code: package edu.cmu.tetrad.algcomparison.algorithm.continuous.dag;
  
  import edu.cmu.tetrad.algcomparison.algorithm.Algorithm;
  import edu.cmu.tetrad.annotation.AlgType;
  import edu.cmu.tetrad.data.DataModel;
  import edu.cmu.tetrad.data.DataSet;
  import edu.cmu.tetrad.data.DataType;
  import edu.cmu.tetrad.data.DataUtils;
  import edu.cmu.tetrad.graph.EdgeListGraph;
  import edu.cmu.tetrad.graph.Graph;
  import edu.cmu.tetrad.util.Parameters;
  import edu.pitt.dbmi.algo.resampling.GeneralResamplingTest;
  import edu.pitt.dbmi.algo.resampling.ResamplingEdgeEnsemble;
  
  import java.util.ArrayList;
  import java.util.List;
  
  /**
   * LiNGAM.
   *
   * @author jdramsey
   */
  @edu.cmu.tetrad.annotation.Algorithm(
          name = "LiNGAM",
          command = "lingam",
          algoType = AlgType.forbid_latent_common_causes
  )
  public class Lingam implements Algorithm {
  
      static final long serialVersionUID = 23L;
  
      public Graph search(DataModel dataSet, Parameters parameters) {
          if (parameters.getInt("numberResampling") < 1) {
              edu.cmu.tetrad.search.Lingam lingam = new edu.cmu.tetrad.search.Lingam();
              lingam.setPenaltyDiscount(parameters.getDouble("penaltyDiscount"));
              return lingam.search(DataUtils.getContinuousDataSet(dataSet));
          } else {
              Lingam algorithm = new Lingam();
  
              DataSet data = (DataSet) dataSet;
              GeneralResamplingTest search = new GeneralResamplingTest(data, algorithm, parameters.getInt("numberResampling"));
              
              search.setPercentResampleSize(parameters.getDouble("percentResampleSize"));
              search.setResamplingWithReplacement(parameters.getBoolean("resamplingWithReplacement"));
              
              ResamplingEdgeEnsemble edgeEnsemble = ResamplingEdgeEnsemble.Highest;
              switch (parameters.getInt("resamplingEnsemble", 1)) {
                  case 0:
                      edgeEnsemble = ResamplingEdgeEnsemble.Preserved;
                      break;
                  case 1:
                      edgeEnsemble = ResamplingEdgeEnsemble.Highest;
                      break;
                  case 2:
                      edgeEnsemble = ResamplingEdgeEnsemble.Majority;
              }
              search.setEdgeEnsemble(edgeEnsemble);
              search.setAddOriginalDataset(parameters.getBoolean("addOriginalDataset"));
              
              search.setParameters(parameters);
              search.setVerbose(parameters.getBoolean("verbose"));
              return search.search();
          }
      }
  
      @Override
      public Graph getComparisonGraph(Graph graph) {
          return new EdgeListGraph(graph);
      }
  
      public String getDescription() {
          return "LiNGAM (Linear Non-Gaussian Acyclic Model";
      }
  
      @Override
      public DataType getDataType() {
          return DataType.Continuous;
      }
  
      @Override
      public List<String> getParameters() {
          List<String> parameters = new ArrayList<>();
          // Resampling
          parameters.add("numberResampling");
          parameters.add("percentResampleSize");
          parameters.add("resamplingWithReplacement");
          parameters.add("resamplingEnsemble");
          parameters.add("addOriginalDataset");
          parameters.add("verbose");
          return parameters;
      }
  }