diff --git a/tetrad-lib/src/test/java/edu/cmu/tetrad/test/TestCheckMarkov.java b/tetrad-lib/src/test/java/edu/cmu/tetrad/test/TestCheckMarkov.java
index fc9b28cb6a..a6f061c37b 100644
--- a/tetrad-lib/src/test/java/edu/cmu/tetrad/test/TestCheckMarkov.java
+++ b/tetrad-lib/src/test/java/edu/cmu/tetrad/test/TestCheckMarkov.java
@@ -111,4 +111,426 @@ public void test2() {
 
         System.out.println(markovCheck.getMarkovCheckRecordString());
     }
+
+    @Test
+    public void testGaussianDAGPrecisionRecallForLocalOnMarkovBlanket() {
+//        Graph trueGraph = RandomGraph.randomDag(100, 0, 400, 100, 100, 100, false);
+        Graph trueGraph = RandomGraph.randomDag(80, 0, 80, 100, 100, 100, false);
+        System.out.println("Test True Graph: " + trueGraph);
+        System.out.println("Test True Graph size: " + trueGraph.getNodes().size());
+
+        SemPm pm = new SemPm(trueGraph);
+        // Parameters without additional setting default tobe Gaussian
+        SemIm im = new SemIm(pm, new Parameters());
+        DataSet data = im.simulateData(10000, false);
+        edu.cmu.tetrad.search.score.SemBicScore score = new SemBicScore(data, true);
+        score.setPenaltyDiscount(2);
+        Graph estimatedCpdag = new PermutationSearch(new Boss(score)).search();
+//        TODO VBC: Next check different search algo to generate estimated graph. e.g. PC
+        System.out.println("Test Estimated CPDAG Graph: " + estimatedCpdag);
+        System.out.println("~~~~~~~~~~~~~~~~~~~~~~~~~~~~");
+        testGaussianDAGPrecisionRecallForLocalOnMarkovBlanketUsingAdjAHConfusionMatrix(data, trueGraph, estimatedCpdag);
+        testGaussianDAGPrecisionRecallForLocalOnMarkovBlanketUsingLGConfusionMatrix(data, trueGraph, estimatedCpdag);
+        System.out.println("~~~~~~~~~~~~~Full Graph~~~~~~~~~~~~~~~");
+        estimatedCpdag = GraphUtils.replaceNodes(estimatedCpdag, trueGraph.getNodes());
+        double whole_ap = new AdjacencyPrecision().getValue(trueGraph, estimatedCpdag, null);
+        double whole_ar = new AdjacencyRecall().getValue(trueGraph, estimatedCpdag, null);
+        double whole_ahp = new ArrowheadPrecision().getValue(trueGraph, estimatedCpdag, null);
+        double whole_ahr = new ArrowheadRecall().getValue(trueGraph, estimatedCpdag, null);
+        double whole_lgp = new LocalGraphPrecision().getValue(trueGraph, estimatedCpdag, null);
+        double whole_lgr = new LocalGraphRecall().getValue(trueGraph, estimatedCpdag, null);
+        System.out.println("whole_ap: " + whole_ap);
+        System.out.println("whole_ar: " + whole_ar );
+        System.out.println("whole_ahp: " + whole_ahp);
+        System.out.println("whole_ahr: " + whole_ahr);
+        System.out.println("whole_lgp: " + whole_lgp);
+        System.out.println("whole_lgr: " + whole_lgr);
+    }
+
+    public void testGaussianDAGPrecisionRecallForLocalOnMarkovBlanketUsingAdjAHConfusionMatrix(DataSet data, Graph trueGraph, Graph estimatedCpdag) {
+        IndependenceTest fisherZTest = new IndTestFisherZ(data, 0.05);
+        MarkovCheck markovCheck = new MarkovCheck(estimatedCpdag, fisherZTest, ConditioningSetType.MARKOV_BLANKET);
+        // Using Adj, AH confusion matrix
+        List<List<Node>> accepts_rejects = markovCheck.getAndersonDarlingTestAcceptsRejectsNodesForAllNodesPlotData(fisherZTest, estimatedCpdag, trueGraph, 0.05, 1.0, 0.8);
+        List<Node> accepts = accepts_rejects.get(0);
+        List<Node> rejects = accepts_rejects.get(1);
+        System.out.println("Accepts size: " + accepts.size());
+        System.out.println("Rejects size: " + rejects.size());
+    }
+
+    public void testGaussianDAGPrecisionRecallForLocalOnMarkovBlanketUsingLGConfusionMatrix(DataSet data, Graph trueGraph, Graph estimatedCpdag) {
+        IndependenceTest fisherZTest = new IndTestFisherZ(data, 0.05);
+        MarkovCheck markovCheck = new MarkovCheck(estimatedCpdag, fisherZTest, ConditioningSetType.MARKOV_BLANKET);
+        // Using Local Graph (LG) confusion matrix
+        // ADTest pass/fail threshold default to be 0.05. shuffleThreshold default to be 0.5
+        List<List<Node>> accepts_rejects = markovCheck.getAndersonDarlingTestAcceptsRejectsNodesForAllNodesPlotData2(fisherZTest, estimatedCpdag, trueGraph, 0.05, 1.0, 0.8);
+        List<Node> accepts = accepts_rejects.get(0);
+        List<Node> rejects = accepts_rejects.get(1);
+        System.out.println("Accepts size: " + accepts.size());
+        System.out.println("Rejects size: " + rejects.size());
+    }
+
+    @Test
+    public void testGaussianCPDAGPrecisionRecallForLocalOnMarkovBlanket() {
+        Graph trueGraph = RandomGraph.randomDag(10, 0, 10, 100, 100, 100, false);
+        // The completed partially directed acyclic graph (CPDAG) for the given DAG.
+        Graph trueGraphCPDAG = GraphTransforms.dagToCpdag(trueGraph);
+        System.out.println("Test True Graph: " + trueGraph);
+        System.out.println("Test True Graph CPDAG: " + trueGraphCPDAG);
+
+        SemPm pm = new SemPm(trueGraph);
+        // Parameters without additional setting default tobe Gaussian
+        SemIm im = new SemIm(pm, new Parameters());
+        DataSet data = im.simulateData(1000, false);
+        edu.cmu.tetrad.search.score.SemBicScore score = new SemBicScore(data, true);
+        score.setPenaltyDiscount(2);
+        Graph estimatedCpdag = new PermutationSearch(new Boss(score)).search();
+        System.out.println("Test Estimated CPDAG Graph: " + estimatedCpdag);
+        System.out.println("~~~~~~~~~~~~~~~~~~~~~~~~~~~~");
+
+        IndependenceTest fisherZTest = new IndTestFisherZ(data, 0.05);
+        MarkovCheck markovCheck = new MarkovCheck(estimatedCpdag, fisherZTest, ConditioningSetType.MARKOV_BLANKET);
+        // ADTest pass/fail threshold default to be 0.05. shuffleThreshold default to be 0.5
+//        List<List<Node>> accepts_rejects = markovCheck.getAndersonDarlingTestAcceptsRejectsNodesForAllNodes(fisherZTest, estimatedCpdag, 0.05, 0.5);
+        List<List<Node>> accepts_rejects = markovCheck.getAndersonDarlingTestAcceptsRejectsNodesForAllNodesPlotData(fisherZTest, estimatedCpdag, trueGraph, 0.05, 0.3, 0.8);
+
+        List<Node> accepts = accepts_rejects.get(0);
+        List<Node> rejects = accepts_rejects.get(1);
+        System.out.println("Accepts size: " + accepts.size());
+        System.out.println("Rejects size: " + rejects.size());
+    }
+
+    @Test
+    public void testNonGaussianDAGPrecisionRecallForLocalOnMarkovBlanket() {
+        Graph trueGraph = RandomGraph.randomDag(10, 0, 10, 100, 100, 100, false);
+        System.out.println("Test True Graph: " + trueGraph);
+        System.out.println("Test True Graph size: " + trueGraph.getNodes().size());
+
+        SemPm pm = new SemPm(trueGraph);
+
+        Parameters params = new Parameters();
+        // Manually set non-Gaussian
+        params.set(Params.SIMULATION_ERROR_TYPE, 3);
+        params.set(Params.SIMULATION_PARAM1, 1);
+
+        SemIm im = new SemIm(pm, params);
+        DataSet data = im.simulateData(1000, false);
+        edu.cmu.tetrad.search.score.SemBicScore score = new SemBicScore(data, true);
+        score.setPenaltyDiscount(2);
+        Graph estimatedCpdag = new PermutationSearch(new Boss(score)).search();
+        System.out.println("Test Estimated CPDAG Graph: " + estimatedCpdag);
+        System.out.println("~~~~~~~~~~~~~~~~~~~~~~~~~~~~");
+
+        IndependenceTest fisherZTest = new IndTestFisherZ(data, 0.05);
+        MarkovCheck markovCheck = new MarkovCheck(estimatedCpdag, fisherZTest, ConditioningSetType.MARKOV_BLANKET);
+        // ADTest pass/fail threshold default to be 0.05. shuffleThreshold default to be 0.5
+        //        List<List<Node>> accepts_rejects = markovCheck.getAndersonDarlingTestAcceptsRejectsNodesForAllNodes(fisherZTest, estimatedCpdag, 0.05, 0.3);
+        List<List<Node>> accepts_rejects = markovCheck.getAndersonDarlingTestAcceptsRejectsNodesForAllNodesPlotData(fisherZTest, estimatedCpdag, trueGraph, 0.05, 0.3, 0.8);
+        List<Node> accepts = accepts_rejects.get(0);
+        List<Node> rejects = accepts_rejects.get(1);
+        System.out.println("Accepts size: " + accepts.size());
+        System.out.println("Rejects size: " + rejects.size());
+    }
+
+    @Test
+    public void testNonGaussianCPDAGPrecisionRecallForLocalOnMarkovBlanket() {
+        Graph trueGraph = RandomGraph.randomDag(10, 0, 10, 100, 100, 100, false);
+        // The completed partially directed acyclic graph (CPDAG) for the given DAG.
+        Graph trueGraphCPDAG = GraphTransforms.dagToCpdag(trueGraph);
+        System.out.println("Test True Graph: " + trueGraph);
+        System.out.println("Test True Graph CPDAG: " + trueGraphCPDAG);
+
+        SemPm pm = new SemPm(trueGraph);
+
+        Parameters params = new Parameters();
+        // Manually set non-Gaussian
+        params.set(Params.SIMULATION_ERROR_TYPE, 3);
+        params.set(Params.SIMULATION_PARAM1, 1);
+
+        SemIm im = new SemIm(pm, params);
+        DataSet data = im.simulateData(1000, false);
+        edu.cmu.tetrad.search.score.SemBicScore score = new SemBicScore(data, true);
+        score.setPenaltyDiscount(2);
+        Graph estimatedCpdag = new PermutationSearch(new Boss(score)).search();
+        System.out.println("Test Estimated CPDAG Graph: " + estimatedCpdag);
+        System.out.println("~~~~~~~~~~~~~~~~~~~~~~~~~~~~");
+
+        IndependenceTest fisherZTest = new IndTestFisherZ(data, 0.05);
+        MarkovCheck markovCheck = new MarkovCheck(estimatedCpdag, fisherZTest, ConditioningSetType.MARKOV_BLANKET);
+        // ADTest pass/fail threshold default to be 0.05. shuffleThreshold default to be 0.5
+        //        List<List<Node>> accepts_rejects = markovCheck.getAndersonDarlingTestAcceptsRejectsNodesForAllNodes(fisherZTest, estimatedCpdag, 0.05, 0.5);
+        List<List<Node>> accepts_rejects = markovCheck.getAndersonDarlingTestAcceptsRejectsNodesForAllNodesPlotData(fisherZTest, estimatedCpdag, trueGraph, 0.05, 0.3, 0.8);
+        List<Node> accepts = accepts_rejects.get(0);
+        List<Node> rejects = accepts_rejects.get(1);
+        System.out.println("Accepts size: " + accepts.size());
+        System.out.println("Rejects size: " + rejects.size());
+    }
+
+
+
+    @Test
+    public void testGaussianDAGPrecisionRecallForLocalOnParents() {
+        Graph trueGraph = RandomGraph.randomDag(10, 0, 10, 100, 100, 100, false);
+        System.out.println("Test True Graph: " + trueGraph);
+        System.out.println("Test True Graph size: " + trueGraph.getNodes().size());
+
+        SemPm pm = new SemPm(trueGraph);
+        // Parameters without additional setting default tobe Gaussian
+        SemIm im = new SemIm(pm, new Parameters());
+        DataSet data = im.simulateData(1000, false);
+        edu.cmu.tetrad.search.score.SemBicScore score = new SemBicScore(data, true);
+        score.setPenaltyDiscount(2);
+        Graph estimatedCpdag = new PermutationSearch(new Boss(score)).search();
+        System.out.println("Test Estimated CPDAG Graph: " + estimatedCpdag);
+        System.out.println("~~~~~~~~~~~~~~~~~~~~~~~~~~~~");
+
+        IndependenceTest fisherZTest = new IndTestFisherZ(data, 0.05);
+        // TODO VBC: confirm on the choice of ConditioningSetType.
+        MarkovCheck markovCheck = new MarkovCheck(estimatedCpdag, fisherZTest, ConditioningSetType.LOCAL_MARKOV);
+        // ADTest pass/fail threshold default to be 0.05. shuffleThreshold default to be 0.5
+        List<List<Node>> accepts_rejects = markovCheck.getAndersonDarlingTestAcceptsRejectsNodesForAllNodes(fisherZTest, estimatedCpdag, 0.05, 0.5);
+        List<Node> accepts = accepts_rejects.get(0);
+        List<Node> rejects = accepts_rejects.get(1);
+        System.out.println("Accepts size: " + accepts.size());
+        System.out.println("Rejects size: " + rejects.size());
+
+        for(Node a: accepts) {
+            System.out.println("=====================");
+            markovCheck.getPrecisionAndRecallOnMarkovBlanketGraph(a, estimatedCpdag, trueGraph);
+            System.out.println("=====================");
+
+        }
+        for (Node a: rejects) {
+            System.out.println("=====================");
+            markovCheck.getPrecisionAndRecallOnMarkovBlanketGraph(a, estimatedCpdag, trueGraph);
+            System.out.println("=====================");
+        }
+    }
+
+    @Test
+    public void testGaussianCPDAGPrecisionRecallForLocalOnParents() {
+        Graph trueGraph = RandomGraph.randomDag(10, 0, 10, 100, 100, 100, false);
+        // The completed partially directed acyclic graph (CPDAG) for the given DAG.
+        Graph trueGraphCPDAG = GraphTransforms.dagToCpdag(trueGraph);
+        System.out.println("Test True Graph: " + trueGraph);
+        System.out.println("Test True Graph CPDAG: " + trueGraphCPDAG);
+
+        SemPm pm = new SemPm(trueGraph);
+        // Parameters without additional setting default tobe Gaussian
+        SemIm im = new SemIm(pm, new Parameters());
+        DataSet data = im.simulateData(1000, false);
+        edu.cmu.tetrad.search.score.SemBicScore score = new SemBicScore(data, true);
+        score.setPenaltyDiscount(2);
+        Graph estimatedCpdag = new PermutationSearch(new Boss(score)).search();
+        System.out.println("Test Estimated CPDAG Graph: " + estimatedCpdag);
+        System.out.println("~~~~~~~~~~~~~~~~~~~~~~~~~~~~");
+
+        IndependenceTest fisherZTest = new IndTestFisherZ(data, 0.05);
+        MarkovCheck markovCheck = new MarkovCheck(estimatedCpdag, fisherZTest, ConditioningSetType.MARKOV_BLANKET);
+        // ADTest pass/fail threshold default to be 0.05. shuffleThreshold default to be 0.5
+        List<List<Node>> accepts_rejects = markovCheck.getAndersonDarlingTestAcceptsRejectsNodesForAllNodes(fisherZTest, estimatedCpdag, 0.05, 0.5);
+        List<Node> accepts = accepts_rejects.get(0);
+        List<Node> rejects = accepts_rejects.get(1);
+        System.out.println("Accepts size: " + accepts.size());
+        System.out.println("Rejects size: " + rejects.size());
+
+        // Compare the Est CPDAG with True graph's CPDAG.
+        for(Node a: accepts) {
+            System.out.println("=====================");
+            markovCheck.getPrecisionAndRecallOnMarkovBlanketGraph(a, estimatedCpdag, trueGraphCPDAG);
+            System.out.println("=====================");
+
+        }
+        for (Node a: rejects) {
+            System.out.println("=====================");
+            markovCheck.getPrecisionAndRecallOnMarkovBlanketGraph(a, estimatedCpdag, trueGraphCPDAG);
+            System.out.println("=====================");
+        }
+    }
+
+    @Test
+    public void testNonGaussianDAGPrecisionRecallForLocalOnParents() {
+        Graph trueGraph = RandomGraph.randomDag(10, 0, 10, 100, 100, 100, false);
+        System.out.println("Test True Graph: " + trueGraph);
+        System.out.println("Test True Graph size: " + trueGraph.getNodes().size());
+
+        SemPm pm = new SemPm(trueGraph);
+        Parameters params = new Parameters();
+        // Manually set non-Gaussian
+        params.set(Params.SIMULATION_ERROR_TYPE, 3);
+        params.set(Params.SIMULATION_PARAM1, 1);
+
+        SemIm im = new SemIm(pm, params);
+        DataSet data = im.simulateData(1000, false);
+        edu.cmu.tetrad.search.score.SemBicScore score = new SemBicScore(data, true);
+        score.setPenaltyDiscount(2);
+        Graph estimatedCpdag = new PermutationSearch(new Boss(score)).search();
+        System.out.println("Test Estimated CPDAG Graph: " + estimatedCpdag);
+        System.out.println("~~~~~~~~~~~~~~~~~~~~~~~~~~~~");
+
+        IndependenceTest fisherZTest = new IndTestFisherZ(data, 0.05);
+        // TODO VBC: confirm on the choice of ConditioningSetType.
+        MarkovCheck markovCheck = new MarkovCheck(estimatedCpdag, fisherZTest, ConditioningSetType.LOCAL_MARKOV);
+        // ADTest pass/fail threshold default to be 0.05. shuffleThreshold default to be 0.5
+        List<List<Node>> accepts_rejects = markovCheck.getAndersonDarlingTestAcceptsRejectsNodesForAllNodes(fisherZTest, estimatedCpdag, 0.05, 0.5);
+        List<Node> accepts = accepts_rejects.get(0);
+        List<Node> rejects = accepts_rejects.get(1);
+        System.out.println("Accepts size: " + accepts.size());
+        System.out.println("Rejects size: " + rejects.size());
+
+        for(Node a: accepts) {
+            System.out.println("=====================");
+            markovCheck.getPrecisionAndRecallOnMarkovBlanketGraph(a, estimatedCpdag, trueGraph);
+            System.out.println("=====================");
+
+        }
+        for (Node a: rejects) {
+            System.out.println("=====================");
+            markovCheck.getPrecisionAndRecallOnMarkovBlanketGraph(a, estimatedCpdag, trueGraph);
+            System.out.println("=====================");
+        }
+    }
+
+    @Test
+    public void testNonGaussianCPDAGPrecisionRecallForLocalOnParents() {
+        Graph trueGraph = RandomGraph.randomDag(10, 0, 10, 100, 100, 100, false);
+        // The completed partially directed acyclic graph (CPDAG) for the given DAG.
+        Graph trueGraphCPDAG = GraphTransforms.dagToCpdag(trueGraph);
+        System.out.println("Test True Graph: " + trueGraph);
+        System.out.println("Test True Graph CPDAG: " + trueGraphCPDAG);
+
+        SemPm pm = new SemPm(trueGraph);
+
+        Parameters params = new Parameters();
+        // Manually set non-Gaussian
+        params.set(Params.SIMULATION_ERROR_TYPE, 3);
+        params.set(Params.SIMULATION_PARAM1, 1);
+
+        SemIm im = new SemIm(pm, params);
+        DataSet data = im.simulateData(1000, false);
+        edu.cmu.tetrad.search.score.SemBicScore score = new SemBicScore(data, true);
+        score.setPenaltyDiscount(2);
+        Graph estimatedCpdag = new PermutationSearch(new Boss(score)).search();
+        System.out.println("Test Estimated CPDAG Graph: " + estimatedCpdag);
+        System.out.println("~~~~~~~~~~~~~~~~~~~~~~~~~~~~");
+
+        IndependenceTest fisherZTest = new IndTestFisherZ(data, 0.05);
+        MarkovCheck markovCheck = new MarkovCheck(estimatedCpdag, fisherZTest, ConditioningSetType.MARKOV_BLANKET);
+        // ADTest pass/fail threshold default to be 0.05. shuffleThreshold default to be 0.5
+        List<List<Node>> accepts_rejects = markovCheck.getAndersonDarlingTestAcceptsRejectsNodesForAllNodes(fisherZTest, estimatedCpdag, 0.05, 0.5);
+        List<Node> accepts = accepts_rejects.get(0);
+        List<Node> rejects = accepts_rejects.get(1);
+        System.out.println("Accepts size: " + accepts.size());
+        System.out.println("Rejects size: " + rejects.size());
+
+        // Compare the Est CPDAG with True graph's CPDAG.
+        for(Node a: accepts) {
+            System.out.println("=====================");
+            markovCheck.getPrecisionAndRecallOnMarkovBlanketGraph(a, estimatedCpdag, trueGraphCPDAG);
+            System.out.println("=====================");
+
+        }
+        for (Node a: rejects) {
+            System.out.println("=====================");
+            markovCheck.getPrecisionAndRecallOnMarkovBlanketGraph(a, estimatedCpdag, trueGraphCPDAG);
+            System.out.println("=====================");
+        }
+    }
+
+
+    @Test
+    public void testGaussianCPDAGPrecisionRecallForLocalOnMarkovBlanket2() {
+        Graph trueGraph = RandomGraph.randomDag(10, 0, 10, 100, 100, 100, false);
+        // The completed partially directed acyclic graph (CPDAG) for the given DAG.
+        Graph trueGraphCPDAG = GraphTransforms.dagToCpdag(trueGraph);
+        System.out.println("Test True Graph: " + trueGraph);
+        System.out.println("Test True Graph CPDAG: " + trueGraphCPDAG);
+
+        SemPm pm = new SemPm(trueGraph);
+        // Parameters without additional setting default tobe Gaussian
+        SemIm im = new SemIm(pm, new Parameters());
+        DataSet data = im.simulateData(1000, false);
+        edu.cmu.tetrad.search.score.SemBicScore score = new SemBicScore(data, true);
+        score.setPenaltyDiscount(2);
+        Graph estimatedCpdag = new PermutationSearch(new Boss(score)).search();
+        System.out.println("Test Estimated CPDAG Graph: " + estimatedCpdag);
+        System.out.println("~~~~~~~~~~~~~~~~~~~~~~~~~~~~");
+
+        IndependenceTest fisherZTest = new IndTestFisherZ(data, 0.05);
+        MarkovCheck markovCheck = new MarkovCheck(estimatedCpdag, fisherZTest, ConditioningSetType.MARKOV_BLANKET);
+        // ADTest pass/fail threshold default to be 0.05. shuffleThreshold default to be 0.5
+//        List<List<Node>> accepts_rejects = markovCheck.getAndersonDarlingTestAcceptsRejectsNodesForAllNodes2(fisherZTest, estimatedCpdag, 0.05, 0.5);
+        List<List<Node>> accepts_rejects = markovCheck.getAndersonDarlingTestAcceptsRejectsNodesForAllNodesPlotData2(fisherZTest, estimatedCpdag, trueGraph, 0.05, 0.3, 0.8);
+
+        List<Node> accepts = accepts_rejects.get(0);
+        List<Node> rejects = accepts_rejects.get(1);
+        System.out.println("Accepts size: " + accepts.size());
+        System.out.println("Rejects size: " + rejects.size());
+    }
+
+    @Test
+    public void testNonGaussianDAGPrecisionRecallForLocalOnMarkovBlanket2() {
+        Graph trueGraph = RandomGraph.randomDag(10, 0, 10, 100, 100, 100, false);
+        System.out.println("Test True Graph: " + trueGraph);
+        System.out.println("Test True Graph size: " + trueGraph.getNodes().size());
+
+        SemPm pm = new SemPm(trueGraph);
+
+        Parameters params = new Parameters();
+        // Manually set non-Gaussian
+        params.set(Params.SIMULATION_ERROR_TYPE, 3);
+        params.set(Params.SIMULATION_PARAM1, 1);
+
+        SemIm im = new SemIm(pm, params);
+        DataSet data = im.simulateData(1000, false);
+        edu.cmu.tetrad.search.score.SemBicScore score = new SemBicScore(data, true);
+        score.setPenaltyDiscount(2);
+        Graph estimatedCpdag = new PermutationSearch(new Boss(score)).search();
+        System.out.println("Test Estimated CPDAG Graph: " + estimatedCpdag);
+        System.out.println("~~~~~~~~~~~~~~~~~~~~~~~~~~~~");
+
+        IndependenceTest fisherZTest = new IndTestFisherZ(data, 0.05);
+        MarkovCheck markovCheck = new MarkovCheck(estimatedCpdag, fisherZTest, ConditioningSetType.MARKOV_BLANKET);
+        // ADTest pass/fail threshold default to be 0.05. shuffleThreshold default to be 0.5
+        //        List<List<Node>> accepts_rejects = markovCheck.getAndersonDarlingTestAcceptsRejectsNodesForAllNodes2(fisherZTest, estimatedCpdag, 0.05, 0.3);
+        List<List<Node>> accepts_rejects = markovCheck.getAndersonDarlingTestAcceptsRejectsNodesForAllNodesPlotData2(fisherZTest, estimatedCpdag, trueGraph, 0.05, 0.3, 0.8);
+        List<Node> accepts = accepts_rejects.get(0);
+        List<Node> rejects = accepts_rejects.get(1);
+        System.out.println("Accepts size: " + accepts.size());
+        System.out.println("Rejects size: " + rejects.size());
+    }
+
+    @Test
+    public void testNonGaussianCPDAGPrecisionRecallForLocalOnMarkovBlanket2() {
+        Graph trueGraph = RandomGraph.randomDag(10, 0, 10, 100, 100, 100, false);
+        // The completed partially directed acyclic graph (CPDAG) for the given DAG.
+        Graph trueGraphCPDAG = GraphTransforms.dagToCpdag(trueGraph);
+        System.out.println("Test True Graph: " + trueGraph);
+        System.out.println("Test True Graph CPDAG: " + trueGraphCPDAG);
+
+        SemPm pm = new SemPm(trueGraph);
+
+        Parameters params = new Parameters();
+        // Manually set non-Gaussian
+        params.set(Params.SIMULATION_ERROR_TYPE, 3);
+        params.set(Params.SIMULATION_PARAM1, 1);
+
+        SemIm im = new SemIm(pm, params);
+        DataSet data = im.simulateData(1000, false);
+        edu.cmu.tetrad.search.score.SemBicScore score = new SemBicScore(data, true);
+        score.setPenaltyDiscount(2);
+        Graph estimatedCpdag = new PermutationSearch(new Boss(score)).search();
+        System.out.println("Test Estimated CPDAG Graph: " + estimatedCpdag);
+        System.out.println("~~~~~~~~~~~~~~~~~~~~~~~~~~~~");
+
+        IndependenceTest fisherZTest = new IndTestFisherZ(data, 0.05);
+        MarkovCheck markovCheck = new MarkovCheck(estimatedCpdag, fisherZTest, ConditioningSetType.MARKOV_BLANKET);
+        // ADTest pass/fail threshold default to be 0.05. shuffleThreshold default to be 0.5
+        //        List<List<Node>> accepts_rejects = markovCheck.getAndersonDarlingTestAcceptsRejectsNodesForAllNodes2(fisherZTest, estimatedCpdag, 0.05, 0.5);
+        List<List<Node>> accepts_rejects = markovCheck.getAndersonDarlingTestAcceptsRejectsNodesForAllNodesPlotData2(fisherZTest, estimatedCpdag, trueGraph, 0.05, 0.3, 0.8);
+        List<Node> accepts = accepts_rejects.get(0);
+        List<Node> rejects = accepts_rejects.get(1);
+        System.out.println("Accepts size: " + accepts.size());
+        System.out.println("Rejects size: " + rejects.size());
+    }
 }