RandLib

With RandLib one can easily work with probability distributions. One of the major advantages of this library (apart from being free and open-source) is that it doesn't require any additional packages. All you need is C++17 compiler support.

What can be done via RandLib? Here are some useful examples:

• Fast sampling. For instance, generate million variates from standard normal distribution:

  NormalRand X(0, 1);
  std::vector<double> data(1e6);
  X.Sample(data);

  

• Calculate moments and other properties:

  LogNormalRand X(1, 1);
  std::cout << " Mean = " << X.Mean()
        << " and Variance = " << X.Variance()
        << "\n Median = " << X.Median()
        << " and Mode = " << X.Mode()
        << "\n Skewness = " << X.Skewness()
        << " and Excess kurtosis = " << X.ExcessKurtosis();

  

  Mean = 4.48169 and Variance = 34.5126
  Median = 2.71828 and Mode = 1
  Skewness = 6.18488 and Excess Kurtosis = 110.936

• Fitting parameters using different estimators:

  
  using std::cout;

NormalRand X(0, 1); std::vector data(10); X.Sample(data); cout << "True distribution: " << X.Name() << "\n"; cout << "Sample: "; for (double var : data) cout << var << " "; cout << "\n";

/// Bayesian estimation NormalInverseGammaRand prior(0, 1, 1, 1); NormalInverseGammaRand posterior = X.FitBayes(data, prior); cout << "Bayesian estimator: " << X.Name() << "\n"; cout << "(Posterior distribution: " << posterior.Name() << ")\n";

/// Uniformly minimum variance unbiased estimator X.Fit(data, true); cout << "UMVU estimator: " << X.Name() << "\n";

/// Maximum-likelihood estimator X.Fit(data); cout << "Maximum-likelihood estimator: " << X.Name() << "\n";

![alt tag](images/normalFit.png)

True distribution: Normal(0, 1) Sample: -0.328154 0.709122 -0.607214 1.11472 -1.23726 -0.123584 0.59374 -1.20573 -0.397376 -1.63173 Bayesian estimator: Normal(-0.283042, 0.951348) (Posterior distribution: Normal-Inverse-Gamma(-0.283042, 11, 6, 4.75674)) UMVU estimator: Normal(-0.311347, 0.82504) Maximum-likelihood estimator: Normal(-0.311347, 0.742536)

For documentation look up [here](documentation/document.pdf)