diff --git a/Operations/CO_AddNoise.m b/Operations/CO_AddNoise.m
index df800b35..609de489 100644
--- a/Operations/CO_AddNoise.m
+++ b/Operations/CO_AddNoise.m
@@ -117,14 +117,14 @@
 case {'std1','std2','quantiles','even'}
     % histogram-based methods using my naive implementation in CO_Histogram
     for i = 1:numRepeats
-        amis(i) = CO_HistogramAMI(y+noiseRange(i)*noise,tau,amiMethod,extraParam);
+        amis(i) = CO_HistogramAMI(y + noiseRange(i)*noise,tau,amiMethod,extraParam);
         if isnan(amis(i))
             error('Error computing AMI: Time series too short (?)');
         end
     end
 case {'gaussian','kernel','kraskov1','kraskov2'}
     for i = 1:numRepeats
-        amis(i) = IN_AutoMutualInfo(y+noiseRange(i)*noise,tau,amiMethod,extraParam);
+        amis(i) = IN_AutoMutualInfo(y + noiseRange(i)*noise,tau,amiMethod,extraParam);
         if isnan(amis(i))
             error('Error computing AMI: Time series too short (?)');
         end
@@ -136,7 +136,7 @@
 %-------------------------------------------------------------------------------
 
 % Proportion decreases:
-out.pdec = sum(diff(amis) < 0)/(numRepeats-1);
+out.pdec = sum(diff(amis) < 0)/(numRepeats - 1);
 
 % Mean change in AMI:
 out.meanch = mean(diff(amis));
@@ -156,7 +156,7 @@
 noiseLevels = [0.5,1,1.5,2];
 for i = 1:length(noiseLevels)
     out.(sprintf('ami_at_%u',noiseLevels(i)*10)) = ...
-                        amis(find(noiseRange>=noiseLevels(i),1,'first'));
+            amis(find(noiseRange >= noiseLevels(i),1,'first'));
 end
 
 % Count number of times the AMI function crosses its mean
diff --git a/PlottingAnalysis/TS_ForwardFS.m b/PlottingAnalysis/TS_ForwardFS.m
index 9dc571ec..7032011b 100644
--- a/PlottingAnalysis/TS_ForwardFS.m
+++ b/PlottingAnalysis/TS_ForwardFS.m
@@ -117,7 +117,7 @@
 % ------------------------------------------------------------------------------
 % ------------------------------------------------------------------------------
 FS_timer = tic;
-fprintf(1,['Performing greedy forward feature selection using ''%s''...\n'],criterion);
+fprintf(1,'Performing greedy forward feature selection using ''%s''...\n',criterion);
 
 opts = statset('display','iter');
 % classify_fn = @(XTrain,yTrain,XTest,yTest) 1 - GiveMeCfn(criterion,XTrain,yTrain,XTest,yTest,numClasses);
diff --git a/README.md b/README.md
index c865ffd9..320731d9 100644
--- a/README.md
+++ b/README.md
@@ -27,13 +27,13 @@ To obtain the latest toolboxes (like the optimized _catch22_ faeture set) you sh
 
 Users _unfamiliar with git_ can instead download the repository by clicking the green "Code" button then "Download ZIP".
 
-Once downloaded, you can install _hctsa_ by running the `install.m` script (see [docs](https://hctsa-users.gitbook.io/hctsa-manual) for details).
+Once downloaded, you can install _hctsa_ by running the `install.m` script (see [docs](https://time-series-features.gitbook.io/hctsa-manual/) for details).
 
 <!-- We recommend working outside of the repository so that incremental updates can be pulled from the upstream repository. -->
 
 ## Documentation and Wiki &#x1F4D6;
 
-__Comprehensive documentation__ for _hctsa_, from getting started through to more advanced analyses is on [GitBook](https://hctsa-users.gitbook.io/hctsa-manual).
+__Comprehensive documentation__ for _hctsa_, from getting started through to more advanced analyses is on [GitBook](https://time-series-features.gitbook.io/hctsa-manual/).
 
 There is also alot of additional information on the [__wiki__](https://github.com/benfulcher/hctsa/wiki/), including: