   65  -3596.618   0.0e+00  5.0e-02  0.180225
-   66  -3590.817   0.0e+00  5.0e-02  0.150830
-   67  -3620.793   0.0e+00  1.0e-01  0.126230
-   68  -3608.739   0.0e+00  0.0e+00  0.150856
-   69  -3613.657   0.0e+00  0.0e+00  0.180287
-   70  -3597.147   1.0e+00  0.0e+00  0.215459
-   71  -3555.650   0.0e+00  5.0e-02  0.257494
-   72  -3594.045   0.0e+00  1.5e-01  0.150908
-   73  -3543.667   1.0e+00  5.0e-02  0.180349
-   74  -3595.043   0.0e+00  1.0e-01  0.215534
-   75  -3572.348   1.0e+00  1.5e-01  0.257583
-   76  -3586.976   1.0e+00  5.0e-02  0.215571
-   77  -3566.529   0.0e+00  0.0e+00  0.257627
-   78  -3538.982   0.0e+00  1.0e-01  0.150986
-   79  -3560.281   0.0e+00  1.0e-01  0.088488
-   80  -3574.456   0.0e+00  5.0e-02  0.074055
-   81  -3568.635   0.0e+00  0.0e+00  0.088503
-   82  -3543.400   0.0e+00  1.0e-01  0.051868
-   83  -3560.974   0.0e+00  5.0e-02  0.061987
-   84  -3549.470   0.0e+00  0.0e+00  0.074081
-   85  -3545.856   0.0e+00  0.0e+00  0.088533
-   86  -3515.315   1.0e+00  5.0e-02  0.105805
-   87  -3527.291   1.0e+00  5.0e-02  0.088549
-   88  -3515.009   0.0e+00  0.0e+00  0.105824
-   89  -3532.274   0.0e+00  0.0e+00  0.126469
-   90  -3518.899   0.0e+00  0.0e+00  0.151142
-   91  -3511.339   0.0e+00  5.0e-02  0.180629
-   92  -3529.833   0.0e+00  0.0e+00  0.215868
-   93  -3565.562   0.0e+00  5.0e-02  0.180660
-   94  -3603.658   0.0e+00  0.0e+00  0.215906
-   95  -3595.164   0.0e+00  5.0e-02  0.180691
-   96  -3539.675   0.0e+00  0.0e+00  0.215943
-   97  -3608.294   0.0e+00  5.0e-02  0.180723
-   98  -3585.861   0.0e+00  0.0e+00  0.215980
-   99  -3594.425   1.0e+00  0.0e+00  0.258116
-  100  -3596.381   0.0e+00  5.0e-02  0.216017
-  101  -3589.803   0.0e+00  1.0e-01  0.258161
-  102  -3619.482   0.0e+00  5.0e-02  0.216055
-  103  -3570.690   0.0e+00  1.0e-01  0.180816
-  104  -3545.787   1.0e+00  1.0e-01  0.216092
-  105  -3515.888   0.0e+00  5.0e-02  0.180847
-  106  -3547.241   0.0e+00  5.0e-02  0.151351
-  107  -3528.950   1.0e+00  5.0e-02  0.126666
-  108  -3526.869   0.0e+00  0.0e+00  0.151377
-  109  -3544.864   0.0e+00  5.0e-02  0.180910
-  110  -3536.383   0.0e+00  0.0e+00  0.216204
-  111  -3585.555   0.0e+00  5.0e-02  0.180941
-  112  -3541.398   0.0e+00  0.0e+00  0.216241
-  113  -3551.327   0.0e+00  5.0e-02  0.180972
-  114  -3476.261   0.0e+00  0.0e+00  0.216278
-  115  -3512.409   0.0e+00  5.0e-02  0.181003
-  116  -3544.347   0.0e+00  0.0e+00  0.216316
-  117  -3506.505   0.0e+00  1.0e-01  0.181034
-  118  -3511.628   0.0e+00  0.0e+00  0.216353
-  119  -3502.283   0.0e+00  1.0e-01  0.181066
-  120  -3510.560   0.0e+00  5.0e-02  0.216390
-  121  -3495.075   1.0e+00  1.0e-01  0.258606
-  122  -3444.407   0.0e+00  5.0e-02  0.216428
-  123  -3525.211   0.0e+00  2.0e-01  0.258651
-  124  -3520.949   0.0e+00  2.0e-01  0.216465
-  125  -3519.523   0.0e+00  5.0e-02  0.181159
-  126  -3492.608   0.0e+00  5.0e-02  0.216502
-  127  -3443.437   1.0e+00  1.5e-01  0.258740
-  128  -3473.191   0.0e+00  1.0e-01  0.216540
-  129  -3445.220   0.0e+00  5.0e-02  0.181222
-  130  -3471.242   0.0e+00  5.0e-02  0.216577
-  131  -3450.983   1.0e+00  0.0e+00  0.258829
-  132  -3477.064   1.0e+00  1.5e-01  0.216614
-  133  -3443.047   0.0e+00  1.5e-01  0.126950
-  134  -3521.749   1.0e+00  5.0e-02  0.106244
-  135  -3543.283   0.0e+00  5.0e-02  0.088916
-  136  -3516.085   0.0e+00  0.0e+00  0.106263
-  137  -3496.080   0.0e+00  1.0e-01  0.126994
-  138  -3557.043   0.0e+00  5.0e-02  0.151769
-  139  -3494.817   0.0e+00  1.0e-01  0.127016
-  140  -3530.436   0.0e+00  1.0e-01  0.151795
-  141  -3498.561   1.0e+00  0.0e+00  0.181409
-  142  -3525.235   0.0e+00  0.0e+00  0.216801
-  143  -3514.645   0.0e+00  5.0e-02  0.181441
-  144  -3461.475   0.0e+00  5.0e-02  0.216838
-  145  -3467.901   0.0e+00  5.0e-02  0.181472
-  146  -3458.071   0.0e+00  0.0e+00  0.216876
-  147  -3494.490   0.0e+00  1.5e-01  0.259187
-  148  -3527.754   0.0e+00  5.0e-02  0.216913
-  149  -3520.285   0.0e+00  0.0e+00  0.259231
-  150  -3508.842   0.0e+00  1.0e-01  0.216951
-  151  -3490.882   0.0e+00  0.0e+00  0.259276
-  152  -3523.176   0.0e+00  1.0e-01  0.216988
-  153  -3539.945   0.0e+00  5.0e-02  0.181597
-  154  -3467.065   0.0e+00  0.0e+00  0.217025
-  155  -3514.794   0.0e+00  0.0e+00  0.259366
-  156  -3542.491   0.0e+00  1.5e-01  0.152005
-  157  -3505.487   0.0e+00  5.0e-02  0.181660
-  158  -3478.542   1.0e+00  1.0e-01  0.152031
-  159  -3475.046   0.0e+00  5.0e-02  0.127235
-  160  -3497.219   0.0e+00  5.0e-02  0.152057
-  161  -3511.755   0.0e+00  5.0e-02  0.181722
-  162  -3488.256   0.0e+00  1.0e-01  0.217175
-  163  -3518.694   0.0e+00  5.0e-02  0.259544
-  164  -3524.963   0.0e+00  0.0e+00  0.310180
-  165  -3501.020   0.0e+00  1.0e-01  0.181785
-  166  -3509.842   0.0e+00  5.0e-02  0.217250
-  167  -3504.392   0.0e+00  0.0e+00  0.259634
-  168  -3499.712   0.0e+00  5.0e-02  0.217288
-  169  -3472.272   0.0e+00  0.0e+00  0.259679
-  170  -3487.399   0.0e+00  1.5e-01  0.310340
-  171  -3501.195   0.0e+00  1.0e-01  0.259724
-  172  -3528.694   0.0e+00  0.0e+00  0.310394
-  173  -3487.743   0.0e+00  1.0e-01  0.181911
-  174  -3443.119   0.0e+00  0.0e+00  0.217400
-  175  -3466.950   0.0e+00  1.5e-01  0.181942
-  176  -3453.988   0.0e+00  5.0e-02  0.217437
-  177  -3439.164   0.0e+00  5.0e-02  0.181973
-  178  -3376.121   0.0e+00  0.0e+00  0.217475
-  179  -3401.337   0.0e+00  1.0e-01  0.259903
-  180  -3462.778   0.0e+00  5.0e-02  0.217512
-  181  -3448.675   0.0e+00  5.0e-02  0.259948
-  182  -3447.430   0.0e+00  1.0e-01  0.217550
-  183  -3475.872   0.0e+00  5.0e-02  0.182067
-  184  -3502.555   0.0e+00  1.0e-01  0.217587
-  185  -3487.736   0.0e+00  5.0e-02  0.182099
-  186  -3508.070   0.0e+00  5.0e-02  0.152398
-  187  -3531.568   0.0e+00  0.0e+00  0.182130
-  188  -3468.904   0.0e+00  0.0e+00  0.217663
-  189  -3552.292   1.0e+00  5.0e-02  0.260127
-  190  -3522.526   0.0e+00  5.0e-02  0.217700
-  191  -3449.378   0.0e+00  1.5e-01  0.260172
-  192  -3482.642   0.0e+00  1.0e-01  0.217738
-  193  -3447.347   0.0e+00  0.0e+00  0.260217
-  194  -3545.778   0.0e+00  5.0e-02  0.217775
-  195  -3454.657   0.0e+00  1.5e-01  0.260261
-  196  -3507.440   0.0e+00  1.0e-01  0.217813
-  197  -3475.018   0.0e+00  5.0e-02  0.182287
-  198  -3513.142   0.0e+00  0.0e+00  0.217850
-  199  -3480.068   0.0e+00  5.0e-02  0.182319
-  200  -3529.343   1.0e+00  0.0e+00  0.217888
-  201  -3492.161   0.0e+00  1.0e-01  0.260396
-  202  -3497.105   0.0e+00  1.5e-01  0.311198
-
- gp hyperparameters: 
- 
-    2.8831   -0.6152   -0.5214    1.5709    1.6167
-
-Demo completed in 42.562 minutes
diff --git a/tests/realValues/multiclass_fig1.fig b/tests/realValues/multiclass_fig1.fig
deleted file mode 100644
index 8686a9ae..00000000
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diff --git a/tests/realValues/multiclass_fig2.fig b/tests/realValues/multiclass_fig2.fig
deleted file mode 100644
index 165b1385..00000000
Binary files a/tests/realValues/multiclass_fig2.fig and /dev/null differ
diff --git a/tests/realValues/multiclass_fig3.fig b/tests/realValues/multiclass_fig3.fig
deleted file mode 100644
index baf1cb0c..00000000
Binary files a/tests/realValues/multiclass_fig3.fig and /dev/null differ
diff --git a/tests/realValues/multiclass_nested_ep.mat b/tests/realValues/multiclass_nested_ep.mat
deleted file mode 100644
index b79ad363..00000000
Binary files a/tests/realValues/multiclass_nested_ep.mat and /dev/null differ
diff --git a/tests/realValues/multiclass_nested_ep.txt b/tests/realValues/multiclass_nested_ep.txt
deleted file mode 100644
index 2d73c1a0..00000000
--- a/tests/realValues/multiclass_nested_ep.txt
+++ /dev/null
@@ -1,23 +0,0 @@
-Optimize the hyperparameters
- Iteration  Func-count   Grad-count         f(x)         Step-size
-     0           1           1             145.286    
-     1           2           2             106.602       0.0541548
-     2           5           5             102.985        0.120757
-     3           8           8             98.2436       0.0807225
-     4          10          10             98.0724        0.254374
-     5          11          11             98.0559               1
-     6          12          12             98.0407               1
-     7          14          14             98.0398        0.216519
- Optimizer Results
-  Algorithm Used: Broyden-Fletcher-Goldfarb-Shanno (BFGS)
-  Exit message : Change in x was smaller than the specified tolerance TolX.
-  Iterations : 8
-  Function Count : 15
-  Minimum found : 98.0398
-  Intern Time : 0.010704 seconds
-  Total Time : 60.4646 seconds
-
-Optimized magnitude sigma^2: 13.1109
-Optimized lengthscales: 0.58525     0.64233      4.8782      4.3622
-The percentage of misclassified points: 0.1525
-Demo completed in 1.082 minutes
diff --git a/tests/realValues/multiclass_nested_ep_fig1.fig b/tests/realValues/multiclass_nested_ep_fig1.fig
deleted file mode 100644
index 01f506c1..00000000
Binary files a/tests/realValues/multiclass_nested_ep_fig1.fig and /dev/null differ
diff --git a/tests/realValues/multinom.txt b/tests/realValues/multinom.txt
deleted file mode 100644
index b5b362ed..00000000
--- a/tests/realValues/multinom.txt
+++ /dev/null
@@ -1,435 +0,0 @@
-  Iteration  Func-count      f(x)      Lambda
-      0          1          667.05    
-      1          3         666.162       10
-      2          5         666.133        5
-      3          7         666.049      2.5
-      4          9          665.97     1.25
-      5         11          665.77    0.625
-      6         13         665.562    0.312
-      7         15          665.52    0.156
-      8         17         665.422   0.0781
-      9         19         665.396   0.0391
-     10         21         665.388   0.0195
-     11         23         665.375   0.00977
-     12         25         665.373   0.00488
-     13         27         665.372   0.00244
-     14         29         665.362   0.00122
-     15         31         665.362   0.00061
-     16         33          665.36   0.000305
-     17         35          665.36   0.000153
-     18         37         665.359   7.63e-05
-     19         39         665.359   3.81e-05
-  Iteration  Func-count      f(x)    Lambda
-     20         41         665.359   1.91e-05
- TolFun reached. Func-count 41. Final f(x)=665.359. Elapsed time 2.47
- Using HMC sampler for hyperparameters and MCMC for latent values
- cycle  etr      hrej     lrej     lvScale    
-    2  156.741   0.0e+00  6.6e-01  0.010430
- Using HMC sampler for hyperparameters and MCMC for latent values
- cycle  etr      hrej     lrej     lvScale    
-    3  149.953   0.0e+00  8.0e-01  0.007839
-    4  147.362   0.0e+00  8.0e-01  0.010673
-    5  148.822   1.0e+00  4.0e-01  0.010893
-    6  148.372   0.0e+00  1.0e+00  0.012840
-    7  139.237   0.0e+00  1.0e+00  0.015136
-    8  142.246   0.0e+00  8.0e-01  0.011580
-    9  138.938   0.0e+00  6.0e-01  0.008859
-   10  130.069   0.0e+00  6.0e-01  0.010444
-   11  134.682   0.0e+00  6.0e-01  0.012311
-   12  142.648   0.0e+00  6.0e-01  0.014512
-   13  147.244   0.0e+00  6.0e-01  0.011102
-   14  141.776   0.0e+00  8.0e-01  0.013088
-   15  153.127   0.0e+00  6.0e-01  0.015428
-   16  158.535   0.0e+00  4.0e-01  0.011803
-   17  158.152   0.0e+00  2.0e-01  0.010430
-   18  145.927   0.0e+00  2.0e-01  0.010644
-   19  146.203   0.0e+00  4.0e-01  0.009406
-   20  147.244   0.0e+00  4.0e-01  0.012806
-   21  151.305   0.0e+00  8.0e-01  0.011316
-   22  154.570   0.0e+00  4.0e-01  0.011549
-   23  156.650   0.0e+00  8.0e-01  0.008836
-   24  146.018   0.0e+00  8.0e-01  0.013895
-   25  140.781   0.0e+00  8.0e-01  0.012278
-   26  143.985   0.0e+00  1.0e+00  0.012531
-   27  141.999   0.0e+00  6.0e-01  0.014772
-   28  143.170   0.0e+00  6.0e-01  0.009784
-   29  146.030   0.0e+00  8.0e-01  0.011534
-   30  145.303   0.0e+00  6.0e-01  0.011772
-   31  147.656   0.0e+00  4.0e-01  0.010402
-   32  151.534   0.0e+00  6.0e-01  0.012262
-   33  154.844   0.0e+00  6.0e-01  0.010835
-   34  158.504   1.0e+00  4.0e-01  0.011058
-   35  151.869   1.0e+00  8.0e-01  0.015056
-   36  157.102   0.0e+00  1.0e+00  0.011519
-   37  160.149   1.0e+00  8.0e-01  0.011756
-   38  158.809   0.0e+00  8.0e-01  0.021352
-   39  145.627   0.0e+00  2.0e-01  0.012245
-   40  149.604   0.0e+00  6.0e-01  0.012498
-   41  152.599   1.0e+00  8.0e-01  0.009561
-   42  153.324   0.0e+00  2.0e-01  0.009758
-   43  149.979   0.0e+00  8.0e-01  0.011503
-   44  148.201   0.0e+00  4.0e-01  0.008801
-   45  152.560   0.0e+00  6.0e-01  0.013839
-   46  157.084   0.0e+00  1.0e+00  0.014124
-   47  163.450   0.0e+00  4.0e-01  0.008100
-   48  171.495   1.0e+00  6.0e-01  0.011029
-   49  173.397   0.0e+00  4.0e-01  0.009745
-   50  158.901   0.0e+00  8.0e-01  0.009946
-   51  165.245   0.0e+00  8.0e-01  0.010151
-   52  164.895   0.0e+00  6.0e-01  0.013821
-   53  161.485   0.0e+00  8.0e-01  0.012213
-   54  162.608   0.0e+00  6.0e-01  0.012464
-   55  165.864   0.0e+00  4.0e-01  0.012721
-   56  167.574   0.0e+00  8.0e-01  0.011241
-   57  160.999   0.0e+00  4.0e-01  0.011473
-   58  164.095   0.0e+00  4.0e-01  0.007599
-   59  161.170   0.0e+00  6.0e-01  0.015942
-   60  162.298   0.0e+00  8.0e-01  0.009143
-   61  166.214   0.0e+00  4.0e-01  0.010777
-   62  168.157   0.0e+00  6.0e-01  0.008245
-   63  172.407   0.0e+00  4.0e-01  0.009719
-   64  171.735   0.0e+00  6.0e-01  0.011457
-   65  173.042   1.0e+00  8.0e-01  0.011693
-   66  173.106   1.0e+00  4.0e-01  0.010333
-   67  170.477   0.0e+00  6.0e-01  0.006844
-   68  174.691   0.0e+00  1.0e+00  0.016583
-   69  172.799   1.0e+00  4.0e-01  0.012687
-   70  171.915   0.0e+00  6.0e-01  0.008404
-   71  173.346   1.0e+00  6.0e-01  0.013216
-   72  176.508   1.0e+00  6.0e-01  0.010111
-   73  174.215   0.0e+00  6.0e-01  0.008934
-   74  169.975   0.0e+00  2.0e-01  0.009118
-   75  169.420   1.0e+00  6.0e-01  0.008057
-   76  174.345   0.0e+00  6.0e-01  0.012670
-   77  175.182   0.0e+00  8.0e-01  0.007266
-   78  180.928   0.0e+00  8.0e-01  0.015244
-   79  180.765   0.0e+00  4.0e-01  0.011662
-   80  184.515   0.0e+00  2.0e-01  0.011902
-   81  179.514   0.0e+00  2.0e-01  0.009106
-   82  183.132   0.0e+00  6.0e-01  0.014320
-   83  186.510   0.0e+00  6.0e-01  0.010955
-   84  177.087   0.0e+00  4.0e-01  0.009681
-   85  175.979   1.0e+00  8.0e-01  0.011412
-   86  180.064   0.0e+00  8.0e-01  0.008730
-   87  178.666   1.0e+00  0.0e+00  0.008910
-   88  180.462   1.0e+00  2.0e-01  0.012132
-   89  184.765   0.0e+00  4.0e-01  0.010720
-   90  188.743   0.0e+00  8.0e-01  0.014595
-   91  198.627   0.0e+00  8.0e-01  0.011166
-   92  193.849   0.0e+00  8.0e-01  0.011396
-   93  199.980   1.0e+00  4.0e-01  0.011631
-   94  191.935   0.0e+00  6.0e-01  0.013711
-   95  184.075   0.0e+00  4.0e-01  0.010490
-   96  188.920   0.0e+00  8.0e-01  0.010706
-   97  190.659   0.0e+00  6.0e-01  0.012620
-   98  186.523   1.0e+00  1.0e+00  0.011151
-   99  181.855   0.0e+00  4.0e-01  0.011381
-  100  185.423   0.0e+00  2.0e-01  0.010057
-  101  184.956   0.0e+00  8.0e-01  0.013692
-  102  199.852   0.0e+00  1.0e+00  0.018642
-  103  205.680   0.0e+00  1.0e+00  0.016473
-  104  205.774   0.0e+00  8.0e-01  0.014556
-  105  191.932   0.0e+00  6.0e-01  0.014856
-  106  191.512   0.0e+00  6.0e-01  0.011366
-  107  191.469   0.0e+00  4.0e-01  0.011600
-  108  195.797   1.0e+00  8.0e-01  0.018242
-  109  194.657   1.0e+00  6.0e-01  0.007842
-  110  186.386   0.0e+00  8.0e-01  0.012332
-  111  183.688   0.0e+00  8.0e-01  0.010897
-  112  188.002   0.0e+00  6.0e-01  0.014837
-  113  182.836   0.0e+00  8.0e-01  0.013110
-  114  189.582   0.0e+00  2.0e-01  0.007519
-  115  188.144   0.0e+00  4.0e-01  0.010237
-  116  181.400   0.0e+00  6.0e-01  0.007832
-  117  187.523   0.0e+00  6.0e-01  0.010663
-  118  181.253   1.0e+00  6.0e-01  0.010883
-  119  197.603   1.0e+00  6.0e-01  0.009616
-  120  189.672   1.0e+00  8.0e-01  0.009814
-  121  193.255   0.0e+00  6.0e-01  0.007509
-  122  191.071   0.0e+00  8.0e-01  0.011808
-  123  197.850   0.0e+00  6.0e-01  0.007821
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-  125  194.622   0.0e+00  8.0e-01  0.012553
-  126  200.623   0.0e+00  6.0e-01  0.009603
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-  128  193.468   0.0e+00  6.0e-01  0.007499
-  129  198.643   0.0e+00  6.0e-01  0.006626
-  130  197.104   0.0e+00  6.0e-01  0.010420
-  131  194.725   0.0e+00  1.0e+00  0.012283
-  132  200.355   0.0e+00  1.0e+00  0.012536
-  133  204.499   0.0e+00  8.0e-01  0.011077
-  134  199.043   0.0e+00  1.0e+00  0.008475
-  135  199.557   0.0e+00  8.0e-01  0.009990
-  136  207.263   0.0e+00  2.0e-01  0.006617
-  137  200.371   1.0e+00  1.0e+00  0.009009
-  138  198.157   1.0e+00  8.0e-01  0.010620
-  139  198.883   0.0e+00  8.0e-01  0.008125
-  140  205.275   0.0e+00  8.0e-01  0.012777
-  141  196.914   1.0e+00  4.0e-01  0.009775
-  142  200.531   0.0e+00  6.0e-01  0.008638
-  143  206.378   0.0e+00  8.0e-01  0.007633
-  144  212.963   1.0e+00  4.0e-01  0.007790
-  145  213.121   0.0e+00  8.0e-01  0.009183
-  146  221.572   0.0e+00  4.0e-01  0.007025
-  147  228.156   0.0e+00  8.0e-01  0.008282
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-  151  207.781   0.0e+00  2.0e-01  0.010378
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-  154  213.229   0.0e+00  1.0e+00  0.009552
-  155  217.119   1.0e+00  8.0e-01  0.009749
-  156  212.965   0.0e+00  6.0e-01  0.008615
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-  158  219.487   0.0e+00  6.0e-01  0.008973
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-  160  225.131   0.0e+00  8.0e-01  0.010796
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-  168  221.324   0.0e+00  1.0e+00  0.009527
-  169  214.574   0.0e+00  6.0e-01  0.009723
-  170  222.213   0.0e+00  6.0e-01  0.009924
-  171  227.018   0.0e+00  8.0e-01  0.007592
-  172  222.231   0.0e+00  6.0e-01  0.008950
-  173  220.035   0.0e+00  4.0e-01  0.014074
-  174  214.461   0.0e+00  4.0e-01  0.009322
-  175  222.390   0.0e+00  6.0e-01  0.008237
-  176  220.229   0.0e+00  8.0e-01  0.012954
-  177  219.790   1.0e+00  6.0e-01  0.009910
-  178  222.420   1.0e+00  4.0e-01  0.007582
-  179  224.933   1.0e+00  6.0e-01  0.008938
-  180  224.942   1.0e+00  4.0e-01  0.010536
-  181  232.543   0.0e+00  2.0e-01  0.010753
-  182  222.190   0.0e+00  4.0e-01  0.008226
-  183  221.722   0.0e+00  8.0e-01  0.012937
-  184  218.829   1.0e+00  6.0e-01  0.011431
-  185  213.759   0.0e+00  8.0e-01  0.010101
-  186  218.961   1.0e+00  6.0e-01  0.007728
-  187  217.089   1.0e+00  4.0e-01  0.007887
-  188  214.426   0.0e+00  6.0e-01  0.008050
-  189  220.135   0.0e+00  8.0e-01  0.012658
-  190  217.947   0.0e+00  6.0e-01  0.009684
-  191  218.909   0.0e+00  6.0e-01  0.011416
-  192  227.147   0.0e+00  2.0e-01  0.011651
-  193  220.770   0.0e+00  2.0e-01  0.007718
-  194  224.109   1.0e+00  8.0e-01  0.012136
-  195  224.455   0.0e+00  6.0e-01  0.010724
-  196  227.509   0.0e+00  8.0e-01  0.010945
-  197  232.729   0.0e+00  6.0e-01  0.011171
-  198  223.208   0.0e+00  8.0e-01  0.006406
-  199  230.646   0.0e+00  4.0e-01  0.006538
-  200  232.548   0.0e+00  1.0e+00  0.010282
-  201  229.470   0.0e+00  6.0e-01  0.006811
-  202  230.399   0.0e+00  2.0e-01  0.008028
-  203  231.355   1.0e+00  4.0e-01  0.009464
-  204  241.935   0.0e+00  6.0e-01  0.012885
-  205  238.580   1.0e+00  4.0e-01  0.008535
-  206  242.889   1.0e+00  1.0e+00  0.010061
-  207  232.102   0.0e+00  6.0e-01  0.011860
-  208  224.876   0.0e+00  8.0e-01  0.010480
-  209  234.434   1.0e+00  1.0e+00  0.014268
-  210  233.074   0.0e+00  8.0e-01  0.014562
-  211  241.748   1.0e+00  8.0e-01  0.011141
-  212  241.469   1.0e+00  4.0e-01  0.011370
-  213  231.886   0.0e+00  8.0e-01  0.013403
-  214  229.974   0.0e+00  6.0e-01  0.008878
-  215  234.781   0.0e+00  4.0e-01  0.012088
-  216  237.931   0.0e+00  2.0e-01  0.009248
-  217  239.706   0.0e+00  2.0e-01  0.009438
-  218  234.219   0.0e+00  1.0e+00  0.012851
-  219  234.878   1.0e+00  6.0e-01  0.009831
-  220  232.927   0.0e+00  6.0e-01  0.007522
-  221  235.485   0.0e+00  1.0e+00  0.013661
-  222  236.736   0.0e+00  8.0e-01  0.012072
-  223  233.293   1.0e+00  1.0e+00  0.007996
-  224  231.648   0.0e+00  4.0e-01  0.009426
-  225  234.705   1.0e+00  8.0e-01  0.009620
-  226  236.161   0.0e+00  1.0e+00  0.009818
-  227  241.674   0.0e+00  6.0e-01  0.011574
-  228  234.566   1.0e+00  6.0e-01  0.008855
-  229  226.260   0.0e+00  1.0e+00  0.010438
-  230  223.157   0.0e+00  4.0e-01  0.006914
-  231  228.623   0.0e+00  4.0e-01  0.010872
-  232  232.463   1.0e+00  4.0e-01  0.008318
-  233  239.122   0.0e+00  6.0e-01  0.007350
-  234  234.880   0.0e+00  8.0e-01  0.008664
-  235  234.098   0.0e+00  8.0e-01  0.011797
-  236  241.289   0.0e+00  1.0e+00  0.013906
-  237  239.993   0.0e+00  6.0e-01  0.009211
-  238  245.382   0.0e+00  6.0e-01  0.009401
-  239  250.210   0.0e+00  6.0e-01  0.007192
-  240  249.312   0.0e+00  4.0e-01  0.007340
-  241  242.819   0.0e+00  6.0e-01  0.007492
-  242  240.454   0.0e+00  0.0e+00  0.008831
-  243  244.747   0.0e+00  4.0e-01  0.007803
-  244  248.635   0.0e+00  6.0e-01  0.007964
-  245  248.841   1.0e+00  4.0e-01  0.007038
-  246  255.468   1.0e+00  8.0e-01  0.007183
-  247  263.691   0.0e+00  2.0e-01  0.009779
-  248  248.709   1.0e+00  6.0e-01  0.009981
-  249  248.787   0.0e+00  4.0e-01  0.005724
-  250  243.080   0.0e+00  1.0e+00  0.010396
-  251  249.804   0.0e+00  6.0e-01  0.006886
-  252  241.678   0.0e+00  6.0e-01  0.006085
-  253  248.100   1.0e+00  4.0e-01  0.006210
-  254  253.727   0.0e+00  8.0e-01  0.008455
-  255  253.157   0.0e+00  8.0e-01  0.005601
-  256  257.970   0.0e+00  8.0e-01  0.007626
-  257  250.823   0.0e+00  1.0e+00  0.006738
-  258  249.139   0.0e+00  4.0e-01  0.007943
-  259  245.462   1.0e+00  8.0e-01  0.008107
-  260  254.723   0.0e+00  6.0e-01  0.008274
-  261  254.922   0.0e+00  4.0e-01  0.006330
-  262  254.007   0.0e+00  6.0e-01  0.007462
-  263  246.327   0.0e+00  6.0e-01  0.007615
-  264  252.634   0.0e+00  6.0e-01  0.008977
-  265  254.028   0.0e+00  1.0e+00  0.009162
-  266  246.421   0.0e+00  1.0e+00  0.008096
-  267  248.003   0.0e+00  2.0e-01  0.008263
-  268  241.774   1.0e+00  6.0e-01  0.007301
-  269  246.111   1.0e+00  8.0e-01  0.009941
-  270  243.710   0.0e+00  6.0e-01  0.006585
-  271  243.119   0.0e+00  6.0e-01  0.007762
-  272  245.204   0.0e+00  6.0e-01  0.012206
-  273  238.438   0.0e+00  6.0e-01  0.007000
-  274  238.050   0.0e+00  4.0e-01  0.006186
-  275  240.704   0.0e+00  8.0e-01  0.009727
-  276  242.427   1.0e+00  8.0e-01  0.011466
-  277  246.923   0.0e+00  1.0e+00  0.011703
-  278  242.469   1.0e+00  8.0e-01  0.011944
-  279  244.823   0.0e+00  6.0e-01  0.007911
-  280  249.867   0.0e+00  8.0e-01  0.008074
-  281  244.933   0.0e+00  6.0e-01  0.010993
-  282  248.466   1.0e+00  6.0e-01  0.008411
-  283  241.345   0.0e+00  8.0e-01  0.009914
-  284  247.532   0.0e+00  4.0e-01  0.006567
-  285  248.473   0.0e+00  8.0e-01  0.008941
-  286  251.554   0.0e+00  8.0e-01  0.009125
-  287  251.492   0.0e+00  4.0e-01  0.009313
-  288  253.213   0.0e+00  6.0e-01  0.008230
-  289  253.943   0.0e+00  4.0e-01  0.008399
-  290  245.151   0.0e+00  2.0e-01  0.006426
-  291  246.703   0.0e+00  8.0e-01  0.007575
-  292  257.618   0.0e+00  8.0e-01  0.007731
-  293  254.717   0.0e+00  8.0e-01  0.009113
-  294  247.522   0.0e+00  6.0e-01  0.006972
-  295  256.533   0.0e+00  6.0e-01  0.008219
-  296  256.720   0.0e+00  8.0e-01  0.009688
-  297  264.403   0.0e+00  6.0e-01  0.008561
-  298  255.942   1.0e+00  4.0e-01  0.006550
-  299  260.738   0.0e+00  4.0e-01  0.007721
-  300  255.045   1.0e+00  1.0e+00  0.012141
-  301  254.900   0.0e+00  8.0e-01  0.006963
-  302  253.813   0.0e+00  4.0e-01  0.006153
-  303  256.530   0.0e+00  4.0e-01  0.006279
-  304  255.189   0.0e+00  6.0e-01  0.008550
-  305  254.844   0.0e+00  6.0e-01  0.007555
-  306  255.741   1.0e+00  4.0e-01  0.005780
-  307  255.612   0.0e+00  8.0e-01  0.005899
-  308  263.507   0.0e+00  8.0e-01  0.008031
-  309  257.700   0.0e+00  6.0e-01  0.007097
-  310  255.341   1.0e+00  8.0e-01  0.011160
-  311  256.443   0.0e+00  1.0e+00  0.007392
-  312  256.664   0.0e+00  6.0e-01  0.007545
-  313  259.956   0.0e+00  1.0e+00  0.010272
-  314  252.183   1.0e+00  4.0e-01  0.006804
-  315  250.422   0.0e+00  8.0e-01  0.008021
-  316  250.592   1.0e+00  2.0e-01  0.007087
-  317  261.947   0.0e+00  2.0e-01  0.009650
-  318  257.370   0.0e+00  2.0e-01  0.007382
-  319  258.826   0.0e+00  4.0e-01  0.007535
-  320  255.383   0.0e+00  8.0e-01  0.008882
-  321  249.565   0.0e+00  2.0e-01  0.005883
-  322  259.877   1.0e+00  8.0e-01  0.009252
-  323  260.117   0.0e+00  4.0e-01  0.007078
-  324  262.326   0.0e+00  4.0e-01  0.007224
-  325  265.207   0.0e+00  8.0e-01  0.011360
-  326  263.777   0.0e+00  8.0e-01  0.007525
-  327  263.399   0.0e+00  4.0e-01  0.007680
-  328  261.947   0.0e+00  8.0e-01  0.006786
-  329  264.246   0.0e+00  1.0e+00  0.007999
-  330  258.059   0.0e+00  1.0e+00  0.009430
-  331  257.171   0.0e+00  8.0e-01  0.006246
-  332  258.881   0.0e+00  6.0e-01  0.007363
-  333  261.520   0.0e+00  8.0e-01  0.007515
-  334  263.638   0.0e+00  4.0e-01  0.004978
-  335  257.345   1.0e+00  1.0e+00  0.010442
-  336  254.285   0.0e+00  6.0e-01  0.007989
-  337  253.287   0.0e+00  4.0e-01  0.008153
-  338  259.284   0.0e+00  6.0e-01  0.009611
-  339  259.789   0.0e+00  8.0e-01  0.011330
-  340  261.148   0.0e+00  6.0e-01  0.006497
-  341  253.759   0.0e+00  8.0e-01  0.006631
-  342  251.806   1.0e+00  8.0e-01  0.009029
-  343  255.814   1.0e+00  6.0e-01  0.006907
-  344  252.236   0.0e+00  4.0e-01  0.006104
-  345  257.603   0.0e+00  1.0e+00  0.011086
-  346  248.843   0.0e+00  8.0e-01  0.008481
-  347  254.425   1.0e+00  8.0e-01  0.007495
-  348  259.312   0.0e+00  1.0e+00  0.008835
-  349  259.008   1.0e+00  6.0e-01  0.005067
-  350  259.639   1.0e+00  4.0e-01  0.005972
-  351  254.628   0.0e+00  6.0e-01  0.008132
-  352  243.759   0.0e+00  8.0e-01  0.008299
-  353  243.477   1.0e+00  8.0e-01  0.006349
-  354  244.410   1.0e+00  1.0e+00  0.005610
-  355  246.901   0.0e+00  4.0e-01  0.010190
-  356  247.522   0.0e+00  4.0e-01  0.007796
-  357  248.622   1.0e+00  4.0e-01  0.005964
-  358  248.394   0.0e+00  4.0e-01  0.006087
-  359  248.934   1.0e+00  8.0e-01  0.006213
-  360  249.832   0.0e+00  4.0e-01  0.005490
-  361  248.175   0.0e+00  2.0e-01  0.007475
-  362  247.629   1.0e+00  4.0e-01  0.010177
-  363  247.659   0.0e+00  6.0e-01  0.007786
-  364  249.739   0.0e+00  6.0e-01  0.006880
-  365  250.666   0.0e+00  2.0e-01  0.006079
-  366  249.526   0.0e+00  8.0e-01  0.011042
-  367  252.039   0.0e+00  4.0e-01  0.007314
-  368  247.189   0.0e+00  6.0e-01  0.006463
-  369  248.710   0.0e+00  8.0e-01  0.006596
-  370  256.541   0.0e+00  8.0e-01  0.007775
-  371  248.149   0.0e+00  6.0e-01  0.007936
-  372  256.840   0.0e+00  8.0e-01  0.008099
-  373  258.809   0.0e+00  4.0e-01  0.006196
-  374  260.056   0.0e+00  8.0e-01  0.009744
-  375  267.134   0.0e+00  8.0e-01  0.007455
-  376  266.418   1.0e+00  2.0e-01  0.010150
-  377  254.053   0.0e+00  4.0e-01  0.005821
-  378  257.370   0.0e+00  6.0e-01  0.009153
-  379  260.243   0.0e+00  4.0e-01  0.009342
-  380  255.864   0.0e+00  4.0e-01  0.009534
-  381  254.064   0.0e+00  1.0e+00  0.009731
-  382  259.944   0.0e+00  8.0e-01  0.007445
-  383  267.322   0.0e+00  2.0e-01  0.006578
-  384  264.692   0.0e+00  8.0e-01  0.008957
-  385  256.688   1.0e+00  4.0e-01  0.007914
-  386  258.340   0.0e+00  8.0e-01  0.008078
-  387  268.025   0.0e+00  8.0e-01  0.010998
-  388  263.174   0.0e+00  6.0e-01  0.008414
-  389  264.371   0.0e+00  6.0e-01  0.006437
-  390  267.127   1.0e+00  4.0e-01  0.006570
-  391  269.984   0.0e+00  2.0e-01  0.006705
-  392  268.496   0.0e+00  8.0e-01  0.005925
-  393  263.443   0.0e+00  4.0e-01  0.005235
-  394  263.938   1.0e+00  4.0e-01  0.006172
-  395  279.883   1.0e+00  4.0e-01  0.006299
-  396  261.173   0.0e+00  6.0e-01  0.005566
-  397  264.522   0.0e+00  6.0e-01  0.006561
-  398  262.476   0.0e+00  4.0e-01  0.006696
-  399  268.589   0.0e+00  1.0e+00  0.006834
-  400  265.954   0.0e+00  8.0e-01  0.006975
-  401  264.191   0.0e+00  8.0e-01  0.010968
-  402  264.745   0.0e+00  6.0e-01  0.008391
-
- gp hyperparameters: 
- 
-    0.8942    0.7866    0.9250    0.0264   -0.1950    1.0640
-
-Demo completed in 1.015 minutes
diff --git a/tests/realValues/multinom_fig1.fig b/tests/realValues/multinom_fig1.fig
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diff --git a/tests/realValues/multinom_fig2.fig b/tests/realValues/multinom_fig2.fig
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index 6416c9bb..00000000
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diff --git a/tests/realValues/multinom_fig3.fig b/tests/realValues/multinom_fig3.fig
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diff --git a/tests/realValues/neuralnetcov.txt b/tests/realValues/neuralnetcov.txt
deleted file mode 100644
index ca9d4930..00000000
--- a/tests/realValues/neuralnetcov.txt
+++ /dev/null
@@ -1,10 +0,0 @@
- TolFun reached. Func-count 34. Final f(x)=-49.0877. Elapsed time 0.81
- TolFun reached. Func-count 33. Final f(x)=-92.1279. Elapsed time 1.22
- TolFun reached. Func-count 48. Final f(x)=-123.011. Elapsed time 0.36
- TolFun reached. Func-count 25. Final f(x)=-126.821. Elapsed time 0.27
-
- gp hyperparameters: 
- 
-   -3.6553   -0.9891   -5.8739
-
-Demo completed in 0.072 minutes
diff --git a/tests/realValues/neuralnetcov_fig1.fig b/tests/realValues/neuralnetcov_fig1.fig
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index 2db07979..00000000
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diff --git a/tests/realValues/neuralnetcov_fig3.fig b/tests/realValues/neuralnetcov_fig3.fig
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index 3b00a341..00000000
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diff --git a/tests/realValues/neuralnetcov_fig4.fig b/tests/realValues/neuralnetcov_fig4.fig
deleted file mode 100644
index bcfb7d30..00000000
Binary files a/tests/realValues/neuralnetcov_fig4.fig and /dev/null differ
diff --git a/tests/realValues/periodic.txt b/tests/realValues/periodic.txt
deleted file mode 100644
index 31403228..00000000
--- a/tests/realValues/periodic.txt
+++ /dev/null
@@ -1,89 +0,0 @@
- Optimizer Results
-  Algorithm Used: Broyden-Fletcher-Goldfarb-Shanno (BFGS)
-  Exit message : Line search cannot find an acceptable point along the current search direction
-  Iterations : 29
-  Function Count : 50
-  Minimum found : 582.4295
-  Intern Time : 0.029314 seconds
-  Total Time : 6.3461 seconds
-GP with sexp+sexp+noise:  l_1= 478.43, s^2_1 = 1880.95, 
- l_2= 2.31, s^2_2 = 5.90 
- s^2_{noise} = 0.04
- Iteration  Func-count   Grad-count         f(x)         Step-size
-     0           1           1             567.966    
-     1           6           6             567.821     3.33254e-07
-     2          11          11             186.598      0.00870757
-     3          15          15             178.531      0.00677027
-     4          17          17              174.01             0.1
-     5          20          20             171.958            0.01
-     6          23          23             170.366       0.0398045
-     7          27          27              163.86       0.0354622
-     8          30          30             160.988        0.287431
-     9          32          32             158.369         0.27121
-    10          33          33               156.7               1
-    11          35          35             156.584        0.378146
-    12          37          37              156.58        0.186455
- Optimizer Results
-  Algorithm Used: Broyden-Fletcher-Goldfarb-Shanno (BFGS)
-  Exit message : Line search cannot find an acceptable point along the current search direction
-  Iterations : 13
-  Function Count : 52
-  Minimum found : 156.5799
-  Intern Time : 0.033706 seconds
-  Total Time : 11.3564 seconds
-GP sexp+periodic+sexp+noise:  l_1= 204.51, s^2_1 = 517.05, 
- l_2= 1.38, s^2_2 = 6.86, p=12.00, s_sexp^2 = 1469.76, 
- l_3= 4.01, s^2_3 = 0.17, 
- s^2_{noise} = 0.04
-[Warning: quad_moments: Reached the limit on the maximum number of intervals in use.] 
-[> In <a href="matlab: opentoline('/m/home/home9/96/siivole1/data/Documents/Codes/gpstuff_monotonic/gp/private/quad_moments.m',122,1)">gp/private/quad_moments at 122</a>
-  In <a href="matlab: opentoline('/m/home/home9/96/siivole1/data/Documents/Codes/gpstuff_monotonic/gp/lik_poisson.m',247,1)">lik_poisson>lik_poisson_tiltedMoments at 247</a>
-  In <a href="matlab: opentoline('/m/home/home9/96/siivole1/data/Documents/Codes/gpstuff_monotonic/gp/gpep_e.m',2117,1)">gpep_e>ep_algorithm at 2117</a>
-  In <a href="matlab: opentoline('/m/home/home9/96/siivole1/data/Documents/Codes/gpstuff_monotonic/gp/gpep_e.m',110,1)">gpep_e at 110</a>
-  In <a href="matlab: opentoline('/m/home/home9/96/siivole1/data/Documents/Codes/gpstuff_monotonic/gp/gp_e.m',54,1)">gp_e at 54</a>
-  In <a href="matlab: opentoline('/m/home/home9/96/siivole1/data/Documents/Codes/gpstuff_monotonic/gp/gp_eg.m',35,1)">gp_eg at 35</a>
-  In <a href="matlab: opentoline('/m/home/home9/96/siivole1/data/Documents/Codes/gpstuff_monotonic/gp/gp_optim.m',66,1)">gp_optim>@(ww)gp_eg(ww,gp,x,y,'z',z) at 66</a>
-  In <a href="matlab: opentoline('/m/home/home9/96/siivole1/data/Documents/Codes/gpstuff_monotonic/optim/fminlbfgs.m',890,1)">fminlbfgs>gradient_function at 890</a>
-  In <a href="matlab: opentoline('/m/home/home9/96/siivole1/data/Documents/Codes/gpstuff_monotonic/optim/fminlbfgs.m',744,1)">fminlbfgs>bracketingPhase at 744</a>
-  In <a href="matlab: opentoline('/m/home/home9/96/siivole1/data/Documents/Codes/gpstuff_monotonic/optim/fminlbfgs.m',583,1)">fminlbfgs>linesearch at 583</a>
-  In <a href="matlab: opentoline('/m/home/home9/96/siivole1/data/Documents/Codes/gpstuff_monotonic/optim/fminlbfgs.m',279,1)">fminlbfgs at 279</a>
-  In <a href="matlab: opentoline('/m/home/home9/96/siivole1/data/Documents/Codes/gpstuff_monotonic/gp/gp_optim.m',121,1)">gp_optim at 121</a>
-  In <a href="matlab: opentoline('/m/home/home9/96/siivole1/data/Documents/Codes/gpstuff_monotonic/gp/demo_periodic.m',286,1)">demo_periodic at 286</a>
-  In <a href="matlab: opentoline('/m/work/modules/Ubuntu/14.04/amd64/common/matlab/2014a/toolbox/matlab/lang/run.m',63,1)">run at 63</a>
-  In <a href="matlab: opentoline('/m/home/home9/96/siivole1/data/Documents/Codes/gpstuff_monotonic/tests/rundemo.m',111,1)">rundemo at 111</a>
-  In <a href="matlab: opentoline('/m/home/home9/96/siivole1/data/Documents/Codes/gpstuff_monotonic/tests/test_periodic.m',44,1)">test_periodic>testRunDemo at 44</a>
-  In <a href="matlab: opentoline('/m/work/modules/Ubuntu/14.04/amd64/common/matlab/2014a/toolbox/matlab/testframework/+matlab/+unittest/FunctionTestCase.m',90,1)">FunctionTestCase>FunctionTestCase.test at 90</a>
-  In <a href="matlab: opentoline('/m/work/modules/Ubuntu/14.04/amd64/common/matlab/2014a/toolbox/matlab/testframework/+matlab/+unittest/TestRunner.m',967,1)">TestRunner>TestRunner.evaluateMethod at 967</a>
-  In <a href="matlab: opentoline('/m/work/modules/Ubuntu/14.04/amd64/common/matlab/2014a/toolbox/matlab/testframework/+matlab/+unittest/TestRunner.m',678,1)">TestRunner>TestRunner.evaluateMethodOnPlugins at 678</a>
-  In <a href="matlab: opentoline('/m/work/modules/Ubuntu/14.04/amd64/common/matlab/2014a/toolbox/matlab/testframework/+matlab/+unittest/TestRunner.m',691,1)">TestRunner>TestRunner.evaluateMethodsOnTestContent at 691</a>
-  In <a href="matlab: opentoline('/m/work/modules/Ubuntu/14.04/amd64/common/matlab/2014a/toolbox/matlab/testframework/+matlab/+unittest/TestRunner.m',953,1)">TestRunner>TestRunner.runTestMethod at 953</a>
-  In <a href="matlab: opentoline('/m/work/modules/Ubuntu/14.04/amd64/common/matlab/2014a/toolbox/matlab/testframework/+matlab/+unittest/TestRunner.m',678,1)">TestRunner>TestRunner.evaluateMethodOnPlugins at 678</a>
-  In <a href="matlab: opentoline('/m/work/modules/Ubuntu/14.04/amd64/common/matlab/2014a/toolbox/matlab/testframework/+matlab/+unittest/TestRunner.m',902,1)">TestRunner>TestRunner.runTest at 902</a>
-  In <a href="matlab: opentoline('/m/work/modules/Ubuntu/14.04/amd64/common/matlab/2014a/toolbox/matlab/testframework/+matlab/+unittest/TestRunner.m',678,1)">TestRunner>TestRunner.evaluateMethodOnPlugins at 678</a>
-  In <a href="matlab: opentoline('/m/work/modules/Ubuntu/14.04/amd64/common/matlab/2014a/toolbox/matlab/testframework/+matlab/+unittest/TestRunner.m',374,1)">TestRunner>TestRunner.runSharedTestCase at 374</a>
-  In <a href="matlab: opentoline('/m/work/modules/Ubuntu/14.04/amd64/common/matlab/2014a/toolbox/matlab/testframework/+matlab/+unittest/TestRunner.m',832,1)">TestRunner>TestRunner.runTestClass at 832</a>
-  In <a href="matlab: opentoline('/m/work/modules/Ubuntu/14.04/amd64/common/matlab/2014a/toolbox/matlab/testframework/+matlab/+unittest/+plugins/TestRunnerPlugin.m',369,1)">TestRunnerPlugin>TestRunnerPlugin.runTestClass at 369</a>
-  In <a href="matlab: opentoline('/m/work/modules/Ubuntu/14.04/amd64/common/matlab/2014a/toolbox/matlab/testframework/+matlab/+unittest/+plugins/+testrunprogress/ConciseProgressPlugin.m',61,1)">ConciseProgressPlugin>ConciseProgressPlugin.runTestClass at 61</a>
-  In <a href="matlab: opentoline('/m/work/modules/Ubuntu/14.04/amd64/common/matlab/2014a/toolbox/matlab/testframework/+matlab/+unittest/TestRunner.m',678,1)">TestRunner>TestRunner.evaluateMethodOnPlugins at 678</a>
-  In <a href="matlab: opentoline('/m/work/modules/Ubuntu/14.04/amd64/common/matlab/2014a/toolbox/matlab/testframework/+matlab/+unittest/TestRunner.m',754,1)">TestRunner>TestRunner.runTestSuite at 754</a>
-  In <a href="matlab: opentoline('/m/work/modules/Ubuntu/14.04/amd64/common/matlab/2014a/toolbox/matlab/testframework/+matlab/+unittest/+plugins/TestRunnerPlugin.m',220,1)">TestRunnerPlugin>TestRunnerPlugin.runTestSuite at 220</a>
-  In <a href="matlab: opentoline('/m/work/modules/Ubuntu/14.04/amd64/common/matlab/2014a/toolbox/matlab/testframework/+matlab/+unittest/+plugins/FailureDiagnosticsPlugin.m',83,1)">FailureDiagnosticsPlugin>FailureDiagnosticsPlugin.runTestSuite at 83</a>
-  In <a href="matlab: opentoline('/m/work/modules/Ubuntu/14.04/amd64/common/matlab/2014a/toolbox/matlab/testframework/+matlab/+unittest/TestRunner.m',678,1)">TestRunner>TestRunner.evaluateMethodOnPlugins at 678</a>
-  In <a href="matlab: opentoline('/m/work/modules/Ubuntu/14.04/amd64/common/matlab/2014a/toolbox/matlab/testframework/+matlab/+unittest/TestRunner.m',220,1)">TestRunner>TestRunner.run at 220</a>
-  In <a href="matlab: opentoline('/m/work/modules/Ubuntu/14.04/amd64/common/matlab/2014a/toolbox/matlab/testframework/+matlab/+unittest/TestSuite.m',568,1)">TestSuite>TestSuite.run at 568</a>
-  In <a href="matlab: opentoline('/m/work/modules/Ubuntu/14.04/amd64/common/matlab/2014a/toolbox/matlab/testframework/runtests.m',73,1)">runtests at 73</a>
-  In <a href="matlab: opentoline('/m/home/home9/96/siivole1/data/Documents/Codes/gpstuff_monotonic/tests/runtestset.m',166,1)">runtestset at 166</a>] 
- Optimizer Results
-  Algorithm Used: Broyden-Fletcher-Goldfarb-Shanno (BFGS)
-  Exit message : Change in the objective function value was less than TolFun.
-  Iterations : 20
-  Function Count : 29
-  Minimum found : 249.6466
-  Intern Time : 0.015026 seconds
-  Total Time : 7.1884 seconds
-
- gp hyperparameters: 
- 
-   -1.6167    3.9826   -0.3658   -0.0967    4.6499   -2.9568   -0.4466    3.2408    5.2019
-
-Demo completed in 0.437 minutes
diff --git a/tests/realValues/periodic_fig1.fig b/tests/realValues/periodic_fig1.fig
deleted file mode 100644
index a98bbac2..00000000
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diff --git a/tests/realValues/periodic_fig2.fig b/tests/realValues/periodic_fig2.fig
deleted file mode 100644
index 1bbfa2b5..00000000
Binary files a/tests/realValues/periodic_fig2.fig and /dev/null differ
diff --git a/tests/realValues/periodic_fig3.fig b/tests/realValues/periodic_fig3.fig
deleted file mode 100644
index 66d9e772..00000000
Binary files a/tests/realValues/periodic_fig3.fig and /dev/null differ
diff --git a/tests/realValues/periodic_fig4.fig b/tests/realValues/periodic_fig4.fig
deleted file mode 100644
index c541cda8..00000000
Binary files a/tests/realValues/periodic_fig4.fig and /dev/null differ
diff --git a/tests/realValues/quantilegp.mat b/tests/realValues/quantilegp.mat
deleted file mode 100644
index 676d9dd7..00000000
Binary files a/tests/realValues/quantilegp.mat and /dev/null differ
diff --git a/tests/realValues/quantilegp.txt b/tests/realValues/quantilegp.txt
deleted file mode 100644
index ace3def6..00000000
--- a/tests/realValues/quantilegp.txt
+++ /dev/null
@@ -1,108 +0,0 @@
- Using SSLS sampler for hyperparameters and ESLS for latent values
- cycle  etr      
-   10  -655.948  
-   20  -814.127  
-   30  -793.237  
-   40  -811.429  
-   50  -790.802  
-   60  -795.628  
-   70  -799.446  
-   80  -790.804  
-   90  -790.947  
-  100  -793.979  
-  110  -819.661  
-  120  -810.312  
-  130  -806.862  
-  140  -799.911  
-  150  -802.424  
-  160  -809.855  
-  170  -803.488  
-  180  -802.406  
-  190  -814.197  
-  200  -815.736  
- Using SSLS sampler for hyperparameters and ESLS for latent values
- cycle  etr      
-   10  -835.532  
-   20  -876.538  
-   30  -873.323  
-   40  -873.999  
-   50  -866.677  
-   60  -865.850  
-   70  -862.013  
-   80  -861.627  
-   90  -881.933  
-  100  -882.073  
-  110  -879.486  
-  120  -858.539  
-  130  -857.283  
-  140  -859.148  
-  150  -874.396  
-  160  -874.116  
-  170  -872.306  
-  180  -873.092  
-  190  -864.090  
-  200  -869.966  
- Using SSLS sampler for hyperparameters and ESLS for latent values
- cycle  etr      
-   10  -674.260  
-   20  -782.390  
-   30  -785.058  
-   40  -777.811  
-   50  -775.691  
-   60  -781.468  
-   70  -780.819  
-   80  -778.977  
-   90  -762.236  
-  100  -775.296  
-  110  -776.123  
-  120  -767.543  
-  130  -755.555  
-  140  -748.529  
-  150  -742.941  
-  160  -743.832  
-  170  -743.400  
-  180  -751.381  
-  190  -736.308  
-  200  -742.827  
- Iteration  Func-count   Grad-count         f(x)         Step-size
-     0           1           1             330.216    
-     1           2           2             265.558       0.0138401
-     2           4           4             212.146        0.497238
-     3           7           7             211.176       0.0434884
-     4           8           8             210.956               1
-     5           9           9             210.917               1
-     6          10          10             210.913               1
-     7          11          11             210.913               1
-     8          12          12             210.913               1
-     9          13          13             210.913               1
- Optimizer Results
-  Algorithm Used: Broyden-Fletcher-Goldfarb-Shanno (BFGS)
-  Exit message : Line search cannot find an acceptable point along the current search direction
-  Iterations : 10
-  Function Count : 18
-  Minimum found : 210.9131
-  Intern Time : 0.012145 seconds
-  Total Time : 46.1157 seconds
- Iteration  Func-count   Grad-count         f(x)         Step-size
-     0           1           1             331.846    
-     1           2           2             266.315       0.0137365
-     2           4           4             212.334        0.467987
-     3           7           7             208.034            0.05
-     4           9           9             207.787        0.305239
-     5          10          10             207.776               1
-     6          11          11             207.774               1
-     7          12          12             207.774               1
- Optimizer Results
-  Algorithm Used: Broyden-Fletcher-Goldfarb-Shanno (BFGS)
-  Exit message : Change in x was smaller than the specified tolerance TolX.
-  Iterations : 8
-  Function Count : 14
-  Minimum found : 207.7743
-  Intern Time : 0.009385 seconds
-  Total Time : 45.9901 seconds
-
- gp hyperparameters: 
- 
-         0         0   -0.6931
-
-Demo completed in 2.888 minutes
diff --git a/tests/realValues/quantilegp_fig1.fig b/tests/realValues/quantilegp_fig1.fig
deleted file mode 100644
index e40fe451..00000000
Binary files a/tests/realValues/quantilegp_fig1.fig and /dev/null differ
diff --git a/tests/realValues/regression1.txt b/tests/realValues/regression1.txt
deleted file mode 100644
index 155dd297..00000000
--- a/tests/realValues/regression1.txt
+++ /dev/null
@@ -1,60 +0,0 @@
-GP with Gaussian noise model
-
-gpcf = 
-
-           type: 'gpcf_sexp'
-    lengthScale: [1.1000 1.2000]
-     magnSigma2: 0.0400
-              p: [1x1 struct]
-             fh: [1x1 struct]
-
-
-K =
-
-    0.0400    0.0187    0.0019
-    0.0187    0.0400    0.0187
-    0.0019    0.0187    0.0400
-
-
-C =
-
-    0.0800    0.0187    0.0019
-    0.0187    0.0800    0.0187
-    0.0019    0.0187    0.0800
-
- MAP estimate for the parameters
- TolFun reached. Func-count 33. Final f(x)=41.4902. Elapsed time 0.65
-    'log(sexp.magnSigma2)'
-    'log(sexp.lengthScale x 2)'
-    'log(gaussian.sigma2)'
-
-    3.5984    0.8838    0.8323    0.0428
-
- Grid integration over the parameters
- IA-grid: finding the mode
- IA-grid: computing Hessian using multiplication
- IA-grid: evaluating density in a grid
- IA-grid: evaluated density at 341 points
-    Elapsed time 11.44 seconds
- IA-grid: Total elapsed time 19.63 seconds
- MCMC integration over the parameters
- Using SLS sampler for hyperparameters
- cycle  etr      slsrej  
-   20  44.827  sls  
-   40  42.705  sls  
-   60  42.124  sls  
-   80  44.214  sls  
-  100  42.967  sls  
-  120  43.217  sls  
-  140  41.865  sls  
-  160  44.764  sls  
-  180  42.241  sls  
-  200  41.686  sls  
-  220  42.984  sls  
-Done
-
- gp hyperparameters: 
- 
-    1.2805   -0.1235   -0.1836   -3.1521
-
-Demo completed in 1.078 minutes
diff --git a/tests/realValues/regression1_fig1.fig b/tests/realValues/regression1_fig1.fig
deleted file mode 100644
index 8e45ee8d..00000000
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diff --git a/tests/realValues/regression1_fig2.fig b/tests/realValues/regression1_fig2.fig
deleted file mode 100644
index 2d95fda2..00000000
Binary files a/tests/realValues/regression1_fig2.fig and /dev/null differ
diff --git a/tests/realValues/regression1_fig3.fig b/tests/realValues/regression1_fig3.fig
deleted file mode 100644
index a3f1bf2f..00000000
Binary files a/tests/realValues/regression1_fig3.fig and /dev/null differ
diff --git a/tests/realValues/regression1_fig4.fig b/tests/realValues/regression1_fig4.fig
deleted file mode 100644
index b23c76bf..00000000
Binary files a/tests/realValues/regression1_fig4.fig and /dev/null differ
diff --git a/tests/realValues/regression_additive1.txt b/tests/realValues/regression_additive1.txt
deleted file mode 100644
index f40b6eda..00000000
--- a/tests/realValues/regression_additive1.txt
+++ /dev/null
@@ -1,87 +0,0 @@
-
-gp = 
-
-            type: 'FULL'
-             lik: [1x1 struct]
-              cf: {[1x1 struct]  [1x1 struct]}
-    infer_params: 'covariance+likelihood'
-    jitterSigma2: 1.0000e-09
-              fh: [1x1 struct]
-
- TolX reached. Func-count 56. Final f(x)=600.635. Elapsed time 4.58
-
-gp_fic = 
-
-            type: 'FIC'
-             lik: [1x1 struct]
-              cf: {[1x1 struct]  [1x1 struct]}
-    infer_params: 'covariance+likelihood'
-    jitterSigma2: 1.0000e-09
-             X_u: [24x1 double]
-            nind: 24
-               p: [1x1 struct]
-              fh: [1x1 struct]
-
- TolFun reached. Func-count 45. Final f(x)=1257.57. Elapsed time 1.97
-
-gp_pic = 
-
-            type: 'PIC'
-             lik: [1x1 struct]
-              cf: {[1x1 struct]  [1x1 struct]}
-    infer_params: 'covariance+likelihood'
-    jitterSigma2: 1.0000e-06
-             X_u: [24x1 double]
-            nind: 24
-               p: [1x1 struct]
-        tr_index: []
-              fh: [1x1 struct]
-
- TolX reached. Func-count 45. Final f(x)=636.321. Elapsed time 4.25
- TolFun reached. Func-count 3. Final f(x)=636.321. Elapsed time 0.28
-
-gp_csfic = 
-
-            type: 'CS+FIC'
-             lik: [1x1 struct]
-              cf: {[1x1 struct]  [1x1 struct]}
-    infer_params: 'covariance+likelihood'
-    jitterSigma2: 1.0000e-09
-             X_u: [24x1 double]
-            nind: 24
-               p: [1x1 struct]
-              fh: [1x1 struct]
-
-[Warning: Function value at xnew not finite or a number] 
-[> In <a href="matlab: opentoline('/proj/bayes/tsivula/gpstuff/optim/fminscg.m',202,1)">fminscg at 202</a>
-  In <a href="matlab: opentoline('/proj/bayes/tsivula/gpstuff/gp/gp_optim.m',121,1)">gp_optim at 121</a>
-  In <a href="matlab: opentoline('/proj/bayes/tsivula/gpstuff/gp/demo_regression_additive1.m',305,1)">demo_regression_additive1 at 305</a>
-  In <a href="matlab: opentoline('/proj/matlab/matlab2013b/toolbox/matlab/lang/run.m',63,1)">run at 63</a>
-  In <a href="matlab: opentoline('/proj/bayes/tsivula/gpstuff/xunit/rundemo.m',111,1)">rundemo at 111</a>
-  In <a href="matlab: opentoline('/proj/bayes/tsivula/gpstuff/xunit/test_regression_additive1.m',44,1)">test_regression_additive1>testRunDemo at 44</a>
-  In <a href="matlab: opentoline('/proj/matlab/matlab2013b/toolbox/matlab/testframework/+matlab/+unittest/FunctionTestCase.m',90,1)">FunctionTestCase>FunctionTestCase.test at 90</a>
-  In <a href="matlab: opentoline('/proj/matlab/matlab2013b/toolbox/matlab/testframework/+matlab/+unittest/TestRunner.m',684,1)">TestRunner>TestRunner.runMethod at 684</a>
-  In <a href="matlab: opentoline('/proj/matlab/matlab2013b/toolbox/matlab/testframework/+matlab/+unittest/+plugins/TestRunnerPlugin.m',99,1)">TestRunnerPlugin>TestRunnerPlugin.runMethod at 99</a>
-  In <a href="matlab: opentoline('/proj/matlab/matlab2013b/toolbox/matlab/testframework/+matlab/+unittest/+plugins/TestRunnerPlugin.m',99,1)">TestRunnerPlugin>TestRunnerPlugin.runMethod at 99</a>
-  In <a href="matlab: opentoline('/proj/matlab/matlab2013b/toolbox/matlab/testframework/+matlab/+unittest/TestRunner.m',538,1)">TestRunner>TestRunner.runMethodOnPlugins at 538</a>
-  In <a href="matlab: opentoline('/proj/matlab/matlab2013b/toolbox/matlab/testframework/+matlab/+unittest/TestRunner.m',555,1)">TestRunner>TestRunner.runMethodsOnTestContent at 555</a>
-  In <a href="matlab: opentoline('/proj/matlab/matlab2013b/toolbox/matlab/testframework/+matlab/+unittest/TestRunner.m',753,1)">TestRunner>TestRunner.runTestMethod at 753</a>
-  In <a href="matlab: opentoline('/proj/matlab/matlab2013b/toolbox/matlab/testframework/+matlab/+unittest/+plugins/TestRunnerPlugin.m',95,1)">TestRunnerPlugin>TestRunnerPlugin.runTestMethod at 95</a>
-  In <a href="matlab: opentoline('/proj/matlab/matlab2013b/toolbox/matlab/testframework/+matlab/+unittest/+plugins/TestRunnerPlugin.m',95,1)">TestRunnerPlugin>TestRunnerPlugin.runTestMethod at 95</a>
-  In <a href="matlab: opentoline('/proj/matlab/matlab2013b/toolbox/matlab/testframework/+matlab/+unittest/TestRunner.m',538,1)">TestRunner>TestRunner.runMethodOnPlugins at 538</a>
-  In <a href="matlab: opentoline('/proj/matlab/matlab2013b/toolbox/matlab/testframework/+matlab/+unittest/TestRunner.m',655,1)">TestRunner>TestRunner.runTestSuite at 655</a>
-  In <a href="matlab: opentoline('/proj/matlab/matlab2013b/toolbox/matlab/testframework/+matlab/+unittest/+plugins/TestRunnerPlugin.m',54,1)">TestRunnerPlugin>TestRunnerPlugin.runTestSuite at 54</a>
-  In <a href="matlab: opentoline('/proj/matlab/matlab2013b/toolbox/matlab/testframework/+matlab/+unittest/+plugins/TestRunnerPlugin.m',54,1)">TestRunnerPlugin>TestRunnerPlugin.runTestSuite at 54</a>
-  In <a href="matlab: opentoline('/proj/matlab/matlab2013b/toolbox/matlab/testframework/+matlab/+unittest/+plugins/FailureDiagnosticsPlugin.m',77,1)">FailureDiagnosticsPlugin>FailureDiagnosticsPlugin.runTestSuite at 77</a>
-  In <a href="matlab: opentoline('/proj/matlab/matlab2013b/toolbox/matlab/testframework/+matlab/+unittest/TestRunner.m',538,1)">TestRunner>TestRunner.runMethodOnPlugins at 538</a>
-  In <a href="matlab: opentoline('/proj/matlab/matlab2013b/toolbox/matlab/testframework/+matlab/+unittest/TestRunner.m',175,1)">TestRunner>TestRunner.run at 175</a>
-  In <a href="matlab: opentoline('/proj/matlab/matlab2013b/toolbox/matlab/testframework/+matlab/+unittest/TestSuite.m',364,1)">TestSuite>TestSuite.run at 364</a>
-  In <a href="matlab: opentoline('/proj/matlab/matlab2013b/toolbox/matlab/testframework/runtests.m',44,1)">runtests at 44</a>
-  In <a href="matlab: opentoline('/proj/bayes/tsivula/gpstuff/xunit/runtestset.m',94,1)">runtestset at 94</a>] 
- TolX reached. Func-count 45. Final f(x)=600.505. Elapsed time 1.31
-
- gp hyperparameters: 
- 
-    5.7476    4.8844    1.6333    2.0965   -3.5407
-
-Demo completed in 0.236 minutes
diff --git a/tests/realValues/regression_additive1_fig1.fig b/tests/realValues/regression_additive1_fig1.fig
deleted file mode 100644
index 9610cc94..00000000
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diff --git a/tests/realValues/regression_additive1_fig2.fig b/tests/realValues/regression_additive1_fig2.fig
deleted file mode 100644
index a75e26e6..00000000
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diff --git a/tests/realValues/regression_additive1_fig3.fig b/tests/realValues/regression_additive1_fig3.fig
deleted file mode 100644
index 352f9f9c..00000000
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diff --git a/tests/realValues/regression_additive1_fig4.fig b/tests/realValues/regression_additive1_fig4.fig
deleted file mode 100644
index 0322d6ff..00000000
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diff --git a/tests/realValues/regression_additive1_fig5.fig b/tests/realValues/regression_additive1_fig5.fig
deleted file mode 100644
index 1b866cb4..00000000
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diff --git a/tests/realValues/regression_additive2.mat b/tests/realValues/regression_additive2.mat
deleted file mode 100644
index 4ccb7696..00000000
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diff --git a/tests/realValues/regression_additive2.txt b/tests/realValues/regression_additive2.txt
deleted file mode 100644
index 2ccf9521..00000000
--- a/tests/realValues/regression_additive2.txt
+++ /dev/null
@@ -1,20 +0,0 @@
-Constant + linear covariance function
- TolFun reached. Func-count 31. Final f(x)=103.189. Elapsed time 0.44
-Constant + squared exponential covariance function
-(w.r.t. the first input dimension) + linear (w.r.t.
-the second input dimension)
- TolFun reached. Func-count 33. Final f(x)=54.0344. Elapsed time 0.82
-Additive squared exponential covariance function
- TolFun reached. Func-count 35. Final f(x)=26.9207. Elapsed time 0.76
-Squared exponential covariance function
- TolFun reached. Func-count 29. Final f(x)=30.6121. Elapsed time 0.46
-Additive neural network covariance function
- TolFun reached. Func-count 33. Final f(x)=24.4157. Elapsed time 1.09
-Neural network covariance function
- TolFun reached. Func-count 54. Final f(x)=24.2467. Elapsed time 1.23
-
- gp hyperparameters: 
- 
-   -3.1648    1.4213    0.5037   -2.9099
-
-Demo completed in 0.273 minutes
diff --git a/tests/realValues/regression_additive2_fig1.fig b/tests/realValues/regression_additive2_fig1.fig
deleted file mode 100644
index 28e1b800..00000000
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diff --git a/tests/realValues/regression_additive2_fig2.fig b/tests/realValues/regression_additive2_fig2.fig
deleted file mode 100644
index 42d91732..00000000
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diff --git a/tests/realValues/regression_additive2_fig3.fig b/tests/realValues/regression_additive2_fig3.fig
deleted file mode 100644
index feb00c75..00000000
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diff --git a/tests/realValues/regression_additive2_fig4.fig b/tests/realValues/regression_additive2_fig4.fig
deleted file mode 100644
index 5e817a63..00000000
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diff --git a/tests/realValues/regression_additive2_fig5.fig b/tests/realValues/regression_additive2_fig5.fig
deleted file mode 100644
index 1ca8d444..00000000
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diff --git a/tests/realValues/regression_additive2_fig6.fig b/tests/realValues/regression_additive2_fig6.fig
deleted file mode 100644
index afe8effd..00000000
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diff --git a/tests/realValues/regression_hier.txt b/tests/realValues/regression_hier.txt
deleted file mode 100644
index 65e6d927..00000000
--- a/tests/realValues/regression_hier.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-1) Linear model with intercept and slope wrt time
- TolFun reached. Func-count 19. Final f(x)=19.4149. Elapsed time 0.21
-2) Linear model with hierarchical intercept
- TolFun reached. Func-count 25. Final f(x)=-37.2922. Elapsed time 0.46
-3) Linear model with hierarchical intercept and slope
- TolFun reached. Func-count 33. Final f(x)=-46.4251. Elapsed time 0.77
-4) Nonlinear model with hierarchical intercept
- TolFun reached. Func-count 40. Final f(x)=-45.8041. Elapsed time 0.95
-5) Non-linear hierarchical model 1 with MAP
- TolFun reached. Func-count 88. Final f(x)=-77.8152. Elapsed time 3.28
-6) Non-linear hierarchical model 1 with IA
- IA-CCD: finding the mode
-    Elapsed time 1.31 seconds
- IA-CCD: computing Hessian using multiplication
- IA-CCD: 147 points for 9 parameters
- IA-CCD: autoscaling in 18 directions
- IA-CCD: scaling minmax [0.63 1.43]
- IA-CCD: evaluating density at 147 points
-    Elapsed time 1.92 seconds
- IA-CCD: Total elapsed time 4.10 seconds
-7) Non-linear hierarchical model 2 with MAP
- TolFun reached. Func-count 39. Final f(x)=-80.2512. Elapsed time 0.91
-8) Non-linear hierarchical model 2 with IA
- IA-CCD: finding the mode
- IA-CCD: computing Hessian using multiplication
- IA-CCD: 45 points for 6 parameters
- IA-CCD: autoscaling in 12 directions
- IA-CCD: scaling minmax [0.84 1.26]
- IA-CCD: evaluating density at 45 points
- IA-CCD: Total elapsed time 1.25 seconds
-9) Non-linear hierarchical model 3 with IA
- TolFun reached. Func-count 43. Final f(x)=-64.4989. Elapsed time 1.50
- IA-CCD: finding the mode
- IA-CCD: computing Hessian using multiplication
- IA-CCD: 79 points for 7 parameters
- IA-CCD: autoscaling in 14 directions
- IA-CCD: scaling minmax [0.70 1.29]
- IA-CCD: evaluating density at 79 points
- IA-CCD: Total elapsed time 2.11 seconds
-10) Missing data example
- TolFun reached. Func-count 49. Final f(x)=-4.10284. Elapsed time 0.99
- IA-CCD: finding the mode
- IA-CCD: computing Hessian using multiplication
- IA-CCD: 79 points for 7 parameters
- IA-CCD: autoscaling in 14 directions
- IA-CCD: scaling minmax [0.64 1.33]
- IA-CCD: evaluating density at 79 points
- IA-CCD: Total elapsed time 1.00 seconds
-
- gp hyperparameters: 
- 
-   -0.1987   -3.4072    1.0782    0.4515   -3.4082   -4.7715   -4.8805
-
-Demo completed in 0.461 minutes
diff --git a/tests/realValues/regression_hier_fig1.fig b/tests/realValues/regression_hier_fig1.fig
deleted file mode 100644
index e4ba19a2..00000000
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diff --git a/tests/realValues/regression_hier_fig2.fig b/tests/realValues/regression_hier_fig2.fig
deleted file mode 100644
index 319c9547..00000000
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diff --git a/tests/realValues/regression_meanf.mat b/tests/realValues/regression_meanf.mat
deleted file mode 100644
index 6384a899..00000000
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diff --git a/tests/realValues/regression_meanf.txt b/tests/realValues/regression_meanf.txt
deleted file mode 100644
index db155f03..00000000
--- a/tests/realValues/regression_meanf.txt
+++ /dev/null
@@ -1,15 +0,0 @@
-Checking gradient ...
-
-   analytic   diffs     delta
-
-   -6.7439   -6.7439    0.0000
-    2.1897    2.1897   -0.0000
-   -2.3321   -2.3321   -0.0000
-
- TolFun reached. Func-count 19. Final f(x)=16.4578. Elapsed time 0.19
-
- gp hyperparameters: 
- 
-    1.6749   -0.3565   -1.4619
-
-Demo completed in 0.009 minutes
diff --git a/tests/realValues/regression_meanf_fig1.fig b/tests/realValues/regression_meanf_fig1.fig
deleted file mode 100644
index d95e5c44..00000000
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diff --git a/tests/realValues/regression_ppcs.mat b/tests/realValues/regression_ppcs.mat
deleted file mode 100644
index 6e3d786e..00000000
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diff --git a/tests/realValues/regression_ppcs.txt b/tests/realValues/regression_ppcs.txt
deleted file mode 100644
index a8409d5a..00000000
--- a/tests/realValues/regression_ppcs.txt
+++ /dev/null
@@ -1,25 +0,0 @@
-  Iteration  Func-count      f(x)      Lambda
-      0          1           15150    
-      1          3         14366.9       10
-      2          5         14366.9        5
-      3          6         14366.9       20
-      4          7         14366.9       80
-      5          8         13860.1      320
-      6         10         13859.9      320
-      7         12         13449.3      160
-      8         14         13274.3      160
-      9         16         13257.4       80
-     10         18           13254       40
-     11         20         13253.7       20
-     12         22         13253.6       10
-     13         24         13253.4        5
-     14         26         13253.4      2.5
-     15         28         13253.4     1.25
- TolX reached. Func-count 28. Final f(x)=13253.4. Elapsed time 3338.85
-Proportion of non-zeros is 0.0475
-
- gp hyperparameters: 
- 
-    2.7497    1.1142    1.4814    1.3157
-
-Demo completed in 55.722 minutes
diff --git a/tests/realValues/regression_ppcs_fig1.fig b/tests/realValues/regression_ppcs_fig1.fig
deleted file mode 100644
index f565c711..00000000
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diff --git a/tests/realValues/regression_ppcs_fig2.fig b/tests/realValues/regression_ppcs_fig2.fig
deleted file mode 100644
index 55662383..00000000
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diff --git a/tests/realValues/regression_ppcs_fig3.fig b/tests/realValues/regression_ppcs_fig3.fig
deleted file mode 100644
index c4e0e0c5..00000000
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diff --git a/tests/realValues/regression_robust.mat b/tests/realValues/regression_robust.mat
deleted file mode 100644
index 3a167d7b..00000000
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diff --git a/tests/realValues/regression_robust.txt b/tests/realValues/regression_robust.txt
deleted file mode 100644
index bfae7205..00000000
--- a/tests/realValues/regression_robust.txt
+++ /dev/null
@@ -1,143 +0,0 @@
-
-gp = 
-
-            type: 'FULL'
-             lik: [1x1 struct]
-              cf: {[1x1 struct]}
-    infer_params: 'covariance+likelihood'
-    jitterSigma2: 1.0000e-09
-              fh: [1x1 struct]
-
-Gaussian noise model and MAP estimate for parameters
- TolFun reached. Func-count 23. Final f(x)=27.0141. Elapsed time 0.18
-
-S1 =
-
-length-scale: 1.066, magnSigma2: 5.420  
-
-
-Scale mixture Gaussian (~=Student-t) noise model                
-using MCMC integration over the latent values and parameters    
- Using SLS sampler for hyperparameters
- cycle  etr      slsrej  
-   20  -38.105  sls  
-   40  -51.018  sls  
-   60  -54.371  sls  
-   80  -54.989  sls  
-  100  -61.803  sls  
-  120  -65.385  sls  
-  140  -50.697  sls  
-  160  -64.071  sls  
-  180  -47.628  sls  
-  200  -53.372  sls  
-  220  -55.422  sls  
-  240  -55.152  sls  
-  260  -59.509  sls  
-  280  -45.947  sls  
-  300  -53.208  sls  
-
-S2 =
-
-length-scale: 1.051, magnSigma2: 7.908 
-
-
-Student-t noise model using Laplace integration over the 
-latent values and MAP estimate for the parameters        
- TolFun reached. Func-count 27. Final f(x)=-24.079. Elapsed time 1.10
-
-S3 =
-
-length-scale: 1.047, magnSigma2: 3.092 
-
-
-Student-t noise model using EP integration over the
-latent values and MAP estimate for parameters      
-  Iteration  Func-count      f(x)      Lambda
-      0          1          5.0753    
-      1          3       -0.390577       10
-      2          5        -20.2665        5
-      3          7        -21.8841      2.5
-      4          9        -23.5458     1.25
-      5         11        -23.9733    0.625
-      6         13        -24.8948    0.312
-      7         15           -25.2    0.156
-      8         17        -25.3813   0.0781
-      9         19        -25.4289   0.0391
-     10         21        -25.5223   0.0195
-     11         23        -25.5358   0.00977
-     12         25        -25.6001   0.00488
-     13         27         -25.619   0.00244
-     14         29        -25.6518   0.00122
-     15         31        -25.6526   0.00061
- TolFun reached. Func-count 31. Final f(x)=-25.6526. Elapsed time 17.56
-
-S4 =
-
-length-scale: 1.040, magnSigma2: 2.920 
-
-
-Student-t noise model with nu= 4 and using MCMC integration
-over the latent values and parameters                      
- Using SSLS sampler for hyperparameters and ESLS for latent values
- cycle  etr      lslsn
-   20  -194.741  7.0e+00  
-   40  -247.772  9.0e+00  
-   60  -263.046  9.0e+00  
-   80  -286.524  4.0e+00  
-  100  -284.345  9.0e+00  
-  120  -283.511  8.0e+00  
-  140  -281.262  7.0e+00  
-  160  -293.818  1.2e+01  
-  180  -294.601  1.6e+01  
-  200  -302.074  8.0e+00  
-  220  -303.498  8.0e+00  
-  240  -303.258  1.0e+01  
-  260  -308.047  1.6e+01  
-  280  -313.108  8.0e+00  
-  300  -308.032  1.2e+01  
-  320  -307.147  1.2e+01  
-  340  -313.006  9.0e+00  
-  360  -315.498  1.0e+01  
-  380  -309.592  1.1e+01  
-  400  -309.290  1.1e+01  
-
-S5 =
-
-length-scale: 0.761, magnSigma2: 2.366 
-
-
-Student-t noise model with nu=4 using Laplace integration over
-the latent values and MAP estimate for the parameters         
- TolFun reached. Func-count 19. Final f(x)=-14.9305. Elapsed time 0.51
-
-S6 =
-
-length-scale: 1.040, magnSigma2: 3.095 
-
-
-Student-t noise model with nu=4 using EP integration over
-the latent values and MAP estimate for parameters        
-  Iteration  Func-count      f(x)      Lambda
-      0          1          5.0753    
-      1          3       -0.138385       10
-      2          5        -13.6439        5
-      3          7        -14.0719      2.5
-      4          9        -14.1846     1.25
-      5         11        -14.8554    0.625
-      6         13        -15.1515    0.312
-      7         15        -15.1629    0.156
-      8         17        -15.1788   0.0781
-      9         19        -15.1794   0.0391
- TolFun reached. Func-count 19. Final f(x)=-15.1794. Elapsed time 7.74
-
-S7 =
-
-length-scale: 1.033, magnSigma2: 2.966 
-
-
-
- gp hyperparameters: 
- 
-    1.0871    0.0321   -4.6357
-
-Demo completed in 1.327 minutes
diff --git a/tests/realValues/regression_robust_fig1.fig b/tests/realValues/regression_robust_fig1.fig
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index 7323074e..00000000
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diff --git a/tests/realValues/regression_robust_fig2.fig b/tests/realValues/regression_robust_fig2.fig
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index 32c23e1d..00000000
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diff --git a/tests/realValues/regression_robust_fig3.fig b/tests/realValues/regression_robust_fig3.fig
deleted file mode 100644
index 166b3c29..00000000
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diff --git a/tests/realValues/regression_robust_fig4.fig b/tests/realValues/regression_robust_fig4.fig
deleted file mode 100644
index 62d75da2..00000000
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diff --git a/tests/realValues/regression_robust_fig5.fig b/tests/realValues/regression_robust_fig5.fig
deleted file mode 100644
index 36e8c941..00000000
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diff --git a/tests/realValues/regression_robust_fig6.fig b/tests/realValues/regression_robust_fig6.fig
deleted file mode 100644
index 8f5b95e1..00000000
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diff --git a/tests/realValues/regression_robust_fig7.fig b/tests/realValues/regression_robust_fig7.fig
deleted file mode 100644
index e25fd2ee..00000000
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diff --git a/tests/realValues/regression_robust_fig8.fig b/tests/realValues/regression_robust_fig8.fig
deleted file mode 100644
index 4b7cec00..00000000
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diff --git a/tests/realValues/regression_sparse1.txt b/tests/realValues/regression_sparse1.txt
deleted file mode 100644
index 29f4fd1b..00000000
--- a/tests/realValues/regression_sparse1.txt
+++ /dev/null
@@ -1,74 +0,0 @@
-
-gp = 
-
-            type: 'FULL'
-             lik: [1x1 struct]
-              cf: {[1x1 struct]}
-    infer_params: 'covariance+likelihood'
-    jitterSigma2: 1.0000e-08
-              fh: [1x1 struct]
-
- TolFun reached. Func-count 27. Final f(x)=52.5982. Elapsed time 1.30
-
-gp_fic = 
-
-            type: 'FIC'
-             lik: [1x1 struct]
-              cf: {[1x1 struct]}
-    infer_params: 'covariance+likelihood'
-    jitterSigma2: 1.0000e-04
-             X_u: [36x2 double]
-            nind: 36
-               p: [1x1 struct]
-              fh: [1x1 struct]
-
- TolFun reached. Func-count 53. Final f(x)=46.3892. Elapsed time 0.68
-
-gp_pic = 
-
-            type: 'PIC'
-             lik: [1x1 struct]
-              cf: {[1x1 struct]}
-    infer_params: 'covariance+likelihood'
-    jitterSigma2: 1.0000e-04
-             X_u: [36x2 double]
-            nind: 36
-               p: [1x1 struct]
-        tr_index: {1x16 cell}
-              fh: [1x1 struct]
-
- TolFun reached. Func-count 29. Final f(x)=45.3107. Elapsed time 1.08
-
-gp_var = 
-
-            type: 'VAR'
-             lik: [1x1 struct]
-              cf: {[1x1 struct]}
-    infer_params: 'covariance+likelihood+inducing'
-    jitterSigma2: 1.0000e-04
-             X_u: [36x2 double]
-            nind: 36
-               p: [1x1 struct]
-              fh: [1x1 struct]
-
- TolFun reached. Func-count 155. Final f(x)=50.7135. Elapsed time 9.72
-
-gp_dtc = 
-
-            type: 'DTC'
-             lik: [1x1 struct]
-              cf: {[1x1 struct]}
-    infer_params: 'covariance+likelihood'
-    jitterSigma2: 1.0000e-04
-             X_u: [36x2 double]
-            nind: 36
-               p: [1x1 struct]
-              fh: [1x1 struct]
-
- TolFun reached. Func-count 61. Final f(x)=46.3105. Elapsed time 0.71
-
- gp hyperparameters: 
- 
-    0.4291    1.1773    1.0540   -3.2363
-
-Demo completed in 0.238 minutes
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@@ -1,14 +0,0 @@
-Full GP
- TolFun reached. Func-count 35. Final f(x)=51.3654. Elapsed time 0.31
-FIC GP
- TolX reached. Func-count 14. Final f(x)=57.5109. Elapsed time 0.18
-VAR GP
- TolFun reached. Func-count 68. Final f(x)=65.8332. Elapsed time 0.87
-DTC GP
- TolX reached. Func-count 14. Final f(x)=48.4277. Elapsed time 0.17
-
- gp hyperparameters: 
- 
-    1.6935   -0.6707   -2.9350
-
-Demo completed in 0.034 minutes
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- TolFun reached. Func-count 35. Final f(x)=  2844. Elapsed time 2.42
-
-S1 =
-
-MAP: length-scale: 14.3, magnSigma: 0.153 
-
-
- IA-grid: finding the mode
- IA-grid: computing Hessian using multiplication
- IA-grid: evaluating density in a grid
- IA-grid: evaluated density at 12 points
- IA-grid: Total elapsed time 2.35 seconds
-
-S2 =
-
-IA-GRID: 90% CI - length-scale: [8.8,33.1], magnSigma: [0.099,0.325] 
-
-
- Using HMC sampler for hyperparameters and MCMC for latent values
- cycle  etr      hrej     lrej 
-    2  732.703   0.0e+00  6.0e-02  0.087940
-
-h1 =
-
-  986.0103
-  987.0098
-
-        mean hmcrej: 0.00 latrej: 0.02
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-
-S3 =
-
-MCMC: 90% CI - length-scale: [7.3,28.7], magnSigma: [0.114,0.332] 
-
-
-MAP: length-scale: 14.3, magnSigma: 0.153 
-
-IA-GRID: 90% CI - length-scale: [8.8,33.1], magnSigma: [0.099,0.325] 
-
-MCMC: 90% CI - length-scale: [7.3,28.7], magnSigma: [0.114,0.332] 
-
-
- gp hyperparameters: 
- 
-   -2.3055    2.8813
-
-Demo completed in 4.450 minutes
diff --git a/tests/realValues/spatial1_fig1.fig b/tests/realValues/spatial1_fig1.fig
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diff --git a/tests/realValues/spatial2.mat b/tests/realValues/spatial2.mat
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diff --git a/tests/realValues/spatial2.txt b/tests/realValues/spatial2.txt
deleted file mode 100644
index 81339926..00000000
--- a/tests/realValues/spatial2.txt
+++ /dev/null
@@ -1,28 +0,0 @@
-GP with negative-binomial observation model, Laplace
-integration over the latent values and MAP estimate
-for the parameters
-  Iteration  Func-count      f(x)      Lambda
-      0          1          5286.4    
-      1          3         5286.36       10
-      2          4          5241.3       40
-      3          6         5221.46       40
-      4          8         5216.89       20
-      5         10         5216.55       10
-      6         12         5216.44        5
-      7         14         5216.44      2.5
- TolX reached. Func-count 14. Final f(x)=5216.44. Elapsed time 239.70
-Proportion of non-zeros is 0.0715
-GP with negative-binomial observation model, EP
-integration over the latent values and MAP estimate
-for the parameters
-  Iteration  Func-count      f(x)      Lambda
-      0          1          5216.4    
-      1          3         5216.42       10
- TolX reached. Func-count 3. Final f(x)=5216.42. Elapsed time 221.78
-Proportion of non-zeros is 0.0715
-
- gp hyperparameters: 
- 
-   -2.1782    2.2583    2.8991
-
-Demo completed in 8.026 minutes
diff --git a/tests/realValues/spatial2_fig1.fig b/tests/realValues/spatial2_fig1.fig
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index a0665282..00000000
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diff --git a/tests/realValues/survival_aft.txt b/tests/realValues/survival_aft.txt
deleted file mode 100644
index 414cde19..00000000
--- a/tests/realValues/survival_aft.txt
+++ /dev/null
@@ -1,43 +0,0 @@
- Iteration  Func-count   Grad-count         f(x)         Step-size
-     0           1           1              1685.5    
-     1           3           3             1677.24     0.000815125
-     2           5           5             1669.25        0.295997
-     3           7           7             1661.47        0.204233
-     4           9           9             1658.18        0.139274
-     5          12          12             1656.38       0.0470118
-     6          14          14             1655.37        0.425986
-     7          16          16              1654.6        0.201154
-     8          17          17             1654.34               1
-     9          18          18             1654.17               1
-    10          19          19             1654.17               1
-    11          20          20             1654.17               1
-    12          21          21             1654.17               1
-    13          22          22             1654.17               1
- Optimizer Results
-  Algorithm Used: Broyden-Fletcher-Goldfarb-Shanno (BFGS)
-  Exit message : Change in x was smaller than the specified tolerance TolX.
-  Iterations : 14
-  Function Count : 23
-  Minimum found : 1654.1655
-  Intern Time : 0.045501 seconds
-  Total Time : 16.0997 seconds
- IA-CCD: computing Hessian using multiplication
-    Elapsed time 13.39 seconds
- IA-CCD: 81 points for 8 parameters
- IA-CCD: autoscaling in 16 directions
- IA-CCD: scaling minmax [0.77 1.22]
-    Elapsed time 6.22 seconds
- IA-CCD: evaluating density at 81 points
-    Elapsed time 27.19 seconds
- IA-CCD: Total elapsed time 46.87 seconds
-
-ans =
-
-  -1.6292e+03
-
-
- gp hyperparameters: 
- 
-   -0.5933   -0.7558   -0.2888    0.6951    2.0332   -0.4531    0.5524   -0.0853
-
-Demo completed in 6.724 minutes
diff --git a/tests/realValues/survival_aft_fig1.fig b/tests/realValues/survival_aft_fig1.fig
deleted file mode 100644
index d24c1e5b..00000000
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diff --git a/tests/realValues/survival_coxph.mat b/tests/realValues/survival_coxph.mat
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index 92010549..00000000
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diff --git a/tests/realValues/survival_coxph.txt b/tests/realValues/survival_coxph.txt
deleted file mode 100644
index 9419205d..00000000
--- a/tests/realValues/survival_coxph.txt
+++ /dev/null
@@ -1,40 +0,0 @@
-  Iteration  Func-count      f(x)      Lambda
-      0          1          592.79    
-      1          3          586.41     21.6
-      2          5         585.087     21.6
-      3          7         572.396     10.8
-      4          9          569.02     5.41
-      5         11         568.097     2.71
-      6         13         567.164     1.35
-      7         15          567.04    0.676
-      8         17         566.904    0.338
-      9         19         566.673    0.169
-     10         21         566.665   0.0846
- TolFun reached. Func-count 21. Final f(x)=566.665. Elapsed time 58.25
-
-ans =
-
-  -5.9203e+03
-
-Evaluating the CV utility. The inference method is MAP.
- The CV-fold number: 1/10 
- The CV-fold number: 2/10 
- The CV-fold number: 3/10 
- The CV-fold number: 4/10 
- The CV-fold number: 5/10 
- The CV-fold number: 6/10 
- The CV-fold number: 7/10 
- The CV-fold number: 8/10 
- The CV-fold number: 9/10 
- The CV-fold number: 10/10 
-
-ans =
-
- -546.5010
-
-
- gp hyperparameters: 
- 
-   -0.6744   -0.3888   -0.6922   -1.1005   -1.3187   -1.5066    0.4186    0.9045   -0.9993    0.4694
-
-Demo completed in 15.805 minutes
diff --git a/tests/realValues/survival_coxph_fig1.fig b/tests/realValues/survival_coxph_fig1.fig
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index 62315ddd..00000000
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diff --git a/tests/realValues/svi_classific.mat b/tests/realValues/svi_classific.mat
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index 41dadecd..00000000
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diff --git a/tests/realValues/svi_classific.txt b/tests/realValues/svi_classific.txt
deleted file mode 100644
index d769aa98..00000000
--- a/tests/realValues/svi_classific.txt
+++ /dev/null
@@ -1,105 +0,0 @@
-SVI GP classification model with probit likelihood
-Select 20 inducing inputs by clustering 10 points from both training classes ... done
-iter=1/1000, e=-7860.360, de=Inf
-iter=11/1000, e=-165.931, de=2.3164
-iter=21/1000, e=-148.428, de=1.1875
-iter=31/1000, e=-139.968, de=0.74236
-iter=41/1000, e=-135.056, de=0.44259
-iter=51/1000, e=-131.841, de=0.59245
-iter=61/1000, e=-129.681, de=0.38347
-iter=71/1000, e=-128.214, de=0.038981
-iter=81/1000, e=-126.864, de=0.07066
-iter=91/1000, e=-126.041, de=0.10476
-iter=101/1000, e=-125.086, de=0.062065
-iter=111/1000, e=-124.379, de=0.03243
-iter=121/1000, e=-123.903, de=0.079169
-iter=131/1000, e=-123.417, de=0.042307
-iter=141/1000, e=-122.934, de=0.072635
-iter=151/1000, e=-122.674, de=0.005947
-iter=161/1000, e=-122.282, de=0.0011458
-iter=171/1000, e=-122.021, de=0.064172
-iter=181/1000, e=-121.737, de=0.027982
-iter=191/1000, e=-121.554, de=0.034569
-iter=201/1000, e=-121.295, de=0.0065123
-iter=211/1000, e=-121.112, de=0.035029
-iter=221/1000, e=-120.938, de=0.023682
-iter=231/1000, e=-120.793, de=0.013486
-iter=241/1000, e=-120.612, de=0.0041615
-iter=251/1000, e=-120.500, de=0.014727
-iter=261/1000, e=-120.344, de=0.0065798
-iter=271/1000, e=-120.224, de=0.032921
-iter=281/1000, e=-120.089, de=0.0040458
-iter=291/1000, e=-120.021, de=0.020417
-iter=301/1000, e=-119.913, de=0.0086777
-iter=311/1000, e=-119.852, de=0.0057643
-iter=321/1000, e=-119.709, de=0.06095
-iter=331/1000, e=-119.689, de=0.011442
-iter=341/1000, e=-119.585, de=0.019807
-iter=351/1000, e=-119.484, de=0.029099
-iter=361/1000, e=-119.437, de=0.026291
-iter=371/1000, e=-119.362, de=0.0074362
-iter=381/1000, e=-119.326, de=0.00062521
-iter=391/1000, e=-119.250, de=0.00043361
-iter=401/1000, e=-119.176, de=0.007927
-iter=411/1000, e=-119.134, de=0.009016
-iter=421/1000, e=-119.076, de=0.015387
-iter=431/1000, e=-119.017, de=0.0072118
-iter=441/1000, e=-118.998, de=0.016271
-iter=451/1000, e=-118.955, de=0.00070405
-iter=461/1000, e=-118.887, de=0.013122
-iter=471/1000, e=-118.862, de=0.018169
-iter=481/1000, e=-118.793, de=0.0204
-iter=491/1000, e=-118.778, de=0.014299
-iter=501/1000, e=-118.709, de=0.014295
-iter=511/1000, e=-118.680, de=0.020085
-iter=521/1000, e=-118.661, de=0.016699
-iter=531/1000, e=-118.639, de=0.014926
-iter=541/1000, e=-118.608, de=0.017074
-iter=551/1000, e=-118.558, de=0.0025758
-iter=561/1000, e=-118.531, de=0.0024666
-iter=571/1000, e=-118.499, de=0.007473
-iter=581/1000, e=-118.462, de=0.0071841
-iter=591/1000, e=-118.439, de=0.014747
-iter=601/1000, e=-118.399, de=0.0069098
-iter=611/1000, e=-118.374, de=0.0081705
-iter=621/1000, e=-118.363, de=0.005196
-iter=631/1000, e=-118.345, de=0.0095211
-iter=641/1000, e=-118.296, de=0.0026426
-iter=651/1000, e=-118.276, de=0.0010179
-iter=661/1000, e=-118.260, de=0.0050527
-iter=671/1000, e=-118.221, de=0.0016515
-iter=681/1000, e=-118.225, de=0.013952
-iter=691/1000, e=-118.196, de=0.0046819
-iter=701/1000, e=-118.169, de=0.0053694
-iter=711/1000, e=-118.136, de=0.0074989
-iter=721/1000, e=-118.134, de=0.010308
-iter=731/1000, e=-118.096, de=0.012854
-iter=741/1000, e=-118.089, de=0.0073019
-iter=751/1000, e=-118.068, de=0.0044747
-iter=761/1000, e=-118.042, de=0.0087049
-iter=771/1000, e=-118.036, de=0.00292
-iter=781/1000, e=-118.005, de=0.0081969
-iter=791/1000, e=-117.987, de=0.0094266
-iter=801/1000, e=-117.977, de=0.0054837
-iter=811/1000, e=-117.967, de=0.0085031
-iter=821/1000, e=-117.953, de=0.0046847
-iter=831/1000, e=-117.934, de=0.0028916
-iter=841/1000, e=-117.925, de=0.003906
-iter=851/1000, e=-117.890, de=0.00077029
-iter=861/1000, e=-117.877, de=0.0079648
-iter=871/1000, e=-117.865, de=0.007645
-iter=881/1000, e=-117.854, de=0.0051175
-iter=891/1000, e=-117.841, de=0.0040959
-iter=901/1000, e=-117.826, de=0.0053202
-iter=911/1000, e=-117.811, de=0.0083797
-iter=921/1000, e=-117.806, de=0.0065045
-iter=931/1000, e=-117.790, de=0.00091282
-iter=941/1000, e=-117.771, de=0.00096074
-iter=951/1000, e=-117.766, de=0.0047903
-iter=961/1000, e=-117.756, de=0.0073944
-iter=971/1000, e=-117.733, de=0.0023123
-iter=981/1000, e=-117.733, de=0.0059698
-iter=991/1000, e=-117.724, de=0.0010356
-Iteration limit 1000 reached while optimising the parameters
-Final energy: -117.699
-Demo completed in 2.713 minutes
diff --git a/tests/realValues/svi_classific_fig1.fig b/tests/realValues/svi_classific_fig1.fig
deleted file mode 100644
index 8ef29f31..00000000
Binary files a/tests/realValues/svi_classific_fig1.fig and /dev/null differ
diff --git a/tests/realValues/svi_regression.mat b/tests/realValues/svi_regression.mat
deleted file mode 100644
index d0870ab2..00000000
Binary files a/tests/realValues/svi_regression.mat and /dev/null differ
diff --git a/tests/realValues/svi_regression.txt b/tests/realValues/svi_regression.txt
deleted file mode 100644
index 924da3ed..00000000
--- a/tests/realValues/svi_regression.txt
+++ /dev/null
@@ -1,22 +0,0 @@
-Assign inducing inputs by clustering
-iter=1/150, e=-806.238, mlpd=-0.00329, rmse=0.08678, de=Inf
-iter=11/150, e=1628.618, mlpd=1.341, rmse=0.01701, de=0.53808
-iter=21/150, e=1638.118, mlpd=1.352, rmse=0.01603, de=0.92957
-iter=31/150, e=1644.276, mlpd=1.354, rmse=0.01486, de=0.00041356
-iter=41/150, e=1648.648, mlpd=1.357, rmse=0.01436, de=0.6143
-iter=51/150, e=1650.748, mlpd=1.353, rmse=0.01441, de=0.011609
-iter=61/150, e=1653.303, mlpd=1.357, rmse=0.01386, de=0.69366
-iter=71/150, e=1654.098, mlpd=1.356, rmse=0.01392, de=0.73379
-iter=81/150, e=1655.407, mlpd=1.362, rmse=0.01336, de=0.15758
-iter=91/150, e=1656.587, mlpd=1.362, rmse=0.01348, de=0.83295
-iter=101/150, e=1657.672, mlpd=1.362, rmse=0.01369, de=0.55494
-iter=111/150, e=1658.012, mlpd=1.359, rmse=0.01341, de=0.028293
-iter=121/150, e=1659.574, mlpd=1.362, rmse=0.0132, de=0.37814
-iter=131/150, e=1660.190, mlpd=1.361, rmse=0.01289, de=0.43749
-iter=141/150, e=1660.848, mlpd=1.361, rmse=0.01271, de=0.026303
-Iteration limit 150 reached while optimising the parameters
-Final values:
-e=1660.428, mlpd=1.362, rmse=0.01325
-[Warning: MATLAB has disabled some advanced graphics rendering features by switching to software
-OpenGL. For more information, click <a href="matlab:opengl('problems')">here</a>.] 
-Demo completed in 1.108 minutes
diff --git a/tests/realValues/svi_regression_fig1.fig b/tests/realValues/svi_regression_fig1.fig
deleted file mode 100644
index 7a1db3d0..00000000
Binary files a/tests/realValues/svi_regression_fig1.fig and /dev/null differ
diff --git a/tests/realValues/svi_regression_fig2.fig b/tests/realValues/svi_regression_fig2.fig
deleted file mode 100644
index 6f1a4b80..00000000
Binary files a/tests/realValues/svi_regression_fig2.fig and /dev/null differ
diff --git a/tests/realValues/zinegbin.mat b/tests/realValues/zinegbin.mat
deleted file mode 100644
index 383f2823..00000000
Binary files a/tests/realValues/zinegbin.mat and /dev/null differ
diff --git a/tests/realValues/zinegbin.txt b/tests/realValues/zinegbin.txt
deleted file mode 100644
index 99479c7f..00000000
--- a/tests/realValues/zinegbin.txt
+++ /dev/null
@@ -1,30 +0,0 @@
- Iteration  Func-count   Grad-count         f(x)         Step-size
-     0           1           1             2964.84    
-     1           2           2             2908.76       0.0146046
-     2           4           4             2900.33             0.1
-     3           5           5             2885.72               1
-     4           7           7              2864.1        0.295668
-     5           8           8             2848.64               1
-     6           9           9             2842.71               1
-     7          10          10             2842.05               1
-     8          12          12             2841.84        0.266463
-     9          14          14             2841.81        0.143598
-    10          15          15             2841.77               1
-    11          16          16             2841.71               1
-    12          17          17              2841.7               1
-    13          18          18             2841.69               1
-    14          19          19             2841.69               1
- Optimizer Results
-  Algorithm Used: Broyden-Fletcher-Goldfarb-Shanno (BFGS)
-  Exit message : Change in x was smaller than the specified tolerance TolX.
-  Iterations : 15
-  Function Count : 20
-  Minimum found : 2841.6901
-  Intern Time : 0.017685 seconds
-  Total Time : 102.8869 seconds
-
- gp hyperparameters: 
- 
-    4.0478    1.3173   -2.6332    1.4244
-
-Demo completed in 1.745 minutes
diff --git a/tests/realValues/zinegbin_fig1.fig b/tests/realValues/zinegbin_fig1.fig
deleted file mode 100644
index 10d61eb4..00000000
Binary files a/tests/realValues/zinegbin_fig1.fig and /dev/null differ
diff --git a/tests/realValues/zinegbin_fig2.fig b/tests/realValues/zinegbin_fig2.fig
deleted file mode 100644
index 64711c23..00000000
Binary files a/tests/realValues/zinegbin_fig2.fig and /dev/null differ
diff --git a/tests/rundemo.m b/tests/rundemo.m
deleted file mode 100644
index 2ca0d7c5..00000000
--- a/tests/rundemo.m
+++ /dev/null
@@ -1,256 +0,0 @@
-function rundemo(name, varToSave, mode)
-%RUNDEMO  Run a GPstuff demo and save the results.
-%
-%   Description
-%     RUNDEMO(name, varToSave, mode) runs the GPstuff demo given in name
-%     and save given variables into the folder 'testValues' or 
-%     'realValues'. This function is used by test_* to compare the results
-%     into the precomputed expected ones.
-%     
-%     The saved components are:
-%       - desired workspace variables into <name>.mat
-%       - command line output log into <name>.txt
-%       - new created figures into <name>_fig#.fig
-%     If diary is on before this function is called, it is left untouched
-%     and the logfile is not created. Figures created before this function
-%     are not touched.
-%
-%   Parameters:
-%     name
-%       The name of the demo without the .m extension or the demo_ prefix,
-%       e.g. 'binomial1'.
-%     varToSave (optional)
-%       Cell array of strings indicates the variables that are saved into
-%       the desired folder. Giving string 'all' saves the whole work
-%       space. String 'same' (default) looks the names of the saved
-%       variables in the file 'realValues/<nameOfTheDemo>.mat' and saves
-%       them. Empty array indicates that nothig is saved. A function can be
-%       applied into the variable before saving by providing a cell array
-%       containing the name of the variable and the function handle (in
-%       that order) in place of the name string in the cell array varToSave
-%       e.g. varToSave = {'var1' {'var2' @(x)diag(x)} 'var3'} saves
-%       variables var1, var2 = diag(var2) and var3. By providing a third
-%       cell array string element, the name of the saved variable can be
-%       changed, e.g. {'varOut' @(x)diag(x), 'varOrig'} saves variable
-%       varOut = diag(varOrig).
-%     mode (optional)
-%       String 'test' (default) or 'real' indicating which folder the
-%       results are saved.
-%
-%   See also
-%     TEST_*, DEMO_*
-%
-% Copyright (c) 2014 Tuomas Sivula
-
-% This software is distributed under the GNU General Public 
-% License (version 3 or later); please refer to the file 
-% License.txt, included with the software, for details.
-
-% Handle parameters
-% Save to 'testValues' by default
-if nargin < 3
-  mode = 'test';
-end
-% If varToSave is not defined, save the same as before
-if nargin < 2
-  varToSave = 'same';
-end
-
-% Validate mode parameter
-if ~(strcmp(mode, 'test') || strcmp(mode, 'real'))
-  error('Mode has to be ''test'' or ''real''')
-end
-
-% Save required variables into the structure run_demo_data
-run_demo_data.name = name;
-run_demo_data.varToSave = varToSave;
-run_demo_data.mode = mode;
-% Store original figures
-run_demo_data.orig_figs =  get(0,'children');
-% Change working directory
-fpath = mfilename('fullpath');
-run_demo_data.origPath = cd(fpath(1:end-length(mfilename)));
-% Try to avoid differences in the results by setting a common random stream
-run_demo_data.origStream = setrandstream(0);
-% Hide figure windows
-run_demo_data.origFigVisibility = get(0,'DefaultFigureVisible');
-set(0,'DefaultFigureVisible','off')
-% Check if diay is already used (no logging then)
-if strcmp(get(0,'Diary'), 'off')
-  run_demo_data.diary = 1;
-else
-  run_demo_data.diary = 0;
-end
-clear name varToSave fpath mode
-
-% Create cleanup function to restore changes
-run_demo_cleanupObj = onCleanup(@() restoreChanges(run_demo_data));
-
-% Create the folder if it does not exist
-if ~(exist([run_demo_data.mode 'Values/'], 'dir') == 7)
-  mkdir([run_demo_data.mode 'Values/'])
-end
-
-% Run the demo and create log into the desired directory
-try
-  
-  fprintf('---- Run demo_%s %s\n\n', ...
-    run_demo_data.name, repmat('-',1,79-15-length(run_demo_data.name)));
-  
-  % Create log, if diary is not currently running
-  if run_demo_data.diary
-    if exist([run_demo_data.mode 'Values/' run_demo_data.name '.txt'] ...
-        ,'file')
-      delete([run_demo_data.mode 'Values/' run_demo_data.name '.txt']);
-    end
-    diary([run_demo_data.mode 'Values/' run_demo_data.name '.txt']);
-  end
-  
-  % Run the demo and measure time
-  run_demo_data.timer_id = tic;
-  run(['demo_' run_demo_data.name])
-  % If variable gp exist, display hypreparameters
-  if exist('gp', 'var')
-    run_demo_data.w = gp_pak(gp);
-    if numel(run_demo_data.w) <= 10
-      fprintf('\n gp hyperparameters: \n \n')
-      disp(run_demo_data.w)
-    end
-  end
-  fprintf('Demo completed in %.3f minutes\n', ...
-    toc(run_demo_data.timer_id)/60)
-  
-  % Close diary at this point (if logged)
-  if run_demo_data.diary
-    diary off;
-  end
-
-catch err
-  % Error running the demo
-  % Here the exception could be included as a cause to a new exeption but
-  % that makes the information related to this exeption harder to see.
-  % ME = MException('run_demo:DemoFailure', 'Could not run demo_%s', ...
-  %   run_demo_data.name);
-  % ME = addCause(ME, err);
-  % throw(ME)
-  rethrow(err)
-end
-
-% Save the results into the desired directory
-try
-  
-  % Save variables
-  if ~isempty(run_demo_data.varToSave)
-    if iscell(run_demo_data.varToSave)
-      % Apply functions
-      broken = 0;
-      for i = 1:length(run_demo_data.varToSave)
-        if iscell(run_demo_data.varToSave{i}) ...
-            && length(run_demo_data.varToSave{i}) >= 2 ...
-            && ischar(run_demo_data.varToSave{i}{1}) ...
-            && isvarname(run_demo_data.varToSave{i}{1}) ...
-            && isa(run_demo_data.varToSave{i}{2}, 'function_handle') ...
-            && (length(run_demo_data.varToSave{i}) == 2 ...
-            || (length(run_demo_data.varToSave{i}) == 3 ...
-            && ischar(run_demo_data.varToSave{i}{3}) ...
-            && isvarname(run_demo_data.varToSave{i}{3})) )
-          if length(run_demo_data.varToSave{i}) == 2
-            if exist(run_demo_data.varToSave{i}{1}, 'var')
-              eval(sprintf('%s = run_demo_data.varToSave{i}{2}(%s);', ...
-                run_demo_data.varToSave{i}{1}, ...
-                run_demo_data.varToSave{i}{1}));
-            else
-              error('Variable %s not found', run_demo_data.varToSave{i}{1})
-            end
-          else
-            if exist(run_demo_data.varToSave{i}{3}, 'var')
-              eval(sprintf('%s = run_demo_data.varToSave{i}{2}(%s);', ...
-                run_demo_data.varToSave{i}{1}, ...
-                run_demo_data.varToSave{i}{3}));
-            else
-              error('Variable %s not found', run_demo_data.varToSave{i}{3})
-            end
-          end
-          run_demo_data.varToSave{i} = run_demo_data.varToSave{i}{1};
-        elseif ~ischar(run_demo_data.varToSave{i})
-          warning('Unsupported parameter varToSave, no variables saved.')
-          broken = 1;
-          break
-        end
-      end
-      if ~broken
-        % Save given variables
-        save([run_demo_data.mode 'Values/' run_demo_data.name], ...
-          run_demo_data.varToSave{:})
-      end
-    elseif ischar(run_demo_data.varToSave) ...
-        && strcmp(run_demo_data.varToSave, 'same')
-      % Save the same variables as in the 'realValues/<nameOfTheDemo>.mat'
-      finfo = whos(matfile(['realValues/' run_demo_data.name '.mat']));
-      if ~isempty(finfo)
-        save([run_demo_data.mode 'Values/' run_demo_data.name], finfo.name)
-      else
-        warning(['File realValues/' run_demo_data.name '.mat not found' ...
-          'or file has no variables.'])
-      end
-    elseif ischar(run_demo_data.varToSave) ...
-        && strcmp(run_demo_data.varToSave, 'all')
-      % Save all variables
-      save([run_demo_data.mode 'Values/' run_demo_data.name])
-    else
-      warning('Unsupported parameter varToSave, no variables saved.')
-    end
-  end
-  
-  % Save all figures into the desired folder
-  for i = setdiff(get(0,'children'), run_demo_data.orig_figs)'
-    if isprop(i, 'Number')
-      filename = [run_demo_data.mode 'Values/' ...
-        run_demo_data.name '_fig' num2str(i.Number) '.fig'];
-    else
-      filename = [run_demo_data.mode 'Values/' ...
-        run_demo_data.name '_fig' num2str(i) '.fig'];
-    end
-    % First save the figure hidden
-    saveas(i,filename)
-    % Then change the visible-flag on
-    % Note that the field hgM_070000 present in some newer Matlab versions
-    % is discarded.
-    f=load(filename,'-mat','hgS_070000');
-    n=fieldnames(f);
-    f.(n{1}).properties.Visible='on';
-    save(filename,'-struct','f')
-  end
-  
-catch err
-  % Error saving the variables
-  ME = MException('run_demo:DemoResultSaveFailure', ...
-    'Could not save the results');
-  ME = addCause(ME, err);
-  throw(ME)
-end
-
-fprintf('Results saved into the folder ''tests/%sValues/''\n\n', ...
-  run_demo_data.mode)
-
-end
-
-
-function restoreChanges(run_demo_data)
-  % Ensure that changes are restored when exiting the function
-  
-  % Close all created 'hidden' figures
-  for i = setdiff(get(0,'children'), run_demo_data.orig_figs)'
-    close(i);
-  end
-  % Change directory
-  cd(run_demo_data.origPath)
-  % Set back previous random stream
-  setrandstream(run_demo_data.origStream);
-  % Set back original figure visibility
-  set(0,'DefaultFigureVisible',run_demo_data.origFigVisibility)
-  % Ensure that diary is closed (if used)
-  if run_demo_data.diary
-    diary off;
-  end
-end
diff --git a/tests/runtestset.m b/tests/runtestset.m
deleted file mode 100644
index a8d88def..00000000
--- a/tests/runtestset.m
+++ /dev/null
@@ -1,174 +0,0 @@
-function log = runtestset(mode)
-%RUNTESTSET  Run a set of tests in GPstuff.
-%
-%  Description
-%    LOG = RUN_GP_TESTS(MODE) runs a collection of tests in tests folder
-%    and returns the test log. Each of the tests runs corresponding demo
-%    and compares the values to previously saved ones for errors. This is
-%    useful e.g. in case user modifies functions provided by GPstuff.
-%
-%    Possible values for parameter MODE:
-%      'fast' - runs a selection of test taking about 2.5 min (default)
-%      'hour' - runs a selection of test taking about one hour
-%      'all'  - runs all the tests taking about 5 hours
-%
-%    Can be used with both xUnit Test Framework package by Steve Eddins
-%    and the built-in Unit Testing Framework.
-%
-%    N.B. The time estimates assumes that SuiteSparse is used. The
-%    estimated runtime of the individual demos (min):
-%       binomial1              0.057
-%       binomial2              2.853
-%       binomial_apc           0.224
-%       classific              1.058
-%       derivativeobs          0.020
-%       epinf                 76.727
-%       hierprior              2.734
-%       hurdle                 2.286
-%       improvemarginals       0.043
-%       kalman1                0.044
-%       kalman2                6.270
-%       lgcp                   0.069
-%       loopred                1.017
-%       memorysave             0.727
-%       modelassesment1       12.892
-%       modelassesment2       45.490
-%       monotonic2             0.406
-%       multiclass            42.372
-%       multiclass_nested_ep   1.088
-%       multinom               1.027
-%       neuralnetcov           0.076
-%       periodic               0.380
-%       quantilegp             2.862
-%       regression1            1.028
-%       regression_additive1   0.258
-%       regression_additive2   0.112
-%       regression_hier        0.409
-%       regression_meanf       0.015
-%       regression_ppcs       58.481
-%       regression_robust      1.352
-%       regression_sparse1     0.273
-%       regression_sparse2     0.044
-%       spatial1               4.473
-%       spatial2               8.209
-%       survival_aft           6.519
-%       survival_coxph        15.872
-%       svi_classific          2.713
-%       svi_regression         0.740
-%       zinegbin               1.763
-%
-%  See also
-%    Readme.txt
-
-% Copyright (c) 2011-2012 Ville Tolvanen
-% Copyright (c) 2014 Tuomas Sivula
-
-% This software is distributed under the GNU General Public 
-% License (version 3 or later); please refer to the file 
-% License.txt, included with the software, for details.
-
-if nargin < 1
-  mode = 'fast';
-end
-
-% Define tests to run on fast mode
-fast_tests = {'test_binomial1' ...
-'test_binomial_apc' ...
-'test_derivativeobs' ...
-'test_improvemarginals' ...
-'test_kalman1' ...
-'test_lgcp' ...
-'test_monotonic2' ...
-'test_neuralnetcov' ...
-'test_periodic' ...
-'test_regression_additive1' ...
-'test_regression_additive2' ...
-'test_regression_hier' ...
-'test_regression_meanf' ...
-'test_regression_sparse1' ...
-'test_regression_sparse2' ...
-'test_svi_regression'}';
-
-% Define tests to run on hour mode
-hour_tests = {'test_binomial1' ...
-'test_binomial2' ...
-'test_binomial_apc' ...
-'test_classific' ...
-'test_derivativeobs' ...
-'test_hierprior' ...
-'test_hurdle' ...
-'test_improvemarginals' ...
-'test_kalman1' ...
-'test_kalman2' ...
-'test_lgcp' ...
-'test_loopred' ...
-'test_memorysave' ...
-'test_monotonic2' ...
-'test_multiclass_nested_ep' ...
-'test_multinom' ...
-'test_neuralnetcov' ...
-'test_periodic' ...
-'test_quantilegp' ...
-'test_regression1' ...
-'test_regression_additive1' ...
-'test_regression_additive2' ...
-'test_regression_hier' ...
-'test_regression_meanf' ...
-'test_regression_robust' ...
-'test_regression_sparse1' ...
-'test_regression_sparse2' ...
-'test_spatial1' ...
-'test_spatial2' ...
-'test_survival_aft' ...
-'test_svi_classific' ...
-'test_svi_regression' ...
-'test_zinegbin'}';
-
-% Path to the unittest folder
-path = mfilename('fullpath');
-path = path(1:end-length(mfilename));
-
-% Check which test package to use
-runtests_path = which('runtests');
-if exist([runtests_path(1:end-10) 'initTestSuite.m'], 'file')
-  
-  % xUnit Test Framework
-  if strcmp(mode, 'all')
-    % Add all the tests
-    suite = TestSuite.fromName(path);
-  elseif strcmp(mode, 'fast')
-    % Add the selected tests
-    suite = TestSuite;
-    for i = 1:length(fast_tests)
-      suite.add(TestSuite(fast_tests{i}));
-    end
-  elseif strcmp(mode, 'hour')
-    % Add the selected tests
-    suite = TestSuite;
-    for i = 1:length(hour_tests)
-      suite.add(TestSuite(hour_tests{i}));
-    end
-  else
-    error('Unsupported mode')
-  end
-  % Run the tests
-  log = TestRunLogger;
-  suite.run(log);
-  
-else
-  
-  % Built-in test framework
-  if strcmp(mode, 'all')
-    % Run all the tests
-    log = runtests(path);
-  elseif strcmp(mode, 'fast')
-    % Run the selected tests
-    log = runtests(fast_tests);
-  elseif strcmp(mode, 'hour')
-    % Run the selected tests
-    log = runtests(hour_tests);
-  else
-    error('Unsupported mode')
-  end
-  
-end
diff --git a/tests/test_binomial1.m b/tests/test_binomial1.m
deleted file mode 100644
index 40f32c95..00000000
--- a/tests/test_binomial1.m
+++ /dev/null
@@ -1,70 +0,0 @@
-function test_suite = test_binomial1
-
-%   Run specific demo, save values and compare the results to the expected.
-%   Works for both xUnit Test Framework package by Steve Eddins and for
-%   the built-in Unit Testing Framework (as of Matlab version 2013b).
-%
-%   See also
-%     TEST_ALL, DEMO_BINOMIAL1
-%
-% Copyright (c) 2011-2012 Ville Tolvanen
-% Copyright (c) 2014 Tuomas Sivula
-
-% This software is distributed under the GNU General Public 
-% License (version 3 or later); please refer to the file 
-% License.txt, included with the software, for details.
-  
-  % Check which package to use
-  if exist('initTestSuite', 'file')
-    initTestSuite_path = which('initTestSuite');
-  else
-    initTestSuite_path = '';
-  end
-  if ~isempty(initTestSuite_path) && ...
-     exist([initTestSuite_path(1:end-15) 'runtests'], 'file')
-    % xUnit package
-    initTestSuite;
-  else
-    % Built-in package
-    % Use all functions except the @setup
-    tests = localfunctions;
-    tests = tests(~cellfun(@(x)strcmp(func2str(x),'setup'),tests));
-    test_suite = functiontests(tests);
-  end
-end
-
-
-% -------------
-%     Tests
-% -------------
-
-function testRunDemo(testCase)
-  % Run the correspondin demo and save the values. Note this test has to
-  % be run at lest once before the other test may succeed.
-  rundemo(getName())
-end
-
-function testPredictiveMeanVarianceAndDensity(testCase)
-  % Test predictive mean, variance and density
-  verifyVarsEqual(testCase, getName(), ...
-    'RelTolElement', 0.05, 'RelTolRange', 0.01)
-end
-
-
-% ------------------------
-%     Helper functions
-% ------------------------
-
-function testCase = setup
-  % Helper function to suply empty array into variable testCase as an
-  % argument for each test function, if using xUnit package. Not to be
-  % used with built-in test framework.
-  testCase = [];
-end
-
-function name = getName
-  % Helperfunction that returns the name of the demo, e.g. 'binomial1'.
-  name = mfilename;
-  name = name(6:end);
-end
-
diff --git a/tests/test_binomial2.m b/tests/test_binomial2.m
deleted file mode 100644
index ba73383c..00000000
--- a/tests/test_binomial2.m
+++ /dev/null
@@ -1,94 +0,0 @@
-function test_suite = test_binomial2
-
-%   Run specific demo, save values and compare the results to the expected.
-%   Works for both xUnit Test Framework package by Steve Eddins and for
-%   the built-in Unit Testing Framework (as of Matlab version 2013b).
-%
-%   See also
-%     TEST_ALL, DEMO_BINOMIAL2
-%
-% Copyright (c) 2011-2012 Ville Tolvanen
-% Copyright (c) 2014 Tuomas Sivula
-
-% This software is distributed under the GNU General Public 
-% License (version 3 or later); please refer to the file 
-% License.txt, included with the software, for details.
-  
-  % Check which package to use
-  if exist('initTestSuite', 'file')
-    initTestSuite_path = which('initTestSuite');
-  else
-    initTestSuite_path = '';
-  end
-  if ~isempty(initTestSuite_path) && ...
-     exist([initTestSuite_path(1:end-15) 'runtests'], 'file')
-    % xUnit package
-    initTestSuite;
-  else
-    % Built-in package
-    % Use all functions except the @setup
-    tests = localfunctions;
-    tests = tests(~cellfun(@(x)strcmp(func2str(x),'setup'),tests));
-    test_suite = functiontests(tests);
-  end
-end
-
-
-% -------------
-%     Tests
-% -------------
-
-function testRunDemo(testCase)
-  % Run the correspondin demo and save the values. Note this test has to
-  % be run at lest once before the other test may succeed.
-  rundemo(getName())
-end
-
-function testPredictiveMeanAndVarianceLaplace(testCase)
-  % Test predictive mean and variance for Laplace
-  verifyVarsEqual(testCase, getName(), ...
-    {'Eft_la','Varft_la','Eft_la_sampled','Varft_la_sampled'}, ...
-    'RelTolElement', 0.1, 'RelTolRange', 0.05)
-  % Test prediction for pbetapositive
-  verifyVarsEqual(testCase, getName(), {'pbetapositive_la'}, ...
-    'AbsTol', 0.01)
-end
-
-function testPredictiveMeanAndVarianceEP(testCase)
-  % Test predictive mean and variance for EP
-  verifyVarsEqual(testCase, getName(), ...
-    {'Eft_ep','Varft_ep','Eft_ep_sampled','Varft_ep_sampled'}, ...
-    'RelTolElement', 0.1, 'RelTolRange', 0.05)
-  % Test prediction for pbetapositive
-  verifyVarsEqual(testCase, getName(), {'pbetapositive_ep'}, ...
-    'AbsTol', 0.01)
-end
-
-function testPredictiveMeanMCMC(testCase)
-  % Test predictive mean for the MCMC samples
-  verifyVarsEqual(testCase, getName(), {'Eft_mcmc'}, ...
-    'RelTolElement', 0.1, 'RelTolRange', 0.05)
-  % Test prediction for pbetapositive
-  verifyVarsEqual(testCase, getName(), {'pbetapositive_mcmc'}, ...
-    'AbsTol', 0.01)
-end
-
-
-% ------------------------
-%     Helper functions
-% ------------------------
-
-function testCase = setup
-  % Helper function to suply empty array into variable testCase as an
-  % argument for each test function, if using xUnit package. Not to be
-  % used with built-in test framework.
-  testCase = [];
-end
-
-function name = getName
-  % Helperfunction that returns the name of the demo, e.g. 'binomial1'.
-  name = mfilename;
-  name = name(6:end);
-end
-
- 
\ No newline at end of file
diff --git a/tests/test_binomial_apc.m b/tests/test_binomial_apc.m
deleted file mode 100644
index 7522cee3..00000000
--- a/tests/test_binomial_apc.m
+++ /dev/null
@@ -1,74 +0,0 @@
-function test_suite = test_binomial_apc
-
-%   Run specific demo, save values and compare the results to the expected.
-%   Works for both xUnit Test Framework package by Steve Eddins and for
-%   the built-in Unit Testing Framework (as of Matlab version 2013b).
-%
-%   See also
-%     TEST_ALL, DEMO_BINOMIAL_APC
-%
-% Copyright (c) 2011-2012 Ville Tolvanen
-% Copyright (c) 2014 Tuomas Sivula
-
-% This software is distributed under the GNU General Public 
-% License (version 3 or later); please refer to the file 
-% License.txt, included with the software, for details.
-  
-  % Check which package to use
-  if exist('initTestSuite', 'file')
-    initTestSuite_path = which('initTestSuite');
-  else
-    initTestSuite_path = '';
-  end
-  if ~isempty(initTestSuite_path) && ...
-     exist([initTestSuite_path(1:end-15) 'runtests'], 'file')
-    % xUnit package
-    initTestSuite;
-  else
-    % Built-in package
-    % Use all functions except the @setup
-    tests = localfunctions;
-    tests = tests(~cellfun(@(x)strcmp(func2str(x),'setup'),tests));
-    test_suite = functiontests(tests);
-  end
-end
-
-
-% -------------
-%     Tests
-% -------------
-
-function testRunDemo(testCase)
-  % Run the correspondin demo and save the values. Note this test has to
-  % be run at lest once before the other test may succeed.
-  rundemo(getName())
-end
-
-function testPredictionsAll(testCase)
-  verifyVarsEqual(testCase, getName(), {'Eft', 'Varft'}, ...
-    'RelTolElement', 0.05, 'RelTolRange', 0.025)
-end
-
-function testPredictionsCohort(testCase)
-  verifyVarsEqual(testCase, getName(), {'Eft_3', 'Varft_3'}, ...
-    'RelTolElement', 0.05, 'RelTolRange', 0.025)
-end
-
-
-% ------------------------
-%     Helper functions
-% ------------------------
-
-function testCase = setup
-  % Helper function to suply empty array into variable testCase as an
-  % argument for each test function, if using xUnit package. Not to be
-  % used with built-in test framework.
-  testCase = [];
-end
-
-function name = getName
-  % Helperfunction that returns the name of the demo, e.g. 'binomial1'.
-  name = mfilename;
-  name = name(6:end);
-end
-
diff --git a/tests/test_classific.m b/tests/test_classific.m
deleted file mode 100644
index 93445c52..00000000
--- a/tests/test_classific.m
+++ /dev/null
@@ -1,86 +0,0 @@
-function test_suite = test_classific
-
-%   Run specific demo, save values and compare the results to the expected.
-%   Works for both xUnit Test Framework package by Steve Eddins and for
-%   the built-in Unit Testing Framework (as of Matlab version 2013b).
-%
-%   See also
-%     TEST_ALL, DEMO_CLASSIFIC
-%
-% Copyright (c) 2011-2012 Ville Tolvanen
-% Copyright (c) 2014 Tuomas Sivula
-
-% This software is distributed under the GNU General Public 
-% License (version 3 or later); please refer to the file 
-% License.txt, included with the software, for details.
-  
-  % Check which package to use
-  if exist('initTestSuite', 'file')
-    initTestSuite_path = which('initTestSuite');
-  else
-    initTestSuite_path = '';
-  end
-  if ~isempty(initTestSuite_path) && ...
-     exist([initTestSuite_path(1:end-15) 'runtests'], 'file')
-    % xUnit package
-    initTestSuite;
-  else
-    % Built-in package
-    % Use all functions except the @setup
-    tests = localfunctions;
-    tests = tests(~cellfun(@(x)strcmp(func2str(x),'setup'),tests));
-    test_suite = functiontests(tests);
-  end
-end
-
-
-% -------------
-%     Tests
-% -------------
-
-function testRunDemo(testCase)
-  % Run the correspondin demo and save the values. Note this test has to
-  % be run at lest once before the other test may succeed.
-  rundemo(getName(), {...
-    {'Eft_la', @(x)x(1:100)}, ...
-    {'Eft_ep', @(x)x(1:100)}, ...
-    {'Efs_mc', @(x)x(1:50,1:50)}, ...
-    {'Varft_la', @(x)x(1:100)}, ...
-    {'Varft_ep', @(x)x(1:100)}, ...
-    {'Varfs_mc', @(x)x(1:50,1:50)} })
-end
-
-function testLaplace(testCase)
-  verifyVarsEqual(testCase, getName(), {'Eft_la','Varft_la'}, ...
-    'RelTolElement', 0.05, 'RelTolRange', 0.01)
-end
-
-function testEP(testCase)
-  verifyVarsEqual(testCase, getName(), {'Eft_ep','Varft_ep'}, ...
-    'RelTolElement', 0.05, 'RelTolRange', 0.01)
-end
-
-% The results vary too much to be tested reliably
-% function testMC(testCase)
-%   verifyVarsEqual(testCase, getName(), {'Efs_mc'}, @(x)mean(x,2), ...
-%     'RelTolRange', 0.2)
-% end
-
-
-% ------------------------
-%     Helper functions
-% ------------------------
-
-function testCase = setup
-  % Helper function to suply empty array into variable testCase as an
-  % argument for each test function, if using xUnit package. Not to be
-  % used with built-in test framework.
-  testCase = [];
-end
-
-function name = getName
-  % Helperfunction that returns the name of the demo, e.g. 'binomial1'.
-  name = mfilename;
-  name = name(6:end);
-end
-
diff --git a/tests/test_derivativeobs.m b/tests/test_derivativeobs.m
deleted file mode 100644
index c4e48a5d..00000000
--- a/tests/test_derivativeobs.m
+++ /dev/null
@@ -1,76 +0,0 @@
-function test_suite = test_derivativeobs
-
-%   Run specific demo, save values and compare the results to the expected.
-%   Works for both xUnit Test Framework package by Steve Eddins and for
-%   the built-in Unit Testing Framework (as of Matlab version 2013b).
-%
-%   See also
-%     TEST_ALL, DEMO_DERIVATIVEOBS
-%
-% Copyright (c) 2011-2012 Ville Tolvanen
-% Copyright (c) 2014 Tuomas Sivula
-
-% This software is distributed under the GNU General Public 
-% License (version 3 or later); please refer to the file 
-% License.txt, included with the software, for details.
-  
-  % Check which package to use
-  if exist('initTestSuite', 'file')
-    initTestSuite_path = which('initTestSuite');
-  else
-    initTestSuite_path = '';
-  end
-  if ~isempty(initTestSuite_path) && ...
-     exist([initTestSuite_path(1:end-15) 'runtests'], 'file')
-    % xUnit package
-    initTestSuite;
-  else
-    % Built-in package
-    % Use all functions except the @setup
-    tests = localfunctions;
-    tests = tests(~cellfun(@(x)strcmp(func2str(x),'setup'),tests));
-    test_suite = functiontests(tests);
-  end
-end
-
-
-% -------------
-%     Tests
-% -------------
-
-function testRunDemo(testCase)
-  % Run the correspondin demo and save the values. Note this test has to
-  % be run at lest once before the other test may succeed.
-  rundemo(getName())
-end
-
-function testPrediction(testCase)
-  % Test predictive mean and variance
-  verifyVarsEqual(testCase, getName(), {'Eft','Varft'}, ...
-    'RelTolElement', 0.05, 'RelTolRange', 0.01)
-end
-
-function testPredictionDerivative(testCase)
-  % Test predictive mean and variance with derivative obs
-  verifyVarsEqual(testCase, getName(), {'Eft2','Varft2'}, ...
-    'RelTolElement', 0.05, 'RelTolRange', 0.01)
-end
-
-
-% ------------------------
-%     Helper functions
-% ------------------------
-
-function testCase = setup
-  % Helper function to suply empty array into variable testCase as an
-  % argument for each test function, if using xUnit package. Not to be
-  % used with built-in test framework.
-  testCase = [];
-end
-
-function name = getName
-  % Helperfunction that returns the name of the demo, e.g. 'binomial1'.
-  name = mfilename;
-  name = name(6:end);
-end
-
diff --git a/tests/test_epinf.m b/tests/test_epinf.m
deleted file mode 100644
index bc3d2eed..00000000
--- a/tests/test_epinf.m
+++ /dev/null
@@ -1,70 +0,0 @@
-function test_suite = test_epinf
-
-%   Run specific demo, save values and compare the results to the expected.
-%   Works for both xUnit Test Framework package by Steve Eddins and for
-%   the built-in Unit Testing Framework (as of Matlab version 2013b).
-%
-%   See also
-%     TEST_ALL, DEMO_EPINF
-%
-% Copyright (c) 2011-2012 Ville Tolvanen
-% Copyright (c) 2014 Tuomas Sivula
-
-% This software is distributed under the GNU General Public 
-% License (version 3 or later); please refer to the file 
-% License.txt, included with the software, for details.
-  
-  % Check which package to use
-  if exist('initTestSuite', 'file')
-    initTestSuite_path = which('initTestSuite');
-  else
-    initTestSuite_path = '';
-  end
-  if ~isempty(initTestSuite_path) && ...
-     exist([initTestSuite_path(1:end-15) 'runtests'], 'file')
-    % xUnit package
-    initTestSuite;
-  else
-    % Built-in package
-    % Use all functions except the @setup
-    tests = localfunctions;
-    tests = tests(~cellfun(@(x)strcmp(func2str(x),'setup'),tests));
-    test_suite = functiontests(tests);
-  end
-end
-
-
-% -------------
-%     Tests
-% -------------
-
-function testRunDemo(testCase)
-  % Run the correspondin demo and save the values. Note this test has to
-  % be run at lest once before the other test may succeed.
-  rundemo(getName())
-end
-
-function testPredictiveMeanAndVariance(testCase)
-  % Test predictive mean, variance and density
-  verifyVarsEqual(testCase, getName(), ...
-    'RelTolElement', 0.05, 'RelTolRange', 0.01)
-end
-
-
-% ------------------------
-%     Helper functions
-% ------------------------
-
-function testCase = setup
-  % Helper function to suply empty array into variable testCase as an
-  % argument for each test function, if using xUnit package. Not to be
-  % used with built-in test framework.
-  testCase = [];
-end
-
-function name = getName
-  % Helperfunction that returns the name of the demo, e.g. 'binomial1'.
-  name = mfilename;
-  name = name(6:end);
-end
-
diff --git a/tests/test_hierprior.m b/tests/test_hierprior.m
deleted file mode 100644
index 4221eb91..00000000
--- a/tests/test_hierprior.m
+++ /dev/null
@@ -1,85 +0,0 @@
-function test_suite = test_hierprior
-
-%   Run specific demo, save values and compare the results to the expected.
-%   Works for both xUnit Test Framework package by Steve Eddins and for
-%   the built-in Unit Testing Framework (as of Matlab version 2013b).
-%
-%   See also
-%     TEST_ALL, DEMO_HIERPRIOR
-%
-% Copyright (c) 2011-2012 Ville Tolvanen
-% Copyright (c) 2014 Tuomas Sivula
-
-% This software is distributed under the GNU General Public 
-% License (version 3 or later); please refer to the file 
-% License.txt, included with the software, for details.
-  
-  % Check which package to use
-  if exist('initTestSuite', 'file')
-    initTestSuite_path = which('initTestSuite');
-  else
-    initTestSuite_path = '';
-  end
-  if ~isempty(initTestSuite_path) && ...
-     exist([initTestSuite_path(1:end-15) 'runtests'], 'file')
-    % xUnit package
-    initTestSuite;
-  else
-    % Built-in package
-    % Use all functions except the @setup
-    tests = localfunctions;
-    tests = tests(~cellfun(@(x)strcmp(func2str(x),'setup'),tests));
-    test_suite = functiontests(tests);
-  end
-end
-
-
-% -------------
-%     Tests
-% -------------
-
-function testRunDemo(testCase)
-  % Run the correspondin demo and save the values. Note this test has to
-  % be run at lest once before the other test may succeed.
-  rundemo(getName())
-end
-
-function testDerivatesRegression(testCase)
-  verifyVarsEqual(testCase, getName(), {'derivs'}, @(x)x{1}, ...
-    'RelTolElement', 0.05, 'RelTolRange', 0.01, 'AbsTol', 0.4)
-  verifyVarsEqual(testCase, getName(), {'derivs'}, @(x)x{2}, ...
-    'RelTolElement', 0.05, 'RelTolRange', 0.01, 'AbsTol', 0.4)
-end
-
-function testDerivatesLaplace(testCase)
-  verifyVarsEqual(testCase, getName(), {'derivs'}, @(x)x{3}, ...
-    'AbsTol', 1e-5)
-  verifyVarsEqual(testCase, getName(), {'derivs'}, @(x)x{4}, ...
-    'AbsTol', 1e-5)
-end
-
-function testDerivatesEP(testCase)
-  verifyVarsEqual(testCase, getName(), {'derivs'}, @(x)x{5}, ...
-    'AbsTol', 1e-5)
-  verifyVarsEqual(testCase, getName(), {'derivs'}, @(x)x{6}, ...
-    'AbsTol', 1e-5)
-end
-
-
-% ------------------------
-%     Helper functions
-% ------------------------
-
-function testCase = setup
-  % Helper function to suply empty array into variable testCase as an
-  % argument for each test function, if using xUnit package. Not to be
-  % used with built-in test framework.
-  testCase = [];
-end
-
-function name = getName
-  % Helperfunction that returns the name of the demo, e.g. 'binomial1'.
-  name = mfilename;
-  name = name(6:end);
-end
-
diff --git a/tests/test_hurdle.m b/tests/test_hurdle.m
deleted file mode 100644
index 91cb2ead..00000000
--- a/tests/test_hurdle.m
+++ /dev/null
@@ -1,75 +0,0 @@
-function test_suite = test_hurdle
-
-%   Run specific demo, save values and compare the results to the expected.
-%   Works for both xUnit Test Framework package by Steve Eddins and for
-%   the built-in Unit Testing Framework (as of Matlab version 2013b).
-%
-%   See also
-%     TEST_ALL, DEMO_HURDLE
-%
-% Copyright (c) 2011-2012 Ville Tolvanen
-% Copyright (c) 2014 Tuomas Sivula
-
-% This software is distributed under the GNU General Public 
-% License (version 3 or later); please refer to the file 
-% License.txt, included with the software, for details.
-  
-  % Check which package to use
-  if exist('initTestSuite', 'file')
-    initTestSuite_path = which('initTestSuite');
-  else
-    initTestSuite_path = '';
-  end
-  if ~isempty(initTestSuite_path) && ...
-     exist([initTestSuite_path(1:end-15) 'runtests'], 'file')
-    % xUnit package
-    initTestSuite;
-  else
-    % Built-in package
-    % Use all functions except the @setup
-    tests = localfunctions;
-    tests = tests(~cellfun(@(x)strcmp(func2str(x),'setup'),tests));
-    test_suite = functiontests(tests);
-  end
-end
-
-
-% -------------
-%     Tests
-% -------------
-
-function testRunDemo(testCase)
-  % Run the correspondin demo and save the values. Note this test has to
-  % be run at lest once before the other test may succeed.
-  rundemo(getName())
-end
-
-function testPredictiveMeanAndVariance_LatentZeroProcess(testCase)
-  verifyVarsEqual(testCase, getName(), {'Efz','Varfz'}, ...
-    'RelTolElement', 0.05, 'RelTolRange', 0.01)
-end
-
-function testPredictiveMeanAndVariance_LatentCountProcess(testCase)
-  verifyVarsEqual(testCase, getName(), {'Efc','Varfc'}, ...
-    'RelTolElement', 0.05, 'RelTolRange', 0.01)
-end
-
-
-% ------------------------
-%     Helper functions
-% ------------------------
-
-function testCase = setup
-  % Helper function to suply empty array into variable testCase as an
-  % argument for each test function, if using xUnit package. Not to be
-  % used with built-in test framework.
-  testCase = [];
-end
-
-function name = getName
-  % Helperfunction that returns the name of the demo, e.g. 'binomial1'.
-  name = mfilename;
-  name = name(6:end);
-end
-
- 
\ No newline at end of file
diff --git a/tests/test_improvemarginals.m b/tests/test_improvemarginals.m
deleted file mode 100644
index 1db43c96..00000000
--- a/tests/test_improvemarginals.m
+++ /dev/null
@@ -1,85 +0,0 @@
-function test_suite = test_improvemarginals
-
-%   Run specific demo, save values and compare the results to the expected.
-%   Works for both xUnit Test Framework package by Steve Eddins and for
-%   the built-in Unit Testing Framework (as of Matlab version 2013b).
-%
-%   See also
-%     TEST_ALL, DEMO_IMPROVEMARGINALS
-%
-% Copyright (c) 2011-2012 Ville Tolvanen
-% Copyright (c) 2014 Tuomas Sivula
-
-% This software is distributed under the GNU General Public 
-% License (version 3 or later); please refer to the file 
-% License.txt, included with the software, for details.
-  
-  % Check which package to use
-  if exist('initTestSuite', 'file')
-    initTestSuite_path = which('initTestSuite');
-  else
-    initTestSuite_path = '';
-  end
-  if ~isempty(initTestSuite_path) && ...
-     exist([initTestSuite_path(1:end-15) 'runtests'], 'file')
-    % xUnit package
-    initTestSuite;
-  else
-    % Built-in package
-    % Use all functions except the @setup
-    tests = localfunctions;
-    tests = tests(~cellfun(@(x)strcmp(func2str(x),'setup'),tests));
-    test_suite = functiontests(tests);
-  end
-end
-
-
-% -------------
-%     Tests
-% -------------
-
-function testRunDemo(testCase)
-  % Run the correspondin demo and save the values. Note this test has to
-  % be run at lest once before the other test may succeed.
-  rundemo(getName())
-end
-
-function test_EP_fact(testCase)
-  verifyVarsEqual(testCase, getName(), ...
-    {'pc_ep','fvec_ep','pc_ep_pred','fvec_ep_pred'}, ...
-    {[], @(x)[x(1),x(end)], [], @(x)[x(1),x(end)]}, ...
-    'RelTolElement', 0.05, 'RelTolRange', 0.01)
-end
-
-function test_Laplace_CM2(testCase)
-  verifyVarsEqual(testCase, getName(), ...
-    {'pc_la','fvec_la','pc_la_pred','fvec_la_pred'}, ...
-    {[], @(x)[x(1),x(end)], [], @(x)[x(1),x(end)]}, ...
-    'RelTolElement', 0.05, 'RelTolRange', 0.01)
-end
-
-function test_Laplace_fact(testCase)
-  verifyVarsEqual(testCase, getName(), ...
-    {'pc_la2','fvec_la2','pc_la_pred2','fvec_la_pred2'}, ...
-    {[], @(x)[x(1),x(end)], [], @(x)[x(1),x(end)]}, ...
-    'RelTolElement', 0.1, 'RelTolRange', 0.05)
-end
-
-
-% ------------------------
-%     Helper functions
-% ------------------------
-
-function testCase = setup
-  % Helper function to suply empty array into variable testCase as an
-  % argument for each test function, if using xUnit package. Not to be
-  % used with built-in test framework.
-  testCase = [];
-end
-
-function name = getName
-  % Helperfunction that returns the name of the demo, e.g. 'binomial1'.
-  name = mfilename;
-  name = name(6:end);
-end
-
diff --git a/tests/test_kalman1.m b/tests/test_kalman1.m
deleted file mode 100644
index 10d59978..00000000
--- a/tests/test_kalman1.m
+++ /dev/null
@@ -1,71 +0,0 @@
-function test_suite = test_kalman1
-
-%   Run specific demo, save values and compare the results to the expected.
-%   Works for both xUnit Test Framework package by Steve Eddins and for
-%   the built-in Unit Testing Framework (as of Matlab version 2013b).
-%
-%   See also
-%     TEST_ALL, DEMO_KALMAN1
-%
-% Copyright (c) 2011-2012 Ville Tolvanen
-% Copyright (c) 2014 Tuomas Sivula
-
-% This software is distributed under the GNU General Public 
-% License (version 3 or later); please refer to the file 
-% License.txt, included with the software, for details.
-  
-  % Check which package to use
-  if exist('initTestSuite', 'file')
-    initTestSuite_path = which('initTestSuite');
-  else
-    initTestSuite_path = '';
-  end
-  if ~isempty(initTestSuite_path) && ...
-     exist([initTestSuite_path(1:end-15) 'runtests'], 'file')
-    % xUnit package
-    initTestSuite;
-  else
-    % Built-in package
-    % Use all functions except the @setup
-    tests = localfunctions;
-    tests = tests(~cellfun(@(x)strcmp(func2str(x),'setup'),tests));
-    test_suite = functiontests(tests);
-  end
-end
-
-
-% -------------
-%     Tests
-% -------------
-
-function testRunDemo(testCase)
-  % Run the correspondin demo and save the values. Note this test has to
-  % be run at lest once before the other test may succeed.
-  rundemo(getName())
-end
-
-function testPredictionsKalman1(testCase)
-  % Test predictive mean and variance
-  verifyVarsEqual(testCase, getName(), ...
-    {'Eft','Varft','Covft'}, ...
-    'RelTolElement', 0.1, 'RelTolRange', 0.02)
-end
-
-
-% ------------------------
-%     Helper functions
-% ------------------------
-
-function testCase = setup
-  % Helper function to suply empty array into variable testCase as an
-  % argument for each test function, if using xUnit package. Not to be
-  % used with built-in test framework.
-  testCase = [];
-end
-
-function name = getName
-  % Helperfunction that returns the name of the demo, e.g. 'binomial1'.
-  name = mfilename;
-  name = name(6:end);
-end
-
diff --git a/tests/test_kalman2.m b/tests/test_kalman2.m
deleted file mode 100644
index 31863373..00000000
--- a/tests/test_kalman2.m
+++ /dev/null
@@ -1,75 +0,0 @@
-function test_suite = test_kalman2
-
-%   Run specific demo, save values and compare the results to the expected.
-%   Works for both xUnit Test Framework package by Steve Eddins and for
-%   the built-in Unit Testing Framework (as of Matlab version 2013b).
-%
-%   See also
-%     TEST_ALL, DEMO_KALMAN2
-%
-% Copyright (c) 2011-2012 Ville Tolvanen
-% Copyright (c) 2014 Tuomas Sivula
-
-% This software is distributed under the GNU General Public 
-% License (version 3 or later); please refer to the file 
-% License.txt, included with the software, for details.
-  
-  % Check which package to use
-  if exist('initTestSuite', 'file')
-    initTestSuite_path = which('initTestSuite');
-  else
-    initTestSuite_path = '';
-  end
-  if ~isempty(initTestSuite_path) && ...
-     exist([initTestSuite_path(1:end-15) 'runtests'], 'file')
-    % xUnit package
-    initTestSuite;
-  else
-    % Built-in package
-    % Use all functions except the @setup
-    tests = localfunctions;
-    tests = tests(~cellfun(@(x)strcmp(func2str(x),'setup'),tests));
-    test_suite = functiontests(tests);
-  end
-end
-
-
-% -------------
-%     Tests
-% -------------
-
-function testRunDemo(testCase)
-  % Run the correspondin demo and save the values. Note this test has to
-  % be run at lest once before the other test may succeed.
-  
-  % Suppress an expected warning
-  Sw = warning('off','MATLAB:nearlySingularMatrix');
-  rundemo(getName())
-  warning(Sw); % Return the original warning visibility status
-end
-
-function testPredictionsKalman2(testCase)
-  % Test predictive mean and variance
-  verifyVarsEqual(testCase, getName(), ...
-    {'Eft','Varft'}, ...
-    'RelTolElement', 0.1, 'RelTolRange', 0.02)
-end
-
-
-% ------------------------
-%     Helper functions
-% ------------------------
-
-function testCase = setup
-  % Helper function to suply empty array into variable testCase as an
-  % argument for each test function, if using xUnit package. Not to be
-  % used with built-in test framework.
-  testCase = [];
-end
-
-function name = getName
-  % Helperfunction that returns the name of the demo, e.g. 'binomial1'.
-  name = mfilename;
-  name = name(6:end);
-end
-
diff --git a/tests/test_lgcp.m b/tests/test_lgcp.m
deleted file mode 100644
index 35f3e99a..00000000
--- a/tests/test_lgcp.m
+++ /dev/null
@@ -1,70 +0,0 @@
-function test_suite = test_lgcp
-
-%   Run specific demo, save values and compare the results to the expected.
-%   Works for both xUnit Test Framework package by Steve Eddins and for
-%   the built-in Unit Testing Framework (as of Matlab version 2013b).
-%
-%   See also
-%     TEST_ALL, DEMO_LGCP
-%
-% Copyright (c) 2011-2012 Ville Tolvanen
-% Copyright (c) 2014 Tuomas Sivula
-
-% This software is distributed under the GNU General Public 
-% License (version 3 or later); please refer to the file 
-% License.txt, included with the software, for details.
-  
-  % Check which package to use
-  if exist('initTestSuite', 'file')
-    initTestSuite_path = which('initTestSuite');
-  else
-    initTestSuite_path = '';
-  end
-  if ~isempty(initTestSuite_path) && ...
-     exist([initTestSuite_path(1:end-15) 'runtests'], 'file')
-    % xUnit package
-    initTestSuite;
-  else
-    % Built-in package
-    % Use all functions except the @setup
-    tests = localfunctions;
-    tests = tests(~cellfun(@(x)strcmp(func2str(x),'setup'),tests));
-    test_suite = functiontests(tests);
-  end
-end
-
-
-% -------------
-%     Tests
-% -------------
-
-function testRunDemo(testCase)
-  % Run the correspondin demo and save the values. Note this test has to
-  % be run at lest once before the other test may succeed.
-  rundemo(getName())
-end
-
-function testLGCP(testCase)
-  verifyVarsEqual(testCase, getName(), ...
-    {'p1', 'p2'}, ...
-    'RelTolElement', 0.05, 'RelTolRange', 0.01)
-end
-
-
-% ------------------------
-%     Helper functions
-% ------------------------
-
-function testCase = setup
-  % Helper function to suply empty array into variable testCase as an
-  % argument for each test function, if using xUnit package. Not to be
-  % used with built-in test framework.
-  testCase = [];
-end
-
-function name = getName
-  % Helperfunction that returns the name of the demo, e.g. 'binomial1'.
-  name = mfilename;
-  name = name(6:end);
-end
-
diff --git a/tests/test_loopred.m b/tests/test_loopred.m
deleted file mode 100644
index 4644d1ed..00000000
--- a/tests/test_loopred.m
+++ /dev/null
@@ -1,88 +0,0 @@
-function test_suite = test_loopred
-
-%   Run specific demo, save values and compare the results to the expected.
-%   Works for both xUnit Test Framework package by Steve Eddins and for
-%   the built-in Unit Testing Framework (as of Matlab version 2013b).
-%
-%   See also
-%     TEST_ALL, DEMO_LOOPRED
-%
-% Copyright (c) 2011-2012 Ville Tolvanen
-% Copyright (c) 2014 Tuomas Sivula
-
-% This software is distributed under the GNU General Public 
-% License (version 3 or later); please refer to the file 
-% License.txt, included with the software, for details.
-  
-  % Check which package to use
-  if exist('initTestSuite', 'file')
-    initTestSuite_path = which('initTestSuite');
-  else
-    initTestSuite_path = '';
-  end
-  if ~isempty(initTestSuite_path) && ...
-     exist([initTestSuite_path(1:end-15) 'runtests'], 'file')
-    % xUnit package
-    initTestSuite;
-  else
-    % Built-in package
-    % Use all functions except the @setup
-    tests = localfunctions;
-    tests = tests(~cellfun(@(x)strcmp(func2str(x),'setup'),tests));
-    test_suite = functiontests(tests);
-  end
-end
-
-
-% -------------
-%     Tests
-% -------------
-
-function testRunDemo(testCase)
-  % Run the correspondin demo and save the values. Note this test has to
-  % be run at lest once before the other test may succeed.
-  rundemo(getName())
-end
-
-function testLrsLOO(testCase)
-  verifyVarsEqual(testCase, getName(), ...
-    {'Eft_lrs','Varft_lrs','lpyt_lrs'}, ...
-    'AbsTol', 0.1)
-end
-
-function testCavityLOO(testCase)
-  verifyVarsEqual(testCase, getName(), ...
-    {'Eft_cav','Varft_cav','lpyt_cav'}, ...
-    'AbsTol', 0.1)
-end
-
-function testInlaLOO(testCase)
-  verifyVarsEqual(testCase, getName(), ...
-    {'Eft_inla','Varft_inla','lpyt_inla'}, ...
-    'AbsTol', 0.1)
-end
-
-function testEPLOO(testCase)
-  verifyVarsEqual(testCase, getName(), ...
-    {'Eft_ep','Varft_ep','lpyt_ep'}, ...
-    'AbsTol', 0.1)
-end
-
-
-% ------------------------
-%     Helper functions
-% ------------------------
-
-function testCase = setup
-  % Helper function to suply empty array into variable testCase as an
-  % argument for each test function, if using xUnit package. Not to be
-  % used with built-in test framework.
-  testCase = [];
-end
-
-function name = getName
-  % Helperfunction that returns the name of the demo, e.g. 'binomial1'.
-  name = mfilename;
-  name = name(6:end);
-end
-
diff --git a/tests/test_memorysave.m b/tests/test_memorysave.m
deleted file mode 100644
index 73b0e310..00000000
--- a/tests/test_memorysave.m
+++ /dev/null
@@ -1,64 +0,0 @@
-function test_suite = test_memorysave
-
-%   Run specific demo, save values and compare the results to the expected.
-%   Works for both xUnit Test Framework package by Steve Eddins and for
-%   the built-in Unit Testing Framework (as of Matlab version 2013b).
-%
-%   See also
-%     TEST_ALL, DEMO_MEMORYSAVE
-%
-% Copyright (c) 2011-2012 Ville Tolvanen
-% Copyright (c) 2014 Tuomas Sivula
-
-% This software is distributed under the GNU General Public 
-% License (version 3 or later); please refer to the file 
-% License.txt, included with the software, for details.
-  
-  % Check which package to use
-  if exist('initTestSuite', 'file')
-    initTestSuite_path = which('initTestSuite');
-  else
-    initTestSuite_path = '';
-  end
-  if ~isempty(initTestSuite_path) && ...
-     exist([initTestSuite_path(1:end-15) 'runtests'], 'file')
-    % xUnit package
-    initTestSuite;
-  else
-    % Built-in package
-    % Use all functions except the @setup
-    tests = localfunctions;
-    tests = tests(~cellfun(@(x)strcmp(func2str(x),'setup'),tests));
-    test_suite = functiontests(tests);
-  end
-end
-
-
-% -------------
-%     Tests
-% -------------
-
-function testRunDemo(testCase)
-  % Run the correspondin demo and save the values. Note this test has to
-  % be run at lest once before the other test may succeed.
-  rundemo(getName(), [])
-end
-
-
-% ------------------------
-%     Helper functions
-% ------------------------
-
-function testCase = setup
-  % Helper function to suply empty array into variable testCase as an
-  % argument for each test function, if using xUnit package. Not to be
-  % used with built-in test framework.
-  testCase = [];
-end
-
-function name = getName
-  % Helperfunction that returns the name of the demo, e.g. 'binomial1'.
-  name = mfilename;
-  name = name(6:end);
-end
-
diff --git a/tests/test_modelassesment1.m b/tests/test_modelassesment1.m
deleted file mode 100644
index 42149d79..00000000
--- a/tests/test_modelassesment1.m
+++ /dev/null
@@ -1,129 +0,0 @@
-function test_suite = test_modelassesment1
-
-%   Run specific demo, save values and compare the results to the expected.
-%   Works for both xUnit Test Framework package by Steve Eddins and for
-%   the built-in Unit Testing Framework (as of Matlab version 2013b).
-%
-%   See also
-%     TEST_ALL, DEMO_MODELASSESMENT1
-%
-% Copyright (c) 2011-2012 Ville Tolvanen
-% Copyright (c) 2014 Tuomas Sivula
-
-% This software is distributed under the GNU General Public 
-% License (version 3 or later); please refer to the file 
-% License.txt, included with the software, for details.
-  
-  % Check which package to use
-  if exist('initTestSuite', 'file')
-    initTestSuite_path = which('initTestSuite');
-  else
-    initTestSuite_path = '';
-  end
-  if ~isempty(initTestSuite_path) && ...
-     exist([initTestSuite_path(1:end-15) 'runtests'], 'file')
-    % xUnit package
-    initTestSuite;
-  else
-    % Built-in package
-    % Use all functions except the @setup
-    tests = localfunctions;
-    tests = tests(~cellfun(@(x)strcmp(func2str(x),'setup'),tests));
-    test_suite = functiontests(tests);
-  end
-end
-
-
-% -------------
-%     Tests
-% -------------
-
-function testRunDemo(testCase)
-  % Run the correspondin demo and save the values. Note this test has to
-  % be run at lest once before the other test may succeed.
-  rundemo(getName())
-end
-
-function testDICParameters(testCase)
-  verifyVarsEqual(testCase, getName(), {'DIC'}, {@nans2zero}, ...
-    'RelTolElement', 0.05, 'RelTolRange', 0.01)
-end
-
-function testDICAll(testCase)
-  verifyVarsEqual(testCase, getName(), {'DIC2'}, {@nans2zero}, ...
-    'RelTolElement', 0.2, 'RelTolRange', 0.05)
-end
-
-function testDICLatent(testCase)
-  verifyVarsEqual(testCase, getName(), {'DIC_latent'}, {@nans2zero}, ...
-    'RelTolElement', 0.2, 'RelTolRange', 0.05)
-end
-
-function testPeffLatentMarginalized(testCase)
-  verifyVarsEqual(testCase, getName(), {'p_eff'}, {@nans2zero}, ...
-    'RelTolElement', 0.2, 'RelTolRange', 0.05)
-end
-
-function testPeffAll(testCase)
-  verifyVarsEqual(testCase, getName(), {'p_eff2'}, {@nans2zero}, ...
-    'RelTolElement', 0.2, 'RelTolRange', 0.05)
-end
-
-function testPeffLatent(testCase)
-  verifyVarsEqual(testCase, getName(), {'p_eff_latent'}, {@nans2zero}, ...
-    'RelTolElement', 0.2, 'RelTolRange', 0.05)
-end
-
-function testPeffLatent2(testCase)
-  verifyVarsEqual(testCase, getName(), {'p_eff_latent2'}, {@nans2zero}, ...
-    'RelTolElement', 0.2, 'RelTolRange', 0.05)
-end
-
-function testLogPredDensity10foldCV(testCase)
-  verifyVarsEqual(testCase, getName(), {'mlpd_cv'}, ...
-    'RelTolElement', 0.2, 'RelTolRange', 0.05)
-end
-
-function testMeanSquaredError10foldCV(testCase)
-  verifyVarsEqual(testCase, getName(), {'rmse_cv'}, ...
-    'RelTolElement', 0.2, 'RelTolRange', 0.05)
-end
-
-function testLogPredDensityLOOPRED(testCase)
-  verifyVarsEqual(testCase, getName(), {'mlpd_loo'}, ...
-    'RelTolElement', 0.2, 'RelTolRange', 0.05)
-end
-
-function testMeanSquaredErrorLOOPRED(testCase)
-  verifyVarsEqual(testCase, getName(), {'rmse_loo'}, ...
-    'RelTolElement', 0.2, 'RelTolRange', 0.05)
-end
-
-function testWAIC(testCase)
-  verifyVarsEqual(testCase, getName(), {'WAICV','WAICG'}, ...
-    'RelTolElement', 0.2, 'RelTolRange', 0.05)
-end
-
-
-% ------------------------
-%     Helper functions
-% ------------------------
-
-function x = nans2zero(x)
-  % Helperfunction to replace NaNs in an array by zeroes.
-  x(isnan(x)) = 0;
-end
-
-function testCase = setup
-  % Helper function to suply empty array into variable testCase as an
-  % argument for each test function, if using xUnit package. Not to be
-  % used with built-in test framework.
-  testCase = [];
-end
-
-function name = getName
-  % Helperfunction that returns the name of the demo, e.g. 'binomial1'.
-  name = mfilename;
-  name = name(6:end);
-end
-
diff --git a/tests/test_modelassesment2.m b/tests/test_modelassesment2.m
deleted file mode 100644
index dbb456b2..00000000
--- a/tests/test_modelassesment2.m
+++ /dev/null
@@ -1,114 +0,0 @@
-function test_suite = test_modelassesment2
-
-%   Run specific demo, save values and compare the results to the expected.
-%   Works for both xUnit Test Framework package by Steve Eddins and for
-%   the built-in Unit Testing Framework (as of Matlab version 2013b).
-%
-%   See also
-%     TEST_ALL, DEMO_MODELASSESMENT2
-%
-% Copyright (c) 2011-2012 Ville Tolvanen
-% Copyright (c) 2014 Tuomas Sivula
-
-% This software is distributed under the GNU General Public 
-% License (version 3 or later); please refer to the file 
-% License.txt, included with the software, for details.
-  
-  % Check which package to use
-  if exist('initTestSuite', 'file')
-    initTestSuite_path = which('initTestSuite');
-  else
-    initTestSuite_path = '';
-  end
-  if ~isempty(initTestSuite_path) && ...
-     exist([initTestSuite_path(1:end-15) 'runtests'], 'file')
-    % xUnit package
-    initTestSuite;
-  else
-    % Built-in package
-    % Use all functions except the @setup
-    tests = localfunctions;
-    tests = tests(~cellfun(@(x)strcmp(func2str(x),'setup'),tests));
-    test_suite = functiontests(tests);
-  end
-end
-
-
-% -------------
-%     Tests
-% -------------
-
-function testRunDemo(testCase)
-  % Run the correspondin demo and save the values. Note this test has to
-  % be run at lest once before the other test may succeed.
-  rundemo(getName())
-end
-
-function testDICParameters(testCase)
-  verifyVarsEqual(testCase, getName(), {'DIC'}, @nans2zero, ...
-    'RelTolElement', 0.2, 'RelTolRange', 0.05)
-end
-
-function testDICAll(testCase)
-  verifyVarsEqual(testCase, getName(), {'DIC2'}, @nans2zero, ...
-    'RelTolElement', 0.2, 'RelTolRange', 0.05)
-end
-
-function testDICLatent(testCase)
-  verifyVarsEqual(testCase, getName(), {'DIC_latent'}, @nans2zero, ...
-    'RelTolElement', 0.2, 'RelTolRange', 0.05)
-end
-
-% function testPeffLatentMarginalized(testCase)
-%   verifyVarsEqual(testCase, getName(), {'p_eff'}, @nans2zero, ...
-%     'AbsTol', 0.5)
-% end
-
-function testPeffLatent(testCase)
-  verifyVarsEqual(testCase, getName(), {'p_eff_latent'}, @nans2zero, ...
-    'AbsTol', 0.6)
-end
-
-function testPeffLatent2(testCase)
-  verifyVarsEqual(testCase, getName(), {'p_eff_latent2'}, @nans2zero, ...
-    'AbsTol', 2.5)
-end
-
-function testLogPredDensity10foldCV(testCase)
-  verifyVarsEqual(testCase, getName(), {'mlpd_cv'}, ...
-    'RelTolElement', 0.2, 'RelTolRange', 0.05)
-end
-
-function testLogPredDensityLOOPRED(testCase)
-  verifyVarsEqual(testCase, getName(), {'mlpd_loo'}, ...
-    'RelTolElement', 0.2, 'RelTolRange', 0.05)
-end
-
-function testWAIC(testCase)
-  verifyVarsEqual(testCase, getName(), {'WAIC'}, ...
-    'RelTolElement', 0.2, 'RelTolRange', 0.05)
-end
-
-
-% ------------------------
-%     Helper functions
-% ------------------------
-
-function x = nans2zero(x)
-  % Helperfunction to replace NaNs in an array by zeroes.
-  x(isnan(x)) = 0;
-end
-
-function testCase = setup
-  % Helper function to suply empty array into variable testCase as an
-  % argument for each test function, if using xUnit package. Not to be
-  % used with built-in test framework.
-  testCase = [];
-end
-
-function name = getName
-  % Helperfunction that returns the name of the demo, e.g. 'binomial1'.
-  name = mfilename;
-  name = name(6:end);
-end
-
diff --git a/tests/test_monotonic2.m b/tests/test_monotonic2.m
deleted file mode 100644
index e59ba889..00000000
--- a/tests/test_monotonic2.m
+++ /dev/null
@@ -1,74 +0,0 @@
-function test_suite = test_monotonic2
-
-%   Run specific demo, save values and compare the results to the expected.
-%   Works for both xUnit Test Framework package by Steve Eddins and for
-%   the built-in Unit Testing Framework (as of Matlab version 2013b).
-%
-%   See also
-%     TEST_ALL, DEMO_MONOTONIC2
-%
-% Copyright (c) 2011-2012 Ville Tolvanen
-% Copyright (c) 2014 Tuomas Sivula
-
-% This software is distributed under the GNU General Public 
-% License (version 3 or later); please refer to the file 
-% License.txt, included with the software, for details.
-  
-  % Check which package to use
-  if exist('initTestSuite', 'file')
-    initTestSuite_path = which('initTestSuite');
-  else
-    initTestSuite_path = '';
-  end
-  if ~isempty(initTestSuite_path) && ...
-     exist([initTestSuite_path(1:end-15) 'runtests'], 'file')
-    % xUnit package
-    initTestSuite;
-  else
-    % Built-in package
-    % Use all functions except the @setup
-    tests = localfunctions;
-    tests = tests(~cellfun(@(x)strcmp(func2str(x),'setup'),tests));
-    test_suite = functiontests(tests);
-  end
-end
-
-
-% -------------
-%     Tests
-% -------------
-
-function testRunDemo(testCase)
-  % Run the correspondin demo and save the values. Note this test has to
-  % be run at lest once before the other test may succeed.
-  rundemo(getName())
-end
-
-function testPredictiveMeanAndVariance_normal(testCase)
-  verifyVarsEqual(testCase, getName(), {'Eft','Varft'}, ...
-    'RelTolElement', 0.05, 'RelTolRange', 0.01)
-end
-
-function testPredictiveMeanAndVariance_monotonic(testCase)
-  verifyVarsEqual(testCase, getName(), {'Eftm','Varftm'}, ...
-    'RelTolElement', 0.05, 'RelTolRange', 0.01)
-end
-
-
-% ------------------------
-%     Helper functions
-% ------------------------
-
-function testCase = setup
-  % Helper function to suply empty array into variable testCase as an
-  % argument for each test function, if using xUnit package. Not to be
-  % used with built-in test framework.
-  testCase = [];
-end
-
-function name = getName
-  % Helperfunction that returns the name of the demo, e.g. 'binomial1'.
-  name = mfilename;
-  name = name(6:end);
-end
-
diff --git a/tests/test_multiclass.m b/tests/test_multiclass.m
deleted file mode 100644
index e8ef8064..00000000
--- a/tests/test_multiclass.m
+++ /dev/null
@@ -1,73 +0,0 @@
-function test_suite = test_multiclass
-
-%   Run specific demo, save values and compare the results to the expected.
-%   Works for both xUnit Test Framework package by Steve Eddins and for
-%   the built-in Unit Testing Framework (as of Matlab version 2013b).
-%
-%   See also
-%     TEST_ALL, DEMO_MULTICLASS
-%
-% Copyright (c) 2011-2012 Ville Tolvanen
-% Copyright (c) 2014 Tuomas Sivula
-
-% This software is distributed under the GNU General Public 
-% License (version 3 or later); please refer to the file 
-% License.txt, included with the software, for details.
-  
-  % Check which package to use
-  if exist('initTestSuite', 'file')
-    initTestSuite_path = which('initTestSuite');
-  else
-    initTestSuite_path = '';
-  end
-  if ~isempty(initTestSuite_path) && ...
-     exist([initTestSuite_path(1:end-15) 'runtests'], 'file')
-    % xUnit package
-    initTestSuite;
-  else
-    % Built-in package
-    % Use all functions except the @setup
-    tests = localfunctions;
-    tests = tests(~cellfun(@(x)strcmp(func2str(x),'setup'),tests));
-    test_suite = functiontests(tests);
-  end
-end
-
-
-% -------------
-%     Tests
-% -------------
-
-function testRunDemo(testCase)
-  % Run the correspondin demo and save the values. Note this test has to
-  % be run at lest once before the other test may succeed.
-  rundemo(getName(), { ...
-    {'Eft', @(x)x(1:100,:)}, ...
-    {'Varft', @(x)x(:,:,1:100)}, ...
-    {'Covft', @(x)x(:,:,1:100)} })
-end
-
-function testPredictions(testCase)
-  verifyVarsEqual(testCase, getName(), ...
-    {'Eft', 'Varft', 'Covft'}, @mean, ...
-    'RelTolElement', 0.01, 'RelTolRange', 0.005)
-end
-
-
-% ------------------------
-%     Helper functions
-% ------------------------
-
-function testCase = setup
-  % Helper function to suply empty array into variable testCase as an
-  % argument for each test function, if using xUnit package. Not to be
-  % used with built-in test framework.
-  testCase = [];
-end
-
-function name = getName
-  % Helperfunction that returns the name of the demo, e.g. 'binomial1'.
-  name = mfilename;
-  name = name(6:end);
-end
-
diff --git a/tests/test_multiclass_nested_ep.m b/tests/test_multiclass_nested_ep.m
deleted file mode 100644
index 5f8b8218..00000000
--- a/tests/test_multiclass_nested_ep.m
+++ /dev/null
@@ -1,69 +0,0 @@
-function test_suite = test_multiclass_nested_ep
-
-%   Run specific demo, save values and compare the results to the expected.
-%   Works for both xUnit Test Framework package by Steve Eddins and for
-%   the built-in Unit Testing Framework (as of Matlab version 2013b).
-%
-%   See also
-%     TEST_ALL, DEMO_MULTICLASS_NESTED_EP
-%
-% Copyright (c) 2011-2012 Ville Tolvanen
-% Copyright (c) 2014 Tuomas Sivula
-
-% This software is distributed under the GNU General Public 
-% License (version 3 or later); please refer to the file 
-% License.txt, included with the software, for details.
-  
-  % Check which package to use
-  if exist('initTestSuite', 'file')
-    initTestSuite_path = which('initTestSuite');
-  else
-    initTestSuite_path = '';
-  end
-  if ~isempty(initTestSuite_path) && ...
-     exist([initTestSuite_path(1:end-15) 'runtests'], 'file')
-    % xUnit package
-    initTestSuite;
-  else
-    % Built-in package
-    % Use all functions except the @setup
-    tests = localfunctions;
-    tests = tests(~cellfun(@(x)strcmp(func2str(x),'setup'),tests));
-    test_suite = functiontests(tests);
-  end
-end
-
-
-% -------------
-%     Tests
-% -------------
-
-function testRunDemo(testCase)
-  % Run the correspondin demo and save the values. Note this test has to
-  % be run at lest once before the other test may succeed.
-  rundemo(getName())
-end
-
-function testPredictionsMulticlassEP(testCase)
-  verifyVarsEqual(testCase, getName(), {'Eft', 'Covft', 'lpg'}, ...
-    'RelTolElement', 0.1, 'RelTolRange', 0.02)
-end
-
-
-% ------------------------
-%     Helper functions
-% ------------------------
-
-function testCase = setup
-  % Helper function to suply empty array into variable testCase as an
-  % argument for each test function, if using xUnit package. Not to be
-  % used with built-in test framework.
-  testCase = [];
-end
-
-function name = getName
-  % Helperfunction that returns the name of the demo, e.g. 'binomial1'.
-  name = mfilename;
-  name = name(6:end);
-end
-
diff --git a/tests/test_multinom.m b/tests/test_multinom.m
deleted file mode 100644
index 19048d6a..00000000
--- a/tests/test_multinom.m
+++ /dev/null
@@ -1,68 +0,0 @@
-function test_suite = test_multinom
-
-%   Run specific demo, save values and compare the results to the expected.
-%   Works for both xUnit Test Framework package by Steve Eddins and for
-%   the built-in Unit Testing Framework (as of Matlab version 2013b).
-%
-%   See also
-%     TEST_ALL, DEMO_MULTINOM
-%
-% Copyright (c) 2011-2012 Ville Tolvanen
-% Copyright (c) 2014 Tuomas Sivula
-
-% This software is distributed under the GNU General Public 
-% License (version 3 or later); please refer to the file 
-% License.txt, included with the software, for details.
-  
-  % Check which package to use
-  if exist('initTestSuite', 'file')
-    initTestSuite_path = which('initTestSuite');
-  else
-    initTestSuite_path = '';
-  end
-  if ~isempty(initTestSuite_path) && ...
-     exist([initTestSuite_path(1:end-15) 'runtests'], 'file')
-    % xUnit package
-    initTestSuite;
-  else
-    % Built-in package
-    % Use all functions except the @setup
-    tests = localfunctions;
-    tests = tests(~cellfun(@(x)strcmp(func2str(x),'setup'),tests));
-    test_suite = functiontests(tests);
-  end
-end
-
-
-% -------------
-%     Tests
-% -------------
-
-function testRunDemo(testCase)
-  % Run the correspondin demo and save the values. Note this test has to
-  % be run at lest once before the other test may succeed.
-  rundemo(getName())
-end
-
-function testPredictionsMultinom(testCase)
-  verifyVarsEqual(testCase, getName(), {'Eft', 'pyt2'}, 'AbsTol', 0.1)
-end
-
-
-% ------------------------
-%     Helper functions
-% ------------------------
-
-function testCase = setup
-  % Helper function to suply empty array into variable testCase as an
-  % argument for each test function, if using xUnit package. Not to be
-  % used with built-in test framework.
-  testCase = [];
-end
-
-function name = getName
-  % Helperfunction that returns the name of the demo, e.g. 'binomial1'.
-  name = mfilename;
-  name = name(6:end);
-end
-
diff --git a/tests/test_neuralnetcov.m b/tests/test_neuralnetcov.m
deleted file mode 100644
index 97ee8713..00000000
--- a/tests/test_neuralnetcov.m
+++ /dev/null
@@ -1,71 +0,0 @@
-function test_suite = test_neuralnetcov
-
-%   Run specific demo, save values and compare the results to the expected.
-%   Works for both xUnit Test Framework package by Steve Eddins and for
-%   the built-in Unit Testing Framework (as of Matlab version 2013b).
-%
-%   See also
-%     TEST_ALL, DEMO_NEURALNETCOV
-%
-% Copyright (c) 2011-2012 Ville Tolvanen
-% Copyright (c) 2014 Tuomas Sivula
-
-% This software is distributed under the GNU General Public 
-% License (version 3 or later); please refer to the file 
-% License.txt, included with the software, for details.
-  
-  % Check which package to use
-  if exist('initTestSuite', 'file')
-    initTestSuite_path = which('initTestSuite');
-  else
-    initTestSuite_path = '';
-  end
-  if ~isempty(initTestSuite_path) && ...
-     exist([initTestSuite_path(1:end-15) 'runtests'], 'file')
-    % xUnit package
-    initTestSuite;
-  else
-    % Built-in package
-    % Use all functions except the @setup
-    tests = localfunctions;
-    tests = tests(~cellfun(@(x)strcmp(func2str(x),'setup'),tests));
-    test_suite = functiontests(tests);
-  end
-end
-
-
-% -------------
-%     Tests
-% -------------
-
-function testRunDemo(testCase)
-  % Run the correspondin demo and save the values. Note this test has to
-  % be run at lest once before the other test may succeed.
-  rundemo(getName())
-end
-
-function testPredictions(testCase)
-  verifyVarsEqual(testCase, getName(), ...
-    {'Eft_map', 'Eft_map2','Varft_map','Varft_map2'}, ...
-    {@mean, @mean, @mean, @mean}, ...
-    'RelTolElement', 0.05, 'RelTolRange', 0.01)
-end
-
-
-% ------------------------
-%     Helper functions
-% ------------------------
-
-function testCase = setup
-  % Helper function to suply empty array into variable testCase as an
-  % argument for each test function, if using xUnit package. Not to be
-  % used with built-in test framework.
-  testCase = [];
-end
-
-function name = getName
-  % Helperfunction that returns the name of the demo, e.g. 'binomial1'.
-  name = mfilename;
-  name = name(6:end);
-end
-
diff --git a/tests/test_periodic.m b/tests/test_periodic.m
deleted file mode 100644
index a6a30df3..00000000
--- a/tests/test_periodic.m
+++ /dev/null
@@ -1,76 +0,0 @@
-function test_suite = test_periodic
-
-%   Run specific demo, save values and compare the results to the expected.
-%   Works for both xUnit Test Framework package by Steve Eddins and for
-%   the built-in Unit Testing Framework (as of Matlab version 2013b).
-%
-%   See also
-%     TEST_ALL, DEMO_PERIODIC
-%
-% Copyright (c) 2011-2012 Ville Tolvanen
-% Copyright (c) 2014 Tuomas Sivula
-
-% This software is distributed under the GNU General Public 
-% License (version 3 or later); please refer to the file 
-% License.txt, included with the software, for details.
-  
-  % Check which package to use
-  if exist('initTestSuite', 'file')
-    initTestSuite_path = which('initTestSuite');
-  else
-    initTestSuite_path = '';
-  end
-  if ~isempty(initTestSuite_path) && ...
-     exist([initTestSuite_path(1:end-15) 'runtests'], 'file')
-    % xUnit package
-    initTestSuite;
-  else
-    % Built-in package
-    % Use all functions except the @setup
-    tests = localfunctions;
-    tests = tests(~cellfun(@(x)strcmp(func2str(x),'setup'),tests));
-    test_suite = functiontests(tests);
-  end
-end
-
-
-% -------------
-%     Tests
-% -------------
-
-function testRunDemo(testCase)
-  % Run the correspondin demo and save the values. Note this test has to
-  % be run at lest once before the other test may succeed.
-  rundemo(getName())
-end
-
-function testPredictionsMaunaLoa(testCase)
-  verifyVarsEqual(testCase, getName(), ...
-    {'Eft_full1', 'Eft_full2','Varft_full1','Varft_full2'}, @mean, ...
-    'RelTolElement', 0.05, 'RelTolRange', 0.01)
-end
-
-function testPredictionsDrowning(testCase)
-  verifyVarsEqual(testCase, getName(), ...
-    {'Eft_full', 'Varft_full'}, @mean, ...
-    'RelTolElement', 0.05, 'RelTolRange', 0.01)
-end
-
-
-% ------------------------
-%     Helper functions
-% ------------------------
-
-function testCase = setup
-  % Helper function to suply empty array into variable testCase as an
-  % argument for each test function, if using xUnit package. Not to be
-  % used with built-in test framework.
-  testCase = [];
-end
-
-function name = getName
-  % Helperfunction that returns the name of the demo, e.g. 'binomial1'.
-  name = mfilename;
-  name = name(6:end);
-end
-
diff --git a/tests/test_quantilegp.m b/tests/test_quantilegp.m
deleted file mode 100644
index 6b7b3db7..00000000
--- a/tests/test_quantilegp.m
+++ /dev/null
@@ -1,75 +0,0 @@
-function test_suite = test_quantilegp
-
-%   Run specific demo, save values and compare the results to the expected.
-%   Works for both xUnit Test Framework package by Steve Eddins and for
-%   the built-in Unit Testing Framework (as of Matlab version 2013b).
-%
-%   See also
-%     TEST_ALL, DEMO_QUANTILEGP
-%
-% Copyright (c) 2011-2012 Ville Tolvanen
-% Copyright (c) 2014 Tuomas Sivula
-
-% This software is distributed under the GNU General Public 
-% License (version 3 or later); please refer to the file 
-% License.txt, included with the software, for details.
-  
-  % Check which package to use
-  if exist('initTestSuite', 'file')
-    initTestSuite_path = which('initTestSuite');
-  else
-    initTestSuite_path = '';
-  end
-  if ~isempty(initTestSuite_path) && ...
-     exist([initTestSuite_path(1:end-15) 'runtests'], 'file')
-    % xUnit package
-    initTestSuite;
-  else
-    % Built-in package
-    % Use all functions except the @setup
-    tests = localfunctions;
-    tests = tests(~cellfun(@(x)strcmp(func2str(x),'setup'),tests));
-    test_suite = functiontests(tests);
-  end
-end
-
-
-% -------------
-%     Tests
-% -------------
-
-function testRunDemo(testCase)
-  % Run the correspondin demo and save the values. Note this test has to
-  % be run at lest once before the other test may succeed.
-  rundemo(getName())
-end
-
-function testPredictiveMeanAndVariance_EP(testCase)
-  verifyVarsEqual(testCase, getName(), {'Ef4','Ef5','Varf4','Varf5'}, ...
-    'RelTolElement', 0.1, 'RelTolRange', 0.02)
-end
-
-function testPredictiveMean_MCMC(testCase)
-  verifyVarsEqual(testCase, getName(), {'Ef1','Ef2','Ef3'}, ...
-    'RelTolRange', 0.1)
-end
-
-
-% ------------------------
-%     Helper functions
-% ------------------------
-
-function testCase = setup
-  % Helper function to suply empty array into variable testCase as an
-  % argument for each test function, if using xUnit package. Not to be
-  % used with built-in test framework.
-  testCase = [];
-end
-
-function name = getName
-  % Helperfunction that returns the name of the demo, e.g. 'binomial1'.
-  name = mfilename;
-  name = name(6:end);
-end
-
-    
\ No newline at end of file
diff --git a/tests/test_regression1.m b/tests/test_regression1.m
deleted file mode 100644
index eed40ee6..00000000
--- a/tests/test_regression1.m
+++ /dev/null
@@ -1,101 +0,0 @@
-function test_suite = test_regression1
-
-%   Run specific demo, save values and compare the results to the expected.
-%   Works for both xUnit Test Framework package by Steve Eddins and for
-%   the built-in Unit Testing Framework (as of Matlab version 2013b).
-%
-%   See also
-%     TEST_ALL, DEMO_REGRESSION1
-%
-% Copyright (c) 2011-2012 Ville Tolvanen
-% Copyright (c) 2014 Tuomas Sivula
-
-% This software is distributed under the GNU General Public 
-% License (version 3 or later); please refer to the file 
-% License.txt, included with the software, for details.
-  
-  % Check which package to use
-  if exist('initTestSuite', 'file')
-    initTestSuite_path = which('initTestSuite');
-  else
-    initTestSuite_path = '';
-  end
-  if ~isempty(initTestSuite_path) && ...
-     exist([initTestSuite_path(1:end-15) 'runtests'], 'file')
-    % xUnit package
-    initTestSuite;
-  else
-    % Built-in package
-    % Use all functions except the @setup
-    tests = localfunctions;
-    tests = tests(~cellfun(@(x)strcmp(func2str(x),'setup'),tests));
-    test_suite = functiontests(tests);
-  end
-end
-
-
-% -------------
-%     Tests
-% -------------
-
-function testRunDemo(testCase)
-  % Run the correspondin demo and save the values. Note this test has to
-  % be run at lest once before the other test may succeed.
-  rundemo(getName(), { ...
-    'K', 'C', 'w', ...
-    {'Eft_map', @(x)x(1:50)}, {'Varft_map', @(x)x(1:50)}, ...
-    {'Eft_ia', @(x)x(1:50)}, {'Varft_ia', @(x)x(1:50)}, ...
-    {'Eft_mc', @(x)x(1:50)}, {'Varft_mc', @(x)x(1:50)} })
-end
-
-function testCovarianceMatrices(testCase)
-  % Test diagonal with 1e-7 tolerance (because of varying jitterSigma2)
-  verifyVarsEqual(testCase, getName(), {'K','C'}, ...
-    @(x)diag(x), 'AbsTol', 1e-7)
-  % Test non-diagonal elements
-  verifyVarsEqual(testCase, getName(), {'K','C'}, ...
-    @(x)x(~eye(size(x))), 'AbsTol', eps)
-end
-
-function testOptimizedParameter(testCase)
-  verifyVarsEqual(testCase, getName(), {'w'}, ...
-    'RelTolElement', 0.05)
-end
-
-function testPredictedMeanVarianceGrid(testCase)
-  verifyVarsEqual(testCase, getName(), ...
-    {'Eft_map','Varft_map'}, ...
-    'RelTolElement', 0.05, 'RelTolRange', 0.01)
-end
-
-function testPredictedMeanVarianceMC(testCase)
-  verifyVarsEqual(testCase, getName(), {'Eft_mc'}, ...
-    'RelTolElement', 0.1, 'RelTolRange', 0.02)
-  verifyVarsEqual(testCase, getName(), {'Varft_mc'}, ...
-    'AbsTol', 0.3)
-end
-
-function testPredictedMeanVarianceIA(testCase)
-  verifyVarsEqual(testCase, getName(), ...
-    {'Eft_ia','Varft_ia'}, ...
-    'RelTolElement', 0.1, 'RelTolRange', 0.02)
-end
-
-
-% ------------------------
-%     Helper functions
-% ------------------------
-
-function testCase = setup
-  % Helper function to suply empty array into variable testCase as an
-  % argument for each test function, if using xUnit package. Not to be
-  % used with built-in test framework.
-  testCase = [];
-end
-
-function name = getName
-  % Helperfunction that returns the name of the demo, e.g. 'binomial1'.
-  name = mfilename;
-  name = name(6:end);
-end
-
diff --git a/tests/test_regression_additive1.m b/tests/test_regression_additive1.m
deleted file mode 100644
index 24639997..00000000
--- a/tests/test_regression_additive1.m
+++ /dev/null
@@ -1,82 +0,0 @@
-function test_suite = test_regression_additive1
-
-%   Run specific demo, save values and compare the results to the expected.
-%   Works for both xUnit Test Framework package by Steve Eddins and for
-%   the built-in Unit Testing Framework (as of Matlab version 2013b).
-%
-%   See also
-%     TEST_ALL, DEMO_REGRESSION_ADDITIVE1
-%
-% Copyright (c) 2011-2012 Ville Tolvanen
-% Copyright (c) 2014 Tuomas Sivula
-
-% This software is distributed under the GNU General Public 
-% License (version 3 or later); please refer to the file 
-% License.txt, included with the software, for details.
-  
-  % Check which package to use
-  if exist('initTestSuite', 'file')
-    initTestSuite_path = which('initTestSuite');
-  else
-    initTestSuite_path = '';
-  end
-  if ~isempty(initTestSuite_path) && ...
-     exist([initTestSuite_path(1:end-15) 'runtests'], 'file')
-    % xUnit package
-    initTestSuite;
-  else
-    % Built-in package
-    % Use all functions except the @setup
-    tests = localfunctions;
-    tests = tests(~cellfun(@(x)strcmp(func2str(x),'setup'),tests));
-    test_suite = functiontests(tests);
-  end
-end
-
-
-% -------------
-%     Tests
-% -------------
-
-function testRunDemo(testCase)
-  % Run the correspondin demo and save the values. Note this test has to
-  % be run at lest once before the other test may succeed.
-  rundemo(getName())
-end
-
-function testPredictionsFIC(testCase)
-  verifyVarsEqual(testCase, getName(), ...
-    {'Eft_fic', 'Varft_fic'}, {[], @mean}, ...
-    'AbsTol', 0.1)
-end
-
-function testPredictionsPIC(testCase)
-  verifyVarsEqual(testCase, getName(), ...
-    {'Eft_pic', 'Varft_pic'}, ...
-    'AbsTol', 0.1)
-end
-
-function testPredictionsSparse(testCase)
-  verifyVarsEqual(testCase, getName(), ...
-    {'Eft_csfic', 'Varft_csfic'}, ...
-    'AbsTol', 0.1)
-end
-
-
-% ------------------------
-%     Helper functions
-% ------------------------
-
-function testCase = setup
-  % Helper function to suply empty array into variable testCase as an
-  % argument for each test function, if using xUnit package. Not to be
-  % used with built-in test framework.
-  testCase = [];
-end
-
-function name = getName
-  % Helperfunction that returns the name of the demo, e.g. 'binomial1'.
-  name = mfilename;
-  name = name(6:end);
-end
-
diff --git a/tests/test_regression_additive2.m b/tests/test_regression_additive2.m
deleted file mode 100644
index 21abf020..00000000
--- a/tests/test_regression_additive2.m
+++ /dev/null
@@ -1,69 +0,0 @@
-function test_suite = test_regression_additive2
-
-%   Run specific demo, save values and compare the results to the expected.
-%   Works for both xUnit Test Framework package by Steve Eddins and for
-%   the built-in Unit Testing Framework (as of Matlab version 2013b).
-%
-%   See also
-%     TEST_ALL, DEMO_REGRESSION_ADDITIVE2
-%
-% Copyright (c) 2011-2012 Ville Tolvanen
-% Copyright (c) 2014 Tuomas Sivula
-
-% This software is distributed under the GNU General Public 
-% License (version 3 or later); please refer to the file 
-% License.txt, included with the software, for details.
-  
-  % Check which package to use
-  if exist('initTestSuite', 'file')
-    initTestSuite_path = which('initTestSuite');
-  else
-    initTestSuite_path = '';
-  end
-  if ~isempty(initTestSuite_path) && ...
-     exist([initTestSuite_path(1:end-15) 'runtests'], 'file')
-    % xUnit package
-    initTestSuite;
-  else
-    % Built-in package
-    % Use all functions except the @setup
-    tests = localfunctions;
-    tests = tests(~cellfun(@(x)strcmp(func2str(x),'setup'),tests));
-    test_suite = functiontests(tests);
-  end
-end
-
-
-% -------------
-%     Tests
-% -------------
-
-function testRunDemo(testCase)
-  % Run the correspondin demo and save the values. Note this test has to
-  % be run at lest once before the other test may succeed.
-  rundemo(getName())
-end
-
-function testNeuralNetworkCFPrediction(testCase)
-  verifyVarsEqual(testCase, getName(), {'Eft_map'}, ...
-    'RelTolElement', 0.05, 'RelTolRange', 0.01)
-end
-
-
-% ------------------------
-%     Helper functions
-% ------------------------
-
-function testCase = setup
-  % Helper function to suply empty array into variable testCase as an
-  % argument for each test function, if using xUnit package. Not to be
-  % used with built-in test framework.
-  testCase = [];
-end
-
-function name = getName
-  % Helperfunction that returns the name of the demo, e.g. 'binomial1'.
-  name = mfilename;
-  name = name(6:end);
-end
-
diff --git a/tests/test_regression_hier.m b/tests/test_regression_hier.m
deleted file mode 100644
index 27dbf50d..00000000
--- a/tests/test_regression_hier.m
+++ /dev/null
@@ -1,69 +0,0 @@
-function test_suite = test_regression_hier
-
-%   Run specific demo, save values and compare the results to the expected.
-%   Works for both xUnit Test Framework package by Steve Eddins and for
-%   the built-in Unit Testing Framework (as of Matlab version 2013b).
-%
-%   See also
-%     TEST_ALL, DEMO_REGRESSION_HIER
-%
-% Copyright (c) 2011-2012 Ville Tolvanen
-% Copyright (c) 2014 Tuomas Sivula
-
-% This software is distributed under the GNU General Public 
-% License (version 3 or later); please refer to the file 
-% License.txt, included with the software, for details.
-  
-  % Check which package to use
-  if exist('initTestSuite', 'file')
-    initTestSuite_path = which('initTestSuite');
-  else
-    initTestSuite_path = '';
-  end
-  if ~isempty(initTestSuite_path) && ...
-     exist([initTestSuite_path(1:end-15) 'runtests'], 'file')
-    % xUnit package
-    initTestSuite;
-  else
-    % Built-in package
-    % Use all functions except the @setup
-    tests = localfunctions;
-    tests = tests(~cellfun(@(x)strcmp(func2str(x),'setup'),tests));
-    test_suite = functiontests(tests);
-  end
-end
-
-
-% -------------
-%     Tests
-% -------------
-
-function testRunDemo(testCase)
-  % Run the correspondin demo and save the values. Note this test has to
-  % be run at lest once before the other test may succeed.
-  rundemo(getName())
-end
-
-function testPredictionMissingData(testCase)
-  verifyVarsEqual(testCase, getName(), {'Eff'}, {@mean}, ...
-    'RelTolElement', 0.01, 'RelTolRange', 0.005)
-end
-
-
-% ------------------------
-%     Helper functions
-% ------------------------
-
-function testCase = setup
-  % Helper function to suply empty array into variable testCase as an
-  % argument for each test function, if using xUnit package. Not to be
-  % used with built-in test framework.
-  testCase = [];
-end
-
-function name = getName
-  % Helperfunction that returns the name of the demo, e.g. 'binomial1'.
-  name = mfilename;
-  name = name(6:end);
-end
-
diff --git a/tests/test_regression_meanf.m b/tests/test_regression_meanf.m
deleted file mode 100644
index a9b24c6c..00000000
--- a/tests/test_regression_meanf.m
+++ /dev/null
@@ -1,69 +0,0 @@
-function test_suite = test_regression_meanf
-
-%   Run specific demo, save values and compare the results to the expected.
-%   Works for both xUnit Test Framework package by Steve Eddins and for
-%   the built-in Unit Testing Framework (as of Matlab version 2013b).
-%
-%   See also
-%     TEST_ALL, DEMO_REGRESSION_MEANF
-%
-% Copyright (c) 2011-2012 Ville Tolvanen
-% Copyright (c) 2014 Tuomas Sivula
-
-% This software is distributed under the GNU General Public 
-% License (version 3 or later); please refer to the file 
-% License.txt, included with the software, for details.
-  
-  % Check which package to use
-  if exist('initTestSuite', 'file')
-    initTestSuite_path = which('initTestSuite');
-  else
-    initTestSuite_path = '';
-  end
-  if ~isempty(initTestSuite_path) && ...
-     exist([initTestSuite_path(1:end-15) 'runtests'], 'file')
-    % xUnit package
-    initTestSuite;
-  else
-    % Built-in package
-    % Use all functions except the @setup
-    tests = localfunctions;
-    tests = tests(~cellfun(@(x)strcmp(func2str(x),'setup'),tests));
-    test_suite = functiontests(tests);
-  end
-end
-
-
-% -------------
-%     Tests
-% -------------
-
-function testRunDemo(testCase)
-  % Run the correspondin demo and save the values. Note this test has to
-  % be run at lest once before the other test may succeed.
-  rundemo(getName())
-end
-
-function testPredictions(testCase)
-  verifyVarsEqual(testCase, getName(), {'Eft'}, {@mean}, ...
-    'RelTolElement', 0.1, 'RelTolRange', 0.02)
-end
-
-
-% ------------------------
-%     Helper functions
-% ------------------------
-
-function testCase = setup
-  % Helper function to suply empty array into variable testCase as an
-  % argument for each test function, if using xUnit package. Not to be
-  % used with built-in test framework.
-  testCase = [];
-end
-
-function name = getName
-  % Helperfunction that returns the name of the demo, e.g. 'binomial1'.
-  name = mfilename;
-  name = name(6:end);
-end
-
diff --git a/tests/test_regression_ppcs.m b/tests/test_regression_ppcs.m
deleted file mode 100644
index 0e394215..00000000
--- a/tests/test_regression_ppcs.m
+++ /dev/null
@@ -1,74 +0,0 @@
-function test_suite = test_regression_ppcs
-
-%   Run specific demo, save values and compare the results to the expected.
-%   Works for both xUnit Test Framework package by Steve Eddins and for
-%   the built-in Unit Testing Framework (as of Matlab version 2013b).
-%
-%   See also
-%     TEST_ALL, DEMO_REGRESSION_PPCS
-%
-% Copyright (c) 2011-2012 Ville Tolvanen
-% Copyright (c) 2014 Tuomas Sivula
-
-% This software is distributed under the GNU General Public 
-% License (version 3 or later); please refer to the file 
-% License.txt, included with the software, for details.
-  
-  % Check which package to use
-  if exist('initTestSuite', 'file')
-    initTestSuite_path = which('initTestSuite');
-  else
-    initTestSuite_path = '';
-  end
-  if ~isempty(initTestSuite_path) && ...
-     exist([initTestSuite_path(1:end-15) 'runtests'], 'file')
-    % xUnit package
-    initTestSuite;
-  else
-    % Built-in package
-    % Use all functions except the @setup
-    tests = localfunctions;
-    tests = tests(~cellfun(@(x)strcmp(func2str(x),'setup'),tests));
-    test_suite = functiontests(tests);
-  end
-end
-
-
-% -------------
-%     Tests
-% -------------
-
-function testRunDemo(testCase)
-  % Run the correspondin demo and save the values. Note this test has to
-  % be run at lest once before the other test may succeed.
-  rundemo(getName(), {{'K', @(x)x(1:50,1:50)}, {'Ef', @(x)x(1:100)}})
-end
-
-function testCovarianceMatrix(testCase)
-  verifyVarsEqual(testCase, getName(), {'K'}, {@(x)mean(full(x))}, ...
-    'RelTolElement', 0.1, 'RelTolRange', 0.02)
-end
-
-function testPrediction(testCase)
-  verifyVarsEqual(testCase, getName(), {'Ef'}, {@(x)mean(x)}, ...
-    'RelTolElement', 0.1, 'RelTolRange', 0.02)
-end
-
-
-% ------------------------
-%     Helper functions
-% ------------------------
-
-function testCase = setup
-  % Helper function to suply empty array into variable testCase as an
-  % argument for each test function, if using xUnit package. Not to be
-  % used with built-in test framework.
-  testCase = [];
-end
-
-function name = getName
-  % Helperfunction that returns the name of the demo, e.g. 'binomial1'.
-  name = mfilename;
-  name = name(6:end);
-end
-
diff --git a/tests/test_regression_robust.m b/tests/test_regression_robust.m
deleted file mode 100644
index d6d99b62..00000000
--- a/tests/test_regression_robust.m
+++ /dev/null
@@ -1,76 +0,0 @@
-function test_suite = test_regression_robust
-
-%   Run specific demo, save values and compare the results to the expected.
-%   Works for both xUnit Test Framework package by Steve Eddins and for
-%   the built-in Unit Testing Framework (as of Matlab version 2013b).
-%
-%   See also
-%     TEST_ALL, DEMO_REGRESSION_ROBUST
-%
-% Copyright (c) 2011-2012 Ville Tolvanen
-% Copyright (c) 2014 Tuomas Sivula
-
-% This software is distributed under the GNU General Public 
-% License (version 3 or later); please refer to the file 
-% License.txt, included with the software, for details.
-  
-  % Check which package to use
-  if exist('initTestSuite', 'file')
-    initTestSuite_path = which('initTestSuite');
-  else
-    initTestSuite_path = '';
-  end
-  if ~isempty(initTestSuite_path) && ...
-     exist([initTestSuite_path(1:end-15) 'runtests'], 'file')
-    % xUnit package
-    initTestSuite;
-  else
-    % Built-in package
-    % Use all functions except the @setup
-    tests = localfunctions;
-    tests = tests(~cellfun(@(x)strcmp(func2str(x),'setup'),tests));
-    test_suite = functiontests(tests);
-  end
-end
-
-
-% -------------
-%     Tests
-% -------------
-
-function testRunDemo(testCase)
-  % Run the correspondin demo and save the values. Note this test has to
-  % be run at lest once before the other test may succeed.
-  rundemo(getName(), {'Eft', 'Varft', {'w', @gp_pak, 'rr'}})
-end
-
-function testPredictionEP(testCase)
-  verifyVarsEqual(testCase, getName(), {'Eft', 'Varft'}, @mean, ...
-    'RelTolElement', 0.05, 'RelTolRange', 0.01)
-end
-
-function testMCMCSamples(testCase)
-  verifyVarsEqual(testCase, getName(), {'w'}, @(x) mean(x(:,1)), ...
-    'AbsTol', 1)
-  verifyVarsEqual(testCase, getName(), {'w'}, @(x) mean(x(:,[2,3])), ...
-    'AbsTol', 0.5)
-end
-
-
-% ------------------------
-%     Helper functions
-% ------------------------
-
-function testCase = setup
-  % Helper function to suply empty array into variable testCase as an
-  % argument for each test function, if using xUnit package. Not to be
-  % used with built-in test framework.
-  testCase = [];
-end
-
-function name = getName
-  % Helperfunction that returns the name of the demo, e.g. 'binomial1'.
-  name = mfilename;
-  name = name(6:end);
-end
-
diff --git a/tests/test_regression_sparse1.m b/tests/test_regression_sparse1.m
deleted file mode 100644
index 796bbccf..00000000
--- a/tests/test_regression_sparse1.m
+++ /dev/null
@@ -1,89 +0,0 @@
-function test_suite = test_regression_sparse1
-
-%   Run specific demo, save values and compare the results to the expected.
-%   Works for both xUnit Test Framework package by Steve Eddins and for
-%   the built-in Unit Testing Framework (as of Matlab version 2013b).
-%
-%   See also
-%     TEST_ALL, DEMO_REGRESSION_SPARSE1
-%
-% Copyright (c) 2011-2012 Ville Tolvanen
-% Copyright (c) 2014 Tuomas Sivula
-
-% This software is distributed under the GNU General Public 
-% License (version 3 or later); please refer to the file 
-% License.txt, included with the software, for details.
-  
-  % Check which package to use
-  if exist('initTestSuite', 'file')
-    initTestSuite_path = which('initTestSuite');
-  else
-    initTestSuite_path = '';
-  end
-  if ~isempty(initTestSuite_path) && ...
-     exist([initTestSuite_path(1:end-15) 'runtests'], 'file')
-    % xUnit package
-    initTestSuite;
-  else
-    % Built-in package
-    % Use all functions except the @setup
-    tests = localfunctions;
-    tests = tests(~cellfun(@(x)strcmp(func2str(x),'setup'),tests));
-    test_suite = functiontests(tests);
-  end
-end
-
-
-% -------------
-%     Tests
-% -------------
-
-function testRunDemo(testCase)
-  % Run the correspondin demo and save the values. Note this test has to
-  % be run at lest once before the other test may succeed.
-  rundemo(getName())
-end
-
-function testPredictionsCS(testCase)
-  verifyVarsEqual(testCase, getName(), {'Eft_cs'}, ...
-    'RelTolRange', 0.02)
-end
-
-function testPredictionsFIC(testCase)
-  verifyVarsEqual(testCase, getName(), {'Eft_fic'}, ...
-    'RelTolRange', 0.02)
-end
-
-function testPredictionsPIC(testCase)
-  verifyVarsEqual(testCase, getName(), {'Eft_pic'}, ...
-    'RelTolRange', 0.02)
-end
-
-function testPredictionsVAR(testCase)
-  verifyVarsEqual(testCase, getName(), {'Eft_var'}, ...
-    'RelTolRange', 0.02)
-end
-
-function testPredictionsDTC(testCase)
-  verifyVarsEqual(testCase, getName(), {'Eft_dtc'}, ...
-    'RelTolRange', 0.02)
-end
-
-
-% ------------------------
-%     Helper functions
-% ------------------------
-
-function testCase = setup
-  % Helper function to suply empty array into variable testCase as an
-  % argument for each test function, if using xUnit package. Not to be
-  % used with built-in test framework.
-  testCase = [];
-end
-
-function name = getName
-  % Helperfunction that returns the name of the demo, e.g. 'binomial1'.
-  name = mfilename;
-  name = name(6:end);
-end
-
diff --git a/tests/test_regression_sparse2.m b/tests/test_regression_sparse2.m
deleted file mode 100644
index 02228017..00000000
--- a/tests/test_regression_sparse2.m
+++ /dev/null
@@ -1,78 +0,0 @@
-function test_suite = test_regression_sparse2
-
-%   Run specific demo, save values and compare the results to the expected.
-%   Works for both xUnit Test Framework package by Steve Eddins and for
-%   the built-in Unit Testing Framework (as of Matlab version 2013b).
-%
-%   See also
-%     TEST_ALL, DEMO_REGRESSION_SPARSE2
-%
-% Copyright (c) 2011-2012 Ville Tolvanen
-% Copyright (c) 2014 Tuomas Sivula
-
-% This software is distributed under the GNU General Public 
-% License (version 3 or later); please refer to the file 
-% License.txt, included with the software, for details.
-  
-  % Check which package to use
-  if exist('initTestSuite', 'file')
-    initTestSuite_path = which('initTestSuite');
-  else
-    initTestSuite_path = '';
-  end
-  if ~isempty(initTestSuite_path) && ...
-     exist([initTestSuite_path(1:end-15) 'runtests'], 'file')
-    % xUnit package
-    initTestSuite;
-  else
-    % Built-in package
-    % Use all functions except the @setup
-    tests = localfunctions;
-    tests = tests(~cellfun(@(x)strcmp(func2str(x),'setup'),tests));
-    test_suite = functiontests(tests);
-  end
-end
-
-
-% -------------
-%     Tests
-% -------------
-
-function testRunDemo(testCase)
-  % Run the correspondin demo and save the values. Note this test has to
-  % be run at lest once before the other test may succeed.
-  rundemo(getName(), { ...
-    {'Eft_full', @(x)x(1:100)}, {'Varft_full', @(x)x(1:100)}, ...
-    {'Eft_var', @(x)x(1:100)}, {'Varft_var', @(x)x(1:100)} })
-end
-
-function testPredictionsFull(testCase)
-  verifyVarsEqual(testCase, getName(), ...
-    {'Eft_full', 'Varft_full'}, @mean, ...
-    'RelTolElement', 0.1)
-end
-
-function testPredictionsVar(testCase)
-  verifyVarsEqual(testCase, getName(), ...
-    {'Eft_var', 'Varft_var'}, ...
-    'RelTolElement', 0.1, 'RelTolRange', 0.02)
-end
-
-
-% ------------------------
-%     Helper functions
-% ------------------------
-
-function testCase = setup
-  % Helper function to suply empty array into variable testCase as an
-  % argument for each test function, if using xUnit package. Not to be
-  % used with built-in test framework.
-  testCase = [];
-end
-
-function name = getName
-  % Helperfunction that returns the name of the demo, e.g. 'binomial1'.
-  name = mfilename;
-  name = name(6:end);
-end
-
diff --git a/tests/test_spatial1.m b/tests/test_spatial1.m
deleted file mode 100644
index fbdd98a0..00000000
--- a/tests/test_spatial1.m
+++ /dev/null
@@ -1,75 +0,0 @@
-function test_suite = test_spatial1
-
-%   Run specific demo, save values and compare the results to the expected.
-%   Works for both xUnit Test Framework package by Steve Eddins and for
-%   the built-in Unit Testing Framework (as of Matlab version 2013b).
-%
-%   See also
-%     TEST_ALL, DEMO_SPATIAL1
-%
-% Copyright (c) 2011-2012 Ville Tolvanen
-% Copyright (c) 2014 Tuomas Sivula
-
-% This software is distributed under the GNU General Public 
-% License (version 3 or later); please refer to the file 
-% License.txt, included with the software, for details.
-  
-  % Check which package to use
-  if exist('initTestSuite', 'file')
-    initTestSuite_path = which('initTestSuite');
-  else
-    initTestSuite_path = '';
-  end
-  if ~isempty(initTestSuite_path) && ...
-     exist([initTestSuite_path(1:end-15) 'runtests'], 'file')
-    % xUnit package
-    initTestSuite;
-  else
-    % Built-in package
-    % Use all functions except the @setup
-    tests = localfunctions;
-    tests = tests(~cellfun(@(x)strcmp(func2str(x),'setup'),tests));
-    test_suite = functiontests(tests);
-  end
-end
-
-
-% -------------
-%     Tests
-% -------------
-
-function testRunDemo(testCase)
-  % Run the correspondin demo and save the values. Note this test has to
-  % be run at lest once before the other test may succeed.
-  rundemo(getName())
-end
-
-function testEstimatesIA(testCase)
-  verifyVarsEqual(testCase, getName(), {'Elth', 'Elth2'}, ...
-    'RelTolElement', 0.1, 'RelTolRange', 0.02)
-end
-
-function testPredictionIA(testCase)
-  verifyVarsEqual(testCase, getName(), {'Ef', 'Varf'}, ...
-    @(x)mean(x(1:100)), ...
-    'RelTolElement', 0.1, 'RelTolRange', 0.02)
-end
-
-
-% ------------------------
-%     Helper functions
-% ------------------------
-
-function testCase = setup
-  % Helper function to suply empty array into variable testCase as an
-  % argument for each test function, if using xUnit package. Not to be
-  % used with built-in test framework.
-  testCase = [];
-end
-
-function name = getName
-  % Helperfunction that returns the name of the demo, e.g. 'binomial1'.
-  name = mfilename;
-  name = name(6:end);
-end
-
diff --git a/tests/test_spatial2.m b/tests/test_spatial2.m
deleted file mode 100644
index 63d5900c..00000000
--- a/tests/test_spatial2.m
+++ /dev/null
@@ -1,76 +0,0 @@
-function test_suite = test_spatial2
-
-%   Run specific demo, save values and compare the results to the expected.
-%   Works for both xUnit Test Framework package by Steve Eddins and for
-%   the built-in Unit Testing Framework (as of Matlab version 2013b).
-%
-%   See also
-%     TEST_ALL, DEMO_SPATIAL2
-%
-% Copyright (c) 2011-2012 Ville Tolvanen
-% Copyright (c) 2014 Tuomas Sivula
-
-% This software is distributed under the GNU General Public 
-% License (version 3 or later); please refer to the file 
-% License.txt, included with the software, for details.
-  
-  % Check which package to use
-  if exist('initTestSuite', 'file')
-    initTestSuite_path = which('initTestSuite');
-  else
-    initTestSuite_path = '';
-  end
-  if ~isempty(initTestSuite_path) && ...
-     exist([initTestSuite_path(1:end-15) 'runtests'], 'file')
-    % xUnit package
-    initTestSuite;
-  else
-    % Built-in package
-    % Use all functions except the @setup
-    tests = localfunctions;
-    tests = tests(~cellfun(@(x)strcmp(func2str(x),'setup'),tests));
-    test_suite = functiontests(tests);
-  end
-end
-
-
-% -------------
-%     Tests
-% -------------
-
-function testRunDemo(testCase)
-  % Run the correspondin demo and save the values. Note this test has to
-  % be run at lest once before the other test may succeed.
-  rundemo(getName(), ...
-    {{'Ef', @(x)x(1:100)}, {'Varf', @(x)x(1:100)}, ...
-    {'C', @(x)x(1:50,1:50)}})
-end
-
-function testPredictionsEP(testCase)
-  verifyVarsEqual(testCase, getName(), {'Ef', 'Varf'}, @mean, ...
-    'RelTolElement', 0.1, 'RelTolRange', 0.02)
-end
-
-function testCovarianceMatrix(testCase)
-  verifyVarsEqual(testCase, getName(), {'C'}, @mean, ...
-    'RelTolElement', 0.1, 'RelTolRange', 0.02)
-end
-
-
-% ------------------------
-%     Helper functions
-% ------------------------
-
-function testCase = setup
-  % Helper function to suply empty array into variable testCase as an
-  % argument for each test function, if using xUnit package. Not to be
-  % used with built-in test framework.
-  testCase = [];
-end
-
-function name = getName
-  % Helperfunction that returns the name of the demo, e.g. 'binomial1'.
-  name = mfilename;
-  name = name(6:end);
-end
-
diff --git a/tests/test_survival_aft.m b/tests/test_survival_aft.m
deleted file mode 100644
index 55475b1a..00000000
--- a/tests/test_survival_aft.m
+++ /dev/null
@@ -1,73 +0,0 @@
-function test_suite = test_survival_aft
-
-%   Run specific demo, save values and compare the results to the expected.
-%   Works for both xUnit Test Framework package by Steve Eddins and for
-%   the built-in Unit Testing Framework (as of Matlab version 2013b).
-%
-%   See also
-%     TEST_ALL, DEMO_SURVIVAL_AFT
-%
-% Copyright (c) 2011-2012 Ville Tolvanen
-% Copyright (c) 2014 Tuomas Sivula
-
-% This software is distributed under the GNU General Public 
-% License (version 3 or later); please refer to the file 
-% License.txt, included with the software, for details.
-  
-  % Check which package to use
-  if exist('initTestSuite', 'file')
-    initTestSuite_path = which('initTestSuite');
-  else
-    initTestSuite_path = '';
-  end
-  if ~isempty(initTestSuite_path) && ...
-     exist([initTestSuite_path(1:end-15) 'runtests'], 'file')
-    % xUnit package
-    initTestSuite;
-  else
-    % Built-in package
-    % Use all functions except the @setup
-    tests = localfunctions;
-    tests = tests(~cellfun(@(x)strcmp(func2str(x),'setup'),tests));
-    test_suite = functiontests(tests);
-  end
-end
-
-
-% -------------
-%     Tests
-% -------------
-
-function testRunDemo(testCase)
-  % Run the correspondin demo and save the values. Note this test has to
-  % be run at lest once before the other test may succeed.
-  rundemo(getName())
-end
-
-function testPredictions(testCase)
-  verifyVarsEqual(testCase, getName(), {'pmu'}, {@(x)x(:,1)}, ...
-    'RelTolElement', 0.05, 'RelTolRange', 0.01)
-  verifyVarsEqual(testCase, getName(), {'pmu'}, {@(x)x(:,2)}, ...
-    'RelTolElement', 0.05, 'RelTolRange', 0.01)
-  verifyVarsEqual(testCase, getName(), {'pmu'}, {@(x)x(:,3)}, ...
-    'RelTolElement', 0.05, 'RelTolRange', 0.01)
-end
-
-
-% ------------------------
-%     Helper functions
-% ------------------------
-
-function testCase = setup
-  % Helper function to suply empty array into variable testCase as an
-  % argument for each test function, if using xUnit package. Not to be
-  % used with built-in test framework.
-  testCase = [];
-end
-
-function name = getName
-  % Helperfunction that returns the name of the demo, e.g. 'binomial1'.
-  name = mfilename;
-  name = name(6:end);
-end
-
diff --git a/tests/test_survival_coxph.m b/tests/test_survival_coxph.m
deleted file mode 100644
index 82f10636..00000000
--- a/tests/test_survival_coxph.m
+++ /dev/null
@@ -1,69 +0,0 @@
-function test_suite = test_survival_coxph
-
-%   Run specific demo, save values and compare the results to the expected.
-%   Works for both xUnit Test Framework package by Steve Eddins and for
-%   the built-in Unit Testing Framework (as of Matlab version 2013b).
-%
-%   See also
-%     TEST_ALL, DEMO_SURVIVAL_COXPH
-%
-% Copyright (c) 2011-2012 Ville Tolvanen
-% Copyright (c) 2014 Tuomas Sivula
-
-% This software is distributed under the GNU General Public 
-% License (version 3 or later); please refer to the file 
-% License.txt, included with the software, for details.
-  
-  % Check which package to use
-  if exist('initTestSuite', 'file')
-    initTestSuite_path = which('initTestSuite');
-  else
-    initTestSuite_path = '';
-  end
-  if ~isempty(initTestSuite_path) && ...
-     exist([initTestSuite_path(1:end-15) 'runtests'], 'file')
-    % xUnit package
-    initTestSuite;
-  else
-    % Built-in package
-    % Use all functions except the @setup
-    tests = localfunctions;
-    tests = tests(~cellfun(@(x)strcmp(func2str(x),'setup'),tests));
-    test_suite = functiontests(tests);
-  end
-end
-
-
-% -------------
-%     Tests
-% -------------
-
-function testRunDemo(testCase)
-  % Run the correspondin demo and save the values. Note this test has to
-  % be run at lest once before the other test may succeed.
-  rundemo(getName())
-end
-
-function testPredictionsCoxph(testCase)
-  verifyVarsEqual(testCase, getName(), {'Ef','Varf'}, ...
-    'AbsTol', 0.1)
-end
-
-
-% ------------------------
-%     Helper functions
-% ------------------------
-
-function testCase = setup
-  % Helper function to suply empty array into variable testCase as an
-  % argument for each test function, if using xUnit package. Not to be
-  % used with built-in test framework.
-  testCase = [];
-end
-
-function name = getName
-  % Helperfunction that returns the name of the demo, e.g. 'binomial1'.
-  name = mfilename;
-  name = name(6:end);
-end
-
diff --git a/tests/test_svi_classific.m b/tests/test_svi_classific.m
deleted file mode 100644
index ff7f51b9..00000000
--- a/tests/test_svi_classific.m
+++ /dev/null
@@ -1,74 +0,0 @@
-function test_suite = test_svi_classific
-
-%   Run specific demo, save values and compare the results to the expected.
-%   Works for both xUnit Test Framework package by Steve Eddins and for
-%   the built-in Unit Testing Framework (as of Matlab version 2013b).
-%
-%   See also
-%     TEST_ALL, DEMO_SVI_CLASSIFIC
-%
-% Copyright (c) 2011-2012 Ville Tolvanen
-% Copyright (c) 2014 Tuomas Sivula
-
-% This software is distributed under the GNU General Public 
-% License (version 3 or later); please refer to the file 
-% License.txt, included with the software, for details.
-  
-  % Check which package to use
-  if exist('initTestSuite', 'file')
-    initTestSuite_path = which('initTestSuite');
-  else
-    initTestSuite_path = '';
-  end
-  if ~isempty(initTestSuite_path) && ...
-     exist([initTestSuite_path(1:end-15) 'runtests'], 'file')
-    % xUnit package
-    initTestSuite;
-  else
-    % Built-in package
-    % Use all functions except the @setup
-    tests = localfunctions;
-    tests = tests(~cellfun(@(x)strcmp(func2str(x),'setup'),tests));
-    test_suite = functiontests(tests);
-  end
-end
-
-
-% -------------
-%     Tests
-% -------------
-
-function testRunDemo(testCase)
-  % Run the correspondin demo and save the values. Note this test has to
-  % be run at lest once before the other test may succeed.
-  rundemo(getName(), {'lpyt', {'w', @(x)gp_pak(x), 'gp'} })
-end
-
-function testPrediction(testCase)
-  verifyVarsEqual(testCase, getName(), {'lpyt'}, ...
-    'RelTolElement', 0.05, 'RelTolRange', 0.01)
-end
-
-function testParameters(testCase)
-  verifyVarsEqual(testCase, getName(), {'w'}, ...
-    'RelTolElement', 0.05, 'RelTolRange', 0.01)
-end
-
-
-% ------------------------
-%     Helper functions
-% ------------------------
-
-function testCase = setup
-  % Helper function to suply empty array into variable testCase as an
-  % argument for each test function, if using xUnit package. Not to be
-  % used with built-in test framework.
-  testCase = [];
-end
-
-function name = getName
-  % Helperfunction that returns the name of the demo, e.g. 'binomial1'.
-  name = mfilename;
-  name = name(6:end);
-end
-
diff --git a/tests/test_svi_regression.m b/tests/test_svi_regression.m
deleted file mode 100644
index 176829f3..00000000
--- a/tests/test_svi_regression.m
+++ /dev/null
@@ -1,85 +0,0 @@
-function test_suite = test_svi_regression
-
-%   Run specific demo, save values and compare the results to the expected.
-%   Works for both xUnit Test Framework package by Steve Eddins and for
-%   the built-in Unit Testing Framework (as of Matlab version 2013b).
-%
-%   See also
-%     TEST_ALL, DEMO_SVI_REGRESSION
-%
-% Copyright (c) 2011-2012 Ville Tolvanen
-% Copyright (c) 2014 Tuomas Sivula
-
-% This software is distributed under the GNU General Public 
-% License (version 3 or later); please refer to the file 
-% License.txt, included with the software, for details.
-  
-  % Check which package to use
-  if exist('initTestSuite', 'file')
-    initTestSuite_path = which('initTestSuite');
-  else
-    initTestSuite_path = '';
-  end
-  if ~isempty(initTestSuite_path) && ...
-     exist([initTestSuite_path(1:end-15) 'runtests'], 'file')
-    % xUnit package
-    initTestSuite;
-  else
-    % Built-in package
-    % Use all functions except the @setup
-    tests = localfunctions;
-    tests = tests(~cellfun(@(x)strcmp(func2str(x),'setup'),tests));
-    test_suite = functiontests(tests);
-  end
-end
-
-
-% -------------
-%     Tests
-% -------------
-
-function testRunDemo(testCase)
-  % Run the correspondin demo and save the values. Note this test has to
-  % be run at lest once before the other test may succeed.
-  rundemo(getName(), {...
-    {'e_final', @(x)x.e(end), 'diagnosis'}, ...
-    {'mlpd_final', @(x)x.mlpd(end), 'diagnosis'}, ...
-    {'rmse_final', @(x)x.rmse(end), 'diagnosis'}, ...
-    {'Eft', @(x)x([411:451, 1231:1271])}, ...
-    {'w', @(x)gp_pak(x), 'gp'} })
-end
-
-function testConvergence(testCase)
-  verifyVarsEqual(testCase, getName(), ...
-    {'e_final', 'mlpd_final', 'rmse_final'}, ...
-    'RelTolElement', 0.1)
-end
-
-function testMean(testCase)
-  verifyVarsEqual(testCase, getName(), {'Eft'}, ...
-    'RelTolElement', 0.05, 'RelTolRange', 0.01)
-end
-
-function testParameters(testCase)
-  verifyVarsEqual(testCase, getName(), {'w'}, ...
-    'RelTolElement', 0.05, 'RelTolRange', 0.01)
-end
-
-
-% ------------------------
-%     Helper functions
-% ------------------------
-
-function testCase = setup
-  % Helper function to suply empty array into variable testCase as an
-  % argument for each test function, if using xUnit package. Not to be
-  % used with built-in test framework.
-  testCase = [];
-end
-
-function name = getName
-  % Helperfunction that returns the name of the demo, e.g. 'binomial1'.
-  name = mfilename;
-  name = name(6:end);
-end
-
diff --git a/tests/test_zinegbin.m b/tests/test_zinegbin.m
deleted file mode 100644
index 7131b353..00000000
--- a/tests/test_zinegbin.m
+++ /dev/null
@@ -1,70 +0,0 @@
-function test_suite = test_zinegbin
-
-%   Run specific demo, save values and compare the results to the expected.
-%   Works for both xUnit Test Framework package by Steve Eddins and for
-%   the built-in Unit Testing Framework (as of Matlab version 2013b).
-%
-%   See also
-%     TEST_ALL, DEMO_ZINEGBIN
-%
-% Copyright (c) 2011-2012 Ville Tolvanen
-% Copyright (c) 2014 Tuomas Sivula
-
-% This software is distributed under the GNU General Public 
-% License (version 3 or later); please refer to the file 
-% License.txt, included with the software, for details.
-  
-  % Check which package to use
-  if exist('initTestSuite', 'file')
-    initTestSuite_path = which('initTestSuite');
-  else
-    initTestSuite_path = '';
-  end
-  if ~isempty(initTestSuite_path) && ...
-     exist([initTestSuite_path(1:end-15) 'runtests'], 'file')
-    % xUnit package
-    initTestSuite;
-  else
-    % Built-in package
-    % Use all functions except the @setup
-    tests = localfunctions;
-    tests = tests(~cellfun(@(x)strcmp(func2str(x),'setup'),tests));
-    test_suite = functiontests(tests);
-  end
-end
-
-
-% -------------
-%     Tests
-% -------------
-
-function testRunDemo(testCase)
-  % Run the correspondin demo and save the values. Note this test has to
-  % be run at lest once before the other test may succeed.
-  rundemo(getName(), ...
-    {{'Ef' @(x)x(1:100)}, {'Varf', @(x)diag(x(1:100,1:100))}})
-end
-
-function testPredictionsZinegbin(testCase)
-  verifyVarsEqual(testCase, getName(), {'Ef', 'Varf'}, ...
-    'RelTolElement', 0.1, 'RelTolRange', 0.02)
-end
-
-
-% ------------------------
-%     Helper functions
-% ------------------------
-
-function testCase = setup
-  % Helper function to suply empty array into variable testCase as an
-  % argument for each test function, if using xUnit package. Not to be
-  % used with built-in test framework.
-  testCase = [];
-end
-
-function name = getName
-  % Helperfunction that returns the name of the demo, e.g. 'binomial1'.
-  name = mfilename;
-  name = name(6:end);
-end
-
diff --git a/tests/verifyVarsEqual.m b/tests/verifyVarsEqual.m
deleted file mode 100644
index 5988534f..00000000
--- a/tests/verifyVarsEqual.m
+++ /dev/null
@@ -1,245 +0,0 @@
-function verifyVarsEqual(testCase, name, varargin)
-%verifyVarsEqual  Verify the equality of demo results within tolerance.
-%
-%  Description
-%    verifyVarsEqual(testCase, name, vars, funcs, OPTIONS)
-%    compares the elements in the results of a demo into the expected ones
-%    using relative tolerance to each element individually and/or to the
-%    range of the expected array and/or using absolute tolerance. The data
-%    is loaded from the 'realValues' and 'testValues' folders. Used by the
-%    gpstuff tests. Can be used with xUnit Test Framework package by Steve
-%    Eddins or with the Matlab's built-in Unit Testing Framework (as of
-%    version 2013b).
-%
-%    Parameters:
-%      testCase
-%        The testCase object, if using the integrated unit test framework,
-%        or empty array, if using the xUnit package.
-%      name
-%        The name of the demo.
-%      vars (optional)
-%        Cell array of strings defining the numerical array variables that
-%        are compared. Giving string 'same' (default) looks the names of
-%        the saved variables in the file 'realValues/<nameOfTheDemo>.mat'
-%        and compares them.
-%      funcs (optional)
-%        Cell array of function handles that are applied into the
-%        corresponding actual and expected arrays before comparison. Cell
-%        containing an empty array indicates that no function is used for
-%        that variable. Empty array in general indicates that no function
-%        is applied for any variable. Providing a single function handle
-%        applies the function to all variables.
-%
-%    OPTIONS is optional parameter-value pair
-%      RelTolElement - tolerance relative to the magnitude of each element
-%                      (default 0.05)
-%      RelTolRange   - tolerance relative to the range of the elements
-%                      (default 0.01)
-%      AbsTol        - absolute tolerance (default 0)
-%
-%   See also
-%     TEST_*
-%
-% Copyright (c) 2014 Tuomas Sivula
-
-% This software is distributed under the GNU General Public 
-% License (version 3 or later); please refer to the file 
-% License.txt, included with the software, for details.
-
-% Parse inputs
-ip = inputParser;
-ip.FunctionName = 'verifyVarsEqual';
-ip.addRequired('testCase',@(x) isempty(x) ...
-    || isa(testCase, 'matlab.unittest.FunctionTestCase'))
-ip.addRequired('name', @ischar)
-ip.addOptional('vars', 'same', @(x) iscellstr(x) ...
-    || (ischar(x) && strcmp(x,'same')))
-ip.addOptional('funcs', [], @(x) isempty(x) || ...
-    isa(x,'function_handle') || (iscell(x) && all( ...
-    cellfun(@(y)isa(y,'function_handle')||isempty(y),x) )))
-ip.addParamValue('RelTolElement', 0, ...
-  @(x) isnumeric(x) && isscalar(x) && isreal(x) && x >= 0)
-ip.addParamValue('RelTolRange', 0, ...
-  @(x) isnumeric(x) && isscalar(x) && isreal(x) && x >= 0)
-ip.addParamValue('AbsTol', 0, ...
-  @(x) isnumeric(x) && isscalar(x) && isreal(x) && x >= 0)
-ip.parse(testCase,name,varargin{:});
-testCase = ip.Results.testCase;
-name = ip.Results.name;
-vars = ip.Results.vars;
-funcs = ip.Results.funcs;
-tol_el = ip.Results.RelTolElement;
-tol_range = ip.Results.RelTolRange;
-tol_abs = ip.Results.AbsTol;
-
-% Handle parameter vars
-if ischar(vars) && strcmp(vars, 'same')
-  % Check the variable names from 'realValues/<nameOfTheDemo>.mat'
-  vars = whos(matfile(['realValues/' name '.mat']));
-  vars = {vars.name};
-end
-
-% Validate parameter funcs
-if ~isempty(funcs) && iscell(funcs) && length(funcs) ~= length(vars)
-  error('Parameter funcs size mismatch')
-end
-
-% Find the path to the test folder
-path = mfilename('fullpath');
-path = path(1:end-length(mfilename));
-
-% For every variable in vars
-for i = 1:length(vars)
-  
-  % Load the variables
-  if isempty(testCase)
-    % xUnit package
-    Sw = warning('off','MATLAB:load:variableNotFound')
-    try
-      actual = load([path, 'testValues/' name '.mat'], vars{i});
-      if ~isfield(actual, vars{i})
-        error(['Variable ' vars{i} ' not found in testValues/' name '.mat'])
-      end
-      expected = load([path, 'realValues/' name '.mat'], vars{i});
-      if ~isfield(expected, vars{i})
-        error(['Variable ' vars{i} ' not found in realValues/' name '.mat'])
-      end
-    catch err
-      warning(Sw)
-      rethrow(err)
-    end
-    warning(Sw)
-  else
-    % Built-in test framework
-    actual = assertWarningFree(testCase, ...
-      @() load([path, 'testValues/' name '.mat'], vars{i}));
-    expected = assertWarningFree(testCase, ...
-      @() load([path, 'realValues/' name '.mat'], vars{i}));
-  end
-  
-  % Error message (if the variable should fail the validation)
-  msg = ['The variable ' vars{i} ' failed the verification.'];
-  
-  % Apply funcs
-  if isa(funcs,'function_handle')
-    actual = funcs(actual.(vars{i}));
-    expected = funcs(expected.(vars{i}));
-    %Add info to the error message
-    msg = sprintf('%s\nFunction applied:\n%s', msg, func2str(funcs));
-  elseif ~isempty(funcs) && ~isempty(funcs{i})
-    actual = funcs{i}(actual.(vars{i}));
-    expected = funcs{i}(expected.(vars{i}));
-    %Add info to the error message
-    msg = sprintf('%s\nFunction applied:\n%s', msg, func2str(funcs{i}));
-  else
-    actual = actual.(vars{i});
-    expected = expected.(vars{i});
-  end
-  
-  
-  
-  if isempty(testCase)
-    
-    % xUnit package (N.B. using assertions instead of verifications)
-    if tol_el == 0 && tol_range == 0 && tol_abs == 0
-      % No tolerance
-      assertEqual(actual, expected, msg)
-    else
-      % Manual assertion
-      
-      % Compare
-      diff = abs(actual(:) - expected(:));
-      viol_el = 1;
-      viol_range = 1;
-      viol_abs = 1;
-      if tol_el > 0
-        viol_el = diff > tol_el*abs(expected(:));
-      end
-      if tol_range > 0
-        viol_range = diff > tol_range ...
-          *( max(expected(:)) - min(expected(:)) );
-      end
-      if tol_abs > 0
-        viol_abs = diff > tol_abs;
-      end
-      
-      if any( viol_el & viol_range & viol_abs )
-        
-        % Compose the message
-        msg = sprintf( ...
-          ['%s\nInput elements are not all equal within tolerances\n' ...
-           '(RelTolElement=%g, RelTolRange=%g, AbsTol=%g)\n\n'], ...
-          msg, tol_el, tol_range, tol_abs);
-        if tol_el > 0 && any(viol_el)
-          msg = sprintf( ...
-            ['%sThe following indexes fail the elementwise relative ' ...
-             'tolerance:\n%s\n\n'], ...
-            msg, xunit.utils.arrayToString(find(viol_el)'));
-        end
-        if tol_range > 0 && any(viol_range)
-          msg = sprintf( ...
-            ['%sThe following indexes fail the range relative ' ...
-             'tolerance:\n%s\n\n'], ...
-            msg, xunit.utils.arrayToString(find(viol_range)'));
-        end
-        if tol_abs > 0 && any(viol_abs)
-          msg = sprintf( ...
-            ['%sThe following indexes fail the absolute ' ...
-             'tolerance:\n%s\n\n'], ...
-            msg, xunit.utils.arrayToString(find(viol_abs)'));
-        end
-        msg = sprintf( ...
-          ['%sThe following indexes fail the comparison(s):\n%s\n\n' ...
-           'First input:\n%s\n\nSecond input:\n%s'], ...
-          msg, ...
-          xunit.utils.arrayToString(find(viol_el & viol_range & viol_abs)'), ...
-          xunit.utils.arrayToString(actual), ...
-          xunit.utils.arrayToString(expected) );
-        
-        % Raise error
-        throwAsCaller(MException('assertVars:tolExceeded', msg));
-        
-      end
-    end
-
-  else
-    
-    if tol_el == 0 && tol_range == 0 && tol_abs == 0
-      % No tolerance
-      verifyEqual(testCase, actual, expected, msg)
-    else
-      % Construct tolerance object
-      if tol_el > 0
-        tolObj = matlab.unittest.constraints.RelativeTolerance(tol_el);
-      end
-      if tol_range > 0
-        if exist('tolObj', 'var')
-          tolObj = tolObj | matlab.unittest.constraints.AbsoluteTolerance( ...
-            tol_range*( max(expected(:)) - min(expected(:)) ));
-        else
-          tolObj = matlab.unittest.constraints.AbsoluteTolerance( ...
-            tol_range*( max(expected(:)) - min(expected(:)) ));
-        end
-      end
-      if tol_abs > 0
-        if exist('tolObj', 'var')
-          tolObj = tolObj | matlab.unittest.constraints.AbsoluteTolerance( ...
-            tol_abs);
-        else
-          tolObj = matlab.unittest.constraints.AbsoluteTolerance(tol_abs);
-        end
-      end
-      
-      % Perform the verification
-      testCase.verifyThat(actual, matlab.unittest.constraints.IsEqualTo( ...
-        expected, 'Within', tolObj), msg);
-    
-    end
-
-  end
-  
-  % Clear variables
-  clear actual expected tolObj
-  
-end
-