diff --git a/src/TR.jl b/src/TR.jl
index 2a0ac86..b9a924f 100644
--- a/src/TR.jl
+++ b/src/TR.jl
@@ -4,8 +4,8 @@ In practice the most naive way of approaching the update problem
 """
 function TRLooCVUpdateNaive(X, y, lambdasu, bOld)
     
-n, p      = size(X);
-rmsecvman = zeros(length(lambdasu));
+n, p   = size(X);
+rmsecv = zeros(length(lambdasu));
 
 for i = 1:n
     inds  = setdiff(1:n, i);
@@ -24,9 +24,9 @@ for i = 1:n
     end
 end
 
-rmsecvman = sqrt.(1/n .* rmsecvman);
+rmsecv = sqrt.(1/n .* rmsecv);
     
-return rmsecvman
+return rmsecv
 end
 
 """
@@ -35,8 +35,8 @@ Hence regression coefficients are calculated for all lambda values
 """
 function TRLooCVUpdateFair(X, y, lambdasu, bOld)
 
-n, p      = size(X);
-rmsecvman = zeros(length(lambdasu))
+n, p   = size(X);
+rmsecv = zeros(length(lambdasu))
     
 for i = 1:n
     inds  = setdiff(1:n, i);
@@ -53,13 +53,13 @@ for i = 1:n
     denom2      = broadcast(.+, ones(n-1), broadcast(./, lambdasu', s.^2))
    
     # Calculating regression coefficients and residual
-    bcoeffs    = V * broadcast(./, (U' * ys), denom) .+ bOld .- V * broadcast(./, V' * bOld, denom2);   
-    rmsecvman += ((y[i] .- ((X[i,:]' .- mX) * bcoeffs .+ my)).^2)';
+    bcoeffs = V * broadcast(./, (U' * ys), denom) .+ bOld .- V * broadcast(./, V' * bOld, denom2);   
+    rmsecv += ((y[i] .- ((X[i,:]' .- mX) * bcoeffs .+ my)).^2)';
 end
 
-rmsecvman = sqrt.(1/n .* rmsecvman);
+rmsecv = sqrt.(1/n .* rmsecv);
 
-return rmsecvman
+return rmsecv
 end
 
 """
@@ -500,9 +500,9 @@ The LS problem is solved explicitly and no shortcuts are used.
 """
 function TRSegCVUpdateNaive(X, y, lambdas, cvfolds, bOld)
     
-n, p      = size(X);
-rmsecvman = zeros(length(lambdas));
-nfolds    = length(unique(cvfolds));
+n, p   = size(X);
+rmsecv = zeros(length(lambdas));
+nfolds = length(unique(cvfolds));
 
 for j = 1:length(lambdas)
     for i = 1:nfolds
@@ -515,14 +515,14 @@ for j = 1:length(lambdas)
         Xs = Xdata .- mX;
         ys = ydata .- my;
         
-        betas         = [Xs; sqrt(lambdas[j]) * I(p)] \ [ys; sqrt(lambdas[j]) * bOld];
-        rmsecvman[j] += sum((y[vec(inds)] - ((X[vec(inds),:] .- mX) * betas .+ my)).^2);
+        betas      = [Xs; sqrt(lambdas[j]) * I(p)] \ [ys; sqrt(lambdas[j]) * bOld];
+        rmsecv[j] += sum((y[vec(inds)] - ((X[vec(inds),:] .- mX) * betas .+ my)).^2);
     end
 end
 
-rmsecvman = sqrt.(1/n .* rmsecvman);
+rmsecv = sqrt.(1/n .* rmsecv);
     
-return rmsecvman
+return rmsecv
 end
 
 
@@ -531,9 +531,9 @@ K-fold CV for the Ridge regression update problem, using the 'SVD-trick' for cal
 """
 function TRSegCVUpdateFair(X, y, lambdas, cv, bOld)
 
-n, p      = size(X);
-rmsecvman = zeros(length(lambdas));
-nfolds    = length(unique(cv));
+n, p   = size(X);
+rmsecv = zeros(length(lambdas));
+nfolds = length(unique(cv));
 
 for i = 1:nfolds
     inds  = (cv .== i);
@@ -551,14 +551,14 @@ for i = 1:nfolds
     denom2      = broadcast(.+, ones(n-sum(inds)), broadcast(./, lambdas', s.^2));
    
     # Calculating regression coefficients
-    bcoeffs    = V * broadcast(./, (U' * ys), denom) .+ bOld .- V * broadcast(./, V' * bOld, denom2);
-    rmsecvman += sum((y[vec(inds)] .- ((X[vec(inds),:] .- mX) * bcoeffs .+ my)).^2, dims=1)';
+    bcoeffs  = V * broadcast(./, (U' * ys), denom) .+ bOld .- V * broadcast(./, V' * bOld, denom2);
+    rmsecv  += sum((y[vec(inds)] .- ((X[vec(inds),:] .- mX) * bcoeffs .+ my)).^2, dims=1)';
 
 end
 
-rmsecvman = sqrt.(1/n .* rmsecvman);
+rmsecv = sqrt.(1/n .* rmsecv);
 
-    return rmsecvman
+    return rmsecv
 end
 
 """