txt: ## dist_cpan-KOBOLDWIZ-Game-Stats.md
  This software is a minimal statistics package which includes probabiltiy
  calculation based on populations which sometimes are a Distribution themselves.
  
  It is to be used in games and was built for speed not general OOP.
  
  Features are populations and distributions of probabilities wherefor an
  estimate and variance can be calculated. A Covariance class provides with
  2 populations the covariance. The same works for a correlation.
  There is also a Probability class which uses a population of probabilities
  and calculates conditional probabilities and thus a multi-variate Bayes
  method. You can access the nth probability in this case and work on
  towards Bayesian Inference.
  
  Examples:
  my $pp = 0.1; ### start probability
  my $pop = Mathx::Stat::DistributionPopulation.new; ### list of probabilties
  
  my @plist;
  my @indices;
  
  ### We fill the distributionpopulation with probabilities
  
  loop (my $i = $pp, my $j = 0; $i <= 1.0; $i+=0.1, $j++) {
  $pop.add($i);
  push(@plist, $i);
  push(@indices, $j);
  }
  
  $pop.Expectance; ### yields 0.55
  
  $pop.Variance; ## yields the variance of the probabilties in the population
  
  $pop.GeneratedNumber ### yields a marginal distribution number based on population
  
  my $cov = Mathx::Stat::Covariance.new;
  my $corr = Mathx::Stat::Correlation.new;
  
  ### calculation of covariance and correlation
  
  $cov.Covariance($pop,$pop);
  
  $corr.Correlation($pop,$pop);
  
  ### make a multi-variate probability instance (again based on a population list)
  
  my $p = Mathx::Stat::Probability.new(xpop => @plist);
  
  ### calculate Bayes on several indexed probabilities a conditional
  
  ### probability list and the index of the precondition probability
  
  $p.Bayes(@indices, @plist, 1);
  
  $p.Bayes(@indices, @plist, 0);
  
  ### caculate conditional probabilities (1-dimensional) of 2 indexed
  
  ### probabilities in the population ofthe variable $p
  
  $p.CalculatedCondP(3,0);
  
  $p.CalculatedCondP(7,4);
  