SSJ
3.3.1
Stochastic Simulation in Java

▼Ntutorial  
CAsianGBM  
CAsianGBMQMC  
CAsianNew  
▼CBankEv  
CArrival  
CDeparture  
▼CCallCenter  
CArrival  
CCall  
CCallCompletion  
CNextPeriod  
CCollision  
CCompareOutputs  
CInventory  
CInventoryCRN  
CNewTestAsianRQMC  
CNonuniform  
▼CPreyPred  
CEndOfSim  
CPreds  
CPreys  
CPrintPoint  
▼CQueueEv  
CArrival  
CCustomer  
CDeparture  
CEndOfSim  
CQueueLindley  
▼CQueueObs  
CLargeWaitsCollector  
CObservationTrace  
▼CRunClass  
CRunClassException  
CSan13Dist  This class simulates a specific stochastic activity network with 9 nodes and 13 links, taken from Elmaghraby (1977) and used again in L'Ecuyer and Lemieux (2000), "Variance Reduction via Lattice Rules" 
CSnippet  
▼Numontreal  
▼Nssj  
▶Ncharts  Charts generation 
CAxis  Represents an axis of a chart encapsulated by an instance of XYChart 
CBoxChart  This class provides tools to create and manage boxandwhisker plots 
CBoxSeriesCollection  This class stores data used in a umontreal.ssj.charts.CategoryChart 
CCategoryChart  This class provides tools to create charts from data in a simple way 
CContinuousDistChart  This class provides tools to plot the density and the cumulative probability of a continuous probability distribution 
CCustomHistogramDataset  A dataset that can be used for creating histograms 
CDiscreteDistIntChart  This class provides tools to plot the mass function and the cumulative probability of a discrete probability distribution over the integers 
CEmpiricalChart  This class provides additional tools to create and manage empirical plots 
CEmpiricalRenderer  A renderer that draws horizontal lines between points and/or draws shapes at each data point to provide an empirical style chart 
CEmpiricalSeriesCollection  Stores data used in a EmpiricalChart 
CHistogramChart  This class provides tools to create and manage histograms 
CHistogramSeriesCollection  Stores data used in a HistogramChart 
CMultipleDatasetChart  Provides tools to plot many datasets on the same chart 
CPlotFormat  Provide tools to import and export data set tables to and from Gnuplot, MATLAB and Mathematica compatible formats, or customized format 
CPPPlot  This class implements PPplot (or probabilityprobability plot) objects that compare two probability distributions 
CQQPlot  This class implements QQplot (or quantilequantile plot) objects that compare two probability distributions 
CScatterChart  This class provides tools to create and manage scatter plots 
CSSJCategorySeriesCollection  Stores data used in a CategoryChart 
CSSJXYSeriesCollection  Stores data used in a XYChart 
CXYChart  This class provides tools to create charts from data in a simple way 
CXYLineChart  This class provides tools to create and manage curve plots 
CXYListSeriesCollection  This class extends umontreal.ssj.charts.SSJXYSeriesCollection 
CYListChart  This class extends the class umontreal.ssj.charts.XYLineChart 
CYListSeriesCollection  This class extends umontreal.ssj.charts.XYListSeriesCollection 
▶Ndiscrepancy  Methods to compute various types of discrepancies for quasiMonte Carlo point sets 
CBigDiscrepancy  This abstract class is the base class of all discrepancy classes programmed with floatingpoint numbers with multiprecision 
CBigDiscShiftBaker1  This class computes the same discrepancy as in umontreal.ssj.discrepancy.DiscShiftBaker1 [see eq 
CBigDiscShiftBaker1Lattice  This class computes the same discrepancy as in umontreal.ssj.discrepancy.DiscShiftBaker1Lattice [see eq 
CDiscL2Hickernell  This class computes the Hickernell \(\mathcal{L}_2\)star discrepancy in [81] (eq 
CDiscL2Star  This class computes the traditional \(\mathcal{L}_2\)star discrepancy \(\mathcal{D}_2^*(\mathcal{P})\) for a set of \(n\) points \(\mathcal{P}\) [236], [237], [83] 
CDiscL2Symmetric  COMPLÉTER LA DOC ICI 
CDiscL2Unanchored  A discrepancy is said to be reflectioninvariant if it has the same value when the points are reflected through any plane \(x_j= 1/2\), passing through the center of the unit hypercube, i.e 
CDiscrepancy  This abstract class is the base class of all discrepancy classes 
CDiscrepancyContainer  This class is used to compute, store and display discrepancies 
CDiscShift1  This class computes the discrepancy for randomly shifted points of a set \(\mathcal{P}\) [84] (eq 
CDiscShift1Lattice  This class computes the same discrepancy for randomly shifted points of a set \(\mathcal{L}\) as given in eq 
CDiscShift2  This class computes the discrepancy in [84] (eq 
CDiscShift2Lattice  This class computes the same discrepancy for randomly shifted points of a set \(\mathcal{L}\) as given in eq 
CDiscShiftBaker1  This class computes the discrepancy for randomly shifted, then baker folded points of a set \(\mathcal{P}\) 
CDiscShiftBaker1Lattice  This class computes the same discrepancy in [84] (eq 
CPalpha  Extends the class Discrepancy and implements the methods required to compute the \(P_{\alpha}\) figure of merit for a lattice point set \(\Psi_s\) which is the intersection of a lattice \(L\) and the unit hypercube \([0, 1)^s\) in \(s\) dimensions 
CSearcher  This class implements methods to search for the best lattices of rank 1, defined as follows [219] 
CSearcherCBC  This class implements searches to find the best rank1 lattices with respect to a given discrepancy, using componentbycomponent (CBC) searches, random or exhaustive for each component 
CSearcherKorobov  This class implements searches to find the best Korobov lattices with respect to a given discrepancy 
▶Nfunctionfit  Function fit utilities 
CBSpline  Represents a Bspline with control points at \((X_i, Y_i)\) 
CLeastSquares  This class implements different linear regression models, using the least squares method to estimate the regression coefficients 
CPolInterp  Represents a polynomial that interpolates through a set of points 
CSmoothingCubicSpline  Represents a cubic spline with nodes at \((x_i, y_i)\) computed with the smoothing cubic spline algorithm of Schoenberg [42], [204] 
▶Nfunctions  Univariate functions as Java objects 
CAverageMathFunction  Represents a function computing the average of several functions 
CIdentityMathFunction  Represents the identity function \(f(x)=x\) 
CMathFunction  This interface should be implemented by classes which represent univariate mathematical functions 
CMathFunctionUtil  Provides utility methods for computing derivatives and integrals of functions 
CMathFunctionWithDerivative  Represents a mathematical function whose \(n\)th derivative can be computed using derivative(double,int) 
CMathFunctionWithFirstDerivative  Represents a mathematical function whose derivative can be computed using derivative(double) 
CMathFunctionWithIntegral  Represents a mathematical function whose integral can be computed by the integral(double,double) method 
CMultiFunction  This interface should be implemented by classes which represent multivariate mathematical functions 
CPiecewiseConstantFunction  Represents a piecewiseconstant function 
CPolynomial  Represents a polynomial of degree \(n\) in power form 
CPowerMathFunction  Represents a function computing \((af(x) + b)^p\) for a userdefined function \(f(x)\) and power \(p\) 
CShiftedMathFunction  Represents a function computing \(f(x)  \delta\) for a userdefined function \(f(x)\) and shift \(\delta\) 
CSqrtMathFunction  Represents a function computing the square root of another function \(f(x)\) 
CSquareMathFunction  Represents a function computing \((af(x) + b)^2\) for a userdefined function \(f(x)\) 
▶Ngof  Goodnessoffit test Statistics 
CFBar  This class is similar to FDist, except that it provides static methods to compute or approximate the complementary distribution function of \(X\), which we define as \(\bar{F} (x) = P[X\ge x]\), instead of \(F (x)=P[X\le x]\) 
CFDist  This class provides methods to compute (or approximate) the distribution functions of special types of goodnessoffit test statistics 
CGofFormat  This class contains methods used to format results of GOF test statistics, or to apply a series of tests simultaneously and format the results 
▶CGofStat  This class provides methods to compute several types of EDF goodnessoffit test statistics and to apply certain transformations to a set of observations 
COutcomeCategoriesChi2  This class helps managing the partitions of possible outcomes into categories for applying chisquare tests 
CKernelDensity  This static class provides methods to compute a kernel density estimator from a set of \(n\) individual observations \(x_0, …, x_{n1}\), which define an empirical distribution 
▶Nhups  Highly Uniform Point Sets 
▶CAntitheticPointSet  This container class provides antithetic versions of the contained points 
CAntitheticPointSetIterator  
▶CBakerTransformedPointSet  This container class embodies a point set to which a baker's transformation (also called a tent transform) is applied (see, e.g., [49], [84], [156]) 
CBakerTransformedPointSetIterator  
▶CCachedPointSet  This container class caches a point set by precomputing and storing its points locally in an array 
CCachedPointSetIterator  This class implements a CachedPointSet iterator which takes the value directly in the array x in which the points are cached, rather than calling getCoordinate inherited from CachedPointSet 
▶CContainerPointSet  This acts as a generic base class for all container classes that contain a point set and apply a specific kind of transformation to the coordinates \(u_{i,j}\) when the points are generated by the iterator 
CContainerPointSetIterator  
CCycleBasedLFSR  Linear feedback shift register (LFSR) random number generators [147], [124], [187], produce numbers by generating a sequence of bits from a linear recurrence modulo 2, and forming fractional numbers by taking blocks of successive bits 
▶CCycleBasedPointSet  This abstract class provides the basic structures for storing and manipulating a point set defined by a set of cycles 
CCycleBasedPointSetIterator  
▶CCycleBasedPointSetBase2  Similar to CycleBasedPointSet, except that the successive values in the cycles are stored as integers in the range \(\{0,\dots,2^k1\}\), where \(1\le k \le31\) 
CCycleBasedPointSetBase2Iterator  
▶CDigitalNet  This class provides the basic structures for storing and manipulating linear digital nets in base \(b\), for an arbitrary base \(b\ge2\) 
CDigitalNetIterator  
CDigitalNetIteratorNoGray  
▶CDigitalNetBase2  A special case of DigitalNet for the base \(b=2\) 
CDigitalNetBase2Iterator  
CDigitalNetBase2IteratorNoGray  
CDigitalNetBase2FromFile  This class permits one to read the parameters that define a digital net in base 2 either from a file, or from a URL address 
▶CDigitalNetFromFile  This class allows us to read the parameters defining a digital net either from a file, or from a URL address on the World Wide Web 
CNetComparator  
▶CDigitalSequence  This abstract class describes methods specific to digital sequences 
CDigitalNetIteratorShiftGenerators  
CDigitalNetIteratorShiftNoGray  
▶CDigitalSequenceBase2  This abstract class describes methods specific to digital sequences in base 2 
CDigitalNetBase2IteratorShiftGenerators  
CDigitalNetBase2IteratorShiftNoGray  
CEmptyRandomization  This class implements an empty umontreal.ssj.hups.PointSetRandomization 
CF2wCycleBasedLFSR  This class creates a point set based upon a linear feedback shift register sequence 
CF2wCycleBasedPolyLCG  This class creates a point set based upon a linear congruential sequence in the finite field \(\mathbb F_{2^w}[z]/P(z)\) 
CF2wNetLFSR  This class implements a digital net in base 2 starting from a linear feedback shift register generator 
CF2wNetPolyLCG  This class implements a digital net in base 2 starting from a polynomial LCG in \(\mathbb F_{2^w}[z]/P(z)\) 
CF2wStructure  This class implements methods and fields needed by the classes umontreal.ssj.hups.F2wNetLFSR, umontreal.ssj.hups.F2wNetPolyLCG, umontreal.ssj.hups.F2wCycleBasedLFSR and umontreal.ssj.hups.F2wCycleBasedPolyLCG 
CFaureSequence  This class implements digital nets or digital sequences formed by the first \(n = b^k\) points of the Faure sequence in base \(b\) 
CHaltonSequence  This class implements the sequence of Halton [76] , which is essentially a modification of Hammersley nets for producing an infinite sequence of points having low discrepancy 
CHammersleyPointSet  This class implements Hammersley point sets, which are defined as follows 
CIndependentPointsCached  Similar to IndependentPoints, but the points are all generated and stored (cached) when the point set is randomized 
CKorobovLattice  This class implements a Korobov lattice, which represents the same point set as in class LCGPointSet, but implemented differently 
CKorobovLatticeSequence  
CLatinHypercube  Implements Latin Hypercube Sampling (LHS) with \(n\) points in the \(s\)dimensional unit hypercube 
CLCGPointSet  Implements a recurrencebased point set defined via a linear congruential recurrence of the form \(x_i = a x_{i1} \mod n\) and \(u_i = x_i / n\) 
CLMScrambleShift  This class implements a umontreal.ssj.hups.PointSetRandomization that performs a left matrix scrambling and adds a random digital shift 
CNestedUniformScrambling  This class implements a PointSetRandomization that performs Owen's nested uniform scrambling [191], [195] 
CNiedSequenceBase2  This class implements digital sequences constructed from the Niederreiter sequence in base 2 
CNiedXingSequenceBase2  This class implements digital sequences based on the NiederreiterXing sequence in base 2 
CPaddedPointSet  This container class realizes padded point sets, constructed by taking some coordinates from a point set \(P_1\), other coordinates from a point set \(P_2\), and so on 
▶CPointSet  This abstract class represents a general point set 
CDefaultPointSetIterator  This class implements a default point set iterator 
CPointSetIterator  This is the interface for iterators that permit one to go through the points of a #PointSet and the successive coordinates of these points 
CPointSetRandomization  This interface is for a randomization that can be used to randomize a umontreal.ssj.hups.PointSet 
CRadicalInverse  This class implements basic methods for working with radical inverses of integers in an arbitrary basis \(b\) 
CRandomShift  This class implements a umontreal.ssj.hups.PointSetRandomization 
CRandomStart  This class implements a umontreal.ssj.hups.PointSetRandomization that randomizes a sequence simply by taking a random starting point 
CRandShiftedMod1PointSet  This container class embodies an arbitrary point set and its iterator adds a random shift modulo 1 to all the points, when producing the coordinates 
▶CRank1Lattice  This class implements point sets specified by integration lattices of rank 
CRank1LatticeIterator  
CRQMCPointSet  This class is used for randomized quasiMonte Carlo (RQMC) simulations [125], [126], [192], [193] 
CSMScrambleShift  This class implements a umontreal.ssj.hups.PointSetRandomization that performs a striped matrix scrambling [195] and adds a random digital shift 
CSobolSequence  This class implements digital nets and digital sequences in base 2 formed by the first \(n = 2^k\) points of a Sobol’ sequence [221], [222] 
▶CSortedAndCutPointSet  This class is useful for the ArrayRQMC method, in the situation where the Markov chain has a multidimensional state, the RQMC points are sorted once for all, based on their first \(\ell\) coordinates, then these coordinates are removed and only the remaining coordinates are kept and randomized at each step 
CSortedAndCutPointSetIterator  
CStratifiedUnitCube  This class implements a stratification of the unit cube in rectangular boxes of same size and orientation 
CStratifiedUnitCubeAnti  This class implements a stratification of the unit cube in rectangular boxes of same size and orientation, similar to StratifiedUnitCube 
CSubsetOfPointSet  This container class permits one to select a subset of a point set 
▶Nlatnetbuilder  Tools to search for good quasiMonte Carlo point sets and sequences defined as integration lattices, polynomial lattices, and digital nets (and sequences) 
▶CDigitalNetSearch  Class for the search of good digital nets using LatNet Builder 
CDigitalNetBase2FromLatNetBuilder  Class for the construction od digital nets 
COrdinaryLatticeSearch  Class for the search of good rank1 ordinary lattice rules using LatNet Builder 
CPolynomialLatticeSearch  Class for the search of good polynomial lattice rules using LatNet Builder 
CSearch  Abstract class for the search of highly uniform point sets with LatNet Builder 
▶Nmarkovchainrqmc  Tools to simulate Markov chains with the ArrayRQMC method 
CArrayOfComparableChains  This class provides tools to simulate an array of MarkovChainComparable objects with the arrayRQMC method of [139], [141] 
CArrayOfDoubleChains  Similar to ArrayOfComparableChains, except that instead of working with \(n\) clones of a MarkovChain, we use a single MarkovChainDouble object for all the chains 
CMarkovChain  This class defines a generic Markov chain and provides basic tools to simulate it for a given number of steps or until it stops, and to recover the performance measure 
CMarkovChainComparable  A subclass of MarkovChain for which there is a total ordering between the states, induced by the implementation of the umontreal.ssj.util.MultiDimComparable interface 
CMarkovChainDouble  A special kind of Markov chain whose state space is a subset of the real numbers 
▶Nmcqmctools  Tools for Simple Monte Carlo and QuasiMonte Carlo Experiments 
▶Nanova  Tools to estimate ANOVA components for Monte Carlo models 
CAnova  This class automates the process of replicating estimators of the ANOVA variances 
CAnovaObserver  ANOVA observer 
CAnovaVarianceCollector  Extends ListOfTallies to collect ANOVA variances 
CAnovaVarianceEstimator  ANOVA variance estimator 
CBasicObservationCollector  Does nothing but counting the total number of observations 
CCoordinateSet  Represents a set of coordinates 
CCoordinateSetLong  Implementation of CoordinateSet using a long bitmask internal representation 
CIntegrator  
CMeanVarExperiment  
CMonteCarloIntegrator  
CMonteCarloModelDoubleRQMC  An interface for a simple simulation model for which Monte Carlo (MC) or RQMC experiments are to be performed 
CMonteCarloModelRQMC  An interface for a simple simulation model for which Monte Carlo (MC) or RQMC experiments are to be performed 
CMonteCarloSampler  Monte Carlo sampler 
CObservationCollector  
CObservationCollectorList  
CPartialVariance  Represents the partial variance of a function with respect to a given coordinate set 
CPartialVarianceEstimator  Partial variance estimator 
CQMCSampler  QMC sampler 
CRandomIntegrator  
CRandomSampler  
CReplicator  Replicator class 
CReport  
CRQMCSampler  QMC sampler 
CSampler  
CSplitStream  Implements a random stream that mixes two input streams by using a coordinate mask 
CMonteCarloExperiment  Provides generic tools to perform simple Monte Carlo experiments with a simulation model that implements one of the interfaces MonteCarloModelDouble, MonteCarloModelDoubleArray, or MonteCarloModelCV 
CMonteCarloModel  An interface for a simple simulation model for which Monte Carlo (MC) or RQMC experiments are to be performed 
CMonteCarloModelCV  An extension of MonteCarloModelDouble that also implements a vector of control variates 
CMonteCarloModelDensityKnown  An interface for a simulation model for which Monte Carlo (MC) and RQMC experiments are to be performed 
CMonteCarloModelDouble  An interface for a very simple simulation model for which Monte Carlo (MC) and RQMC experiments are to be performed 
CMonteCarloModelDoubleArray  Similar to MonteCarloModelDouble except that the returned performance is an array of real numbers 
CRQMCExperiment  Provides basic generic tools to perform RQMC experiments with a simulation model that implements the MonteCarloModelDouble interface 
CRQMCExperimentSeries  This class offers facilities to perform experiments on the convergence of the variance when estimating a mean (expectation) with a series of RQMC point sets usually of the same type, but different sizes \(n\) 
▶Nprobdist  Probability distributions 
CAndersonDarlingDist  Extends the class ContinuousDistribution for the Anderson–Darling distribution (see [6], [165], [173], [225] ) 
CAndersonDarlingDistQuick  Extends the class AndersonDarlingDist for the Anderson–Darling distribution (see [6], [165], [225] ) 
CBernoulliDist  Extends the class DiscreteDistributionInt for the Bernoulli distribution [118] with parameter \(p\), where \(0\le p\le1\) 
CBetaDist  Extends the class ContinuousDistribution for the beta distribution [100] (page 210) with shape parameters \(\alpha> 0\) and \(\beta> 0\), over the interval \([a,b]\), where \(a < b\) 
CBetaSymmetricalDist  Specializes the class BetaDist to the case of a symmetrical beta distribution over the interval \([0,1]\), with shape parameters \(\alpha= \beta\) 
CBinomialDist  Extends the class DiscreteDistributionInt for the binomial distribution [118] (page 321) with parameters \(n\) and \(p\), where \(n\) is a positive integer and \(0\le p\le1\) 
CCauchyDist  Extends the class ContinuousDistribution for the Cauchy distribution [99] (page 299) with location parameter \(\alpha\) and scale parameter \(\beta> 0\) 
CChiDist  Extends the class ContinuousDistribution for the chi distribution [99] (page 417) with shape parameter \(\nu > 0\), where the number of degrees of freedom \(\nu\) is a positive integer 
CChiSquareDist  Extends the class ContinuousDistribution for the chisquare distribution with \(n\) degrees of freedom, where \(n\) is a positive integer [99] (page 416) 
CChiSquareDistQuick  Provides a variant of ChiSquareDist with faster but less accurate methods 
CChiSquareNoncentralDist  Extends the class ContinuousDistribution for the noncentral chisquare distribution with \(\nu\) degrees of freedom and noncentrality parameter \(\lambda\), where \(\nu> 0\) and \(\lambda> 0\) [100] (page 436) 
CConstantDist  Represents a constant discrete distribution taking a single real value with probability 1 
CConstantIntDist  Represents a constant discrete distribution taking a single integer value with probability 1 
CContinuousDistribution  Classes implementing continuous distributions should inherit from this base class 
CCramerVonMisesDist  Extends the class ContinuousDistribution for the Cramérvon Mises distribution (see [55], [224], [225] ) 
CDiscreteDistribution  This class implements discrete distributions over a finite set of real numbers (also over integers as a particular case) 
CDiscreteDistributionInt  Classes implementing discrete distributions over the integers should inherit from this class 
CDistribution  This interface should be implemented by all classes supporting discrete and continuous distributions 
CDistributionFactory  This class implements a string API for the package probdist 
CEmpiricalDist  Extends DiscreteDistribution to an empirical distribution function, based on the observations \(X_{(1)},…,X_{(n)}\) (sorted by increasing order) 
CErlangDist  Extends the class GammaDist for the special case of the Erlang distribution with shape parameter \(k > 0\) and scale parameter \(\lambda> 0\) 
CExponentialDist  Extends the class ContinuousDistribution for the exponential distribution [99] (page 494) with mean \(1/\lambda\) where \(\lambda> 0\) 
CExponentialDistFromMean  Extends the ExponentialDist class with a constructor accepting as argument the mean \(1/\lambda\) instead of the rate \(\lambda\) 
CExtremeValueDist  This class has been replaced by GumbelDist 
CFatigueLifeDist  Extends the class ContinuousDistribution for the fatigue life distribution [20] with location parameter \(\mu\), scale parameter \(\beta\) and shape parameter \(\gamma\) 
CFisherFDist  Extends the class ContinuousDistribution for the Fisher F distribution with \(n_1\) and \(n_2\) degrees of freedom, where \(n_1\) and \(n_2\) are positive integers 
CFoldedNormalDist  Extends the class ContinuousDistribution for the folded normal distribution with parameters \(\mu\ge0\) and \(\sigma> 0\) 
CFrechetDist  Extends the class ContinuousDistribution for the Fréchet distribution [100] (page 3), with location parameter \(\delta\), scale parameter \(\beta> 0\), and shape parameter \(\alpha> 0\), where we use the notation \(z = (x\delta)/\beta\) 
CGammaDist  Extends the class ContinuousDistribution for the gamma distribution [99] (page 337) with shape parameter \(\alpha> 0\) and scale parameter \(\lambda> 0\) 
CGammaDistFromMoments  Extends the GammaDist distribution with constructors accepting the mean \(\mu\) and variance \(\sigma^2\) as arguments instead of a shape parameter \(\alpha\) and a scale parameter \(\lambda\) 
CGeometricDist  Extends the class DiscreteDistributionInt for the geometric distribution [118] (page 322) with parameter \(p\), where \(0 < p < 1\) 
CGumbelDist  Extends the class ContinuousDistribution for the Gumbel distribution [100] (page 2), with location parameter \(\delta\) and scale parameter \(\beta\neq0\) 
CHalfNormalDist  Extends the class ContinuousDistribution for the halfnormal distribution with parameters \(\mu\) and \(\sigma> 0\) 
CHyperbolicSecantDist  Extends the class ContinuousDistribution for the hyperbolic secant distribution with location parameter \(\mu\) and scale parameter \(\sigma> 0\) 
CHypergeometricDist  Extends the class DiscreteDistributionInt for the hypergeometric distribution [66] (page 101) with \(k\) elements chosen among \(l\), \(m\) being of one type, and \(lm\) of the other 
CHypoExponentialDist  This class implements the hypoexponential distribution, also called the generalized Erlang distribution 
CHypoExponentialDistEqual  This class implements the hypoexponential distribution for the case of equidistant \(\lambda_i = (n+1i)h\) 
CHypoExponentialDistQuick  This class is a subclass of HypoExponentialDist and also implements the hypoexponential distribution 
CInverseDistFromDensity  Implements a method for computing the inverse of an arbitrary continuous distribution function when only the probability density is known [43] 
CInverseGammaDist  Extends the class ContinuousDistribution for the inverse gamma distribution with shape parameter \(\alpha> 0\) and scale parameter \(\beta> 0\), also known as the Pearson type V distribution 
CInverseGaussianDist  Extends the class ContinuousDistribution for the inverse Gaussian distribution with location parameter \(\mu> 0\) and scale parameter \(\lambda> 0\) 
CJohnsonSBDist  Extends the class ContinuousDistribution for the Johnson \(S_B\) distribution [101], [118], [63] with shape parameters \(\gamma\) and \(\delta> 0\), location parameter \(\xi\), and scale parameter \(\lambda>0\) 
CJohnsonSLDist  Extends the class ContinuousDistribution for the Johnson \(S_L\) distribution (see [101], [99] ) 
CJohnsonSUDist  Extends the class ContinuousDistribution for the Johnson \(S_U\) distribution (see [118] (page 316)) 
CJohnsonSystem  This class contains common parameters and methods for the Johnson system of distributions [101], [99] with shape parameters \(\gamma\) and \(\delta> 0\), location parameter \(\xi\), and scale parameter \(\lambda>0\) 
CKolmogorovSmirnovDist  Extends the class ContinuousDistribution for the KolmogorovSmirnov distribution with parameter \(n\) [55] 
CKolmogorovSmirnovDistQuick  Extends the class KolmogorovSmirnovDist for the Kolmogorov–Smirnov distribution 
CKolmogorovSmirnovPlusDist  Extends the class ContinuousDistribution for the Kolmogorov–Smirnov+ distribution (see [40], [55], [27] ) 
CLaplaceDist  Extends the class ContinuousDistribution for the Laplace distribution (see, e.g., [100] (page 165)) 
CLogarithmicDist  Extends the class DiscreteDistributionInt for the logarithmic distribution 
CLogisticDist  Extends the class ContinuousDistribution for the logistic distribution (e.g., [100] (page 115)) 
CLoglogisticDist  Extends the class ContinuousDistribution for the LogLogistic distribution with shape parameter \(\alpha> 0\) and scale parameter \(\beta> 0\) 
CLognormalDist  Extends the class ContinuousDistribution for the lognormal distribution [99] 
CLognormalDistFromMoments  Extends the LognormalDist class with a constructor accepting the mean \(m\) and the variance \(v\) of the distribution as arguments 
CNakagamiDist  Extends the class ContinuousDistribution for the Nakagami distribution with location parameter \(a\), scale parameter \(\lambda> 0\) and shape parameter \(c > 0\) 
CNegativeBinomialDist  Extends the class DiscreteDistributionInt for the negative binomial distribution [118] (page 324) with real parameters \(n\) and \(p\), where \(n > 0\) and \(0\le p\le1\) 
CNormalDist  Extends the class ContinuousDistribution for the normal distribution (e.g., [99] (page 80)) 
CNormalDistQuick  A variant of the class NormalDist (for the normal distribution with mean \(\mu\) and variance \(\sigma^2\)) 
CNormalInverseGaussianDist  Extends the class ContinuousDistribution for the normal inverse gaussian distribution with location parameter \(\mu\), scale parameter \(\delta> 0\), tail heavyness \(\alpha> 0\), and asymmetry parameter \(\beta\) such that \(0 \le\beta < \alpha\) 
CParetoDist  Extends the class ContinuousDistribution for a distribution from the Pareto family, with shape parameter \(\alpha> 0\) and location parameter \(\beta> 0\) [99] (page 574) 
CPascalDist  The Pascal distribution is a special case of the negative binomial distribution [118] (page 324) with parameters \(n\) and \(p\), where \(n\) is a positive integer and \(0\le p\le1\) 
CPearson5Dist  THIS CLASS HAS BEEN RENAMED InverseGammaDist 
CPearson6Dist  Extends the class ContinuousDistribution for the Pearson type VI distribution with shape parameters \(\alpha_1 > 0\) and \(\alpha_2 > 0\), and scale parameter \(\beta> 0\) 
CPiecewiseLinearEmpiricalDist  Extends the class ContinuousDistribution for a piecewiselinear approximation of the empirical distribution function, based on the observations \(X_{(1)},…,X_{(n)}\) (sorted by increasing order), and defined as follows (e.g., [118] (page 318)) 
CPoissonDist  Extends the class DiscreteDistributionInt for the Poisson distribution [118] (page 325) with mean \(\lambda\ge0\) 
CPowerDist  Extends the class ContinuousDistribution for the power distribution [57] (page 161) with shape parameter \(c > 0\), over the interval \([a,b]\), where \(a < b\) 
CRayleighDist  This class extends the class ContinuousDistribution for the Rayleigh distribution [57] with location parameter \(a\), and scale parameter \(\beta> 0\) 
CStudentDist  Extends the class ContinuousDistribution for the Student \(t\)distribution [100] (page 362) with \(n\) degrees of freedom, where \(n\) is a positive integer 
CStudentDistQuick  Extends the class StudentDist for the Student \(t\)distribution 
CTriangularDist  Extends the class ContinuousDistribution for the triangular distribution (see [100] (page 297) and [118] (page 317)) with domain \([a,b]\) and mode (or shape parameter) \(m\), where \(a\le m\le b\) 
CTruncatedDist  This container class takes an arbitrary continuous distribution and truncates it to an interval \([a,b]\), where \(a\) and \(b\) can be finite or infinite 
CUniformDist  Extends the class ContinuousDistribution for the uniform distribution [100] (page 276) over the interval \([a,b]\) 
CUniformIntDist  Extends the class DiscreteDistributionInt for the discrete uniform distribution over the range \([i,j]\) 
CWatsonGDist  Extends the class ContinuousDistribution for the Watson \(G\) distribution (see [41], [238] ) 
CWatsonUDist  Extends the class ContinuousDistribution for the Watson U distribution (see [55], [224], [225] ) 
CWeibullDist  This class extends the class ContinuousDistribution for the Weibull distribution [99] (page 628) with shape parameter \(\alpha> 0\), location parameter \(\delta\), and scale parameter \(\lambda> 0\) 
▶Nprobdistmulti  Multivariate Probability Distributions 
▶Nnorta  This package implements the correlation matching algorithms proposed in [13] for the situation where one wants to use the NORTA method to fit a multivariate distribution with discrete marginals 
CNI1  Extends the class NortaInitDisc and implements the algorithm NI1 
CNI2a  Extends the class NortaInitDisc and implements the algorithm NI2a 
CNI2b  Extends the class NortaInitDisc and implements the algorithm NI2b 
CNI3  Extends the class NortaInitDisc and implements the algorithm NI3 
CNortaInitDisc  This abstract class defines the algorithms used for NORTA initialization when the marginal distributions are discrete 
CBiNormalDist  Extends the class ContinuousDistribution2Dim for the bivariate normal distribution [98] (page 84) 
CBiNormalDonnellyDist  Extends the class BiNormalDist for the bivariate normal distribution [98] (page 84) using a translation of Donnelly’s Fortran code in [53] 
CBiNormalGenzDist  Extends the class BiNormalDist for the bivariate normal distribution [98] (page 84) using Genz’s algorithm as described in [67] 
CBiStudentDist  Extends the class ContinuousDistribution2Dim for the standard bivariate Student’s \(t\) distribution [98] (page 132) 
CContinuousDistribution2Dim  Classes implementing 2dimensional continuous distributions should inherit from this class 
CContinuousDistributionMulti  Classes implementing continuous multidimensional distributions should inherit from this class 
CDirichletDist  Implements the abstract class ContinuousDistributionMulti for the Dirichlet distribution with parameters ( \(\alpha_1\),…, \(\alpha_d\)), \(\alpha_i > 0\) 
CDiscreteDistributionIntMulti  Classes implementing multidimensional discrete distributions over the integers should inherit from this class 
CMultinomialDist  Implements the abstract class DiscreteDistributionIntMulti for the multinomial distribution with parameters \(n\) and ( \(p_1\), …, \(p_d\)) 
CMultiNormalDist  Implements the abstract class ContinuousDistributionMulti for the multinormal distribution with mean vector \(\boldsymbol{\mu}\) and covariance matrix \(\boldsymbol{\Sigma}\) 
CNegativeMultinomialDist  Implements the class DiscreteDistributionIntMulti for the negative multinomial distribution with parameters \(n > 0\) and ( \(p_1, …, p_d\)) such that all \(0<p_i<1\) and \(\sum_{i=1}^d p_i < 1\) 
▶Nrandvar  Generating NonUniform Random Numbers 
CBernoulliGen  This class implements random variate generators for the Bernoulli distribution (see class umontreal.ssj.probdist.BernoulliDist ) 
CBetaGen  This class implements random variate generators with the beta distribution with shape parameters \(\alpha> 0\) and \(\beta> 0\), over the interval \((a,b)\), where \(a < b\) 
CBetaRejectionLoglogisticGen  Implements Beta random variate generators using the rejection method with loglogistic envelopes from [34] 
CBetaStratifiedRejectionGen  This class implements Beta random variate generators using the stratified rejection/patchwork rejection method from [210], [223] 
CBetaSymmetricalBestGen  This class implements symmetrical beta random variate generators using Devroye’s oneliner method 
CBetaSymmetricalGen  This class implements random variate generators with the symmetrical beta distribution with shape parameters \(\alpha= \beta\), over the interval \((0,1)\) 
CBetaSymmetricalPolarGen  This class implements symmetrical beta random variate generators using Ulrich’s polar method [232] 
CBinomialConvolutionGen  Implements binomial random variate generators using the convolution method 
CBinomialGen  This class implements random variate generators for the binomial distribution 
CCauchyGen  This class implements random variate generators for the Cauchy distribution 
CChiGen  This class implements random variate generators for the chi distribution 
CChiRatioOfUniformsGen  This class implements Chi random variate generators using the ratio of uniforms method with shift 
CChiSquareGen  This class implements random variate generators with the chi square distribution with \(n>0\) degrees of freedom 
CChiSquareNoncentralGamGen  This class implements noncentral chi square random variate generators using the additive property of the noncentral chi square distribution [115] 
CChiSquareNoncentralGen  This class implements random variate generators for the noncentral chi square distribution with \(\nu> 0\) degrees of freedom and noncentrality parameter \(\lambda> 0\) 
CChiSquareNoncentralPoisGen  This class implements noncentral chi square random variate generators using Poisson and central chi square generators 
CConstantGen  This class implements a random variate generator that returns a constant value 
CErlangConvolutionGen  This class implements Erlang random variate generators using the convolution method 
CErlangGen  This class implements random variate generators for the Erlang distribution with parameters \(k > 0\) and \(\lambda> 0\) 
CExponentialGen  This class implements random variate generators for the exponential distribution 
CExponentialInverseFromDensityGen  This class implements exponential random variate generators using numerical inversion of the exponential density as described in [44] 
CExtremeValueGen  This class has been replaced by GumbelGen 
CFatigueLifeGen  This class implements random variate generators for the fatigue life distribution [20] with location parameter \(\mu\), scale parameter \(\beta\) and shape parameter \(\gamma\) 
CFisherFGen  This class implements random variate generators for the Fisher F distribution with \(n\) and \(m\) degrees of freedom, where \(n\) and \(m\) are positive integers 
CFNoncentralGen  This class implements random variate generators for the noncentral Fdistribution 
CFoldedNormalGen  This class implements methods for generating random variates from the folded normal distribution with parameters \(\mu\ge0\) and \(\sigma> 0\) 
CFrechetGen  This class implements methods for generating random variates from the Fréchet distribution, with location parameter \(\delta\), scale parameter \(\beta> 0\), and shape parameter \(\alpha> 0\), where we use the notation \(z = (x\delta)/\beta\) 
CGammaAcceptanceRejectionGen  This class implements gamma random variate generators using a method that combines acceptancerejection with acceptancecomplement, and proposed in [2], [4] 
CGammaGen  This class implements random variate generators for the gamma distribution 
CGammaRejectionLoglogisticGen  This class implements gamma random variate generators using a rejection method with loglogistic envelopes, from [33] 
CGeometricGen  This class implements a random variate generator for the geometric distribution 
CGumbelGen  This class implements methods for generating random variates from the Gumbel distribution 
CHalfNormalGen  This class implements methods for generating random variates from the halfnormal distribution with parameters \(\mu\) and \(\sigma> 0\) 
CHyperbolicSecantGen  This class implements random variate generators for the hyperbolic secant distribution with location parameter \(\mu\) and scale parameter \(\sigma\) 
CHypergeometricGen  This class implements random variate generators for the hypergeometric distribution 
CHypoExponentialGen  This class implements random variate generators for the hypoexponential distribution (see classes umontreal.ssj.probdist.HypoExponentialDist and umontreal.ssj.probdist.HypoExponentialDistQuick in package probdist for the definition) 
CInverseFromDensityGen  Implements a method for generating random variates by numerical inversion of an arbitrary continuous distribution when only the probability density is known [44] 
CInverseGammaGen  This class implements random variate generators for the inverse gamma distribution with shape parameter \(\alpha> 0\) and scale parameter \(\beta> 0\) 
CInverseGaussianGen  This class implements random variate generators for the inverse Gaussian distribution with location parameter \(\mu> 0\) and scale parameter \(\lambda> 0\) 
CInverseGaussianMSHGen  This class implements inverse gaussian random variate generators using the manytoone transformation method of Michael, Schucany and Haas (MHS) [180], [48] 
CJohnsonSBGen  This class implements random variate generators for the Johnson \(S_B\) distribution 
CJohnsonSLGen  This class implements random variate generators for the Johnson \(S_L\) distribution 
CJohnsonSUGen  This class implements random variate generators for the Johnson \(S_U\) distribution 
CJohnsonSystemG  This class contains common parameters and methods for the random variate generators associated with the Johnson system of distributions [101], [99] 
CKernelDensityGen  This class implements random variate generators for distributions obtained via kernel density estimation methods from a set of \(n\) individual observations \(x_1,…,x_n\) [45], [46], [89], [90], [215] 
CKernelDensityVarCorrectGen  This class is a variant of KernelDensityGen, but with a rescaling of the empirical distribution so that the variance of the density used to generate the random variates is equal to the empirical variance, as suggested by [215] 
CLaplaceGen  This class implements methods for generating random variates from the Laplace distribution 
CLogarithmicGen  This class implements random variate generators for the (discrete) logarithmic distribution 
CLogisticGen  This class implements random variate generators for the logistic distribution 
CLoglogisticGen  This class implements random variate generators for the loglogistic distribution with shape parameter \(\alpha> 0\) and scale parameter \(\beta> 0\) 
CLognormalGen  This class implements methods for generating random variates from the lognormal distribution 
CLognormalSpecialGen  Implements methods for generating random variates from the lognormal distribution using an arbitrary normal random variate generator 
CMixtureGen  This class implements random variate generators for a mixture distribution 
CNakagamiGen  This class implements random variate generators for the Nakagami distribution 
CNegativeBinomialGen  This class implements random variate generators having the negative binomial distribution 
CNormalACRGen  This class implements normal random variate generators using the acceptancecomplement ratio method [87] 
CNormalBoxMullerGen  This class implements normal random variate generators using the BoxMuller method from [23] 
CNormalGen  This class implements methods for generating random variates from the normal distribution \(N(\mu, \sigma)\) 
CNormalInverseFromDensityGen  This class implements normal random variate generators using numerical inversion of the normal density as described in [44] 
CNormalInverseGaussianGen  This class implements random variate generators for the normal inverse gaussian ( \(\mathcal{NIG}\)) distribution 
CNormalInverseGaussianIGGen  This class implements a normal inverse gaussian ( \({NIG}\)) random variate generator by using a normal generator ( \(N\)) and an inverse gaussian generator ( \(IG\)), as described in the following [239], [103] 
CNormalKindermannRamageGen  This class implements normal random variate generators using the KindermannRamage method [106] 
CNormalPolarGen  This class implements normal random variate generators using the polar method with rejection [176] 
CParetoGen  This class implements random variate generators for one of the Pareto distributions, with parameters \(\alpha>0\) and \(\beta>0\) 
CPascalConvolutionGen  Implements Pascal random variate generators by the convolution method (see [118] ) 
CPascalGen  Implements Pascal random variate generators, which is a special case of the negative binomial generator with parameter \(\gamma\) equal to a positive integer 
CPearson5Gen  THIS CLASS HAS BEEN RENAMED InverseGammaGen 
CPearson6Gen  This class implements random variate generators for the Pearson type VI distribution with shape parameters \(\alpha_1 > 0\) and \(\alpha_2 > 0\), and scale parameter \(\beta> 0\) 
CPoissonGen  This class implements random variate generators having the Poisson distribution 
CPoissonTIACGen  This class implements random variate generators having the Poisson distribution (see PoissonGen ) 
CPowerGen  This class implements random variate generators for the power distribution with shape parameter \(c > 0\), over the interval \([a,b]\) 
CRandomVariateGen  This is the base class for all random variate generators over the real line 
CRandomVariateGenInt  This is the base class for all generators of discrete random variates over the set of integers 
CRandomVariateGenWithCache  This class represents a random variate generator whose values are cached for more efficiency when using common random numbers 
CRandUnuran  This internal class provides the access point to the C package UNURAN 
CRayleighGen  This class implements random variate generators for the Rayleigh distribution 
CStudentGen  This class implements methods for generating random variates from the Student distribution with \(n>0\) degrees of freedom 
CStudentNoncentralGen  This class implements random variate generators for the noncentral Studentt distribution with \(n>0\) degrees of freedom and noncentrality parameter \(\delta\) 
CStudentPolarGen  This class implements Student random variate generators using the polar method of [14] 
CTriangularGen  This class implements random variate generators for the triangular distribution 
CUniformGen  This class implements random variate generators for the (continuous) uniform distribution over the interval \((a,b)\), where \(a\) and \(b\) are real numbers with \(a < b\) 
CUniformIntGen  This class implements a random variate generator for the uniform distribution over integers, over the interval \([i,j]\) 
CUnuranContinuous  This class permits one to create continuous univariate generators using UNURAN via its string API 
CUnuranDiscreteInt  This class permits one to create a discrete univariate generator using UNURAN via its string API 
CUnuranEmpirical  This class permits one to create generators for empirical and quasiempirical univariate distributions using UNURAN via its string interface 
CUnuranException  This type of unchecked exception is thrown when an error occurs inside the UNURAN package 
CWeibullGen  This class implements random variate generators for the Weibull distribution 
▶Nrandvarmulti  Generating Random Vectors 
CDirichletGen  Extends RandomMultivariateGen for a Dirichlet [98] distribution 
CIIDMultivariateGen  Extends RandomMultivariateGen for a vector of independent identically distributed (i.i.d.) random variables 
CMultinormalCholeskyGen  Extends MultinormalGen for a multivariate normal distribution [98] , generated via a Cholesky decomposition of the covariance matrix 
CMultinormalGen  Extends RandomMultivariateGen for a multivariate normal (or multinormal) distribution [98] 
CMultinormalPCAGen  Extends MultinormalGen for a multivariate normal distribution [98] , generated via the method of principal components analysis (PCA) of the covariance matrix 
CRandomMultivariateGen  This class is the multivariate counterpart of umontreal.ssj.randvar.RandomVariateGen 
▶Nrng  Random Number Generators 
CAntitheticStream  This container class allows the user to force any RandomStream to return antithetic variates 
CBakerTransformedStream  This container class permits one to apply the baker’s transformation to the output of any RandomStream 
CBasicRandomStreamFactory  Represents a basic random stream factory that can constructs new instances of a given RandomStream implementation via the newInstance method 
CCloneableRandomStream  CloneableRandomStream extends RandomStream and Cloneable 
CF2NL607  Implements the RandomStream interface by using as a backbone generator the combination of the WELL607 proposed in [199], [198] (and implemented in WELL607 ) with a nonlinear generator 
▶CF2wPoly  
▶CF2w  
CF2wElem  
CF2wPolyElem  
CGenF2w32  Implements the RandomStream interface via inheritance from RandomStreamBase 
CLFSR113  Extends RandomStreamBase using a composite linear feedback shift register (LFSR) (or Tausworthe) RNG as defined in [147], [229] 
CLFSR258  Extends RandomStreamBase using a 64bit composite linear feedback shift register (LFSR) (or Tausworthe) RNG as defined in [147], [229] 
CMRG31k3p  Extends the abstract class RandomStreamBase, thus implementing the RandomStream interface indirectly 
CMRG32k3a  Extends the abstract class RandomStreamBase by using as a backbone (or main) generator the combined multiple recursive generator (CMRG) MRG32k3a proposed by L’Ecuyer [148] , implemented in 64bit floatingpoint arithmetic 
CMRG32k3aL  The same generator as MRG32k3a, except here it is implemented with type long instead of double 
CMT19937  Implements the RandomStream interface via inheritance from RandomStreamBase 
CRandMrg  USE MRG32k3a INSTEAD of this class 
CRandomPermutation  Provides methods to randomly shuffle arrays or lists using a random stream 
CRandomStream  This interface defines the basic structures to handle multiple streams of uniform (pseudo)random numbers and convenient tools to move around within and across these streams 
CRandomStreamBase  This class provides a convenient foundation on which RNGs can be built 
CRandomStreamFactory  Represents a random stream factory capable of constructing instances of a given type of random stream by invoking the newInstance method each time a new random stream is needed, instead of invoking directly the specific constructor of the desired type 
CRandomStreamInstantiationException  This exception is thrown when a random stream factory cannot instantiate a stream on a call to its umontreal.ssj.rng.RandomStreamFactory.newInstance method 
CRandomStreamManager  Manages a list of random streams for more convenient synchronization 
CRandomStreamWithCache  This class represents a random stream whose uniforms are cached for more efficiency when using common random numbers 
CRandRijndael  Implements a RNG using the Rijndael block cipher algorithm (AES) with key and block lengths of 128 bits 
CRijndael_Algorithm  Rijndael –pronounced Reindaal– is a variable blocksize (128, 192 and 256bit), variable keysize (128, 192 and 256bit) symmetric cipher 
CRijndael_Properties  This class acts as a central repository for an algorithm specific properties 
CTruncatedRandomStream  Represents a container random stream generating numbers in an interval \((a,b)\) instead of in \((0,1)\), where \(0\le a < b \le1\), by using the contained stream 
CWELL1024  Implements the RandomStream interface via inheritance from RandomStreamBase 
CWELL512  This class implements the RandomStream interface via inheritance from RandomStreamBase 
CWELL607  This class implements the RandomStream interface via inheritance from RandomStreamBase 
CWELL607base  
▶Nsimevents  Simulation Clock and Event List Management 
▶Neventlist  This package provides different kinds of event list implementations 
CBinaryTree  An implementation of EventList using a binary search tree 
CDoublyLinked  An implementation of EventList using a doubly linked linear list 
CEventList  An interface for implementations of event lists 
CHenriksen  An implementation of EventList using the doublylinked indexed list of Henriksen [108] (see also [62] (p 
CRedblackTree  An implementation of EventList using a red black tree, which is similar to a binary search tree except that every node is colored red or black 
CSplayTree  An implementation of EventList using a splay tree [218] 
CAccumulate  A subclass of umontreal.ssj.stat.StatProbe, for collecting statistics on a variable that evolves in simulation time, with a piecewiseconstant trajectory 
CContinuous  Represents a variable in a continuoustime simulation 
▶CContinuousState  Represents the portion of the simulator’s state associated with continuoustime simulation 
CIntegMethod  
CEvent  This abstract class provides event scheduling tools 
CLinkedListStat  This class extends ListWithStat, and uses a linked list as the internal data structure 
▶CListWithStat  Implements a list with integrated statistical probes to provide automatic collection of statistics on the sojourn times of objects in the list and on the size of the list as a function of time given by a simulator 
CNode  Represents a node that can be part of a list with statistical collecting 
CSim  This static class contains the executive of a discreteevent simulation 
CSimulator  Represents the executive of a discreteevent simulator 
▶Nsimexp  Tools for Simulation Experiments 
CBatchMeansSim  Performs a simulation experiment on an infinite horizon, for estimating steadystate performance measures, using batch means 
CRepSim  Performs a simulation experiment on a finite horizon, using a certain number of independent runs or replications 
CSimExp  Represents a framework for performing experiments using simulation 
▶Nstat  Tools for Collecting Statistics and computing some estimators 
▶Ndensity  Univariate density estimation 
CDEDerivativeGaussian  This class implements a density derivative estimator (DDE) with a Gaussian ( i.e., standard normal) kernel function 
CDEHistogram  Histogram density estimator for a univariate density 
CDEKernelDensity  This class provides methods to construct a kernel density estimator (KDE) for univariate densities from a set of \(n\) individual observations \(x_0,\dots, x_{n1}\), and to evaluate it at a single point or at a set of selected evaluation points 
CDensityDerivativeEstimator  This class implements a density derivative estimator (DDE) based on a kernel density estimator (KDE) with a sufficiently smooth kernel function \(k\), see umontreal.ssj.stat.density.DEKernelDensity 
CDensityEstimator  This abstract class represents a univariate density estimator (DE) 
▶Nlist  
▶Nlincv  
CFunctionOfMultipleMeansTallyWithCV  Represents a function of multiple means tally for an estimator with linear control variables 
CListOfTalliesWithCV  Represents a list of tallies with control variables that inherits the functionalities of a list of tallies, and accepts vectors of length \(p+q\) 
CArrayOfObservationListener  Represents an object that can listen to observations broadcast by lists of statistical probes 
CListOfFunctionOfMultipleMeansTallies  Represents a list of tally statistical collectors for a vector of functions of multiple means 
CListOfStatProbes  Represents a list of statistical probes that can be managed simultaneously 
CListOfTallies  Represents a list of tally statistical collectors 
CListOfTalliesWithCovariance  Extends ListOfTallies to add support for the computation of the sample covariance between each pair of elements in a list, without storing all observations 
▶Nmatrix  
CMatrixOfFunctionOfMultipleMeansTallies  Represents a matrix of statistical collectors for functions of multiple means 
CMatrixOfObservationListener  Represents an object that can listen to observations broadcast by matrices of statistical probes 
CMatrixOfStatProbes  Represents a matrix of statistical probes that can be managed simultaneously 
CMatrixOfTallies  Represents a matrix of tally statistical collectors 
CFunctionOfMultipleMeansTally  Represents a statistical collector for estimating a function of multiple means with a confidence interval based on the delta theorem [214] 
CHistogramChartToLatex  
CHistogramOnly  This class is similar to TallyHistogram, except that it does not maintain the min, max, average, and variance of the observations 
CObservationListener  Represents an object that can listen to observations broadcast by statistical probes 
CPgfDataTable  Represents a data table which has a name, a number of observations (rows), a number of fields (columns), an array that contains the names (identifiers) of the fields, and a twodimensional array that contains the data 
CScaledHistogram  This class provides histograms for which the bin counts (heights of rectangles) are replaced by realvalued frequencies (in double ) and can be rescaled 
CStatProbe  The objects of this class are statistical probes or collectors, which are elementary devices for collecting statistics 
CTally  A subclass of StatProbe 
CTallyHistogram  This class extends Tally 
CTallyStore  This class is a variant of Tally for which the individual observations are stored in a list implemented as a DoubleArrayList 
▶Nstochprocess  Stochastic Processes 
CBrownianMotion  This class represents a Brownian motion process \(\{X(t) : t \geq0 \}\), sampled at times \(0 = t_0 < t_1 < \cdots< t_d\) 
CBrownianMotionBridge  Represents a Brownian motion process \(\{X(t) : t \geq0 \}\) sampled using the bridge sampling technique (see for example [69] ) 
CBrownianMotionPCA  A Brownian motion process \(\{X(t) : t \geq0 \}\) sampled using the principal component decomposition (PCA) [69], [95], [153] 
CBrownianMotionPCAEqualSteps  Same as BrownianMotionPCA, but uses a trick to speed up the calculation when the time steps are equidistant 
CCIRProcess  This class represents a CIR (Cox, Ingersoll, Ross) process [36] \(\{X(t) : t \geq0 \}\), sampled at times \(0 = t_0 < t_1 < \cdots< t_d\) 
CCIRProcessEuler  This class represents a CIR process as in CIRProcess, but the process is generated using the simple Euler scheme 
CGammaProcess  This class represents a gamma process [171] (page 82) \(\{ S(t) = G(t; \mu, \nu) : t \geq0 \}\) with mean parameter \(\mu\) and variance parameter \(\nu\) 
CGammaProcessBridge  This class represents a gamma process \(\{ S(t) = G(t; \mu, \nu) : t \geq0 \}\) with mean parameter \(\mu\) and variance parameter \(\nu\), sampled using the gamma bridge method (see for example [208], [11] ) 
CGammaProcessPCA  Represents a gamma process sampled using the principal component analysis (PCA) 
CGammaProcessPCABridge  Same as GammaProcessPCA, but the generated uniforms correspond to a bridge transformation of the BrownianMotionPCA instead of a sequential transformation 
CGammaProcessPCASymmetricalBridge  Same as GammaProcessPCABridge, but uses the fast inversion method for the symmetrical beta distribution, proposed by L’Ecuyer and Simard [134] , to accelerate the generation of the beta random variables 
CGammaProcessSymmetricalBridge  This class differs from GammaProcessBridge only in that it requires the number of interval of the path to be a power of 2 and of equal size 
CGeometricBrownianMotion  Represents a geometric Brownian motion (GBM) process \(\{S(t), t\ge0\}\), which evolves according to the stochastic differential equation 
CGeometricLevyProcess  Abstract class used as a parent class for the exponentiation of a Lévy process \(X(t)\): \[ S(t) = S(0) \exp\left(X(t) + (r  \omega_{RN}) t\right). \] The interest rate is denoted \(r\) and is referred to as 
CGeometricNormalInverseGaussianProcess  The geometric normal inverse gaussian (GNIG) process is the exponentiation of a NormalInverseGaussianProcess : \[ S(t) = S_0 \exp\left[ (r\omega_{RN})t + \mbox{NIG}(t;\alpha,\beta,\mu,\delta) \right], \] where \(r\) is the interest rate 
CGeometricVarianceGammaProcess  This class represents a geometric variance gamma process \(S(t)\) (see [171] (page 86)) 
CInverseGaussianProcess  The inverse Gaussian process is a nondecreasing process where the increments are additive and are given by the inverse gaussian distribution, umontreal.ssj.probdist.InverseGaussianDist 
CInverseGaussianProcessBridge  Samples the path by bridge sampling: first finding the process value at the final time and then the middle time, etc 
▶CInverseGaussianProcessMSH  Uses a faster generating method (MSH) [180] than the simple inversion of the distribution function used by InverseGaussianProcess 
CNonRandomStream  NonRandomStream: Given a double array, this class will return those values as if it where a random stream 
CInverseGaussianProcessPCA  Approximates a principal component analysis (PCA) decomposition of the InverseGaussianProcess 
CMultivariateBrownianMotion  This class represents a multivariate Brownian motion process \(\{\mathbf{X}(t) = (X_1(t),…, X_c(t)), t \geq0 \}\), sampled at times \(0 = t_0 < t_1 < \cdots< t_d\) 
CMultivariateBrownianMotionBridge  A multivariate Brownian motion process \(\{\mathbf{X}(t) : t \geq0 \}\) sampled via bridge sampling 
CMultivariateBrownianMotionPCA  A multivariate Brownian motion process \(\{\mathbf{X}(t) : t \geq0 \}\) sampled entirely using the principal component decomposition (PCA), as explained in [69] , page 92 
CMultivariateBrownianMotionPCABigSigma  A multivariate Brownian motion process \(\{\mathbf{X}(t) : t \geq0 \}\) sampled entirely using the principal component decomposition (PCA) 
CMultivariateGeometricBrownianMotion  This class is a multivariate version of GeometricBrownianMotion 
CMultivariateStochasticProcess  This class is a multivariate version of StochasticProcess where the process evolves in the \(c\)dimensional real space 
CNormalInverseGaussianProcess  This class represents a normal inverse gaussian process (NIG) 
COrnsteinUhlenbeckProcess  This class represents an OrnsteinUhlenbeck process \(\{X(t) : t \geq0 \}\), sampled at times \(0 = t_0 < t_1 < \cdots< t_d\) 
COrnsteinUhlenbeckProcessEuler  This class represents an OrnsteinUhlenbeck process as in OrnsteinUhlenbeckProcess, but the process is generated using the simple Euler scheme 
CStochasticProcess  Abstract base class for a stochastic process \(\{X(t) : t \geq 0 \}\) sampled (or observed) at a finite number of time points, \(0 = t_0 < t_1 < \cdots< t_d\) 
CVarianceGammaProcess  This class represents a variance gamma (VG) process \(\{S(t) = X(t; \theta, \sigma, \nu) : t \geq0\}\) 
CVarianceGammaProcessAlternate  This is a VarianceGammaProcess for which the successive random numbers are used in a different order to generate the sample path 
CVarianceGammaProcessDiff  This class represents a variance gamma (VG) process \(\{S(t) = X(t; \theta, \sigma, \nu) : t \geq0\}\) 
CVarianceGammaProcessDiffPCA  Same as VarianceGammaProcessDiff, but the two inner GammaProcess ’es are of PCA type 
CVarianceGammaProcessDiffPCABridge  Same as VarianceGammaProcessDiff, but the two inner GammaProcess ’es are of the type PCABridge 
CVarianceGammaProcessDiffPCASymmetricalBridge  Same as VarianceGammaProcessDiff, but the two inner GammaProcess ’es are of the PCASymmetricalBridge type 
▶Nutil  General basic utilities 
▶Nio  This package provides tools for exporting data to text and binary files, as well as for importing data from files 
CAbstractDataReader  This abstract class implements shared functionality for data readers 
CAbstractDataWriter  This abstract class implements shared functionality for data writers 
CBinaryDataReader  Binary data reader 
CBinaryDataWriter  Binary data writer 
CCachedDataWriter  This abstract class implements shared functionality for data writers that store all fields in memory before outputing them with umontreal.ssj.util.io.DataWriter.close 
CDataField  This class represents a data field from a file read by an instance of a class implementing DataReader 
CDataReader  Data reader interface 
CDataWriter  Data writer interface 
▶CTextDataWriter  Text data writer 
CFormat  Output format: organize fields as columns or as rows 
▶Nsort  This package contains classes for sorting, in particular to sort multidimensional points 
CBatchSort  This class implements a MultiDimSortComparable that performs a batch sort on multivariate arrays 
CBatchSortPow2  This is a subclass of BatchSort for which the batch numbers \(n_j\) are always powers of 2 
CDoubleArrayComparator  This provides an implementation of Comparator in which arrays of double in \(d\) dimensions are compared by comparing their coordinate \(j\) in the natural order of real numbers, where \(j \in\{0,…,d1\}\) is given in the constructor 
CHilbertCurveBatchSort  This sort is similar to BatchSortPow2, except that after applying the batch sort, the objects are given labels that map them to the \(d\)dimensional unit hypercube \([0,1)^d\) as explained below, and then reordered by following a Hilbert curve as in the HilbertCurveSort 
CHilbertCurveMap  This class implements the mapping of a Hilbert curve in the \(d\)dimensional unit hypercube \([0,1)^d\) 
▶CHilbertCurveSort  This class implements a MultiDimSort01<T extends MultiDim01> that can sort an array of points in the \(d\)dimensional unit hypercube \([0,1)^d\), by following a Hilbert curve, and using (at most) the first \(m\) bits of each point 
CLongIndexComparator2  The comparator class used by sortIndexOfLong2 
CMultiDim01  This interface represents a point or array of \(d\) dimensions in a unit hypercube \([0, 1)^d\) 
CMultiDimComparable  This interface is an extension (or variant) of the Comparable interface in Java 
CMultiDimComparator  This class is useful if one wishes to perform an ordinary onedimensional sort on MultiDimComparable<T> objects based on a single coordinate \(j\), which is specified in the constructor 
CMultiDimSort  This interface is meant to be implemented by certain multivariate sorting algorithms that sort objects based on different fields (or dimensions) 
CMultiDimSort01  This interface extends MultiDimSort<T> to implement multivariate sorting algorithms that sort points of \(d\) dimensions in the unit hypercube \([0, 1)^d\) 
CMultiDimSortComparable  This interface extends MultiDimSort<T> to implement multivariate sorting algorithms that sort objects that are pairwise comparable 
COneDimSort  This class implements a MultiDimSortComparable that simply sorts the objects according to a given sorting coordinate \(j \ge0\) specified in the constructor 
CSplitSort  Implements a MultiDimSortComparable that performs a split sort on a MultiDimComparable<T> array based on its first \(d\) dimensions 
CAbstractChrono  AbstractChrono is a class that acts as an interface to the system clock and calculates the CPU or system time consumed by parts of a program 
CArithmeticMod  This class provides facilities to compute multiplications of scalars, of vectors and of matrices modulo m 
▶CBitMatrix  This class implements matrices of bits of arbitrary dimensions 
CIncompatibleDimensionException  Runtime exception raised when the dimensions of the BitMatrix are not appropriate for the operation 
CBitVector  This class implements vectors of bits and the operations needed to use them 
CChrono  The Chrono class extends the umontreal.ssj.util.AbstractChrono class and computes the CPU time for the current thread only 
CChronoSingleThread  This class is deprecated but kept for compatibility with older versions of SSJ 
CClassFinder  Utility class used to convert a simple class name to a fully qualified class object 
CDMatrix  This class implements a few methods for matrix calculations with double numbers 
CGlobalCPUTimeChrono  Extends the AbstractChrono class to compute the global CPU time used by the Java Virtual Machine 
CIntrospection  Provides utility methods for introspection using Java Reflection API 
CJDBCManager  This class provides some facilities to connect to a SQL database and to retrieve data stored in it 
CMisc  This class provides miscellaneous functions that are hard to classify 
CMultivariateFunction  Represents a function of multiple variables 
CNameConflictException  This exception is thrown by a ClassFinder when two or more fully qualified class names can be associated with a simple class name 
CNativeUtils  A simple library class which helps with loading dynamic libraries stored in the JAR archive 
CNum  This class provides a few constants and some methods to compute numerical quantities such as factorials, combinations, gamma functions, and so on 
CPrintfFormat  This class acts like a StringBuffer which defines new types of append methods 
CRatioFunction  Represents a function computing a ratio of two values 
CRootFinder  This class provides methods to solve nonlinear equations 
CSysteme  This class provides a few tools related to the system or the computer 
CSystemTimeChrono  Extends the AbstractChrono class to compute the total system time using Java’s builtin System.nanoTime 
CTableFormat  This class provides methods to format arrays and matrices into String s in different styles 
CTextDataReader  Provides static methods to read data from text files 
CThreadCPUTimeChrono  Extends the AbstractChrono class to compute the CPU time for a single thread 
CTimeUnit  Represents a time unit for conversion of time durations 
CTransformingList  Represents a list that dynamically transforms the elements of another list 