nanmean -> mean( ,'omitnan')

src/Import/fieldtrip/fileio/private/read_neuralynx_ncs.m

@@ -149,7 +149,7 @@
   % detect the number of timestamps per block while avoiding influencce of gaps
   d = double(TimeStamp(2:end)-TimeStamp(1:end-1));
   maxJump  = ceil(10^6./(Fs-1))*512;
-  gapCorrectedTimeStampPerSample =  nanmean(d(d<maxJump))/512;
+  gapCorrectedTimeStampPerSample = mean(d(d<maxJump),'omitnan') / 512;
 
   % read the timestamp from the first and last record
   if (ispc), fclose(fid); end

src/ext/VBA/stats&plots/nanzscore.m

@@ -37,7 +37,7 @@
 end
 
 % Compute X's mean and sd, and standardize it
-mu = nanmean(x,dim);
+mu = mean(x, dim, 'omitnan');
 sigma = nanstd(x,flag,dim);
 sigma0 = sigma;
 sigma0(sigma0==0) = 1;

src/helper/pspm_segment_mean.m

@@ -126,7 +126,7 @@
     conditions{c}.data(:,s) = subj_seg{s}{c}.mean(1:min_size);
   end
   m = conditions{c}.data;
-  conditions{c}.mean = nanmean(m,2);
+  conditions{c}.mean = mean(m, 2, 'omitnan');
   conditions{c}.std = nanstd(m,0,2);
   conditions{c}.sem = conditions{c}.std/sqrt(n_subjects);
   conditions{c}.name = subj_seg{s}{c}.name;

src/pspm_load1.m

@@ -269,7 +269,7 @@
     else
       for iCond = 1:numel(indata.(mdltype).condnames)
         condindx = strcmpi(indata.(mdltype).condnames{iCond}, indata.(mdltype).trlnames);
-        data.stats(iCond, :) = nanmean(indata.(mdltype).stats(condindx, :), 1);
+        data.stats(iCond, :) = mean(indata.(mdltype).stats(condindx, :), 1, 'omitnan');
       end
       data.names = indata.(mdltype).names;
       data.trlnames = indata.(mdltype).trlnames;