diff --git a/Project.toml b/Project.toml
index 92ec7a9..65a78dc 100644
--- a/Project.toml
+++ b/Project.toml
@@ -1,7 +1,7 @@
 name = "WaveletsExt"
 uuid = "8f464e1e-25db-479f-b0a5-b7680379e03f"
 authors = ["Zeng Fung Liew <lzfliew@ucdavis.edu>", "Shozen Dan <shodan@ucdavis.edu>"]
-version = "0.1.14"
+version = "0.1.15"
 
 [deps]
 AverageShiftedHistograms = "77b51b56-6f8f-5c3a-9cb4-d71f9594ea6e"
diff --git a/paper/bestbasis.png b/paper/bestbasis.png
index c415c32..064dfa9 100644
Binary files a/paper/bestbasis.png and b/paper/bestbasis.png differ
diff --git a/paper/denoising.png b/paper/denoising.png
index 7dc19c2..16e0c27 100644
Binary files a/paper/denoising.png and b/paper/denoising.png differ
diff --git a/paper/examples.jl b/paper/examples.jl
index e3b29eb..2720432 100644
--- a/paper/examples.jl
+++ b/paper/examples.jl
@@ -24,7 +24,7 @@ p2 = wiggle(y) |> p -> plot!(p, yticks=1:9, title="Stationary WT")
 
 # Combine and save plot
 p = plot(p1, p2, layout=(1,2))
-savefig(p, "transforms.png")
+savefig(p, "tpaper/ransforms.png")
 
 # 2. Best Basis Algorithms -----------------------------------------------------------------
 #    Compared to Wavelets.jl, WaveletsExt.jl has an extended ability of catering toward
@@ -39,12 +39,12 @@ xw = wpdall(x, wt, 6)
 
 # ----- Joint Best Basis (JBB)
 tree = bestbasistree(xw, JBB())
-p1 = plot_tfbdry(tree, 6, nd_col=:green, ln_col=:black, bg_col=:white) |> 
+p1 = plot_tfbdry(tree, 6, node_color=:green, line_color=:black, background_color=:white) |> 
      p -> plot!(p, title="JBB")
 
 # ----- Least Statistically Dependent Basis (LSDB)
 tree = bestbasistree(xw, LSDB())
-p2 = plot_tfbdry(tree, 6, nd_col=:green, ln_col=:black, bg_col=:white) |> 
+p2 = plot_tfbdry(tree, 6, node_color=:green, line_color=:black, background_color=:white) |> 
      p -> plot!(p, title="LSDB")
 
 # Combine and save plot
@@ -118,7 +118,7 @@ ldb = LocalDiscriminantBasis(wt=wt,
 X̂ = fit_transform(ldb, X, y)
 
 # Plot the best basis for feature extraction
-p2 = plot_tfbdry(ldb.tree, 6, nd_col=:green, ln_col=:black, bg_col=:white)
+p2 = plot_tfbdry(ldb.tree, 6, node_color=:green, line_color=:black, background_color=:white)
 plot!(p2, title="Basis Selection using LDB")
 
 p = plot(p1, p2, size=(600,300))
diff --git a/paper/ldb.png b/paper/ldb.png
index 70d18f8..baaa3e0 100644
Binary files a/paper/ldb.png and b/paper/ldb.png differ
diff --git a/paper/paper.md b/paper/paper.md
index da6a6c5..3e9bb68 100644
--- a/paper/paper.md
+++ b/paper/paper.md
@@ -71,12 +71,12 @@ xw = wpdall(x, wt, 6)
 
 # ----- Joint Best Basis (JBB)
 tree = bestbasistree(xw, JBB())
-p1 = plot_tfbdry(tree, nd_col=:black, ln_col=:black, bg_col=:white) |> 
+p1 = plot_tfbdry(tree, node_color=:green, line_color=:black, background_color=:white) |> 
      p -> plot!(p, title="JBB")
 
 # ----- Least Statistically Dependent Basis (LSDB)
 tree = bestbasistree(xw, LSDB())
-p2 = plot_tfbdry(tree, nd_col=:black, ln_col=:black, bg_col=:white) |> 
+p2 = plot_tfbdry(tree, node_color=:green, line_color=:black, background_color=:white) |> 
      p -> plot!(p, title="LSDB")
 
 # Combine and save plot
@@ -159,7 +159,7 @@ ldb = LocalDiscriminantBasis(
 X̂ = fit_transform(ldb, X, y)
 
 # Plot the best basis for feature extraction
-p2 = plot_tfbdry(ldb.tree, nd_col=:black, ln_col=:black, bg_col=:white)
+p2 = plot_tfbdry(ldb.tree, node_color=:green, line_color=:black, background_color=:white)
 plot!(p2, title="Basis Selection using LDB")
 
 # Combine and save plot