All Classes

Class	Description
AbstractL128	This abstract class is a base for algorithms from the LXM family of generators with a 128-bit LCG sub-generator.
AbstractL64	This abstract class is a base for algorithms from the LXM family of generators with a 64-bit LCG sub-generator.
AbstractL64X128	This abstract class is a base for algorithms from the LXM family of generators with a 64-bit LCG and 128-bit XBG sub-generator.
AbstractPcg6432	This abstract class is a base for algorithms from the Permuted Congruential Generator (PCG) family that use an internal 64-bit Linear Congruential Generator (LCG) and output 32-bits per cycle.
AbstractPcgMcg6432	This abstract class is a base for algorithms from the Permuted Congruential Generator (PCG) family that use an internal 64-bit Multiplicative Congruential Generator (MCG) and output 32-bits per cycle.
AbstractWell	This abstract class implements the WELL class of pseudo-random number generator from François Panneton, Pierre L'Ecuyer and Makoto Matsumoto.
AbstractWell.IndexTable	Inner class used to store the indirection index table which is fixed for a given type of WELL class of pseudo-random number generator.
AbstractXoRoShiRo1024	This abstract class is a base for algorithms from the Xor-Shift-Rotate family of 64-bit generators with 1024-bits of state.
AbstractXoRoShiRo128	This abstract class is a base for algorithms from the Xor-Shift-Rotate family of 64-bit generators with 128-bits of state.
AbstractXoRoShiRo64	This abstract class is a base for algorithms from the Xor-Shift-Rotate family of 32-bit generators with 64-bits of state.
AbstractXoShiRo128	This abstract class is a base for algorithms from the Xor-Shift-Rotate family of 32-bit generators with 128-bits of state.
AbstractXoShiRo256	This abstract class is a base for algorithms from the Xor-Shift-Rotate family of 64-bit generators with 256-bits of state.
AbstractXoShiRo512	This abstract class is a base for algorithms from the Xor-Shift-Rotate family of 64-bit generators with 512-bits of state.
AhrensDieterExponentialSampler	Sampling from an exponential distribution.
AhrensDieterMarsagliaTsangGammaSampler	Sampling from the gamma distribution.
AhrensDieterMarsagliaTsangGammaSampler.AhrensDieterGammaSampler	Class to sample from the Gamma distribution when 0 < alpha < 1.
AhrensDieterMarsagliaTsangGammaSampler.BaseGammaSampler	Base class for a sampler from the Gamma distribution.
AhrensDieterMarsagliaTsangGammaSampler.MarsagliaTsangGammaSampler	Class to sample from the Gamma distribution when the alpha >= 1.
AliasMethodDiscreteSampler	Distribution sampler that uses the Alias method.
AliasMethodDiscreteSampler.SmallTableAliasMethodDiscreteSampler	Sample from the computed tables exploiting the small power-of-two table size.
BaseProvider	Base class with default implementation for common methods.
BoxMullerGaussianSampler	Deprecated. Since version 1.1.
BoxMullerLogNormalSampler	Deprecated. Since version 1.1.
BoxMullerNormalizedGaussianSampler	Box-Muller algorithm for sampling from Gaussian distribution with mean 0 and standard deviation 1.
BoxSampler	Generate points uniformly distributed within a n-dimension box (hyperrectangle).
BoxSampler.BoxSampler2D	Sample uniformly from a box in 2D.
BoxSampler.BoxSampler3D	Sample uniformly from a box in 3D.
BoxSampler.BoxSamplerND	Sample uniformly from a box in ND.
ByteArray2IntArray	Creates a int[] from a byte[].
ByteArray2LongArray	Creates a long[] from a byte[].
ChengBetaSampler	Sampling from a beta distribution.
ChengBetaSampler.BaseChengBetaSampler	Base class to implement Cheng's algorithms for the beta distribution.
ChengBetaSampler.ChengBBBetaSampler	Computes one sample using Cheng's BB algorithm, when beta distribution alpha and beta shape parameters are both larger than 1.
ChengBetaSampler.ChengBCBetaSampler	Computes one sample using Cheng's BC algorithm, when at least one of beta distribution alpha or beta shape parameters is smaller than 1.
CollectionSampler<T>	Sampling from a Collection.
CombinationSampler	Class for representing combinations of a sequence of integers.
CompositeSamplers	Factory class to create a sampler that combines sampling from multiple samplers.
CompositeSamplers.Builder<S>	Builds a composite sampler.
CompositeSamplers.CompositeSampler<S>	A composite sampler.
CompositeSamplers.ContinuousSamplerFactory	A factory for creating a composite ContinuousSampler.
CompositeSamplers.ContinuousSamplerFactory.CompositeContinuousSampler	A composite continuous sampler.
CompositeSamplers.DiscreteProbabilitySampler	The DiscreteProbabilitySampler class defines implementations that sample from a user-defined discrete probability distribution.
CompositeSamplers.DiscreteProbabilitySamplerFactory	A factory for creating a sampler of a user-defined discrete probability distribution.
CompositeSamplers.DiscreteSamplerFactory	A factory for creating a composite DiscreteSampler.
CompositeSamplers.DiscreteSamplerFactory.CompositeDiscreteSampler	A composite discrete sampler.
CompositeSamplers.LongSamplerFactory	A factory for creating a composite LongSampler.
CompositeSamplers.LongSamplerFactory.CompositeLongSampler	A composite long sampler.
CompositeSamplers.ObjectSamplerFactory<T>	A factory for creating a composite ObjectSampler.
CompositeSamplers.ObjectSamplerFactory.CompositeObjectSampler<T>	A composite object sampler.
CompositeSamplers.SamplerBuilder<S>	Builds a composite sampler.
CompositeSamplers.SamplerBuilder.SamplerFactory<S>	A factory for creating composite samplers.
CompositeSamplers.SamplerBuilder.Specialisation	The specialisation of composite sampler to build.
CompositeSamplers.SamplerBuilder.WeightedSampler<S>	Contains a weighted sampler.
CompositeSamplers.SharedStateContinuousSamplerFactory	A factory for creating a composite SharedStateContinuousSampler.
CompositeSamplers.SharedStateContinuousSamplerFactory.CompositeSharedStateContinuousSampler	A composite continuous sampler with shared state support.
CompositeSamplers.SharedStateDiscreteProbabilitySampler	A class to implement the SharedStateDiscreteSampler interface for a discrete probability sampler given a factory and the probability distribution.
CompositeSamplers.SharedStateDiscreteSamplerFactory	A factory for creating a composite SharedStateDiscreteSampler.
CompositeSamplers.SharedStateDiscreteSamplerFactory.CompositeSharedStateDiscreteSampler	A composite discrete sampler with shared state support.
CompositeSamplers.SharedStateLongSamplerFactory	A factory for creating a composite SharedStateLongSampler.
CompositeSamplers.SharedStateLongSamplerFactory.CompositeSharedStateLongSampler	A composite long sampler with shared state support.
CompositeSamplers.SharedStateObjectSamplerFactory<T>	A factory for creating a composite SharedStateObjectSampler.
CompositeSamplers.SharedStateObjectSamplerFactory.CompositeSharedStateObjectSampler<T>	A composite object sampler with shared state support.
ContinuousInverseCumulativeProbabilityFunction	Interface for a continuous distribution that can be sampled using the inversion method.
ContinuousSampler	Sampler that generates values of type double.
ContinuousUniformSampler	Sampling from a uniform distribution.
ContinuousUniformSampler.OpenIntervalContinuousUniformSampler	Specialization to sample from an open interval (lo, hi).
Conversions	Performs seed conversions.
Coordinates	Utility class for common coordinate operations for shape samplers.
DirichletSampler	Sampling from a Dirichlet distribution.
DirichletSampler.GeneralDirichletSampler	Sample from a Dirichlet distribution with different concentration parameters for each category.
DirichletSampler.SymmetricDirichletSampler	Sample from a symmetric Dirichlet distribution with the same concentration parameter for each category.
DiscreteInverseCumulativeProbabilityFunction	Interface for a discrete distribution that can be sampled using the inversion method.
DiscreteProbabilityCollectionSampler<T>	Sampling from a collection of items with user-defined probabilities.
DiscreteSampler	Sampler that generates values of type int.
DiscreteUniformSampler	Discrete uniform distribution sampler.
DiscreteUniformSampler.AbstractDiscreteUniformSampler	Base class for a sampler from a discrete uniform distribution.
DiscreteUniformSampler.FixedDiscreteUniformSampler	Discrete uniform distribution sampler when the sample value is fixed.
DiscreteUniformSampler.LargeRangeDiscreteUniformSampler	Discrete uniform distribution sampler when the range between lower and upper is too large to fit in a positive integer.
DiscreteUniformSampler.OffsetDiscreteUniformSampler	Adds an offset to an underlying discrete sampler.
DiscreteUniformSampler.PowerOf2RangeDiscreteUniformSampler	Discrete uniform distribution sampler when the range is a power of 2 and greater than 1.
DiscreteUniformSampler.SmallRangeDiscreteUniformSampler	Discrete uniform distribution sampler when the range is small enough to fit in a positive integer.
DotyHumphreySmallFastCounting32	Implement the Small, Fast, Counting (SFC) 32-bit generator of Chris Doty-Humphrey.
DotyHumphreySmallFastCounting64	Implement the Small, Fast, Counting (SFC) 64-bit generator of Chris Doty-Humphrey.
FastLoadedDiceRollerDiscreteSampler	Distribution sampler that uses the Fast Loaded Dice Roller (FLDR).
FastLoadedDiceRollerDiscreteSampler.FixedValueDiscreteSampler	Class to handle the edge case of observations in only one category.
FastLoadedDiceRollerDiscreteSampler.FLDRSampler	Class to implement the FLDR sample algorithm.
GaussianSampler	Sampling from a Gaussian distribution with given mean and standard deviation.
GeometricSampler	Sampling from a geometric distribution.
GeometricSampler.GeometricExponentialSampler	Sample from the geometric distribution by using a related exponential distribution.
GeometricSampler.GeometricP1Sampler	Sample from the geometric distribution when the probability of success is 1.
GuideTableDiscreteSampler	Compute a sample from n values each with an associated probability.
Int2Long	Converts a Integer to an Long.
IntArray2Int	Creates a single value by "xor" of all the values in the input array.
IntArray2LongArray	Creates a long[] from an int[].
InternalGamma	Adapted and stripped down copy of class "org.apache.commons.math4.special.Gamma".
InternalUtils	Functions used by some of the samplers.
InternalUtils.FactorialLog	Class for computing the natural logarithm of the factorial of n.
IntProvider	Base class for all implementations that provide an int-based source randomness.
InverseTransformContinuousSampler	Distribution sampler that uses the inversion method.
InverseTransformDiscreteSampler	Distribution sampler that uses the inversion method.
InverseTransformParetoSampler	Sampling from a Pareto distribution.
ISAACRandom	A fast cryptographic pseudo-random number generator.
JDKRandom	A provider that uses the Random.nextInt() method of the JDK's Random class as the source of randomness.
JDKRandom.ValidatingObjectInputStream	An ObjectInputStream that's restricted to deserialize only Random using look-ahead deserialization.
JDKRandomBridge	Subclass of Random that delegates to a RestorableUniformRandomProvider instance but will otherwise rely on the base class for generating all the random types.
JDKRandomWrapper	Wraps a Random instance to implement UniformRandomProvider.
JenkinsSmallFast32	Implement Bob Jenkins's small fast (JSF) 32-bit generator.
JenkinsSmallFast64	Implement Bob Jenkins's small fast (JSF) 64-bit generator.
JumpableUniformRandomProvider	Applies to generators that can be advanced a large number of steps of the output sequence in a single operation.
KempSmallMeanPoissonSampler	Sampler for the Poisson distribution.
KISSRandom	Port from Marsaglia's "KISS" algorithm.
L128X1024Mix	A 64-bit all purpose generator.
L128X128Mix	A 64-bit all purpose generator.
L128X256Mix	A 64-bit all purpose generator.
L32X64Mix	A 32-bit all purpose generator.
L64X1024Mix	A 64-bit all purpose generator.
L64X128Mix	A 64-bit all purpose generator.
L64X128StarStar	A 64-bit all purpose generator.
L64X256Mix	A 64-bit all purpose generator.
LargeMeanPoissonSampler	Sampler for the Poisson distribution.
LargeMeanPoissonSampler.LargeMeanPoissonSamplerState	Encapsulate the state of the sampler.
LevySampler	Sampling from a Lévy distribution.
LineSampler	Generate points uniformly distributed on a line.
LineSampler.LineSampler1D	Sample uniformly from a line in 1D.
LineSampler.LineSampler2D	Sample uniformly from a line in 2D.
LineSampler.LineSampler3D	Sample uniformly from a line in 3D.
LineSampler.LineSamplerND	Sample uniformly from a line in ND.
ListSampler	Sampling from a List.
LogNormalSampler	Sampling from a log-normal distribution.
Long2Int	Converts a Long to an Integer.
Long2IntArray	Uses a long value to seed a SplitMix64 RNG and create a int[] with the requested number of random values.
Long2LongArray	Uses a Long value to seed a SplitMix64 RNG and create a long[] with the requested number of random values.
LongArray2IntArray	Creates an int[] from a long[].
LongArray2Long	Creates a single value by "xor" of all the values in the input array.
LongJumpableUniformRandomProvider	Applies to generators that can be advanced a very large number of steps of the output sequence in a single operation.
LongProvider	Base class for all implementations that provide a long-based source randomness.
LongSampler	Sampler that generates values of type long.
LXMSupport	Utility support for the LXM family of generators.
LXMSupport	Utility support for the LXM family of generators.
MarsagliaNormalizedGaussianSampler	Marsaglia polar method for sampling from a Gaussian distribution with mean 0 and standard deviation 1.
MarsagliaTsangWangDiscreteSampler	Sampler for a discrete distribution using an optimised look-up table.
MarsagliaTsangWangDiscreteSampler.AbstractMarsagliaTsangWangDiscreteSampler	The base class for Marsaglia-Tsang-Wang samplers.
MarsagliaTsangWangDiscreteSampler.Binomial	Create a sampler for the Binomial distribution.
MarsagliaTsangWangDiscreteSampler.Binomial.MarsagliaTsangWangFixedResultBinomialSampler	Return a fixed result for the Binomial distribution.
MarsagliaTsangWangDiscreteSampler.Binomial.MarsagliaTsangWangInversionBinomialSampler	Return an inversion result for the Binomial distribution.
MarsagliaTsangWangDiscreteSampler.Enumerated	Create a sampler for an enumerated distribution of n values each with an associated probability.
MarsagliaTsangWangDiscreteSampler.MarsagliaTsangWangBase64Int16DiscreteSampler	An implementation for the sample algorithm based on the decomposition of the index in the range [0,2^30) into 5 base-64 digits with 16-bit backing storage.
MarsagliaTsangWangDiscreteSampler.MarsagliaTsangWangBase64Int32DiscreteSampler	An implementation for the sample algorithm based on the decomposition of the index in the range [0,2^30) into 5 base-64 digits with 32-bit backing storage.
MarsagliaTsangWangDiscreteSampler.MarsagliaTsangWangBase64Int8DiscreteSampler	An implementation for the sample algorithm based on the decomposition of the index in the range [0,2^30) into 5 base-64 digits with 8-bit backing storage.
MarsagliaTsangWangDiscreteSampler.Poisson	Create a sampler for the Poisson distribution.
MersenneTwister	This class implements a powerful pseudo-random number generator developed by Makoto Matsumoto and Takuji Nishimura during 1996-1997.
MersenneTwister64	This class provides the 64-bits version of the originally 32-bits Mersenne Twister.
MiddleSquareWeylSequence	Middle Square Weyl Sequence Random Number Generator.
MixFunctions	Performs mixing of bits.
MultiplyWithCarry256	Port from Marsaglia's "Multiply-With-Carry" algorithm.
NativeSeedType	The native seed type.
NoOpConverter<SEED>	Dummy converter that simply passes on its input.
NormalizedGaussianSampler	Marker interface for a sampler that generates values from an N(0,1) Gaussian distribution.
NumberFactory	Utility for creating number types from one or two int values or one long value, or a sequence of bytes.
ObjectSampler<T>	Sampler that generates values of a specified type.
PcgMcgXshRr32	A Permuted Congruential Generator (PCG) that is composed of a 64-bit Multiplicative Congruential Generator (MCG) combined with the XSH-RR (xorshift; random rotate) output transformation to create 32-bit output.
PcgMcgXshRs32	A Permuted Congruential Generator (PCG) that is composed of a 64-bit Multiplicative Congruential Generator (MCG) combined with the XSH-RS (xorshift; random shift) output transformation to create 32-bit output.
PcgRxsMXs64	A Permuted Congruential Generator (PCG) that is composed of a 64-bit Linear Congruential Generator (LCG) combined with the RXS-M-XS (random xorshift; multiply; xorshift) output transformation to create 64-bit output.
PcgXshRr32	A Permuted Congruential Generator (PCG) that is composed of a 64-bit Linear Congruential Generator (LCG) combined with the XSH-RR (xorshift; random rotate) output transformation to create 32-bit output.
PcgXshRs32	A Permuted Congruential Generator (PCG) that is composed of a 64-bit Linear Congruential Generator (LCG) combined with the XSH-RS (xorshift; random shift) output transformation to create 32-bit output.
PermutationSampler	Class for representing permutations of a sequence of integers.
PoissonSampler	Sampler for the Poisson distribution.
PoissonSamplerCache	Create a sampler for the Poisson distribution using a cache to minimise construction cost.
ProviderBuilder	RNG builder.
ProviderBuilder.RandomSourceInternal	Identifiers of the generators.
RandomIntSource	Source of randomness that generates values of type int.
RandomLongSource	Source of randomness that generates values of type long.
RandomProviderDefaultState	Wraps the internal state of a generator instance.
RandomProviderState	Marker interface for objects that represents the state of a random generator.
RandomSource	This class provides the API for creating generators of random numbers.
RandomStreams	Utility for creating streams using a source of randomness.
RandomStreams.SeededObjectFactory<T>	A factory for creating objects using a seed and a using a source of randomness.
RandomStreams.SeededObjectSpliterator<T>	Spliterator for streams of a given object type that can be created from a seed and source of randomness.
RejectionInversionZipfSampler	Implementation of the Zipf distribution.
RejectionInversionZipfSampler.RejectionInversionZipfSamplerImpl	Implements the rejection-inversion method for the Zipf distribution.
RestorableUniformRandomProvider	Applies to generators whose internal state can be saved and restored.
SamplerBase	Deprecated. Since version 1.1.
Seed2ArrayConverter<IN,OUT>	Seed converter to create an output array type.
SeedConverter<IN,OUT>	Seed converter.
SeedConverterComposer<IN,TRANS,OUT>	Composes two converters.
SeedFactory	Utilities related to seeding.
SeedUtils	Utility for creating seeds.
SeedUtils.UnsignedByteProvider	Provider of unsigned 8-bit integers.
SharedStateContinuousSampler	Sampler that generates values of type double and can create new instances to sample from the same state with a given source of randomness.
SharedStateDiscreteSampler	Sampler that generates values of type int and can create new instances to sample from the same state with a given source of randomness.
SharedStateLongSampler	Sampler that generates values of type long and can create new instances to sample from the same state with a given source of randomness.
SharedStateObjectSampler<T>	Sampler that generates values of a specified type and can create new instances to sample from the same state with a given source of randomness.
SharedStateSampler<R>	Applies to samplers that can share state between instances.
SmallMeanPoissonSampler	Sampler for the Poisson distribution.
SplitMix64	A fast RNG, with 64 bits of state, that can be used to initialize the state of other generators.
SplittableUniformRandomProvider	Applies to generators that can be split into two objects (the original and a new instance) each of which implements the same interface (and can be recursively split indefinitely).
StableSampler	Samples from a stable distribution.
StableSampler.Alpha1CMSStableSampler	Implement the stable distribution case: alpha == 1 and beta != 0.
StableSampler.BaseStableSampler	Base class for implementations of a stable distribution that requires an exponential random deviate.
StableSampler.Beta0CMSStableSampler	Implement the generic stable distribution case: alpha < 2 and beta == 0.
StableSampler.Beta0WeronStableSampler	Implement the generic stable distribution case: alpha < 2 and beta == 0.
StableSampler.CauchyStableSampler	Implement the alpha = 1 and beta = 0 stable distribution case (Cauchy distribution).
StableSampler.CMSStableSampler	Implement the generic stable distribution case: alpha < 2 and beta != 0.
StableSampler.GaussianStableSampler	Implement the alpha = 2 stable distribution case (Gaussian distribution).
StableSampler.LevyStableSampler	Implement the alpha = 0.5 and beta = 1 stable distribution case (Levy distribution).
StableSampler.SpecialMath	Implement special math functions required by the CMS algorithm.
StableSampler.TransformedStableSampler	Class for implementations of a stable distribution transformed by scale and location.
StableSampler.WeronStableSampler	Implement the generic stable distribution case: alpha < 2 and beta != 0.
SubsetSamplerUtils	Utility class for selecting a subset of a sequence of integers.
TetrahedronSampler	Generate points uniformly distributed within a tetrahedron.
ThreadLocalRandomSource	This class provides a thread-local UniformRandomProvider.
ThreadLocalRandomSource.ThreadLocalRng	Extend the ThreadLocal to allow creation of the desired RandomSource.
TriangleSampler	Generate points uniformly distributed within a triangle.
TriangleSampler.TriangleSampler2D	Sample uniformly from a triangle in 2D.
TriangleSampler.TriangleSampler3D	Sample uniformly from a triangle in 3D.
TriangleSampler.TriangleSamplerND	Sample uniformly from a triangle in ND.
TSampler	Sampling from a T distribution.
TSampler.NormalTSampler	Sample from a t-distribution using a normal distribution.
TSampler.StudentsTSampler	Sample from a t-distribution using Bailey's algorithm.
TwoCmres	Random number generator designed by Mark D. Overton.
TwoCmres.Cmres	Subcycle generator.
TwoCmres.Cmres.Factory	Factory.
UniformLongSampler	Discrete uniform distribution sampler generating values of type long.
UniformLongSampler.FixedUniformLongSampler	Discrete uniform distribution sampler when the sample value is fixed.
UniformLongSampler.LargeRangeUniformLongSampler	Discrete uniform distribution sampler when the range between lower and upper is too large to fit in a positive long.
UniformLongSampler.OffsetUniformLongSampler	Adds an offset to an underlying discrete sampler.
UniformLongSampler.PowerOf2RangeUniformLongSampler	Discrete uniform distribution sampler when the range is a power of 2 and greater than 1.
UniformLongSampler.SmallRangeUniformLongSampler	Discrete uniform distribution sampler when the range is small enough to fit in a positive long.
UniformRandomProvider	Applies to generators of random number sequences that follow a uniform distribution.
UniformRandomProviderSupport	Support for UniformRandomProvider default methods.
UniformRandomProviderSupport.ProviderDoublesSpliterator	Spliterator for streams of double values that may be recursively split.
UniformRandomProviderSupport.ProviderIntsSpliterator	Spliterator for streams of int values that may be recursively split.
UniformRandomProviderSupport.ProviderLongsSpliterator	Spliterator for streams of long values that may be recursively split.
UniformRandomProviderSupport.ProviderSpliterator	Base class for spliterators for streams of values.
UniformRandomProviderSupport.ProviderSplitsSpliterator	Spliterator for streams of SplittableUniformRandomProvider.
UnitBallSampler	Generate coordinates uniformly distributed within the unit n-ball.
UnitBallSampler.UnitBallSampler1D	Sample uniformly from a 1D unit line.
UnitBallSampler.UnitBallSampler2D	Sample uniformly from a 2D unit disk.
UnitBallSampler.UnitBallSampler3D	Sample uniformly from a 3D unit ball.
UnitBallSampler.UnitBallSamplerND	Sample using ball point picking.
UnitSphereSampler	Generate vectors isotropically located on the surface of a sphere.
UnitSphereSampler.UnitSphereSampler1D	Sample uniformly from the ends of a 1D unit line.
UnitSphereSampler.UnitSphereSampler2D	Sample uniformly from a 2D unit circle.
UnitSphereSampler.UnitSphereSampler3D	Sample uniformly from a 3D unit sphere.
UnitSphereSampler.UnitSphereSamplerND	Sample uniformly from a ND unit sphere.
Well1024a	This class implements the WELL1024a pseudo-random number generator from François Panneton, Pierre L'Ecuyer and Makoto Matsumoto.
Well19937a	This class implements the WELL19937a pseudo-random number generator from François Panneton, Pierre L'Ecuyer and Makoto Matsumoto.
Well19937c	This class implements the WELL19937c pseudo-random number generator from François Panneton, Pierre L'Ecuyer and Makoto Matsumoto.
Well44497a	This class implements the WELL44497a pseudo-random number generator from François Panneton, Pierre L'Ecuyer and Makoto Matsumoto.
Well44497b	This class implements the WELL44497b pseudo-random number generator from François Panneton, Pierre L'Ecuyer and Makoto Matsumoto.
Well512a	This class implements the WELL512a pseudo-random number generator from François Panneton, Pierre L'Ecuyer and Makoto Matsumoto.
XoRoShiRo1024PlusPlus	A large-state all-purpose 64-bit generator.
XoRoShiRo1024Star	A large-state 64-bit generator suitable for double generation.
XoRoShiRo1024StarStar	A large-state all-purpose 64-bit generator.
XoRoShiRo128Plus	A fast 64-bit generator suitable for double generation.
XoRoShiRo128PlusPlus	A fast all-purpose 64-bit generator.
XoRoShiRo128StarStar	A fast all-purpose 64-bit generator.
XoRoShiRo64Star	A fast 32-bit generator suitable for float generation.
XoRoShiRo64StarStar	A fast all-purpose 32-bit generator.
XorShift1024Star	A fast RNG implementing the XorShift1024* algorithm.
XorShift1024StarPhi	A fast RNG implementing the XorShift1024* algorithm.
XoShiRo128Plus	A fast 32-bit generator suitable for float generation.
XoShiRo128PlusPlus	A fast all-purpose 32-bit generator.
XoShiRo128StarStar	A fast all-purpose 32-bit generator.
XoShiRo256Plus	A fast 64-bit generator suitable for double generation.
XoShiRo256PlusPlus	A fast all-purpose 64-bit generator.
XoShiRo256StarStar	A fast all-purpose 64-bit generator.
XoShiRo512Plus	A fast 64-bit generator suitable for double generation.
XoShiRo512PlusPlus	A fast all-purpose generator.
XoShiRo512StarStar	A fast all-purpose generator.
ZigguratNormalizedGaussianSampler	Marsaglia and Tsang "Ziggurat" method for sampling from a Gaussian distribution with mean 0 and standard deviation 1.
ZigguratSampler	Modified ziggurat method for sampling from Gaussian and exponential distributions.
ZigguratSampler.Exponential	Modified ziggurat method for sampling from an exponential distribution.
ZigguratSampler.Exponential.ExponentialMean	Specialisation which multiplies the standard exponential result by a specified mean.
ZigguratSampler.NormalizedGaussian	Modified ziggurat method for sampling from a Gaussian distribution with mean 0 and standard deviation 1.