diff --git a/src/TR.jl b/src/TR.jl
index 951fa2d..7383c58 100644
--- a/src/TR.jl
+++ b/src/TR.jl
@@ -2,7 +2,7 @@
 Solves the model update problem explicitly as a least squares problem with stacked matrices.
 In practice the most naive way of approaching the update problem
 """
-function TRLooCVUpdateNaive(X, y, lambdasu, bold)
+function TRLooCVUpdateNaive(X, y, lambdasu, bOld)
     
 n, p      = size(X);
 rmsecvman = zeros(length(lambdasu));
@@ -19,7 +19,7 @@ for i = 1:n
     p2 = size(Xdata, 2);
     
     for j = 1:length(lambdasu)
-        betas         = [Xs; sqrt(lambdasu[j]) * I(p2)] \ [ys ; sqrt(lambdasu[j]) * bold];
+        betas         = [Xs; sqrt(lambdasu[j]) * I(p2)] \ [ys ; sqrt(lambdasu[j]) * bOld];
         rmsecvman[j] += (y[i] - (((X[i,:]' .- mX) * betas)[1] + my))^2;
     end
 end
@@ -33,7 +33,7 @@ end
 Uses the 'svd-trick' for efficient calculation of regression coefficients, but does not use leverage corrections.
 Hence regression coefficients are calculated for all lambda values
 """
-function TRLooCVUpdateFair(X, y, lambdasu, bold)
+function TRLooCVUpdateFair(X, y, lambdasu, bOld)
 
 n, p      = size(X);
 rmsecvman = zeros(length(lambdasu))
@@ -53,7 +53,7 @@ for i = 1:n
     denom2      = broadcast(.+, ones(n-1), broadcast(./, lambdasu', s.^2)) # denom2 = 1 + lambda/(s's) = (s's + lambda) / (s's)
    
     # Calculating regression coefficients and residual
-    bcoeffs      = V * broadcast(./, (U' * ys), denom) .+ bold .- V * broadcast(./, V' * bold, denom2);   
+    bcoeffs    = V * broadcast(./, (U' * ys), denom) .+ bOld .- V * broadcast(./, V' * bOld, denom2);   
     rmsecvman += ((y[i] .- ((X[i,:]' .- mX) * bcoeffs .+ my)).^2)';
 end
 
@@ -131,7 +131,7 @@ end
 # Calculating rmsecv and regression coefficients
 press   = sum(rescv.^2, dims=1)';
 rmsecv  = sqrt.(1/n .* press);
-bcoeffs      = V * broadcast(./, (U' * y), denom) .+ bold .- V * broadcast(./, V' * bold, denom2);
+bcoeffs = V * broadcast(./, (U' * y), denom) .+ bOld .- V * broadcast(./, V' * bOld, denom2);
 bcoeffs = regMat \ bcoeffs;
 
 # Creating regression coefficients for uncentred data