code: function (jvmObject) {
  
          this.logger = Logger.getLogger("IsotonicRegression_js");
          if (!jvmObject || !(jvmObject instanceof org.apache.spark.mllib.regression.IsotonicRegression)) {
              jvmObject = new org.apache.spark.mllib.regression.IsotonicRegression();
          }
  
          JavaWrapper.call(this, jvmObject);
  
      }
docs: Isotonic regression.
  Currently implemented using parallelized pool adjacent violators algorithm.
  Only univariate (single feature) algorithm supported.
  
  Sequential PAV implementation based on:
  Tibshirani, Ryan J., Holger Hoefling, and Robert Tibshirani.
  "Nearly-isotonic regression." Technometrics 53.1 (2011): 54-61.
  Available from [[http://www.stat.cmu.edu/~ryantibs/papers/neariso.pdf]]
  
  Sequential PAV parallelization based on:
  Kearsley, Anthony J., Richard A. Tapia, and Michael W. Trosset.
  "An approach to parallelizing isotonic regression."
  Applied Mathematics and Parallel Computing. Physica-Verlag HD, 1996. 141-147.
  Available from [[http://softlib.rice.edu/pub/CRPC-TRs/reports/CRPC-TR96640.pdf]]
  
  @see [[http://en.wikipedia.org/wiki/Isotonic_regression Isotonic regression (Wikipedia)]]
  @memberof module:eclairjs/mllib/regression
  @classdesc
   
  Constructs IsotonicRegression instance with default parameter isotonic = true.
  
  @class