The aim of this package is to provide the basic components needed to generate random variables (correlated or not in the case of vectorial variables) that could be used in a simulation application and to provide some basic statistical classes in order to analyze the simulation results.

At the lowest level, number generation one at a time for any primitive type is provided by either the {@link org.spaceroots.mantissa.random.FourTapRandom FourTapRandom} class or the {@link org.spaceroots.mantissa.random.MersenneTwister MersenneTwister} class which both extend the java.util.Random standard class with much better algorithms. The {@link org.spaceroots.mantissa.random.FourTapRandom FourTapRandom} algorithm is due to Robert M. Ziff (this class was kindly contributed to mantissa by Bill Maier), whereas the {@link org.spaceroots.mantissa.random.MersenneTwister MersenneTwister} class is due to Makoto Matsumoto and Takuji Nishimura.

Vectorial generators are build by embedding a {@link org.spaceroots.mantissa.random.NormalizedRandomGenerator normalized} scalar generator into the vectorial generator classes {@link org.spaceroots.mantissa.random.CorrelatedRandomVectorGenerator CorrelatedRandomVectorGenerator} or {@link org.spaceroots.mantissa.random.UncorrelatedRandomVectorGenerator UncorrelatedRandomVectorGenerator} that will be responsible for packaging all numbers into vectors with the specified mean values, standard deviations and correlation coefficients. Since most practical problems make the assumption the probability distribution is a gaussian one, the normalized generator will often be an instance of {@link org.spaceroots.mantissa.random.GaussianRandomGenerator GaussianRandomGenerator}, but uniform distribution are also available using instances of {@link org.spaceroots.mantissa.random.UniformRandomGenerator UniformRandomGenerator}.