diff --git a/java/source/infodynamics/measures/continuous/kraskov/ConditionalMutualInfoCalculatorMultiVariateKraskov.java b/java/source/infodynamics/measures/continuous/kraskov/ConditionalMutualInfoCalculatorMultiVariateKraskov.java
index a4f23c5..1e5dcc5 100755
--- a/java/source/infodynamics/measures/continuous/kraskov/ConditionalMutualInfoCalculatorMultiVariateKraskov.java
+++ b/java/source/infodynamics/measures/continuous/kraskov/ConditionalMutualInfoCalculatorMultiVariateKraskov.java
@@ -19,6 +19,7 @@
 package infodynamics.measures.continuous.kraskov;
 
 import java.util.Calendar;
+import java.util.PriorityQueue;
 
 import infodynamics.measures.continuous.ConditionalMutualInfoCalculatorMultiVariate;
 import infodynamics.measures.continuous.ConditionalMutualInfoMultiVariateCommon;
@@ -27,6 +28,7 @@ import infodynamics.utils.KdTree;
 import infodynamics.utils.MathsUtils;
 import infodynamics.utils.MatrixUtils;
 import infodynamics.utils.NearestNeighbourSearcher;
+import infodynamics.utils.NeighbourNodeData;
 import infodynamics.utils.UnivariateNearestNeighbourSearcher;
 import infodynamics.utils.EmpiricalMeasurementDistribution;
 import infodynamics.utils.NativeUtils;
@@ -910,4 +912,68 @@ public abstract class ConditionalMutualInfoCalculatorMultiVariateKraskov
 	// public ConditionalMutualInfoCalculatorMultiVariateKraskov clone() {
 	//	return this;
 	// }
+	
+    /**
+     * Debug method to return the k nearest neighbour distances that 
+     *  would be utilised for each sample point here.
+     * Note that this is specifically the max-norm across the three variables, which is used for each
+     *  range search in algorithm 1 (although algorithm 2 would use the max distance
+     *  for each variable within the kNNs in their separate range searches). 
+     *  
+     * @param startTimePoint
+     * @param numTimePoints
+     * @return
+     * @throws Exception
+     */
+	public double[] kNNDistances(int startTimePoint, int numTimePoints) throws Exception {
+		double[] kNNdistances = new double[numTimePoints];
+		
+		for (int t = startTimePoint; t < startTimePoint + numTimePoints; t++) {
+			// Compute eps for this time step by
+			//  finding the kth closest neighbour for point t:
+			PriorityQueue<NeighbourNodeData> nnPQ =
+					kdTreeJoint.findKNearestNeighbours(k, t, dynCorrExclTime);
+			// First element in the PQ is the kth NN,
+			//  and epsilon = kthNnData.distance
+			NeighbourNodeData kthNnData = nnPQ.poll();
+
+			kNNdistances[t - startTimePoint] = kthNnData.distance;
+		}
+		return kNNdistances;
+	}
+
+	/**
+	 * Debug method to return the k nearest neighbour distances that
+	 *  would be utilised in {@link #computeLocalUsingPreviousObservations(double[][], double[][], double[][])}
+	 *  for a cross conditional MI.
+     * Note that this is specifically the max-norm across the three variables, which is used for each
+     *  range search in algorithm 1 (although algorithm 2 would use the max distance
+     *  for each variable within the kNNs in their separate range searches). 
+	 *  
+	 * @param startTimePoint
+	 * @param numTimePoints
+	 * @param newVar1Observations
+	 * @param newVar2Observations
+	 * @return
+	 * @throws Exception
+	 */
+	public double[] kNNDistancesForNewSamples(int startTimePoint, int numTimePoints,
+			double[][] newStates1, double[][] newStates2, double[][] newCondStates) throws Exception {
+
+		double[] kNNdistances = new double[numTimePoints];
+		
+		for (int t = startTimePoint; t < startTimePoint + numTimePoints; t++) {
+			// Compute eps for this time step by
+			//  finding the kth closest neighbour for the new sample:
+			PriorityQueue<NeighbourNodeData> nnPQ =
+					kdTreeJoint.findKNearestNeighbours(k,
+							new double[][] {newStates1[t], newStates2[t], newCondStates[t]});
+			// First element in the PQ is the kth NN,
+			//  and epsilon = kthNnData.distance
+			NeighbourNodeData kthNnData = nnPQ.poll();
+			
+			kNNdistances[t - startTimePoint] = kthNnData.distance;
+		}
+		return kNNdistances;
+	}
 }
diff --git a/java/source/infodynamics/measures/continuous/kraskov/ConditionalMutualInfoCalculatorMultiVariateKraskov1.java b/java/source/infodynamics/measures/continuous/kraskov/ConditionalMutualInfoCalculatorMultiVariateKraskov1.java
index dcb4320..0ef07ee 100755
--- a/java/source/infodynamics/measures/continuous/kraskov/ConditionalMutualInfoCalculatorMultiVariateKraskov1.java
+++ b/java/source/infodynamics/measures/continuous/kraskov/ConditionalMutualInfoCalculatorMultiVariateKraskov1.java
@@ -18,19 +18,13 @@
 
 package infodynamics.measures.continuous.kraskov;
 
-import java.util.Arrays;
 import java.util.Calendar;
 import java.util.PriorityQueue;
 
 import infodynamics.measures.continuous.ConditionalMutualInfoCalculatorMultiVariate;
-import infodynamics.utils.EmpiricalMeasurementDistribution;
-import infodynamics.utils.FirstIndexComparatorDouble;
-import infodynamics.utils.KdTree;
 import infodynamics.utils.MathsUtils;
 import infodynamics.utils.MatrixUtils;
-import infodynamics.utils.NearestNeighbourSearcher;
 import infodynamics.utils.NeighbourNodeData;
-import infodynamics.utils.UnivariateNearestNeighbourSearcher;
 
 /**
  * <p>Computes the differential conditional mutual information of two multivariate
@@ -340,7 +334,7 @@ public class ConditionalMutualInfoCalculatorMultiVariateKraskov1
 
 		for (int t = startTimePoint; t < startTimePoint + numTimePoints; t++) {
 			// Compute eps for this time step by
-			//  finding the kth closest neighbour for point t:
+			//  finding the kth closest neighbour for the new sample:
 			long methodStartTime = Calendar.getInstance().getTimeInMillis();
 			PriorityQueue<NeighbourNodeData> nnPQ =
 					kdTreeJoint.findKNearestNeighbours(k,
diff --git a/java/source/infodynamics/measures/continuous/kraskov/MutualInfoCalculatorMultiVariateKraskov.java b/java/source/infodynamics/measures/continuous/kraskov/MutualInfoCalculatorMultiVariateKraskov.java
index 88bd932..5cc8e6d 100755
--- a/java/source/infodynamics/measures/continuous/kraskov/MutualInfoCalculatorMultiVariateKraskov.java
+++ b/java/source/infodynamics/measures/continuous/kraskov/MutualInfoCalculatorMultiVariateKraskov.java
@@ -1106,5 +1106,72 @@ public abstract class MutualInfoCalculatorMultiVariateKraskov
     }
     
     return totalErrors;
-  } 
+  }
+
+    /**
+     * Debug method to return the k nearest neighbour distances that 
+     *  would be utilised for each sample point here.
+     * Note that this is specifically the max-norm across the two variables, which is used for both
+     *  variables in the range searches in algorithm 1 (although algorithm 2 would use the max distance
+     *  for each variable within the kNNs in their separate range searches). 
+     *  
+     * @param startTimePoint
+     * @param numTimePoints
+     * @return
+     * @throws Exception
+     */
+	public double[] kNNDistances(int startTimePoint, int numTimePoints) throws Exception {
+		
+		double[] kNNdistances = new double[numTimePoints];
+		
+		for (int t = startTimePoint; t < startTimePoint + numTimePoints; t++) {
+			// Compute eps for this time step by
+			//  finding the kth closest neighbour for point t:
+			PriorityQueue<NeighbourNodeData> nnPQ =
+					kdTreeJoint.findKNearestNeighbours(k, t, dynCorrExclTime);
+			// First element in the PQ is the kth NN,
+			//  and epsilon = kthNnData.distance
+			NeighbourNodeData kthNnData = nnPQ.poll();
+
+			kNNdistances[t - startTimePoint] = kthNnData.distance;
+		}
+		return kNNdistances;
+	}
+	
+
+	/**
+	 * Debug method to return the k nearest neighbour distances that
+	 *  would be utilised in {@link #computeLocalUsingPreviousObservations(double[][], double[][])}
+	 *  for a cross MI.
+     * Note that this is specifically the max-norm across the two variables, which is used for both
+     *  variables in the range searches in algorithm 1 (although algorithm 2 would use the max distance
+     *  for each variable within the kNNs in their separate range searches). 
+	 *  
+	 * @param startTimePoint
+	 * @param numTimePoints
+	 * @param newVar1Observations
+	 * @param newVar2Observations
+	 * @return
+	 * @throws Exception
+	 */
+	public double[] kNNDistancesForNewSamples(int startTimePoint, int numTimePoints,
+			double[][] newVar1Observations, double[][] newVar2Observations) throws Exception {
+
+		double[] kNNdistances = new double[numTimePoints];
+		
+		for (int t = startTimePoint; t < startTimePoint + numTimePoints; t++) {
+			// Compute eps for this time step by
+			//  finding the kth closest neighbour for the new sample:
+			PriorityQueue<NeighbourNodeData> nnPQ =
+					kdTreeJoint.findKNearestNeighbours(k,
+							new double[][] {newVar1Observations[t], newVar2Observations[t]});
+			// First element in the PQ is the kth NN,
+			//  and epsilon = kthNnData.distance
+			NeighbourNodeData kthNnData = nnPQ.poll();
+			
+			kNNdistances[t - startTimePoint] = kthNnData.distance;
+		}
+		return kNNdistances;
+	}
+
 }
diff --git a/java/source/infodynamics/measures/continuous/kraskov/MutualInfoCalculatorMultiVariateKraskov1.java b/java/source/infodynamics/measures/continuous/kraskov/MutualInfoCalculatorMultiVariateKraskov1.java
index c68040e..bdd86b5 100644
--- a/java/source/infodynamics/measures/continuous/kraskov/MutualInfoCalculatorMultiVariateKraskov1.java
+++ b/java/source/infodynamics/measures/continuous/kraskov/MutualInfoCalculatorMultiVariateKraskov1.java
@@ -72,7 +72,7 @@ public class MutualInfoCalculatorMultiVariateKraskov1
 		double sumNx = 0;
 		double sumNy = 0;
 		double sum2xkNNDist = 0;
-				
+		
 		for (int t = startTimePoint; t < startTimePoint + numTimePoints; t++) {
 			// Compute eps for this time step by
 			//  finding the kth closest neighbour for point t:
@@ -196,7 +196,7 @@ public class MutualInfoCalculatorMultiVariateKraskov1
 		
 		for (int t = startTimePoint; t < startTimePoint + numTimePoints; t++) {
 			// Compute eps for this time step by
-			//  finding the kth closest neighbour for point t:
+			//  finding the kth closest neighbour for the new sample:
 			PriorityQueue<NeighbourNodeData> nnPQ =
 					kdTreeJoint.findKNearestNeighbours(k,
 							new double[][] {newVar1Observations[t], newVar2Observations[t]});