Example 1

  Uses MtxExpr, Math387, Statistics;
  procedure Example;
  var vec1: Vector;
    resA, resB : TSample;
    CIA,CIB: TTwoElmReal;
  begin
	  // first, generate 1000 randomly Weibull distributed
	  // numbers with parameters a=0.5 and b =1.2
	  vec1.Size(1000);
	  RandomWeibull(0.5,1.2,vec1);
    // Now extract the a,b and their 95% confidence intervals.
	  // Use at max 400 iterations and tolerance 0.0001
    WeibullFit(vec1,resA,resB,CIA,CIB,400,1e-4,0.05);
  end;

  #include "StatRandom.hpp"
  #include "MtxVecCpp.h"
  #include "Statistics.hpp"
  void __fastcall Example();
  {
    Vector vec1;
	  // first, generate 1000 randomly gamma distributed
	  // numbers with parameters a=0.5 and b =1.2
	  vec1->Size(1000,false);
	  RandomWeibull(0.5,1.2,vec1);
    // Now extract the a,b and their 95% confidence intervals.
	  // Use at max 400 iterations and tolerance 0.0001
    double resA, ResB;
    double CIA[2];
    double CIB[2];
    WeibullFit(vec1,resA,resB,CIA,CIB,400,1e-4,0.05);
 }

  using Dew.Math;
  using Dew.Stats;
  using Dew.Stats.Units;
  namespace Dew.Examples;
  {
    private void Example()
    {
      Vector vec1 = new Vector(1000,false);
  	  // first, generate 1000 randomly gamma distributed
  	  // numbers with parameters a=0.5 and b =1.2
  	  StatRandom.RandomWeibull(0.5,1.2,vec1);
      // Now extract the a,b and their 95% confidence intervals.
  	  // Use at max 400 iterations and tolerance 0.0001
      double resA, ResB;
      double[2] CIA;
      double[2] CIB;
      Statistics.WeibullFit(vec1,out resA,out resB,out CIA,out CIB,400,1e-4,0.05);
    }
  }