SSJ
V. 1.2.5.
A B C D E F G H I J K L M N O P Q R S T U V W X

A

AbstractSimProcess - Class in umontreal.iro.lecuyer.simprocs
This abstract class provides process scheduling tools.
AbstractSimProcess() - Constructor for class umontreal.iro.lecuyer.simprocs.AbstractSimProcess
Constructs a new process without scheduling it.
Accumulate - Class in umontreal.iro.lecuyer.simevents
A subclass of StatProbe, for collecting statistics on a variable that evolves in simulation time, with a piecewise-constant trajectory.
Accumulate() - Constructor for class umontreal.iro.lecuyer.simevents.Accumulate
Constructs a new Accumulate statistical probe and initializes it by invoking init().
Accumulate(String) - Constructor for class umontreal.iro.lecuyer.simevents.Accumulate
Construct and initializes a new Accumulate statistical probe with name name and initial time 0.
actions() - Method in class umontreal.iro.lecuyer.simevents.Event
This is the method that is executed when this event occurs.
actions() - Method in class umontreal.iro.lecuyer.simprocs.AbstractSimProcess
This is the method that is called when this process is executing.
activeTests - Static variable in class umontreal.iro.lecuyer.gof.GofFormat
The set of EDF tests that are to be performed when calling the methods activeTests, formatActiveTests, etc.
activeTests(DoubleArrayList, double[], double[]) - Static method in class umontreal.iro.lecuyer.gof.GofFormat
Computes the EDF test statistics by calling tests, then computes the p-values of those that currently belong to activeTests, and return these quantities in sVal and pVal, respectively.
activeTests(DoubleArrayList, ContinuousDistribution, double[], double[]) - Static method in class umontreal.iro.lecuyer.gof.GofFormat
The observations are in data, not necessarily sorted, and we want to compare their empirical distribution with the distribution dist.
AD - Static variable in class umontreal.iro.lecuyer.gof.GofFormat
Anderson-Darling test
add(RandomStream) - Method in class umontreal.iro.lecuyer.rng.RandomStreamManager
Adds the given stream to the internal list of this random stream manager and returns the added stream.
add(Event) - Method in class umontreal.iro.lecuyer.simevents.eventlist.BinaryTree
 
add(Event) - Method in class umontreal.iro.lecuyer.simevents.eventlist.DoublyLinked
 
add(Event) - Method in interface umontreal.iro.lecuyer.simevents.eventlist.EventList
Adds a new event in the event list, according to the time of ev.
add(Event) - Method in class umontreal.iro.lecuyer.simevents.eventlist.Henriksen
 
add(Event) - Method in class umontreal.iro.lecuyer.simevents.eventlist.RedblackTree
 
add(Event) - Method in class umontreal.iro.lecuyer.simevents.eventlist.SplayTree
 
add(int, Object) - Method in class umontreal.iro.lecuyer.simevents.LinkedListStat
 
add(Object) - Method in class umontreal.iro.lecuyer.simevents.LinkedListStat
 
add(double) - Method in class umontreal.iro.lecuyer.stat.Tally
Gives a new observation x to the statistical collector.
add(double) - Method in class umontreal.iro.lecuyer.stat.TallyStore
 
addAfter(Event, Event) - Method in class umontreal.iro.lecuyer.simevents.eventlist.BinaryTree
 
addAfter(Event, Event) - Method in class umontreal.iro.lecuyer.simevents.eventlist.DoublyLinked
 
addAfter(Event, Event) - Method in interface umontreal.iro.lecuyer.simevents.eventlist.EventList
Same as add, but adds the new event ev immediately after the event other in the list.
addAfter(Event, Event) - Method in class umontreal.iro.lecuyer.simevents.eventlist.Henriksen
 
addAfter(Event, Event) - Method in class umontreal.iro.lecuyer.simevents.eventlist.RedblackTree
 
addAfter(Event, Event) - Method in class umontreal.iro.lecuyer.simevents.eventlist.SplayTree
 
addAll(Collection) - Method in class umontreal.iro.lecuyer.simevents.LinkedListStat
 
addAll(int, Collection) - Method in class umontreal.iro.lecuyer.simevents.LinkedListStat
 
addBefore(Event, Event) - Method in class umontreal.iro.lecuyer.simevents.eventlist.BinaryTree
 
addBefore(Event, Event) - Method in class umontreal.iro.lecuyer.simevents.eventlist.DoublyLinked
 
addBefore(Event, Event) - Method in interface umontreal.iro.lecuyer.simevents.eventlist.EventList
Same as add, but adds the new event ev immediately before the event other in the list.
addBefore(Event, Event) - Method in class umontreal.iro.lecuyer.simevents.eventlist.Henriksen
 
addBefore(Event, Event) - Method in class umontreal.iro.lecuyer.simevents.eventlist.RedblackTree
 
addBefore(Event, Event) - Method in class umontreal.iro.lecuyer.simevents.eventlist.SplayTree
 
addFaurePermutations() - Method in class umontreal.iro.lecuyer.hups.HaltonSequence
Permutes the digits using Faure permutations for all coordinates.
addFaurePermutations() - Method in class umontreal.iro.lecuyer.hups.HammersleyPointSet
Permutes the digits using Faure permutations for all coordinates.
addFirst(Event) - Method in class umontreal.iro.lecuyer.simevents.eventlist.BinaryTree
 
addFirst(Event) - Method in class umontreal.iro.lecuyer.simevents.eventlist.DoublyLinked
 
addFirst(Event) - Method in interface umontreal.iro.lecuyer.simevents.eventlist.EventList
Adds a new event at the beginning of the event list.
addFirst(Event) - Method in class umontreal.iro.lecuyer.simevents.eventlist.Henriksen
 
addFirst(Event) - Method in class umontreal.iro.lecuyer.simevents.eventlist.RedblackTree
 
addFirst(Event) - Method in class umontreal.iro.lecuyer.simevents.eventlist.SplayTree
 
addFirst(Object) - Method in class umontreal.iro.lecuyer.simevents.LinkedListStat
 
addLast(Object) - Method in class umontreal.iro.lecuyer.simevents.LinkedListStat
 
addRandomShift(int, int, RandomStream) - Method in class umontreal.iro.lecuyer.hups.ContainerPointSet
 
addRandomShift(int, int) - Method in class umontreal.iro.lecuyer.hups.ContainerPointSet
 
addRandomShift(RandomStream) - Method in class umontreal.iro.lecuyer.hups.ContainerPointSet
 
addRandomShift() - Method in class umontreal.iro.lecuyer.hups.ContainerPointSet
 
addRandomShift(int, int, RandomStream) - Method in class umontreal.iro.lecuyer.hups.CycleBasedPointSet
Adds a random shift to all the points of the point set, using stream stream to generate the random numbers, for coordinates d1 to d2 - 1.
addRandomShift(int, int, RandomStream) - Method in class umontreal.iro.lecuyer.hups.CycleBasedPointSetBase2
Adds a random digital shift in base 2 to all the points of the point set, using stream stream to generate the random numbers, for coordinates d1 to d2 - 1.
addRandomShift(int, int, RandomStream) - Method in class umontreal.iro.lecuyer.hups.DigitalNet
Adds a random digital shift to all the points of the point set, using stream stream to generate the random numbers.
addRandomShift(RandomStream) - Method in class umontreal.iro.lecuyer.hups.DigitalNet
Same as addRandomShift(0, dim, stream), where dim is the dimension of the digital net.
addRandomShift(RandomStream) - Method in class umontreal.iro.lecuyer.hups.DigitalNetBase2
 
addRandomShift(int, int, RandomStream) - Method in class umontreal.iro.lecuyer.hups.DigitalNetBase2
 
addRandomShift(int, int, RandomStream) - Method in class umontreal.iro.lecuyer.hups.PointSet
This method does nothing for this generic class.
addRandomShift(RandomStream) - Method in class umontreal.iro.lecuyer.hups.PointSet
Similar to addRandomShift (0, d2, stream), with d2 the dimension of the current random shift.
addRandomShift(int, int) - Method in class umontreal.iro.lecuyer.hups.PointSet
Deprecated: Similar to addRandomShift (d1, d2, stream), with the current random stream.
addRandomShift() - Method in class umontreal.iro.lecuyer.hups.PointSet
 
addRandomShift(int, int, RandomStream) - Method in class umontreal.iro.lecuyer.hups.RandShiftedPointSet
Changes the stream used for the random shifts to stream, then refreshes the shift for coordinates d1 to d2-1.
addRandomShift(RandomStream) - Method in class umontreal.iro.lecuyer.hups.RandShiftedPointSet
Changes the stream used for the random shifts to stream, then refreshes all coordinates of the random shift, up to its current dimension.
addRandomShift(int, int) - Method in class umontreal.iro.lecuyer.hups.RandShiftedPointSet
Refreshes the random shift (generates new uniform values for the random shift coordinates) for coordinates d1 to d2-1.
addRandomShift() - Method in class umontreal.iro.lecuyer.hups.RandShiftedPointSet
Refreshes all coordinates of the random shift, up to its current dimension.
addRandomShift(int, int, RandomStream) - Method in class umontreal.iro.lecuyer.hups.Rank1Lattice
Adds a random shift to all the points of the point set, using stream stream to generate the random numbers.
advanceState(int, int) - Method in class umontreal.iro.lecuyer.rng.RandMrg
Advances the state of this stream by k values, without modifying the states of other streams (as in setSeed), nor the values of Bg and Ig associated with this stream.
afterEachStep() - Method in class umontreal.iro.lecuyer.simevents.Continuous
This method is executed after each integration step for this Continuous variable.
and(BitMatrix) - Method in class umontreal.iro.lecuyer.util.BitMatrix
Returns the BitMatrix resulting from the application of the and operator on the original BitMatrix and that.
and(BitVector) - Method in class umontreal.iro.lecuyer.util.BitVector
Returns a BitVector which is the result of the and operator with both the this and that BitVector's.
andersonDarling(int, double) - Static method in class umontreal.iro.lecuyer.gof.FBar
Returns 1.0 - FDist.andersonDarling (n, x).
andersonDarling(int, double) - Static method in class umontreal.iro.lecuyer.gof.FDist
Returns P[AN2 <= x], where AN2 is the Anderson-Darling statistic for a sample of independent uniforms over (0, 1).
andersonDarling(DoubleArrayList) - Static method in class umontreal.iro.lecuyer.gof.GofStat
Computes and returns the Anderson-Darling statistic AN2.
AntitheticPointSet - Class in umontreal.iro.lecuyer.hups
This container class provides antithetic points.
AntitheticPointSet(PointSet) - Constructor for class umontreal.iro.lecuyer.hups.AntitheticPointSet
Constructs an antithetic point set from the given point set P.
AntitheticStream - Class in umontreal.iro.lecuyer.rng
This container class allows the user to force any RandomStream to return antithetic variates.
AntitheticStream(RandomStream) - Constructor for class umontreal.iro.lecuyer.rng.AntitheticStream
Constructs a new antithetic stream, using the random numbers from the base stream stream.
append(String) - Method in class umontreal.iro.lecuyer.util.PrintfFormat
Appends str to the buffer.
append(int, String) - Method in class umontreal.iro.lecuyer.util.PrintfFormat
Uses the s static method to append str to the buffer.
append(double) - Method in class umontreal.iro.lecuyer.util.PrintfFormat
Appends x to the buffer.
append(int, double) - Method in class umontreal.iro.lecuyer.util.PrintfFormat
Uses the f static method to append x to the buffer.
append(int, int, double) - Method in class umontreal.iro.lecuyer.util.PrintfFormat
Uses the f static method to append x to the buffer.
append(int) - Method in class umontreal.iro.lecuyer.util.PrintfFormat
Appends x to the buffer.
append(int, int) - Method in class umontreal.iro.lecuyer.util.PrintfFormat
Uses the d static method to append x to the buffer.
append(long) - Method in class umontreal.iro.lecuyer.util.PrintfFormat
Appends x to the buffer.
append(int, long) - Method in class umontreal.iro.lecuyer.util.PrintfFormat
Uses the d static method to append x to the buffer.
append(int, int, int, double) - Method in class umontreal.iro.lecuyer.util.PrintfFormat
Uses the format static method with the same four arguments to append x to the buffer.
append(char) - Method in class umontreal.iro.lecuyer.util.PrintfFormat
Appends a single character to the buffer.
apply(RandomStream) - Method in interface umontreal.iro.lecuyer.hups.Randomization
Applies this randomization to the point set using stream stream.
ArithmeticMod - Class in umontreal.iro.lecuyer.util
This class provides facilities to compute multiplications of scalars, of vectors and of matrices modulo m.
average() - Method in class umontreal.iro.lecuyer.simevents.Accumulate
Returns the time-average since the last initialization to the last call to update.
average() - Method in class umontreal.iro.lecuyer.stat.StatProbe
Returns the average for this collector.
average() - Method in class umontreal.iro.lecuyer.stat.Tally
 

B

BakerTransformedPointSet - Class in umontreal.iro.lecuyer.hups
This container class embodies a point set to which a Baker transformation is applied.
BakerTransformedPointSet(PointSet) - Constructor for class umontreal.iro.lecuyer.hups.BakerTransformedPointSet
Constructs a Baker-transformed point set from the given point set P.
BakerTransformedStream - Class in umontreal.iro.lecuyer.rng
This container class permits one to apply the baker's transformation to the output of any RandomStream.
BakerTransformedStream(RandomStream) - Constructor for class umontreal.iro.lecuyer.rng.BakerTransformedStream
Constructs a new baker transformed stream, using the random numbers from the base stream stream.
barF(double, double, int, double) - Static method in class umontreal.iro.lecuyer.probdist.BetaDist
Same as barF (alpha, beta, 0, 1, d, x).
barF(double, double, double, double, int, double) - Static method in class umontreal.iro.lecuyer.probdist.BetaDist
Computes the complementary distribution function.
barF(double, int, double) - Static method in class umontreal.iro.lecuyer.probdist.BetaSymmetricalDist
Same as barF (alpha, beta, d, x).
barF(int) - Method in class umontreal.iro.lecuyer.probdist.BinomialDist
 
barF(double) - Method in class umontreal.iro.lecuyer.probdist.CauchyDist
 
barF(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.CauchyDist
Computes the complementary distribution.
barF(double) - Method in class umontreal.iro.lecuyer.probdist.ChiDist
 
barF(int, double) - Static method in class umontreal.iro.lecuyer.probdist.ChiDist
Computes the complementary distribution.
barF(double) - Method in class umontreal.iro.lecuyer.probdist.ChiSquareDist
 
barF(int, int, double) - Static method in class umontreal.iro.lecuyer.probdist.ChiSquareDist
Computes the complementary chi-square distribution function with n degrees of freedom.
barF(double) - Method in class umontreal.iro.lecuyer.probdist.ContinuousDistribution
 
barF(double) - Method in class umontreal.iro.lecuyer.probdist.DiscreteDistribution
 
barF(double) - Method in class umontreal.iro.lecuyer.probdist.DiscreteDistributionInt
Returns bar(F)(x), the complementary distribution function.
barF(int) - Method in class umontreal.iro.lecuyer.probdist.DiscreteDistributionInt
Returns bar(F)(x), the complementary distribution function.
barF(double) - Method in interface umontreal.iro.lecuyer.probdist.Distribution
Returns bar(F)(x) = 1 - F(x).
barF(double) - Method in class umontreal.iro.lecuyer.probdist.EmpiricalDist
 
barF(int, double, int, double) - Static method in class umontreal.iro.lecuyer.probdist.ErlangDist
Computes the complementary distribution function.
barF(double) - Method in class umontreal.iro.lecuyer.probdist.ExponentialDist
 
barF(double, double) - Static method in class umontreal.iro.lecuyer.probdist.ExponentialDist
Computes the complementary distribution function.
barF(double) - Method in class umontreal.iro.lecuyer.probdist.ExtremeValueDist
 
barF(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.ExtremeValueDist
Computes the complementary distribution function.
barF(double) - Method in class umontreal.iro.lecuyer.probdist.FatigueLifeDist
 
barF(double, double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.FatigueLifeDist
Computes the complementary distribution function of the fatigue life distribution with parameters μ, β and γ.
barF(double) - Method in class umontreal.iro.lecuyer.probdist.FisherFDist
 
barF(int, int, int, double) - Static method in class umontreal.iro.lecuyer.probdist.FisherFDist
Computes the complementary distribution function of the Fisher F-distribution with parameters n and m, evaluated at x, with roughly d decimal digits of precision.
barF(double, double, int, double) - Static method in class umontreal.iro.lecuyer.probdist.GammaDist
Computes the complementary distribution function.
barF(double, int, double) - Static method in class umontreal.iro.lecuyer.probdist.GammaDist
Same as barF (alpha, 1.0, d, x).
barF(int) - Method in class umontreal.iro.lecuyer.probdist.GeometricDist
 
barF(double, int) - Static method in class umontreal.iro.lecuyer.probdist.GeometricDist
Computes the complementary distribution function bar(F)(x).
barF(double) - Method in class umontreal.iro.lecuyer.probdist.HyperbolicSecantDist
 
barF(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.HyperbolicSecantDist
Computes the complementary distribution function of the hyperbolic secant distribution with parameters μ and σ.
barF(int) - Method in class umontreal.iro.lecuyer.probdist.HypergeometricDist
 
barF(int, int, int, int) - Static method in class umontreal.iro.lecuyer.probdist.HypergeometricDist
Computes the complementary distribution function bar(F)(x).
barF(double) - Method in class umontreal.iro.lecuyer.probdist.InverseGaussianDist
 
barF(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.InverseGaussianDist
Computes the complementary distribution function of the inverse gaussian distribution with parameters μ and λ, evaluated at x.
barF(double, double, double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.JohnsonSBDist
Computes the complementary distribution.
barF(double, double, double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.JohnsonSUDist
Computes the complementary distribution function 1 - F(x).
barF(double) - Method in class umontreal.iro.lecuyer.probdist.LaplaceDist
 
barF(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.LaplaceDist
Computes the Laplace complementary distribution function.
barF(int) - Method in class umontreal.iro.lecuyer.probdist.LogarithmicDist
 
barF(double, int) - Static method in class umontreal.iro.lecuyer.probdist.LogarithmicDist
Computes the complementary distribution function bar(F)(x).
barF(double) - Method in class umontreal.iro.lecuyer.probdist.LogisticDist
 
barF(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.LogisticDist
Computes the complementary distribution function 1 - F(x).
barF(double) - Method in class umontreal.iro.lecuyer.probdist.LoglogisticDist
 
barF(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.LoglogisticDist
Computes the complementary distribution function of the log-logistic distribution with parameters α and β.
barF(double) - Method in class umontreal.iro.lecuyer.probdist.LognormalDist
 
barF(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.LognormalDist
Computes the lognormal complementary distribution function bar(F)(x), using NormalDist.barF01.
barF(int) - Method in class umontreal.iro.lecuyer.probdist.NegativeBinomialDist
 
barF(double) - Method in class umontreal.iro.lecuyer.probdist.NormalDist
 
barF(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.NormalDist
Computes the complementary normal distribution function bar(F)(x) = 1 - Φ((x - μ)/σ), with mean μ and variance σ.
barF(double) - Method in class umontreal.iro.lecuyer.probdist.NormalDistQuick
 
barF(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.NormalDistQuick
Returns an approximation of 1 - Φ(x), where Φ is the standard normal distribution function, with mean 0 and variance 1.
barF(double) - Method in class umontreal.iro.lecuyer.probdist.ParetoDist
 
barF(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.ParetoDist
Computes the complementary distribution function.
barF(double) - Method in class umontreal.iro.lecuyer.probdist.Pearson5Dist
 
barF(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.Pearson5Dist
Computes the complementary distribution function of a Pearson V distribution with shape parameter α and scale parameter β.
barF(double) - Method in class umontreal.iro.lecuyer.probdist.Pearson6Dist
 
barF(double, double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.Pearson6Dist
Computes the complementary distribution function of a Pearson VI distribution with shape parameters α1 and α2, and scale parameter β.
barF(double) - Method in class umontreal.iro.lecuyer.probdist.PiecewiseLinearEmpiricalDist
 
barF(int) - Method in class umontreal.iro.lecuyer.probdist.PoissonDist
 
barF(double, int) - Static method in class umontreal.iro.lecuyer.probdist.PoissonDist
Computes and returns the value of the complementary Poisson distribution function, bar(F)(x), for λ = lambda.
barF(int, double) - Static method in class umontreal.iro.lecuyer.probdist.StudentDist
Computes the complementary distribution function bar(F)(x).
barF(double) - Method in class umontreal.iro.lecuyer.probdist.TriangularDist
 
barF(double, double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.TriangularDist
Computes the complementary distribution function.
barF(double) - Method in class umontreal.iro.lecuyer.probdist.TruncatedDist
 
barF(double) - Method in class umontreal.iro.lecuyer.probdist.UniformDist
 
barF(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.UniformDist
Computes the uniform complementary distribution function bar(F)(x).
barF(int) - Method in class umontreal.iro.lecuyer.probdist.UniformIntDist
 
barF(int, int, int) - Static method in class umontreal.iro.lecuyer.probdist.UniformIntDist
Computes the discrete uniform complementary distribution function bar(F)(x).
barF(double) - Method in class umontreal.iro.lecuyer.probdist.WeibullDist
 
barF(double, double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.WeibullDist
Computes the complementary distribution function.
barF(double, double) - Static method in class umontreal.iro.lecuyer.probdist.WeibullDist
Same as barF (alpha, 1.0, 0.0, x).
barF(double, double, double) - Static method in class umontreal.iro.lecuyer.probdistmulti.BiNormalDist
Computes the standard upper binormal distribution with μ1 = μ2 = 0 and σ1 = σ2 = 1.
barF(double, double) - Method in class umontreal.iro.lecuyer.probdistmulti.BiNormalDist
 
barF(double, double, double, double, double, double, double) - Static method in class umontreal.iro.lecuyer.probdistmulti.BiNormalDist
Computes the upper binormal distribution function with parameters μ1 = mu1, μ2 = mu2, σ1 = sigma1, σ2 = sigma2 and ρ = rho.
barF(double, double, double, double, double, double, double, int) - Static method in class umontreal.iro.lecuyer.probdistmulti.BiNormalDonnellyDist
Computes the upper binormal distribution function with parameters μ1 = mu1, μ2 = mu2, σ1 = sigma1, σ2 = sigma2, ρ = rho and ndig decimal digits of accuracy.
barF(double, double, double, int) - Static method in class umontreal.iro.lecuyer.probdistmulti.BiNormalDonnellyDist
Computes the upper standard binormal distribution function with parameters ρ = rho and ndig decimal digits of accuracy.
barF(double, double) - Method in class umontreal.iro.lecuyer.probdistmulti.BiNormalDonnellyDist
 
barF(double, double, double, double, double, double, double) - Static method in class umontreal.iro.lecuyer.probdistmulti.BiNormalDonnellyDist
 
barF(double, double, double) - Static method in class umontreal.iro.lecuyer.probdistmulti.BiNormalDonnellyDist
 
barF(double, double) - Method in class umontreal.iro.lecuyer.probdistmulti.BiNormalGenzDist
 
barF(double, double, double, double, double, double, double) - Static method in class umontreal.iro.lecuyer.probdistmulti.BiNormalGenzDist
 
barF(double, double, double) - Static method in class umontreal.iro.lecuyer.probdistmulti.BiNormalGenzDist
 
barF(double, double) - Method in class umontreal.iro.lecuyer.probdistmulti.BiStudentDist
 
barF(int, double, double, double) - Static method in class umontreal.iro.lecuyer.probdistmulti.BiStudentDist
Computes the standard upper bivariate Student's t distribution.
barF(double, double) - Method in class umontreal.iro.lecuyer.probdistmulti.ContinuousDistribution2Dim
.
barF01(double) - Static method in class umontreal.iro.lecuyer.probdist.NormalDist
Same as barF (0.0, 1.0, x).
barF01(double) - Static method in class umontreal.iro.lecuyer.probdist.NormalDistQuick
Same as barF (0.0, 1.0, x).
BasicRandomStreamFactory - Class in umontreal.iro.lecuyer.rng
Represents a basic random stream factory that can constructs new instances of a given RandomStream implementation via the newInstance method.
BasicRandomStreamFactory(Class) - Constructor for class umontreal.iro.lecuyer.rng.BasicRandomStreamFactory
Constructs a new basic random stream factory with random stream class rsClass.
besselK025(double) - Static method in class umontreal.iro.lecuyer.util.Num
Returns the value of K1/4(x), where Kais the modified Bessel's function of the second kind.
BetaDist - Class in umontreal.iro.lecuyer.probdist
Extends the class ContinuousDistribution for the beta distribution with shape parameters α > 0 and β > 0, over the interval (a, b), where a < b.
BetaDist(double, double) - Constructor for class umontreal.iro.lecuyer.probdist.BetaDist
Constructs a BetaDist object with parameters α = alpha and β = beta and default domain (0, 1).
BetaDist(double, double, double, double) - Constructor for class umontreal.iro.lecuyer.probdist.BetaDist
Constructs a BetaDist object with parameters α = alpha and β = beta, and domain (a, b).
BetaDist(double, double, int) - Constructor for class umontreal.iro.lecuyer.probdist.BetaDist
Constructs a BetaDist object with parameters α = alpha and β = beta, and approximations of roughly d decimal digits of precision when computing the distribution, complementary distribution, and inverse functions.
BetaDist(double, double, double, double, int) - Constructor for class umontreal.iro.lecuyer.probdist.BetaDist
Constructs a BetaDist object with parameters α = alpha and β = beta, and approximations of roughly d decimal digits of precision when computing distribution, complementary distribution, and inverse functions.
BetaGen - Class in umontreal.iro.lecuyer.randvar
This class implements random variate generators with the beta distribution with shape parameters α > 0 and β > 0, over the interval (a, b), where a < b.
BetaGen(RandomStream, BetaDist) - Constructor for class umontreal.iro.lecuyer.randvar.BetaGen
Creates a new generator for the distribution dist, using stream s.
BetaRejectionLoglogisticGen - Class in umontreal.iro.lecuyer.randvar
Implements Beta random variate generators using the rejection method with log-logistic envelopes.
BetaRejectionLoglogisticGen(RandomStream, RandomStream, BetaDist) - Constructor for class umontreal.iro.lecuyer.randvar.BetaRejectionLoglogisticGen
Creates a new generator for the distribution dist, using stream s and auxiliary stream aux.
BetaRejectionLoglogisticGen(RandomStream, BetaDist) - Constructor for class umontreal.iro.lecuyer.randvar.BetaRejectionLoglogisticGen
Same as BetaRejectionLoglogisticGen (s, s, dist).
BetaStratifiedRejectionGen - Class in umontreal.iro.lecuyer.randvar
This class implements Beta random variate generators using the stratified rejection/patchwork rejection method.
BetaStratifiedRejectionGen(RandomStream, RandomStream, BetaDist) - Constructor for class umontreal.iro.lecuyer.randvar.BetaStratifiedRejectionGen
Creates a new generator for the distribution dist, using the given stream s and auxiliary stream aux.
BetaStratifiedRejectionGen(RandomStream, BetaDist) - Constructor for class umontreal.iro.lecuyer.randvar.BetaStratifiedRejectionGen
Same as BetaStratifiedRejectionGen(s, s, dist).
BetaSymmetricalBestGen - Class in umontreal.iro.lecuyer.randvar
This class implements symmetrical beta random variate generators using Devroye's one-liner method.
BetaSymmetricalBestGen(RandomStream, RandomStream, RandomStream, BetaSymmetricalDist) - Constructor for class umontreal.iro.lecuyer.randvar.BetaSymmetricalBestGen
Creates a new generator for the distribution dist, using stream stream to generate U1, stream s2 to generate U2 and stream s3 to generate S as given in equation.
BetaSymmetricalBestGen(RandomStream, BetaSymmetricalDist) - Constructor for class umontreal.iro.lecuyer.randvar.BetaSymmetricalBestGen
Creates a new generator for the distribution dist, using only one stream stream.
BetaSymmetricalDist - Class in umontreal.iro.lecuyer.probdist
Specializes the class BetaDist to the case of a symmetrical beta distribution over the interval [0, 1], with shape parameters α = β.
BetaSymmetricalDist(double) - Constructor for class umontreal.iro.lecuyer.probdist.BetaSymmetricalDist
Constructs a BetaSymmetricalDist object with parameters α = β = alpha, over the unit interval (0, 1).
BetaSymmetricalDist(double, int) - Constructor for class umontreal.iro.lecuyer.probdist.BetaSymmetricalDist
Same as BetaSymmetricalDist (alpha), but using approximations of roughly d decimal digits of precision when computing the distribution, complementary distribution, and inverse functions.
BetaSymmetricalGen - Class in umontreal.iro.lecuyer.randvar
This class implements random variate generators with the symmetrical beta distribution with shape parameters α = β, over the interval (0, 1).
BetaSymmetricalGen(RandomStream, BetaSymmetricalDist) - Constructor for class umontreal.iro.lecuyer.randvar.BetaSymmetricalGen
Creates a new generator for the distribution dist, using stream s.
BetaSymmetricalPolarGen - Class in umontreal.iro.lecuyer.randvar
This class implements symmetrical beta random variate generators using Ulrich's polar method.
BetaSymmetricalPolarGen(RandomStream, RandomStream, BetaSymmetricalDist) - Constructor for class umontreal.iro.lecuyer.randvar.BetaSymmetricalPolarGen
Creates a new generator for the distribution dist, using stream stream to generate x, and stream s2 to generate y as described in eq.
BetaSymmetricalPolarGen(RandomStream, BetaSymmetricalDist) - Constructor for class umontreal.iro.lecuyer.randvar.BetaSymmetricalPolarGen
Creates a new generator for the distribution dist, using only one stream stream.
Bin - Class in umontreal.iro.lecuyer.simprocs
A Bin corresponds to a pile of identical tokens, and a list of processes waiting for the tokens when the list is empty.
Bin() - Constructor for class umontreal.iro.lecuyer.simprocs.Bin
Constructs a new bin, initially empty, with service policy FIFO.
Bin(String) - Constructor for class umontreal.iro.lecuyer.simprocs.Bin
Constructs a new bin, initially empty, with service policy FIFO and identifier name.
BinaryTree - Class in umontreal.iro.lecuyer.simevents.eventlist
An implementation of EventList using a binary search tree.
BinaryTree() - Constructor for class umontreal.iro.lecuyer.simevents.eventlist.BinaryTree
 
binomial(RandomStream, int, double) - Static method in class umontreal.iro.lecuyer.randvar.Rand1
Deprecated. Returns a random variate having the binomial distribution with parameters n and p, using stream s.
BinomialConvolutionGen - Class in umontreal.iro.lecuyer.randvar
Implements binomial random variate generators using the convolution method.
BinomialConvolutionGen(RandomStream, BinomialDist) - Constructor for class umontreal.iro.lecuyer.randvar.BinomialConvolutionGen
Creates a new random variate generator for distribution dist and stream s.
BinomialDist - Class in umontreal.iro.lecuyer.probdist
Extends the class DiscreteDistributionInt for the binomial distribution with parameters n and p, where n is a positive integer and 0 <= p <= 1.
BinomialDist(int, double) - Constructor for class umontreal.iro.lecuyer.probdist.BinomialDist
Creates an object that contains the binomial terms, for 0 <= x <= n, and the corresponding cumulative function.
BinomialGen - Class in umontreal.iro.lecuyer.randvar
This class implements random variate generators for the binomial distribution.
BinomialGen(RandomStream, BinomialDist) - Constructor for class umontreal.iro.lecuyer.randvar.BinomialGen
Creates a new random variate generator for the binomial distribution dist and the random stream s.
BiNormalDist - Class in umontreal.iro.lecuyer.probdistmulti
Extends the class ContinuousDistribution2Dim for the bivariate normal distribution.
BiNormalDist(double) - Constructor for class umontreal.iro.lecuyer.probdistmulti.BiNormalDist
Constructs a BiNormalDist object with default parameters μ1 = μ2 = 0, σ1 = σ2 = 1 and correlation ρ = rho.
BiNormalDist(double, double, double, double, double) - Constructor for class umontreal.iro.lecuyer.probdistmulti.BiNormalDist
Constructs a BiNormalDist object with parameters μ1 = mu1, μ2 = mu2, σ1 = sigma1, σ2 = sigma2 and ρ = rho.
BiNormalDonnellyDist - Class in umontreal.iro.lecuyer.probdistmulti
Extends the class BiNormalDist for the bivariate normal distribution using a translation of Donnelly's FORTRAN code.
BiNormalDonnellyDist(double, int) - Constructor for class umontreal.iro.lecuyer.probdistmulti.BiNormalDonnellyDist
Constructor with default parameters μ1 = μ2 = 0, σ1 = σ2 = 1, correlation ρ = rho, and d = ndig digits of accuracy (the absolute error is smaller than 10-d).
BiNormalDonnellyDist(double) - Constructor for class umontreal.iro.lecuyer.probdistmulti.BiNormalDonnellyDist
Same as BiNormalDonnellyDist (rho, 15).
BiNormalDonnellyDist(double, double, double, double, double, int) - Constructor for class umontreal.iro.lecuyer.probdistmulti.BiNormalDonnellyDist
Constructor with parameters μ1 = mu1, μ2 = mu2, σ1 = sigma1, σ2 = sigma2, ρ = rho, and d = ndig digits of accuracy.
BiNormalDonnellyDist(double, double, double, double, double) - Constructor for class umontreal.iro.lecuyer.probdistmulti.BiNormalDonnellyDist
Same as BiNormalDonnellyDist (mu1, sigma1, mu2, sigma2, rho, 15).
BiNormalGenzDist - Class in umontreal.iro.lecuyer.probdistmulti
Extends the class BiNormalDist for the bivariate normal distribution using Genz's algorithm as described in.
BiNormalGenzDist(double) - Constructor for class umontreal.iro.lecuyer.probdistmulti.BiNormalGenzDist
Constructs a BiNormalGenzDist object with default parameters μ1 = μ2 = 0, σ1 = σ2 = 1 and correlation ρ = rho.
BiNormalGenzDist(double, double, double, double, double) - Constructor for class umontreal.iro.lecuyer.probdistmulti.BiNormalGenzDist
Constructs a BiNormalGenzDist object with parameters μ1 = mu1, μ2 = mu2, σ1 = sigma1, σ2 = sigma2 and ρ = rho.
BiStudentDist - Class in umontreal.iro.lecuyer.probdistmulti
Extends the class ContinuousDistribution2Dim for the standard bivariate Student's t distribution.
BiStudentDist(int, double) - Constructor for class umontreal.iro.lecuyer.probdistmulti.BiStudentDist
Constructs a BiStudentDist object with correlation ρ = rho and ν = nu degrees of freedom.
BitMatrix - Class in umontreal.iro.lecuyer.util
This class implements matrices of bits of arbitrary dimensions.
BitMatrix(int, int) - Constructor for class umontreal.iro.lecuyer.util.BitMatrix
Creates a new BitMatrix with r rows and c columns filled with 0's.
BitMatrix(BitVector[]) - Constructor for class umontreal.iro.lecuyer.util.BitMatrix
Creates a new BitMatrix using the data in rows.
BitMatrix(int[][], int, int) - Constructor for class umontreal.iro.lecuyer.util.BitMatrix
Creates a new BitMatrix with r rows and c columns using the data in data.
BitMatrix(BitMatrix) - Constructor for class umontreal.iro.lecuyer.util.BitMatrix
Copy constructor.
BitMatrix.IncompatibleDimensionException - Exception in umontreal.iro.lecuyer.util
Runtime exception raised when the dimensions of the BitMatrix are not appropriate for the operation.
BitVector - Class in umontreal.iro.lecuyer.util
This class implements vectors of bits and the operations needed to use them.
BitVector(int) - Constructor for class umontreal.iro.lecuyer.util.BitVector
Creates a new BitVector of length length with all its bits set to 0.
BitVector(int[], int) - Constructor for class umontreal.iro.lecuyer.util.BitVector
Creates a new BitVector of length length using the data in vect.
BitVector(int[]) - Constructor for class umontreal.iro.lecuyer.util.BitVector
Creates a new BitVector using the data in vect.
BitVector(BitVector) - Constructor for class umontreal.iro.lecuyer.util.BitVector
Creates a copy of the BitVector that.
brentDekker(double, double, MathFunction, double) - Static method in class umontreal.iro.lecuyer.util.RootFinder
Computes a root x of the function in f using the Brent-Dekker method.

C

CachedPointSet - Class in umontreal.iro.lecuyer.hups
This container class caches a point set by precomputing and storing its points locally in an array.
CachedPointSet(PointSet, int, int) - Constructor for class umontreal.iro.lecuyer.hups.CachedPointSet
Creates a new PointSet object that contains an array storing the first dim coordinates of the first n points of P.
CachedPointSet(PointSet) - Constructor for class umontreal.iro.lecuyer.hups.CachedPointSet
Creates a new PointSet object that contains an array storing the points of P.
calcMatStirling(int, int) - Static method in class umontreal.iro.lecuyer.util.Num
Computes and returns the Stirling numbers of the second kind
cancel() - Method in class umontreal.iro.lecuyer.simevents.Event
Cancels this event before it occurs.
cancel(String) - Static method in class umontreal.iro.lecuyer.simevents.Event
Finds the first occurence of an event of class ``type'' in the event list, and cancels it.
cancel() - Method in class umontreal.iro.lecuyer.simprocs.AbstractSimProcess
Cancel the activating event that was supposed to resume this process, and place the process in the SUSPENDED state.
CauchyDist - Class in umontreal.iro.lecuyer.probdist
Extends the class ContinuousDistribution for the Cauchy distribution with location parameter α and scale parameter β > 0.
CauchyDist() - Constructor for class umontreal.iro.lecuyer.probdist.CauchyDist
Constructs a CauchyDist object with parameters α = 0 and β = 1.
CauchyDist(double, double) - Constructor for class umontreal.iro.lecuyer.probdist.CauchyDist
Constructs a CauchyDist object with parameters α = alpha and β = beta.
CauchyGen - Class in umontreal.iro.lecuyer.randvar
This class implements random variate generators for the Cauchy distribution.
CauchyGen(RandomStream, CauchyDist) - Constructor for class umontreal.iro.lecuyer.randvar.CauchyGen
Create a new generator for the distribution dist, using stream s.
cdf(double) - Method in class umontreal.iro.lecuyer.probdist.BetaDist
 
cdf(double, double, int, double) - Static method in class umontreal.iro.lecuyer.probdist.BetaDist
Same as cdf (alpha, beta, 0, 1, d, x).
cdf(double, double, double, double, int, double) - Static method in class umontreal.iro.lecuyer.probdist.BetaDist
Computes an approximation of the distribution function, with roughly d decimal digits of precision.
cdf(double) - Method in class umontreal.iro.lecuyer.probdist.BetaSymmetricalDist
 
cdf(double, int, double) - Static method in class umontreal.iro.lecuyer.probdist.BetaSymmetricalDist
Same as cdf (alpha, alpha, d, x).
cdf(int) - Method in class umontreal.iro.lecuyer.probdist.BinomialDist
 
cdf(int, double, int) - Static method in class umontreal.iro.lecuyer.probdist.BinomialDist
Computes F(x), the distribution function of a binomial random variable with parameters n and p, evaluated at x.
cdf(double) - Method in class umontreal.iro.lecuyer.probdist.CauchyDist
 
cdf(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.CauchyDist
Computes the distribution function.
cdf(double) - Method in class umontreal.iro.lecuyer.probdist.ChiDist
 
cdf(int, double) - Static method in class umontreal.iro.lecuyer.probdist.ChiDist
Computes the distribution function by using the gamma distribution function.
cdf(double) - Method in class umontreal.iro.lecuyer.probdist.ChiSquareDist
 
cdf(int, int, double) - Static method in class umontreal.iro.lecuyer.probdist.ChiSquareDist
Computes an approximation of the chi-square distribution function with n degrees of freedom.
cdf(double) - Method in class umontreal.iro.lecuyer.probdist.DiscreteDistribution
 
cdf(double) - Method in class umontreal.iro.lecuyer.probdist.DiscreteDistributionInt
Returns the distribution function F evaluated at x (see).
cdf(int) - Method in class umontreal.iro.lecuyer.probdist.DiscreteDistributionInt
Returns the distribution function F evaluated at x (see).
cdf(double) - Method in interface umontreal.iro.lecuyer.probdist.Distribution
Computes and returns the distribution function F(x).
cdf(double) - Method in class umontreal.iro.lecuyer.probdist.EmpiricalDist
 
cdf(int, double, int, double) - Static method in class umontreal.iro.lecuyer.probdist.ErlangDist
Computes the distribution function using the gamma distribution function.
cdf(double) - Method in class umontreal.iro.lecuyer.probdist.ExponentialDist
 
cdf(double, double) - Static method in class umontreal.iro.lecuyer.probdist.ExponentialDist
Computes the distribution function.
cdf(double) - Method in class umontreal.iro.lecuyer.probdist.ExtremeValueDist
 
cdf(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.ExtremeValueDist
Computes the distribution function.
cdf(double) - Method in class umontreal.iro.lecuyer.probdist.FatigueLifeDist
 
cdf(double, double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.FatigueLifeDist
Computes the fatigue life distribution function with parameters μ, β and γ.
cdf(double) - Method in class umontreal.iro.lecuyer.probdist.FisherFDist
 
cdf(int, int, int, double) - Static method in class umontreal.iro.lecuyer.probdist.FisherFDist
Computes the distribution function of the Fisher F-distribution with parameters n and m, evaluated at x, with roughly d decimal digits of precision.
cdf(double) - Method in class umontreal.iro.lecuyer.probdist.GammaDist
 
cdf(double, double, int, double) - Static method in class umontreal.iro.lecuyer.probdist.GammaDist
Returns an approximation of the gamma distribution function with parameters α = alpha and λ = lambda.
cdf(double, int, double) - Static method in class umontreal.iro.lecuyer.probdist.GammaDist
Equivalent to cdf (alpha, 1.0, d, x).
cdf(int) - Method in class umontreal.iro.lecuyer.probdist.GeometricDist
 
cdf(double, int) - Static method in class umontreal.iro.lecuyer.probdist.GeometricDist
Computes the distribution function F(x).
cdf(double) - Method in class umontreal.iro.lecuyer.probdist.HyperbolicSecantDist
 
cdf(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.HyperbolicSecantDist
Computes the distribution function of the hyperbolic secant distribution with parameters μ and σ.
cdf(int) - Method in class umontreal.iro.lecuyer.probdist.HypergeometricDist
 
cdf(int, int, int, int) - Static method in class umontreal.iro.lecuyer.probdist.HypergeometricDist
Computes the distribution function F(x).
cdf(double) - Method in class umontreal.iro.lecuyer.probdist.InverseGaussianDist
 
cdf(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.InverseGaussianDist
Computes the distribution function of the inverse gaussian distribution with parameters μ and λ, evaluated at x.
cdf(double) - Method in class umontreal.iro.lecuyer.probdist.JohnsonSBDist
 
cdf(double, double, double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.JohnsonSBDist
Computes the distribution function.
cdf(double) - Method in class umontreal.iro.lecuyer.probdist.JohnsonSUDist
 
cdf(double, double, double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.JohnsonSUDist
Computes the distribution function F(x).
cdf(double) - Method in class umontreal.iro.lecuyer.probdist.LaplaceDist
 
cdf(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.LaplaceDist
Computes the Laplace distribution function.
cdf(int) - Method in class umontreal.iro.lecuyer.probdist.LogarithmicDist
 
cdf(double, int) - Static method in class umontreal.iro.lecuyer.probdist.LogarithmicDist
Computes the distribution function F(x).
cdf(double) - Method in class umontreal.iro.lecuyer.probdist.LogisticDist
 
cdf(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.LogisticDist
Computes the distribution function F(x).
cdf(double) - Method in class umontreal.iro.lecuyer.probdist.LoglogisticDist
 
cdf(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.LoglogisticDist
Computes the distribution function of the log-logistic distribution with parameters α and β.
cdf(double) - Method in class umontreal.iro.lecuyer.probdist.LognormalDist
 
cdf(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.LognormalDist
Computes the lognormal distribution function, using cdf01.
cdf(int) - Method in class umontreal.iro.lecuyer.probdist.NegativeBinomialDist
 
cdf(double, double, int) - Static method in class umontreal.iro.lecuyer.probdist.NegativeBinomialDist
Computes the distribution function.
cdf(double) - Method in class umontreal.iro.lecuyer.probdist.NormalDist
 
cdf(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.NormalDist
Computes the normal distribution function with mean μ and variance σ2.
cdf(double) - Method in class umontreal.iro.lecuyer.probdist.NormalDistQuick
 
cdf(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.NormalDistQuick
Returns an approximation of Φ(x), where Φ is the standard normal distribution function, with mean 0 and variance 1.
cdf(double) - Method in class umontreal.iro.lecuyer.probdist.ParetoDist
 
cdf(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.ParetoDist
Computes the distribution function.
cdf(double) - Method in class umontreal.iro.lecuyer.probdist.Pearson5Dist
 
cdf(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.Pearson5Dist
Computes the density function of a Pearson V distribution with shape parameter α and scale parameter β.
cdf(double) - Method in class umontreal.iro.lecuyer.probdist.Pearson6Dist
 
cdf(double, double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.Pearson6Dist
Computes the distribution function of a Pearson VI distribution with shape parameters α1 and α2, and scale parameter β.
cdf(double) - Method in class umontreal.iro.lecuyer.probdist.PiecewiseLinearEmpiricalDist
 
cdf(int) - Method in class umontreal.iro.lecuyer.probdist.PoissonDist
 
cdf(double, int) - Static method in class umontreal.iro.lecuyer.probdist.PoissonDist
Computes and returns the value of the Poisson distribution function, F(x), for λ = lambda.
cdf(double) - Method in class umontreal.iro.lecuyer.probdist.StudentDist
 
cdf(int, double) - Static method in class umontreal.iro.lecuyer.probdist.StudentDist
Returns an approximation for the Student-t distribution function with n degrees of freedom.
cdf(double) - Method in class umontreal.iro.lecuyer.probdist.TriangularDist
 
cdf(double, double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.TriangularDist
Computes the distribution function.
cdf(double) - Method in class umontreal.iro.lecuyer.probdist.TruncatedDist
 
cdf(double) - Method in class umontreal.iro.lecuyer.probdist.UniformDist
 
cdf(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.UniformDist
Computes the uniform distribution function as in.
cdf(int) - Method in class umontreal.iro.lecuyer.probdist.UniformIntDist
 
cdf(int, int, int) - Static method in class umontreal.iro.lecuyer.probdist.UniformIntDist
Computes the discrete uniform distribution function as in.
cdf(double) - Method in class umontreal.iro.lecuyer.probdist.WeibullDist
 
cdf(double, double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.WeibullDist
Computes the distribution function.
cdf(double, double) - Static method in class umontreal.iro.lecuyer.probdist.WeibullDist
Same as cdf (alpha, 1.0, 0.0, x).
cdf(double, double, double) - Static method in class umontreal.iro.lecuyer.probdistmulti.BiNormalDist
Computes the standard binormal distribution using the fast Drezner-Wesolowsky method described in.
cdf(double, double) - Method in class umontreal.iro.lecuyer.probdistmulti.BiNormalDist
 
cdf(double, double, double, double, double, double, double) - Static method in class umontreal.iro.lecuyer.probdistmulti.BiNormalDist
Computes the binormal distribution function with parameters μ1 = mu1, μ2 = mu2, σ1 = sigma1, σ2 = sigma2 and ρ = rho.
cdf(double, double, double, int) - Static method in class umontreal.iro.lecuyer.probdistmulti.BiNormalDonnellyDist
Computes the standard binormal distribution with the method described in, where ndig is the number of decimal digits of accuracy provided (ndig  <= 15).
cdf(double, double, double, double, double, double, double, int) - Static method in class umontreal.iro.lecuyer.probdistmulti.BiNormalDonnellyDist
Computes the binormal distribution function with parameters μ1 = mu1, μ2 = mu2, σ1 = sigma1, σ2 = sigma2, correlation ρ = rho and ndig decimal digits of accuracy.
cdf(double, double) - Method in class umontreal.iro.lecuyer.probdistmulti.BiNormalDonnellyDist
 
cdf(double, double, double) - Static method in class umontreal.iro.lecuyer.probdistmulti.BiNormalDonnellyDist
 
cdf(double, double, double, double, double, double, double) - Static method in class umontreal.iro.lecuyer.probdistmulti.BiNormalDonnellyDist
 
cdf(double, double, double) - Static method in class umontreal.iro.lecuyer.probdistmulti.BiNormalGenzDist
Computes the standard binormal distribution with the method described in.
cdf(double, double, double, double, double, double, double) - Static method in class umontreal.iro.lecuyer.probdistmulti.BiNormalGenzDist
 
cdf(double, double) - Method in class umontreal.iro.lecuyer.probdistmulti.BiNormalGenzDist
 
cdf(double, double) - Method in class umontreal.iro.lecuyer.probdistmulti.BiStudentDist
 
cdf(int, double, double, double) - Static method in class umontreal.iro.lecuyer.probdistmulti.BiStudentDist
Computes the standard bivariate Student's t distribution using the method described in.
cdf(double, double) - Method in class umontreal.iro.lecuyer.probdistmulti.ContinuousDistribution2Dim
.
cdf(double, double, double, double) - Method in class umontreal.iro.lecuyer.probdistmulti.ContinuousDistribution2Dim
.
cdf(int[]) - Method in class umontreal.iro.lecuyer.probdistmulti.DiscreteDistributionIntMulti
Computes the cumulative probability function F of the distribution evaluated at x, assuming the lowest values start at 0, i.e.
cdf(int[]) - Method in class umontreal.iro.lecuyer.probdistmulti.MultinomialDist
 
cdf(int, double[], int[]) - Static method in class umontreal.iro.lecuyer.probdistmulti.MultinomialDist
Computes the function F of the multinomial distribution with parameters n and (p1,...,pd) evaluated at x.
cdf(double, double[], int[]) - Static method in class umontreal.iro.lecuyer.probdistmulti.NegativeMultinomialDist
Computes the cumulative probability function F of the negative multinomial distribution with parameters γ and (p1,...,pk), evaluated at x.
cdf01(double) - Static method in class umontreal.iro.lecuyer.probdist.NormalDist
Same as cdf (0.0, 1.0, x).
cdf01(double) - Static method in class umontreal.iro.lecuyer.probdist.NormalDistQuick
Same as cdf (0.0, 1.0, x).
cdf2(int, int, double) - Static method in class umontreal.iro.lecuyer.probdist.StudentDist
Returns an approximation of the Student-t distribution function with n degrees of freedom.
changeCapacity(int) - Method in class umontreal.iro.lecuyer.simprocs.Resource
Modifies by diff units (increases if diff > 0, decreases if diff < 0) the capacity (i.e., the number of units) of the resource.
chi2(double[], int[], int, int) - Static method in class umontreal.iro.lecuyer.gof.GofStat
Computes and returns the chi-square statistic for the observations oi in count[smin...smax], for which the corresponding expected values ei are in nbExp[smin...smax].
chi2(IntArrayList, DiscreteDistributionInt, int, int, double, int[]) - Static method in class umontreal.iro.lecuyer.gof.GofStat
Computes and returns the chi-square statistic for the observations stored in data, assuming that these observations follow the discrete distribution dist.
chi2Equal(double, int[], int, int) - Static method in class umontreal.iro.lecuyer.gof.GofStat
Similar to chi2, except that the expected number of observations per category is assumed to be the same for all categories, and equal to nbExp.
chi2Equal(DoubleArrayList, double) - Static method in class umontreal.iro.lecuyer.gof.GofStat
Computes the chi-square statistic for a continuous distribution.
chi2Equal(DoubleArrayList) - Static method in class umontreal.iro.lecuyer.gof.GofStat
Equivalent to chi2Equal (data, 10).
ChiDist - Class in umontreal.iro.lecuyer.probdist
Extends the class ContinuousDistribution for the chi distribution with shape parameter v > 0, where the number of degrees of freedom v is a positive integer.
ChiDist(int) - Constructor for class umontreal.iro.lecuyer.probdist.ChiDist
Constructs a ChiDist object.
ChiGen - Class in umontreal.iro.lecuyer.randvar
This class implements random variate generators for the chi distribution.
ChiGen(RandomStream, ChiDist) - Constructor for class umontreal.iro.lecuyer.randvar.ChiGen
Create a new generator for the distribution dist, using stream s.
ChiRatioOfUniformsGen - Class in umontreal.iro.lecuyer.randvar
This class implements Chi random variate generators using the ratio of uniforms method with shift.
ChiRatioOfUniformsGen(RandomStream, ChiDist) - Constructor for class umontreal.iro.lecuyer.randvar.ChiRatioOfUniformsGen
Create a new generator for the distribution dist, using stream s.
chiSquare(RandomStream, int) - Static method in class umontreal.iro.lecuyer.randvar.Rand1
Deprecated. Returns a random variate having the ChiSquare distribution with parameters n.
ChiSquareDist - Class in umontreal.iro.lecuyer.probdist
Extends the class ContinuousDistribution for the chi-square distribution with n degrees of freedom, where n is a positive integer.
ChiSquareDist(int) - Constructor for class umontreal.iro.lecuyer.probdist.ChiSquareDist
Constructs a chi-square distribution with n degrees of freedom.
ChiSquareDistQuick - Class in umontreal.iro.lecuyer.probdist
Provides a variant of ChiSquareDist with faster but less accurate methods.
ChiSquareDistQuick(int) - Constructor for class umontreal.iro.lecuyer.probdist.ChiSquareDistQuick
Constructs a chi-square distribution with n degrees of freedom.
ChiSquareGen - Class in umontreal.iro.lecuyer.randvar
This class implements random variate generators with the chi square distribution with n > 0 degrees of freedom.
ChiSquareGen(RandomStream, ChiSquareDist) - Constructor for class umontreal.iro.lecuyer.randvar.ChiSquareGen
Create a new generator for the distribution dist and stream s.
Chrono - Class in umontreal.iro.lecuyer.util
Chrono is a small class that acts as an interface to the system clock and calculates the CPU time consumed by parts of a program.
Chrono() - Constructor for class umontreal.iro.lecuyer.util.Chrono
Constructs a Chrono object and initializes it to zero.
ChronoSingleThread - Class in umontreal.iro.lecuyer.util
The ChronoSingleThread class extends the Chrono class and computes the CPU time for the current thread only.
ChronoSingleThread() - Constructor for class umontreal.iro.lecuyer.util.ChronoSingleThread
Constructs a ChronoSingleThread object and initializes it to zero.
clear() - Method in class umontreal.iro.lecuyer.rng.RandomStreamManager
Removes all the streams from the internal list of this random stream manager.
clear() - Method in class umontreal.iro.lecuyer.simevents.eventlist.BinaryTree
 
clear() - Method in class umontreal.iro.lecuyer.simevents.eventlist.DoublyLinked
 
clear() - Method in interface umontreal.iro.lecuyer.simevents.eventlist.EventList
Empties the event list, i.e., cancels all events.
clear() - Method in class umontreal.iro.lecuyer.simevents.eventlist.Henriksen
 
clear() - Method in class umontreal.iro.lecuyer.simevents.eventlist.RedblackTree
 
clear() - Method in class umontreal.iro.lecuyer.simevents.eventlist.SplayTree
 
clear() - Method in class umontreal.iro.lecuyer.simevents.LinkedListStat
 
clear() - Method in class umontreal.iro.lecuyer.util.PrintfFormat
Clears the contents of the buffer.
clearCache() - Method in class umontreal.iro.lecuyer.randvar.RandomVariateGenWithCache
Clears the cached values for this cached generator.
clearCache() - Method in class umontreal.iro.lecuyer.rng.RandomStreamWithCache
Clears the cached values for this random stream.
clearRandomShift() - Method in class umontreal.iro.lecuyer.hups.ContainerPointSet
 
clearRandomShift() - Method in class umontreal.iro.lecuyer.hups.CycleBasedPointSet
 
clearRandomShift() - Method in class umontreal.iro.lecuyer.hups.CycleBasedPointSetBase2
 
clearRandomShift() - Method in class umontreal.iro.lecuyer.hups.DigitalNet
 
clearRandomShift() - Method in class umontreal.iro.lecuyer.hups.DigitalNetBase2
 
clearRandomShift() - Method in class umontreal.iro.lecuyer.hups.PointSet
Erases the current random shift, if any.
clearRandomShift() - Method in class umontreal.iro.lecuyer.hups.Rank1Lattice
Clears the random shift.
clone() - Method in class umontreal.iro.lecuyer.simevents.Accumulate
Clone this object.
clone() - Method in class umontreal.iro.lecuyer.simevents.LinkedListStat
 
clone() - Method in class umontreal.iro.lecuyer.stat.StatProbe
 
clone() - Method in class umontreal.iro.lecuyer.stat.Tally
Clone this object.
clone() - Method in class umontreal.iro.lecuyer.stat.TallyStore
Clone this object.
clone() - Method in class umontreal.iro.lecuyer.util.BitMatrix
Creates a copy of the BitMatrix.
clone() - Method in class umontreal.iro.lecuyer.util.BitVector
Creates a copy of the BitVector.
CM - Static variable in class umontreal.iro.lecuyer.gof.GofFormat
Cramér-von Mises test
combination(int, int) - Static method in class umontreal.iro.lecuyer.util.Num
Returns the number of different combinations of s objects amongst n.
Condition - Class in umontreal.iro.lecuyer.simprocs
A Condition is a boolean indicator, with a list of processes waiting for the indicator to be true (when it is false).
Condition(boolean) - Constructor for class umontreal.iro.lecuyer.simprocs.Condition
Constructs a new Condition with initial value val.
Condition(boolean, String) - Constructor for class umontreal.iro.lecuyer.simprocs.Condition
Constructs a new Condition with initial value val and identifier name.
confidenceIntervalStudent(double, double[]) - Method in class umontreal.iro.lecuyer.stat.Tally
Returns, in elements 0 and 1 of the array object centerAndRadius[], the center and half-length (radius) of a confidence interval on the true mean of the random variable X, with confidence level level, assuming that the observations given to this collector are independent and identically distributed (i.i.d.) copies of X, and that X has the normal distribution.
connectToDatabase(Properties) - Static method in class umontreal.iro.lecuyer.util.JDBCManager
Connects to the database using the properties prop and returns the an object representing the connection.
connectToDatabase(InputStream) - Static method in class umontreal.iro.lecuyer.util.JDBCManager
Returns a connection to the database using the properties read from stream is.
connectToDatabase(File) - Static method in class umontreal.iro.lecuyer.util.JDBCManager
Equivalent to connectToDatabase (new FileInputStream (file)).
connectToDatabase(String) - Static method in class umontreal.iro.lecuyer.util.JDBCManager
Equivalent to connectToDatabase (new FileInputStream (fileName)).
connectToDatabaseFromResource(String) - Static method in class umontreal.iro.lecuyer.util.JDBCManager
Uses connectToDatabase with the stream obtained from the resource resource.
ContainerPointSet - Class in umontreal.iro.lecuyer.hups
This acts as a generic base class for all container classes that contain a point set and apply some kind of transformation to the coordinates to define a new point set.
ContainerPointSet() - Constructor for class umontreal.iro.lecuyer.hups.ContainerPointSet
 
contains(Object) - Method in class umontreal.iro.lecuyer.simevents.LinkedListStat
 
Continuous - Class in umontreal.iro.lecuyer.simevents
This abstract class provides the basic structures and tools for continuous-time simulation, where certain variables evolve continuously in time, according to differential equations.
Continuous() - Constructor for class umontreal.iro.lecuyer.simevents.Continuous
Constructs a new continuous-time variable, without initializing it.
ContinuousDistribution - Class in umontreal.iro.lecuyer.probdist
Classes implementing continuous distributions should inherit from this class.
ContinuousDistribution() - Constructor for class umontreal.iro.lecuyer.probdist.ContinuousDistribution
 
ContinuousDistribution2Dim - Class in umontreal.iro.lecuyer.probdistmulti
Classes implementing 2-dimensional continuous distributions should inherit from this class.
ContinuousDistribution2Dim() - Constructor for class umontreal.iro.lecuyer.probdistmulti.ContinuousDistribution2Dim
 
ContinuousDistributionMulti - Class in umontreal.iro.lecuyer.probdistmulti
Classes implementing continuous multi-dimensional distributions should inherit from this class.
ContinuousDistributionMulti() - Constructor for class umontreal.iro.lecuyer.probdistmulti.ContinuousDistributionMulti
 
COR - Static variable in class umontreal.iro.lecuyer.gof.GofFormat
Correlation
covariance(TallyStore) - Method in class umontreal.iro.lecuyer.stat.TallyStore
Returns the sample covariance of the observations contained in this tally, and the other tally t2.
cramerVonMises(int, double) - Static method in class umontreal.iro.lecuyer.gof.FBar
Returns 1.0 - FDist.cramerVonMises (n, x).
cramerVonMises(int, double) - Static method in class umontreal.iro.lecuyer.gof.FDist
Returns an approximation of P[WN2 <= x], where WN2 is the Cramér-von Mises statistic for a sample of independent uniforms over (0, 1).
cramerVonMises(DoubleArrayList) - Static method in class umontreal.iro.lecuyer.gof.GofStat
Computes and returns the Cramér-von Mises statistic WN2.
createForSingleThread() - Static method in class umontreal.iro.lecuyer.util.Chrono
Creates a Chrono instance adapted for a program using a single thread.
currentProcess() - Static method in class umontreal.iro.lecuyer.simprocs.AbstractSimProcess
Returns the process that is currently executing, if any.
CycleBasedPointSet - Class in umontreal.iro.lecuyer.hups
This abstract class provides the basic structures for storing and manipulating a highly uniform point set defined by a set of cycles.
CycleBasedPointSet() - Constructor for class umontreal.iro.lecuyer.hups.CycleBasedPointSet
 
CycleBasedPointSet.CycleBasedPointSetIterator - Class in umontreal.iro.lecuyer.hups
 
CycleBasedPointSet.CycleBasedPointSetIterator() - Constructor for class umontreal.iro.lecuyer.hups.CycleBasedPointSet.CycleBasedPointSetIterator
 
CycleBasedPointSetBase2 - Class in umontreal.iro.lecuyer.hups
Similar to CycleBasedPointSet, except that the successive values in the cycles are stored as integers in the range {0,..., 2k -1}, where 1 <= k <= 31.
CycleBasedPointSetBase2() - Constructor for class umontreal.iro.lecuyer.hups.CycleBasedPointSetBase2
 
CycleBasedPointSetBase2.CycleBasedPointSetBase2Iterator - Class in umontreal.iro.lecuyer.hups
 
CycleBasedPointSetBase2.CycleBasedPointSetBase2Iterator() - Constructor for class umontreal.iro.lecuyer.hups.CycleBasedPointSetBase2.CycleBasedPointSetBase2Iterator
 

D

d(long) - Static method in class umontreal.iro.lecuyer.util.PrintfFormat
Same as d (0, 1, x).
d(int, long) - Static method in class umontreal.iro.lecuyer.util.PrintfFormat
Same as d (fieldwidth, 1, x).
d(int, int, long) - Static method in class umontreal.iro.lecuyer.util.PrintfFormat
Formats the long integer x into a string like %d in the C printf function.
DBL_DIG - Static variable in class umontreal.iro.lecuyer.util.Num
Number of decimal digits of precision in a double.
DBL_EPSILON - Static variable in class umontreal.iro.lecuyer.util.Num
Difference between 1.0 and the smallest double greater than 1.0.
DBL_MAX_10_EXP - Static variable in class umontreal.iro.lecuyer.util.Num
Largest int x such that 10x is representable (approximately) as a double.
DBL_MAX_EXP - Static variable in class umontreal.iro.lecuyer.util.Num
Largest int x such that 2x-1 is representable (approximately) as a double.
DBL_MIN - Static variable in class umontreal.iro.lecuyer.util.Num
Smallest normalized positive floating-point double.
DBL_MIN_EXP - Static variable in class umontreal.iro.lecuyer.util.Num
Smallest int x such that 2x-1 is representable (approximately) as a normalised double.
DEAD - Static variable in class umontreal.iro.lecuyer.simprocs.AbstractSimProcess
The process has terminated its execution.
decPrec - Variable in class umontreal.iro.lecuyer.probdist.ContinuousDistribution
 
decPrec - Variable in class umontreal.iro.lecuyer.probdistmulti.ContinuousDistribution2Dim
Defines the target number of decimals of accuracy when approximating a distribution function, but there is no guarantee that this target is always attained.
delay(double) - Method in class umontreal.iro.lecuyer.simprocs.AbstractSimProcess
Suspends the execution of the currently executing process and schedules it to resume its execution in delay units of simulation time.
delay(double) - Method in class umontreal.iro.lecuyer.simprocs.dsol.SimProcess
 
delay(double) - Method in class umontreal.iro.lecuyer.simprocs.SimProcess
 
DELAYED - Static variable in class umontreal.iro.lecuyer.simprocs.AbstractSimProcess
The process is not executing but has an event in the event list to reactivate it later on.
density(double) - Method in class umontreal.iro.lecuyer.probdist.BetaDist
 
density(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.BetaDist
Same as density (alpha, beta, 0, 1, x).
density(double, double, double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.BetaDist
Computes the density function of the beta distribution.
density(double, double) - Static method in class umontreal.iro.lecuyer.probdist.BetaSymmetricalDist
Returns the density evaluated at x.
density(double) - Method in class umontreal.iro.lecuyer.probdist.CauchyDist
 
density(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.CauchyDist
Computes the density function.
density(double) - Method in class umontreal.iro.lecuyer.probdist.ChiDist
 
density(int, double) - Static method in class umontreal.iro.lecuyer.probdist.ChiDist
Computes the density function.
density(double) - Method in class umontreal.iro.lecuyer.probdist.ChiSquareDist
 
density(int, double) - Static method in class umontreal.iro.lecuyer.probdist.ChiSquareDist
Computes the density function for a chi-square distribution with n degrees of freedom.
density(double) - Method in class umontreal.iro.lecuyer.probdist.ContinuousDistribution
Returns f (x), the density of X evaluated at x.
density(int, double, double) - Static method in class umontreal.iro.lecuyer.probdist.ErlangDist
Computes the density function.
density(double) - Method in class umontreal.iro.lecuyer.probdist.ExponentialDist
 
density(double, double) - Static method in class umontreal.iro.lecuyer.probdist.ExponentialDist
Computes the density function.
density(double) - Method in class umontreal.iro.lecuyer.probdist.ExtremeValueDist
 
density(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.ExtremeValueDist
Computes the density function.
density(double) - Method in class umontreal.iro.lecuyer.probdist.FatigueLifeDist
 
density(double, double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.FatigueLifeDist
Computes the density for the fatigue life distribution with parameters μ, β and γ.
density(double) - Method in class umontreal.iro.lecuyer.probdist.FisherFDist
 
density(int, int, double) - Static method in class umontreal.iro.lecuyer.probdist.FisherFDist
Computes the density function for a Fisher F-distribution with n and m degrees of freedom.
density(double) - Method in class umontreal.iro.lecuyer.probdist.GammaDist
 
density(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.GammaDist
Computes the density function.
density(double) - Method in class umontreal.iro.lecuyer.probdist.HyperbolicSecantDist
 
density(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.HyperbolicSecantDist
Computes the density function for a hyperbolic secant distribution with parameters μ and σ.
density(double) - Method in class umontreal.iro.lecuyer.probdist.InverseGaussianDist
 
density(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.InverseGaussianDist
Computes the density function for the inverse gaussian distribution with parameters μ and λ, evaluated at x.
density(double) - Method in class umontreal.iro.lecuyer.probdist.JohnsonSBDist
 
density(double, double, double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.JohnsonSBDist
Computes the density function.
density(double) - Method in class umontreal.iro.lecuyer.probdist.JohnsonSUDist
 
density(double, double, double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.JohnsonSUDist
Computes the density function f (x).
density(double) - Method in class umontreal.iro.lecuyer.probdist.LaplaceDist
 
density(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.LaplaceDist
Computes the Laplace density function.
density(double) - Method in class umontreal.iro.lecuyer.probdist.LogisticDist
 
density(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.LogisticDist
Computes the density function f (x).
density(double) - Method in class umontreal.iro.lecuyer.probdist.LoglogisticDist
 
density(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.LoglogisticDist
Computes the density function for a log-logisitic distribution with parameters α and β.
density(double) - Method in class umontreal.iro.lecuyer.probdist.LognormalDist
 
density(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.LognormalDist
Computes the lognormal density function f (x).
density(double) - Method in class umontreal.iro.lecuyer.probdist.NormalDist
 
density(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.NormalDist
Computes the normal density function.
density(double) - Method in class umontreal.iro.lecuyer.probdist.ParetoDist
 
density(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.ParetoDist
Computes the density function.
density(double) - Method in class umontreal.iro.lecuyer.probdist.Pearson5Dist
 
density(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.Pearson5Dist
Computes the density function of a Pearson V distribution with shape parameter α and scale parameter β.
density(double) - Method in class umontreal.iro.lecuyer.probdist.Pearson6Dist
 
density(double, double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.Pearson6Dist
Computes the density function of a Pearson VI distribution with shape parameters α1 and α2, and scale parameter β.
density(double) - Method in class umontreal.iro.lecuyer.probdist.PiecewiseLinearEmpiricalDist
 
density(double) - Method in class umontreal.iro.lecuyer.probdist.StudentDist
 
density(int, double) - Static method in class umontreal.iro.lecuyer.probdist.StudentDist
Computes the density function for a Student-t distribution with n degrees of freedom.
density(double) - Method in class umontreal.iro.lecuyer.probdist.TriangularDist
 
density(double, double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.TriangularDist
Computes the density function.
density(double) - Method in class umontreal.iro.lecuyer.probdist.TruncatedDist
Returns an approximation of the mean obtained using the Simpson 1/3 numerical integration, or throws an UnsupportedOperationException if a or b are infinite.
density(double) - Method in class umontreal.iro.lecuyer.probdist.UniformDist
 
density(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.UniformDist
Computes the uniform density function f (x).
density(double) - Method in class umontreal.iro.lecuyer.probdist.WeibullDist
 
density(double, double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.WeibullDist
Computes the density function.
density(double, double) - Static method in class umontreal.iro.lecuyer.probdist.WeibullDist
Same as density (alpha, 1.0, 0.0, x).
density(double, double) - Method in class umontreal.iro.lecuyer.probdistmulti.BiNormalDist
 
density(double, double, double) - Static method in class umontreal.iro.lecuyer.probdistmulti.BiNormalDist
Computes the standard binormal density function with μ1 = μ2 = 0 and σ1 = σ2 = 1.
density(double, double, double, double, double, double, double) - Static method in class umontreal.iro.lecuyer.probdistmulti.BiNormalDist
Computes the binormal density function with parameters μ1 = mu1, μ2 = mu2, σ1 = sigma1, σ2 = sigma2 and ρ = rho.
density(double, double) - Method in class umontreal.iro.lecuyer.probdistmulti.BiStudentDist
 
density(int, double, double, double) - Static method in class umontreal.iro.lecuyer.probdistmulti.BiStudentDist
Computes the standard bivariate Student's t density function with correlation ρ = rho and ν = nu degrees of freedom.
density(double, double) - Method in class umontreal.iro.lecuyer.probdistmulti.ContinuousDistribution2Dim
Returns f (x, y), the density of (X, Y) evaluated at (x, y).
density(double[]) - Method in class umontreal.iro.lecuyer.probdistmulti.ContinuousDistribution2Dim
Simply calls density (x[0], x[1]).
density(double[]) - Method in class umontreal.iro.lecuyer.probdistmulti.ContinuousDistributionMulti
Returns f (x1, x2,…, xd), the probability density of X evaluated at the point x, where x = {x1, x2,…, xd}.
density(double[]) - Method in class umontreal.iro.lecuyer.probdistmulti.DirichletDist
 
density(double[], double[]) - Static method in class umontreal.iro.lecuyer.probdistmulti.DirichletDist
Computes the density of the Dirichlet distribution with parameters (α1,...,αd).
density(double[]) - Method in class umontreal.iro.lecuyer.probdistmulti.MultiNormalDist
 
density(double[], double[][], double[]) - Static method in class umontreal.iro.lecuyer.probdistmulti.MultiNormalDist
Computes the density of the multinormal distribution with parameters μ = mu and Σ = sigma, evaluated at x.
derivative(double) - Method in class umontreal.iro.lecuyer.simevents.Continuous
This method should return the derivative of this variable with respect to time, at time t.
diff(DoubleArrayList, DoubleArrayList, int, int, double, double) - Static method in class umontreal.iro.lecuyer.gof.GofStat
Same as diff for the continuous case.
diff(IntArrayList, IntArrayList, int, int, int, int) - Static method in class umontreal.iro.lecuyer.gof.GofStat
Assumes that the real-valued observations U0,..., UN-1 contained in sortedData are already sorted in increasing order and computes the differences between the successive observations.
digamma(double) - Static method in class umontreal.iro.lecuyer.util.Num
Returns the logarithmic derivative of the Gamma function ψ(x) = Γ'(x)/Γ(x).
DigitalNet - Class in umontreal.iro.lecuyer.hups
This class provides the basic structures for storing and manipulating linear digital nets in base b, for an arbitrary base b >= 2.
DigitalNet() - Constructor for class umontreal.iro.lecuyer.hups.DigitalNet
Empty constructor.
DigitalNetBase2 - Class in umontreal.iro.lecuyer.hups
A special case of DigitalNet for the base b = 2.
DigitalNetBase2() - Constructor for class umontreal.iro.lecuyer.hups.DigitalNetBase2
 
DigitalNetBase2FromFile - Class in umontreal.iro.lecuyer.hups
This class allows us to read the parameters defining a digital net in base 2 either from a file, or from a URL address on the World Wide Web.
DigitalNetBase2FromFile(String, int, int, int) - Constructor for class umontreal.iro.lecuyer.hups.DigitalNetBase2FromFile
Constructs a digital net in base 2 after reading its parameters from file filename.
DigitalNetBase2FromFile(String, int) - Constructor for class umontreal.iro.lecuyer.hups.DigitalNetBase2FromFile
Same as DigitalNetBase2FromFile(filename, r, 31, s1) where s1 is the dimension and r is given in data file filename.
DigitalNetFromFile - Class in umontreal.iro.lecuyer.hups
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.
DigitalNetFromFile(String, int, int, int) - Constructor for class umontreal.iro.lecuyer.hups.DigitalNetFromFile
Constructs a digital net after reading its parameters from file filename.
DigitalNetFromFile(String, int) - Constructor for class umontreal.iro.lecuyer.hups.DigitalNetFromFile
Same as DigitalNetFromFile(filename, r, r, s) where s is the dimension and r is given in data file filename.
DigitalSequence - Class in umontreal.iro.lecuyer.hups
This abstract class describes methods specific to digital sequences.
DigitalSequence() - Constructor for class umontreal.iro.lecuyer.hups.DigitalSequence
 
DigitalSequenceBase2 - Class in umontreal.iro.lecuyer.hups
This abstract class describes methods specific to digital sequences in base 2.
DigitalSequenceBase2() - Constructor for class umontreal.iro.lecuyer.hups.DigitalSequenceBase2
 
DirichletDist - Class in umontreal.iro.lecuyer.probdistmulti
Implements the abstract class ContinuousDistributionMulti for the Dirichlet distribution with parameters (α1,...,αd), αi > 0.
DirichletDist(double[]) - Constructor for class umontreal.iro.lecuyer.probdistmulti.DirichletDist
 
DirichletGen - Class in umontreal.iro.lecuyer.randvarmulti
Extends RandomMultiVariateGen for a Dirichlet distribution.
DirichletGen(RandomStream, double[]) - Constructor for class umontreal.iro.lecuyer.randvarmulti.DirichletGen
Constructs a new Dirichlet generator with parameters αi+1 = alphas[i], for i = 0,…, k - 1, and the stream stream.
DiscreteDistribution - Class in umontreal.iro.lecuyer.probdist
Classes implementing discrete distributions over a finite set of real numbers should inherit from this class.
DiscreteDistribution(int, double[], double[]) - Constructor for class umontreal.iro.lecuyer.probdist.DiscreteDistribution
Constructs a discrete distribution over the n values contained in array obs, with probabilities given in array prob.
DiscreteDistributionInt - Class in umontreal.iro.lecuyer.probdist
Classes implementing discrete distributions over the integers should inherit from this class.
DiscreteDistributionInt() - Constructor for class umontreal.iro.lecuyer.probdist.DiscreteDistributionInt
 
DiscreteDistributionIntMulti - Class in umontreal.iro.lecuyer.probdistmulti
Classes implementing multi-dimensional discrete distributions over the integers should inherit from this class.
DiscreteDistributionIntMulti() - Constructor for class umontreal.iro.lecuyer.probdistmulti.DiscreteDistributionIntMulti
 
Distribution - Interface in umontreal.iro.lecuyer.probdist
This interface should be implemented by all classes supporting discrete and continuous distributions.
DistributionFactory - Class in umontreal.iro.lecuyer.probdist
This class implements a string API for the package probdist.
DoublyLinked - Class in umontreal.iro.lecuyer.simevents.eventlist
An implementation of EventList using a doubly linked linear list.
DoublyLinked() - Constructor for class umontreal.iro.lecuyer.simevents.eventlist.DoublyLinked
 

E

E(double) - Static method in class umontreal.iro.lecuyer.util.PrintfFormat
Same as E (0, 6, x).
E(int, double) - Static method in class umontreal.iro.lecuyer.util.PrintfFormat
Same as E (fieldwidth, 6, x).
E(int, int, double) - Static method in class umontreal.iro.lecuyer.util.PrintfFormat
Formats a double-precision number x like %E in C printf.
e(double) - Static method in class umontreal.iro.lecuyer.util.PrintfFormat
Same as e (0, 6, x).
e(int, double) - Static method in class umontreal.iro.lecuyer.util.PrintfFormat
Same as e (fieldwidth, 6, x).
e(int, int, double) - Static method in class umontreal.iro.lecuyer.util.PrintfFormat
The same as E, except that `e' is used as the exponent character instead of `E'.
EBASE - Static variable in class umontreal.iro.lecuyer.util.Num
The constant e.
EmpiricalDist - Class in umontreal.iro.lecuyer.probdist
Extends DiscreteDistribution to an empirical distribution function, based on the observations X(1),..., X(n) (sorted by increasing order).
EmpiricalDist(double[]) - Constructor for class umontreal.iro.lecuyer.probdist.EmpiricalDist
Constructs a new empirical distribution using all the observations stored in obs, and which are assumed to have been sorted in increasing numerical order.
EmpiricalDist(Reader) - Constructor for class umontreal.iro.lecuyer.probdist.EmpiricalDist
Constructs a new empirical distribution using the observations read from the reader in.
enlarge(int, boolean) - Method in class umontreal.iro.lecuyer.util.BitVector
Resizes the BitVector so that its length is equal to size.
enlarge(int) - Method in class umontreal.iro.lecuyer.util.BitVector
Resizes the BitVector so that its length is equal to size.
EPSILON - Static variable in class umontreal.iro.lecuyer.probdist.DiscreteDistributionInt
Environment variable that determines what probability terms can be considered as negligible when building precomputed tables for distribution and mass functions.
EPSILONAD - Static variable in class umontreal.iro.lecuyer.gof.GofStat
 
EPSILONP - Static variable in class umontreal.iro.lecuyer.gof.GofFormat
Environment variable used in formatp0 to determine which p-values are too close to 0 or 1 to be printed explicitly.
equals(BitMatrix) - Method in class umontreal.iro.lecuyer.util.BitMatrix
Verifies that this and that are strictly identical.
equals(BitVector) - Method in class umontreal.iro.lecuyer.util.BitVector
Verifies if two BitVector's have the same length and the same data.
eraseOriginalGeneratorMatrices() - Method in class umontreal.iro.lecuyer.hups.DigitalNet
Erases the original generator matrices and replaces them by the current ones.
ErasePermutations() - Method in class umontreal.iro.lecuyer.hups.HaltonSequence
Erases the Faure permutations: from now on, the digits will not be Faure permuted.
ErasePermutations() - Method in class umontreal.iro.lecuyer.hups.HammersleyPointSet
Erases the Faure permutations: from now on, the digits will not be Faure permuted.
erlang(RandomStream, int, double) - Static method in class umontreal.iro.lecuyer.randvar.Rand1
Deprecated. Returns a random variate having the Erlang distribution.
ErlangConvolutionGen - Class in umontreal.iro.lecuyer.randvar
This class implements Erlang random variate generators using the convolution method.
ErlangConvolutionGen(RandomStream, ErlangDist) - Constructor for class umontreal.iro.lecuyer.randvar.ErlangConvolutionGen
Creates a new generator for the distribution dist and stream s.
ErlangDist - Class in umontreal.iro.lecuyer.probdist
Extends the class GammaDist for the special case of the Erlang distribution with shape parameter k > 0 and scale parameter λ > 0.
ErlangDist(int) - Constructor for class umontreal.iro.lecuyer.probdist.ErlangDist
Constructs a ErlangDist object with parameters k = k and λ = 1.
ErlangDist(int, double) - Constructor for class umontreal.iro.lecuyer.probdist.ErlangDist
Constructs a ErlangDist object with parameters k = k and λ = lambda.
ErlangGen - Class in umontreal.iro.lecuyer.randvar
This class implements random variate generators for the Erlang distribution with parameters k > 0 and λ > 0.
ErlangGen(RandomStream, ErlangDist) - Constructor for class umontreal.iro.lecuyer.randvar.ErlangGen
Creates a new generator for the distribution dist and stream s.
EULER - Static variable in class umontreal.iro.lecuyer.util.Num
The Euler-Mascheroni constant.
evalCheby(double[], int, double) - Static method in class umontreal.iro.lecuyer.util.Num
Evaluates a series of Chebyshev polynomials Tj at x over the basic interval [- 1,  1].
evalChebyStar(double[], int, double) - Static method in class umontreal.iro.lecuyer.util.Num
Evaluates a series of shifted Chebyshev polynomials Tj* at x over the basic interval [0,  1].
evalPoly(int, double[], double[], double) - Static method in class umontreal.iro.lecuyer.util.Misc
Given n, X and C as described in interpol(n, X, Y, C), this function returns the value of the interpolating polynomial evaluated at z.
evaluate(double) - Method in interface umontreal.iro.lecuyer.util.MathFunction
Returns the value of the function evaluated at x.
Event - Class in umontreal.iro.lecuyer.simevents
This abstract class provides event scheduling tools.
Event() - Constructor for class umontreal.iro.lecuyer.simevents.Event
Constructs a new event instance, which can be placed afterwards into the event list.
EventList - Interface in umontreal.iro.lecuyer.simevents.eventlist
An interface for implementations of event lists.
EXECUTING - Static variable in class umontreal.iro.lecuyer.simprocs.AbstractSimProcess
The process is the one currently executing its actions method.
expon(RandomStream, double) - Static method in class umontreal.iro.lecuyer.randvar.Rand1
Deprecated. Returns a random variate having the exponential distribution with mean = mean, using stream s.
ExponentialDist - Class in umontreal.iro.lecuyer.probdist
Extends the class ContinuousDistribution for the exponential distribution with mean 1/λ where λ > 0.
ExponentialDist() - Constructor for class umontreal.iro.lecuyer.probdist.ExponentialDist
Constructs an ExponentialDist object with parameter λ = 1.
ExponentialDist(double) - Constructor for class umontreal.iro.lecuyer.probdist.ExponentialDist
Constructs an ExponentialDist object with parameter λ = lambda.
ExponentialGen - Class in umontreal.iro.lecuyer.randvar
This class implements random variate generators for the exponential distribution.
ExponentialGen(RandomStream, ExponentialDist) - Constructor for class umontreal.iro.lecuyer.randvar.ExponentialGen
Creates a new generator for the exponential distribution dist and stream s.
extendSequence(int) - Method in class umontreal.iro.lecuyer.hups.DigitalSequence
Increases the number of points to n = bk from now on.
extendSequence(int) - Method in class umontreal.iro.lecuyer.hups.DigitalSequenceBase2
Increases the number of points to n = 2k from now on.
extendSequence(int) - Method in class umontreal.iro.lecuyer.hups.FaureSequence
 
extendSequence(int) - Method in class umontreal.iro.lecuyer.hups.NiedSequenceBase2
 
extendSequence(int) - Method in class umontreal.iro.lecuyer.hups.NiedXingSequenceBase2
 
extendSequence(int) - Method in class umontreal.iro.lecuyer.hups.SobolSequence
 
ExtremeValueDist - Class in umontreal.iro.lecuyer.probdist
Extends the class ContinuousDistribution for the extreme value (or Gumbel) distribution, with location parameter α and scale parameter λ > 0.
ExtremeValueDist() - Constructor for class umontreal.iro.lecuyer.probdist.ExtremeValueDist
Constructs a ExtremeValueDist object with parameters α = 0 and λ = 1.
ExtremeValueDist(double, double) - Constructor for class umontreal.iro.lecuyer.probdist.ExtremeValueDist
Constructs a ExtremeValueDist object with parameters α = alpha and λ = lambda.
ExtremeValueGen - Class in umontreal.iro.lecuyer.randvar
This class implements random variate generators for the Gumbel (or extreme value) distribution.
ExtremeValueGen(RandomStream, ExtremeValueDist) - Constructor for class umontreal.iro.lecuyer.randvar.ExtremeValueGen
Creates a new generator object for distribution dist and stream s.

F

f(double) - Static method in class umontreal.iro.lecuyer.util.PrintfFormat
Same as f (0, 6, x).
f(int, double) - Static method in class umontreal.iro.lecuyer.util.PrintfFormat
Same as f (fieldwidth, 6, x).
f(int, int, double) - Static method in class umontreal.iro.lecuyer.util.PrintfFormat
Formats the double-precision x into a string like %f in C printf.
F2NL607 - Class in umontreal.iro.lecuyer.rng
Implements the RandomStream interface by using as a backbone generator the combination of the WELL607 proposed in (and implemented in WELL607) with a nonlinear generator.
F2NL607() - Constructor for class umontreal.iro.lecuyer.rng.F2NL607
Constructs a new stream, initializing it at its beginning.
F2NL607(String) - Constructor for class umontreal.iro.lecuyer.rng.F2NL607
Constructs a new stream with the identifier name (used in the toString method).
F2wCycleBasedLFSR - Class in umontreal.iro.lecuyer.hups
This class creates a point set based upon a linear feedback shift register sequence.
F2wCycleBasedLFSR(int, int, int, int, int, int[], int[]) - Constructor for class umontreal.iro.lecuyer.hups.F2wCycleBasedLFSR
Constructs a point set with 2rw points.
F2wCycleBasedLFSR(String, int) - Constructor for class umontreal.iro.lecuyer.hups.F2wCycleBasedLFSR
Constructs a point set after reading its parameters from file filename; the parameters are located at line numbered no of filename.
F2wCycleBasedPolyLCG - Class in umontreal.iro.lecuyer.hups
This class creates a point set based upon a linear congruential sequence in the finite field F2w[z]/P(z).
F2wCycleBasedPolyLCG(int, int, int, int, int, int[], int[]) - Constructor for class umontreal.iro.lecuyer.hups.F2wCycleBasedPolyLCG
Constructs a point set with 2rw points.
F2wCycleBasedPolyLCG(String, int) - Constructor for class umontreal.iro.lecuyer.hups.F2wCycleBasedPolyLCG
Constructs a point set after reading its parameters from file filename; the parameters are located at line numbered no of filename.
F2wNetLFSR - Class in umontreal.iro.lecuyer.hups
This class implements a digital net in base 2 starting from a linear feedback shift register generator.
F2wNetLFSR(int, int, int, int, int, int[], int[], int) - Constructor for class umontreal.iro.lecuyer.hups.F2wNetLFSR
Constructs a point set with 2rw points.
F2wNetLFSR(String, int, int) - Constructor for class umontreal.iro.lecuyer.hups.F2wNetLFSR
Constructs a point set after reading its parameters from file filename; the parameters are located at line numbered no of filename.
F2wNetPolyLCG - Class in umontreal.iro.lecuyer.hups
This class implements a digital net in base 2 starting from a polynomial LCG in F2w[z]/P(z).
F2wNetPolyLCG(int, int, int, int, int, int, int[], int[], int) - Constructor for class umontreal.iro.lecuyer.hups.F2wNetPolyLCG
Constructs a point set with 2rw points.
F2wNetPolyLCG(String, int, int) - Constructor for class umontreal.iro.lecuyer.hups.F2wNetPolyLCG
Constructs a point set after reading its parameters from file filename; the parameters are located at line numbered no of filename.
F2wStructure - Class in umontreal.iro.lecuyer.hups
This class implements methods and fields needed by the classes F2wNetLFSR, F2wNetPolyLCG, F2wCycleBasedLFSR and F2wCycleBasedPolyLCG.
factorial(int) - Static method in class umontreal.iro.lecuyer.util.Num
Returns the value of factorial n.
FatigueLifeDist - Class in umontreal.iro.lecuyer.probdist
Extends the class ContinuousDistribution for the Fatigue Life distribution with location parameter μ, scale parameter β and shape parameter γ.
FatigueLifeDist(double, double, double) - Constructor for class umontreal.iro.lecuyer.probdist.FatigueLifeDist
Constructs a fatigue life distribution with parameters μ, β and γ.
FatigueLifeGen - Class in umontreal.iro.lecuyer.randvar
This class implements random variate generators for the Fatigue Life distribution with location parameter μ, scale parameter β and shape parameter γ.
FatigueLifeGen(RandomStream, FatigueLifeDist) - Constructor for class umontreal.iro.lecuyer.randvar.FatigueLifeGen
Creates a new generator for the distribution dist, using stream s.
FaureSequence - Class in umontreal.iro.lecuyer.hups
This class implements digital nets or digital sequences formed by the first n = bk points of the Faure sequence in base b.
FaureSequence(int, int, int, int, int) - Constructor for class umontreal.iro.lecuyer.hups.FaureSequence
Constructs a digital net in base b, with n = bk points and w output digits, in dim dimensions.
FaureSequence(int, int) - Constructor for class umontreal.iro.lecuyer.hups.FaureSequence
Same as FaureSequence(b, k, w, w, dim) with base b equal to the smallest prime larger or equal to dim, and with at least n points.
FBar - Class in umontreal.iro.lecuyer.gof
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 >= x], instead of F(x) = P[X <= x].
FDist - Class in umontreal.iro.lecuyer.gof
This class provides methods to compute (or approximate) the distribution functions of various types of goodness-of-fit test statistics.
FisherFDist - Class in umontreal.iro.lecuyer.probdist
Extends the class ContinuousDistribution for the Fisher F-distribution with n and m degrees of freedom, where n and m are positive integers.
FisherFDist(int, int) - Constructor for class umontreal.iro.lecuyer.probdist.FisherFDist
Constructs a Fisher F-distribution with n and m degrees of freedom.
FisherFGen - Class in umontreal.iro.lecuyer.randvar
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.
FisherFGen(RandomStream, FisherFDist) - Constructor for class umontreal.iro.lecuyer.randvar.FisherFGen
Creates a new generator for the distribution dist, using stream s.
format() - Method in class umontreal.iro.lecuyer.util.Chrono
Converts the CPU time used by the program since its last call to init for this Chrono to a String in the HH:MM:SS.xx format.
format(double) - Static method in class umontreal.iro.lecuyer.util.Chrono
Converts the time time, given in seconds, to a String in the HH:MM:SS.xx format.
format(long) - Static method in class umontreal.iro.lecuyer.util.PrintfFormat
Same as d (0, 1, x).
format(int, long) - Static method in class umontreal.iro.lecuyer.util.PrintfFormat
Converts a long integer to a String with a minimum length of fieldwidth, the result is right-padded with spaces if necessary but it is not truncated.
format(int, int, int, double) - Static method in class umontreal.iro.lecuyer.util.PrintfFormat
Returns a String containing x.
format(int[], int, int, int, int) - Static method in class umontreal.iro.lecuyer.util.TableFormat
Formats a String containing the elements n1 to n2 (inclusive) of table V, k elements per line, p positions per element.
format(double[], int, int, int, int, int, int) - Static method in class umontreal.iro.lecuyer.util.TableFormat
Similar to the previous method, but for an array of double's.
format(int[][], int, int, int, int, int, int, int, String) - Static method in class umontreal.iro.lecuyer.util.TableFormat
Formats a submatrix of integers.
format(double[][], int, int, int, int, int, int, int, String) - Static method in class umontreal.iro.lecuyer.util.TableFormat
Formats the submatrix with lines i1  <= i <=  i2 and columns j1  <= j <=  j2 of the matrix Mat, using the formatting style style.
formatActiveTests(int, double[], double[]) - Static method in class umontreal.iro.lecuyer.gof.GofFormat
Gets the p-values of the active EDF test statistics, which are in activeTests.
formatBase(int, long) - Static method in class umontreal.iro.lecuyer.util.PrintfFormat
Same as formatBase (0, b, x).
formatBase(int, int, long) - Static method in class umontreal.iro.lecuyer.util.PrintfFormat
Converts the integer x to a String representation in base b.
formatChi2(int, int, double) - Static method in class umontreal.iro.lecuyer.gof.GofFormat
Computes the p-value of the chi-square statistic chi2 for a test with k intervals.
formatCIStudent(double, int) - Method in class umontreal.iro.lecuyer.stat.Tally
An alias for formatConfidenceIntervalStudent.
formatCIStudent(double) - Method in class umontreal.iro.lecuyer.stat.Tally
An alias for formatConfidenceIntervalStudent.
formatConfidenceIntervalStudent(double, int) - Method in class umontreal.iro.lecuyer.stat.Tally
Similar to confidenceIntervalStudent, but returns the confidence interval in a formatted string of the form ``95% confidence interval for mean: (32.431, 32.487)'', using d decimal digits of accuracy.
formatConfidenceIntervalStudent(double) - Method in class umontreal.iro.lecuyer.stat.Tally
 
formatKS(int, double, double, double) - Static method in class umontreal.iro.lecuyer.gof.GofFormat
Computes the p-values of the three Kolmogorov-Smirnov statistics DN+, DN-, and DN, whose values are in dp, dm, d, respectively, assuming a sample of size n.
formatKS(DoubleArrayList, ContinuousDistribution) - Static method in class umontreal.iro.lecuyer.gof.GofFormat
Computes the KS test statistics to compare the empirical distribution of the observations in data with the theoretical distribution dist and formats the results.
formatKSJumpOne(int, double, double) - Static method in class umontreal.iro.lecuyer.gof.GofFormat
Similar to formatKS, but for the KS statistic DN+(a).
formatKSJumpOne(DoubleArrayList, ContinuousDistribution, double) - Static method in class umontreal.iro.lecuyer.gof.GofFormat
Similar to formatKS, but for DN+(a).
formatp0(double) - Static method in class umontreal.iro.lecuyer.gof.GofFormat
Returns the significance level (or p-value) p of a test, in the format ``1 - p'' if p is close to 1, and p otherwise.
formatp1(double) - Static method in class umontreal.iro.lecuyer.gof.GofFormat
Returns the string ``Significance level of test : '', then calls formatp0 to print p, and adds the marker ``****'' if p is considered suspect (uses the environment variable RSUSPECTP for this).
formatp2(double, double) - Static method in class umontreal.iro.lecuyer.gof.GofFormat
Returns x on a single line, then go to the next line and calls formatp1.
formatp3(String, double, double) - Static method in class umontreal.iro.lecuyer.gof.GofFormat
Formats the test statistic x for a test named testName with p-value p.
formatPoints() - Method in class umontreal.iro.lecuyer.hups.AntitheticPointSet
 
formatPoints() - Method in class umontreal.iro.lecuyer.hups.BakerTransformedPointSet
 
formatPoints() - Method in class umontreal.iro.lecuyer.hups.ContainerPointSet
 
formatPoints() - Method in class umontreal.iro.lecuyer.hups.CycleBasedPointSet
 
formatPoints() - Method in class umontreal.iro.lecuyer.hups.CycleBasedPointSetBase2
 
formatPoints() - Method in class umontreal.iro.lecuyer.hups.PointSet
Same as invoking formatPoints with n and d equal to the number of points and the dimension, respectively.
formatPoints(int, int) - Method in class umontreal.iro.lecuyer.hups.PointSet
Formats a string that displays the same information as returned by toString, together with the first d coordinates of the first n points.
formatState() - Method in class umontreal.iro.lecuyer.hups.CycleBasedPointSet.CycleBasedPointSetIterator
 
formatState() - Method in class umontreal.iro.lecuyer.rng.RandomStreamBase
Deprecated: use the toString method.
formatStateFull() - Method in class umontreal.iro.lecuyer.rng.RandomStreamBase
Eliminated: use the toStringFull method.
formatWithError(int, int, int, int, double, double, String[]) - Static method in class umontreal.iro.lecuyer.util.PrintfFormat
Stores a string containing x into res[0], and a string containing error into res[1], both strings being formatted with the same notation.
formatWithError(int, int, int, double, double, String[]) - Static method in class umontreal.iro.lecuyer.util.PrintfFormat
Stores a string containing x into res[0], and a string containing error into res[1], both strings being formatted with the same notation.

G

G(double) - Static method in class umontreal.iro.lecuyer.util.PrintfFormat
Same as G (0, 6, x).
G(int, double) - Static method in class umontreal.iro.lecuyer.util.PrintfFormat
Same as G (fieldwidth, 6, x).
G(int, int, double) - Static method in class umontreal.iro.lecuyer.util.PrintfFormat
Formats the double-precision x into a string like %G in C printf.
g(double) - Static method in class umontreal.iro.lecuyer.util.PrintfFormat
Same as g (0, 6, x).
g(int, double) - Static method in class umontreal.iro.lecuyer.util.PrintfFormat
Same as g (fieldwidth, 6, x).
g(int, int, double) - Static method in class umontreal.iro.lecuyer.util.PrintfFormat
The same as G, except that `e' is used in the scientific notation.
GammaAcceptanceRejectionGen - Class in umontreal.iro.lecuyer.randvar
This class implements gamma random variate generators using a method that combines acceptance-rejection with acceptance-complement.
GammaAcceptanceRejectionGen(RandomStream, GammaDist) - Constructor for class umontreal.iro.lecuyer.randvar.GammaAcceptanceRejectionGen
Creates a new generator object for the gamma distribution dist and stream s for both the main and auxiliary stream.
GammaAcceptanceRejectionGen(RandomStream, RandomStream, GammaDist) - Constructor for class umontreal.iro.lecuyer.randvar.GammaAcceptanceRejectionGen
Creates a new generator object for the gamma distribution dist, using main stream s and auxiliary stream aux.
GammaDist - Class in umontreal.iro.lecuyer.probdist
Extends the class ContinuousDistribution for the gamma distribution with shape parameter α > 0 and scale parameter λ > 0.
GammaDist(double) - Constructor for class umontreal.iro.lecuyer.probdist.GammaDist
Constructs a GammaDist object with parameters α = alpha and λ = 1.
GammaDist(double, double) - Constructor for class umontreal.iro.lecuyer.probdist.GammaDist
Constructs a GammaDist object with parameters α = alpha and λ = lambda.
GammaDist(double, double, int) - Constructor for class umontreal.iro.lecuyer.probdist.GammaDist
Constructs a GammaDist object with parameters α = alpha and λ = lambda, and approximations of roughly d decimal digits of precision when computing functions.
GammaGen - Class in umontreal.iro.lecuyer.randvar
This class implements random variate generators for the gamma distribution.
GammaGen(RandomStream, GammaDist) - Constructor for class umontreal.iro.lecuyer.randvar.GammaGen
Creates a new generator object for the gamma distribution dist and stream s.
GammaRejectionLoglogisticGen - Class in umontreal.iro.lecuyer.randvar
This class implements gamma random variate generators using a rejection method with loglogistic envelopes,.
GammaRejectionLoglogisticGen(RandomStream, GammaDist) - Constructor for class umontreal.iro.lecuyer.randvar.GammaRejectionLoglogisticGen
Creates a new generator object for the gamma distribution dist and stream s for both the main and auxiliary stream.
GammaRejectionLoglogisticGen(RandomStream, RandomStream, GammaDist) - Constructor for class umontreal.iro.lecuyer.randvar.GammaRejectionLoglogisticGen
Creates a new generator object for the gamma distribution dist, using main stream s and auxiliary stream aux.
GenF2w32 - Class in umontreal.iro.lecuyer.rng
Implements the RandomStream interface via inheritance from RandomStreamBase.
GenF2w32() - Constructor for class umontreal.iro.lecuyer.rng.GenF2w32
Constructs a new stream.
GenF2w32(String) - Constructor for class umontreal.iro.lecuyer.rng.GenF2w32
Constructs a new stream with the identifier name (used in the toString method).
geometric(RandomStream, double) - Static method in class umontreal.iro.lecuyer.randvar.Rand1
Deprecated. Returns a random variate having the geometric distribution with parameter p, where 0 < p < 1, using stream s.
GeometricDist - Class in umontreal.iro.lecuyer.probdist
Extends the class DiscreteDistributionInt for the geometric distribution with parameter p, where 0 < p < 1.
GeometricDist(double) - Constructor for class umontreal.iro.lecuyer.probdist.GeometricDist
Constructs a geometric distribution with parameter p.
GeometricGen - Class in umontreal.iro.lecuyer.randvar
This class implements a random variate generator for the geometric distribution.
GeometricGen(RandomStream, GeometricDist) - Constructor for class umontreal.iro.lecuyer.randvar.GeometricGen
Creates a new generator for the distribution dist, using stream s.
get(int) - Method in class umontreal.iro.lecuyer.simevents.LinkedListStat
 
geta() - Method in class umontreal.iro.lecuyer.hups.LCGPointSet
Returns the value of the multiplier a.
getA() - Method in class umontreal.iro.lecuyer.probdist.BetaDist
Returns the parameter a of this object.
getA() - Method in class umontreal.iro.lecuyer.probdist.TriangularDist
Returns the value of a for this object.
getA() - Method in class umontreal.iro.lecuyer.probdist.TruncatedDist
Returns the value of a.
getA() - Method in class umontreal.iro.lecuyer.probdist.UniformDist
Returns the parameter a.
getAlpha() - Method in class umontreal.iro.lecuyer.probdist.BetaDist
Returns the parameter α of this object.
getAlpha() - Method in class umontreal.iro.lecuyer.probdist.CauchyDist
Returns the value of α for this object.
getAlpha() - Method in class umontreal.iro.lecuyer.probdist.ExtremeValueDist
Returns the parameter α of this object.
getAlpha() - Method in class umontreal.iro.lecuyer.probdist.GammaDist
Return the parameter α for this object.
getAlpha() - Method in class umontreal.iro.lecuyer.probdist.LogisticDist
Return the parameter α of this object.
getAlpha() - Method in class umontreal.iro.lecuyer.probdist.LoglogisticDist
Return the parameter α of this object.
getAlpha() - Method in class umontreal.iro.lecuyer.probdist.ParetoDist
Returns the parameter α.
getAlpha() - Method in class umontreal.iro.lecuyer.probdist.Pearson5Dist
Returns the α parameter of this object.
getAlpha() - Method in class umontreal.iro.lecuyer.probdist.WeibullDist
Returns the parameter α.
getAlpha() - Method in class umontreal.iro.lecuyer.probdistmulti.DirichletDist
Returns the parameters (α1,...,αd) of this object.
getAlpha(int) - Method in class umontreal.iro.lecuyer.probdistmulti.DirichletDist
Returns the ith component of the alpha vector.
getAlpha(int) - Method in class umontreal.iro.lecuyer.randvarmulti.DirichletGen
Returns the αi+1 parameter for this Dirichlet generator.
getAlpha1() - Method in class umontreal.iro.lecuyer.probdist.Pearson6Dist
Returns the α1 parameter of this object.
getAlpha2() - Method in class umontreal.iro.lecuyer.probdist.Pearson6Dist
Returns the α2 parameter of this object.
getArea() - Method in class umontreal.iro.lecuyer.probdist.TruncatedDist
Returns the value of F(b) - F(a), the area under the truncated density function.
getArray() - Method in class umontreal.iro.lecuyer.stat.TallyStore
Returns the DoubleArrayList object that contains the observations for this probe.
getAuxStream() - Method in class umontreal.iro.lecuyer.randvar.BetaRejectionLoglogisticGen
Returns the auxiliary stream associated with that object.
getAuxStream() - Method in class umontreal.iro.lecuyer.randvar.BetaStratifiedRejectionGen
Returns the auxiliary stream associated with this object.
getAuxStream() - Method in class umontreal.iro.lecuyer.randvar.GammaAcceptanceRejectionGen
Returns the auxiliary stream associated with this object.
getAuxStream() - Method in class umontreal.iro.lecuyer.randvar.GammaRejectionLoglogisticGen
Returns the auxiliary stream associated with this object.
getAuxStream() - Method in class umontreal.iro.lecuyer.randvar.UnuranContinuous
Returns the auxiliary random number stream.
getAuxStream() - Method in class umontreal.iro.lecuyer.randvar.UnuranDiscreteInt
Returns the auxiliary random number stream.
getAuxStream() - Method in class umontreal.iro.lecuyer.randvar.UnuranEmpirical
Returns the auxiliary random number stream.
getAvailable() - Method in class umontreal.iro.lecuyer.simprocs.Bin
Returns the number of available tokens for this bin.
getAvailable() - Method in class umontreal.iro.lecuyer.simprocs.Resource
Returns the number of available units, i.e., the capacity minus the number of units busy.
getB() - Method in class umontreal.iro.lecuyer.probdist.BetaDist
Returns the parameter b of this object.
getB() - Method in class umontreal.iro.lecuyer.probdist.TriangularDist
Returns the value of b for this object.
getB() - Method in class umontreal.iro.lecuyer.probdist.TruncatedDist
Returns the value of b.
getB() - Method in class umontreal.iro.lecuyer.probdist.UniformDist
Returns the parameter b.
getBaseBandwidth(EmpiricalDist) - Static method in class umontreal.iro.lecuyer.randvar.KernelDensityGen
Computes and returns the value of h0 in.
getBeta() - Method in class umontreal.iro.lecuyer.probdist.BetaDist
Returns the parameter β of this object.
getBeta() - Method in class umontreal.iro.lecuyer.probdist.CauchyDist
Returns the value of β for this object.
getBeta() - Method in class umontreal.iro.lecuyer.probdist.FatigueLifeDist
Returns the parameter β of this object.
getBeta() - Method in class umontreal.iro.lecuyer.probdist.LoglogisticDist
Returns the parameter β of this object.
getBeta() - Method in class umontreal.iro.lecuyer.probdist.ParetoDist
Returns the parameter β.
getBeta() - Method in class umontreal.iro.lecuyer.probdist.Pearson5Dist
Returns the β parameter of this object.
getBeta() - Method in class umontreal.iro.lecuyer.probdist.Pearson6Dist
Returns the β parameter of this object.
getBool(int, int) - Method in class umontreal.iro.lecuyer.util.BitMatrix
Returns the value of the bit in the specified row and column.
getBool(int) - Method in class umontreal.iro.lecuyer.util.BitVector
Gives the value of the bit in position pos.
getBuffer() - Method in class umontreal.iro.lecuyer.util.PrintfFormat
Returns the StringBuffer associated with that object.
getCachedGen() - Method in class umontreal.iro.lecuyer.randvar.RandomVariateGenWithCache
Returns a reference to the random variate generator whose values are cached.
getCachedStream() - Method in class umontreal.iro.lecuyer.rng.RandomStreamWithCache
Returns a reference to the random stream whose values are cached.
getCachedValues() - Method in class umontreal.iro.lecuyer.randvar.RandomVariateGenWithCache
Returns an array list containing the values cached by this random variate generator.
getCachedValues() - Method in class umontreal.iro.lecuyer.rng.RandomStreamWithCache
Returns an array list containing the values cached by this random stream.
getCacheIndex() - Method in class umontreal.iro.lecuyer.randvar.RandomVariateGenWithCache
Return the index of the next cached value that will be returned by the generator.
getCacheIndex() - Method in class umontreal.iro.lecuyer.rng.RandomStreamWithCache
Return the index of the next cached value that will be returned by the stream.
getCapacity() - Method in class umontreal.iro.lecuyer.simprocs.Resource
Returns the current capacity of the resource.
getCholeskyDecompSigma() - Method in class umontreal.iro.lecuyer.randvarmulti.MultiNormalGen
Returns the Cholesky decomposition of the covariance matrix used for generating the vectors.
getContinuousDistribution(String) - Static method in class umontreal.iro.lecuyer.probdist.DistributionFactory
Uses the Java Reflection API to construct a ContinuousDistribution object by executing the code contained in the string str.
getCoordinate(int, int) - Method in class umontreal.iro.lecuyer.hups.AntitheticPointSet
 
getCoordinate(int, int) - Method in class umontreal.iro.lecuyer.hups.BakerTransformedPointSet
 
getCoordinate(int, int) - Method in class umontreal.iro.lecuyer.hups.CachedPointSet
 
getCoordinate(int, int) - Method in class umontreal.iro.lecuyer.hups.ContainerPointSet
 
getCoordinate(int, int) - Method in class umontreal.iro.lecuyer.hups.CycleBasedPointSet
 
getCoordinate(int, int) - Method in class umontreal.iro.lecuyer.hups.CycleBasedPointSetBase2
 
getCoordinate(int, int) - Method in class umontreal.iro.lecuyer.hups.DigitalNet
 
getCoordinate(int, int) - Method in class umontreal.iro.lecuyer.hups.DigitalNetBase2
 
getCoordinate(int, int) - Method in class umontreal.iro.lecuyer.hups.HaltonSequence
 
getCoordinate(int, int) - Method in class umontreal.iro.lecuyer.hups.HammersleyPointSet
 
getCoordinate(int, int) - Method in class umontreal.iro.lecuyer.hups.KorobovLatticeSequence
 
getCoordinate(int, int) - Method in class umontreal.iro.lecuyer.hups.PaddedPointSet
 
getCoordinate(int, int) - Method in class umontreal.iro.lecuyer.hups.PointSet
Returns ui, j, the coordinate j of the point i.
getCoordinate(int, int) - Method in class umontreal.iro.lecuyer.hups.Rank1Lattice
 
getCoordinate(int, int) - Method in class umontreal.iro.lecuyer.hups.SubsetOfPointSet
 
getCoordinateNoGray(int, int) - Method in class umontreal.iro.lecuyer.hups.DigitalNet
Returns ui, j, the coordinate j of point i, the points being enumerated in the standard order (no Gray code).
getCoordinateNoGray(int, int) - Method in class umontreal.iro.lecuyer.hups.DigitalNetBase2
 
getCorrelation() - Method in class umontreal.iro.lecuyer.probdistmulti.BiNormalDist
 
getCorrelation(double, double, double, double, double) - Static method in class umontreal.iro.lecuyer.probdistmulti.BiNormalDist
Return the correlation matrix of the binormal distribution.
getCorrelation() - Method in class umontreal.iro.lecuyer.probdistmulti.BiStudentDist
 
getCorrelation(int, double) - Static method in class umontreal.iro.lecuyer.probdistmulti.BiStudentDist
Returns the correlation matrix of the bivariate Student's t distribution.
getCorrelation() - Method in class umontreal.iro.lecuyer.probdistmulti.ContinuousDistributionMulti
Returns the correlation matrix of the distribution, defined as ρij = σij/(σ_iiσ_jj)1/2.
getCorrelation() - Method in class umontreal.iro.lecuyer.probdistmulti.DirichletDist
 
getCorrelation(double[]) - Static method in class umontreal.iro.lecuyer.probdistmulti.DirichletDist
Computes the correlation matrix of the Dirichlet distribution with parameters (α1,...,αd).
getCorrelation() - Method in class umontreal.iro.lecuyer.probdistmulti.DiscreteDistributionIntMulti
Returns the correlation matrix of the distribution, defined as ρij = σij/(σ_iiσ_jj)1/2.
getCorrelation() - Method in class umontreal.iro.lecuyer.probdistmulti.MultinomialDist
 
getCorrelation(int, double[]) - Static method in class umontreal.iro.lecuyer.probdistmulti.MultinomialDist
Computes the correlation matrix of the multinomial distribution with parameters n and (p1,...,pd).
getCorrelation() - Method in class umontreal.iro.lecuyer.probdistmulti.MultiNormalDist
 
getCorrelation(double[], double[][]) - Static method in class umontreal.iro.lecuyer.probdistmulti.MultiNormalDist
Computes the correlation matrix of the multinormal distribution with parameters μ and Σ).
getCorrelation() - Method in class umontreal.iro.lecuyer.probdistmulti.NegativeMultinomialDist
 
getCorrelation(double, double[]) - Static method in class umontreal.iro.lecuyer.probdistmulti.NegativeMultinomialDist
Computes the correlation matrix of the negative multinomial distribution with parameters γ and (p1,...,pd).
getCovariance() - Method in class umontreal.iro.lecuyer.probdistmulti.BiNormalDist
 
getCovariance(double, double, double, double, double) - Static method in class umontreal.iro.lecuyer.probdistmulti.BiNormalDist
Return the covariance matrix of the binormal distribution.
getCovariance() - Method in class umontreal.iro.lecuyer.probdistmulti.BiStudentDist
 
getCovariance(int, double) - Static method in class umontreal.iro.lecuyer.probdistmulti.BiStudentDist
Returns the covariance matrix of the bivariate Student's t distribution.
getCovariance() - Method in class umontreal.iro.lecuyer.probdistmulti.ContinuousDistributionMulti
Returns the variance-covariance matrix of the distribution, defined as
σij = E[(Xi - μi)(Xj - μj)].
getCovariance() - Method in class umontreal.iro.lecuyer.probdistmulti.DirichletDist
 
getCovariance(double[]) - Static method in class umontreal.iro.lecuyer.probdistmulti.DirichletDist
Computes the covariance matrix of the Dirichlet distribution with parameters (α1,...,αd).
getCovariance() - Method in class umontreal.iro.lecuyer.probdistmulti.DiscreteDistributionIntMulti
Returns the variance-covariance matrix of the distribution, defined as
σij = E[(Xi - μi)(Xj - μj)].
getCovariance() - Method in class umontreal.iro.lecuyer.probdistmulti.MultinomialDist
 
getCovariance(int, double[]) - Static method in class umontreal.iro.lecuyer.probdistmulti.MultinomialDist
Computes the covariance matrix of the multinomial distribution with parameters n and (p1,...,pd).
getCovariance() - Method in class umontreal.iro.lecuyer.probdistmulti.MultiNormalDist
 
getCovariance(double[], double[][]) - Static method in class umontreal.iro.lecuyer.probdistmulti.MultiNormalDist
Computes the covariance matrix of the multinormal distribution with parameters μ and Σ.
getCovariance() - Method in class umontreal.iro.lecuyer.probdistmulti.NegativeMultinomialDist
 
getCovariance(double, double[]) - Static method in class umontreal.iro.lecuyer.probdistmulti.NegativeMultinomialDist
Computes the covariance matrix of the negative multinomial distribution with parameters γ and (p1,...,pd).
getCurCoordIndex() - Method in interface umontreal.iro.lecuyer.hups.PointSetIterator
Returns the index j of the current coordinate.
getCurPointIndex() - Method in interface umontreal.iro.lecuyer.hups.PointSetIterator
Returns the index i of the current point.
getDelay() - Method in class umontreal.iro.lecuyer.simprocs.AbstractSimProcess
If the process is in the DELAYED state, returns the remaining time until the planned occurrence of its activating event.
getDelta() - Method in class umontreal.iro.lecuyer.probdist.JohnsonSBDist
Returns the value of δ for this object.
getDelta() - Method in class umontreal.iro.lecuyer.probdist.JohnsonSUDist
Returns the value of δ for this object.
getDelta() - Method in class umontreal.iro.lecuyer.probdist.WeibullDist
Returns the parameter δ.
getDimension() - Method in class umontreal.iro.lecuyer.hups.ContainerPointSet
 
getDimension() - Method in class umontreal.iro.lecuyer.hups.CycleBasedPointSet
 
getDimension() - Method in class umontreal.iro.lecuyer.hups.PointSet
Returns the dimension (number of available coordinates) of the point set.
getDimension() - Method in class umontreal.iro.lecuyer.probdistmulti.ContinuousDistributionMulti
Returns the dimension d of the distribution.
getDimension() - Method in class umontreal.iro.lecuyer.probdistmulti.DiscreteDistributionIntMulti
Returns the dimension d of the distribution.
getDimension() - Method in class umontreal.iro.lecuyer.probdistmulti.MultiNormalDist
Returns the dimension d of the distribution.
getDimension() - Method in class umontreal.iro.lecuyer.randvarmulti.RandomMultiVariateGen
Returns the dimension of this multi-variate generator (the dimension of the random points).
getDiscreteDistribution(String) - Static method in class umontreal.iro.lecuyer.probdist.DistributionFactory
Same as getContinuousDistribution, but for discrete distributions over the real numbers.
getDiscreteDistributionInt(String) - Static method in class umontreal.iro.lecuyer.probdist.DistributionFactory
Same as getContinuousDistribution, but for discrete distributions over the integers.
getDistribution(String) - Static method in class umontreal.iro.lecuyer.probdist.DistributionFactory
 
getDistribution(String, double[], int) - Static method in class umontreal.iro.lecuyer.probdist.DistributionFactory
Uses the Java Reflection API to construct a ContinuousDistribution object by estimating parameters of the distribution using the maximum likelihood method based on the n observations in table x[i], i = 0, 1,…, n - 1.
getDistribution(String, int[], int) - Static method in class umontreal.iro.lecuyer.probdist.DistributionFactory
Uses the Java Reflection API to construct a DiscreteDistributionInt object by estimating parameters of the distribution using the maximum likelihood method based on the n observations in table x[i], i = 0, 1,…, n - 1.
getDistribution(Class, double[], int) - Static method in class umontreal.iro.lecuyer.probdist.DistributionFactory
Uses the Java Reflection API to construct a ContinuousDistribution object by estimating parameters of the distribution using the maximum likelihood method based on the n observations in table x[i], i = 0, 1,…, n - 1.
getDistribution(Class, int[], int) - Static method in class umontreal.iro.lecuyer.probdist.DistributionFactory
Uses the Java Reflection API to construct a DiscreteDistributionInt object by estimating parameters of the distribution using the maximum likelihood method based on the n observations in table x[i], i = 0, 1,…, n - 1.
getDistribution() - Method in class umontreal.iro.lecuyer.randvar.RandomVariateGen
Returns the Distribution used by this generator.
getDistribution() - Method in class umontreal.iro.lecuyer.randvar.RandomVariateGenWithCache
 
getDistribution() - Method in class umontreal.iro.lecuyer.randvar.UnuranContinuous
 
getDistribution() - Method in class umontreal.iro.lecuyer.randvar.UnuranDiscreteInt
 
getDistribution() - Method in class umontreal.iro.lecuyer.randvar.UnuranEmpirical
 
getEventList() - Static method in class umontreal.iro.lecuyer.simevents.Sim
Gets the currently used event list.
getFa() - Method in class umontreal.iro.lecuyer.probdist.TruncatedDist
Returns the value of F(a).
getFaurePermutation(int, int[]) - Static method in class umontreal.iro.lecuyer.hups.RadicalInverse
Computes the Faure permutation σb of the set {0,…, b - 1} and puts it in array pi.
getFb() - Method in class umontreal.iro.lecuyer.probdist.TruncatedDist
Returns the value of F(b).
getFirst() - Method in class umontreal.iro.lecuyer.simevents.eventlist.BinaryTree
 
getFirst() - Method in class umontreal.iro.lecuyer.simevents.eventlist.DoublyLinked
 
getFirst() - Method in interface umontreal.iro.lecuyer.simevents.eventlist.EventList
Returns the first event in the event list.
getFirst() - Method in class umontreal.iro.lecuyer.simevents.eventlist.Henriksen
 
getFirst() - Method in class umontreal.iro.lecuyer.simevents.eventlist.RedblackTree
 
getFirst() - Method in class umontreal.iro.lecuyer.simevents.eventlist.SplayTree
 
getFirst() - Method in class umontreal.iro.lecuyer.simevents.LinkedListStat
 
getFirstOfClass(String) - Method in class umontreal.iro.lecuyer.simevents.eventlist.BinaryTree
 
getFirstOfClass(String) - Method in class umontreal.iro.lecuyer.simevents.eventlist.DoublyLinked
 
getFirstOfClass(String) - Method in interface umontreal.iro.lecuyer.simevents.eventlist.EventList
Returns the first event of the class cl (a subclass of Event) in the event list.
getFirstOfClass(String) - Method in class umontreal.iro.lecuyer.simevents.eventlist.Henriksen
 
getFirstOfClass(String) - Method in class umontreal.iro.lecuyer.simevents.eventlist.RedblackTree
 
getFirstOfClass(String) - Method in class umontreal.iro.lecuyer.simevents.eventlist.SplayTree
 
getGamma() - Method in class umontreal.iro.lecuyer.probdist.FatigueLifeDist
Returns the parameter γ of this object.
getGamma() - Method in class umontreal.iro.lecuyer.probdist.JohnsonSBDist
Returns the value of γ for this object.
getGamma() - Method in class umontreal.iro.lecuyer.probdist.JohnsonSUDist
Returns the value of γ for this object.
getGamma() - Method in class umontreal.iro.lecuyer.probdist.NegativeBinomialDist
Returns the parameter γ of this object.
getGamma() - Method in class umontreal.iro.lecuyer.probdistmulti.NegativeMultinomialDist
Returns the parameter γ of this object.
getGen1() - Method in class umontreal.iro.lecuyer.randvarmulti.RandomMultiVariateGen
Returns the one-dimensional RandomVariateGen used by this object.
getHours() - Method in class umontreal.iro.lecuyer.util.Chrono
Returns the CPU time in hours used by the program since the last call to init for this Chrono.
getI() - Method in class umontreal.iro.lecuyer.probdist.UniformIntDist
Returns the parameter i.
getInitTime() - Method in class umontreal.iro.lecuyer.simevents.Accumulate
Returns the initialization time for this object.
getInstanceFromMLE(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.BetaDist
Creates a new instance of a beta distribution with parameters α and β over the interval [0, 1] estimated using the maximum likelihood method based on the n observations in table x[i], i = 0, 1,…, n - 1.
getInstanceFromMLE(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.BetaSymmetricalDist
Creates a new instance of a symmetrical beta distribution with parameter α estimated using the maximum likelihood method based on the n observations in table x[i], i = 0, 1,…, n - 1.
getInstanceFromMLE(int[], int) - Static method in class umontreal.iro.lecuyer.probdist.BinomialDist
Creates a new instance of a binomial distribution with both parameters hat(n) and hat(p) estimated using the maximum likelihood method based on the m observations x[i], i = 0, 1,…, m - 1.
getInstanceFromMLE(int[], int, int) - Static method in class umontreal.iro.lecuyer.probdist.BinomialDist
Creates a new instance of a binomial distribution with given parameter n and estimated parameter hat(p) using the maximum likelihood method based on the m observations in table x[i], i = 0, 1,…, m - 1.
getInstanceFromMLE(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.CauchyDist
Creates a new instance of a Cauchy distribution with parameters α and β estimated using the maximum likelihood method based on the n observations in table x[i], i = 0, 1,…, n - 1.
getInstanceFromMLE(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.ChiDist
Creates a new instance of a chi distribution with parameter ν estimated using the maximum likelihood method based on the n observations in table x[i], i = 0, 1,…, n - 1.
getInstanceFromMLE(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.ChiSquareDist
Creates a new instance of a chi-square distribution with parameter n estimated using the maximum likelihood method based on the m observations in table x[i], i = 0, 1,…, m - 1.
getInstanceFromMLE(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.ErlangDist
Creates a new instance of an Erlang distribution with parameters k and λ estimated using the maximum likelihood method based on the n observations in table x[i], i = 0, 1,…, n - 1.
getInstanceFromMLE(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.ExponentialDist
Creates a new instance of an exponential distribution with parameter λ estimated using the maximum likelihood method based on the n observations in table x[i], i = 0, 1,…, n - 1.
getInstanceFromMLE(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.ExtremeValueDist
Creates a new instance of an extreme value distribution with parameters α and λ estimated using the maximum likelihood method based on the n observations in table x[i], i = 0, 1,…, n - 1.
getInstanceFromMLE(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.GammaDist
Creates a new instance of a gamma distribution with parameters α and λ estimated using the maximum likelihood method based on the n observations in table x[i], i = 0, 1,…, n - 1.
getInstanceFromMLE(int[], int) - Static method in class umontreal.iro.lecuyer.probdist.GeometricDist
Creates a new instance of a geometric distribution with parameter p estimated using the maximum likelihood method based on the n observations in table x[i], i = 0, 1,…, n - 1.
getInstanceFromMLE(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.HyperbolicSecantDist
Creates a new instance of a hyperbolic secant distribution with parameters μ and σ estimated using the maximum likelihood method based on the n observations in table x[i], i = 0, 1,…, n - 1.
getInstanceFromMLE(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.InverseGaussianDist
Creates a new instance of an inverse gaussian distribution with parameters μ and λ estimated using the maximum likelihood method based on the n observations in table x[i], i = 0, 1,…, n - 1.
getInstanceFromMLE(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.LaplaceDist
Creates a new instance of a Laplace distribution with parameters θ and φ estimated using the maximum likelihood method based on the n observations in table x[i], i = 0, 1,…, n - 1.
getInstanceFromMLE(int[], int) - Static method in class umontreal.iro.lecuyer.probdist.LogarithmicDist
Creates a new instance of a logarithmic distribution with parameter θ estimated using the maximum likelihood method based on the n observations in table x[i], i = 0, 1,…, n - 1.
getInstanceFromMLE(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.LogisticDist
Creates a new instance of a logistic distribution with parameters α and λ estimated using the maximum likelihood method based on the n observations in table x[i], i = 0, 1,…, n - 1.
getInstanceFromMLE(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.LoglogisticDist
Creates a new instance of a log-logistic distribution with parameters α and β estimated using the maximum likelihood method based on the n observations in table x[i], i = 0, 1,…, n - 1.
getInstanceFromMLE(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.LognormalDist
Creates a new instance of a lognormal distribution with parameters μ and σ estimated using the maximum likelihood method based on the n observations in table x[i], i = 0, 1,…, n - 1.
getInstanceFromMLE(int[], int, double) - Static method in class umontreal.iro.lecuyer.probdist.NegativeBinomialDist
Creates a new instance of a negative binomial distribution with parameters γ = gamma given and hat(p) estimated using the maximum likelihood method based on the n observations in table x[i], i = 0, 1,…, n - 1.
getInstanceFromMLE(int[], int) - Static method in class umontreal.iro.lecuyer.probdist.NegativeBinomialDist
Creates a new instance of a negative binomial distribution with parameters γ and p estimated using the maximum likelihood method based on the n observations in table x[i], i = 0, 1,…, n - 1.
getInstanceFromMLE(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.NormalDist
Creates a new instance of a normal distribution with parameters μ and σ estimated using the maximum likelihood method based on the n observations in table x[i], i = 0, 1,…, n - 1.
getInstanceFromMLE(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.ParetoDist
Creates a new instance of a Pareto distribution with parameters α and β estimated using the maximum likelihood method based on the n observations in table x[i], i = 0, 1,…, n - 1.
getInstanceFromMLE(int[], int) - Static method in class umontreal.iro.lecuyer.probdist.PascalDist
Creates a new instance of a Pascal distribution with parameters n and p estimated using the maximum likelihood method based on the m observations in table x[i], i = 0, 1,…, m - 1.
getInstanceFromMLE(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.Pearson5Dist
Creates a new instance of a Pearson V distribution with parameters α and β estimated using the maximum likelihood method based on the n observations in table x[i], i = 0, 1,…, n - 1.
getInstanceFromMLE(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.Pearson6Dist
Creates a new instance of a Pearson VI distribution with parameters α1, α2 and β, estimated using the maximum likelihood method based on the n observations in table x[i], i = 0, 1,…, n - 1.
getInstanceFromMLE(int[], int) - Static method in class umontreal.iro.lecuyer.probdist.PoissonDist
Creates a new instance of a Poisson distribution with parameter λ estimated using the maximum likelihood method based on the n observations in table x[i], i = 0, 1,…, n - 1.
getInstanceFromMLE(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.StudentDist
Creates a new instance of a Student-t distribution with parameter n estimated using the maximum likelihood method based on the m observations in table x[i], i = 0, 1,…, m - 1.
getInstanceFromMLE(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.UniformDist
Creates a new instance of a uniform distribution with parameters a and b estimated using the maximum likelihood method based on the n observations in table x[i], i = 0, 1,…, n - 1.
getInstanceFromMLE(int[], int) - Static method in class umontreal.iro.lecuyer.probdist.UniformIntDist
Creates a new instance of a discrete uniform distribution over integers with parameters i and j estimated using the maximum likelihood method based on the n observations in table x[k], k = 0, 1,…, n - 1.
getInstanceFromMLE(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.WeibullDist
Creates a new instance of a Weibull distribution with parameters α, λ and δ estimated using the maximum likelihood method based on the n observations in table x[i], i = 0, 1,…, n - 1.
getInt(int) - Method in class umontreal.iro.lecuyer.util.BitVector
Returns an int containing all the bits in the interval [pos×32,pos×32 + 31].
getInterQuartileRange() - Method in class umontreal.iro.lecuyer.probdist.EmpiricalDist
Returns the interquartile range of the observations, defined as the difference between the third and first quartiles.
getJ() - Method in class umontreal.iro.lecuyer.probdist.UniformIntDist
Returns the parameter j.
getK() - Method in class umontreal.iro.lecuyer.probdist.ErlangDist
Returns the parameter k for this object.
getK() - Method in class umontreal.iro.lecuyer.probdist.HypergeometricDist
Returns the k associated with this object.
getL() - Method in class umontreal.iro.lecuyer.probdist.HypergeometricDist
Returns the l associated with this object.
getLambda() - Method in class umontreal.iro.lecuyer.probdist.ExponentialDist
Returns the value of λ for this object.
getLambda() - Method in class umontreal.iro.lecuyer.probdist.ExtremeValueDist
Returns the parameter λ of this object.
getLambda() - Method in class umontreal.iro.lecuyer.probdist.GammaDist
Return the parameter λ for this object.
getLambda() - Method in class umontreal.iro.lecuyer.probdist.InverseGaussianDist
Returns the parameter λ of this object.
getLambda() - Method in class umontreal.iro.lecuyer.probdist.JohnsonSBDist
Returns the value of λ for this object.
getLambda() - Method in class umontreal.iro.lecuyer.probdist.JohnsonSUDist
Returns the value of λ for this object.
getLambda() - Method in class umontreal.iro.lecuyer.probdist.LogisticDist
Returns the parameter λ of this object.
getLambda() - Method in class umontreal.iro.lecuyer.probdist.PoissonDist
Returns the λ associated with this object.
getLambda() - Method in class umontreal.iro.lecuyer.probdist.WeibullDist
Returns the parameter λ.
getLast() - Method in class umontreal.iro.lecuyer.simevents.LinkedListStat
 
getLastTime() - Method in class umontreal.iro.lecuyer.simevents.Accumulate
Returns the last update time for this object.
getLastValue() - Method in class umontreal.iro.lecuyer.simevents.Accumulate
Returns the value passed to this probe by the last call to its update method (or the initial value if update was never called after init).
getLinearState() - Method in class umontreal.iro.lecuyer.rng.F2NL607
Returns the current state of the linear part of the stream, represented as an array of 19 integers.
getM() - Method in class umontreal.iro.lecuyer.probdist.FisherFDist
Returns the parameter m of this object.
getM() - Method in class umontreal.iro.lecuyer.probdist.HypergeometricDist
Returns the m associated with this object.
getM() - Method in class umontreal.iro.lecuyer.probdist.TriangularDist
Returns the value of m for this object.
getMaximumLikelihoodEstimate(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.BetaDist
Estimates and returns the parameters [ hat(α), hat(β)] of the beta distribution over the interval [0, 1] using the maximum likelihood method based on the n observations in table x[i], i = 0, 1,…, n - 1.
getMaximumLikelihoodEstimate(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.BetaSymmetricalDist
Estimates and returns the parameter [ hat(α)] of the symmetrical beta distribution using the maximum likelihood method based on the n observations in table x[i], i = 0, 1,…, n - 1.
getMaximumLikelihoodEstimate(int[], int) - Static method in class umontreal.iro.lecuyer.probdist.BinomialDist
Estimates the parameters [hat(n), hat(p)] of the binomial distribution using the maximum likelihood method based on the m observations in table x[i], i = 0, 1,…, m - 1.
getMaximumLikelihoodEstimate(int[], int, int) - Static method in class umontreal.iro.lecuyer.probdist.BinomialDist
Estimates the parameter [hat(p)] of the binomial distribution with given parameter n using the maximum likelihood method based on the m observations x[i], i = 0, 1,…, m - 1.
getMaximumLikelihoodEstimate(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.CauchyDist
Estimates and returns the parameters [ hat(α), hat(β)] of the Cauchy distribution using the maximum likelihood method based on the n observations in table x[i], i = 0, 1,…, n - 1.
getMaximumLikelihoodEstimate(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.ChiDist
Estimates and returns the parameter [hat(ν)] of the chi distribution using the maximum likelihood method based on the n observations in table x[i], i = 0, 1,…, n - 1.
getMaximumLikelihoodEstimate(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.ChiSquareDist
Estimates and returns the parameter [hat(n)] of the chi-square distribution using the maximum likelihood method based on the m observations in table x[i], i = 0, 1,…, m - 1.
getMaximumLikelihoodEstimate(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.ErlangDist
Estimates and returns the parameters [hat(k), hat(λ)] of the Erlang distribution using the maximum likelihood method based on the n observations in table x[i], i = 0, 1,…, n - 1.
getMaximumLikelihoodEstimate(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.ExponentialDist
Estimates and returns the parameter [ hat(λ)] of the exponential distribution using the maximum likelihood method based on the n observations in table x[i], i = 0, 1,…, n - 1.
getMaximumLikelihoodEstimate(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.ExtremeValueDist
Estimates and returns the parameters [ hat(α), hat(λ)] of the extreme value distribution using the maximum likelihood method based on the n observations in table x[i], i = 0, 1,…, n - 1.
getMaximumLikelihoodEstimate(double[], int, double) - Static method in class umontreal.iro.lecuyer.probdist.FatigueLifeDist
Estimates and returns the parameters [ hat(β), hat(γ)] of the fatigue life distribution using the maximum likelihood method based on the n observations in table x[i], i = 0, 1,…, n - 1.
getMaximumLikelihoodEstimate(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.GammaDist
Estimates and returns the parameters [ hat(α), hat(λ)] of the gamma distribution using the maximum likelihood method based on the n observations in table x[i], i = 0, 1,…, n - 1.
getMaximumLikelihoodEstimate(int[], int) - Static method in class umontreal.iro.lecuyer.probdist.GeometricDist
Estimates and returns the parameter [hat(p)] of the geometric distribution using the maximum likelihood method based on the n observations in table x[i], i = 0, 1,…, n - 1.
getMaximumLikelihoodEstimate(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.HyperbolicSecantDist
Estimates and returns the parameters [hat(μ), hat(σ)] of the hyperbolic secant distribution using the maximum likelihood method based on the n observations in table x[i], i = 0, 1,…, n - 1.
getMaximumLikelihoodEstimate(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.InverseGaussianDist
Estimates and returns the parameters [hat(μ), hat(λ)] of the inverse gaussian distribution using the maximum likelihood method based on the n observations in table x[i], i = 0, 1,…, n - 1.
getMaximumLikelihoodEstimate(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.LaplaceDist
Estimates and returns the parameters [ hat(&thetas;), hat(&phis;)] of the Laplace distribution using the maximum likelihood method based on the n observations in table x[i], i = 0, 1,…, n - 1.
getMaximumLikelihoodEstimate(int[], int) - Static method in class umontreal.iro.lecuyer.probdist.LogarithmicDist
Estimates and returns the parameter [ hat(&thetas;)] of the logarithmic distribution using the maximum likelihood method based on the n observations in table x[i], i = 0, 1,…, n - 1.
getMaximumLikelihoodEstimate(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.LogisticDist
Estimates and returns the parameters [ hat(α), hat(λ)] of the log-normal distribution using the maximum likelihood method based on the n observations in table x[i], i = 0, 1,…, n - 1.
getMaximumLikelihoodEstimate(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.LoglogisticDist
Estimates and returns the parameters [ hat(α), hat(β)] of the log-logistic distribution using the maximum likelihood method based on the n observations in table x[i], i = 0, 1,…, n - 1.
getMaximumLikelihoodEstimate(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.LognormalDist
Estimates and returns the parameters [hat(μ), hat(σ)] of the log-normal distribution using the maximum likelihood method based on the n observations in table x[i], i = 0, 1,…, n - 1.
getMaximumLikelihoodEstimate(int[], int, double) - Static method in class umontreal.iro.lecuyer.probdist.NegativeBinomialDist
Estimates and returns the parameter [hat(p)] of the negative binomial distribution using the maximum likelihood method based on the n observations in table x[i], i = 0, 1,…, n - 1.
getMaximumLikelihoodEstimate(int[], int) - Static method in class umontreal.iro.lecuyer.probdist.NegativeBinomialDist
Estimates and returns the parameters [ hat(γ), hat(p)] of the negative binomial distribution using the maximum likelihood method based on the n observations in table x[i], i = 0, 1,…, n - 1.
getMaximumLikelihoodEstimate(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.NormalDist
Estimates and returns the parameters [hat(μ), hat(σ)] of the normal distribution using the maximum likelihood method based on the n observations in table x[i], i = 0, 1,…, n - 1.
getMaximumLikelihoodEstimate(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.ParetoDist
Estimates and returns the parameters [ hat(α), hat(β)] of the Pareto distribution using the maximum likelihood method based on the n observations in table x[i], i = 0, 1,…, n - 1.
getMaximumLikelihoodEstimate(int[], int) - Static method in class umontreal.iro.lecuyer.probdist.PascalDist
Estimates and returns the parameters [hat(n), hat(p)] of the Pascal distribution using the maximum likelihood method based on the m observations in table x[i], i = 0, 1,…, m - 1.
getMaximumLikelihoodEstimate(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.Pearson5Dist
Estimates and returns the parameters [ hat(α), hat(β)] of the Pearson V distribution using the maximum likelihood method based on the n observations in table x[i], i = 0, 1,…, n - 1.
getMaximumLikelihoodEstimate(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.Pearson6Dist
Estimates and returns the parameters [ hat(α_1), hat(α_2), hat(β)] of the Pearson VI distribution using the maximum likelihood method based on the n observations in table x[i], i = 0, 1,…, n - 1.
getMaximumLikelihoodEstimate(int[], int) - Static method in class umontreal.iro.lecuyer.probdist.PoissonDist
Estimates and returns the parameter [ hat(λ)] of the Poisson distribution using the maximum likelihood method based on the n observations in table x[i], i = 0, 1,…, n - 1.
getMaximumLikelihoodEstimate(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.StudentDist
Estimates and returns the parameter [hat(n)] of the Student-t distribution using the maximum likelihood method based on the m observations in table x[i], i = 0, 1,…, m - 1.
getMaximumLikelihoodEstimate(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.UniformDist
Estimates and returns the parameters [hat(a), hat(b)] of the uniform distribution using the maximum likelihood method based on the n observations in table x[i], i = 0, 1,…, n - 1.
getMaximumLikelihoodEstimate(int[], int) - Static method in class umontreal.iro.lecuyer.probdist.UniformIntDist
Estimates and returns the parameters [hat(ı), hat(j)] of the uniform distribution over integers using the maximum likelihood method based on the n observations in table x[k], k = 0, 1,…, n - 1.
getMaximumLikelihoodEstimate(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.WeibullDist
Estimates and returns the parameters [ hat(α), hat(λ), hat(δ) = 0] of the Weibull distribution using the maximum likelihood method based on the n observations in table x[i], i = 0, 1,…, n - 1.
getMaximumLikelihoodEstimate(double[][], int, int) - Static method in class umontreal.iro.lecuyer.probdistmulti.DirichletDist
Estimates and returns the parameters [ hat(α_1),…, hat(α_d)] of the Dirichlet distribution using the maximum likelihood method based on the n observations of d components in table x[i][j], i = 0, 1,…, n - 1 and j = 0, 1,…, d - 1.
getMaximumLikelihoodEstimate(int[][], int, int, int) - Static method in class umontreal.iro.lecuyer.probdistmulti.MultinomialDist
Estimates and returns the parameters [hat(p_i),...,hat(p_d)] of the multinomial distribution using the maximum likelihood method based on the m observations of d components in table x[i][j], i = 0, 1,…, m - 1 and j = 0, 1,…, d - 1.
getMaximumLikelihoodEstimate(int[][], int, int) - Static method in class umontreal.iro.lecuyer.probdistmulti.NegativeMultinomialDist
Estimates the parameters [ hat(γ), hat(p_1),...,hat(p_d)] of the negative multinomial distribution using the maximum likelihood method based on the n observations of d components in table x[i][j], i = 0, 1,…, n - 1 and j = 0, 1,…, d - 1.
getMaximumLikelihoodEstimateMu(double[][], int, int) - Static method in class umontreal.iro.lecuyer.probdistmulti.MultiNormalDist
Estimates the parameters μ of the multinormal distribution using the maximum likelihood method based on the n observations of d components in table x[i][j], i = 0, 1,…, n - 1 and j = 0, 1,…, d - 1.
getMaximumLikelihoodEstimateSigma(double[][], int, int) - Static method in class umontreal.iro.lecuyer.probdistmulti.MultiNormalDist
Estimates the parameters Σ of the multinormal distribution using the maximum likelihood method based on the n observations of d components in table x[i][j], i = 0, 1,…, n - 1 and j = 0, 1,…, d - 1.
getMean() - Method in class umontreal.iro.lecuyer.probdist.BetaDist
 
getMean(double, double) - Static method in class umontreal.iro.lecuyer.probdist.BetaDist
Computes and returns the mean E[X] = α/(α + β) of the beta distribution with parameters α and β.
getMean() - Method in class umontreal.iro.lecuyer.probdist.BetaSymmetricalDist
 
getMean(double) - Static method in class umontreal.iro.lecuyer.probdist.BetaSymmetricalDist
Computes and returns the mean E[X] = 1/2 of the symmetrical beta distribution with parameter α.
getMean() - Method in class umontreal.iro.lecuyer.probdist.BinomialDist
 
getMean(int, double) - Static method in class umontreal.iro.lecuyer.probdist.BinomialDist
Computes the mean E[X] = np of the binomial distribution with parameters n and p.
getMean() - Method in class umontreal.iro.lecuyer.probdist.CauchyDist
 
getMean(double, double) - Static method in class umontreal.iro.lecuyer.probdist.CauchyDist
Throws an exception since the mean does not exist.
getMean() - Method in class umontreal.iro.lecuyer.probdist.ChiDist
 
getMean(int) - Static method in class umontreal.iro.lecuyer.probdist.ChiDist
Computes and returns the mean of the chi distribution with parameter ν.
getMean() - Method in class umontreal.iro.lecuyer.probdist.ChiSquareDist
 
getMean(int) - Static method in class umontreal.iro.lecuyer.probdist.ChiSquareDist
Computes and returns the mean E[X] = n of the chi-square distribution with parameter n.
getMean() - Method in class umontreal.iro.lecuyer.probdist.DiscreteDistribution
Computes the mean E[X] = ∑i=1npixi of the distribution.
getMean() - Method in interface umontreal.iro.lecuyer.probdist.Distribution
Returns the mean of the distribution function.
getMean() - Method in class umontreal.iro.lecuyer.probdist.EmpiricalDist
 
getMean(int, double) - Static method in class umontreal.iro.lecuyer.probdist.ErlangDist
Computes and returns the mean, E[X] = k/λ, of the Erlang distribution with parameters k and λ.
getMean() - Method in class umontreal.iro.lecuyer.probdist.ExponentialDist
 
getMean(double) - Static method in class umontreal.iro.lecuyer.probdist.ExponentialDist
Computes and returns the mean, E[X] = 1/λ, of the exponential distribution with parameter λ.
getMean() - Method in class umontreal.iro.lecuyer.probdist.ExtremeValueDist
 
getMean(double, double) - Static method in class umontreal.iro.lecuyer.probdist.ExtremeValueDist
Computes and returns the mean, E[X] = α + γ/λ, of the extreme value distribution with parameters α and λ, where γ = 0.5772156649 is the Euler-Mascheroni constant.
getMean() - Method in class umontreal.iro.lecuyer.probdist.FatigueLifeDist
 
getMean(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.FatigueLifeDist
Computes and returns the mean E[X] = μ + β(1 + γ2/2) of the fatigue life distribution with parameters μ, β and γ.
getMean() - Method in class umontreal.iro.lecuyer.probdist.FisherFDist
 
getMean(int, int) - Static method in class umontreal.iro.lecuyer.probdist.FisherFDist
Computes and returns the mean E[X] = m/(m - 2) of the Fisher F-distribution with parameters n and m.
getMean() - Method in class umontreal.iro.lecuyer.probdist.GammaDist
 
getMean(double, double) - Static method in class umontreal.iro.lecuyer.probdist.GammaDist
Computes and returns the mean E[X] = α/λ of the gamma distribution with parameters α and λ.
getMean() - Method in class umontreal.iro.lecuyer.probdist.GeometricDist
 
getMean(double) - Static method in class umontreal.iro.lecuyer.probdist.GeometricDist
Computes and returns the mean E[X] = (1 - p)/p of the geometric distribution with parameter p.
getMean() - Method in class umontreal.iro.lecuyer.probdist.HyperbolicSecantDist
 
getMean(double, double) - Static method in class umontreal.iro.lecuyer.probdist.HyperbolicSecantDist
Computes and returns the mean E[X] = μ of the hyperbolic secant distribution with parameters μ and σ.
getMean() - Method in class umontreal.iro.lecuyer.probdist.HypergeometricDist
 
getMean(int, int, int) - Static method in class umontreal.iro.lecuyer.probdist.HypergeometricDist
Computes and returns the mean E[X] = km/l of the Hypergeometric distribution with parameters m, l and k.
getMean() - Method in class umontreal.iro.lecuyer.probdist.InverseGaussianDist
 
getMean(double, double) - Static method in class umontreal.iro.lecuyer.probdist.InverseGaussianDist
Returns the mean E[X] = μ of the inverse gaussian distribution with parameters μ and λ.
getMean() - Method in class umontreal.iro.lecuyer.probdist.JohnsonSBDist
 
getMean(double, double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.JohnsonSBDist
Computes and returns the mean of the Johnson SB distribution with parameters γ, δ, ξ and λ.
getMean() - Method in class umontreal.iro.lecuyer.probdist.JohnsonSUDist
 
getMean(double, double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.JohnsonSUDist
Computes and returns the mean of the Johnson SU distribution with parameters γ, δ, ξ and λ.
getMean() - Method in class umontreal.iro.lecuyer.probdist.LaplaceDist
 
getMean(double, double) - Static method in class umontreal.iro.lecuyer.probdist.LaplaceDist
Computes and returns the mean E[X] = θ of the Laplace distribution with parameters θ and φ.
getMean() - Method in class umontreal.iro.lecuyer.probdist.LogarithmicDist
 
getMean(double) - Static method in class umontreal.iro.lecuyer.probdist.LogarithmicDist
Computes and returns the mean of the logarithmic distribution with parameter θ = theta.
getMean() - Method in class umontreal.iro.lecuyer.probdist.LogisticDist
 
getMean(double, double) - Static method in class umontreal.iro.lecuyer.probdist.LogisticDist
Computes and returns the mean E[X] = α of the logistic distribution with parameters α and λ.
getMean() - Method in class umontreal.iro.lecuyer.probdist.LoglogisticDist
 
getMean(double, double) - Static method in class umontreal.iro.lecuyer.probdist.LoglogisticDist
Computes and returns the mean of the log-logistic distribution with parameters α and β.
getMean() - Method in class umontreal.iro.lecuyer.probdist.LognormalDist
 
getMean(double, double) - Static method in class umontreal.iro.lecuyer.probdist.LognormalDist
Computes and returns the mean E[X] = eμ+σ2/2 of the lognormal distribution with parameters μ and σ.
getMean() - Method in class umontreal.iro.lecuyer.probdist.NegativeBinomialDist
 
getMean(double, double) - Static method in class umontreal.iro.lecuyer.probdist.NegativeBinomialDist
Computes and returns the mean E[X] = γ(1 - p)/p of the negative binomial distribution with parameters γ and p.
getMean() - Method in class umontreal.iro.lecuyer.probdist.NormalDist
 
getMean(double, double) - Static method in class umontreal.iro.lecuyer.probdist.NormalDist
Computes and returns the mean E[X] = μ of the normal distribution with parameters μ and σ.
getMean() - Method in class umontreal.iro.lecuyer.probdist.ParetoDist
 
getMean(double, double) - Static method in class umontreal.iro.lecuyer.probdist.ParetoDist
Computes and returns the mean E[X] = αβ/(α - 1) of the Pareto distribution with parameters α and β.
getMean() - Method in class umontreal.iro.lecuyer.probdist.Pearson5Dist
 
getMean(double, double) - Static method in class umontreal.iro.lecuyer.probdist.Pearson5Dist
Computes and returns the mean E[X] = β/(α - 1) of a Pearson V distribution with shape parameter α and scale parameter β.
getMean() - Method in class umontreal.iro.lecuyer.probdist.Pearson6Dist
 
getMean(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.Pearson6Dist
Computes and returns the mean E[X] = (βα1)/(α2 - 1) of a Pearson VI distribution with shape parameters α1 and α2, and scale parameter β.
getMean() - Method in class umontreal.iro.lecuyer.probdist.PiecewiseLinearEmpiricalDist
 
getMean() - Method in class umontreal.iro.lecuyer.probdist.PoissonDist
 
getMean(double) - Static method in class umontreal.iro.lecuyer.probdist.PoissonDist
Computes and returns the mean E[X] = λ of the Poisson distribution with parameter λ.
getMean() - Method in class umontreal.iro.lecuyer.probdist.StudentDist
 
getMean(int) - Static method in class umontreal.iro.lecuyer.probdist.StudentDist
Returns the mean E[X] = 0 of the Student-t distribution with parameter n.
getMean() - Method in class umontreal.iro.lecuyer.probdist.TriangularDist
 
getMean(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.TriangularDist
Computes and returns the mean E[X] = (a + b + m)/3 of the triangular distribution with parameters a, b, m.
getMean() - Method in class umontreal.iro.lecuyer.probdist.TruncatedDist
 
getMean() - Method in class umontreal.iro.lecuyer.probdist.UniformDist
 
getMean(double, double) - Static method in class umontreal.iro.lecuyer.probdist.UniformDist
Computes and returns the mean E[X] = (a + b)/2 of the uniform distribution with parameters a and b.
getMean() - Method in class umontreal.iro.lecuyer.probdist.UniformIntDist
 
getMean(int, int) - Static method in class umontreal.iro.lecuyer.probdist.UniformIntDist
Computes and returns the mean E[X] = (i + j)/2 of the discrete uniform distribution.
getMean() - Method in class umontreal.iro.lecuyer.probdist.WeibullDist
 
getMean(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.WeibullDist
Computes and returns the mean of the Weibull distribution with parameters α, λ and δ.
getMean() - Method in class umontreal.iro.lecuyer.probdistmulti.BiNormalDist
 
getMean(double, double, double, double, double) - Static method in class umontreal.iro.lecuyer.probdistmulti.BiNormalDist
Return the mean vector E[X] = (μ1, μ2) of the binormal distribution.
getMean() - Method in class umontreal.iro.lecuyer.probdistmulti.BiStudentDist
 
getMean(int, double) - Static method in class umontreal.iro.lecuyer.probdistmulti.BiStudentDist
Returns the mean vector E[X] = (0, 0) of the bivariate Student's t distribution.
getMean() - Method in class umontreal.iro.lecuyer.probdistmulti.ContinuousDistributionMulti
Returns the mean vector of the distribution, defined as μi = E[Xi].
getMean() - Method in class umontreal.iro.lecuyer.probdistmulti.DirichletDist
 
getMean(double[]) - Static method in class umontreal.iro.lecuyer.probdistmulti.DirichletDist
Computes the mean E[X] = αi/α0 of the Dirichlet distribution with parameters (α1,...,αd), where α0 = ∑i=1dαi.
getMean() - Method in class umontreal.iro.lecuyer.probdistmulti.DiscreteDistributionIntMulti
Returns the mean vector of the distribution, defined as μi = E[Xi].
getMean() - Method in class umontreal.iro.lecuyer.probdistmulti.MultinomialDist
 
getMean(int, double[]) - Static method in class umontreal.iro.lecuyer.probdistmulti.MultinomialDist
Computes the mean E[Xi] = npi of the multinomial distribution with parameters n and (p1,...,pd).
getMean() - Method in class umontreal.iro.lecuyer.probdistmulti.MultiNormalDist
 
getMean(double[], double[][]) - Static method in class umontreal.iro.lecuyer.probdistmulti.MultiNormalDist
Returns the mean E[X] = μ of the multinormal distribution with parameters μ and Σ.
getMean() - Method in class umontreal.iro.lecuyer.probdistmulti.NegativeMultinomialDist
 
getMean(double, double[]) - Static method in class umontreal.iro.lecuyer.probdistmulti.NegativeMultinomialDist
Computes the mean E[X] = γpi/p0 of the negative multinomial distribution with parameters γ and (p1,...,pd).
getMedian() - Method in class umontreal.iro.lecuyer.probdist.EmpiricalDist
Returns the n/2th item of the sorted observations when the number of items is odd, and the mean of the n/2th and the (n/2 + 1)th items when the number of items is even.
getMedian(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.EmpiricalDist
Returns the n/2th item of the array obs when the number of items is odd, and the mean of the n/2th and the (n/2 + 1)th items when the number of items is even.
getMinutes() - Method in class umontreal.iro.lecuyer.util.Chrono
Returns the CPU time in minutes used by the program since the last call to init for this Chrono.
getMomentsEstimate(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.ChiSquareDist
Estimates and returns the parameter [hat(n)] of the chi-square distribution using the moments method based on the m observations in table x[i], i = 0, 1,…, m - 1.
getMu() - Method in class umontreal.iro.lecuyer.probdist.FatigueLifeDist
Returns the parameter μ of this object.
getMu() - Method in class umontreal.iro.lecuyer.probdist.HyperbolicSecantDist
Returns the parameter μ of this object.
getMu() - Method in class umontreal.iro.lecuyer.probdist.InverseGaussianDist
Returns the parameter μ of this object.
getMu() - Method in class umontreal.iro.lecuyer.probdist.LognormalDist
Returns the parameter μ of this object.
getMu() - Method in class umontreal.iro.lecuyer.probdist.NormalDist
Returns the parameter μ.
getMu() - Method in class umontreal.iro.lecuyer.probdistmulti.MultiNormalDist
Returns the parameter μ of this object.
getMu(int) - Method in class umontreal.iro.lecuyer.probdistmulti.MultiNormalDist
Returns the i-th component of the parameter μ of this object.
getMu() - Method in class umontreal.iro.lecuyer.randvarmulti.MultiNormalGen
Returns the mean vector used by this generator.
getMu(int) - Method in class umontreal.iro.lecuyer.randvarmulti.MultiNormalGen
Returns the i-th component of the mean vector for this generator.
getMu1() - Method in class umontreal.iro.lecuyer.probdistmulti.BiNormalDist
Returns the parameter μ1.
getMu2() - Method in class umontreal.iro.lecuyer.probdistmulti.BiNormalDist
Returns the parameter μ2.
getN() - Method in class umontreal.iro.lecuyer.probdist.BinomialDist
Returns the parameter n of this object.
getN() - Method in class umontreal.iro.lecuyer.probdist.ChiSquareDist
Returns the parameter n of this object.
getN() - Method in class umontreal.iro.lecuyer.probdist.EmpiricalDist
Returns n, the number of observations.
getN() - Method in class umontreal.iro.lecuyer.probdist.FisherFDist
Returns the parameter n of this object.
getN() - Method in class umontreal.iro.lecuyer.probdist.PascalDist
Returns the parameter n of this object.
getN() - Method in class umontreal.iro.lecuyer.probdist.PiecewiseLinearEmpiricalDist
Returns n, the number of observations.
getN() - Method in class umontreal.iro.lecuyer.probdist.StudentDist
Returns the parameter n associated with this object.
getN() - Method in class umontreal.iro.lecuyer.probdistmulti.MultinomialDist
Returns the parameter n of this object.
getName() - Method in class umontreal.iro.lecuyer.simevents.LinkedListStat
Returns the name associated to this list, or null if no name was assigned.
getName() - Method in class umontreal.iro.lecuyer.simprocs.Condition
Returns the name (or identifier) associated to this condition.
getName() - Method in class umontreal.iro.lecuyer.simprocs.Resource
Returns the name (or identifier) associated to this resource.
getName() - Method in class umontreal.iro.lecuyer.stat.StatProbe
Returns the name associated with this probe, or null if no name was specified upon construction.
getNonLinearData() - Static method in class umontreal.iro.lecuyer.rng.F2NL607
Return the data of all the components of the non-linear part of the random number generator.
getNonLinearState() - Method in class umontreal.iro.lecuyer.rng.F2NL607
Returns the current state of the non-linear part of the stream, represented as an array of n integers, where n is the number of components in the non-linear generator.
getNu() - Method in class umontreal.iro.lecuyer.probdist.ChiDist
Returns the value of ν for this object.
getNumCachedValues() - Method in class umontreal.iro.lecuyer.randvar.RandomVariateGenWithCache
Returns the total number of values cached by this generator.
getNumCachedValues() - Method in class umontreal.iro.lecuyer.rng.RandomStreamWithCache
Returns the total number of values cached by this random stream.
getNumPoints() - Method in class umontreal.iro.lecuyer.hups.ContainerPointSet
 
getNumPoints() - Method in class umontreal.iro.lecuyer.hups.HaltonSequence
 
getNumPoints() - Method in class umontreal.iro.lecuyer.hups.PointSet
Returns the number of points.
getNumUnits() - Method in class umontreal.iro.lecuyer.simprocs.UserRecord
Returns the number of units requested or used by the associated process.
getObs(int) - Method in class umontreal.iro.lecuyer.probdist.EmpiricalDist
Returns the value of X(i).
getObs(int) - Method in class umontreal.iro.lecuyer.probdist.PiecewiseLinearEmpiricalDist
Returns the value of X(i).
getP() - Method in class umontreal.iro.lecuyer.probdist.BinomialDist
Returns the parameter p of this object.
getP() - Method in class umontreal.iro.lecuyer.probdist.GeometricDist
Returns the p associated with this object.
getP() - Method in class umontreal.iro.lecuyer.probdist.NegativeBinomialDist
Returns the parameter p of this object.
getP() - Method in class umontreal.iro.lecuyer.probdistmulti.MultinomialDist
Returns the parameters (p1,...,pd) of this object.
getP() - Method in class umontreal.iro.lecuyer.probdistmulti.NegativeMultinomialDist
Returns the parameters (p1,...,pd) of this object.
getParams() - Method in class umontreal.iro.lecuyer.probdist.PoissonDist
Return a table containing the parameter of the current distribution.
getPhi() - Method in class umontreal.iro.lecuyer.probdist.LaplaceDist
Returns the parameter φ.
getPrimes(int) - Static method in class umontreal.iro.lecuyer.hups.RadicalInverse
Provides an elementary method for obtaining the first n prime numbers larger than 1.
getProcess() - Method in class umontreal.iro.lecuyer.simprocs.UserRecord
Returns the process object associated with this record.
getRandomStreamClass() - Method in class umontreal.iro.lecuyer.rng.BasicRandomStreamFactory
Returns the random stream class associated with this object.
getRandomStreamFactory() - Method in exception umontreal.iro.lecuyer.rng.RandomStreamInstantiationException
Returns the random stream factory concerned by this exception.
getRequestTime() - Method in class umontreal.iro.lecuyer.simprocs.UserRecord
Return the time of creation of this record.
getSampleMean() - Method in class umontreal.iro.lecuyer.probdist.EmpiricalDist
Returns the sample mean of the observations.
getSampleMean() - Method in class umontreal.iro.lecuyer.probdist.PiecewiseLinearEmpiricalDist
Returns the sample mean of the observations.
getSampleStandardDeviation() - Method in class umontreal.iro.lecuyer.probdist.EmpiricalDist
Returns the sample standard deviation of the observations.
getSampleStandardDeviation() - Method in class umontreal.iro.lecuyer.probdist.PiecewiseLinearEmpiricalDist
Returns the sample standard deviation of the observations.
getSampleVariance() - Method in class umontreal.iro.lecuyer.probdist.EmpiricalDist
Returns the sample variance of the observations.
getSampleVariance() - Method in class umontreal.iro.lecuyer.probdist.PiecewiseLinearEmpiricalDist
Returns the sample variance of the observations.
getSeconds() - Method in class umontreal.iro.lecuyer.util.Chrono
Returns the CPU time in seconds used by the program since the last call to init for this Chrono.
getShiftDimension() - Method in class umontreal.iro.lecuyer.hups.RandShiftedPointSet
Returns the number of dimensions of the current random shift.
getSigma() - Method in class umontreal.iro.lecuyer.probdist.HyperbolicSecantDist
Returns the parameter σ of this object.
getSigma() - Method in class umontreal.iro.lecuyer.probdist.LognormalDist
Returns the parameter σ of this object.
getSigma() - Method in class umontreal.iro.lecuyer.probdist.NormalDist
Returns the parameter σ.
getSigma() - Method in class umontreal.iro.lecuyer.probdistmulti.MultiNormalDist
Returns the parameter Σ of this object.
getSigma() - Method in class umontreal.iro.lecuyer.randvarmulti.MultiNormalGen
Returns the covariance matrix Σ used by this generator.
getSigma1() - Method in class umontreal.iro.lecuyer.probdistmulti.BiNormalDist
Returns the parameter σ1.
getSigma2() - Method in class umontreal.iro.lecuyer.probdistmulti.BiNormalDist
Returns the parameter σ2.
getStandardDeviation() - Method in class umontreal.iro.lecuyer.probdist.BetaDist
 
getStandardDeviation(double, double) - Static method in class umontreal.iro.lecuyer.probdist.BetaDist
Computes the standard deviation of the beta distribution with parameters α and β.
getStandardDeviation() - Method in class umontreal.iro.lecuyer.probdist.BetaSymmetricalDist
 
getStandardDeviation(double) - Static method in class umontreal.iro.lecuyer.probdist.BetaSymmetricalDist
Computes and returns the standard deviation of the symmetrical beta distribution with parameter α.
getStandardDeviation() - Method in class umontreal.iro.lecuyer.probdist.BinomialDist
 
getStandardDeviation(int, double) - Static method in class umontreal.iro.lecuyer.probdist.BinomialDist
Computes the standard deviation of the Binomial distribution with parameters n and p.
getStandardDeviation() - Method in class umontreal.iro.lecuyer.probdist.CauchyDist
 
getStandardDeviation(double, double) - Static method in class umontreal.iro.lecuyer.probdist.CauchyDist
Returns since the standard deviation does not exist.
getStandardDeviation() - Method in class umontreal.iro.lecuyer.probdist.ChiDist
 
getStandardDeviation(int) - Static method in class umontreal.iro.lecuyer.probdist.ChiDist
Computes and returns the standard deviation of the chi distribution with parameter ν.
getStandardDeviation() - Method in class umontreal.iro.lecuyer.probdist.ChiSquareDist
 
getStandardDeviation(int) - Static method in class umontreal.iro.lecuyer.probdist.ChiSquareDist
Computes and returns the standard deviation of the chi-square distribution with parameter n.
getStandardDeviation() - Method in class umontreal.iro.lecuyer.probdist.DiscreteDistribution
Computes the standard deviation of the distribution.
getStandardDeviation() - Method in interface umontreal.iro.lecuyer.probdist.Distribution
Returns the standard deviation of the distribution function.
getStandardDeviation() - Method in class umontreal.iro.lecuyer.probdist.EmpiricalDist
 
getStandardDeviation(int, double) - Static method in class umontreal.iro.lecuyer.probdist.ErlangDist
Computes and returns the standard deviation of the Erlang distribution with parameters k and λ.
getStandardDeviation() - Method in class umontreal.iro.lecuyer.probdist.ExponentialDist
 
getStandardDeviation(double) - Static method in class umontreal.iro.lecuyer.probdist.ExponentialDist
Computes and returns the standard deviation of the exponential distribution with parameter λ.
getStandardDeviation() - Method in class umontreal.iro.lecuyer.probdist.ExtremeValueDist
 
getStandardDeviation(double, double) - Static method in class umontreal.iro.lecuyer.probdist.ExtremeValueDist
Computes and returns the standard deviation of the extreme value distribution with parameters α and λ.
getStandardDeviation() - Method in class umontreal.iro.lecuyer.probdist.FatigueLifeDist
 
getStandardDeviation(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.FatigueLifeDist
Computes and returns the standard deviation of the fatigue life distribution with parameters μ, β and γ.
getStandardDeviation() - Method in class umontreal.iro.lecuyer.probdist.FisherFDist
 
getStandardDeviation(int, int) - Static method in class umontreal.iro.lecuyer.probdist.FisherFDist
Computes and returns the standard deviation of the Fisher F-distribution with parameters n and m.
getStandardDeviation() - Method in class umontreal.iro.lecuyer.probdist.GammaDist
 
getStandardDeviation(double, double) - Static method in class umontreal.iro.lecuyer.probdist.GammaDist
Computes and returns the standard deviation of the gamma distribution with parameters α and λ.
getStandardDeviation() - Method in class umontreal.iro.lecuyer.probdist.GeometricDist
 
getStandardDeviation(double) - Static method in class umontreal.iro.lecuyer.probdist.GeometricDist
Computes and returns the standard deviation of the geometric distribution with parameter p.
getStandardDeviation() - Method in class umontreal.iro.lecuyer.probdist.HyperbolicSecantDist
 
getStandardDeviation(double, double) - Static method in class umontreal.iro.lecuyer.probdist.HyperbolicSecantDist
Computes and returns the standard deviation of the hyperbolic secant distribution with parameters μ and σ.
getStandardDeviation() - Method in class umontreal.iro.lecuyer.probdist.HypergeometricDist
 
getStandardDeviation(int, int, int) - Static method in class umontreal.iro.lecuyer.probdist.HypergeometricDist
Computes and returns the standard deviation of the hypergeometric distribution with parameters m, l and k.
getStandardDeviation() - Method in class umontreal.iro.lecuyer.probdist.InverseGaussianDist
 
getStandardDeviation(double, double) - Static method in class umontreal.iro.lecuyer.probdist.InverseGaussianDist
Computes and returns the standard deviation of the inverse gaussian distribution with parameters μ and λ.
getStandardDeviation() - Method in class umontreal.iro.lecuyer.probdist.JohnsonSBDist
 
getStandardDeviation(double, double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.JohnsonSBDist
Computes and returns the standard deviation of the Johnson SB distribution with parameters γ, δ, ξ and λ.
getStandardDeviation() - Method in class umontreal.iro.lecuyer.probdist.JohnsonSUDist
 
getStandardDeviation(double, double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.JohnsonSUDist
Computes and returns the standard deviation of the Johnson SU distribution with parameters γ, δ, ξ and λ.
getStandardDeviation() - Method in class umontreal.iro.lecuyer.probdist.LaplaceDist
 
getStandardDeviation(double, double) - Static method in class umontreal.iro.lecuyer.probdist.LaplaceDist
Computes and returns the standard deviation of the Laplace distribution with parameters θ and φ.
getStandardDeviation() - Method in class umontreal.iro.lecuyer.probdist.LogarithmicDist
 
getStandardDeviation(double) - Static method in class umontreal.iro.lecuyer.probdist.LogarithmicDist
Computes and returns the standard deviation of the logarithmic distribution with parameter θ = theta.
getStandardDeviation() - Method in class umontreal.iro.lecuyer.probdist.LogisticDist
 
getStandardDeviation(double, double) - Static method in class umontreal.iro.lecuyer.probdist.LogisticDist
Computes and returns the standard deviation of the logistic distribution with parameters α and λ.
getStandardDeviation() - Method in class umontreal.iro.lecuyer.probdist.LoglogisticDist
 
getStandardDeviation(double, double) - Static method in class umontreal.iro.lecuyer.probdist.LoglogisticDist
Computes and returns the standard deviation of the log-logistic distribution with parameters α and β.
getStandardDeviation() - Method in class umontreal.iro.lecuyer.probdist.LognormalDist
 
getStandardDeviation(double, double) - Static method in class umontreal.iro.lecuyer.probdist.LognormalDist
Computes and returns the standard deviation of the lognormal distribution with parameters μ and σ.
getStandardDeviation() - Method in class umontreal.iro.lecuyer.probdist.NegativeBinomialDist
 
getStandardDeviation(double, double) - Static method in class umontreal.iro.lecuyer.probdist.NegativeBinomialDist
Computes and returns the standard deviation of the negative binomial distribution with parameters γ and p.
getStandardDeviation() - Method in class umontreal.iro.lecuyer.probdist.NormalDist
 
getStandardDeviation(double, double) - Static method in class umontreal.iro.lecuyer.probdist.NormalDist
Computes and returns the standard deviation σ of the normal distribution with parameters μ and σ.
getStandardDeviation() - Method in class umontreal.iro.lecuyer.probdist.ParetoDist
 
getStandardDeviation(double, double) - Static method in class umontreal.iro.lecuyer.probdist.ParetoDist
Computes and returns the standard deviation of the Pareto distribution with parameters α and β.
getStandardDeviation() - Method in class umontreal.iro.lecuyer.probdist.Pearson5Dist
 
getStandardDeviation(double, double) - Static method in class umontreal.iro.lecuyer.probdist.Pearson5Dist
Computes and returns the standard deviation of a Pearson V distribution with shape parameter α and scale parameter β.
getStandardDeviation() - Method in class umontreal.iro.lecuyer.probdist.Pearson6Dist
 
getStandardDeviation(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.Pearson6Dist
Computes and returns the standard deviation of a Pearson VI distribution with shape parameters α1 and α2, and scale parameter β.
getStandardDeviation() - Method in class umontreal.iro.lecuyer.probdist.PiecewiseLinearEmpiricalDist
 
getStandardDeviation() - Method in class umontreal.iro.lecuyer.probdist.PoissonDist
 
getStandardDeviation(double) - Static method in class umontreal.iro.lecuyer.probdist.PoissonDist
Computes and returns the standard deviation of the Poisson distribution with parameter λ.
getStandardDeviation() - Method in class umontreal.iro.lecuyer.probdist.StudentDist
 
getStandardDeviation(int) - Static method in class umontreal.iro.lecuyer.probdist.StudentDist
Computes and returns the standard deviation of the Student-t distribution with parameter n.
getStandardDeviation() - Method in class umontreal.iro.lecuyer.probdist.TriangularDist
 
getStandardDeviation(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.TriangularDist
Computes and returns the standard deviation of the triangular distribution with parameters a, b, m.
getStandardDeviation() - Method in class umontreal.iro.lecuyer.probdist.TruncatedDist
 
getStandardDeviation() - Method in class umontreal.iro.lecuyer.probdist.UniformDist
 
getStandardDeviation(double, double) - Static method in class umontreal.iro.lecuyer.probdist.UniformDist
Computes and returns the standard deviation of the uniform distribution with parameters a and b.
getStandardDeviation() - Method in class umontreal.iro.lecuyer.probdist.UniformIntDist
 
getStandardDeviation(int, int) - Static method in class umontreal.iro.lecuyer.probdist.UniformIntDist
Computes and returns the standard deviation of the discrete uniform distribution.
getStandardDeviation() - Method in class umontreal.iro.lecuyer.probdist.WeibullDist
 
getStandardDeviation(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.WeibullDist
Computes and returns the standard deviation of the Weibull distribution with parameters α, λ and δ.
getState() - Method in class umontreal.iro.lecuyer.rng.GenF2w32
Returns the current state of the stream, represented as an array of 25 integers.
getState() - Method in class umontreal.iro.lecuyer.rng.LFSR113
Returns the current state of the stream, represented as an array of four integers.
getState() - Method in class umontreal.iro.lecuyer.rng.LFSR258
Returns the current state of the stream, represented as an array of five integers.
getState() - Method in class umontreal.iro.lecuyer.rng.MRG31k3p
Returns the current state Cg of this stream.
getState() - Method in class umontreal.iro.lecuyer.rng.MRG32k3a
Returns the current state Cg of this stream.
getState() - Method in class umontreal.iro.lecuyer.rng.RandMrg
Returns the current state Cg of this stream.
getState() - Method in class umontreal.iro.lecuyer.rng.RandRijndael
Returns the current state of the stream, represented as an array of four integers.
getState() - Method in class umontreal.iro.lecuyer.rng.WELL1024
Returns the current state of the stream, represented as an array of 32 integers.
getState() - Method in class umontreal.iro.lecuyer.rng.WELL512
Returns the current state of the stream, represented as an array of 16 integers.
getState() - Method in class umontreal.iro.lecuyer.rng.WELL607
Returns the current state of the stream, represented as an array of 19 integers.
getState() - Method in class umontreal.iro.lecuyer.simprocs.AbstractSimProcess
Returns the state of the process.
getStream() - Method in class umontreal.iro.lecuyer.randvar.RandomVariateGen
Returns the RandomStream used by this generator.
getStream() - Method in class umontreal.iro.lecuyer.randvar.RandomVariateGenWithCache
 
getStream() - Method in class umontreal.iro.lecuyer.randvar.UnuranContinuous
 
getStream() - Method in class umontreal.iro.lecuyer.randvar.UnuranDiscreteInt
 
getStream() - Method in class umontreal.iro.lecuyer.randvar.UnuranEmpirical
 
getStream() - Method in class umontreal.iro.lecuyer.randvarmulti.RandomMultiVariateGen
Returns the RandomStream used by this object.
getStream2() - Method in class umontreal.iro.lecuyer.randvar.BetaSymmetricalBestGen
Returns stream s2 associated with this object.
getStream2() - Method in class umontreal.iro.lecuyer.randvar.BetaSymmetricalPolarGen
Returns stream s2 associated with this object.
getStream3() - Method in class umontreal.iro.lecuyer.randvar.BetaSymmetricalBestGen
Returns stream s3 associated with this object.
getStreams() - Method in class umontreal.iro.lecuyer.rng.RandomStreamManager
Returns an unmodifiable list containing all the random streams in this random stream manager.
getTheta() - Method in class umontreal.iro.lecuyer.probdist.LaplaceDist
Returns the parameter θ.
getTheta() - Method in class umontreal.iro.lecuyer.probdist.LogarithmicDist
Returns the θ associated with this object.
getTimeInterval(double[], int, int, double) - Static method in class umontreal.iro.lecuyer.util.Misc
Returns the index of the time interval corresponding to time t.
getVariance() - Method in class umontreal.iro.lecuyer.probdist.BetaDist
 
getVariance(double, double) - Static method in class umontreal.iro.lecuyer.probdist.BetaDist
.
getVariance() - Method in class umontreal.iro.lecuyer.probdist.BetaSymmetricalDist
 
getVariance(double) - Static method in class umontreal.iro.lecuyer.probdist.BetaSymmetricalDist
Computes and returns the variance, Var[X] = 1/(8α + 4), of the symmetrical beta distribution with parameter α.
getVariance() - Method in class umontreal.iro.lecuyer.probdist.BinomialDist
 
getVariance(int, double) - Static method in class umontreal.iro.lecuyer.probdist.BinomialDist
Computes the variance Var[X] = np(1 - p) of the binomial distribution with parameters n and p.
getVariance() - Method in class umontreal.iro.lecuyer.probdist.CauchyDist
 
getVariance(double, double) - Static method in class umontreal.iro.lecuyer.probdist.CauchyDist
Returns since the variance does not exist.
getVariance() - Method in class umontreal.iro.lecuyer.probdist.ChiDist
 
getVariance(int) - Static method in class umontreal.iro.lecuyer.probdist.ChiDist
Computes and returns the variance of the chi distribution with parameter ν.
getVariance() - Method in class umontreal.iro.lecuyer.probdist.ChiSquareDist
 
getVariance(int) - Static method in class umontreal.iro.lecuyer.probdist.ChiSquareDist
Computes and returns the variance Var[X] = 2n of the chi-square distribution with parameter n.
getVariance() - Method in class umontreal.iro.lecuyer.probdist.DiscreteDistribution
Computes the variance Var[X] = ∑i=1npi(xi - E[X])2 of the distribution.
getVariance() - Method in interface umontreal.iro.lecuyer.probdist.Distribution
Returns the variance of the distribution function.
getVariance() - Method in class umontreal.iro.lecuyer.probdist.EmpiricalDist
 
getVariance(int, double) - Static method in class umontreal.iro.lecuyer.probdist.ErlangDist
Computes and returns the variance, Var[X] = k/λ2, of the Erlang distribution with parameters k and λ.
getVariance() - Method in class umontreal.iro.lecuyer.probdist.ExponentialDist
 
getVariance(double) - Static method in class umontreal.iro.lecuyer.probdist.ExponentialDist
Computes and returns the variance, Var[X] = 1/λ2, of the exponential distribution with parameter λ.
getVariance() - Method in class umontreal.iro.lecuyer.probdist.ExtremeValueDist
 
getVariance(double, double) - Static method in class umontreal.iro.lecuyer.probdist.ExtremeValueDist
Computes and returns the variance, Var[X] = π2/(6λ2), of the extreme value distribution with parameters α and λ.
getVariance() - Method in class umontreal.iro.lecuyer.probdist.FatigueLifeDist
 
getVariance(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.FatigueLifeDist
Computes and returns the variance Var[X] = β2γ2(1 + 5γ2/4) of the fatigue life distribution with parameters μ, β and γ.
getVariance() - Method in class umontreal.iro.lecuyer.probdist.FisherFDist
 
getVariance(int, int) - Static method in class umontreal.iro.lecuyer.probdist.FisherFDist
Computes and returns the variance of the Fisher F-distribution with parameters n and m.
getVariance() - Method in class umontreal.iro.lecuyer.probdist.GammaDist
 
getVariance(double, double) - Static method in class umontreal.iro.lecuyer.probdist.GammaDist
Computes and returns the variance Var[X] = α/λ2 of the gamma distribution with parameters α and λ.
getVariance() - Method in class umontreal.iro.lecuyer.probdist.GeometricDist
 
getVariance(double) - Static method in class umontreal.iro.lecuyer.probdist.GeometricDist
Computes and returns the variance Var[X] = (1 - p)/p2 of the geometric distribution with parameter p.
getVariance() - Method in class umontreal.iro.lecuyer.probdist.HyperbolicSecantDist
 
getVariance(double, double) - Static method in class umontreal.iro.lecuyer.probdist.HyperbolicSecantDist
Computes and returns the variance Var[X] = σ2 of the hyperbolic secant distribution with parameters μ and σ.
getVariance() - Method in class umontreal.iro.lecuyer.probdist.HypergeometricDist
 
getVariance(int, int, int) - Static method in class umontreal.iro.lecuyer.probdist.HypergeometricDist
Computes and returns the variance of the hypergeometric distribution with parameters m, l and k.
getVariance() - Method in class umontreal.iro.lecuyer.probdist.InverseGaussianDist
 
getVariance(double, double) - Static method in class umontreal.iro.lecuyer.probdist.InverseGaussianDist
Computes and returns the variance Var[X] = μ3/λ of the inverse gaussian distribution with parameters μ and λ.
getVariance() - Method in class umontreal.iro.lecuyer.probdist.JohnsonSBDist
 
getVariance(double, double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.JohnsonSBDist
Computes and returns the variance of the Johnson SB distribution with parameters γ, δ, ξ and λ.
getVariance() - Method in class umontreal.iro.lecuyer.probdist.JohnsonSUDist
 
getVariance(double, double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.JohnsonSUDist
Computes and returns the variance of the Johnson SU distribution with parameters γ, δ, ξ and λ.
getVariance() - Method in class umontreal.iro.lecuyer.probdist.LaplaceDist
 
getVariance(double, double) - Static method in class umontreal.iro.lecuyer.probdist.LaplaceDist
Computes and returns the variance Var[X] = 2φ2 of the Laplace distribution with parameters θ and φ.
getVariance() - Method in class umontreal.iro.lecuyer.probdist.LogarithmicDist
 
getVariance(double) - Static method in class umontreal.iro.lecuyer.probdist.LogarithmicDist
Computes and returns the variance of the logarithmic distribution with parameter θ = theta.
getVariance() - Method in class umontreal.iro.lecuyer.probdist.LogisticDist
 
getVariance(double, double) - Static method in class umontreal.iro.lecuyer.probdist.LogisticDist
Computes and returns the variance Var[X] = π2/(3λ2) of the logistic distribution with parameters α and λ.
getVariance() - Method in class umontreal.iro.lecuyer.probdist.LoglogisticDist
 
getVariance(double, double) - Static method in class umontreal.iro.lecuyer.probdist.LoglogisticDist
Computes and returns the variance of the log-logistic distribution with parameters α and β.
getVariance() - Method in class umontreal.iro.lecuyer.probdist.LognormalDist
 
getVariance(double, double) - Static method in class umontreal.iro.lecuyer.probdist.LognormalDist
Computes and returns the variance Var[X] = e2μ+σ2(eσ2 - 1) of the lognormal distribution with parameters μ and σ.
getVariance() - Method in class umontreal.iro.lecuyer.probdist.NegativeBinomialDist
 
getVariance(double, double) - Static method in class umontreal.iro.lecuyer.probdist.NegativeBinomialDist
Computes and returns the variance Var[X] = γ(1 - p)/p2 of the negative binomial distribution with parameters γ and p.
getVariance() - Method in class umontreal.iro.lecuyer.probdist.NormalDist
 
getVariance(double, double) - Static method in class umontreal.iro.lecuyer.probdist.NormalDist
Computes and returns the variance Var[X] = σ2 of the normal distribution with parameters μ and σ.
getVariance() - Method in class umontreal.iro.lecuyer.probdist.ParetoDist
 
getVariance(double, double) - Static method in class umontreal.iro.lecuyer.probdist.ParetoDist
Computes and returns the variance of the Pareto distribution with parameters α and β.
getVariance() - Method in class umontreal.iro.lecuyer.probdist.Pearson5Dist
 
getVariance(double, double) - Static method in class umontreal.iro.lecuyer.probdist.Pearson5Dist
Computes and returns the variance Var[X] = β2/((α -1)2(α - 2) of a Pearson V distribution with shape parameter α and scale parameter β.
getVariance() - Method in class umontreal.iro.lecuyer.probdist.Pearson6Dist
 
getVariance(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.Pearson6Dist
Computes and returns the variance Var[X] = [β2α1(α1 + α2 -1)]/[(α2 -1)2(α2 - 2)] of a Pearson VI distribution with shape parameters α1 and α2, and scale parameter β.
getVariance() - Method in class umontreal.iro.lecuyer.probdist.PiecewiseLinearEmpiricalDist
 
getVariance() - Method in class umontreal.iro.lecuyer.probdist.PoissonDist
 
getVariance(double) - Static method in class umontreal.iro.lecuyer.probdist.PoissonDist
Computes and returns the variance = λ of the Poisson distribution with parameter λ.
getVariance() - Method in class umontreal.iro.lecuyer.probdist.StudentDist
 
getVariance(int) - Static method in class umontreal.iro.lecuyer.probdist.StudentDist
Computes and returns the variance Var[X] = n/(n - 2) of the Student-t distribution with parameter n.
getVariance() - Method in class umontreal.iro.lecuyer.probdist.TriangularDist
 
getVariance(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.TriangularDist
Computes and returns the variance Var[X] = (a2 + b2 + m2 - ab - am - bm)/18 of the triangular distribution with parameters a, b, m.
getVariance() - Method in class umontreal.iro.lecuyer.probdist.TruncatedDist
Returns an approximation of the variance obtained using the Simpson 1/3 numerical integration, or throws an UnsupportedOperationException if a or b are infinite.
getVariance() - Method in class umontreal.iro.lecuyer.probdist.UniformDist
 
getVariance(double, double) - Static method in class umontreal.iro.lecuyer.probdist.UniformDist
Computes and returns the variance Var[X] = (b - a)2/12 of the uniform distribution with parameters a and b.
getVariance() - Method in class umontreal.iro.lecuyer.probdist.UniformIntDist
 
getVariance(int, int) - Static method in class umontreal.iro.lecuyer.probdist.UniformIntDist
Computes and returns the variance Var[X] = [(j - i + 1)2 -1]/12 of the discrete uniform distribution.
getVariance() - Method in class umontreal.iro.lecuyer.probdist.WeibullDist
 
getVariance(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.WeibullDist
Computes and returns the variance of the Weibull distribution with parameters α, λ and δ.
getXi() - Method in class umontreal.iro.lecuyer.probdist.JohnsonSBDist
Returns the value of ξ for this object.
getXi() - Method in class umontreal.iro.lecuyer.probdist.JohnsonSUDist
Returns the value of ξ for this object.
GNUPLOT - Static variable in class umontreal.iro.lecuyer.gof.GofFormat
Data file format used for plotting functions with Gnuplot.
GofFormat - Class in umontreal.iro.lecuyer.gof
This class contains methods used to format results of GOF test statistics, or to apply a series of tests simultaneously and format the results.
GofStat - Class in umontreal.iro.lecuyer.gof
This class provides methods to compute several types of EDF goodness-of-fit test statistics and to apply certain transformations to a set of observations.
GofStat.OutcomeCategoriesChi2 - Class in umontreal.iro.lecuyer.gof
This class helps managing the partitions of possible outcomes into categories for applying chi-square tests.
GofStat.OutcomeCategoriesChi2(double[]) - Constructor for class umontreal.iro.lecuyer.gof.GofStat.OutcomeCategoriesChi2
Constructs an OutcomeCategoriesChi2 object using the array nbExp for the number of expected observations in each category.
GofStat.OutcomeCategoriesChi2(double[], int, int) - Constructor for class umontreal.iro.lecuyer.gof.GofStat.OutcomeCategoriesChi2
Constructs an OutcomeCategoriesChi2 object using the given nbExp expected observations array.
GofStat.OutcomeCategoriesChi2(double[], int[], int, int, int) - Constructor for class umontreal.iro.lecuyer.gof.GofStat.OutcomeCategoriesChi2
Constructs an OutcomeCategoriesChi2 object.
graphDistUnif(DoubleArrayList, String) - Static method in class umontreal.iro.lecuyer.gof.GofFormat
Formats data to plot the empirical distribution of U(1),..., U(N), which are assumed to be in data[0...N-1], and to compare it with the uniform distribution.
graphFunc(ContinuousDistribution, double, double, int, int, String) - Static method in class umontreal.iro.lecuyer.gof.GofFormat
Prints data to plot the graph of function F over the interval [a, b], and returns the result as a String.
graphSoft - Static variable in class umontreal.iro.lecuyer.gof.GofFormat
Environment variable that selects the type of software to be used for plotting the graphs of functions.
gumbel(RandomStream, double, double) - Static method in class umontreal.iro.lecuyer.randvar.Rand1
Deprecated. Returns a random variate having the Gumbel distribution with parameters alpha and lambda (see, Problem 8.1).

H

HaltonSequence - Class in umontreal.iro.lecuyer.hups
This class implements the sequence of Halton, which is essentially a modification of Hammersley nets for producing an infinite sequence of points having low discrepancy.
HaltonSequence(int) - Constructor for class umontreal.iro.lecuyer.hups.HaltonSequence
Constructs a new Halton sequence in dim dimensions.
HammersleyPointSet - Class in umontreal.iro.lecuyer.hups
This class implements Hammersley point sets, which are defined as follows.
HammersleyPointSet(int, int) - Constructor for class umontreal.iro.lecuyer.hups.HammersleyPointSet
Constructs a new Hammersley point set with n points in dim dimensions.
hasNextCoordinate() - Method in class umontreal.iro.lecuyer.hups.CycleBasedPointSet.CycleBasedPointSetIterator
 
hasNextCoordinate() - Method in interface umontreal.iro.lecuyer.hups.PointSetIterator
Returns true if the current point has another coordinate.
hasNextPoint() - Method in interface umontreal.iro.lecuyer.hups.PointSetIterator
Returns true if there is a next point.
Henriksen - Class in umontreal.iro.lecuyer.simevents.eventlist
An implementation of EventList using the doubly-linked index list of Henriksen.
Henriksen() - Constructor for class umontreal.iro.lecuyer.simevents.eventlist.Henriksen
 
HyperbolicSecantDist - Class in umontreal.iro.lecuyer.probdist
Extends the class ContinuousDistribution for the Hyperbolic Secant distribution with location parameter μ and scale parameter σ > 0.
HyperbolicSecantDist(double, double) - Constructor for class umontreal.iro.lecuyer.probdist.HyperbolicSecantDist
Constructs a hyperbolic secant distribution with parameters μ and σ.
HyperbolicSecantGen - Class in umontreal.iro.lecuyer.randvar
This class implements random variate generators for the Hyperbolic Secant distribution with location parameter μ and scale parameter σ.
HyperbolicSecantGen(RandomStream, HyperbolicSecantDist) - Constructor for class umontreal.iro.lecuyer.randvar.HyperbolicSecantGen
Creates a new generator for the distribution dist, using stream s.
HypergeometricDist - Class in umontreal.iro.lecuyer.probdist
Extends the class DiscreteDistributionInt for the hypergeometric distribution with k elements chosen among l, m being of one type, and l - m of the other.
HypergeometricDist(int, int, int) - Constructor for class umontreal.iro.lecuyer.probdist.HypergeometricDist
Constructs an hypergeometric distribution with parameters m, l and k.
HypergeometricGen - Class in umontreal.iro.lecuyer.randvar
This class implements random variate generators for the hypergeometric distribution.
HypergeometricGen(RandomStream, HypergeometricDist) - Constructor for class umontreal.iro.lecuyer.randvar.HypergeometricGen
Creates a new generator for distribution dist, using stream s.

I

iBinomialMatrixScramble(RandomStream) - Method in class umontreal.iro.lecuyer.hups.DigitalNet
Applies the i-binomial matrix scramble proposed by Tezuka .
iBinomialMatrixScramble(RandomStream) - Method in class umontreal.iro.lecuyer.hups.DigitalNetBase2
 
iBinomialMatrixScrambleFaurePermut(RandomStream, int) - Method in class umontreal.iro.lecuyer.hups.DigitalNet
Similar to iBinomialMatrixScramble except that the diagonal elements of each matrix Mj are chosen as in leftMatrixScrambleFaurePermut.
iBinomialMatrixScrambleFaurePermut(RandomStream, int) - Method in class umontreal.iro.lecuyer.hups.DigitalNetBase2
 
iBinomialMatrixScrambleFaurePermutAll(RandomStream, int) - Method in class umontreal.iro.lecuyer.hups.DigitalNet
Similar to iBinomialMatrixScrambleFaurePermut except that the elements under the diagonal are also chosen from the same restricted set as the diagonal elements.
iBinomialMatrixScrambleFaurePermutAll(RandomStream, int) - Method in class umontreal.iro.lecuyer.hups.DigitalNetBase2
 
iBinomialMatrixScrambleFaurePermutDiag(RandomStream, int) - Method in class umontreal.iro.lecuyer.hups.DigitalNet
Similar to iBinomialMatrixScrambleFaurePermut except that all the off-diagonal elements are 0.
iBinomialMatrixScrambleFaurePermutDiag(RandomStream, int) - Method in class umontreal.iro.lecuyer.hups.DigitalNetBase2
 
ILN2 - Static variable in class umontreal.iro.lecuyer.util.Num
The values of 1/ln 2.
increasedPrecis(boolean) - Method in class umontreal.iro.lecuyer.rng.RandMrg
After calling this method with incp = true, each call to the generator (direct or indirect) for this stream will return a uniform random number with (roughly) 53 bits of resolution instead of 32 bits, and will advance the state of the stream by 2 steps instead of 1.
increasedPrecision(boolean) - Method in class umontreal.iro.lecuyer.rng.RandomStreamBase
After calling this method with incp = true, each call to the RNG (direct or indirect) for this stream will return a uniform random number with more bits of precision than what is returned by nextValue, and will advance the state of the stream by 2 steps instead of 1 (i.e., nextValue will be called twice for each random number).
indexOf(Object) - Method in class umontreal.iro.lecuyer.simevents.LinkedListStat
 
init(double[]) - Method in class umontreal.iro.lecuyer.hups.HaltonSequence
Initializes the Halton sequence starting at point x0.
init() - Method in class umontreal.iro.lecuyer.simevents.Accumulate
Initializes the statistical collector and puts the current value of the corresponding variable to 0.
init(double) - Method in class umontreal.iro.lecuyer.simevents.Accumulate
Same as init followed by update(x).
init(double) - Method in class umontreal.iro.lecuyer.simevents.Continuous
Initializes or reinitializes the continuous-time variable to val.
init() - Static method in class umontreal.iro.lecuyer.simevents.Sim
Reinitializes the simulation executive by clearing up the event list, and resetting the simulation clock to zero.
init(EventList) - Static method in class umontreal.iro.lecuyer.simevents.Sim
Same as init, but also chooses evlist as the event list to be used.
init() - Method in class umontreal.iro.lecuyer.simprocs.Bin
Reinitializes this bin by clearing up its pile of tokens and its waiting list.
init(boolean) - Method in class umontreal.iro.lecuyer.simprocs.Condition
Reinitializes this Condition by clearing up its waiting list and resetting its state to val.
init() - Static method in class umontreal.iro.lecuyer.simprocs.dsol.SimProcess
Initializes the process-driven simulation.
init(EventList) - Static method in class umontreal.iro.lecuyer.simprocs.dsol.SimProcess
Initializes the simulation and sets the given event list evlist to be used by the simulation executive.
init() - Method in class umontreal.iro.lecuyer.simprocs.Resource
Reinitializes this resource by clearing up its waiting list and service list.
init() - Static method in class umontreal.iro.lecuyer.simprocs.SimProcess
Initializes the process-driven simulation.
init(EventList) - Static method in class umontreal.iro.lecuyer.simprocs.SimProcess
Initializes the simulation and sets the given event list evlist to be used by the simulation executive.
init() - Method in class umontreal.iro.lecuyer.stat.StatProbe
Initializes the statistical collector.
init() - Method in class umontreal.iro.lecuyer.stat.Tally
 
init() - Method in class umontreal.iro.lecuyer.stat.TallyStore
 
init() - Method in class umontreal.iro.lecuyer.util.Chrono
Initializes this Chrono to zero.
initCache() - Method in class umontreal.iro.lecuyer.randvar.RandomVariateGenWithCache
Resets this generator to recover values from the cache.
initCache() - Method in class umontreal.iro.lecuyer.rng.RandomStreamWithCache
Resets this random stream to recover values from the cache.
INITIAL - Static variable in class umontreal.iro.lecuyer.simprocs.AbstractSimProcess
The process has been created but not yet scheduled.
initStat() - Method in class umontreal.iro.lecuyer.simevents.LinkedListStat
Reinitializes the two statistical probes created by setStatCollecting (true) and makes an update for the probe on the list size.
initStat() - Method in class umontreal.iro.lecuyer.simprocs.Bin
Reinitializes all the statistical collectors for this bin.
initStat() - Method in class umontreal.iro.lecuyer.simprocs.Resource
Reinitializes all the statistical collectors for this resource.
integerRadicalInverse(int, int) - Static method in class umontreal.iro.lecuyer.hups.RadicalInverse
Computes the integer radical inverse of i in base b, equal to bkψb(i) if i has k b-ary digits.
interpol(int, double[], double[], double[]) - Static method in class umontreal.iro.lecuyer.util.Misc
.
inverseBisection(double) - Method in class umontreal.iro.lecuyer.probdist.ContinuousDistribution
 
inverseBrent(double, double, double, double) - Method in class umontreal.iro.lecuyer.probdist.ContinuousDistribution
Computes the inverse distribution function x = F-1(u) using the Brent-Dekker method.
inverseF(double) - Method in class umontreal.iro.lecuyer.probdist.BetaDist
 
inverseF(double, double, int, double) - Static method in class umontreal.iro.lecuyer.probdist.BetaDist
Same as inverseF (alpha, beta, 0, 1, d, u).
inverseF(double, double, double, double, int, double) - Static method in class umontreal.iro.lecuyer.probdist.BetaDist
Returns the inverse beta distribution function using the algorithm implemented in the Cephes math library.
inverseF(double) - Method in class umontreal.iro.lecuyer.probdist.BetaSymmetricalDist
 
inverseF(double, double) - Static method in class umontreal.iro.lecuyer.probdist.BetaSymmetricalDist
Returns the inverse distribution function evaluated at u, for the symmetrical beta distribution over the interval [0, 1], with shape parameters 0 < α = β = alpha.
inverseF(int, double, double) - Static method in class umontreal.iro.lecuyer.probdist.BinomialDist
Computes the inverse of the binomial distribution, x = F-1(u), using a linear search starting at the mode if n is small.
inverseF(double) - Method in class umontreal.iro.lecuyer.probdist.CauchyDist
 
inverseF(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.CauchyDist
Computes the inverse of the distribution.
inverseF(double) - Method in class umontreal.iro.lecuyer.probdist.ChiDist
 
inverseF(int, double) - Static method in class umontreal.iro.lecuyer.probdist.ChiDist
Returns the inverse distribution function computed using the gamma inversion.
inverseF(double) - Method in class umontreal.iro.lecuyer.probdist.ChiSquareDist
 
inverseF(int, double) - Static method in class umontreal.iro.lecuyer.probdist.ChiSquareDist
Computes an approximation of F-1(u), where F is the chi-square distribution with n degrees of freedom.
inverseF(double) - Method in class umontreal.iro.lecuyer.probdist.ChiSquareDistQuick
 
inverseF(int, double) - Static method in class umontreal.iro.lecuyer.probdist.ChiSquareDistQuick
Computes a quick-and-dirty approximation of F-1(u), where F is the chi-square distribution with n degrees of freedom.
inverseF(double) - Method in class umontreal.iro.lecuyer.probdist.ContinuousDistribution
 
inverseF(double) - Method in class umontreal.iro.lecuyer.probdist.DiscreteDistribution
 
inverseF(double) - Method in class umontreal.iro.lecuyer.probdist.DiscreteDistributionInt
Returns the inverse distribution function F-1(u), where 0 <= u <= 1.
inverseF(double) - Method in interface umontreal.iro.lecuyer.probdist.Distribution
Computes and returns the inverse distribution function F-1(u), defined in.
inverseF(double) - Method in class umontreal.iro.lecuyer.probdist.EmpiricalDist
 
inverseF(int, double, int, double) - Static method in class umontreal.iro.lecuyer.probdist.ErlangDist
Returns the inverse distribution function.
inverseF(double) - Method in class umontreal.iro.lecuyer.probdist.ExponentialDist
 
inverseF(double, double) - Static method in class umontreal.iro.lecuyer.probdist.ExponentialDist
Computes the inverse distribution function.
inverseF(double) - Method in class umontreal.iro.lecuyer.probdist.ExtremeValueDist
 
inverseF(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.ExtremeValueDist
Computes the inverse distribution function.
inverseF(double) - Method in class umontreal.iro.lecuyer.probdist.FatigueLifeDist
 
inverseF(double, double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.FatigueLifeDist
Computes the inverse of the fatigue life distribution with parameters μ, β and γ.
inverseF(double) - Method in class umontreal.iro.lecuyer.probdist.FisherFDist
 
inverseF(int, int, int, double) - Static method in class umontreal.iro.lecuyer.probdist.FisherFDist
Computes the inverse of the Fisher F-distribution with parameters n and m, evaluated at x, with roughly d decimal digits of precision.
inverseF(double) - Method in class umontreal.iro.lecuyer.probdist.GammaDist
 
inverseF(double, double, int, double) - Static method in class umontreal.iro.lecuyer.probdist.GammaDist
Computes the inverse distribution function using the algorithm implemented in the Cephes Math Library.
inverseF(double, int, double) - Static method in class umontreal.iro.lecuyer.probdist.GammaDist
Same as inverseF (alpha, 1, d, u).
inverseF(double, double) - Static method in class umontreal.iro.lecuyer.probdist.GeometricDist
Computes the inverse of the geometric distribution.
inverseF(double) - Method in class umontreal.iro.lecuyer.probdist.HyperbolicSecantDist
 
inverseF(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.HyperbolicSecantDist
Computes the inverse of the hyperbolic secant distribution with parameters μ and σ.
inverseF(int, int, int, double) - Static method in class umontreal.iro.lecuyer.probdist.HypergeometricDist
Computes F-1(u) for the hypergeometric distribution without using precomputed tables.
inverseF(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.InverseGaussianDist
Computes the inverse of the inverse gaussian distribution with parameters μ and λ.
inverseF(double) - Method in class umontreal.iro.lecuyer.probdist.JohnsonSBDist
 
inverseF(double, double, double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.JohnsonSBDist
Computes the inverse of the distribution.
inverseF(double) - Method in class umontreal.iro.lecuyer.probdist.JohnsonSUDist
 
inverseF(double, double, double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.JohnsonSUDist
Computes the inverse distribution function F-1(u).
inverseF(double) - Method in class umontreal.iro.lecuyer.probdist.LaplaceDist
 
inverseF(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.LaplaceDist
Computes the inverse Laplace distribution function.
inverseF(double, double) - Static method in class umontreal.iro.lecuyer.probdist.LogarithmicDist
 
inverseF(double) - Method in class umontreal.iro.lecuyer.probdist.LogisticDist
 
inverseF(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.LogisticDist
Computes the inverse distribution function F-1(u).
inverseF(double) - Method in class umontreal.iro.lecuyer.probdist.LoglogisticDist
 
inverseF(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.LoglogisticDist
Computes the inverse of the log-logistic distribution with parameters α and β.
inverseF(double) - Method in class umontreal.iro.lecuyer.probdist.LognormalDist
 
inverseF(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.LognormalDist
Computes the inverse of the lognormal distribution function, using NormalDist.inverseF01.
inverseF(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.NegativeBinomialDist
Computes the inverse function without precomputing tables.
inverseF(double) - Method in class umontreal.iro.lecuyer.probdist.NormalDist
 
inverseF(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.NormalDist
Computes the inverse normal distribution function with mean μ and variance σ2.
inverseF(double) - Method in class umontreal.iro.lecuyer.probdist.NormalDistQuick
 
inverseF(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.NormalDistQuick
Returns an approximation of Φ-1(u), where Φ is the standard normal distribution function, with mean 0 and variance 1.
inverseF(double) - Method in class umontreal.iro.lecuyer.probdist.ParetoDist
 
inverseF(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.ParetoDist
Computes the inverse of the distribution function.
inverseF(double) - Method in class umontreal.iro.lecuyer.probdist.Pearson5Dist
 
inverseF(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.Pearson5Dist
Computes the inverse distribution function of a Pearson V distribution with shape parameter α and scale parameter β.
inverseF(double) - Method in class umontreal.iro.lecuyer.probdist.Pearson6Dist
 
inverseF(double, double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.Pearson6Dist
Computes the inverse distribution function of a Pearson VI distribution with shape parameters α1 and α2, and scale parameter β.
inverseF(double) - Method in class umontreal.iro.lecuyer.probdist.PiecewiseLinearEmpiricalDist
 
inverseF(double, double) - Static method in class umontreal.iro.lecuyer.probdist.PoissonDist
Performs a linear search to get the inverse function without precomputed tables.
inverseF(double) - Method in class umontreal.iro.lecuyer.probdist.StudentDist
 
inverseF(int, double) - Static method in class umontreal.iro.lecuyer.probdist.StudentDist
Returns an approximation of F-1(u), where F is the Student-t distribution function with n degrees of freedom.
inverseF(double) - Method in class umontreal.iro.lecuyer.probdist.TriangularDist
 
inverseF(double, double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.TriangularDist
Computes the inverse distribution function.
inverseF(double) - Method in class umontreal.iro.lecuyer.probdist.TruncatedDist
 
inverseF(double) - Method in class umontreal.iro.lecuyer.probdist.UniformDist
 
inverseF(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.UniformDist
Computes the inverse of the uniform distribution function.
inverseF(int, int, double) - Static method in class umontreal.iro.lecuyer.probdist.UniformIntDist
Computes the inverse of the discrete uniform distribution function.
inverseF(double) - Method in class umontreal.iro.lecuyer.probdist.WeibullDist
 
inverseF(double, double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.WeibullDist
Computes the inverse of the distribution function.
inverseF(double, double) - Static method in class umontreal.iro.lecuyer.probdist.WeibullDist
Same as inverseF (alpha, 1.0, 0.0, x).
inverseF01(double) - Static method in class umontreal.iro.lecuyer.probdist.NormalDist
Same as inverseF (0.0, 1.0, u).
inverseF01(double) - Static method in class umontreal.iro.lecuyer.probdist.NormalDistQuick
Same as inverseF (0.0, 1.0, u).
inverseFInt(double) - Method in class umontreal.iro.lecuyer.probdist.BinomialDist
 
inverseFInt(double) - Method in class umontreal.iro.lecuyer.probdist.DiscreteDistributionInt
Returns the inverse distribution function F-1(u), where 0 <= u <= 1.
inverseFInt(double) - Method in class umontreal.iro.lecuyer.probdist.GeometricDist
 
inverseFInt(double) - Method in class umontreal.iro.lecuyer.probdist.HypergeometricDist
 
inverseFInt(double) - Method in class umontreal.iro.lecuyer.probdist.LogarithmicDist
 
inverseFInt(double) - Method in class umontreal.iro.lecuyer.probdist.NegativeBinomialDist
 
inverseFInt(double) - Method in class umontreal.iro.lecuyer.probdist.PoissonDist
 
inverseFInt(double) - Method in class umontreal.iro.lecuyer.probdist.UniformIntDist
 
InverseGaussianDist - Class in umontreal.iro.lecuyer.probdist
Extends the class ContinuousDistribution for the inverse Gaussian distribution with location parameter μ > 0 and scale parameter λ > 0.
InverseGaussianDist(double, double) - Constructor for class umontreal.iro.lecuyer.probdist.InverseGaussianDist
Constructs the inverse Gaussian distribution with parameters μ and λ.
InverseGaussianGen - Class in umontreal.iro.lecuyer.randvar
This class implements random variate generators for the inverse Gaussian distribution with location parameter μ > 0 and scale parameter λ > 0.
InverseGaussianGen(RandomStream, InverseGaussianDist) - Constructor for class umontreal.iro.lecuyer.randvar.InverseGaussianGen
Creates a new generator for the distribution dist, using stream s.
invStudentDist(int, double) - Static method in class umontreal.iro.lecuyer.randvar.Rand1
Deprecated. Returns y = F-1(u), where F is the Student distribution function with n degrees of freedom.
IRAC2 - Static variable in class umontreal.iro.lecuyer.util.Num
The value of 1/(2)1/2.
isAlive() - Method in class umontreal.iro.lecuyer.simprocs.AbstractSimProcess
Returns true if the process is alive, otherwise false.
isCaching() - Method in class umontreal.iro.lecuyer.randvar.RandomVariateGenWithCache
Determines if the random variate generator is caching values, default being true.
isCaching() - Method in class umontreal.iro.lecuyer.rng.RandomStreamWithCache
Determines if the random stream is caching values, default being true.
isEmpty() - Method in class umontreal.iro.lecuyer.simevents.eventlist.BinaryTree
 
isEmpty() - Method in class umontreal.iro.lecuyer.simevents.eventlist.DoublyLinked
 
isEmpty() - Method in interface umontreal.iro.lecuyer.simevents.eventlist.EventList
Returns true if and only if the event list is empty (no event is scheduled).
isEmpty() - Method in class umontreal.iro.lecuyer.simevents.eventlist.Henriksen
 
isEmpty() - Method in class umontreal.iro.lecuyer.simevents.eventlist.RedblackTree
 
isEmpty() - Method in class umontreal.iro.lecuyer.simevents.eventlist.SplayTree
 
iterateSpacings(DoubleArrayList, DoubleArrayList) - Static method in class umontreal.iro.lecuyer.gof.GofStat
Applies one iteration of the iterated spacings transformation.
iterator() - Method in class umontreal.iro.lecuyer.hups.AntitheticPointSet
 
iterator() - Method in class umontreal.iro.lecuyer.hups.BakerTransformedPointSet
 
iterator() - Method in class umontreal.iro.lecuyer.hups.ContainerPointSet
 
iterator() - Method in class umontreal.iro.lecuyer.hups.CycleBasedPointSet
 
iterator() - Method in class umontreal.iro.lecuyer.hups.CycleBasedPointSetBase2
 
iterator() - Method in class umontreal.iro.lecuyer.hups.DigitalNet
 
iterator() - Method in class umontreal.iro.lecuyer.hups.DigitalNetBase2
 
iterator() - Method in class umontreal.iro.lecuyer.hups.PaddedPointSet
 
iterator() - Method in class umontreal.iro.lecuyer.hups.PointSet
Constructs and returns a point set iterator.
iterator() - Method in class umontreal.iro.lecuyer.hups.RandShiftedPointSet
 
iterator() - Method in class umontreal.iro.lecuyer.hups.Rank1Lattice
 
iterator() - Method in class umontreal.iro.lecuyer.hups.SubsetOfPointSet
 
iteratorNoGray() - Method in class umontreal.iro.lecuyer.hups.DigitalNet
This iterator does not use the Gray code.
iteratorNoGray() - Method in class umontreal.iro.lecuyer.hups.DigitalNetBase2
This iterator does not use the Gray code.
iteratorShift() - Method in class umontreal.iro.lecuyer.hups.DigitalSequence
Similar to iterator, except that the first coordinate of the points is i/n, the second coordinate is obtained via the generating matrix C0, the next one via C1, and so on.
iteratorShift() - Method in class umontreal.iro.lecuyer.hups.DigitalSequenceBase2
Similar to iterator, except that the first coordinate of the points is i/n, the second coordinate is obtained via the generating matrix C0, the next one via C1, and so on.
iteratorShiftNoGray() - Method in class umontreal.iro.lecuyer.hups.DigitalSequence
This iterator shifts all coordinates of each point one position to the right and sets the first coordinate of point i to i/n, so that the points enumerated with this iterator have one more dimension.
iteratorShiftNoGray() - Method in class umontreal.iro.lecuyer.hups.DigitalSequenceBase2
This iterator shifts all coordinates of each point one position to the right and sets the first coordinate of point i to i/n, so that the points enumerated with this iterator have one more dimension.
iterPowRatioTests(DoubleArrayList, int, boolean, boolean, PrintWriter) - Static method in class umontreal.iro.lecuyer.gof.GofFormat
Similar to iterSpacingsTests, but with the GofStat.powerRatios transformation.
iterSpacingsTests(DoubleArrayList, int, boolean, boolean, PrintWriter) - Static method in class umontreal.iro.lecuyer.gof.GofFormat
Repeats the following k times: Applies the GofStat.iterateSpacings transformation to the U(0),..., U(N-1), assuming that these observations are in sortedData, then computes the EDF test statistics and calls activeTests after each transformation.

J

JDBCManager - Class in umontreal.iro.lecuyer.util
This class provides some facilities to connect to a SQL database and to retrieve data stored in it.
JDBCManager() - Constructor for class umontreal.iro.lecuyer.util.JDBCManager
 
JohnsonSBDist - Class in umontreal.iro.lecuyer.probdist
Extends the class ContinuousDistribution for the Johnson SB distribution with shape parameters γ and δ > 0, location parameter ξ, and scale parameter λ > 0.
JohnsonSBDist(double, double, double, double) - Constructor for class umontreal.iro.lecuyer.probdist.JohnsonSBDist
Constructs a JohnsonSBDist object with shape parameters γ and δ, location parameter ξ and scale parameter λ.
JohnsonSUDist - Class in umontreal.iro.lecuyer.probdist
Extends the class ContinuousDistribution for the Johnson SU distribution.
JohnsonSUDist(double, double) - Constructor for class umontreal.iro.lecuyer.probdist.JohnsonSUDist
Same as JohnsonSUDist (gamma, delta, 0.0, 1.0).
JohnsonSUDist(double, double, double, double) - Constructor for class umontreal.iro.lecuyer.probdist.JohnsonSUDist
Constructs a JohnsonSUDist object with shape parameters γ and δ, location parameter ξ, and scale parameter λ.

K

KernelDensityGen - Class in umontreal.iro.lecuyer.randvar
This class implements random variate generators for distributions obtained via kernel density estimation methods from a set of n individual observations x1,..., xn.
KernelDensityGen(RandomStream, EmpiricalDist, RandomVariateGen, double) - Constructor for class umontreal.iro.lecuyer.randvar.KernelDensityGen
Creates a new generator for a kernel density estimated from the observations given by the empirical distribution dist, using stream s to select the observations, generator kGen to generate the added noise from the kernel density, and bandwidth h.
KernelDensityGen(RandomStream, EmpiricalDist, NormalGen) - Constructor for class umontreal.iro.lecuyer.randvar.KernelDensityGen
This constructor uses a gaussian kernel and the default bandwidth h = αkh0 with the αk suggested in Table  for the gaussian distribution.
KernelDensityVarCorrectGen - Class in umontreal.iro.lecuyer.randvar
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 Silverman.
KernelDensityVarCorrectGen(RandomStream, EmpiricalDist, RandomVariateGen, double, double) - Constructor for class umontreal.iro.lecuyer.randvar.KernelDensityVarCorrectGen
Creates a new generator for a kernel density estimated from the observations given by the empirical distribution dist, using stream s to select the observations, generator kGen to generate the added noise from the kernel density, bandwidth h, and σk2 = sigmak2 used for the variance correction.
KernelDensityVarCorrectGen(RandomStream, EmpiricalDist, NormalGen) - Constructor for class umontreal.iro.lecuyer.randvar.KernelDensityVarCorrectGen
This constructor uses a gaussian kernel and the default bandwidth suggested in Table  for the gaussian distribution.
kill() - Method in class umontreal.iro.lecuyer.simprocs.AbstractSimProcess
Terminates the life of this process and sets its state to DEAD, after canceling its activating event if there is one.
kill() - Method in class umontreal.iro.lecuyer.simprocs.dsol.SimProcess
 
kill() - Method in class umontreal.iro.lecuyer.simprocs.SimProcess
 
killAll() - Static method in class umontreal.iro.lecuyer.simprocs.SimProcess
Kills all instances of the class SimProcess.
kolmogorovSmirnov(int, double) - Static method in class umontreal.iro.lecuyer.gof.FBar
Returns 1.0 - FDist.kolmogorovSmirnov (n, x).
kolmogorovSmirnov(int, double) - Static method in class umontreal.iro.lecuyer.gof.FDist
Returns p(x) = P[DN <= x], where DN = max(DN+, DN-) is the two-sided Kolmogorov-Smirnov statistic for a sample of size N.
kolmogorovSmirnov(DoubleArrayList) - Static method in class umontreal.iro.lecuyer.gof.GofStat
Computes the Kolmogorov-Smirnov (KS) test statistics DN+, DN-, and DN.
kolmogorovSmirnovJumpOne(DoubleArrayList, double) - Static method in class umontreal.iro.lecuyer.gof.GofStat
Compute the KS statistics DN+(a) and DN-(a) defined in the description of the method FDist.kolmogorovSmirnovPlusJumpOne, assuming that F is the uniform distribution over [0, 1] and that U(1),..., U(N) are in sortedData.
kolmogorovSmirnovPlus(int, double) - Static method in class umontreal.iro.lecuyer.gof.FBar
Returns 1.0 - FDist.kolmogorovSmirnovPlus (n, x).
kolmogorovSmirnovPlus(int, double) - Static method in class umontreal.iro.lecuyer.gof.FDist
Returns p(x) = P[DN+ <= x], the distribution function of the positive Kolmogorov-Smirnov statistic.
kolmogorovSmirnovPlusJumpOne(int, double, double) - Static method in class umontreal.iro.lecuyer.gof.FDist
Similar to kolmogorovSmirnovPlus but for the case where the distribution function F has a jump of size a at a given point x0, is zero at the left of x0, and is continuous at the right of x0.
KorobovLattice - Class in umontreal.iro.lecuyer.hups
This class implements Korobov lattices, which are the same point sets as in class LCGPointSet, but implemented differently.
KorobovLattice(int, int, int) - Constructor for class umontreal.iro.lecuyer.hups.KorobovLattice
Instantiates a Korobov lattice point set with modulus n and multiplier a in dimension s.
KorobovLattice(int, int, int, int) - Constructor for class umontreal.iro.lecuyer.hups.KorobovLattice
Instantiates a shifted Korobov lattice point set with modulus n and multiplier a in dimension s.
KorobovLatticeSequence - Class in umontreal.iro.lecuyer.hups
This class implements Korobov lattice sequences, defined as follows.
KorobovLatticeSequence(int, int) - Constructor for class umontreal.iro.lecuyer.hups.KorobovLatticeSequence
Constructs a new lattice sequence with base b and generator = a.
KS - Static variable in class umontreal.iro.lecuyer.gof.GofFormat
Kolmogorov-Smirnov test
KSM - Static variable in class umontreal.iro.lecuyer.gof.GofFormat
Kolmogorov-Smirnov- test
KSP - Static variable in class umontreal.iro.lecuyer.gof.GofFormat
Kolmogorov-Smirnov+ test

L

LaplaceDist - Class in umontreal.iro.lecuyer.probdist
Extends the class ContinuousDistribution for the Laplace distribution.
LaplaceDist() - Constructor for class umontreal.iro.lecuyer.probdist.LaplaceDist
Constructs a LaplaceDist object with default parameters θ = 0 and φ = 1.
LaplaceDist(double, double) - Constructor for class umontreal.iro.lecuyer.probdist.LaplaceDist
Constructs a LaplaceDist object with parameters θ = theta and φ = phi.
LaplaceGen - Class in umontreal.iro.lecuyer.randvar
This class implements methods for generating random variates from the Laplace distribution.
LaplaceGen(RandomStream, LaplaceDist) - Constructor for class umontreal.iro.lecuyer.randvar.LaplaceGen
Creates a new generator for the Laplace distribution dist and stream s.
lastIndexOf(Object) - Method in class umontreal.iro.lecuyer.simevents.LinkedListStat
 
LCGPointSet - Class in umontreal.iro.lecuyer.hups
Implements a recurrence-based point set defined via a linear congruential recurrence of the form xi = axi-1mod n and ui = xi/n.
LCGPointSet(int, int) - Constructor for class umontreal.iro.lecuyer.hups.LCGPointSet
Constructs and stores the set of cycles for an LCG with modulus n and multiplier a.
LCGPointSet(int, int, int, int) - Constructor for class umontreal.iro.lecuyer.hups.LCGPointSet
Constructs and stores the set of cycles for an LCG with modulus n = be + c and multiplier a.
leftMatrixScramble(RandomStream) - Method in class umontreal.iro.lecuyer.hups.DigitalNet
Applies a linear scramble by multiplying each Cj on the left by a w×w nonsingular lower-triangular matrix Mj, as suggested by Matoušek and implemented by Hong and Hickernell.
leftMatrixScramble(RandomStream) - Method in class umontreal.iro.lecuyer.hups.DigitalNetBase2
 
leftMatrixScrambleDiag(RandomStream) - Method in class umontreal.iro.lecuyer.hups.DigitalNet
Similar to leftMatrixScramble except that all the off-diagonal elements of the Mj are 0.
leftMatrixScrambleDiag(RandomStream) - Method in class umontreal.iro.lecuyer.hups.DigitalNetBase2
 
leftMatrixScrambleFaurePermut(RandomStream, int) - Method in class umontreal.iro.lecuyer.hups.DigitalNet
Similar to leftMatrixScramble except that the diagonal elements of each matrix Mj are chosen from a restricted set of the best integers as calculated by Faure.
leftMatrixScrambleFaurePermut(RandomStream, int) - Method in class umontreal.iro.lecuyer.hups.DigitalNetBase2
 
leftMatrixScrambleFaurePermutAll(RandomStream, int) - Method in class umontreal.iro.lecuyer.hups.DigitalNet
Similar to leftMatrixScrambleFaurePermut except that the elements under the diagonal are also chosen from the same restricted set as the diagonal elements.
leftMatrixScrambleFaurePermutAll(RandomStream, int) - Method in class umontreal.iro.lecuyer.hups.DigitalNetBase2
 
leftMatrixScrambleFaurePermutDiag(RandomStream, int) - Method in class umontreal.iro.lecuyer.hups.DigitalNet
Similar to leftMatrixScrambleFaurePermut except that all off-diagonal elements are 0.
leftMatrixScrambleFaurePermutDiag(RandomStream, int) - Method in class umontreal.iro.lecuyer.hups.DigitalNetBase2
 
leftMatrixScrambleRest(RandomStream, int) - Method in class umontreal.iro.lecuyer.hups.DigitalNet
 
LFSR113 - Class in umontreal.iro.lecuyer.rng
Extends RandomStreamBase using a composite linear feedback shift register (LFSR) (or Tausworthe) RNG as defined in.
LFSR113() - Constructor for class umontreal.iro.lecuyer.rng.LFSR113
Constructs a new stream.
LFSR113(String) - Constructor for class umontreal.iro.lecuyer.rng.LFSR113
Constructs a new stream with the identifier name.
LFSR258 - Class in umontreal.iro.lecuyer.rng
Extends RandomStreamBase using a 64-bit composite linear feedback shift register (LFSR) (or Tausworthe) RNG as defined in.
LFSR258() - Constructor for class umontreal.iro.lecuyer.rng.LFSR258
Constructs a new stream.
LFSR258(String) - Constructor for class umontreal.iro.lecuyer.rng.LFSR258
Constructs a new stream with the identifier name.
LinkedListStat - Class in umontreal.iro.lecuyer.simevents
This class extends LinkedList, with statistical probes integrated in the class to provide automatic collection of statistics on the sojourn times of objects in the list and the size of the list as a function of time.
LinkedListStat() - Constructor for class umontreal.iro.lecuyer.simevents.LinkedListStat
Constructs a new list, initially empty.
LinkedListStat(Collection) - Constructor for class umontreal.iro.lecuyer.simevents.LinkedListStat
Constructs a list containing the elements of the specified collection.
LinkedListStat(String) - Constructor for class umontreal.iro.lecuyer.simevents.LinkedListStat
Constructs a new list with name name.
LinkedListStat(Collection, String) - Constructor for class umontreal.iro.lecuyer.simevents.LinkedListStat
Constructs a new list containing the elements of the specified collection c and with name name.
LinkedListStat.ListIterator2 - Interface in umontreal.iro.lecuyer.simevents
This interface is for internal use only.
listDir(String) - Static method in class umontreal.iro.lecuyer.hups.DigitalNetBase2FromFile
Lists all files (or directories) in directory dirname.
listDir(String) - Static method in class umontreal.iro.lecuyer.hups.DigitalNetFromFile
Lists all files (or directories) in directory dirname.
listDirHTML(String, String) - Static method in class umontreal.iro.lecuyer.hups.DigitalNetFromFile
Creates a list of all data files in directory dirname and writes that list in format HTML in output file filename.
listIterator() - Method in class umontreal.iro.lecuyer.simevents.eventlist.BinaryTree
 
listIterator() - Method in class umontreal.iro.lecuyer.simevents.eventlist.DoublyLinked
 
listIterator() - Method in interface umontreal.iro.lecuyer.simevents.eventlist.EventList
Returns a list iterator over the elements of the class Event in this list.
listIterator() - Method in class umontreal.iro.lecuyer.simevents.eventlist.Henriksen
 
listIterator() - Method in class umontreal.iro.lecuyer.simevents.eventlist.RedblackTree
 
listIterator() - Method in class umontreal.iro.lecuyer.simevents.eventlist.SplayTree
 
listIterator(int) - Method in class umontreal.iro.lecuyer.simevents.LinkedListStat
 
LN2 - Static variable in class umontreal.iro.lecuyer.util.Num
The values of ln 2.
LN_DBL_MIN - Static variable in class umontreal.iro.lecuyer.util.Num
Natural logarithm of DBL_MIN.
lnFactorial(int) - Static method in class umontreal.iro.lecuyer.util.Num
Returns the value of the natural logarithm of factorial n.
lnGamma(double) - Static method in class umontreal.iro.lecuyer.util.Num
Returns the natural logarithm of the gamma function Γ(x) evaluated at x.
loc - Variable in class umontreal.iro.lecuyer.gof.GofStat.OutcomeCategoriesChi2
loc[i] gives the relocation of the category i in the nbExp array.
log1p(double) - Static method in class umontreal.iro.lecuyer.util.Num
Deprecated: Use Math.log1p instead.
log2(double) - Static method in class umontreal.iro.lecuyer.util.Num
Returns log2(x).
LogarithmicDist - Class in umontreal.iro.lecuyer.probdist
Extends the class DiscreteDistributionInt for the logarithmic distribution.
LogarithmicDist(double) - Constructor for class umontreal.iro.lecuyer.probdist.LogarithmicDist
Constructs a logarithmic distribution with parameter θ = theta.
LogarithmicGen - Class in umontreal.iro.lecuyer.randvar
This class implements random variate generators for the (discrete) logarithmic distribution.
LogarithmicGen(RandomStream, LogarithmicDist) - Constructor for class umontreal.iro.lecuyer.randvar.LogarithmicGen
Creates a new generator with distribution dist and stream s, with default value θ0 = 0.96.
LogarithmicGen(RandomStream, LogarithmicDist, double) - Constructor for class umontreal.iro.lecuyer.randvar.LogarithmicGen
Creates a new generator with distribution dist and stream s, with θ0 = theta0.
Logistic(RandomStream, double, double) - Static method in class umontreal.iro.lecuyer.randvar.Rand1
Deprecated. Returns a Logistic random variate with parameters alpha and standard deviation lambda, using stream s.
LogisticDist - Class in umontreal.iro.lecuyer.probdist
Extends the class ContinuousDistribution for the logistic distribution.
LogisticDist() - Constructor for class umontreal.iro.lecuyer.probdist.LogisticDist
Constructs a LogisticDist object with default parameters α = 0 and λ = 1.
LogisticDist(double, double) - Constructor for class umontreal.iro.lecuyer.probdist.LogisticDist
Constructs a LogisticDist object with parameters α = alpha and λ = lambda.
LogisticGen - Class in umontreal.iro.lecuyer.randvar
This class implements random variate generators for the logistic distribution.
LogisticGen(RandomStream, LogisticDist) - Constructor for class umontreal.iro.lecuyer.randvar.LogisticGen
Creates a new generator for the logistic distribution dist and stream s.
LoglogisticDist - Class in umontreal.iro.lecuyer.probdist
Extends the class ContinuousDistribution for the Log-Logistic distribution with shape parameter α > 0 and scale parameter β > 0.
LoglogisticDist(double, double) - Constructor for class umontreal.iro.lecuyer.probdist.LoglogisticDist
Constructs a log-logistic distribution with parameters α and β.
LoglogisticGen - Class in umontreal.iro.lecuyer.randvar
This class implements random variate generators for the Log-Logistic distribution with shape parameter α > 0 and scale parameter β > 0.
LoglogisticGen(RandomStream, LoglogisticDist) - Constructor for class umontreal.iro.lecuyer.randvar.LoglogisticGen
Creates a new generator for the distribution dist, using stream s.
lognormal(RandomStream, double, double) - Static method in class umontreal.iro.lecuyer.randvar.Rand1
Deprecated. Returns a random variate having the lognormal distribution.
LognormalDist - Class in umontreal.iro.lecuyer.probdist
Extends the class ContinuousDistribution for the lognormal distribution.
LognormalDist() - Constructor for class umontreal.iro.lecuyer.probdist.LognormalDist
Constructs a LognormalDist object with default parameters μ = 0 and σ = 1.
LognormalDist(double, double) - Constructor for class umontreal.iro.lecuyer.probdist.LognormalDist
Constructs a LognormalDist object with parameters μ = mu and σ = sigma.
LognormalGen - Class in umontreal.iro.lecuyer.randvar
This class implements methods for generating random variates from the lognormal distribution.
LognormalGen(RandomStream, LognormalDist) - Constructor for class umontreal.iro.lecuyer.randvar.LognormalGen
Create a random variate generator for the lognormal distribution dist and stream s.
LognormalSpecialGen - Class in umontreal.iro.lecuyer.randvar
Implements methods for generating random variates from the lognormal distribution using an arbitrary normal random variate generator.
LognormalSpecialGen(NormalGen) - Constructor for class umontreal.iro.lecuyer.randvar.LognormalSpecialGen
Create a lognormal random variate generator using the normal generator g and with the same parameters.

M

main(String[]) - Static method in class umontreal.iro.lecuyer.rng.GenF2w32
This method is only meant to be used during the compilation process.
main(String[]) - Static method in class umontreal.iro.lecuyer.rng.WELL1024
This method is only meant to be used during the compilation process.
main(String[]) - Static method in class umontreal.iro.lecuyer.rng.WELL512
This method is only meant to be used during the compilation process.
main(String[]) - Static method in class umontreal.iro.lecuyer.rng.WELL607
This method is only meant to be used during the compilation process.
MATHEMATICA - Static variable in class umontreal.iro.lecuyer.gof.GofFormat
Data file format used for creating graphics with Mathematica.
MATHEMATICA - Static variable in class umontreal.iro.lecuyer.util.TableFormat
Mathematica matrix printing style
MathFunction - Interface in umontreal.iro.lecuyer.util
This interface should be implemented by classes which represent univariate mathematical functions.
MATLAB - Static variable in class umontreal.iro.lecuyer.util.TableFormat
Matlab matrix printing style
matMatModM(double[][], double[][], double[][], double) - Static method in class umontreal.iro.lecuyer.util.ArithmeticMod
Computes A×B mod m and puts the result in C.
matMatModM(int[][], int[][], int[][], int) - Static method in class umontreal.iro.lecuyer.util.ArithmeticMod
Exactly like matMatModM using double, but with int instead of double.
matMatModM(long[][], long[][], long[][], long) - Static method in class umontreal.iro.lecuyer.util.ArithmeticMod
Exactly like matMatModM using double, but with long instead of double.
matPowModM(double[][], double[][], double, int) - Static method in class umontreal.iro.lecuyer.util.ArithmeticMod
Computes Ac mod m and puts the result in B.
matPowModM(int[][], int[][], int, int) - Static method in class umontreal.iro.lecuyer.util.ArithmeticMod
Exactly like matPowModM using double, but with int instead of double.
matPowModM(long[][], long[][], long, int) - Static method in class umontreal.iro.lecuyer.util.ArithmeticMod
Exactly like matPowModM using double, but with long instead of double.
matTwoPowModM(double[][], double[][], double, int) - Static method in class umontreal.iro.lecuyer.util.ArithmeticMod
Computes A2e mod m and puts the result in B.
matTwoPowModM(int[][], int[][], int, int) - Static method in class umontreal.iro.lecuyer.util.ArithmeticMod
Exactly like matTwoPowModM using double, but with int instead of double.
matTwoPowModM(long[][], long[][], long, int) - Static method in class umontreal.iro.lecuyer.util.ArithmeticMod
Exactly like matTwoPowModM using double, but with long instead of double.
matVecModM(double[][], double[], double[], double) - Static method in class umontreal.iro.lecuyer.util.ArithmeticMod
Computes the result of A×s mod m and puts the result in v.
matVecModM(int[][], int[], int[], int) - Static method in class umontreal.iro.lecuyer.util.ArithmeticMod
Exactly like matVecModM using double, but with int instead of double.
matVecModM(long[][], long[], long[], long) - Static method in class umontreal.iro.lecuyer.util.ArithmeticMod
Exactly like matVecModM using double, but with long instead of double.
max() - Method in class umontreal.iro.lecuyer.stat.StatProbe
Returns the largest value taken by the variable since the last initialization of this probe.
MAXINTDOUBLE - Static variable in class umontreal.iro.lecuyer.util.Num
Largest integer n0 = 253 such that any integer n <= n0 is represented exactly as a double.
MAXLAMBDA - Static variable in class umontreal.iro.lecuyer.probdist.PoissonDist
 
MAXN - Static variable in class umontreal.iro.lecuyer.probdist.BinomialDist
 
MAXN - Static variable in class umontreal.iro.lecuyer.probdist.HypergeometricDist
 
MAXN - Static variable in class umontreal.iro.lecuyer.probdist.NegativeBinomialDist
 
MAXTWOEXP - Static variable in class umontreal.iro.lecuyer.util.Num
Powers of 2 up to MAXTWOEXP are stored exactly in the array TWOEXP.
MEAN - Static variable in class umontreal.iro.lecuyer.gof.GofFormat
Mean
min() - Method in class umontreal.iro.lecuyer.stat.StatProbe
Returns the smallest value taken by the variable since the last initialization of this probe.
Misc - Class in umontreal.iro.lecuyer.util
This class provides miscellaneous functions that are hard to classify.
MRG31k3p - Class in umontreal.iro.lecuyer.rng
Extends the abstract class RandomStreamBase, thus implementing the RandomStream interface indirectly.
MRG31k3p() - Constructor for class umontreal.iro.lecuyer.rng.MRG31k3p
Constructs a new stream, initialized at its beginning.
MRG31k3p(String) - Constructor for class umontreal.iro.lecuyer.rng.MRG31k3p
Constructs a new stream with the identifier name (used when formatting the stream state).
MRG32k3a - Class in umontreal.iro.lecuyer.rng
Extends the abstract class RandomStreamBase by using as a backbone (or main) generator the combined multiple recursive generator (CMRG) MRG32k3a proposed by L'Ecuyer, implemented in 64-bit floating-point arithmetic.
MRG32k3a() - Constructor for class umontreal.iro.lecuyer.rng.MRG32k3a
Constructs a new stream, initializes its seed Ig, sets Bg and Cg equal to Ig, and sets its antithetic switch to false.
MRG32k3a(String) - Constructor for class umontreal.iro.lecuyer.rng.MRG32k3a
Constructs a new stream with an identifier name (used when printing the stream state).
MT19937 - Class in umontreal.iro.lecuyer.rng
Implements the RandomStream interface via inheritance from RandomStreamBase.
MT19937(RandomStream) - Constructor for class umontreal.iro.lecuyer.rng.MT19937
Constructs a new stream, using rng to fill its initial state.
MT19937(RandomStream, String) - Constructor for class umontreal.iro.lecuyer.rng.MT19937
Constructs a new stream with the identifier name (used in the toString method).
MultinomialDist - Class in umontreal.iro.lecuyer.probdistmulti
Implements the abstract class DiscreteDistributionIntMulti for the multinomial distribution with parameters n and (p1, ...,pd).
MultinomialDist(int, double[]) - Constructor for class umontreal.iro.lecuyer.probdistmulti.MultinomialDist
Creates a MultinomialDist object with parameters n and (p1,...,pd) such that i=1dpi = 1.
MultiNormalDist - Class in umontreal.iro.lecuyer.probdistmulti
Implements the abstract class ContinuousDistributionMulti for the multinormal distribution with mean vector μ and covariance matrix Σ.
MultiNormalDist(double[], double[][]) - Constructor for class umontreal.iro.lecuyer.probdistmulti.MultiNormalDist
 
MultiNormalGen - Class in umontreal.iro.lecuyer.randvarmulti
Extends RandomMultiVariateGen for a multivariate normal distribution.
MultiNormalGen(NormalGen, int) - Constructor for class umontreal.iro.lecuyer.randvarmulti.MultiNormalGen
Constructs a standard d-dimensional multinormal generator, using the one-dimensional generator gen1.
MultiNormalGen(NormalGen, double[], DoubleMatrix2D) - Constructor for class umontreal.iro.lecuyer.randvarmulti.MultiNormalGen
Constructs a multinormal generator with mean vector mu and covariance matrix sigma, using the one-dimensional normal generator gen1.
MultiNormalGen(NormalGen, double[], double[][]) - Constructor for class umontreal.iro.lecuyer.randvarmulti.MultiNormalGen
Equivalent to MultiNormalGen (gen1, mu, new DenseDoubleMatrix2D (sigma)).
multiply(BitVector) - Method in class umontreal.iro.lecuyer.util.BitMatrix
Multiplies the column BitVector by a BitMatrix and returns the result.
multiply(int) - Method in class umontreal.iro.lecuyer.util.BitMatrix
Multiplies vect, seen as a column BitVector, by a BitMatrix.
multiply(BitMatrix) - Method in class umontreal.iro.lecuyer.util.BitMatrix
Multiplies two BitMatrix's together.
multMod(int, int, int, int) - Static method in class umontreal.iro.lecuyer.util.Num
Returns (as + c)mod m.
multMod(long, long, long, long) - Static method in class umontreal.iro.lecuyer.util.Num
Returns (as + c)mod m.
multMod(double, double, double, double) - Static method in class umontreal.iro.lecuyer.util.Num
Returns (as + c)mod m.
multModM(double, double, double, double) - Static method in class umontreal.iro.lecuyer.util.ArithmeticMod
Computes (a×s + c) mod m.
multModM(int, int, int, int) - Static method in class umontreal.iro.lecuyer.util.ArithmeticMod
Computes (a×s + c) mod m.
multModM(long, long, long, long) - Static method in class umontreal.iro.lecuyer.util.ArithmeticMod
Computes (a×s + c) mod m.

N

nbCategories - Variable in class umontreal.iro.lecuyer.gof.GofStat.OutcomeCategoriesChi2
Total number of categories.
nbExp - Variable in class umontreal.iro.lecuyer.gof.GofStat.OutcomeCategoriesChi2
Expected number of observations for each category.
negativeBinomial(RandomStream, int, double) - Static method in class umontreal.iro.lecuyer.randvar.Rand1
Deprecated. Returns a random variate having the binomial distribution with parameters n and p, using stream s.
NegativeBinomialDist - Class in umontreal.iro.lecuyer.probdist
Extends the class DiscreteDistributionInt for the negative binomial distribution with real parameters γ and p, where γ > 0 and 0 <= p <= 1.
NegativeBinomialDist(double, double) - Constructor for class umontreal.iro.lecuyer.probdist.NegativeBinomialDist
Creates an object that contains the probability terms and the distribution function for the negative binomial distribution with parameters γ and p.
NegativeBinomialGen - Class in umontreal.iro.lecuyer.randvar
This class implements random variate generators having the negative binomial distribution.
NegativeBinomialGen(RandomStream, NegativeBinomialDist) - Constructor for class umontreal.iro.lecuyer.randvar.NegativeBinomialGen
Creates a new generator for the distribution dist, using stream s.
NegativeMultinomialDist - Class in umontreal.iro.lecuyer.probdistmulti
Implements the abstract class DiscreteDistributionIntMulti for the negative multinomial distribution with parameters γ > 0 and (p1,...,pd).
NegativeMultinomialDist(double, double[]) - Constructor for class umontreal.iro.lecuyer.probdistmulti.NegativeMultinomialDist
Creates a NegativeMultinomialDist object with parameters γ = gamma and (p1,...,pd) such that i=1dpi < 1, as described above.
newInstance() - Method in class umontreal.iro.lecuyer.rng.BasicRandomStreamFactory
 
newInstance() - Method in interface umontreal.iro.lecuyer.rng.RandomStreamFactory
Constructs and returns a new random stream.
nextArrayOfDouble(double[], int, int) - Method in class umontreal.iro.lecuyer.randvar.RandomVariateGen
Generates n random numbers from the continuous distribution contained in this object.
nextArrayOfDouble(double[], int, int) - Method in class umontreal.iro.lecuyer.randvar.RandomVariateGenWithCache
 
nextArrayOfDouble(double[], int, int) - Method in class umontreal.iro.lecuyer.randvar.UnuranContinuous
 
nextArrayOfDouble(double[], int, int) - Method in class umontreal.iro.lecuyer.randvar.UnuranEmpirical
 
nextArrayOfDouble(double[], int, int) - Method in class umontreal.iro.lecuyer.rng.AntitheticStream
Calls nextArrayOfDouble (u, start, n) for the base stream, then replaces each u[i] by 1.0 - u[i].
nextArrayOfDouble(double[], int, int) - Method in class umontreal.iro.lecuyer.rng.BakerTransformedStream
Calls nextArrayOfDouble (u, start, n) for the base stream, then applies the baker transformation.
nextArrayOfDouble(double[], int, int) - Method in class umontreal.iro.lecuyer.rng.RandMrg
 
nextArrayOfDouble(double[], int, int) - Method in interface umontreal.iro.lecuyer.rng.RandomStream
Generates n (pseudo)random numbers from the uniform distribution and stores them into the array u starting at index start.
nextArrayOfDouble(double[], int, int) - Method in class umontreal.iro.lecuyer.rng.RandomStreamBase
Calls nextDouble n times to fill the array u.
nextArrayOfDouble(double[], int, int) - Method in class umontreal.iro.lecuyer.rng.RandomStreamWithCache
 
nextArrayOfDouble(double[], int, int) - Method in class umontreal.iro.lecuyer.rng.TruncatedRandomStream
 
nextArrayOfInt(int[], int, int) - Method in class umontreal.iro.lecuyer.randvar.RandomVariateGenInt
Generates n random numbers from the discrete distribution contained in this object.
nextArrayOfInt(int[], int, int) - Method in class umontreal.iro.lecuyer.randvar.UnuranDiscreteInt
 
nextArrayOfInt(int, int, int[], int, int) - Method in class umontreal.iro.lecuyer.rng.AntitheticStream
Calls nextArrayOfInt (i, j, u, start, n) for the base stream, then replaces each u[i] by j - i - u[i].
nextArrayOfInt(int, int, int[], int, int) - Method in class umontreal.iro.lecuyer.rng.BakerTransformedStream
Fills up the array by calling nextInt (i, j).
nextArrayOfInt(int, int, int[], int, int) - Method in class umontreal.iro.lecuyer.rng.RandMrg
 
nextArrayOfInt(int, int, int[], int, int) - Method in interface umontreal.iro.lecuyer.rng.RandomStream
Generates n (pseudo)random numbers from the discrete uniform distribution over the integers {i, i + 1,..., j}, using this stream and stores the result in the array u starting at index start.
nextArrayOfInt(int, int, int[], int, int) - Method in class umontreal.iro.lecuyer.rng.RandomStreamBase
Calls nextInt n times to fill the array u.
nextArrayOfInt(int, int, int[], int, int) - Method in class umontreal.iro.lecuyer.rng.RandomStreamWithCache
 
nextArrayOfInt(int, int, int[], int, int) - Method in class umontreal.iro.lecuyer.rng.TruncatedRandomStream
 
nextArrayOfPoints(double[][], int, int) - Method in class umontreal.iro.lecuyer.randvarmulti.RandomMultiVariateGen
Generates n random points.
nextCoordinate() - Method in class umontreal.iro.lecuyer.hups.CycleBasedPointSet.CycleBasedPointSetIterator
 
nextCoordinate() - Method in class umontreal.iro.lecuyer.hups.CycleBasedPointSetBase2.CycleBasedPointSetBase2Iterator
 
nextCoordinate() - Method in interface umontreal.iro.lecuyer.hups.PointSetIterator
Returns the current coordinate ui, j and advances to the next one.
nextCoordinates(double[], int) - Method in class umontreal.iro.lecuyer.hups.CycleBasedPointSet.CycleBasedPointSetIterator
 
nextCoordinates(double[], int) - Method in class umontreal.iro.lecuyer.hups.CycleBasedPointSetBase2.CycleBasedPointSetBase2Iterator
 
nextCoordinates(double[], int) - Method in interface umontreal.iro.lecuyer.hups.PointSetIterator
Returns the next d coordinates of the current point in p and advances the current coordinate index by d.
nextDouble() - Method in class umontreal.iro.lecuyer.hups.CycleBasedPointSet.CycleBasedPointSetIterator
 
nextDouble() - Method in class umontreal.iro.lecuyer.randvar.BetaGen
 
nextDouble(RandomStream, double, double, double, double) - Static method in class umontreal.iro.lecuyer.randvar.BetaGen
Generates a variate from the beta distribution with parameters α = alpha, β = beta, over the interval (a, b), using stream s.
nextDouble() - Method in class umontreal.iro.lecuyer.randvar.BetaRejectionLoglogisticGen
 
nextDouble() - Method in class umontreal.iro.lecuyer.randvar.BetaStratifiedRejectionGen
 
nextDouble(RandomStream, double, double, double, double) - Static method in class umontreal.iro.lecuyer.randvar.BetaStratifiedRejectionGen
 
nextDouble(RandomStream, RandomStream, RandomStream, double) - Static method in class umontreal.iro.lecuyer.randvar.BetaSymmetricalBestGen
Generates a random number using Devroye's one-liner method.
nextDouble(RandomStream, double) - Static method in class umontreal.iro.lecuyer.randvar.BetaSymmetricalBestGen
Generates a random number using Devroye's one-liner method with only one stream s.
nextDouble() - Method in class umontreal.iro.lecuyer.randvar.BetaSymmetricalBestGen
 
nextDouble() - Method in class umontreal.iro.lecuyer.randvar.BetaSymmetricalGen
 
nextDouble(RandomStream, double) - Static method in class umontreal.iro.lecuyer.randvar.BetaSymmetricalGen
 
nextDouble(RandomStream, RandomStream, double) - Static method in class umontreal.iro.lecuyer.randvar.BetaSymmetricalPolarGen
Generates a random number using Ulrich's polar method.
nextDouble(RandomStream, double) - Static method in class umontreal.iro.lecuyer.randvar.BetaSymmetricalPolarGen
Generates a random number using Ulrich's polar method with only one stream s.
nextDouble() - Method in class umontreal.iro.lecuyer.randvar.BetaSymmetricalPolarGen
 
nextDouble() - Method in class umontreal.iro.lecuyer.randvar.CauchyGen
 
nextDouble(RandomStream, double, double) - Static method in class umontreal.iro.lecuyer.randvar.CauchyGen
Generates a new variate from the Cauchy distribution with parameters α = alpha and β = beta, using stream s.
nextDouble() - Method in class umontreal.iro.lecuyer.randvar.ChiGen
 
nextDouble(RandomStream, int) - Static method in class umontreal.iro.lecuyer.randvar.ChiGen
Generates a random variate from the chi distribution with ν = nu degrees of freedom, using stream s.
nextDouble() - Method in class umontreal.iro.lecuyer.randvar.ChiRatioOfUniformsGen
 
nextDouble(RandomStream, int) - Static method in class umontreal.iro.lecuyer.randvar.ChiRatioOfUniformsGen
 
nextDouble() - Method in class umontreal.iro.lecuyer.randvar.ChiSquareGen
 
nextDouble(RandomStream, int) - Static method in class umontreal.iro.lecuyer.randvar.ChiSquareGen
Generates a new variate from the chi square distribution with n degrees of freedom, using stream s.
nextDouble() - Method in class umontreal.iro.lecuyer.randvar.ErlangConvolutionGen
 
nextDouble(RandomStream, int, double) - Static method in class umontreal.iro.lecuyer.randvar.ErlangConvolutionGen
 
nextDouble(RandomStream, int, double) - Static method in class umontreal.iro.lecuyer.randvar.ErlangGen
Generates a new variate from the Erlang distribution with parameters k = k and λ = lambda, using stream s.
nextDouble() - Method in class umontreal.iro.lecuyer.randvar.ExponentialGen
 
nextDouble(RandomStream, double) - Static method in class umontreal.iro.lecuyer.randvar.ExponentialGen
Uses inversion to generate a new exponential variate with parameter λ = lambda, using stream s.
nextDouble() - Method in class umontreal.iro.lecuyer.randvar.ExtremeValueGen
 
nextDouble(RandomStream, double, double) - Static method in class umontreal.iro.lecuyer.randvar.ExtremeValueGen
Uses inversion to generate a new variate from the extreme value distribution with parameters α = alpha and λ = lambda, using stream s.
nextDouble() - Method in class umontreal.iro.lecuyer.randvar.FatigueLifeGen
 
nextDouble(RandomStream, double, double, double) - Static method in class umontreal.iro.lecuyer.randvar.FatigueLifeGen
Generates a variate from the Fatigue Life distribution with location parameter μ, scale parameter β and shape parameter γ.
nextDouble() - Method in class umontreal.iro.lecuyer.randvar.FisherFGen
 
nextDouble(RandomStream, int, int) - Static method in class umontreal.iro.lecuyer.randvar.FisherFGen
Generates a variate from the Fisher F-distribution with n and m degrees of freedom, using stream s.
nextDouble(RandomStream, RandomStream, double, double) - Static method in class umontreal.iro.lecuyer.randvar.GammaAcceptanceRejectionGen
Generates a new gamma variate with parameters α = alpha and λ = lambda, using main stream s and auxiliary stream aux.
nextDouble() - Method in class umontreal.iro.lecuyer.randvar.GammaAcceptanceRejectionGen
 
nextDouble(RandomStream, double, double) - Static method in class umontreal.iro.lecuyer.randvar.GammaAcceptanceRejectionGen
Same as nextDouble (s, s, alpha, lambda).
nextDouble() - Method in class umontreal.iro.lecuyer.randvar.GammaGen
 
nextDouble(RandomStream, double, double) - Static method in class umontreal.iro.lecuyer.randvar.GammaGen
Generates a new gamma random variate with parameters α = alpha and λ = lambda, using stream s.
nextDouble() - Method in class umontreal.iro.lecuyer.randvar.GammaRejectionLoglogisticGen
 
nextDouble(RandomStream, RandomStream, double, double) - Static method in class umontreal.iro.lecuyer.randvar.GammaRejectionLoglogisticGen
Generates a new gamma variate with parameters α = alpha and λ = lambda, using main stream s and auxiliary stream aux.
nextDouble(RandomStream, double, double) - Static method in class umontreal.iro.lecuyer.randvar.GammaRejectionLoglogisticGen
Same as nextDouble (s, s, alpha, lambda).
nextDouble() - Method in class umontreal.iro.lecuyer.randvar.HyperbolicSecantGen
 
nextDouble(RandomStream, double, double) - Static method in class umontreal.iro.lecuyer.randvar.HyperbolicSecantGen
Generates a variate from the Hyperbolic Secant distribution with location parameter μ and scale parameter σ.
nextDouble() - Method in class umontreal.iro.lecuyer.randvar.InverseGaussianGen
 
nextDouble(RandomStream, double, double) - Static method in class umontreal.iro.lecuyer.randvar.InverseGaussianGen
Generates a variate from the inverse gaussian distribution with location parameter μ > 0 and scale parameter λ > 0.
nextDouble() - Method in class umontreal.iro.lecuyer.randvar.KernelDensityGen
 
nextDouble() - Method in class umontreal.iro.lecuyer.randvar.KernelDensityVarCorrectGen
 
nextDouble() - Method in class umontreal.iro.lecuyer.randvar.LaplaceGen
 
nextDouble(RandomStream, double, double) - Static method in class umontreal.iro.lecuyer.randvar.LaplaceGen
Generates a new variate from the Laplace distribution with parameters θ = theta and φ = phi, using stream s.
nextDouble() - Method in class umontreal.iro.lecuyer.randvar.LogisticGen
 
nextDouble(RandomStream, double, double) - Static method in class umontreal.iro.lecuyer.randvar.LogisticGen
Generates a new variate from the logistic distribution with parameters α = alpha and λ = lambda, using stream s.
nextDouble() - Method in class umontreal.iro.lecuyer.randvar.LoglogisticGen
 
nextDouble(RandomStream, double, double) - Static method in class umontreal.iro.lecuyer.randvar.LoglogisticGen
Generates a variate from the Log-Logistic distribution with shape parameter α > 0 and scale parameter β > 0.
nextDouble() - Method in class umontreal.iro.lecuyer.randvar.LognormalGen
 
nextDouble(RandomStream, double, double) - Static method in class umontreal.iro.lecuyer.randvar.LognormalGen
Generates a new variate from the lognormal distribution with parameters μ = mu and σ = sigma, using stream s.
nextDouble() - Method in class umontreal.iro.lecuyer.randvar.LognormalSpecialGen
 
nextDouble() - Method in class umontreal.iro.lecuyer.randvar.NormalACRGen
 
nextDouble(RandomStream, double, double) - Static method in class umontreal.iro.lecuyer.randvar.NormalACRGen
 
nextDouble() - Method in class umontreal.iro.lecuyer.randvar.NormalBoxMullerGen
 
nextDouble(RandomStream, double, double) - Static method in class umontreal.iro.lecuyer.randvar.NormalBoxMullerGen
 
nextDouble() - Method in class umontreal.iro.lecuyer.randvar.NormalGen
 
nextDouble(RandomStream, double, double) - Static method in class umontreal.iro.lecuyer.randvar.NormalGen
Generates a variate from the normal distribution with parameters μ = mu and σ = sigma, using stream s.
nextDouble() - Method in class umontreal.iro.lecuyer.randvar.NormalKindermannRamageGen
 
nextDouble(RandomStream, double, double) - Static method in class umontreal.iro.lecuyer.randvar.NormalKindermannRamageGen
 
nextDouble() - Method in class umontreal.iro.lecuyer.randvar.NormalPolarGen
 
nextDouble(RandomStream, double, double) - Static method in class umontreal.iro.lecuyer.randvar.NormalPolarGen
 
nextDouble() - Method in class umontreal.iro.lecuyer.randvar.ParetoGen
 
nextDouble(RandomStream, double, double) - Static method in class umontreal.iro.lecuyer.randvar.ParetoGen
Generates a new variate from the Pareto distribution with parameters α = alpha and β = beta, using stream s.
nextDouble() - Method in class umontreal.iro.lecuyer.randvar.Pearson5Gen
 
nextDouble(RandomStream, double, double) - Static method in class umontreal.iro.lecuyer.randvar.Pearson5Gen
Generates a variate from the Pearson V distribution with shape parameter α > 0 and scale parameter β > 0.
nextDouble() - Method in class umontreal.iro.lecuyer.randvar.Pearson6Gen
 
nextDouble(RandomStream, double, double, double) - Static method in class umontreal.iro.lecuyer.randvar.Pearson6Gen
Generates a variate from the Pearson VI distribution with shape parameters α1 > 0 and α2 > 0, and scale parameter β > 0.
nextDouble() - Method in class umontreal.iro.lecuyer.randvar.RandomVariateGen
Generates a random number from the continuous distribution contained in this object.
nextDouble() - Method in class umontreal.iro.lecuyer.randvar.RandomVariateGenWithCache
 
nextDouble() - Method in class umontreal.iro.lecuyer.randvar.StudentGen
 
nextDouble(RandomStream, int) - Static method in class umontreal.iro.lecuyer.randvar.StudentGen
Generates a new variate from the Student distribution with n = n degrees of freedom, using stream s.
nextDouble() - Method in class umontreal.iro.lecuyer.randvar.StudentPolarGen
 
nextDouble(RandomStream, int) - Static method in class umontreal.iro.lecuyer.randvar.StudentPolarGen
 
nextDouble() - Method in class umontreal.iro.lecuyer.randvar.TriangularGen
 
nextDouble(RandomStream, double, double, double) - Static method in class umontreal.iro.lecuyer.randvar.TriangularGen
Generates a new variate from the triangular distribution with parameters a = a, b = b and m = m and stream s, using inversion.
nextDouble() - Method in class umontreal.iro.lecuyer.randvar.UniformGen
 
nextDouble(RandomStream, double, double) - Static method in class umontreal.iro.lecuyer.randvar.UniformGen
Generates a new uniform random variate over the interval (a, b) by inversion, using stream s.
nextDouble() - Method in class umontreal.iro.lecuyer.randvar.UnuranContinuous
 
nextDouble() - Method in class umontreal.iro.lecuyer.randvar.UnuranEmpirical
 
nextDouble() - Method in class umontreal.iro.lecuyer.randvar.WeibullGen
 
nextDouble(RandomStream, double, double, double) - Static method in class umontreal.iro.lecuyer.randvar.WeibullGen
Uses inversion to generate a new variate from the Weibull distribution with parameters α = alpha, λ = lambda, and δ = delta, using stream s.
nextDouble() - Method in class umontreal.iro.lecuyer.rng.AntitheticStream
Returns 1.0 - s.nextDouble() where s is the base stream.
nextDouble() - Method in class umontreal.iro.lecuyer.rng.BakerTransformedStream
Returns the baker transformation of s.nextDouble() where s is the base stream.
nextDouble() - Method in class umontreal.iro.lecuyer.rng.RandMrg
Returns a (pseudo)random number from the uniform distribution over the interval (0, 1), using this stream, after advancing its state by one step.
nextDouble() - Method in interface umontreal.iro.lecuyer.rng.RandomStream
Returns a (pseudo)random number from the uniform distribution over the interval [0, 1), using this stream, after advancing its state by one step.
nextDouble() - Method in class umontreal.iro.lecuyer.rng.RandomStreamBase
Returns a random number between 0 and 1 from the stream.
nextDouble() - Method in class umontreal.iro.lecuyer.rng.RandomStreamWithCache
 
nextDouble() - Method in class umontreal.iro.lecuyer.rng.TruncatedRandomStream
 
nextInt() - Method in class umontreal.iro.lecuyer.randvar.BinomialConvolutionGen
 
nextInt(RandomStream, int, double) - Static method in class umontreal.iro.lecuyer.randvar.BinomialConvolutionGen
 
nextInt() - Method in class umontreal.iro.lecuyer.randvar.BinomialGen
 
nextInt(RandomStream, int, double) - Static method in class umontreal.iro.lecuyer.randvar.BinomialGen
Generates a new integer from the binomial distribution with parameters n = n and p = p, using the given stream s.
nextInt() - Method in class umontreal.iro.lecuyer.randvar.GeometricGen
 
nextInt(RandomStream, double) - Static method in class umontreal.iro.lecuyer.randvar.GeometricGen
Generates a new geometric random variate with parameter p = p, using stream s, by inversion.
nextInt() - Method in class umontreal.iro.lecuyer.randvar.HypergeometricGen
 
nextInt(RandomStream, int, int, int) - Static method in class umontreal.iro.lecuyer.randvar.HypergeometricGen
Generates a new variate from the hypergeometric distribution with parameters m = m, l = l and k = k, using stream s.
nextInt() - Method in class umontreal.iro.lecuyer.randvar.LogarithmicGen
 
nextInt(RandomStream, double) - Static method in class umontreal.iro.lecuyer.randvar.LogarithmicGen
Uses stream s to generate a new variate from the logarithmic distribution with parameter θ = theta.
nextInt() - Method in class umontreal.iro.lecuyer.randvar.NegativeBinomialGen
 
nextInt(RandomStream, int, double) - Static method in class umontreal.iro.lecuyer.randvar.NegativeBinomialGen
Generates a new variate from the negative binomial distribution, with parameters n = n and p = p, using stream s.
nextInt() - Method in class umontreal.iro.lecuyer.randvar.PascalConvolutionGen
 
nextInt(RandomStream, int, double) - Static method in class umontreal.iro.lecuyer.randvar.PascalConvolutionGen
 
nextInt() - Method in class umontreal.iro.lecuyer.randvar.PoissonGen
 
nextInt(RandomStream, double) - Static method in class umontreal.iro.lecuyer.randvar.PoissonGen
A static method for generating a random variate from a Poisson distribution with parameter λ = lambda.
nextInt() - Method in class umontreal.iro.lecuyer.randvar.PoissonTIACGen
 
nextInt(RandomStream, double) - Static method in class umontreal.iro.lecuyer.randvar.PoissonTIACGen
 
nextInt() - Method in class umontreal.iro.lecuyer.randvar.RandomVariateGenInt
Generates a random number (an integer) from the discrete distribution contained in this object.
nextInt() - Method in class umontreal.iro.lecuyer.randvar.UniformIntGen
 
nextInt(RandomStream, int, int) - Static method in class umontreal.iro.lecuyer.randvar.UniformIntGen
Generates a new uniform random variate over the interval [i, j], using stream s, by inversion.
nextInt() - Method in class umontreal.iro.lecuyer.randvar.UnuranDiscreteInt
 
nextInt(int, int) - Method in class umontreal.iro.lecuyer.rng.AntitheticStream
Returns j - i - s.nextInt(i, j) where s is the base stream.
nextInt(int, int) - Method in class umontreal.iro.lecuyer.rng.BakerTransformedStream
Generates a random integer in {i,..., j} via nextDouble (in which the baker transformation is applied).
nextInt(int, int) - Method in class umontreal.iro.lecuyer.rng.LFSR113
 
nextInt(int, int) - Method in class umontreal.iro.lecuyer.rng.LFSR258
 
nextInt(int, int) - Method in class umontreal.iro.lecuyer.rng.RandMrg
 
nextInt(int, int) - Method in interface umontreal.iro.lecuyer.rng.RandomStream
Returns a (pseudo)random number from the discrete uniform distribution over the integers {i, i + 1,..., j}, using this stream.
nextInt(int, int) - Method in class umontreal.iro.lecuyer.rng.RandomStreamBase
Calls nextDouble once to create one integer between i and j.
nextInt(int, int) - Method in class umontreal.iro.lecuyer.rng.RandomStreamWithCache
 
nextInt(int, int) - Method in class umontreal.iro.lecuyer.rng.TruncatedRandomStream
 
nextPoint(double[], int) - Method in class umontreal.iro.lecuyer.hups.CycleBasedPointSet.CycleBasedPointSetIterator
 
nextPoint(double[], int) - Method in class umontreal.iro.lecuyer.hups.CycleBasedPointSetBase2.CycleBasedPointSetBase2Iterator
 
nextPoint(double[], int) - Method in interface umontreal.iro.lecuyer.hups.PointSetIterator
Returns the first d coordinates of the current point in p, advances to the next point, and returns the index of the new current point.
nextPoint(RandomStream, double[], double[]) - Static method in class umontreal.iro.lecuyer.randvarmulti.DirichletGen
Generates a new point from the Dirichlet distribution with parameters alphas, using the stream stream.
nextPoint(double[]) - Method in class umontreal.iro.lecuyer.randvarmulti.DirichletGen
Generates a point from the Dirichlet distribution.
nextPoint(NormalGen, double[], DoubleMatrix2D, double[]) - Static method in class umontreal.iro.lecuyer.randvarmulti.MultiNormalGen
Generates a point from the multinormal distribution with mean vector mu, and covariance matrix sigma, using the one-dimensional normal generator gen1.
nextPoint(NormalGen, double[], double[][], double[]) - Static method in class umontreal.iro.lecuyer.randvarmulti.MultiNormalGen
Equivalent to nextPoint (gen1, mu, new DenseDoubleMatrix2D (sigma), p).
nextPoint(double[]) - Method in class umontreal.iro.lecuyer.randvarmulti.MultiNormalGen
Generates a point from the multinormal distribution.
nextPoint(double[]) - Method in class umontreal.iro.lecuyer.randvarmulti.RandomMultiVariateGen
Generates a random point p using the one-dimensional generator or the stream contained in this object.
nextRadicalInverse(double, double) - Static method in class umontreal.iro.lecuyer.hups.RadicalInverse
A fast method that incrementally computes the radical inverse xi+1 in base b from xi = x = ψb(i), using addition with rigthward carry.
nextRadicalInverse() - Method in class umontreal.iro.lecuyer.hups.RadicalInverse
A fast method that incrementally computes the radical inverse xi+1 in base b from xi = ψb(i), using addition with rigthward carry as described in Wang and Hickernell.
nextRadicalInverseDigits(int, int, int[]) - Static method in class umontreal.iro.lecuyer.hups.RadicalInverse
Given the k digits of the integer radical inverse of i in bdigits, in base b, this method replaces them by the digits of the integer radical inverse of i + 1 and returns their number.
NiedSequenceBase2 - Class in umontreal.iro.lecuyer.hups
This class implements digital sequences constructed from the Niederreiter sequence in base 2.
NiedSequenceBase2(int, int, int) - Constructor for class umontreal.iro.lecuyer.hups.NiedSequenceBase2
Constructs a new digital sequence in base 2 from the first n = 2k points of the Niederreiter sequence, with w output digits, in dim dimensions.
NiedXingSequenceBase2 - Class in umontreal.iro.lecuyer.hups
This class implements digital sequences based on the Niederreiter-Xing sequence in base 2.
NiedXingSequenceBase2(int, int, int) - Constructor for class umontreal.iro.lecuyer.hups.NiedXingSequenceBase2
Constructs a new Niederreiter-Xing digital sequence in base 2 with n = 2k points and w output digits, in dim dimensions.
normal(RandomStream, double, double) - Static method in class umontreal.iro.lecuyer.randvar.Rand1
Deprecated. Returns a normal random variate with mean mu and standard deviation sigma using stream s.
NormalACRGen - Class in umontreal.iro.lecuyer.randvar
This class implements normal random variate generators using the acceptance-complement ratio method.
NormalACRGen(RandomStream, NormalDist) - Constructor for class umontreal.iro.lecuyer.randvar.NormalACRGen
Creates a random variate generator for the normal distribution dist and stream s.
NormalBoxMullerGen - Class in umontreal.iro.lecuyer.randvar
This class implements normal random variate generators using the Box-Muller method.
NormalBoxMullerGen(RandomStream, NormalDist) - Constructor for class umontreal.iro.lecuyer.randvar.NormalBoxMullerGen
Creates a random variate generator for the normal distribution dist and stream s.
NormalDist - Class in umontreal.iro.lecuyer.probdist
Extends the class ContinuousDistribution for the normal distribution (e.g.,).
NormalDist() - Constructor for class umontreal.iro.lecuyer.probdist.NormalDist
Constructs a NormalDist object with default parameters μ = 0 and σ = 1.
NormalDist(double, double) - Constructor for class umontreal.iro.lecuyer.probdist.NormalDist
Constructs a NormalDist object with parameters μ = mu and σ = sigma.
NormalDistQuick - Class in umontreal.iro.lecuyer.probdist
A variant of the class NormalDist (for the normal distribution with mean μ and variance σ2).
NormalDistQuick() - Constructor for class umontreal.iro.lecuyer.probdist.NormalDistQuick
Constructs a NormalDistQuick object with default parameters μ = 0 and σ = 1.
NormalDistQuick(double, double) - Constructor for class umontreal.iro.lecuyer.probdist.NormalDistQuick
Constructs a NormalDistQuick object with parameters μ = mu and σ = sigma.
NormalGen - Class in umontreal.iro.lecuyer.randvar
This class implements methods for generating random variates from the normal distribution N(μ, σ).
NormalGen(RandomStream, NormalDist) - Constructor for class umontreal.iro.lecuyer.randvar.NormalGen
Creates a random variate generator for the normal distribution dist and stream s.
NormalKindermannRamageGen - Class in umontreal.iro.lecuyer.randvar
This class implements normal random variate generators using the Kindermann-Ramage method.
NormalKindermannRamageGen(RandomStream, NormalDist) - Constructor for class umontreal.iro.lecuyer.randvar.NormalKindermannRamageGen
Creates a random variate generator for the normal distribution dist and stream s.
NormalPolarGen - Class in umontreal.iro.lecuyer.randvar
This class implements normal random variate generators using the polar method with rejection.
NormalPolarGen(RandomStream, NormalDist) - Constructor for class umontreal.iro.lecuyer.randvar.NormalPolarGen
Creates a random variate generator for the normal distribution dist and stream s.
not() - Method in class umontreal.iro.lecuyer.util.BitMatrix
Returns the BitMatrix resulting from the application of the not operator on the original BitMatrix.
not() - Method in class umontreal.iro.lecuyer.util.BitVector
Returns a BitVector which is the result of the not operator on the current BitVector.
NTESTTYPES - Static variable in class umontreal.iro.lecuyer.gof.GofFormat
Total number of test types
Num - Class in umontreal.iro.lecuyer.util
This class provides a few constants and some methods to compute numerical quantities such as factorials, combinations, gamma functions, and so on.
numberObs() - Method in class umontreal.iro.lecuyer.stat.Tally
Returns the number of observations given to this probe since its last initialization.
numColumns() - Method in class umontreal.iro.lecuyer.util.BitMatrix
Returns the number of columns of the BitMatrix.
NUMINTERVALS - Static variable in class umontreal.iro.lecuyer.probdist.TruncatedDist
 
numRows() - Method in class umontreal.iro.lecuyer.util.BitMatrix
Returns the number of rows of the BitMatrix.

O

or(BitMatrix) - Method in class umontreal.iro.lecuyer.util.BitMatrix
Returns the BitMatrix resulting from the application of the or operator on the original BitMatrix and that.
or(BitVector) - Method in class umontreal.iro.lecuyer.util.BitVector
Returns a BitVector which is the result of the or operator with both the this and that BitVector's.

P

PaddedPointSet - Class in umontreal.iro.lecuyer.hups
This container class realizes padded point sets, constructed by taking some coordinates from a point set P1, other coordinates from a point set P2, and so on.
PaddedPointSet(int) - Constructor for class umontreal.iro.lecuyer.hups.PaddedPointSet
Constructs a structure for padding at most maxPointSets point sets.
padPointSet(PointSet) - Method in class umontreal.iro.lecuyer.hups.PaddedPointSet
Pads the point set P to the present structure.
padPointSetPermute(PointSet) - Method in class umontreal.iro.lecuyer.hups.PaddedPointSet
Pads the point set P, which is assumed to be finite.
pareto(RandomStream, double, double) - Static method in class umontreal.iro.lecuyer.randvar.Rand1
Deprecated. Returns a random variate having the Pareto distribution with parameters alpha and beta (see, Problem 8.1).
ParetoDist - Class in umontreal.iro.lecuyer.probdist
Extends the class ContinuousDistribution for a distribution from the Pareto family, with shape parameter α > 0 and location parameter β > 0.
ParetoDist(double) - Constructor for class umontreal.iro.lecuyer.probdist.ParetoDist
Constructs a ParetoDist object with parameters α = alpha and β = 1.
ParetoDist(double, double) - Constructor for class umontreal.iro.lecuyer.probdist.ParetoDist
Constructs a ParetoDist object with parameters α = alpha and β = beta.
ParetoGen - Class in umontreal.iro.lecuyer.randvar
This class implements random variate generators for one of the Pareto distributions, with parameters α > 0 and β > 0.
ParetoGen(RandomStream, ParetoDist) - Constructor for class umontreal.iro.lecuyer.randvar.ParetoGen
Creates a new generator for the Pareto distribution dist and stream s.
PascalConvolutionGen - Class in umontreal.iro.lecuyer.randvar
Implements Pascal random variate generators by the convolution method.
PascalConvolutionGen(RandomStream, PascalDist) - Constructor for class umontreal.iro.lecuyer.randvar.PascalConvolutionGen
Creates a new generator for the distribution dist, using stream s.
PascalDist - Class in umontreal.iro.lecuyer.probdist
The Pascal distribution is a special case of the negative binomial distribution with parameters n and p, where n is a positive integer and 0 <= p <= 1.
PascalDist(int, double) - Constructor for class umontreal.iro.lecuyer.probdist.PascalDist
Creates an object that contains the probability terms and the distribution function for the Pascal distribution with parameter n and p.
pDisc(double, double) - Static method in class umontreal.iro.lecuyer.gof.GofStat
Computes a variant of the p-value p whenever a test statistic has a discrete probability distribution.
Pearson5Dist - Class in umontreal.iro.lecuyer.probdist
Extends the class ContinuousDistribution for the Pearson type V distribution with shape parameter α > 0 and scale parameter β > 0.
Pearson5Dist(double, double) - Constructor for class umontreal.iro.lecuyer.probdist.Pearson5Dist
Constructs a Pearson5Dist object with parameters α = alpha and β = beta.
Pearson5Gen - Class in umontreal.iro.lecuyer.randvar
This class implements random variate generators for the Pearson type V distribution with shape parameter α > 0 and scale parameter β > 0.
Pearson5Gen(RandomStream, Pearson5Dist) - Constructor for class umontreal.iro.lecuyer.randvar.Pearson5Gen
Creates a new generator for the distribution dist, using stream s.
Pearson6Dist - Class in umontreal.iro.lecuyer.probdist
Extends the class ContinuousDistribution for the Pearson type VI distribution with shape parameters α1 > 0 and α2 > 0, and scale parameter β > 0.
Pearson6Dist(double, double, double) - Constructor for class umontreal.iro.lecuyer.probdist.Pearson6Dist
Constructs a Pearson6Dist object with parameters α1 = alpha1, α2 = alpha2 and β = beta.
Pearson6Gen - Class in umontreal.iro.lecuyer.randvar
This class implements random variate generators for the Pearson type VI distribution with shape parameters α1 > 0 and α2 > 0, and scale parameter β > 0.
Pearson6Gen(RandomStream, Pearson6Dist) - Constructor for class umontreal.iro.lecuyer.randvar.Pearson6Gen
Creates a new generator for the distribution dist, using stream s.
permutedRadicalInverse(int, int[], int) - Static method in class umontreal.iro.lecuyer.hups.RadicalInverse
Computes the radical inverse of i in base b, where the digits are permuted using the permutation π.
PiecewiseLinearEmpiricalDist - Class in umontreal.iro.lecuyer.probdist
Extends the class ContinuousDistribution for a piecewise-linear approximation of the empirical distribution function, based on the observations X(1),..., X(n) (sorted by increasing order), and defined as follows (e.g.,).
PiecewiseLinearEmpiricalDist(double[]) - Constructor for class umontreal.iro.lecuyer.probdist.PiecewiseLinearEmpiricalDist
Constructs a new piecewise-linear distribution using all the observations stored in obs.
PiecewiseLinearEmpiricalDist(Reader) - Constructor for class umontreal.iro.lecuyer.probdist.PiecewiseLinearEmpiricalDist
Constructs a new empirical distribution using the observations read from the reader in.
PLAIN - Static variable in class umontreal.iro.lecuyer.util.TableFormat
Plain text matrix printing style
PointSet - Class in umontreal.iro.lecuyer.hups
This abstract class defines the basic methods for accessing and manipulating point sets.
PointSet() - Constructor for class umontreal.iro.lecuyer.hups.PointSet
 
PointSetIterator - Interface in umontreal.iro.lecuyer.hups
Objects of classes that implement this interface are iterators that permit one to enumerate (or observe) the successive points of a point set and the successive coordinates of these points.
poisson(RandomStream, double) - Static method in class umontreal.iro.lecuyer.randvar.Rand1
Deprecated. Returns a random variate having the Poisson distribution with parameter (mean) lambda.
PoissonDist - Class in umontreal.iro.lecuyer.probdist
Extends the class DiscreteDistributionInt for the Poisson distribution with mean λ >=  0.
PoissonDist(double) - Constructor for class umontreal.iro.lecuyer.probdist.PoissonDist
Creates an object that contains the probability and distribution functions, for the Poisson distribution with parameter lambda, which are computed and stored in dynamic arrays inside that object.
PoissonGen - Class in umontreal.iro.lecuyer.randvar
This class implements random variate generators having the Poisson distribution.
PoissonGen(RandomStream, PoissonDist) - Constructor for class umontreal.iro.lecuyer.randvar.PoissonGen
Creates a new random variate generator using the Poisson distribution dist and stream s.
PoissonTIACGen - Class in umontreal.iro.lecuyer.randvar
This class implements random variate generators having the Poisson distribution (see PoissonGen).
PoissonTIACGen(RandomStream, PoissonDist) - Constructor for class umontreal.iro.lecuyer.randvar.PoissonTIACGen
Creates a new random variate generator using the Poisson distribution dist and stream s.
power(long) - Method in class umontreal.iro.lecuyer.util.BitMatrix
Raises the BitMatrix to the power p.
power2e(int) - Method in class umontreal.iro.lecuyer.util.BitMatrix
Raises the BitMatrix to power 2e.
powerRatios(DoubleArrayList) - Static method in class umontreal.iro.lecuyer.gof.GofStat
Applies the power ratios transformation W.
print(String) - Static method in class umontreal.iro.lecuyer.hups.F2wStructure
Prints the content of file filename.
printData() - Method in class umontreal.iro.lecuyer.util.BitMatrix
Creates a String containing all the data of the BitMatrix.
PrintfFormat - Class in umontreal.iro.lecuyer.util
This class acts like a StringBuffer which defines new types of append methods.
PrintfFormat() - Constructor for class umontreal.iro.lecuyer.util.PrintfFormat
Constructs a new buffer object containing an empty string.
PrintfFormat(int) - Constructor for class umontreal.iro.lecuyer.util.PrintfFormat
Constructs a new buffer object with an initial capacity of length.
PrintfFormat(String) - Constructor for class umontreal.iro.lecuyer.util.PrintfFormat
Constructs a new buffer object containing the initial string str.
printGeneratorMatrices(int) - Method in class umontreal.iro.lecuyer.hups.DigitalNet
Prints the generator matrices in standard form for dimensions 1 to s.
printGeneratorMatrices(int) - Method in class umontreal.iro.lecuyer.hups.DigitalNetBase2
Prints the generator matrices as bit matrices in standard form for dimensions 1 to s.
printGeneratorMatricesTrans(int) - Method in class umontreal.iro.lecuyer.hups.DigitalNetBase2
Prints the generator matrices transposed in the form of integers for dimensions 1 to s.
prob(int) - Method in class umontreal.iro.lecuyer.probdist.BinomialDist
 
prob(int, double, int) - Static method in class umontreal.iro.lecuyer.probdist.BinomialDist
Computes and returns the mass function p(x).
prob(int, double, double, int) - Static method in class umontreal.iro.lecuyer.probdist.BinomialDist
A generalization of the previous method.
prob(int) - Method in class umontreal.iro.lecuyer.probdist.DiscreteDistribution
Returns pk, the probability of the k-th observation, for 0 <= k < n.
prob(int) - Method in class umontreal.iro.lecuyer.probdist.DiscreteDistributionInt
Returns p(x), the probability of x, which should be a real number in the interval [0, 1].
prob(int) - Method in class umontreal.iro.lecuyer.probdist.EmpiricalDist
 
prob(int) - Method in class umontreal.iro.lecuyer.probdist.GeometricDist
 
prob(double, int) - Static method in class umontreal.iro.lecuyer.probdist.GeometricDist
Computes the probability mass function p(x).
prob(int) - Method in class umontreal.iro.lecuyer.probdist.HypergeometricDist
 
prob(int, int, int, int) - Static method in class umontreal.iro.lecuyer.probdist.HypergeometricDist
Computes the probability mass function p(x).
prob(int) - Method in class umontreal.iro.lecuyer.probdist.LogarithmicDist
 
prob(double, int) - Static method in class umontreal.iro.lecuyer.probdist.LogarithmicDist
Computes the probability mass function p(x).
prob(int) - Method in class umontreal.iro.lecuyer.probdist.NegativeBinomialDist
 
prob(double, double, int) - Static method in class umontreal.iro.lecuyer.probdist.NegativeBinomialDist
Computes the probability mass function.
prob(int) - Method in class umontreal.iro.lecuyer.probdist.PoissonDist
 
prob(double, int) - Static method in class umontreal.iro.lecuyer.probdist.PoissonDist
Computes and returns the value of the Poisson probability p(x), for λ = lambda.
prob(int) - Method in class umontreal.iro.lecuyer.probdist.UniformIntDist
 
prob(int, int, int) - Static method in class umontreal.iro.lecuyer.probdist.UniformIntDist
Computes the discrete uniform density function f (x).
prob(int[]) - Method in class umontreal.iro.lecuyer.probdistmulti.DiscreteDistributionIntMulti
Returns the probability mass function p(x1, x2,…, xd), which should be a real number in [0, 1].
prob(int[]) - Method in class umontreal.iro.lecuyer.probdistmulti.MultinomialDist
 
prob(int, double[], int[]) - Static method in class umontreal.iro.lecuyer.probdistmulti.MultinomialDist
Computes the probability mass function of the multinomial distribution with parameters n and (p1,...,pd) evaluated at x.
prob(int[]) - Method in class umontreal.iro.lecuyer.probdistmulti.NegativeMultinomialDist
 
prob(double, double[], int[]) - Static method in class umontreal.iro.lecuyer.probdistmulti.NegativeMultinomialDist
Computes the probability mass function of the negative multinomial distribution with parameters γ and (p1,...,pd), evaluated at x.
put(int) - Method in class umontreal.iro.lecuyer.simprocs.Bin
Adds n tokens to this bin.

Q

quickSelect(double[], int, int) - Static method in class umontreal.iro.lecuyer.util.Misc
Returns the kth smallest item of the array t of size n.
quickSelect(int[], int, int) - Static method in class umontreal.iro.lecuyer.util.Misc
Returns the kth smallest item of the array t of size n.

R

RAC2 - Static variable in class umontreal.iro.lecuyer.util.Num
The value of (2)1/2.
RadicalInverse - Class in umontreal.iro.lecuyer.hups
This class implements basic methods for working with radical inverses of integers in an arbitrary basis b.
RadicalInverse(int, double) - Constructor for class umontreal.iro.lecuyer.hups.RadicalInverse
Initializes the base of this object to b and its first value of x to x0.
radicalInverse(int, long) - Static method in class umontreal.iro.lecuyer.hups.RadicalInverse
Computes the radical inverse of i in base b.
Rand1 - Class in umontreal.iro.lecuyer.randvar
Deprecated. Use RandomVariateGen and RandomVariateGenInt instead.
RandMrg - Class in umontreal.iro.lecuyer.rng
This class is DEPRECATED.
RandMrg() - Constructor for class umontreal.iro.lecuyer.rng.RandMrg
Constructs a new stream, initializes its seed Ig, sets Bg and Cg equal to Ig, and sets its antithetic switch to false.
RandMrg(String) - Constructor for class umontreal.iro.lecuyer.rng.RandMrg
Constructs a new stream with an identifier name (can be used when printing the stream state, in error messages, etc.).
Randomization - Interface in umontreal.iro.lecuyer.hups
Each type of randomization that can be applied to a general point set should be defined in a class that implements this interface.
randomize(RandomStream) - Method in class umontreal.iro.lecuyer.hups.PaddedPointSet
 
randomize(int, int, RandomStream) - Method in class umontreal.iro.lecuyer.hups.PointSet
By default, this method simply calls addRandomShift(d1, d2, stream).
randomize(RandomStream) - Method in class umontreal.iro.lecuyer.hups.PointSet
By default, this method simply calls addRandomShift(stream).
randomize(int, int) - Method in class umontreal.iro.lecuyer.hups.PointSet
Deprecated: By default, this method simply calls addRandomShift(d1, d2).
randomize() - Method in class umontreal.iro.lecuyer.hups.PointSet
 
RandomMultiVariateGen - Class in umontreal.iro.lecuyer.randvarmulti
This is the base class for all random variate generators over Rd, the d-dimensional space over the reals.
RandomMultiVariateGen(RandomVariateGen) - Constructor for class umontreal.iro.lecuyer.randvarmulti.RandomMultiVariateGen
Creates a new multi-variate random generator using the one-dimensional generator gen1.
RandomMultiVariateGen(RandomStream) - Constructor for class umontreal.iro.lecuyer.randvarmulti.RandomMultiVariateGen
Creates a new multi-variate random generator using stream s.
RandomStream - Interface in umontreal.iro.lecuyer.rng
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.
RandomStreamBase - Class in umontreal.iro.lecuyer.rng
This class provides a convenient foundation on which RNGs can be built.
RandomStreamBase() - Constructor for class umontreal.iro.lecuyer.rng.RandomStreamBase
 
RandomStreamFactory - Interface in umontreal.iro.lecuyer.rng
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.
RandomStreamInstantiationException - Exception in umontreal.iro.lecuyer.rng
This exception is thrown when a random stream factory cannot instantiate a stream on a call to its newInstance method.
RandomStreamInstantiationException(RandomStreamFactory) - Constructor for exception umontreal.iro.lecuyer.rng.RandomStreamInstantiationException
Constructs a new random stream instantiation exception with no message, no cause, and thrown by the given factory.
RandomStreamInstantiationException(RandomStreamFactory, String) - Constructor for exception umontreal.iro.lecuyer.rng.RandomStreamInstantiationException
Constructs a new random stream instantiation exception with the given message, no cause, and concerning factory.
RandomStreamInstantiationException(RandomStreamFactory, Throwable) - Constructor for exception umontreal.iro.lecuyer.rng.RandomStreamInstantiationException
Constructs a new random stream instantiation exception with no message, the given cause, and concerning factory.
RandomStreamInstantiationException(RandomStreamFactory, String, Throwable) - Constructor for exception umontreal.iro.lecuyer.rng.RandomStreamInstantiationException
Constructs a new random stream instantiation exception with the given message, the supplied cause, and concerning factory.
RandomStreamManager - Class in umontreal.iro.lecuyer.rng
Manages a list of random streams for more convenient synchronization.
RandomStreamManager() - Constructor for class umontreal.iro.lecuyer.rng.RandomStreamManager
 
RandomStreamWithCache - Class in umontreal.iro.lecuyer.rng
This class represents a random stream whose uniforms are cached for more efficiency when using common random numbers.
RandomStreamWithCache(RandomStream) - Constructor for class umontreal.iro.lecuyer.rng.RandomStreamWithCache
Constructs a new cached random stream with internal stream stream.
RandomStreamWithCache(RandomStream, int) - Constructor for class umontreal.iro.lecuyer.rng.RandomStreamWithCache
Constructs a new cached random stream with internal stream stream.
RandomVariateGen - Class in umontreal.iro.lecuyer.randvar
This is the base class for all random variate generators over the real line.
RandomVariateGen(RandomStream, Distribution) - Constructor for class umontreal.iro.lecuyer.randvar.RandomVariateGen
Creates a new random variate generator from the distribution dist, using stream s.
RandomVariateGenInt - Class in umontreal.iro.lecuyer.randvar
This is the base class for all generators of discrete random variates over the set of integers.
RandomVariateGenInt(RandomStream, DiscreteDistributionInt) - Constructor for class umontreal.iro.lecuyer.randvar.RandomVariateGenInt
Creates a new random variate generator for the discrete distribution dist, using stream s.
RandomVariateGenWithCache - Class in umontreal.iro.lecuyer.randvar
This class represents a random variate generator whose values are cached for more efficiency when using common random numbers.
RandomVariateGenWithCache(RandomVariateGen) - Constructor for class umontreal.iro.lecuyer.randvar.RandomVariateGenWithCache
Constructs a new cached random variate generator with internal generator rvg.
RandomVariateGenWithCache(RandomVariateGen, int) - Constructor for class umontreal.iro.lecuyer.randvar.RandomVariateGenWithCache
Constructs a new cached random variate generator with internal generator rvg.
RandRijndael - Class in umontreal.iro.lecuyer.rng
Implements a RNG using the Rijndael block cipher algorithm (AES) with key and block lengths of 128 bits.
RandRijndael() - Constructor for class umontreal.iro.lecuyer.rng.RandRijndael
Constructs a new stream.
RandRijndael(String) - Constructor for class umontreal.iro.lecuyer.rng.RandRijndael
Constructs a new stream with the identifier name (used in the toString method).
RandShiftedPointSet - Class in umontreal.iro.lecuyer.hups
This container class embodies a point set to which a random shift modulo 1 is applied (i.e., a single uniform random point is added to all points, modulo 1, to randomize the inner point set).
RandShiftedPointSet(PointSet, int, RandomStream) - Constructor for class umontreal.iro.lecuyer.hups.RandShiftedPointSet
Constructs a structure to contain a randomly shifted version of P.
Rank1Lattice - Class in umontreal.iro.lecuyer.hups
This class implements point sets defined by integration lattices of rank 1, defined as follows.
Rank1Lattice(int, int[], int) - Constructor for class umontreal.iro.lecuyer.hups.Rank1Lattice
Instantiates a Rank1Lattice with n points and lattice vector a of dimension s.
readDoubleData(Statement, String) - Static method in class umontreal.iro.lecuyer.util.JDBCManager
Copies the result of the SQL query query into an array of double-precision values.
readDoubleData(Connection, String) - Static method in class umontreal.iro.lecuyer.util.JDBCManager
Copies the result of the SQL query query into an array of double-precision values.
readDoubleData(Statement, String, String) - Static method in class umontreal.iro.lecuyer.util.JDBCManager
Returns the values of the column column of the table table.
readDoubleData(Connection, String, String) - Static method in class umontreal.iro.lecuyer.util.JDBCManager
Returns the values of the column column of the table table.
readDoubleData(Reader) - Static method in class umontreal.iro.lecuyer.util.TextDataReader
Reads an array of double-precision values from the reader input.
readDoubleData(File) - Static method in class umontreal.iro.lecuyer.util.TextDataReader
Opens the file referred to by the file object file, and calls readDoubleData to obtain an array of double-precision values from the file.
readDoubleData(String) - Static method in class umontreal.iro.lecuyer.util.TextDataReader
Opens the file with name file, and calls readDoubleData to obtain an array of double-precision values from the file.
readDoubleData2D(Statement, String) - Static method in class umontreal.iro.lecuyer.util.JDBCManager
Copies the result of the SQL query query into a rectangular 2D array of double-precision values.
readDoubleData2D(Connection, String) - Static method in class umontreal.iro.lecuyer.util.JDBCManager
Copies the result of the SQL query query into a rectangular 2D array of double-precision values.
readDoubleData2D(Reader) - Static method in class umontreal.iro.lecuyer.util.TextDataReader
Uses the reader input to obtain a 2-dimensional array of double-precision values.
readDoubleData2D(File) - Static method in class umontreal.iro.lecuyer.util.TextDataReader
Opens the file referred to by the file object file, and calls readDoubleData2D to obtain a matrix of double-precision values from the file.
readDoubleData2D(String) - Static method in class umontreal.iro.lecuyer.util.TextDataReader
Opens the file with name file, and calls readDoubleData2D to obtain a matrix of double-precision values from the file.
readDoubleData2DTable(Statement, String) - Static method in class umontreal.iro.lecuyer.util.JDBCManager
Returns the values of the columns of the table table.
readDoubleData2DTable(Connection, String) - Static method in class umontreal.iro.lecuyer.util.JDBCManager
Returns the values of the columns of the table table.
readIntData(Statement, String) - Static method in class umontreal.iro.lecuyer.util.JDBCManager
Copies the result of the SQL query query into an array of integers.
readIntData(Connection, String) - Static method in class umontreal.iro.lecuyer.util.JDBCManager
Copies the result of the SQL query query into an array of integers.
readIntData(Statement, String, String) - Static method in class umontreal.iro.lecuyer.util.JDBCManager
Returns the values of the column column of the table table.
readIntData(Connection, String, String) - Static method in class umontreal.iro.lecuyer.util.JDBCManager
Returns the values of the column column of the table table.
readIntData(Reader) - Static method in class umontreal.iro.lecuyer.util.TextDataReader
This is equivalent to readDoubleData, for reading integers.
readIntData(File) - Static method in class umontreal.iro.lecuyer.util.TextDataReader
This is equivalent to readDoubleData, for reading integers.
readIntData(String) - Static method in class umontreal.iro.lecuyer.util.TextDataReader
This is equivalent to readDoubleData, for reading integers.
readIntData2D(Statement, String) - Static method in class umontreal.iro.lecuyer.util.JDBCManager
Copies the result of the SQL query query into a rectangular 2D array of integers.
readIntData2D(Connection, String) - Static method in class umontreal.iro.lecuyer.util.JDBCManager
Copies the result of the SQL query query into a rectangular 2D array of integers.
readIntData2D(Reader) - Static method in class umontreal.iro.lecuyer.util.TextDataReader
This is equivalent to readDoubleData2D, for reading integers.
readIntData2D(File) - Static method in class umontreal.iro.lecuyer.util.TextDataReader
This is equivalent to readDoubleData2D, for reading integers.
readIntData2D(String) - Static method in class umontreal.iro.lecuyer.util.TextDataReader
This is equivalent to readDoubleData2D, for reading integers.
readIntData2DTable(Statement, String) - Static method in class umontreal.iro.lecuyer.util.JDBCManager
Returns the values of the columns of the table table.
readIntData2DTable(Connection, String) - Static method in class umontreal.iro.lecuyer.util.JDBCManager
Returns the values of the columns of the table table.
RedblackTree - Class in umontreal.iro.lecuyer.simevents.eventlist
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.
RedblackTree() - Constructor for class umontreal.iro.lecuyer.simevents.eventlist.RedblackTree
 
regroupCategories(double) - Method in class umontreal.iro.lecuyer.gof.GofStat.OutcomeCategoriesChi2
Regroup categories as explained earlier, so that the expected number of observations in each category is at least minExp.
release(int) - Method in class umontreal.iro.lecuyer.simprocs.Resource
The executing process that invokes this method releases n units of the resource.
remove(RandomStream) - Method in class umontreal.iro.lecuyer.rng.RandomStreamManager
Removes the given stream from the internal list of this random stream manager.
remove(Event) - Method in class umontreal.iro.lecuyer.simevents.eventlist.BinaryTree
 
remove(Event) - Method in class umontreal.iro.lecuyer.simevents.eventlist.DoublyLinked
 
remove(Event) - Method in interface umontreal.iro.lecuyer.simevents.eventlist.EventList
Removes the event ev from the event list (cancels this event).
remove(Event) - Method in class umontreal.iro.lecuyer.simevents.eventlist.Henriksen
 
remove(Event) - Method in class umontreal.iro.lecuyer.simevents.eventlist.RedblackTree
 
remove(Event) - Method in class umontreal.iro.lecuyer.simevents.eventlist.SplayTree
 
remove(Object) - Method in class umontreal.iro.lecuyer.simevents.LinkedListStat
 
remove(int) - Method in class umontreal.iro.lecuyer.simevents.LinkedListStat
 
removeFirst() - Method in class umontreal.iro.lecuyer.simevents.eventlist.BinaryTree
 
removeFirst() - Method in class umontreal.iro.lecuyer.simevents.eventlist.DoublyLinked
 
removeFirst() - Method in interface umontreal.iro.lecuyer.simevents.eventlist.EventList
Removes the first event from the event list (to cancel or execute this event).
removeFirst() - Method in class umontreal.iro.lecuyer.simevents.eventlist.Henriksen
 
removeFirst() - Method in class umontreal.iro.lecuyer.simevents.eventlist.RedblackTree
 
removeFirst() - Method in class umontreal.iro.lecuyer.simevents.eventlist.SplayTree
 
removeFirst() - Method in class umontreal.iro.lecuyer.simevents.LinkedListStat
 
removeFirstEvent() - Static method in class umontreal.iro.lecuyer.simevents.Sim
This method is used by the package simprocs; it should not be used directly by a simulation program.
removeLast() - Method in class umontreal.iro.lecuyer.simevents.LinkedListStat
 
report() - Method in class umontreal.iro.lecuyer.simevents.Accumulate
 
report() - Method in class umontreal.iro.lecuyer.simevents.LinkedListStat
Returns a string containing a statistical report on the list, provided that setStatCollecting (true) has been called before for this list.
report() - Method in class umontreal.iro.lecuyer.simprocs.Bin
Returns a string containing a complete statistical report on this bin.
report() - Method in class umontreal.iro.lecuyer.simprocs.Resource
Returns a string containing a complete statistical report on this resource.
report() - Method in class umontreal.iro.lecuyer.stat.StatProbe
Returns a string containing a report for this statistical collector.
report() - Method in class umontreal.iro.lecuyer.stat.Tally
Returns a formatted string that contains a report on this probe.
reportAndCIStudent(double, int) - Method in class umontreal.iro.lecuyer.stat.Tally
An alias for reportAndConfidenceIntervalStudent.
reportAndCIStudent(double) - Method in class umontreal.iro.lecuyer.stat.Tally
An alias for reportAndConfidenceIntervalStudent.
reportAndConfidenceIntervalStudent(double, int) - Method in class umontreal.iro.lecuyer.stat.Tally
Returns a formatted string that contains a report on this probe (as in report), followed by a confidence interval (as in formatConfidenceIntervalStudent), using d decimal digits of accuracy.
reportAndConfidenceIntervalStudent(double) - Method in class umontreal.iro.lecuyer.stat.Tally
Same as reportAndConfidenceIntervalStudent(level, 3).
request(int) - Method in class umontreal.iro.lecuyer.simprocs.Resource
The executing process invoking this method requests for n units of this resource.
reschedule(double) - Method in class umontreal.iro.lecuyer.simevents.Event
Cancels this event and reschedules it to happen in delay time units.
reschedule(double) - Method in class umontreal.iro.lecuyer.simprocs.AbstractSimProcess
If the process is in the DELAYED state, removes it from the event list and reschedules it in delay units of time.
resetCurCoordIndex() - Method in class umontreal.iro.lecuyer.hups.CycleBasedPointSet.CycleBasedPointSetIterator
 
resetCurCoordIndex() - Method in interface umontreal.iro.lecuyer.hups.PointSetIterator
Equivalent to setCurCoordIndex (0).
resetCurCycle(int) - Method in class umontreal.iro.lecuyer.hups.CycleBasedPointSet.CycleBasedPointSetIterator
 
resetCurCycle(int) - Method in class umontreal.iro.lecuyer.hups.CycleBasedPointSetBase2.CycleBasedPointSetBase2Iterator
 
resetCurPointIndex() - Method in class umontreal.iro.lecuyer.hups.CycleBasedPointSet.CycleBasedPointSetIterator
 
resetCurPointIndex() - Method in interface umontreal.iro.lecuyer.hups.PointSetIterator
Equivalent to setCurPointIndex (0).
resetGeneratorMatrices() - Method in class umontreal.iro.lecuyer.hups.DigitalNet
Restores the original generator matrices.
resetNextSubstream() - Method in class umontreal.iro.lecuyer.rng.AntitheticStream
 
resetNextSubstream() - Method in class umontreal.iro.lecuyer.rng.BakerTransformedStream
 
resetNextSubstream() - Method in class umontreal.iro.lecuyer.rng.F2NL607
 
resetNextSubstream() - Method in class umontreal.iro.lecuyer.rng.GenF2w32
 
resetNextSubstream() - Method in class umontreal.iro.lecuyer.rng.LFSR113
 
resetNextSubstream() - Method in class umontreal.iro.lecuyer.rng.LFSR258
 
resetNextSubstream() - Method in class umontreal.iro.lecuyer.rng.MRG31k3p
 
resetNextSubstream() - Method in class umontreal.iro.lecuyer.rng.MRG32k3a
 
resetNextSubstream() - Method in class umontreal.iro.lecuyer.rng.MT19937
 
resetNextSubstream() - Method in class umontreal.iro.lecuyer.rng.RandMrg
 
resetNextSubstream() - Method in interface umontreal.iro.lecuyer.rng.RandomStream
Reinitializes the stream to the beginning of its next substream: Ng is computed, and Cg and Bg are set to Ng.
resetNextSubstream() - Method in class umontreal.iro.lecuyer.rng.RandomStreamBase
 
resetNextSubstream() - Method in class umontreal.iro.lecuyer.rng.RandomStreamManager
Forwards to the resetNextSubstream methods of all streams in the list.
resetNextSubstream() - Method in class umontreal.iro.lecuyer.rng.RandomStreamWithCache
 
resetNextSubstream() - Method in class umontreal.iro.lecuyer.rng.RandRijndael
 
resetNextSubstream() - Method in class umontreal.iro.lecuyer.rng.TruncatedRandomStream
 
resetNextSubstream() - Method in class umontreal.iro.lecuyer.rng.WELL1024
 
resetNextSubstream() - Method in class umontreal.iro.lecuyer.rng.WELL512
 
resetNextSubstream() - Method in class umontreal.iro.lecuyer.rng.WELL607
 
resetStartStream() - Method in class umontreal.iro.lecuyer.rng.AntitheticStream
 
resetStartStream() - Method in class umontreal.iro.lecuyer.rng.BakerTransformedStream
 
resetStartStream() - Method in class umontreal.iro.lecuyer.rng.F2NL607
 
resetStartStream() - Method in class umontreal.iro.lecuyer.rng.GenF2w32
 
resetStartStream() - Method in class umontreal.iro.lecuyer.rng.LFSR113
 
resetStartStream() - Method in class umontreal.iro.lecuyer.rng.LFSR258
 
resetStartStream() - Method in class umontreal.iro.lecuyer.rng.MRG31k3p
 
resetStartStream() - Method in class umontreal.iro.lecuyer.rng.MRG32k3a
 
resetStartStream() - Method in class umontreal.iro.lecuyer.rng.MT19937
 
resetStartStream() - Method in class umontreal.iro.lecuyer.rng.RandMrg
 
resetStartStream() - Method in interface umontreal.iro.lecuyer.rng.RandomStream
Reinitializes the stream to its initial state Ig: Cg and Bg are set to Ig.
resetStartStream() - Method in class umontreal.iro.lecuyer.rng.RandomStreamBase
 
resetStartStream() - Method in class umontreal.iro.lecuyer.rng.RandomStreamManager
Forwards to the resetStartStream methods of all streams in the list.
resetStartStream() - Method in class umontreal.iro.lecuyer.rng.RandomStreamWithCache
 
resetStartStream() - Method in class umontreal.iro.lecuyer.rng.RandRijndael
 
resetStartStream() - Method in class umontreal.iro.lecuyer.rng.TruncatedRandomStream
 
resetStartStream() - Method in class umontreal.iro.lecuyer.rng.WELL1024
 
resetStartStream() - Method in class umontreal.iro.lecuyer.rng.WELL512
 
resetStartStream() - Method in class umontreal.iro.lecuyer.rng.WELL607
 
resetStartSubstream() - Method in class umontreal.iro.lecuyer.rng.AntitheticStream
 
resetStartSubstream() - Method in class umontreal.iro.lecuyer.rng.BakerTransformedStream
 
resetStartSubstream() - Method in class umontreal.iro.lecuyer.rng.F2NL607
 
resetStartSubstream() - Method in class umontreal.iro.lecuyer.rng.GenF2w32
 
resetStartSubstream() - Method in class umontreal.iro.lecuyer.rng.LFSR113
 
resetStartSubstream() - Method in class umontreal.iro.lecuyer.rng.LFSR258
 
resetStartSubstream() - Method in class umontreal.iro.lecuyer.rng.MRG31k3p
 
resetStartSubstream() - Method in class umontreal.iro.lecuyer.rng.MRG32k3a
 
resetStartSubstream() - Method in class umontreal.iro.lecuyer.rng.MT19937
 
resetStartSubstream() - Method in class umontreal.iro.lecuyer.rng.RandMrg
 
resetStartSubstream() - Method in interface umontreal.iro.lecuyer.rng.RandomStream
Reinitializes the stream to the beginning of its current substream: Cg is set to Bg.
resetStartSubstream() - Method in class umontreal.iro.lecuyer.rng.RandomStreamBase
 
resetStartSubstream() - Method in class umontreal.iro.lecuyer.rng.RandomStreamManager
Forwards to the resetStartSubstream methods of all streams in the list.
resetStartSubstream() - Method in class umontreal.iro.lecuyer.rng.RandomStreamWithCache
 
resetStartSubstream() - Method in class umontreal.iro.lecuyer.rng.RandRijndael
 
resetStartSubstream() - Method in class umontreal.iro.lecuyer.rng.TruncatedRandomStream
 
resetStartSubstream() - Method in class umontreal.iro.lecuyer.rng.WELL1024
 
resetStartSubstream() - Method in class umontreal.iro.lecuyer.rng.WELL512
 
resetStartSubstream() - Method in class umontreal.iro.lecuyer.rng.WELL607
 
resetToNextPoint() - Method in class umontreal.iro.lecuyer.hups.CycleBasedPointSet.CycleBasedPointSetIterator
 
resetToNextPoint() - Method in interface umontreal.iro.lecuyer.hups.PointSetIterator
Increases the current point index by 1 and returns its new value.
Resource - Class in umontreal.iro.lecuyer.simprocs
Objects of this class are resources having limited capacity, and which can be requested and released by AbstractSimProcess objects.
Resource(int) - Constructor for class umontreal.iro.lecuyer.simprocs.Resource
Constructs a new resource, with initial capacity capacity, and service policy FIFO.
Resource(int, String) - Constructor for class umontreal.iro.lecuyer.simprocs.Resource
Constructs a new resource, with initial capacity capacity, service policy FIFO, and identifier (or name) name.
resume() - Method in class umontreal.iro.lecuyer.simprocs.AbstractSimProcess
Places this process at the beginning of the event list to resume its execution.
reverseDigits(int, int[], int[]) - Static method in class umontreal.iro.lecuyer.hups.RadicalInverse
Given the k b-ary digits of i in bdigits, returns the k digits of the integer radical inverse of i in idigits.
rightMatrixScramble(RandomStream) - Method in class umontreal.iro.lecuyer.hups.DigitalNet
Applies a linear scramble by multiplying each Cj on the right by a single k×k nonsingular upper-triangular matrix M, as suggested by Faure and Tezuka.
rightMatrixScramble(RandomStream) - Method in class umontreal.iro.lecuyer.hups.DigitalNetBase2
 
RootFinder - Class in umontreal.iro.lecuyer.util
This class provides methods to solve non-linear equations.

S

s(String) - Static method in class umontreal.iro.lecuyer.util.PrintfFormat
Same as s (0, str).
s(int, String) - Static method in class umontreal.iro.lecuyer.util.PrintfFormat
Formats the string str like the %s in the C printf function.
scalarProduct(BitVector) - Method in class umontreal.iro.lecuyer.util.BitVector
Returns the scalar product of two BitVector's modulo 2.
scan(int, double, int) - Static method in class umontreal.iro.lecuyer.gof.FBar
Return P[SN(d ) >= m], where SN(d ) is the scan statistic.
scan(int, double, int) - Static method in class umontreal.iro.lecuyer.gof.FDist
Returns F(m), the distribution function of the scan statistic with parameters N and d, evaluated at m.
scan(DoubleArrayList, double) - Static method in class umontreal.iro.lecuyer.gof.GofStat
Computes and returns the scan statistic SN(d ), defined in FBar.scan.
schedule(double) - Method in class umontreal.iro.lecuyer.simevents.Event
Schedules this event to happen in delay time units, i.e., at time Sim.time() + delay, by inserting it in the event list.
schedule(double) - Method in class umontreal.iro.lecuyer.simprocs.AbstractSimProcess
Schedules the process to start in delay time units.
scheduleAfter(Event) - Method in class umontreal.iro.lecuyer.simevents.Event
Schedules this event to happen just after, and at the same time, as the event other.
scheduleBefore(Event) - Method in class umontreal.iro.lecuyer.simevents.Event
Schedules this event to happen just before, and at the same time, as the event other.
scheduleNext() - Method in class umontreal.iro.lecuyer.simevents.Event
Schedules this event as the first event in the event list, to be executed at the current time (as the next event).
scheduleNext() - Method in class umontreal.iro.lecuyer.simprocs.AbstractSimProcess
Schedules this process to start at the current time, by placing it at the beginning of the event list.
selectCoordinates(int[], int) - Method in class umontreal.iro.lecuyer.hups.SubsetOfPointSet
Selects the numCoord coordinates whose numbers are provided in the array coordIndices.
selectCoordinatesRange(int, int) - Method in class umontreal.iro.lecuyer.hups.SubsetOfPointSet
Selects the coordinates from ``from'' to ``to - 1'' from the original point set.
selectEuler(double) - Static method in class umontreal.iro.lecuyer.simevents.Continuous
Selects the Euler method as the integration method, with the integration step size h, in time units.
selectPoints(int[], int) - Method in class umontreal.iro.lecuyer.hups.SubsetOfPointSet
Selects the numPoints points whose numbers are provided in the array pointIndices.
selectPointsRange(int, int) - Method in class umontreal.iro.lecuyer.hups.SubsetOfPointSet
Selects the points numbered from ``from'' to ``to - 1'' from the original point set.
selectRungeKutta2(double) - Static method in class umontreal.iro.lecuyer.simevents.Continuous
Selects a Runge-Kutta method of order 2 as the integration method to be used, with step size h.
selectRungeKutta4(double) - Static method in class umontreal.iro.lecuyer.simevents.Continuous
Selects a Runge-Kutta method of order 4 as the integration method to be used, with step size h.
selfAnd(BitVector) - Method in class umontreal.iro.lecuyer.util.BitVector
Applies the and operator on this with that.
selfNot() - Method in class umontreal.iro.lecuyer.util.BitVector
Applies the not operator on the current BitVector and returns it.
selfOr(BitVector) - Method in class umontreal.iro.lecuyer.util.BitVector
Applies the or operator on this with that.
selfShift(int) - Method in class umontreal.iro.lecuyer.util.BitVector
Shift all the bits of the current BitVector j positions to the right if j is positive, and j positions to the left if j is negative.
selfXor(BitVector) - Method in class umontreal.iro.lecuyer.util.BitVector
Applies the xor operator on this with that.
servList() - Method in class umontreal.iro.lecuyer.simprocs.Resource
Returns the list that contains the UserRecord objects for the processes in the service list for this resource.
set(boolean) - Method in class umontreal.iro.lecuyer.simprocs.Condition
Sets the condition to val.
setAntithetic(boolean) - Method in class umontreal.iro.lecuyer.rng.RandMrg
 
setBandwidth(double) - Method in class umontreal.iro.lecuyer.randvar.KernelDensityGen
Sets the bandwidth to h.
setBandwidth(double) - Method in class umontreal.iro.lecuyer.randvar.KernelDensityVarCorrectGen
 
setBool(int, int, boolean) - Method in class umontreal.iro.lecuyer.util.BitMatrix
Changes the value of the bit in the specified row and column.
setBool(int, boolean) - Method in class umontreal.iro.lecuyer.util.BitVector
Sets the value of the bit in position pos.
setBroadcasting(boolean) - Method in class umontreal.iro.lecuyer.simprocs.Condition
Instructs the condition to start or stop observation broadcasting.
setBroadcasting(boolean) - Method in class umontreal.iro.lecuyer.stat.StatProbe
Instructs the probe to turn its broadcasting ON or OFF.
setCachedGen(RandomVariateGen) - Method in class umontreal.iro.lecuyer.randvar.RandomVariateGenWithCache
Sets the random variate generator whose values are cached to rvg.
setCachedStream(RandomStream) - Method in class umontreal.iro.lecuyer.rng.RandomStreamWithCache
Sets the random stream whose values are cached to stream.
setCachedValues(DoubleArrayList) - Method in class umontreal.iro.lecuyer.randvar.RandomVariateGenWithCache
Sets the array list containing the cached values to values.
setCachedValues(DoubleArrayList) - Method in class umontreal.iro.lecuyer.rng.RandomStreamWithCache
Sets the array list containing the cached values to values.
setCacheIndex(int) - Method in class umontreal.iro.lecuyer.randvar.RandomVariateGenWithCache
Sets the index, in the cache, of the next value returned by nextDouble.
setCacheIndex(int) - Method in class umontreal.iro.lecuyer.rng.RandomStreamWithCache
Sets the index, in the cache, of the next value returned by nextDouble.
setCaching(boolean) - Method in class umontreal.iro.lecuyer.randvar.RandomVariateGenWithCache
Sets the caching indicator to caching.
setCaching(boolean) - Method in class umontreal.iro.lecuyer.rng.RandomStreamWithCache
Sets the caching indicator to caching.
setCapacity(int) - Method in class umontreal.iro.lecuyer.simprocs.Resource
Sets the capacity to newcap.
setCollecting(boolean) - Method in class umontreal.iro.lecuyer.stat.StatProbe
Turns ON or OFF the statistical collection of statistical observations.
setCurCoordIndex(int) - Method in class umontreal.iro.lecuyer.hups.CycleBasedPointSet.CycleBasedPointSetIterator
 
setCurCoordIndex(int) - Method in interface umontreal.iro.lecuyer.hups.PointSetIterator
Sets the current coordinate index to j, so that the next calls to nextCoordinate or nextCoordinates will return the values ui, j, ui, j+1,..., where i is the index of the current point.
setCurPointIndex(int) - Method in class umontreal.iro.lecuyer.hups.CycleBasedPointSet.CycleBasedPointSetIterator
 
setCurPointIndex(int) - Method in interface umontreal.iro.lecuyer.hups.PointSetIterator
Resets the current point index to i and the current coordinate index to zero.
setGen1(RandomVariateGen) - Method in class umontreal.iro.lecuyer.randvarmulti.RandomMultiVariateGen
Sets the RandomVariateGen used by this object to gen1.
setIterator() - Method in interface umontreal.iro.lecuyer.simevents.LinkedListStat.ListIterator2
 
setLambda(double) - Method in class umontreal.iro.lecuyer.probdist.ExponentialDist
Sets the value of λ for this object.
setLambda(double) - Method in class umontreal.iro.lecuyer.probdist.PoissonDist
Sets the λ associated with this object.
setLinearSeed(int[]) - Method in class umontreal.iro.lecuyer.rng.F2NL607
This method is discouraged for normal use.
setMu(double[]) - Method in class umontreal.iro.lecuyer.randvarmulti.MultiNormalGen
Sets the mean vector to mu.
setMu(int, double) - Method in class umontreal.iro.lecuyer.randvarmulti.MultiNormalGen
Sets the i-th component of the mean vector to mui.
setN(int) - Method in class umontreal.iro.lecuyer.probdist.ChiSquareDist
Sets the parameter n of this object.
setN(int) - Method in class umontreal.iro.lecuyer.probdist.StudentDist
Sets the parameter n associated with this object.
setName(String) - Method in class umontreal.iro.lecuyer.stat.StatProbe
Sets the name of this statistical collector to name.
setNonLinearData(int[][]) - Static method in class umontreal.iro.lecuyer.rng.F2NL607
Selects new data for the components of the non-linear generator.
setNonLinearSeed(int[]) - Method in class umontreal.iro.lecuyer.rng.F2NL607
This method is discouraged for normal use.
setNu(int) - Method in class umontreal.iro.lecuyer.probdist.ChiDist
Sets the value of ν for this object.
setP(double) - Method in class umontreal.iro.lecuyer.probdist.GeometricDist
Resets the value of p associated with this object.
setPackageLinearSeed(int[]) - Static method in class umontreal.iro.lecuyer.rng.F2NL607
Sets the initial seed of the linear part of the class F2NL607 to the 19 integers of the vector seed[0..18].
setPackageNonLinearSeed(int[]) - Static method in class umontreal.iro.lecuyer.rng.F2NL607
Sets the non-linear part of the initial seed of the class F2NL607 to the n integers of the vector seed[0..n-1], where n is the number of components of the non-linear part.
setPackageSeed(int[]) - Static method in class umontreal.iro.lecuyer.rng.GenF2w32
Sets the initial seed of the class GenF2w2r32 to the 25 integers of the vector seed[0..24].
setPackageSeed(int[]) - Static method in class umontreal.iro.lecuyer.rng.LFSR113
Sets the initial seed for the class LFSR113 to the four integers of the vector seed[0..3].
setPackageSeed(long[]) - Static method in class umontreal.iro.lecuyer.rng.LFSR258
Sets the initial seed for the class LFSR258 to the five integers of array seed[0..4].
setPackageSeed(int[]) - Static method in class umontreal.iro.lecuyer.rng.MRG31k3p
Sets the initial seed for the class MRG31k3p to the six integers of the vector seed[0..5].
setPackageSeed(long[]) - Static method in class umontreal.iro.lecuyer.rng.MRG32k3a
Sets the initial seed for the class MRG32k3a to the six integers in the vector seed[0..5].
setPackageSeed(long[]) - Static method in class umontreal.iro.lecuyer.rng.RandMrg
Sets the initial seed for the class RandMrg to the six integers in the vector seed[0..5].
setPackageSeed(byte[]) - Static method in class umontreal.iro.lecuyer.rng.RandRijndael
Sets the initial seed for the class RandRijndael to the 16 bytes of the vector seed[0..15].
setPackageSeed(int[]) - Static method in class umontreal.iro.lecuyer.rng.WELL1024
Sets the initial seed of the class WELL1024 to the 32 integers of the vector seed[0..31].
setPackageSeed(int[]) - Static method in class umontreal.iro.lecuyer.rng.WELL512
Sets the initial seed of the class WELL512 to the 16 integers of the vector seed[0..15].
setPackageSeed(int[]) - Static method in class umontreal.iro.lecuyer.rng.WELL607
Sets the initial seed of the class WELL607 to the 19 integers of the vector seed[0..18].
setParam(double, double) - Method in class umontreal.iro.lecuyer.probdist.Pearson5Dist
Sets the parameters α and β of this object.
setParam(double, double, double) - Method in class umontreal.iro.lecuyer.probdist.Pearson6Dist
Sets the parameters α1, α2 and β of this object.
setParams(double, double, double, double, int) - Method in class umontreal.iro.lecuyer.probdist.BetaDist
 
setParams(double, double, double, double, int) - Method in class umontreal.iro.lecuyer.probdist.BetaSymmetricalDist
 
setParams(int, double) - Method in class umontreal.iro.lecuyer.probdist.BinomialDist
Resets the parameters to these new values and recomputes everything as in the constructor.
setParams(double, double) - Method in class umontreal.iro.lecuyer.probdist.CauchyDist
Sets the value of the parameters α and β for this object.
setParams(int, double, int) - Method in class umontreal.iro.lecuyer.probdist.ErlangDist
Sets the parameters k and λ of the distribution for this object.
setParams(double, double) - Method in class umontreal.iro.lecuyer.probdist.ExtremeValueDist
Sets the parameters α and λ of this object.
setParams(double, double, double) - Method in class umontreal.iro.lecuyer.probdist.FatigueLifeDist
Sets the parameters μ, β and γ of this object.
setParams(int, int) - Method in class umontreal.iro.lecuyer.probdist.FisherFDist
Sets the parameters n and m of this object.
setParams(double, double, int) - Method in class umontreal.iro.lecuyer.probdist.GammaDist
 
setParams(double, double) - Method in class umontreal.iro.lecuyer.probdist.HyperbolicSecantDist
Sets the parameters μ and σ of this object.
setParams(int, int, int) - Method in class umontreal.iro.lecuyer.probdist.HypergeometricDist
Resets the parameters of this object to m, l and k.
setParams(double, double) - Method in class umontreal.iro.lecuyer.probdist.InverseGaussianDist
Sets the parameters μ and λ of this object.
setParams(double, double, double, double) - Method in class umontreal.iro.lecuyer.probdist.JohnsonSBDist
Sets the value of the parameters γ, δ, ξ and λ for this object.
setParams(double, double, double, double) - Method in class umontreal.iro.lecuyer.probdist.JohnsonSUDist
Sets the value of the parameters γ, δ, ξ and λ for this object.
setParams(double, double) - Method in class umontreal.iro.lecuyer.probdist.LogisticDist
Sets the parameters α and λ of this object.
setParams(double, double) - Method in class umontreal.iro.lecuyer.probdist.LoglogisticDist
Sets the parameters α and β of this object.
setParams(double, double) - Method in class umontreal.iro.lecuyer.probdist.LognormalDist
Sets the parameters μ and σ of this object.
setParams(double, double) - Method in class umontreal.iro.lecuyer.probdist.NegativeBinomialDist
Sets the parameter γ and p of this object.
setParams(double, double) - Method in class umontreal.iro.lecuyer.probdist.NormalDist
Sets the parameters μ and σ of this object.
setParams(double, double) - Method in class umontreal.iro.lecuyer.probdist.ParetoDist
Sets the parameter α and β for this object.
setParams(int, double) - Method in class umontreal.iro.lecuyer.probdist.PascalDist
Sets the parameter n and p of this object.
setParams(double, double, double) - Method in class umontreal.iro.lecuyer.probdist.TriangularDist
Sets the value of the parameters a, b and m for this object.
setParams(ContinuousDistribution, double, double) - Method in class umontreal.iro.lecuyer.probdist.TruncatedDist
Sets the parameters dist, a and b for this object.
setParams(double, double) - Method in class umontreal.iro.lecuyer.probdist.UniformDist
Sets the parameters a and b for this object.
setParams(int, int) - Method in class umontreal.iro.lecuyer.probdist.UniformIntDist
Sets the parameters i and j for this object.
setParams(double, double, double) - Method in class umontreal.iro.lecuyer.probdist.WeibullDist
Sets the parameters α, λ and δ for this object.
setParams(double[]) - Method in class umontreal.iro.lecuyer.probdistmulti.DirichletDist
Sets the parameters (α1,...,αd) of this object.
setParams(int, double[]) - Method in class umontreal.iro.lecuyer.probdistmulti.MultinomialDist
Sets the parameters n and (p1,...,pd) of this object.
setParams(double[], double[][]) - Method in class umontreal.iro.lecuyer.probdistmulti.MultiNormalDist
Sets the parameters μ and Σ of this object.
setParams(double, double[]) - Method in class umontreal.iro.lecuyer.probdistmulti.NegativeMultinomialDist
Sets the parameters γ and (p1,...,pd) of this object.
setPolicyFIFO() - Method in class umontreal.iro.lecuyer.simprocs.Bin
Sets the service policy for ordering processes waiting for tokens on the bin to FIFO (first in, first out): the processes are placed in the list (and served) according to their order of arrival.
setPolicyFIFO() - Method in class umontreal.iro.lecuyer.simprocs.Resource
Set the service policy to FIFO (first in, first out): the processes are placed in the list (and served) according to their order of arrival.
setPolicyLIFO() - Method in class umontreal.iro.lecuyer.simprocs.Bin
Sets the service policy for ordering processes waiting for tokens on the bin to LIFO (last in, first out): the processes are placed in the list (and served) according to their inverse order of arrival, the last arrived are served first.
setPolicyLIFO() - Method in class umontreal.iro.lecuyer.simprocs.Resource
Set the service policy to LIFO (last in, first out): the processes are placed in the list (and served) according to their inverse order of arrival, the last arrived are served first.
setPositiveReflection(boolean) - Method in class umontreal.iro.lecuyer.randvar.KernelDensityGen
After this method is called with true, the generator will produce only positive values, by using the reflection method: replace all negative values by their absolute values.
setRandomStreamClass(Class) - Method in class umontreal.iro.lecuyer.rng.BasicRandomStreamFactory
Sets the associated random stream class to rsClass.
setScrambleData(RandomStream, int, int[]) - Static method in class umontreal.iro.lecuyer.rng.F2NL607
Selects new data for the components of the non-linear generator.
setSeed(int[]) - Method in class umontreal.iro.lecuyer.rng.GenF2w32
This method is discouraged for normal use.
setSeed(int[]) - Method in class umontreal.iro.lecuyer.rng.LFSR113
This method is discouraged for normal use.
setSeed(long[]) - Method in class umontreal.iro.lecuyer.rng.LFSR258
This method is discouraged for normal use.
setSeed(int[]) - Method in class umontreal.iro.lecuyer.rng.MRG31k3p
Use of this method is strongly discouraged.
setSeed(long[]) - Method in class umontreal.iro.lecuyer.rng.MRG32k3a
Sets the initial seed Ig of this stream to the vector seed[0..5].
setSeed(long[]) - Method in class umontreal.iro.lecuyer.rng.RandMrg
Sets the initial seed Ig of this stream to the vector seed[0..5].
setSeed(byte[]) - Method in class umontreal.iro.lecuyer.rng.RandRijndael
This method is discouraged for normal use.
setSeed(int[]) - Method in class umontreal.iro.lecuyer.rng.WELL1024
This method is discouraged for normal use.
setSeed(int[]) - Method in class umontreal.iro.lecuyer.rng.WELL512
This method is discouraged for normal use.
setSeed(int[]) - Method in class umontreal.iro.lecuyer.rng.WELL607
This method is discouraged for normal use.
setSigma(DoubleMatrix2D) - Method in class umontreal.iro.lecuyer.randvarmulti.MultiNormalGen
Sets the covariance matrix of this multinormal generator to sigma.
setStatCollecting(boolean) - Method in class umontreal.iro.lecuyer.simevents.LinkedListStat
Starts or stops collecting statistics on this list.
setStatCollecting(boolean) - Method in class umontreal.iro.lecuyer.simprocs.Bin
Starts or stops collecting statistics on the list returned by waitList for this bin.
setStatCollecting(boolean) - Method in class umontreal.iro.lecuyer.simprocs.Resource
Starts or stops collecting statistics on the lists returned by waitList and servList for this resource.
setStream(RandomStream) - Method in class umontreal.iro.lecuyer.randvar.RandomVariateGen
Sets the RandomStream used by this generator to stream.
setStream(RandomStream) - Method in class umontreal.iro.lecuyer.randvarmulti.RandomMultiVariateGen
Sets the RandomStream used by this object to stream.
setTheta(double) - Method in class umontreal.iro.lecuyer.probdist.LogarithmicDist
Sets the θ associated with this object.
setTime(double) - Method in class umontreal.iro.lecuyer.simevents.Event
Sets the (planned) time of occurence of this event to time.
shift(int) - Method in class umontreal.iro.lecuyer.util.BitVector
Returns a BitVector equal to the original with all the bits shifted j positions to the right if j is positive, and shifted j positions to the left if j is negative.
shouldBeInterpreted(Method) - Method in class umontreal.iro.lecuyer.simprocs.dsol.SSJInterpretationOracle
 
Sim - Class in umontreal.iro.lecuyer.simevents
This static class contains the executive of a discrete-event simulation.
SimProcess - Class in umontreal.iro.lecuyer.simprocs.dsol
Represents a simulation process whose actions method is interpreted by the DSOL interpreter, written by Peter Jacobs (http://www.tbm.tudelft.nl/webstaf/peterja/index.htm).
SimProcess() - Constructor for class umontreal.iro.lecuyer.simprocs.dsol.SimProcess
Constructs a new process without scheduling it.
SimProcess - Class in umontreal.iro.lecuyer.simprocs
Represents a simulation process with an associated Java thread.
SimProcess() - Constructor for class umontreal.iro.lecuyer.simprocs.SimProcess
Constructs a new process without scheduling it.
simpsonIntegral(MathFunction, double, double, int) - Static method in class umontreal.iro.lecuyer.util.Misc
Computes and returns an approximation of the integral of func over [a, b], using the Simpsons 1/3 method with numIntervals intervals.
size() - Method in class umontreal.iro.lecuyer.simevents.LinkedListStat
 
size() - Method in class umontreal.iro.lecuyer.util.BitVector
Returns the length of the BitVector.
smax - Variable in class umontreal.iro.lecuyer.gof.GofStat.OutcomeCategoriesChi2
Maximum index for valid expected numbers in the array nbExp.
smin - Variable in class umontreal.iro.lecuyer.gof.GofStat.OutcomeCategoriesChi2
Minimum index for valid expected numbers in the array nbExp.
SobolSequence - Class in umontreal.iro.lecuyer.hups
This class implements digital nets or digital sequences in base 2 formed by the first n = 2k points of a Sobol' sequence.
SobolSequence(int, int, int) - Constructor for class umontreal.iro.lecuyer.hups.SobolSequence
Constructs a new digital net with n = 2k points and w = 31 output digits, in dim dimensions, formed by taking the first n points of the Sobol' sequence.
SobolSequence(int, int) - Constructor for class umontreal.iro.lecuyer.hups.SobolSequence
Constructs a Sobol point set with at least n points in dim dimensions.
SplayTree - Class in umontreal.iro.lecuyer.simevents.eventlist
An implementation of EventList using a splay tree.
SplayTree() - Constructor for class umontreal.iro.lecuyer.simevents.eventlist.SplayTree
 
SSJInterpretationOracle - Class in umontreal.iro.lecuyer.simprocs.dsol
Determines which classes should be interpreted by the DSOL interpreter during process simulation.
SSJInterpretationOracle() - Constructor for class umontreal.iro.lecuyer.simprocs.dsol.SSJInterpretationOracle
 
standardDeviation() - Method in class umontreal.iro.lecuyer.stat.Tally
Returns the standard deviation of the observations since the last initialization.
start() - Static method in class umontreal.iro.lecuyer.simevents.Sim
Starts the simulation executive.
startInteg() - Method in class umontreal.iro.lecuyer.simevents.Continuous
Starts the integration process that will change the state of this variable at each integration step.
startInteg(double) - Method in class umontreal.iro.lecuyer.simevents.Continuous
Same as startInteg, after initializing the variable to val.
state() - Method in class umontreal.iro.lecuyer.simprocs.Condition
Returns the state (true or false) of the condition.
statOnAvail() - Method in class umontreal.iro.lecuyer.simprocs.Bin
Returns the statistical collector for the available tokens on the bin as a function of time.
statOnCapacity() - Method in class umontreal.iro.lecuyer.simprocs.Resource
Returns the statistical collector that measures the evolution of the capacity of the resource as a function of time.
statOnSojourn() - Method in class umontreal.iro.lecuyer.simprocs.Resource
Returns the statistical collector for the sojourn times of the UserRecord for this resource.
statOnUtil() - Method in class umontreal.iro.lecuyer.simprocs.Resource
Returns the statistical collector for the utilization of the resource (number of units busy) as a function of time.
StatProbe - Class in umontreal.iro.lecuyer.stat
The objects of this class are statistical probes or collectors, which are elementary devices for collecting statistics.
StatProbe() - Constructor for class umontreal.iro.lecuyer.stat.StatProbe
 
statSize() - Method in class umontreal.iro.lecuyer.simevents.LinkedListStat
Returns the statistical probe on the evolution of the size of the list as a function of the simulation time.
statSojourn() - Method in class umontreal.iro.lecuyer.simevents.LinkedListStat
Returns the statistical probe on the sojourn times of the objects in the list.
stop() - Static method in class umontreal.iro.lecuyer.simevents.Sim
Tells the simulation executive to stop as soon as it takes control, and to return control to the program that called start.
stopInteg() - Method in class umontreal.iro.lecuyer.simevents.Continuous
Stops the integration process for this continuous variable.
stripedMatrixScramble(RandomStream) - Method in class umontreal.iro.lecuyer.hups.DigitalNet
Applies the striped matrix scramble proposed by Owen.
stripedMatrixScramble(RandomStream) - Method in class umontreal.iro.lecuyer.hups.DigitalNetBase2
 
stripedMatrixScrambleFaurePermutAll(RandomStream, int) - Method in class umontreal.iro.lecuyer.hups.DigitalNet
Similar to stripedMatrixScramble except that the elements on and under the diagonal of each matrix Mj are chosen as in leftMatrixScrambleFaurePermut.
stripedMatrixScrambleFaurePermutAll(RandomStream, int) - Method in class umontreal.iro.lecuyer.hups.DigitalNetBase2
 
student(RandomStream, int) - Static method in class umontreal.iro.lecuyer.randvar.Rand1
Deprecated. Returns a random variate having the Student distribution with n degrees of freedom, using stream s.
StudentDist - Class in umontreal.iro.lecuyer.probdist
Extends the class ContinuousDistribution for the Student-t distribution with n degrees of freedom, where n is a positive integer.
StudentDist(int) - Constructor for class umontreal.iro.lecuyer.probdist.StudentDist
Constructs a StudentDist object with n degrees of freedom.
StudentGen - Class in umontreal.iro.lecuyer.randvar
This class implements methods for generating random variates from the Student distribution with n > 0 degrees of freedom.
StudentGen(RandomStream, StudentDist) - Constructor for class umontreal.iro.lecuyer.randvar.StudentGen
Creates a new generator for the Student distribution dist and stream s.
StudentPolarGen - Class in umontreal.iro.lecuyer.randvar
This class implements Student random variate generators using the polar method of.
StudentPolarGen(RandomStream, StudentDist) - Constructor for class umontreal.iro.lecuyer.randvar.StudentPolarGen
Creates a new generator for the Student distribution dist and stream s.
SubsetOfPointSet - Class in umontreal.iro.lecuyer.hups
This container class permits one to select a subset of a point set.
SubsetOfPointSet(PointSet) - Constructor for class umontreal.iro.lecuyer.hups.SubsetOfPointSet
Constructs a new PointSet object, initially identical to P, and from which a subset of the points and/or a subset of the coordinates is to be extracted.
sum() - Method in class umontreal.iro.lecuyer.simevents.Accumulate
 
sum() - Method in class umontreal.iro.lecuyer.stat.StatProbe
Returns the sum cumulated so far for this probe.
SUSPECTP - Static variable in class umontreal.iro.lecuyer.gof.GofFormat
Environment variable used in formatp1 to determine which p-values should be marked as suspect when printing test results.
suspend() - Method in class umontreal.iro.lecuyer.simprocs.AbstractSimProcess
This method can only be invoked for the EXECUTING or a DELAYED process.
suspend() - Method in class umontreal.iro.lecuyer.simprocs.dsol.SimProcess
 
suspend() - Method in class umontreal.iro.lecuyer.simprocs.SimProcess
 
SUSPENDED - Static variable in class umontreal.iro.lecuyer.simprocs.AbstractSimProcess
The process is not executing and will have to be reactivated by another process or event later on.

T

TableFormat - Class in umontreal.iro.lecuyer.util
This class provides methods to format arrays and matrices into Strings in different styles.
take(int) - Method in class umontreal.iro.lecuyer.simprocs.Bin
The executing process invoking this method requests n tokens from this bin.
Tally - Class in umontreal.iro.lecuyer.stat
This type of statistical collector takes a sequence of real-valued observations and can return the average, the variance, a confidence interval for the theoretical mean, etc.
Tally() - Constructor for class umontreal.iro.lecuyer.stat.Tally
Constructs a new Tally statistical probe.
Tally(String) - Constructor for class umontreal.iro.lecuyer.stat.Tally
Constructs a new Tally statistical probe with name name.
TallyStore - Class in umontreal.iro.lecuyer.stat
This class is a variant of Tally, but for which the individual observations are stored in a list implemented as a DoubleArrayList.
TallyStore() - Constructor for class umontreal.iro.lecuyer.stat.TallyStore
Construct a new TallyStore statistical probe.
TallyStore(int) - Constructor for class umontreal.iro.lecuyer.stat.TallyStore
Construct a new TallyStore statistical probe with given initial capacity for its associated array.
TallyStore(DoubleArrayList) - Constructor for class umontreal.iro.lecuyer.stat.TallyStore
Construct a new TallyStore statistical probe with given associated array.
TEN_NEG_POW - Static variable in class umontreal.iro.lecuyer.util.Num
Contains the precomputed negative powers of 10.
TESTNAMES - Static variable in class umontreal.iro.lecuyer.gof.GofFormat
Name of each testType test.
tests(DoubleArrayList, double[]) - Static method in class umontreal.iro.lecuyer.gof.GofFormat
Computes all EDF test statistics to compare the empirical distribution of U(0),..., U(N-1) with the uniform distribution, assuming that these sorted observations are in sortedData.
tests(DoubleArrayList, ContinuousDistribution, double[]) - Static method in class umontreal.iro.lecuyer.gof.GofFormat
The observations V are in data, not necessarily sorted, and their empirical distribution is compared with the continuous distribution dist.
tetragamma(double) - Static method in class umontreal.iro.lecuyer.util.Num
Returns the value of the tetragamma function d2ψ(x)/d2x, the second derivative of the digamma function, evaluated at x.
TextDataReader - Class in umontreal.iro.lecuyer.util
Provides static methods to read data from text files.
TextDataReader() - Constructor for class umontreal.iro.lecuyer.util.TextDataReader
 
time() - Method in class umontreal.iro.lecuyer.simevents.Event
Returns the (planned) time of occurence of this event.
time() - Static method in class umontreal.iro.lecuyer.simevents.Sim
Returns the current value of the simulation clock.
toArray() - Method in class umontreal.iro.lecuyer.simevents.LinkedListStat
 
toArray(Object[]) - Method in class umontreal.iro.lecuyer.simevents.LinkedListStat
 
toNet() - Method in class umontreal.iro.lecuyer.hups.DigitalSequence
Transforms this digital sequence into a digital net without changing the coordinates of the points.
toNet() - Method in class umontreal.iro.lecuyer.hups.DigitalSequenceBase2
Transforms this digital sequence into a digital net without changing the coordinates of the points.
toNetShiftCj() - Method in class umontreal.iro.lecuyer.hups.DigitalSequence
Transforms this digital sequence into a digital net by adding one dimension and shifting all coordinates by one position.
toNetShiftCj() - Method in class umontreal.iro.lecuyer.hups.DigitalSequenceBase2
Transforms this digital sequence into a digital net by adding one dimension and shifting all coordinates by one position.
toString() - Method in class umontreal.iro.lecuyer.gof.GofStat.OutcomeCategoriesChi2
Provides a report on the categories.
toString() - Method in class umontreal.iro.lecuyer.hups.AntitheticPointSet
 
toString() - Method in class umontreal.iro.lecuyer.hups.BakerTransformedPointSet
 
toString() - Method in class umontreal.iro.lecuyer.hups.CachedPointSet
 
toString() - Method in class umontreal.iro.lecuyer.hups.ContainerPointSet
 
toString() - Method in class umontreal.iro.lecuyer.hups.CycleBasedPointSet
 
toString() - Method in class umontreal.iro.lecuyer.hups.DigitalNet
 
toString() - Method in class umontreal.iro.lecuyer.hups.DigitalNetBase2
 
toString() - Method in class umontreal.iro.lecuyer.hups.DigitalNetBase2FromFile
 
toString() - Method in class umontreal.iro.lecuyer.hups.DigitalNetFromFile
 
toString() - Method in class umontreal.iro.lecuyer.hups.F2wCycleBasedLFSR
 
toString() - Method in class umontreal.iro.lecuyer.hups.F2wCycleBasedPolyLCG
 
toString() - Method in class umontreal.iro.lecuyer.hups.F2wNetLFSR
 
toString() - Method in class umontreal.iro.lecuyer.hups.F2wNetPolyLCG
 
toString() - Method in class umontreal.iro.lecuyer.hups.F2wStructure
This method returns a string containing the polynomial P(z) and the stepping parameter.
toString() - Method in class umontreal.iro.lecuyer.hups.FaureSequence
 
toString() - Method in class umontreal.iro.lecuyer.hups.KorobovLattice
 
toString() - Method in class umontreal.iro.lecuyer.hups.LCGPointSet
 
toString() - Method in class umontreal.iro.lecuyer.hups.NiedSequenceBase2
 
toString() - Method in class umontreal.iro.lecuyer.hups.NiedXingSequenceBase2
 
toString() - Method in class umontreal.iro.lecuyer.hups.PaddedPointSet
 
toString() - Method in class umontreal.iro.lecuyer.hups.PointSet
Formats a string that contains the information about the point set.
toString() - Method in class umontreal.iro.lecuyer.hups.Rank1Lattice
 
toString() - Method in class umontreal.iro.lecuyer.hups.SobolSequence
 
toString() - Method in class umontreal.iro.lecuyer.hups.SubsetOfPointSet
 
toString() - Method in class umontreal.iro.lecuyer.probdist.PoissonDist
Returns a String containing information about the current distribution.
toString() - Method in class umontreal.iro.lecuyer.rng.AntitheticStream
Returns a string starting with "Antithetic of " and finishing with the result of the call to the toString method of the generator.
toString() - Method in class umontreal.iro.lecuyer.rng.BakerTransformedStream
Returns a string starting with "Baker transformation of " and finishing with the result of the call to the toString method of the generator.
toString() - Method in class umontreal.iro.lecuyer.rng.BasicRandomStreamFactory
 
toString() - Method in class umontreal.iro.lecuyer.rng.F2NL607
 
toString() - Method in class umontreal.iro.lecuyer.rng.GenF2w32
 
toString() - Method in class umontreal.iro.lecuyer.rng.LFSR113
 
toString() - Method in class umontreal.iro.lecuyer.rng.LFSR258
 
toString() - Method in class umontreal.iro.lecuyer.rng.MRG31k3p
 
toString() - Method in class umontreal.iro.lecuyer.rng.MRG32k3a
Returns a string containing the name and the current state Cg of this stream.
toString() - Method in class umontreal.iro.lecuyer.rng.MT19937
 
toString() - Method in class umontreal.iro.lecuyer.rng.RandMrg
 
toString() - Method in interface umontreal.iro.lecuyer.rng.RandomStream
Returns a string containing the current state of this stream.
toString() - Method in class umontreal.iro.lecuyer.rng.RandomStreamBase
 
toString() - Method in exception umontreal.iro.lecuyer.rng.RandomStreamInstantiationException
Returns a short description of the exception.
toString() - Method in class umontreal.iro.lecuyer.rng.RandomStreamManager
 
toString() - Method in class umontreal.iro.lecuyer.rng.RandRijndael
 
toString() - Method in class umontreal.iro.lecuyer.rng.WELL1024
 
toString() - Method in class umontreal.iro.lecuyer.rng.WELL512
 
toString() - Method in class umontreal.iro.lecuyer.rng.WELL607
 
toString() - Method in class umontreal.iro.lecuyer.simevents.eventlist.BinaryTree
 
toString() - Method in class umontreal.iro.lecuyer.simevents.eventlist.DoublyLinked
 
toString() - Method in class umontreal.iro.lecuyer.simevents.eventlist.Henriksen
 
toString() - Method in class umontreal.iro.lecuyer.simevents.eventlist.RedblackTree
 
toString() - Method in class umontreal.iro.lecuyer.simevents.eventlist.SplayTree
 
toString() - Method in class umontreal.iro.lecuyer.util.BitMatrix
Creates a String containing all the data of the BitMatrix.
toString() - Method in class umontreal.iro.lecuyer.util.BitVector
Returns a string containing all the bits of the BitVector, starting with the highest order bit and finishing with the lowest order bit.
toString() - Method in class umontreal.iro.lecuyer.util.PrintfFormat
Converts the buffer into a String.
toStringDetailed() - Method in class umontreal.iro.lecuyer.hups.DigitalNetBase2FromFile
Writes the parameters and the generating matrices of this digital net to a string.
toStringDetailed() - Method in class umontreal.iro.lecuyer.hups.DigitalNetFromFile
Writes the parameters and the generating matrices of this digital net to a string.
toStringFull() - Method in class umontreal.iro.lecuyer.rng.MRG32k3a
Returns a string containing the name of this stream and the values of all its internal variables.
toStringFull() - Method in class umontreal.iro.lecuyer.rng.RandMrg
Returns a string containing the name of this stream and the values of all its internal variables.
transpose() - Method in class umontreal.iro.lecuyer.util.BitMatrix
Returns the transposed matrix.
TriangularDist - Class in umontreal.iro.lecuyer.probdist
Extends the class ContinuousDistribution for the triangular distribution with domain [a, b] and mode (or shape parameter) m, where a <= m <= b.
TriangularDist() - Constructor for class umontreal.iro.lecuyer.probdist.TriangularDist
Constructs a TriangularDist object with default parameters a = 0, b = 1, and m = 0.5.
TriangularDist(double) - Constructor for class umontreal.iro.lecuyer.probdist.TriangularDist
Constructs a TriangularDist object with parameters a = 0 , b = 1 and m = m.
TriangularDist(double, double, double) - Constructor for class umontreal.iro.lecuyer.probdist.TriangularDist
Constructs a TriangularDist object with parameters a, b and m.
TriangularGen - Class in umontreal.iro.lecuyer.randvar
This class implements random variate generators for the triangular distribution.
TriangularGen(RandomStream, TriangularDist) - Constructor for class umontreal.iro.lecuyer.randvar.TriangularGen
Creates a new generator for the triangular distribution dist and stream s.
trigamma(double) - Static method in class umontreal.iro.lecuyer.util.Num
Returns the value of the trigamma function (x)/dx, the derivative of the digamma function, evaluated at x.
TruncatedDist - Class in umontreal.iro.lecuyer.probdist
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.
TruncatedDist(ContinuousDistribution, double, double) - Constructor for class umontreal.iro.lecuyer.probdist.TruncatedDist
Constructs a new distribution by truncating distribution dist to the interval [a, b].
TruncatedRandomStream - Class in umontreal.iro.lecuyer.rng
Represents a container random stream generating numbers in an interval [a, b], where a < b and a, b∈[0, 1), by using another stream.
TruncatedRandomStream(RandomStream, double, double) - Constructor for class umontreal.iro.lecuyer.rng.TruncatedRandomStream
 
TWOEXP - Static variable in class umontreal.iro.lecuyer.util.Num
Contains the precomputed positive powers of 2.

U

umontreal.iro.lecuyer.gof - package umontreal.iro.lecuyer.gof
This package contains tools for performing univariate goodness-of-fit (GOF) statistical tests.
umontreal.iro.lecuyer.hups - package umontreal.iro.lecuyer.hups
Monte Carlo and quasi-Monte Carlo
umontreal.iro.lecuyer.probdist - package umontreal.iro.lecuyer.probdist
This package contains a set of Java classes providing methods to compute mass, density, distribution, complementary distribution, and inverse distribution functions for some discrete and continuous probability distributions.
umontreal.iro.lecuyer.probdistmulti - package umontreal.iro.lecuyer.probdistmulti
This package contains Java classes providing methods to compute mass, density, distribution and complementary distribution functions for some multi-dimensional discrete and continuous probability distributions.
umontreal.iro.lecuyer.randvar - package umontreal.iro.lecuyer.randvar
This package provides a collection of classes for non-uniform random variate generation, primarily from standard distributions.
umontreal.iro.lecuyer.randvarmulti - package umontreal.iro.lecuyer.randvarmulti
This package provides a collection of classes for non-uniform random variate generation for multi-dimensional distributions.
umontreal.iro.lecuyer.rng - package umontreal.iro.lecuyer.rng
This package offers the basic facilities for generating uniform random numbers.
umontreal.iro.lecuyer.simevents - package umontreal.iro.lecuyer.simevents
This package provides the simulation clock and tools to manage the future events list.
umontreal.iro.lecuyer.simevents.eventlist - package umontreal.iro.lecuyer.simevents.eventlist
This package provides different kinds of event list implementations.
umontreal.iro.lecuyer.simprocs - package umontreal.iro.lecuyer.simprocs
Process-oriented simulation is managed through this package.
umontreal.iro.lecuyer.simprocs.dsol - package umontreal.iro.lecuyer.simprocs.dsol
 
umontreal.iro.lecuyer.stat - package umontreal.iro.lecuyer.stat
This package provides elementary tools for collecting statistics and computing confidence intervals.
umontreal.iro.lecuyer.util - package umontreal.iro.lecuyer.util
This package contains utility classes used in the Java software developed in the simulation laboratory of the DIRO, at the Université de Montréal.
uniform(RandomStream, double, double) - Static method in class umontreal.iro.lecuyer.randvar.Rand1
Deprecated. Returns a random variate having the uniform distribution over the real interval (a, b), using stream s.
UniformDist - Class in umontreal.iro.lecuyer.probdist
Extends the class ContinuousDistribution for the uniform distribution over the interval [a, b].
UniformDist() - Constructor for class umontreal.iro.lecuyer.probdist.UniformDist
Constructs a uniform distribution over the interval (a, b) = (0, 1).
UniformDist(double, double) - Constructor for class umontreal.iro.lecuyer.probdist.UniformDist
Constructs a uniform distribution over the interval (a, b).
UniformGen - Class in umontreal.iro.lecuyer.randvar
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.
UniformGen(RandomStream, UniformDist) - Constructor for class umontreal.iro.lecuyer.randvar.UniformGen
Creates a new generator for the uniform distribution dist and stream s.
UniformIntDist - Class in umontreal.iro.lecuyer.probdist
Extends the class DiscreteDistributionInt for the discrete uniform distribution over the range [i, j].
UniformIntDist(int, int) - Constructor for class umontreal.iro.lecuyer.probdist.UniformIntDist
Constructs a discrete uniform distribution over the interval [i, j].
UniformIntGen - Class in umontreal.iro.lecuyer.randvar
This class implements a random variate generator for the uniform distribution over integers, over the interval [i, j].
UniformIntGen(RandomStream, UniformIntDist) - Constructor for class umontreal.iro.lecuyer.randvar.UniformIntGen
Creates a new generator for the distribution dist, using stream s.
unifTransform(DoubleArrayList, ContinuousDistribution) - Static method in class umontreal.iro.lecuyer.gof.GofStat
Applies the transformation Ui = F(Vi) for 0 <= i < N, where F is a continuous distribution function, and returns the result as an array of length N.
unifTransform(DoubleArrayList, DiscreteDistribution) - Static method in class umontreal.iro.lecuyer.gof.GofStat
Applies the transformation Ui = F(Vi) for 0 <= i < N, where F is a discrete distribution function, and returns the result as an array of length N.
unrandomize() - Method in class umontreal.iro.lecuyer.hups.DigitalNet
 
unrandomize() - Method in class umontreal.iro.lecuyer.hups.PaddedPointSet
 
unrandomize() - Method in class umontreal.iro.lecuyer.hups.PointSet
By default, this method simply calls clearRandomShift().
UnuranContinuous - Class in umontreal.iro.lecuyer.randvar
This class permits one to create continuous univariate distribution using UNURAN via its string API.
UnuranContinuous(RandomStream, String) - Constructor for class umontreal.iro.lecuyer.randvar.UnuranContinuous
Same as UnuranContinuous(s, s, genStr).
UnuranContinuous(RandomStream, RandomStream, String) - Constructor for class umontreal.iro.lecuyer.randvar.UnuranContinuous
Constructs a new continuous random number generator using the UNURAN generator specification string genStr, main stream s, and auxiliary stream aux.
UnuranDiscreteInt - Class in umontreal.iro.lecuyer.randvar
This class permits one to create a discrete univariate distribution using UNURAN via its string API.
UnuranDiscreteInt(RandomStream, String) - Constructor for class umontreal.iro.lecuyer.randvar.UnuranDiscreteInt
Same as UnuranDiscreteInt (s, s, genStr).
UnuranDiscreteInt(RandomStream, RandomStream, String) - Constructor for class umontreal.iro.lecuyer.randvar.UnuranDiscreteInt
Constructs a new discrete random number generator using the UNURAN generator specification string genStr, main stream s, and auxiliary stream aux.
UnuranEmpirical - Class in umontreal.iro.lecuyer.randvar
This class permits one to create generators for empirical and quasi-empirical univariate distributions using UNURAN via its string interface.
UnuranEmpirical(RandomStream, String) - Constructor for class umontreal.iro.lecuyer.randvar.UnuranEmpirical
Constructs a new empirical univariate generator using the specification string genStr and stream s.
UnuranEmpirical(RandomStream, RandomStream, String) - Constructor for class umontreal.iro.lecuyer.randvar.UnuranEmpirical
Constructs a new empirical univariate generator using the specification string genStr, with main stream s and auxiliary stream aux.
UnuranEmpirical(RandomStream, PiecewiseLinearEmpiricalDist, String) - Constructor for class umontreal.iro.lecuyer.randvar.UnuranEmpirical
Same as UnuranEmpirical(s, s, dist, genStr).
UnuranEmpirical(RandomStream, RandomStream, PiecewiseLinearEmpiricalDist, String) - Constructor for class umontreal.iro.lecuyer.randvar.UnuranEmpirical
Same as UnuranEmpirical(s, aux, genStr), but reading the observations from the empirical distribution dist.
UnuranException - Exception in umontreal.iro.lecuyer.randvar
This type of unchecked exception is thrown when an error occurs inside the UNURAN package.
UnuranException() - Constructor for exception umontreal.iro.lecuyer.randvar.UnuranException
Constructs a new generic UNURAN exception.
UnuranException(String) - Constructor for exception umontreal.iro.lecuyer.randvar.UnuranException
Constructs a UNURAN exception with the error message message
update() - Method in class umontreal.iro.lecuyer.simevents.Accumulate
Updates the accumulator using the last value passed to update.
update(double) - Method in class umontreal.iro.lecuyer.simevents.Accumulate
Gives a new observation x to the statistical collector.
UserRecord - Class in umontreal.iro.lecuyer.simprocs
This class represents a record object to store information related to the request of a process for a Resource or for Bin tokens, or when a process waits for a Condition.

V

value() - Method in class umontreal.iro.lecuyer.simevents.Continuous
Returns the current value of this continuous-time variable.
variance() - Method in class umontreal.iro.lecuyer.stat.Tally
Returns the variance of the observations since the last initialization.
volumeSphere(double, int) - Static method in class umontreal.iro.lecuyer.util.Num
Returns the volume V of a sphere of radius 1 in t dimensions using the norm Lp.

W

waitFor() - Method in class umontreal.iro.lecuyer.simprocs.Condition
The executing process invoking this method must wait for this condition to be true.
waitList() - Method in class umontreal.iro.lecuyer.simprocs.Bin
Returns the list of UserRecord for the processes waiting for tokens from this bin.
waitList() - Method in class umontreal.iro.lecuyer.simprocs.Condition
Returns the list of UserRecord for the processes waiting for this condition.
waitList() - Method in class umontreal.iro.lecuyer.simprocs.Resource
Returns the list that contains the UserRecord objects for the processes in the waiting list for this resource.
watsonG(int, double) - Static method in class umontreal.iro.lecuyer.gof.FBar
Returns 1.0 - FDist.watsonG (n, x).
watsonG(int, double) - Static method in class umontreal.iro.lecuyer.gof.FDist
Returns an approximation of P[GN <= x], where GN is the Watson statistic defined in watsonU, for a sample of independent uniforms over (0, 1).
watsonG(DoubleArrayList) - Static method in class umontreal.iro.lecuyer.gof.GofStat
Computes and returns the Watson statistic GN.
watsonU(int, double) - Static method in class umontreal.iro.lecuyer.gof.FBar
Returns 1.0 - FDist.watsonU (n, x).
watsonU(int, double) - Static method in class umontreal.iro.lecuyer.gof.FDist
Returns P[U2 <= x], where U2 is the Watson statistic in the limit when N -> ∞, for a sample of independent uniforms over (0, 1).
watsonU(DoubleArrayList) - Static method in class umontreal.iro.lecuyer.gof.GofStat
Computes and returns the Watson statistic UN2.
weibull(RandomStream, double, double, double) - Static method in class umontreal.iro.lecuyer.randvar.Rand1
Deprecated. Returns a random variate having the Weibull distribution.
WeibullDist - Class in umontreal.iro.lecuyer.probdist
This class extends the class ContinuousDistribution for the Weibull distribution with shape parameter α > 0, location parameter δ, and scale parameter λ > 0.
WeibullDist(double) - Constructor for class umontreal.iro.lecuyer.probdist.WeibullDist
Constructs a WeibullDist object with parameters α = alpha, λ = 1, and δ = 0.
WeibullDist(double, double, double) - Constructor for class umontreal.iro.lecuyer.probdist.WeibullDist
Constructs a WeibullDist object with parameters α = alpha, λ = lambda, and δ = delta.
WeibullGen - Class in umontreal.iro.lecuyer.randvar
This class implements random variate generators for the Weibull distribution.
WeibullGen(RandomStream, WeibullDist) - Constructor for class umontreal.iro.lecuyer.randvar.WeibullGen
Creates a new generator for the Weibull distribution dist and stream s.
WELL1024 - Class in umontreal.iro.lecuyer.rng
Implements the RandomStream interface via inheritance from RandomStreamBase.
WELL1024() - Constructor for class umontreal.iro.lecuyer.rng.WELL1024
Constructs a new stream.
WELL1024(String) - Constructor for class umontreal.iro.lecuyer.rng.WELL1024
Constructs a new stream with the identifier name (used in the toString method).
WELL512 - Class in umontreal.iro.lecuyer.rng
This class implements the RandomStream interface via inheritance from RandomStreamBase.
WELL512() - Constructor for class umontreal.iro.lecuyer.rng.WELL512
Constructs a new stream.
WELL512(String) - Constructor for class umontreal.iro.lecuyer.rng.WELL512
Constructs a new stream with the identifier name (used in the toString method).
WELL607 - Class in umontreal.iro.lecuyer.rng
This class implements the RandomStream interface via inheritance from RandomStreamBase.
WELL607() - Constructor for class umontreal.iro.lecuyer.rng.WELL607
Constructs a new stream.
WELL607(String) - Constructor for class umontreal.iro.lecuyer.rng.WELL607
Constructs a new stream with the identifier name (used in the toString method).
WG - Static variable in class umontreal.iro.lecuyer.gof.GofFormat
Watson G test
WU - Static variable in class umontreal.iro.lecuyer.gof.GofFormat
Watson U test

X

xor(BitMatrix) - Method in class umontreal.iro.lecuyer.util.BitMatrix
Returns the BitMatrix resulting from the application of the xor operator on the original BitMatrix and that.
xor(BitVector) - Method in class umontreal.iro.lecuyer.util.BitVector
Returns a BitVector which is the result of the xor operator applied on this and that.

A B C D E F G H I J K L M N O P Q R S T U V W X
SSJ
V. 1.2.5.

To submit a bug or ask questions, send an e-mail to Pierre L'Ecuyer.