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

## A

AbstractChrono - Class in umontreal.iro.lecuyer.util
  `AbstractChrono` is a class that acts as an interface to the system clock and calculates the CPU or system time consumed by parts of a program.
AbstractChrono() - Constructor for class umontreal.iro.lecuyer.util.AbstractChrono
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 using the default simulator and initializes it by invoking `init()`.
Accumulate(Simulator) - Constructor for class umontreal.iro.lecuyer.simevents.Accumulate
  Constructs a new `Accumulate` statistical probe linked to the given simulator, 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, using the default simulator.
Accumulate(Simulator, 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.SimProcess
  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(double[]) - Method in class umontreal.iro.lecuyer.charts.BoxChart
  Adds a data series into the series collection.
add(double[], int) - Method in class umontreal.iro.lecuyer.charts.BoxChart
  Adds a data series into the series collection.
add(double[]) - Method in class umontreal.iro.lecuyer.charts.BoxSeriesCollection
  Adds a data series into the series collection.
add(double[], int) - Method in class umontreal.iro.lecuyer.charts.BoxSeriesCollection
  Adds a data series into the series collection.
add(double[]) - Method in class umontreal.iro.lecuyer.charts.EmpiricalSeriesCollection
  Adds a data series into the series collection.
add(double[], int) - Method in class umontreal.iro.lecuyer.charts.EmpiricalSeriesCollection
  Adds a data series into the series collection.
add(DoubleArrayList) - Method in class umontreal.iro.lecuyer.charts.EmpiricalSeriesCollection
  Adds a data series into the series collection.
add(TallyStore) - Method in class umontreal.iro.lecuyer.charts.EmpiricalSeriesCollection
  Adds a data series into the series collection.
add(double[]) - Method in class umontreal.iro.lecuyer.charts.HistogramSeriesCollection
  Adds a data series into the series collection.
add(double[], int) - Method in class umontreal.iro.lecuyer.charts.HistogramSeriesCollection
  Adds a data series into the series collection.
add(DoubleArrayList) - Method in class umontreal.iro.lecuyer.charts.HistogramSeriesCollection
  Adds a data series into the series collection.
add(TallyStore) - Method in class umontreal.iro.lecuyer.charts.HistogramSeriesCollection
  Adds a data series into the series collection.
add(SSJXYSeriesCollection) - Method in class umontreal.iro.lecuyer.charts.MultipleDatasetChart
  Adds a new dataset to the chart at the end of the list and returns its position.
add(double[], double[], String, String) - Method in class umontreal.iro.lecuyer.charts.ScatterChart
  Adds a data series into the series collection.
add(double[], double[]) - Method in class umontreal.iro.lecuyer.charts.ScatterChart
  Adds a data series into the series collection.
add(double[], double[], int) - Method in class umontreal.iro.lecuyer.charts.ScatterChart
  Adds a data series into the series collection.
add(double[], double[], String, String) - Method in class umontreal.iro.lecuyer.charts.XYLineChart
  Adds a data series into the series collection.
add(double[], double[]) - Method in class umontreal.iro.lecuyer.charts.XYLineChart
  Adds a data series into the series collection.
add(double[], double[], int) - Method in class umontreal.iro.lecuyer.charts.XYLineChart
  Adds a data series into the series collection.
add(double[][]) - Method in class umontreal.iro.lecuyer.charts.XYLineChart
  Adds the new collection of data series `data` into the series collection.
add(double[][], int) - Method in class umontreal.iro.lecuyer.charts.XYLineChart
  Adds the new collection of data series `data` into the series collection.
add(double[], double[]) - Method in class umontreal.iro.lecuyer.charts.XYListSeriesCollection
  Adds a data series into the series collection.
add(double[], double[], int) - Method in class umontreal.iro.lecuyer.charts.XYListSeriesCollection
  Adds a data series into the series collection.
add(double[][]) - Method in class umontreal.iro.lecuyer.charts.XYListSeriesCollection
  Adds a data series into the series collection.
add(double[][], int) - Method in class umontreal.iro.lecuyer.charts.XYListSeriesCollection
  Adds data series into the series collection.
add(DoubleArrayList) - Method in class umontreal.iro.lecuyer.charts.XYListSeriesCollection
  Adds a data series into the series collection.
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, E) - Method in class umontreal.iro.lecuyer.simevents.ListWithStat
add(E) - Method in class umontreal.iro.lecuyer.stat.list.ListOfStatProbes
add(int, E) - Method in class umontreal.iro.lecuyer.stat.list.ListOfStatProbes
add(double[]) - Method in class umontreal.iro.lecuyer.stat.list.ListOfTallies
  Adds the observation `x[i]` in tally `i` of this list, for `i = 0, ..., size() - 1`.
add(double[]) - Method in class umontreal.iro.lecuyer.stat.list.ListOfTalliesWithCovariance
  Adds a new vector of observations `x` to this list of tallies, and updates the internal data structures computing averages, and sums of products.
add(DoubleMatrix1D) - Method in class umontreal.iro.lecuyer.stat.list.ListOfTalliesWithCovariance
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
add(int, OE) - Method in class umontreal.iro.lecuyer.util.TransformingList
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<? extends E>) - Method in class umontreal.iro.lecuyer.stat.list.ListOfStatProbes
addAll(int, Collection<? extends E>) - Method in class umontreal.iro.lecuyer.stat.list.ListOfStatProbes
addArrayOfObservationListener(ArrayOfObservationListener) - Method in class umontreal.iro.lecuyer.stat.list.ListOfStatProbes
  Adds the observation listener `l` to the list of observers of this list of statistical probes.
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(E) - Method in class umontreal.iro.lecuyer.simevents.LinkedListStat
addLast(E) - Method in class umontreal.iro.lecuyer.simevents.LinkedListStat
addObservationListener(ObservationListener) - Method in class umontreal.iro.lecuyer.stat.StatProbe
  Adds the observation listener `l` to the list of observers of this statistical probe.
addRandomShift(int, int, RandomStream) - Method in class umontreal.iro.lecuyer.hups.ContainerPointSet
  Calls `addRandomShift(d1, d2, stream)` of the contained point set.
addRandomShift(RandomStream) - Method in class umontreal.iro.lecuyer.hups.ContainerPointSet
  Calls `addRandomShift(stream)` of the contained point set.
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.
addRandomShift() - Method in class umontreal.iro.lecuyer.hups.PointSet
  Deprecated.
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.
addSeries(Comparable, double[], int) - Method in class umontreal.iro.lecuyer.charts.CustomHistogramDataset
  Adds a series to the dataset, using the specified number of bins.
addSeries(Comparable, double[], int, int) - Method in class umontreal.iro.lecuyer.charts.CustomHistogramDataset
  Adds a series to the dataset, using the specified number of bins.
addSeries(Comparable, double[], int, double, double) - Method in class umontreal.iro.lecuyer.charts.CustomHistogramDataset
  Adds a series to the dataset.
addSeries(Comparable, double[], int, int, double, double) - Method in class umontreal.iro.lecuyer.charts.CustomHistogramDataset
  Adds a series to the dataset.
addSeries(Comparable, double[], HistogramBin[]) - Method in class umontreal.iro.lecuyer.charts.CustomHistogramDataset
  Adds a series to the dataset.
addSeries(Comparable, double[], int, HistogramBin[]) - Method in class umontreal.iro.lecuyer.charts.CustomHistogramDataset
  Adds a series to the dataset.
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
  Deprecated.
andersonDarling(int, double) - Static method in class umontreal.iro.lecuyer.gof.FDist
  Deprecated.
andersonDarling(DoubleArrayList) - Static method in class umontreal.iro.lecuyer.gof.GofStat
  Computes and returns the Anderson-Darling statistic AN2.
AndersonDarlingDist - Class in umontreal.iro.lecuyer.probdist
  Extends the class `ContinuousDistribution` for the Anderson-Darling distribution (see).
AndersonDarlingDist(int) - Constructor for class umontreal.iro.lecuyer.probdist.AndersonDarlingDist
  Constructs an Anderson-Darling distribution for a sample of size n.
AndersonDarlingDistQuick - Class in umontreal.iro.lecuyer.probdist
  Extends the class `AndersonDarlingDist` for the distribution (see).
AndersonDarlingDistQuick(int) - Constructor for class umontreal.iro.lecuyer.probdist.AndersonDarlingDistQuick
  Constructs an distribution for a sample of size n.
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.
append(CharSequence) - Method in class umontreal.iro.lecuyer.util.PrintfFormat
append(CharSequence, int, int) - Method in class umontreal.iro.lecuyer.util.PrintfFormat
apply(RandomStream) - Method in interface umontreal.iro.lecuyer.hups.Randomization
  Deprecated. Applies this randomization to the point set using stream `stream`.
areAllNumberObsEqual() - Method in class umontreal.iro.lecuyer.stat.list.ListOfTallies
  Tests that every tally in this list contains the same number of observations.
ArithmeticMod - Class in umontreal.iro.lecuyer.util
  This class provides facilities to compute multiplications of scalars, of vectors and of matrices modulo m.
ArrayOfObservationListener - Interface in umontreal.iro.lecuyer.stat.list
  Represents an object that can listen to observations broadcast by lists of statistical probes.
average() - Method in class umontreal.iro.lecuyer.simevents.Accumulate
  Returns the time-average since the last initialization to the last call to `update`.
average(double[]) - Method in class umontreal.iro.lecuyer.stat.list.ListOfStatProbes
  For each probe in this list, computes the average by calling `average`, and stores the results into the array `a`.
average(double[]) - Method in class umontreal.iro.lecuyer.stat.list.ListOfTallies
  Computes the average for each tally in this list, and stores the averages in the array `r`.
average() - Method in class umontreal.iro.lecuyer.stat.StatProbe
  Returns the average for this collector.
average() - Method in class umontreal.iro.lecuyer.stat.Tally
AverageMathFunction - Class in umontreal.iro.lecuyer.functions
  Represents a function computing the average of several functions.
AverageMathFunction(MathFunction...) - Constructor for class umontreal.iro.lecuyer.functions.AverageMathFunction
  Constructs a function computing the average of the functions in the array `func`.
Axis - Class in umontreal.iro.lecuyer.charts
  Represents an axis of a chart encapsulated by an instance of `XYChart`.
Axis(NumberAxis, boolean) - Constructor for class umontreal.iro.lecuyer.charts.Axis
  Create a new `Axis` instance from an existing `NumberAxis` instance with vertical (y-axis) or horizontal (x-axis) orientation.

## 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) - Method in class umontreal.iro.lecuyer.probdist.AndersonDarlingDist
barF(int, double) - Static method in class umontreal.iro.lecuyer.probdist.AndersonDarlingDist
  Computes the complementary distribution function bar(F)n(x) with parameter n.
barF(double) - Method in class umontreal.iro.lecuyer.probdist.AndersonDarlingDistQuick
barF(int, double) - Static method in class umontreal.iro.lecuyer.probdist.AndersonDarlingDistQuick
  Computes the complementary distribution function bar(F)n(x) with parameter n.
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.ChiSquareNoncentralDist
barF(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.ChiSquareNoncentralDist
  Computes the complementary noncentral chi-square distribution function with ν = `nu` degrees of freedom and parameter λ = `lambda`.
barF(double) - Method in class umontreal.iro.lecuyer.probdist.ContinuousDistribution
  Returns the complementary distribution function.
barF(double) - Method in class umontreal.iro.lecuyer.probdist.CramerVonMisesDist
barF(int, double) - Static method in class umontreal.iro.lecuyer.probdist.CramerVonMisesDist
  Computes the complementary distribution function bar(F)n(x) with parameter n.
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, double) - Static method in class umontreal.iro.lecuyer.probdist.FoldedNormalDist
  Computes the complementary distribution function.
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, double, double) - Static method in class umontreal.iro.lecuyer.probdist.HalfNormalDist
  Computes the complementary distribution function.
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.KolmogorovSmirnovDist
barF(int, double) - Static method in class umontreal.iro.lecuyer.probdist.KolmogorovSmirnovDist
  Computes the complementary distribution function bar(F)(x) with parameter n.
barF(double) - Method in class umontreal.iro.lecuyer.probdist.KolmogorovSmirnovDistQuick
barF(int, double) - Static method in class umontreal.iro.lecuyer.probdist.KolmogorovSmirnovDistQuick
  Computes the complementary distribution function bar(F)(x) with parameter n.
barF(double) - Method in class umontreal.iro.lecuyer.probdist.KolmogorovSmirnovPlusDist
barF(int, double) - Static method in class umontreal.iro.lecuyer.probdist.KolmogorovSmirnovPlusDist
  Computes the complementary distribution function bar(F)n(x) with parameter n.
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(double, double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.NakagamiDist
  Computes the complementary distribution function.
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 σ2.
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.NormalInverseGaussianDist
barF(double, double, double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.NormalInverseGaussianDist
  NOT IMPLEMENTED.
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(double) - Method in class umontreal.iro.lecuyer.probdist.PowerDist
barF(double, double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.PowerDist
  Computes the complementary distribution function.
barF(double) - Method in class umontreal.iro.lecuyer.probdist.RayleighDist
barF(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.RayleighDist
  Computes the complementary distribution function.
barF(double, double) - Static method in class umontreal.iro.lecuyer.probdist.RayleighDist
  Same as `barF (0, beta, x)`.
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.WatsonGDist
barF(int, double) - Static method in class umontreal.iro.lecuyer.probdist.WatsonGDist
  Computes the complementary distribution function bar(F)n(x) with parameter n.
barF(double) - Method in class umontreal.iro.lecuyer.probdist.WatsonUDist
barF(int, double) - Static method in class umontreal.iro.lecuyer.probdist.WatsonUDist
  Computes the complementary distribution function bar(F)n(x), where Fn is the Watson U distribution with parameter n.
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, 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, 1, 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 Ka is 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, double, double, double, double) - Constructor for class umontreal.iro.lecuyer.randvar.BetaGen
  Creates a new beta generator with parameters α = `alpha` and β = `beta`, over the interval (`a`, `b`), using stream `s`.
BetaGen(RandomStream, double, double) - Constructor for class umontreal.iro.lecuyer.randvar.BetaGen
  Creates a new beta generator with parameters α = `alpha` and β = `beta`, over the interval (0, 1), using stream `s`.
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, double, double) - Constructor for class umontreal.iro.lecuyer.randvar.BetaRejectionLoglogisticGen
  Creates a beta random variate generator with parameters α = `alpha` and β = `beta` over the interval (0, 1), using main stream `s` and auxiliary stream `aux`.
BetaRejectionLoglogisticGen(RandomStream, double, double) - Constructor for class umontreal.iro.lecuyer.randvar.BetaRejectionLoglogisticGen
  Creates a beta random variate generator with parameters α = `alpha` and β = `beta`, over the interval (0, 1), using stream `s`.
BetaRejectionLoglogisticGen(RandomStream, RandomStream, double, double, double, double) - Constructor for class umontreal.iro.lecuyer.randvar.BetaRejectionLoglogisticGen
  Creates a beta random variate generator with parameters α = `alpha` and β = `beta` over the interval (`a`, `b`), using main stream `s` and auxiliary stream `aux`.
BetaRejectionLoglogisticGen(RandomStream, double, double, double, double) - Constructor for class umontreal.iro.lecuyer.randvar.BetaRejectionLoglogisticGen
  Creates a beta random variate generator with parameters α = `alpha` and β = `beta`, over the interval (`a`, `b`), using stream `s`.
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, double, double) - Constructor for class umontreal.iro.lecuyer.randvar.BetaStratifiedRejectionGen
  Creates a beta random variate generator with parameters α = `alpha` and β = `beta`, over the interval (0, 1), using main stream `s` and auxiliary stream `aux`.
BetaStratifiedRejectionGen(RandomStream, double, double) - Constructor for class umontreal.iro.lecuyer.randvar.BetaStratifiedRejectionGen
  Creates a beta random variate generator with parameters α = `alpha` and β = `beta`, over the interval (0, 1), using stream `s`.
BetaStratifiedRejectionGen(RandomStream, RandomStream, double, double, double, double) - Constructor for class umontreal.iro.lecuyer.randvar.BetaStratifiedRejectionGen
  Creates a beta random variate generator with parameters α = `alpha` and β = `beta`, over the interval (`a`, `b`), using main stream `s` and auxiliary stream `aux`.
BetaStratifiedRejectionGen(RandomStream, double, double, double, double) - Constructor for class umontreal.iro.lecuyer.randvar.BetaStratifiedRejectionGen
  Creates a beta random variate generator with parameters α = `alpha` and β = `beta`, over the interval (`a`, `b`), using stream `s`.
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, double) - Constructor for class umontreal.iro.lecuyer.randvar.BetaSymmetricalBestGen
  Creates a symmetrical beta random variate generator with parameter α = `alpha`, using stream `s1` to generate U1, stream `s2` to generate U2 and stream `s3` to generate S, as given in equation.
BetaSymmetricalBestGen(RandomStream, double) - Constructor for class umontreal.iro.lecuyer.randvar.BetaSymmetricalBestGen
  Creates a symmetrical beta random variate generator with parameter α = `alpha`, using only one stream `s1` to generate U1, U2, and S as given in equation.
BetaSymmetricalBestGen(RandomStream, RandomStream, RandomStream, BetaSymmetricalDist) - Constructor for class umontreal.iro.lecuyer.randvar.BetaSymmetricalBestGen
  Creates a new generator for the distribution `dist`, using stream `s1` 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 `s1`.
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, double) - Constructor for class umontreal.iro.lecuyer.randvar.BetaSymmetricalGen
  Creates a new symmetrical beta generator with parameters α = `alpha`, over the interval (0, 1), using stream `s`.
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, double) - Constructor for class umontreal.iro.lecuyer.randvar.BetaSymmetricalPolarGen
  Creates a symmetrical beta random variate generator with parameter α = `alpha`, using stream `s1` to generate x and stream `s2` to generate y, as in above.
BetaSymmetricalPolarGen(RandomStream, double) - Constructor for class umontreal.iro.lecuyer.randvar.BetaSymmetricalPolarGen
  Creates a symmetrical beta random variate generator with parameter α = `alpha`, using stream `s1` to generate x and y, as in above.
BetaSymmetricalPolarGen(RandomStream, RandomStream, BetaSymmetricalDist) - Constructor for class umontreal.iro.lecuyer.randvar.BetaSymmetricalPolarGen
  Creates a new generator for the distribution `dist`, using stream `s1` to generate x and stream `s2` to generate y, as in above.
BetaSymmetricalPolarGen(RandomStream, BetaSymmetricalDist) - Constructor for class umontreal.iro.lecuyer.randvar.BetaSymmetricalPolarGen
  Creates a new generator for the distribution `dist`, using only one stream `s1`.
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 and linked with the default simulator.
Bin(ProcessSimulator) - Constructor for class umontreal.iro.lecuyer.simprocs.Bin
  Constructs a new bin, initially empty, with service policy FIFO and linked with simulator sim.
Bin(String) - Constructor for class umontreal.iro.lecuyer.simprocs.Bin
  Constructs a new bin, initially empty, with service policy FIFO, identifier `name` and linked with the default simulator.
Bin(ProcessSimulator, String) - Constructor for class umontreal.iro.lecuyer.simprocs.Bin
  Constructs a new bin, initially empty, with service policy FIFO, identifier `name` and linked with simulator sim.
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, int, double) - Constructor for class umontreal.iro.lecuyer.randvar.BinomialConvolutionGen
  Creates a binomial random variate generator with parameters n and p, using stream `s`.
BinomialConvolutionGen(RandomStream, BinomialDist) - Constructor for class umontreal.iro.lecuyer.randvar.BinomialConvolutionGen
  Creates a random variate generator for the binomial 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, int, double) - Constructor for class umontreal.iro.lecuyer.randvar.BinomialGen
  Creates a binomial random variate generator with parameters n and p, using stream `s`.
BinomialGen(RandomStream, BinomialDist) - Constructor for class umontreal.iro.lecuyer.randvar.BinomialGen
  Creates a 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`.
BoxChart - Class in umontreal.iro.lecuyer.charts
  Extends `CategoryChart`.
BoxChart() - Constructor for class umontreal.iro.lecuyer.charts.BoxChart
  Initializes a new `BoxChart` instance with an empty data set.
BoxChart(String, String, String, double[], int) - Constructor for class umontreal.iro.lecuyer.charts.BoxChart
  Initializes a new `BoxChart` instance with data `data`.
BoxChart(String, String, String, double[]...) - Constructor for class umontreal.iro.lecuyer.charts.BoxChart
  Initializes a new `BoxChart` instance with data `data`.
BoxSeriesCollection - Class in umontreal.iro.lecuyer.charts
  This class stores data used in a `CategoryChart`.
BoxSeriesCollection() - Constructor for class umontreal.iro.lecuyer.charts.BoxSeriesCollection
  Creates a new `BoxSeriesCollection` instance with an empty dataset.
BoxSeriesCollection(double[], int) - Constructor for class umontreal.iro.lecuyer.charts.BoxSeriesCollection
  Creates a new `BoxSeriesCollection` instance with default parameters and input series `data`.
BoxSeriesCollection(double[]...) - Constructor for class umontreal.iro.lecuyer.charts.BoxSeriesCollection
  Creates a new `BoxSeriesCollection` instance with default parameters and given data series.
BoxSeriesCollection(DefaultBoxAndWhiskerCategoryDataset) - Constructor for class umontreal.iro.lecuyer.charts.BoxSeriesCollection
  Creates a new `BoxSeriesCollection` instance with default parameters and given data series.
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.
BrownianMotion - Class in umontreal.iro.lecuyer.stochprocess
  This class represents a Brownian motion process {X(t) : t >= 0}, sampled at times 0 = t0 < t1 < ...
BrownianMotion(double, double, double, RandomStream) - Constructor for class umontreal.iro.lecuyer.stochprocess.BrownianMotion
  .
BrownianMotion(double, double, double, NormalGen) - Constructor for class umontreal.iro.lecuyer.stochprocess.BrownianMotion
  Constructs a new `BrownianMotion` with parameters μ = [tex2html_wrap_inline273], σ = [tex2html_wrap_inline275] and initial value X(t0) = [tex2html_wrap_inline277].
BrownianMotionBridge - Class in umontreal.iro.lecuyer.stochprocess
  Represents a Brownian motion process {X(t) : t >= 0} sampled using the bridge sampling technique (see for example).
BrownianMotionBridge(double, double, double, RandomStream) - Constructor for class umontreal.iro.lecuyer.stochprocess.BrownianMotionBridge
  .
BrownianMotionBridge(double, double, double, NormalGen) - Constructor for class umontreal.iro.lecuyer.stochprocess.BrownianMotionBridge
  Constructs a new `BrownianMotionBridge` with parameters μ = [tex2html_wrap_inline205], σ = [tex2html_wrap_inline207] and initial value X(t0) = [tex2html_wrap_inline209].
BrownianMotionPCA - Class in umontreal.iro.lecuyer.stochprocess
  A Brownian motion process {X(t) : t >= 0} sampled using the principal component decomposition (PCA).
BrownianMotionPCA(double, double, double, RandomStream) - Constructor for class umontreal.iro.lecuyer.stochprocess.BrownianMotionPCA
  .
BrownianMotionPCA(double, double, double, NormalGen) - Constructor for class umontreal.iro.lecuyer.stochprocess.BrownianMotionPCA
  Constructs a new `BrownianMotionBridge` with parameters μ = [tex2html_wrap_inline118], σ = [tex2html_wrap_inline120] and initial value X(t0) = [tex2html_wrap_inline122].
BrownianMotionPCAEqualSteps - Class in umontreal.iro.lecuyer.stochprocess
  Same as BrownianMotionPCA, but uses a trick to speed up the calculation when the time steps are equidistant.
BrownianMotionPCAEqualSteps(double, double, double, RandomStream) - Constructor for class umontreal.iro.lecuyer.stochprocess.BrownianMotionPCAEqualSteps
  Constructs a new `BrownianMotionPCAEqualSteps`.
BrownianMotionPCAEqualSteps(double, double, double, NormalGen) - Constructor for class umontreal.iro.lecuyer.stochprocess.BrownianMotionPCAEqualSteps
  Constructs a new `BrownianMotionPCAEqualSteps`.

## 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) - 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.SimProcess
  Cancel the activating event that was supposed to resume this process, and place the process in the `SUSPENDED` state.
CategoryChart - Class in umontreal.iro.lecuyer.charts
  This class provides tools to create charts from data in a simple way.
CategoryChart() - Constructor for class umontreal.iro.lecuyer.charts.CategoryChart
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, double, double) - Constructor for class umontreal.iro.lecuyer.randvar.CauchyGen
  Creates a Cauchy random variate generator with parameters α = `alpha` and β = `beta`, using stream `s`.
CauchyGen(RandomStream) - Constructor for class umontreal.iro.lecuyer.randvar.CauchyGen
  Creates a Cauchy random variate generator with parameters α = 0 and β = 1, using stream `s`.
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.AndersonDarlingDist
cdf(int, double) - Static method in class umontreal.iro.lecuyer.probdist.AndersonDarlingDist
  Computes the Anderson-Darling distribution function Fn(x), with parameter n, using Marsaglia's and al.
cdf(double) - Method in class umontreal.iro.lecuyer.probdist.AndersonDarlingDistQuick
cdf(int, double) - Static method in class umontreal.iro.lecuyer.probdist.AndersonDarlingDistQuick
  Computes the distribution function Fn(x) with parameter n.
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.ChiSquareNoncentralDist
cdf(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.ChiSquareNoncentralDist
  Computes the noncentral chi-square distribution function with ν = `nu` degrees of freedom and parameter λ = `lambda`.
cdf(double) - Method in class umontreal.iro.lecuyer.probdist.CramerVonMisesDist
cdf(int, double) - Static method in class umontreal.iro.lecuyer.probdist.CramerVonMisesDist
  Computes the Cramér-von Mises distribution function with parameter n.
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
  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.FoldedNormalDist
cdf(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.FoldedNormalDist
  Computes the distribution function.
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.HalfNormalDist
cdf(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.HalfNormalDist
  Computes the distribution function.
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.KolmogorovSmirnovDist
cdf(int, double) - Static method in class umontreal.iro.lecuyer.probdist.KolmogorovSmirnovDist
  Computes the distribution function F(x) with parameter n.
cdf(double) - Method in class umontreal.iro.lecuyer.probdist.KolmogorovSmirnovDistQuick
cdf(int, double) - Static method in class umontreal.iro.lecuyer.probdist.KolmogorovSmirnovDistQuick
  Computes the distribution function F(x) with parameter n.
cdf(double) - Method in class umontreal.iro.lecuyer.probdist.KolmogorovSmirnovPlusDist
cdf(int, double) - Static method in class umontreal.iro.lecuyer.probdist.KolmogorovSmirnovPlusDist
  Computes the Kolmogorov-Smirnov+ distribution function Fn(x) with parameter n.
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(double) - Method in class umontreal.iro.lecuyer.probdist.NakagamiDist
cdf(double, double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.NakagamiDist
  Computes the distribution function.
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.NormalInverseGaussianDist
cdf(double, double, double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.NormalInverseGaussianDist
  NOT IMPLEMENTED.
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.PowerDist
cdf(double, double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.PowerDist
  Computes the distribution function.
cdf(double) - Method in class umontreal.iro.lecuyer.probdist.RayleighDist
cdf(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.RayleighDist
  Computes the distribution function.
cdf(double, double) - Static method in class umontreal.iro.lecuyer.probdist.RayleighDist
  Same as `cdf (0, beta, x)`.
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.WatsonGDist
cdf(int, double) - Static method in class umontreal.iro.lecuyer.probdist.WatsonGDist
  Computes the Watson G distribution function Fn(x), with parameter n.
cdf(double) - Method in class umontreal.iro.lecuyer.probdist.WatsonUDist
cdf(int, double) - Static method in class umontreal.iro.lecuyer.probdist.WatsonUDist
  Computes the Watson U distribution function, i.e.
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, 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, 1, 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.
charAt(int) - Method in class umontreal.iro.lecuyer.util.PrintfFormat
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, int) - Constructor for class umontreal.iro.lecuyer.randvar.ChiGen
  Creates a chi random variate generator with ν = `nu` degrees of freedom, using stream `s`.
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, int) - Constructor for class umontreal.iro.lecuyer.randvar.ChiRatioOfUniformsGen
  Creates a chi random variate generator with ν = `nu` degrees of freedom, using stream `s`.
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, int) - Constructor for class umontreal.iro.lecuyer.randvar.ChiSquareGen
  Creates a chi square random variate generator with n degrees of freedom, using stream `s`.
ChiSquareGen(RandomStream, ChiSquareDist) - Constructor for class umontreal.iro.lecuyer.randvar.ChiSquareGen
  Create a new generator for the distribution `dist` and stream `s`.
ChiSquareNoncentralDist - Class in umontreal.iro.lecuyer.probdist
  Extends the class `ContinuousDistribution` for the noncentral chi-square distribution with ν degrees of freedom and noncentrality parameter λ, where ν > 0 and λ > 0.
ChiSquareNoncentralDist(double, double) - Constructor for class umontreal.iro.lecuyer.probdist.ChiSquareNoncentralDist
  Constructs a noncentral chi-square distribution with ν = `nu` degrees of freedom and noncentrality parameter λ = `lambda`.
ChiSquareNoncentralGamGen - Class in umontreal.iro.lecuyer.randvar
  This class implements noncentral chi square random variate generators using the additive property of the noncentral chi square distribution.
ChiSquareNoncentralGamGen(RandomStream, double, double) - Constructor for class umontreal.iro.lecuyer.randvar.ChiSquareNoncentralGamGen
  Creates a noncentral chi square random variate generator with with ν = `nu` degrees of freedom and noncentrality parameter λ = `lambda` using stream `stream`, as described above.
ChiSquareNoncentralGen - Class in umontreal.iro.lecuyer.randvar
  This class implements random variate generators for the noncentral chi square distribution with ν degrees of freedom and noncentrality parameter λ.
ChiSquareNoncentralGen(RandomStream, double, double) - Constructor for class umontreal.iro.lecuyer.randvar.ChiSquareNoncentralGen
  Creates a noncentral chi square random variate generator with ν > 0 degrees of freedom and noncentrality parameter λ > 0, using stream `s`.
ChiSquareNoncentralGen(RandomStream, ChiSquareNoncentralDist) - Constructor for class umontreal.iro.lecuyer.randvar.ChiSquareNoncentralGen
  Create a new generator for the distribution `dist` and stream `s`.
ChiSquareNoncentralPoisGen - Class in umontreal.iro.lecuyer.randvar
  This class implements noncentral chi square random variate generators using Poisson and central chi square generators.
ChiSquareNoncentralPoisGen(RandomStream, double, double) - Constructor for class umontreal.iro.lecuyer.randvar.ChiSquareNoncentralPoisGen
  Creates a noncentral chi square random variate generator with ν = `nu` degrees of freedom and noncentrality parameter λ = `lambda` using stream `stream`, as described above.
CholeskyDecompose(double[][], double[][]) - Static method in class umontreal.iro.lecuyer.util.DMatrix
  Given a symmetric positive-definite matrix M, performs the Cholesky decomposition of M and returns the result as a lower triangular matrix L, such that M = LLT.
CholeskyDecompose(DoubleMatrix2D) - Static method in class umontreal.iro.lecuyer.util.DMatrix
  Given a symmetric positive-definite matrix M, performs the Cholesky decomposition of M and returns the result as a lower triangular matrix L, such that M = LLT.
Chrono - Class in umontreal.iro.lecuyer.util
  The `Chrono` class extends the `AbstractChrono` class and computes the CPU time for the current thread only.
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
  Deprecated.
ChronoSingleThread() - Constructor for class umontreal.iro.lecuyer.util.ChronoSingleThread
  Deprecated. Constructs a `ChronoSingleThread` object and initializes it to zero.
CIRProcess - Class in umontreal.iro.lecuyer.stochprocess
  This class represents a CIR (Cox, Ingersoll, Ross) process {X(t) : t >= 0}, sampled at times 0 = t0 < t1 < ...
CIRProcess(double, double, double, double, RandomStream) - Constructor for class umontreal.iro.lecuyer.stochprocess.CIRProcess
  Constructs a new `CIRProcess` with parameters α = alpha, b, σ = sigma and initial value X(t0) = x0.
CIRProcess(double, double, double, double, NormalGen) - Constructor for class umontreal.iro.lecuyer.stochprocess.CIRProcess
  Here, the normal variate generator is specified directly instead of specifying the stream.
ClassFinder - Class in umontreal.iro.lecuyer.util
  Utility class used to convert a simple class name to a fully qualified class object.
ClassFinder() - Constructor for class umontreal.iro.lecuyer.util.ClassFinder
  Constructs a new class finder with an empty list of import declarations.
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.ListWithStat
clear() - Method in class umontreal.iro.lecuyer.stat.list.ListOfStatProbes
clear() - Method in class umontreal.iro.lecuyer.util.PrintfFormat
  Clears the contents of the buffer.
clear() - Method in class umontreal.iro.lecuyer.util.TransformingList
clearArrayOfObservationListeners() - Method in class umontreal.iro.lecuyer.stat.list.ListOfStatProbes
  Removes all observation listeners from the list of observers of this list of statistical probes.
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.
clearObservationListeners() - Method in class umontreal.iro.lecuyer.stat.StatProbe
  Removes all observation listeners from the list of observers of this statistical probe.
clearRandomShift() - Method in class umontreal.iro.lecuyer.hups.ContainerPointSet
  Calls `clearRandomShift()` of the contained point set.
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.charts.CustomHistogramDataset
  Returns a clone of the dataset.
clone() - Method in class umontreal.iro.lecuyer.charts.EmpiricalRenderer
  Returns a clone of the renderer.
clone() - Method in class umontreal.iro.lecuyer.functions.Polynomial
clone() - Method in interface umontreal.iro.lecuyer.rng.CloneableRandomStream
  Clones the current object and returns its copy.
clone() - Method in class umontreal.iro.lecuyer.rng.F2NL607
  Clones the current generator and return its copy.
clone() - Method in class umontreal.iro.lecuyer.rng.GenF2w32
  Clones the current generator and return its copy.
clone() - Method in class umontreal.iro.lecuyer.rng.LFSR113
  Clones the current generator and return its copy.
clone() - Method in class umontreal.iro.lecuyer.rng.LFSR258
  Clones the current generator and return its copy.
clone() - Method in class umontreal.iro.lecuyer.rng.MRG31k3p
  Clones the current generator and return its copy.
clone() - Method in class umontreal.iro.lecuyer.rng.MRG32k3a
  Clones the current generator and return its copy.
clone() - Method in class umontreal.iro.lecuyer.rng.MRG32k3aL
clone() - Method in class umontreal.iro.lecuyer.rng.MT19937
  Clones the current generator and return its copy.
clone() - Method in class umontreal.iro.lecuyer.rng.RandMrg
  Clones the current generator and return its copy.
clone() - Method in class umontreal.iro.lecuyer.rng.RandomStreamBase
  Clones the current generator and return its copy.
clone() - Method in class umontreal.iro.lecuyer.rng.RandRijndael
  Clones the current generator and return its copy.
clone() - Method in class umontreal.iro.lecuyer.rng.WELL1024
  Clones the current generator and return its copy.
clone() - Method in class umontreal.iro.lecuyer.rng.WELL512
  Clones the current generator and return its copy.
clone() - Method in class umontreal.iro.lecuyer.rng.WELL607
  Clones the current generator and return its copy.
clone() - Method in class umontreal.iro.lecuyer.simevents.Accumulate
  Clone this object.
clone() - Method in class umontreal.iro.lecuyer.stat.list.ListOfStatProbes
  Clones this object.
clone() - Method in class umontreal.iro.lecuyer.stat.list.ListOfTallies
  Clones this object.
clone() - Method in class umontreal.iro.lecuyer.stat.list.ListOfTalliesWithCovariance
  Clones this object.
clone() - Method in class umontreal.iro.lecuyer.stat.StatProbe
clone() - Method in class umontreal.iro.lecuyer.stat.Tally
  Clones this object.
clone() - Method in class umontreal.iro.lecuyer.stat.TallyStore
  Clones this object and the array which stores the observations.
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`.
clone() - Method in class umontreal.iro.lecuyer.util.ClassFinder
  Clones this class finder, and copies its lists of import declarations.
CloneableRandomStream - Interface in umontreal.iro.lecuyer.rng
  `CloneableRandomStream` extends `RandomStream` and `Cloneable`.
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.
compareTo(Event) - Method in class umontreal.iro.lecuyer.simevents.Event
  Compares this object with the specified object for order.
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`, linked with the default simulator.
Condition(ProcessSimulator, boolean) - Constructor for class umontreal.iro.lecuyer.simprocs.Condition
  Constructs a new `Condition` with initial value `val`, linked with simulator `sim`.
Condition(boolean, String) - Constructor for class umontreal.iro.lecuyer.simprocs.Condition
  Constructs a new `Condition` with initial value `val`, identifier `name` and linked with the default simulator.
Condition(ProcessSimulator, boolean, String) - Constructor for class umontreal.iro.lecuyer.simprocs.Condition
  Constructs a new `Condition` with initial value `val`, identifier `name` and linked with simulator `sim`.
confidenceIntervalNormal(double, double[]) - Method in class umontreal.iro.lecuyer.stat.Tally
  Computes a confidence interval on the mean.
confidenceIntervalStudent(double, double[]) - Method in class umontreal.iro.lecuyer.stat.Tally
  Computes a confidence interval on the mean.
confidenceIntervalVarianceChi2(double, double[]) - Method in class umontreal.iro.lecuyer.stat.Tally
  Computes a confidence interval on the variance.
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(URL) - Static method in class umontreal.iro.lecuyer.util.JDBCManager
  Equivalent to `connectToDatabase` `(url.openStream())`.
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.stat.list.ListOfStatProbes
containsAll(Collection<?>) - Method in class umontreal.iro.lecuyer.stat.list.ListOfStatProbes
Continuous - Class in umontreal.iro.lecuyer.simevents
  Represents a variable in a continuous-time simulation.
Continuous() - Constructor for class umontreal.iro.lecuyer.simevents.Continuous
  Constructs a new continuous-time variable linked to the default simulator, without initializing it.
Continuous(Simulator) - Constructor for class umontreal.iro.lecuyer.simevents.Continuous
  Constructs a new continuous-time variable linked to the given simulator, without initializing it.
ContinuousDistChart - Class in umontreal.iro.lecuyer.charts
  This class provides tools to plot the density and the cumulative probability of a continuous probability distribution.
ContinuousDistChart(ContinuousDistribution, double, double, int) - Constructor for class umontreal.iro.lecuyer.charts.ContinuousDistChart
  Constructor for a new `ContinuousDistChart` instance.
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
ContinuousState - Class in umontreal.iro.lecuyer.simevents
  Represents the portion of the simulator's state associated with continuous-time simulation.
continuousState() - Method in class umontreal.iro.lecuyer.simevents.Simulator
  Returns the current state of continuous variables being integrated during the simulation.
ContinuousState.IntegMethod - Enum in umontreal.iro.lecuyer.simevents
convert(double, TimeUnit, TimeUnit) - Static method in enum umontreal.iro.lecuyer.util.TimeUnit
  Converts `value` expressed in time unit `srcUnit` to a time duration expressed in `dstUnit` and returns the result of the conversion.
convertFromInnerType(ListWithStat.Node<E>) - Method in class umontreal.iro.lecuyer.simevents.ListWithStat
convertFromInnerType(IE) - Method in class umontreal.iro.lecuyer.util.TransformingList
  Converts an element in the inner list to an element of the outer type.
convertToInnerType(E) - Method in class umontreal.iro.lecuyer.simevents.ListWithStat
convertToInnerType(OE) - Method in class umontreal.iro.lecuyer.util.TransformingList
  Converts an element of the outer type to an element for the inner list.
COR - Static variable in class umontreal.iro.lecuyer.gof.GofFormat
  Correlation
correlation(int, int) - Method in class umontreal.iro.lecuyer.stat.list.ListOfTallies
  Returns the empirical correlation between the observations in tallies with indices `i` and `j`.
correlation(DoubleMatrix2D) - Method in class umontreal.iro.lecuyer.stat.list.ListOfTallies
  Similar to `covariance` for computing the sample correlation matrix.
covariance(int, int) - Method in class umontreal.iro.lecuyer.stat.list.ListOfTallies
  Returns the empirical covariance of the observations in tallies with indices `i` and `j`.
covariance(DoubleMatrix2D) - Method in class umontreal.iro.lecuyer.stat.list.ListOfTallies
  Constructs and returns the sample covariance matrix for the tallies in this list.
covariance(int, int) - Method in class umontreal.iro.lecuyer.stat.list.ListOfTalliesWithCovariance
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
  Deprecated.
cramerVonMises(int, double) - Static method in class umontreal.iro.lecuyer.gof.FDist
  Deprecated.
cramerVonMises(DoubleArrayList) - Static method in class umontreal.iro.lecuyer.gof.GofStat
  Computes and returns the Cramér-von Mises statistic WN2.
CramerVonMisesDist - Class in umontreal.iro.lecuyer.probdist
  Extends the class `ContinuousDistribution` for the Cramér-von Mises distribution (see).
CramerVonMisesDist(int) - Constructor for class umontreal.iro.lecuyer.probdist.CramerVonMisesDist
  Constructs a Cramér-von Mises distribution for a sample of size n.
createControlEvent(SimProcess) - Method in class umontreal.iro.lecuyer.simprocs.DSOLProcessSimulator
createControlEvent(SimProcess) - Method in class umontreal.iro.lecuyer.simprocs.ProcessSimulator
  Constructs and returns a new `Event` object used for synchronization.
createControlEvent(SimProcess) - Method in class umontreal.iro.lecuyer.simprocs.ThreadProcessSimulator
createForSingleThread() - Static method in class umontreal.iro.lecuyer.util.Chrono
  Creates a `Chrono` instance adapted for a program using a single thread.
createWithTally(int) - Static method in class umontreal.iro.lecuyer.stat.list.ListOfTallies
  This factory method constructs and returns a list of tallies with `size` instances of `Tally`.
createWithTally(int) - Static method in class umontreal.iro.lecuyer.stat.list.ListOfTalliesWithCovariance
  This factory method constructs and returns a list of tallies with `size` instances of `Tally`.
createWithTallyStore(int) - Static method in class umontreal.iro.lecuyer.stat.list.ListOfTallies
  This factory method constructs and returns a list of tallies with `size` instances of `TallyStore`.
createWithTallyStore(int) - Static method in class umontreal.iro.lecuyer.stat.list.ListOfTalliesWithCovariance
  This factory method constructs and returns a list of tallies with `size` instances of `TallyStore`.
currentProcess() - Method in class umontreal.iro.lecuyer.simprocs.ProcessSimulator
  Returns the currently active process for this simulator.
CustomHistogramDataset - Class in umontreal.iro.lecuyer.charts
  A dataset that can be used for creating histograms.
CustomHistogramDataset() - Constructor for class umontreal.iro.lecuyer.charts.CustomHistogramDataset
  Creates a new (empty) dataset with a default type of `HistogramType`.FREQUENCY.
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.SimProcess
  The process has terminated its execution.
decompPCA(double[][]) - Static method in class umontreal.iro.lecuyer.randvarmulti.MultinormalPCAGen
  Computes the decomposition `sigma` = Σ = V Λ Vt.
decompPCA(DoubleMatrix2D) - Static method in class umontreal.iro.lecuyer.randvarmulti.MultinormalPCAGen
  Computes the decomposition `sigma` = Σ = V Λ Vt.
decompPCA(double[][]) - Method in class umontreal.iro.lecuyer.stochprocess.BrownianMotionPCA
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.
defaultSimulator - Static variable in class umontreal.iro.lecuyer.simevents.Simulator
  Represents the default simulator being used by the class `Sim`, and the no-argument constructor of `Event`.
delay(SimProcess, double) - Method in class umontreal.iro.lecuyer.simprocs.DSOLProcessSimulator
delay(SimProcess, double) - Method in class umontreal.iro.lecuyer.simprocs.ProcessSimulator
  Suspends the execution of `process` and schedules it to resume its execution in `delay` units of simulation time.
delay(double) - Method in class umontreal.iro.lecuyer.simprocs.SimProcess
  Suspends the execution of the currently executing process and schedules it to resume its execution in `delay` units of simulation time.
delay(SimProcess, double) - Method in class umontreal.iro.lecuyer.simprocs.ThreadProcessSimulator
DELAYED - Static variable in class umontreal.iro.lecuyer.simprocs.SimProcess
  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.AndersonDarlingDist
density(int, double) - Static method in class umontreal.iro.lecuyer.probdist.AndersonDarlingDist
  Computes the density of the Anderson-Darling distribution with parameter n.
density(double) - Method in class umontreal.iro.lecuyer.probdist.AndersonDarlingDistQuick
density(int, double) - Static method in class umontreal.iro.lecuyer.probdist.AndersonDarlingDistQuick
  Computes the density of the distribution with parameter n.
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.ChiSquareNoncentralDist
density(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.ChiSquareNoncentralDist
  Computes the density function for a noncentral chi-square distribution with ν = `nu` degrees of freedom and parameter λ = `lambda`.
density(double) - Method in class umontreal.iro.lecuyer.probdist.ContinuousDistribution
  Returns f (x), the density evaluated at x.
density(double) - Method in class umontreal.iro.lecuyer.probdist.CramerVonMisesDist
density(int, double) - Static method in class umontreal.iro.lecuyer.probdist.CramerVonMisesDist
  Computes the density function for a Cramér-von Mises distribution with parameter n.
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.FoldedNormalDist
density(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.FoldedNormalDist
  Computes the density function of the folded normal distribution.
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.HalfNormalDist
density(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.HalfNormalDist
  Computes the density function of the half-normal distribution.
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.KolmogorovSmirnovDist
density(int, double) - Static method in class umontreal.iro.lecuyer.probdist.KolmogorovSmirnovDist
  Computes the density for the distribution with parameter n.
density(double) - Method in class umontreal.iro.lecuyer.probdist.KolmogorovSmirnovDistQuick
density(int, double) - Static method in class umontreal.iro.lecuyer.probdist.KolmogorovSmirnovDistQuick
  Computes the density for the distribution with parameter n.
density(double) - Method in class umontreal.iro.lecuyer.probdist.KolmogorovSmirnovPlusDist
density(int, double) - Static method in class umontreal.iro.lecuyer.probdist.KolmogorovSmirnovPlusDist
  Computes the density of the Kolmogorov-Smirnov+ distribution with parameter n.
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.NakagamiDist
density(double, double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.NakagamiDist
  Computes the density function of the Nakagami distribution.
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.NormalInverseGaussianDist
density(double, double, double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.NormalInverseGaussianDist
  Computes the density function for the normal inverse gaussian distribution with parameters α, β, μ and δ, evaluated at x.
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.PowerDist
density(double, double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.PowerDist
  Computes the density function.
density(double) - Method in class umontreal.iro.lecuyer.probdist.RayleighDist
density(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.RayleighDist
  Computes the density function.
density(double, double) - Static method in class umontreal.iro.lecuyer.probdist.RayleighDist
  Same as `density (0, beta, x)`.
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.WatsonGDist
density(int, double) - Static method in class umontreal.iro.lecuyer.probdist.WatsonGDist
  Computes the density function for a Watson G distribution with parameter n.
density(double) - Method in class umontreal.iro.lecuyer.probdist.WatsonUDist
density(int, double) - Static method in class umontreal.iro.lecuyer.probdist.WatsonUDist
  Computes the density of the Watson U distribution with parameter n.
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, 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`.
density01(double) - Static method in class umontreal.iro.lecuyer.probdist.NormalDist
  Same as `density` `(0, 1, x)`.
derivative(double, int) - Method in class umontreal.iro.lecuyer.functions.AverageMathFunction
derivative(double) - Method in class umontreal.iro.lecuyer.functions.AverageMathFunction
derivative(double) - Method in class umontreal.iro.lecuyer.functions.IdentityMathFunction
derivative(double, int) - Method in class umontreal.iro.lecuyer.functions.IdentityMathFunction
derivative(MathFunction, double) - Static method in class umontreal.iro.lecuyer.functions.MathFunctionUtil
  Returns the first derivative of the function `func` evaluated at `x`.
derivative(MathFunction, double, int) - Static method in class umontreal.iro.lecuyer.functions.MathFunctionUtil
  Returns the nth derivative of function `func` evaluated at `x`.
derivative(double, int) - Method in interface umontreal.iro.lecuyer.functions.MathFunctionWithDerivative
  Computes (or estimates) the nth derivative of the function at point `x`.
derivative(double) - Method in interface umontreal.iro.lecuyer.functions.MathFunctionWithFirstDerivative
  Computes (or estimates) the first derivative of the function at point `x`.
derivative(double) - Method in class umontreal.iro.lecuyer.functions.Polynomial
derivative(double, int) - Method in class umontreal.iro.lecuyer.functions.Polynomial
derivative(double) - Method in class umontreal.iro.lecuyer.functions.PowerMathFunction
derivative(double) - Method in class umontreal.iro.lecuyer.functions.ShiftedMathFunction
derivative(double, int) - Method in class umontreal.iro.lecuyer.functions.ShiftedMathFunction
derivative(double) - Method in class umontreal.iro.lecuyer.functions.SquareMathFunction
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.
derivativePolynomial(int) - Method in class umontreal.iro.lecuyer.functions.Polynomial
  Returns a polynomial corresponding to the nth derivative of this polynomial.
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.
diff(DoubleArrayList, DoubleArrayList, int, int, double, double) - Static method in class umontreal.iro.lecuyer.gof.GofStat
  Same as method `diff``(IntArrayList,IntArrayList,int,int,int,int)`, but for the continuous case.
digamma(double) - Static method in class umontreal.iro.lecuyer.util.Num
  Returns the value of 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`.
disableAutoCompletion() - Method in class umontreal.iro.lecuyer.charts.XYListSeriesCollection
  Disables auto completion option.
disableCustomLabels() - Method in class umontreal.iro.lecuyer.charts.Axis
  Disables the use of custom labels for the current axis.
disableGrid() - Method in class umontreal.iro.lecuyer.charts.CategoryChart
  Disables the background grid.
disableGrid() - Method in class umontreal.iro.lecuyer.charts.MultipleDatasetChart
  Disables the background grid.
disableGrid() - Method in class umontreal.iro.lecuyer.charts.XYChart
  Disables the background grid.
DiscreteDistIntChart - Class in umontreal.iro.lecuyer.charts
  This class provides tools to plot the mass function and the cumulative probability of a discrete probability distribution over the integers.
DiscreteDistIntChart(DiscreteDistributionInt) - Constructor for class umontreal.iro.lecuyer.charts.DiscreteDistIntChart
  Constructor for a new `DiscreteDistIntChart` instance used to plot the probabilities of the discrete distribution `dist` over the integers.
DiscreteDistIntChart(DiscreteDistributionInt, int, int) - Constructor for class umontreal.iro.lecuyer.charts.DiscreteDistIntChart
  Constructor for a new `DiscreteDistIntChart` instance used to plot the probabilities of the discrete distribution `dist` over the interval [a, b].
DiscreteDistribution - Class in umontreal.iro.lecuyer.probdist
  Classes implementing discrete distributions over a finite set of real numbers should inherit from this class.
DiscreteDistribution(double[], double[], int) - 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`.
DiscreteDistribution(int, double[], double[]) - Constructor for class umontreal.iro.lecuyer.probdist.DiscreteDistribution
  Deprecated.
DiscreteDistribution(double[]) - Constructor for class umontreal.iro.lecuyer.probdist.DiscreteDistribution
  Constructs a discrete distribution whose parameters are given in a single ordered array: `params[0]` contains n, the number of values to consider.
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`.
DMatrix - Class in umontreal.iro.lecuyer.util
  This class implements a few methods for matrix calculations with `double` numbers.
DMatrix(int, int) - Constructor for class umontreal.iro.lecuyer.util.DMatrix
  Creates a new `DMatrix` with `r` rows and `c` columns.
DMatrix(double[][], int, int) - Constructor for class umontreal.iro.lecuyer.util.DMatrix
  Creates a new `DMatrix` with `r` rows and `c` columns using the data in `data`.
DMatrix(DMatrix) - Constructor for class umontreal.iro.lecuyer.util.DMatrix
  Copy constructor.
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
drawCdf(ContinuousDistribution, double, double, int, String) - Static method in class umontreal.iro.lecuyer.gof.GofFormat
  Formats data to plot the graph of the distribution function F over the interval [a, b], and returns the result as a `String`.
drawDensity(ContinuousDistribution, double, double, int, String) - Static method in class umontreal.iro.lecuyer.gof.GofFormat
  Formats data to plot the graph of the density f (x) over the interval [a, b], and returns the result as a `String`.
drawItem(Graphics2D, XYItemRendererState, Rectangle2D, PlotRenderingInfo, XYPlot, ValueAxis, ValueAxis, XYDataset, int, int, CrosshairState, int) - Method in class umontreal.iro.lecuyer.charts.EmpiricalRenderer
  Draws the visual representation of a single data item.
DSOLProcessSimulator - Class in umontreal.iro.lecuyer.simprocs
  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`).
DSOLProcessSimulator() - Constructor for class umontreal.iro.lecuyer.simprocs.DSOLProcessSimulator
  Constructs a new DSOLProcessSimulator variable without initialization.

## 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.
EmpiricalChart - Class in umontreal.iro.lecuyer.charts
  This class provides additional tools to create and manage empirical plots.
EmpiricalChart() - Constructor for class umontreal.iro.lecuyer.charts.EmpiricalChart
  Initializes a new `EmpiricalChart` instance with an empty data set.
EmpiricalChart(String, String, String, double[]...) - Constructor for class umontreal.iro.lecuyer.charts.EmpiricalChart
  Initializes a new `EmpiricalChart` instance with data `data`.
EmpiricalChart(String, String, String, double[], int) - Constructor for class umontreal.iro.lecuyer.charts.EmpiricalChart
  Initializes a new `EmpiricalChart` instance with a set of points `data`.
EmpiricalChart(String, String, String, DoubleArrayList...) - Constructor for class umontreal.iro.lecuyer.charts.EmpiricalChart
  Similar to the above constructor, but with `DoubleArrayList`.
EmpiricalChart(String, String, String, TallyStore...) - Constructor for class umontreal.iro.lecuyer.charts.EmpiricalChart
  Initializes a new `EmpiricalChart` instance with data arrays contained in each `TallyStore` object.
EmpiricalChart(String, String, String, XYSeriesCollection) - Constructor for class umontreal.iro.lecuyer.charts.EmpiricalChart
  Initializes a new `EmpiricalChart` instance with data `data`.
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`.
EmpiricalRenderer - Class in umontreal.iro.lecuyer.charts
  A renderer that draws horizontal lines between points and/or draws shapes at each data point to provide an empirical style chart.
EmpiricalRenderer() - Constructor for class umontreal.iro.lecuyer.charts.EmpiricalRenderer
  Creates a new renderer.
EmpiricalRenderer(XYToolTipGenerator, XYURLGenerator) - Constructor for class umontreal.iro.lecuyer.charts.EmpiricalRenderer
  Creates a new renderer with selected tool tip and url generators.
EmpiricalSeriesCollection - Class in umontreal.iro.lecuyer.charts
  Stores data used in a `EmpiricalChart`.
EmpiricalSeriesCollection() - Constructor for class umontreal.iro.lecuyer.charts.EmpiricalSeriesCollection
  Creates a new `EmpiricalSeriesCollection` instance with empty dataset.
EmpiricalSeriesCollection(double[]...) - Constructor for class umontreal.iro.lecuyer.charts.EmpiricalSeriesCollection
  Creates a new `EmpiricalSeriesCollection` instance with default parameters and given data series.
EmpiricalSeriesCollection(double[], int) - Constructor for class umontreal.iro.lecuyer.charts.EmpiricalSeriesCollection
  Creates a new `EmpiricalSeriesCollection` instance with default parameters and a given series `data`.
EmpiricalSeriesCollection(DoubleArrayList...) - Constructor for class umontreal.iro.lecuyer.charts.EmpiricalSeriesCollection
  Creates a new `EmpiricalSeriesCollection` instance with default parameters and given data.
EmpiricalSeriesCollection(TallyStore...) - Constructor for class umontreal.iro.lecuyer.charts.EmpiricalSeriesCollection
  Creates a new `EmpiricalSeriesCollection` instance with default parameters and given data.
EmpiricalSeriesCollection(XYSeriesCollection) - Constructor for class umontreal.iro.lecuyer.charts.EmpiricalSeriesCollection
  Creates a new `EmpiricalSeriesCollection` instance with default parameters and given data series.
enableAutoCompletion() - Method in class umontreal.iro.lecuyer.charts.XYListSeriesCollection
  Enables the auto completion option.
enableCustomLabels() - Method in class umontreal.iro.lecuyer.charts.Axis
  Enables the use of custom labels for the current axis.
enableGrid(double, double) - Method in class umontreal.iro.lecuyer.charts.CategoryChart
  Puts a grid on the background.
enableGrid(double, double) - Method in class umontreal.iro.lecuyer.charts.MultipleDatasetChart
  Puts grid on the background.
enableGrid(double, double) - Method in class umontreal.iro.lecuyer.charts.XYChart
  Puts a grid on the background.
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(Object) - Method in class umontreal.iro.lecuyer.charts.CustomHistogramDataset
  Tests this dataset for equality with an arbitrary object.
equals(Object) - Method in class umontreal.iro.lecuyer.stat.list.ListOfStatProbes
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.
erf(double) - Static method in class umontreal.iro.lecuyer.util.Num
  Returns the value of erf(x), the error function.
erfc(double) - Static method in class umontreal.iro.lecuyer.util.Num
  Returns the value of erfc(x), the complementary error function.
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, int, double) - Constructor for class umontreal.iro.lecuyer.randvar.ErlangConvolutionGen
  Creates an Erlang random variate generator with parameters `k` and λ = `lambda`, using stream `s`.
ErlangConvolutionGen(RandomStream, int) - Constructor for class umontreal.iro.lecuyer.randvar.ErlangConvolutionGen
  Creates an Erlang random variate generator with parameters `k` and λ = 1, using stream `s`.
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, int, double) - Constructor for class umontreal.iro.lecuyer.randvar.ErlangGen
  Creates an Erlang random variate generator with parameters `k` and λ = `lambda`, using stream `s`.
ErlangGen(RandomStream, int) - Constructor for class umontreal.iro.lecuyer.randvar.ErlangGen
  Creates an Erlang random variate generator with parameters `k` and λ = 1, using stream `s`.
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
  Polynomial evaluation at a given point.
evaluate(double) - Method in class umontreal.iro.lecuyer.functions.AverageMathFunction
evaluate(double) - Method in class umontreal.iro.lecuyer.functions.IdentityMathFunction
evaluate(double) - Method in interface umontreal.iro.lecuyer.functions.MathFunction
  Returns the value of the function evaluated at x.
evaluate(double) - Method in class umontreal.iro.lecuyer.functions.PiecewiseConstantFunction
evaluate(double) - Method in class umontreal.iro.lecuyer.functions.Polynomial
evaluate(double) - Method in class umontreal.iro.lecuyer.functions.PowerMathFunction
evaluate(double) - Method in class umontreal.iro.lecuyer.functions.ShiftedMathFunction
evaluate(double) - Method in class umontreal.iro.lecuyer.functions.SqrtMathFunction
evaluate(double) - Method in class umontreal.iro.lecuyer.functions.SquareMathFunction
evaluate(double...) - Method in interface umontreal.iro.lecuyer.util.MultivariateFunction
  Computes the function g(x) for the vector `x`.
evaluate(double...) - Method in class umontreal.iro.lecuyer.util.RatioFunction
evaluateGradient(int, double...) - Method in interface umontreal.iro.lecuyer.util.MultivariateFunction
  Computes ∂g(x)/∂xi, the derivative of g(x) with respect to xi.
evaluateGradient(int, double...) - Method in class umontreal.iro.lecuyer.util.RatioFunction
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 of the default simulator.
Event(Simulator) - Constructor for class umontreal.iro.lecuyer.simevents.Event
  Construct a new event instance associated with the given simulator.
EventList - Interface in umontreal.iro.lecuyer.simevents.eventlist
  An interface for implementations of event lists.
EXECUTING - Static variable in class umontreal.iro.lecuyer.simprocs.SimProcess
  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`.
ExponentialDistFromMean - Class in umontreal.iro.lecuyer.probdist
  Extends the `ExponentialDist` class with a constructor accepting as argument the mean 1/λ instead of the rate λ.
ExponentialDistFromMean(double) - Constructor for class umontreal.iro.lecuyer.probdist.ExponentialDistFromMean
  Constructs a new exponential distribution with mean `mean`.
ExponentialGen - Class in umontreal.iro.lecuyer.randvar
  This class implements random variate generators for the exponential distribution.
ExponentialGen(RandomStream, double) - Constructor for class umontreal.iro.lecuyer.randvar.ExponentialGen
  Creates an exponential random variate generator with parameter λ = `lambda`, using stream `s`.
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, double, double) - Constructor for class umontreal.iro.lecuyer.randvar.ExtremeValueGen
  Creates an extreme value random variate generator with parameters α = `alpha` and λ = `lambda`, using stream `s`.
ExtremeValueGen(RandomStream) - Constructor for class umontreal.iro.lecuyer.randvar.ExtremeValueGen
  Creates an extreme value random variate generator with parameters α = 0 and λ = 1, using stream `s`.
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, double, double, double) - Constructor for class umontreal.iro.lecuyer.randvar.FatigueLifeGen
  Creates a fatigue life random variate generator with parameters μ = `mu`, β = `beta` and γ = `gamma`, using stream `s`.
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
  WARNING: All methods in this class are deprecated, except the method `scan`.
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.
findClass(String) - Method in class umontreal.iro.lecuyer.util.ClassFinder
  Tries to find the class corresponding to the simple name `name`.
finiteCenteredDifferenceDerivative(MathFunction, double, double) - Static method in class umontreal.iro.lecuyer.functions.MathFunctionUtil
  Returns (f (x + h) - f (x - h))/(2h), an estimate of the first derivative of f (x) using centered differences.
finiteCenteredDifferenceDerivative(MathFunction, double, int, double) - Static method in class umontreal.iro.lecuyer.functions.MathFunctionUtil
  Computes and returns an estimate of the nth derivative of the function f (x) using finite centered differences.
finiteDifferenceDerivative(MathFunction, double, int, double) - Static method in class umontreal.iro.lecuyer.functions.MathFunctionUtil
  Computes and returns an estimate of the nth derivative of the function f (x).
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, int, int) - Constructor for class umontreal.iro.lecuyer.randvar.FisherFGen
  Creates a Fisher F random variate generator with n and m degrees of freedom, using stream `s`.
FisherFGen(RandomStream, FisherFDist) - Constructor for class umontreal.iro.lecuyer.randvar.FisherFGen
  Creates a new generator for the distribution `dist`, using stream `s`.
FoldedNormalDist - Class in umontreal.iro.lecuyer.probdist
  Extends the class `ContinuousDistribution` for the folded normal distribution with parameters μ >= 0 and σ > 0.
FoldedNormalDist(double, double) - Constructor for class umontreal.iro.lecuyer.probdist.FoldedNormalDist
  Constructs a `FoldedNormalDist` object with parameters μ = `mu` and σ = `sigma`.
FoldedNormalGen - Class in umontreal.iro.lecuyer.randvar
  This class implements methods for generating random variates from the folded normal distribution with parameters μ >= 0 and σ > 0.
FoldedNormalGen(RandomStream, double, double) - Constructor for class umontreal.iro.lecuyer.randvar.FoldedNormalGen
  Creates a new folded normal generator with parameters μ = `mu` and σ = `sigma`, using stream `s`.
FoldedNormalGen(RandomStream, FoldedNormalDist) - Constructor for class umontreal.iro.lecuyer.randvar.FoldedNormalGen
  Creates a new generator for the distribution `dist`, using stream `s`.
format() - Method in class umontreal.iro.lecuyer.util.AbstractChrono
  Converts the CPU time used by the program since its last call to `init` for this `AbstractChrono` to a `String` in the `HH:MM:SS.xx` format.
format(double) - Static method in class umontreal.iro.lecuyer.util.AbstractChrono
  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(Locale, int, int, int, double) - Static method in class umontreal.iro.lecuyer.util.PrintfFormat
  This method is equivalent to `format`, except it formats the given value for the locale `locale`.
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.
formatCINormal(double, int) - Method in class umontreal.iro.lecuyer.stat.Tally
  .
formatCINormal(double) - Method in class umontreal.iro.lecuyer.stat.Tally
  Equivalent to `formatCINormal (level, 3)`.
formatCIStudent(double, int) - Method in class umontreal.iro.lecuyer.stat.Tally
  .
formatCIStudent(double) - Method in class umontreal.iro.lecuyer.stat.Tally
  Equivalent to `formatCIStudent (level, 3)`.
formatCIVarianceChi2(double, int) - Method in class umontreal.iro.lecuyer.stat.Tally
  .
formatConfidenceIntervalNormal(double, int) - Method in class umontreal.iro.lecuyer.stat.Tally
  Deprecated.
formatConfidenceIntervalNormal(double) - Method in class umontreal.iro.lecuyer.stat.Tally
  Deprecated.
formatConfidenceIntervalStudent(double, int) - Method in class umontreal.iro.lecuyer.stat.Tally
  Deprecated.
formatConfidenceIntervalStudent(double) - Method in class umontreal.iro.lecuyer.stat.Tally
  Deprecated.
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.
formatStateFull() - Method in class umontreal.iro.lecuyer.rng.RandomStreamBase
  Deprecated.
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.
formatWithError(Locale, int, int, int, int, double, double, String[]) - Static method in class umontreal.iro.lecuyer.util.PrintfFormat
  This method is equivalent to `formatWithError`, except that it formats the given value and error for the locale `locale`.
formatWithError(Locale, int, int, int, double, double, String[]) - Static method in class umontreal.iro.lecuyer.util.PrintfFormat
  This method is equivalent to `formatWithError`, except that it formats the given value and error for the locale `locale`.
fromCSV(String) - Static method in class umontreal.iro.lecuyer.charts.PlotFormat
  Parses `data` according to the standard CSV format and stores the extracted values in the returned table.
fromCustomizedFormat(String, String, String) - Static method in class umontreal.iro.lecuyer.charts.PlotFormat
  Parses `data` according to a user defined format and stores the extracted values in the returned table.
fromGNUPlot(String) - Static method in class umontreal.iro.lecuyer.charts.PlotFormat
  Parses `data` according to the standard GNUPlot format and stores the extracted values in the returned table.

## 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, RandomStream, double, double) - Constructor for class umontreal.iro.lecuyer.randvar.GammaAcceptanceRejectionGen
  Creates a gamma random variate generator with parameters α = `alpha` and λ = `lambda`, using main stream `s` and auxiliary stream `aux`.
GammaAcceptanceRejectionGen(RandomStream, double, double) - Constructor for class umontreal.iro.lecuyer.randvar.GammaAcceptanceRejectionGen
  Creates a gamma random variate generator with parameters α = `alpha` and λ = `lambda`, using stream `s`.
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`.
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.
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.
GammaDistFromMoments - Class in umontreal.iro.lecuyer.probdist
  Extends the `GammaDist` distribution with constructors accepting the mean μ and variance σ2 as arguments instead of a shape parameter α and a scale parameter λ.
GammaDistFromMoments(double, double, int) - Constructor for class umontreal.iro.lecuyer.probdist.GammaDistFromMoments
  Constructs a gamma distribution with mean `mean`, variance `var`, and `d` decimal of precision.
GammaDistFromMoments(double, double) - Constructor for class umontreal.iro.lecuyer.probdist.GammaDistFromMoments
  Constructs a gamma distribution with mean `mean`, and variance `var`.
GammaGen - Class in umontreal.iro.lecuyer.randvar
  This class implements random variate generators for the gamma distribution.
GammaGen(RandomStream, double, double) - Constructor for class umontreal.iro.lecuyer.randvar.GammaGen
  Creates a gamma random variate generator with parameters α = `alpha` and λ = `lambda`, using stream `s`.
GammaGen(RandomStream, double) - Constructor for class umontreal.iro.lecuyer.randvar.GammaGen
  Creates a gamma random variate generator with parameters α = `alpha` and λ = 1, using stream `s`.
GammaGen(RandomStream, GammaDist) - Constructor for class umontreal.iro.lecuyer.randvar.GammaGen
  Creates a new generator object for the gamma distribution `dist` and stream `s`.
GammaProcess - Class in umontreal.iro.lecuyer.stochprocess
  This class represents a gamma process {S(t) = G(t;μ, ν) : t >= 0} with mean parameter μ and variance parameter ν.
GammaProcess(double, double, double, RandomStream) - Constructor for class umontreal.iro.lecuyer.stochprocess.GammaProcess
  .
GammaProcess(double, double, double, GammaGen) - Constructor for class umontreal.iro.lecuyer.stochprocess.GammaProcess
  Constructs a new `GammaProcess` with parameters μ = [tex2html_wrap_inline291], ν = [tex2html_wrap_inline293] and initial value S(t0) = [tex2html_wrap_inline295].
GammaProcessBridge - Class in umontreal.iro.lecuyer.stochprocess
  This class represents a gamma process {S(t) = G(t;μ, ν) : t >= 0} with mean parameter μ and variance parameter ν, sampled using the gamma bridge method (see for example).
GammaProcessBridge(double, double, double, RandomStream) - Constructor for class umontreal.iro.lecuyer.stochprocess.GammaProcessBridge
  .
GammaProcessBridge(double, double, double, GammaGen, BetaGen) - Constructor for class umontreal.iro.lecuyer.stochprocess.GammaProcessBridge
  Constructs a new `GammaProcessBridge`.
GammaProcessPCA - Class in umontreal.iro.lecuyer.stochprocess
  Represents a gamma process sampled using the principal component analysis (PCA).
GammaProcessPCA(double, double, double, RandomStream) - Constructor for class umontreal.iro.lecuyer.stochprocess.GammaProcessPCA
  .
GammaProcessPCA(double, double, double, GammaGen) - Constructor for class umontreal.iro.lecuyer.stochprocess.GammaProcessPCA
  Constructs a new `GammaProcessPCA` with parameters μ = [tex2html_wrap_inline221], ν = [tex2html_wrap_inline223] and initial value S(t0) = [tex2html_wrap_inline225].
GammaProcessPCABridge - Class in umontreal.iro.lecuyer.stochprocess
  Same as `GammaProcessPCA`, but the generated uniforms correspond to a bridge transformation of the `BrownianMotionPCA` instead of a sequential transformation.
GammaProcessPCABridge(double, double, double, RandomStream) - Constructor for class umontreal.iro.lecuyer.stochprocess.GammaProcessPCABridge
  .
GammaProcessPCASymmetricalBridge - Class in umontreal.iro.lecuyer.stochprocess
  Same as `GammaProcessPCABridge`, but uses the fast inversion method for the symmetrical beta distribution, proposed by L'Ecuyer and Simard, to accelerate the generation of the beta random variables.
GammaProcessPCASymmetricalBridge(double, double, double, RandomStream) - Constructor for class umontreal.iro.lecuyer.stochprocess.GammaProcessPCASymmetricalBridge
  .
GammaProcessSymmetricalBridge - Class in umontreal.iro.lecuyer.stochprocess
  This class differs from `GammaProcessBridge` only in that it requires the number of interval of the path to be a power of 2 and of equal size.
GammaProcessSymmetricalBridge(double, double, double, RandomStream) - Constructor for class umontreal.iro.lecuyer.stochprocess.GammaProcessSymmetricalBridge
  .
GammaProcessSymmetricalBridge(double, double, double, GammaGen, BetaSymmetricalGen) - Constructor for class umontreal.iro.lecuyer.stochprocess.GammaProcessSymmetricalBridge
  Constructs a new `GammaProcessSymmetricalBridge` with parameters μ = [tex2html_wrap_inline128], ν = [tex2html_wrap_inline130] and initial value S(t0) = [tex2html_wrap_inline132].
gammaRatioHalf(double) - Static method in class umontreal.iro.lecuyer.util.Num
  Returns the value of the ratio Γ(x + 1/2)/Γ(x) of two gamma functions, evaluated in a numerically stable way.
GammaRejectionLoglogisticGen - Class in umontreal.iro.lecuyer.randvar
  This class implements gamma random variate generators using a rejection method with loglogistic envelopes,.
GammaRejectionLoglogisticGen(RandomStream, RandomStream, double, double) - Constructor for class umontreal.iro.lecuyer.randvar.GammaRejectionLoglogisticGen
  Creates a gamma random variate generator with parameters α = `alpha` and λ = `lambda`, using main stream `s` and auxiliary stream `aux`.
GammaRejectionLoglogisticGen(RandomStream, double, double) - Constructor for class umontreal.iro.lecuyer.randvar.GammaRejectionLoglogisticGen
  Creates a gamma random variate generator with parameters α = `alpha` and λ = `lambda`, using stream `s`.
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`.
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.
generatePath() - Method in class umontreal.iro.lecuyer.stochprocess.BrownianMotion
generatePath(double[]) - Method in class umontreal.iro.lecuyer.stochprocess.BrownianMotion
  Same as generatePath(), but a vector of uniform random numbers must be provided to the method.
generatePath(RandomStream) - Method in class umontreal.iro.lecuyer.stochprocess.BrownianMotion
generatePath() - Method in class umontreal.iro.lecuyer.stochprocess.BrownianMotionBridge
generatePath(double[]) - Method in class umontreal.iro.lecuyer.stochprocess.BrownianMotionBridge
generatePath() - Method in class umontreal.iro.lecuyer.stochprocess.BrownianMotionPCA
generatePath(double[]) - Method in class umontreal.iro.lecuyer.stochprocess.BrownianMotionPCA
generatePath() - Method in class umontreal.iro.lecuyer.stochprocess.BrownianMotionPCAEqualSteps
generatePath(double[]) - Method in class umontreal.iro.lecuyer.stochprocess.BrownianMotionPCAEqualSteps
generatePath() - Method in class umontreal.iro.lecuyer.stochprocess.CIRProcess
generatePath(RandomStream) - Method in class umontreal.iro.lecuyer.stochprocess.CIRProcess
generatePath() - Method in class umontreal.iro.lecuyer.stochprocess.GammaProcess
  Generates, returns and saves the path {X(t0), X(t1),…, X(td)}.
generatePath(double[]) - Method in class umontreal.iro.lecuyer.stochprocess.GammaProcess
  Generates, returns and saves the path {X(t0), X(t1),…, X(td)}.
generatePath(double[]) - Method in class umontreal.iro.lecuyer.stochprocess.GammaProcessBridge
generatePath() - Method in class umontreal.iro.lecuyer.stochprocess.GammaProcessBridge
generatePath() - Method in class umontreal.iro.lecuyer.stochprocess.GammaProcessPCA
generatePath(double[]) - Method in class umontreal.iro.lecuyer.stochprocess.GammaProcessPCA
generatePath(double[]) - Method in class umontreal.iro.lecuyer.stochprocess.GammaProcessPCABridge
generatePath() - Method in class umontreal.iro.lecuyer.stochprocess.GammaProcessPCABridge
generatePath(double[]) - Method in class umontreal.iro.lecuyer.stochprocess.GammaProcessPCASymmetricalBridge
generatePath() - Method in class umontreal.iro.lecuyer.stochprocess.GammaProcessPCASymmetricalBridge
generatePath() - Method in class umontreal.iro.lecuyer.stochprocess.GammaProcessSymmetricalBridge
generatePath(double[]) - Method in class umontreal.iro.lecuyer.stochprocess.GammaProcessSymmetricalBridge
generatePath() - Method in class umontreal.iro.lecuyer.stochprocess.GeometricBrownianMotion
generatePath(RandomStream) - Method in class umontreal.iro.lecuyer.stochprocess.GeometricBrownianMotion
generatePath() - Method in class umontreal.iro.lecuyer.stochprocess.GeometricLevyProcess
  Generates a path.
generatePath() - Method in class umontreal.iro.lecuyer.stochprocess.GeometricVarianceGammaProcess
generatePath(double[]) - Method in class umontreal.iro.lecuyer.stochprocess.GeometricVarianceGammaProcess
generatePath() - Method in class umontreal.iro.lecuyer.stochprocess.InverseGaussianProcess
generatePath(double[]) - Method in class umontreal.iro.lecuyer.stochprocess.InverseGaussianProcess
  Instead of using the internal stream to generate the path, uses an array of uniforms U[0, 1).
generatePath(double[], double[]) - Method in class umontreal.iro.lecuyer.stochprocess.InverseGaussianProcess
  This method does not work for this class, but will be useful for the subclasses that require two streams.
generatePath() - Method in class umontreal.iro.lecuyer.stochprocess.InverseGaussianProcessBridge
  Generates the path.
generatePath(double[], double[]) - Method in class umontreal.iro.lecuyer.stochprocess.InverseGaussianProcessBridge
  Instead of using the internal streams to generate the path, it uses two arrays of uniforms U[0, 1).
generatePath() - Method in class umontreal.iro.lecuyer.stochprocess.InverseGaussianProcessMSH
  Generates the path.
generatePath(double[], double[]) - Method in class umontreal.iro.lecuyer.stochprocess.InverseGaussianProcessMSH
  Instead of using the internal streams to generate the path, uses two arrays of uniforms U[0, 1).
generatePath(double[]) - Method in class umontreal.iro.lecuyer.stochprocess.InverseGaussianProcessMSH
  Not implemented, requires two `RandomStream`'s.
generatePath() - Method in class umontreal.iro.lecuyer.stochprocess.InverseGaussianProcessPCA
generatePath(double[]) - Method in class umontreal.iro.lecuyer.stochprocess.InverseGaussianProcessPCA
  Instead of using the internal stream to generate the path, uses an array of uniforms U[0, 1).
generatePath() - Method in class umontreal.iro.lecuyer.stochprocess.NormalInverseGaussianProcess
  Generates the path.
generatePath() - Method in class umontreal.iro.lecuyer.stochprocess.OrnsteinUhlenbeckProcess
generatePath(RandomStream) - Method in class umontreal.iro.lecuyer.stochprocess.OrnsteinUhlenbeckProcess
generatePath() - Method in class umontreal.iro.lecuyer.stochprocess.StochasticProcess
  Generates, returns, and saves the sample path {X(t0), X(t1),…, X(td)}, which can then be accessed via `getPath`, `getSubpath`, or `getObservation`.
generatePath(RandomStream) - Method in class umontreal.iro.lecuyer.stochprocess.StochasticProcess
  Same as `generatePath()`, but first resets the stream to `stream`.
generatePath() - Method in class umontreal.iro.lecuyer.stochprocess.VarianceGammaProcess
  Generates and returns the path.
generatePath(double[]) - Method in class umontreal.iro.lecuyer.stochprocess.VarianceGammaProcess
  Similar to the usual `generatePath()`, but here the uniform random numbers used for the simulation must be provided to the method.
generatePath() - Method in class umontreal.iro.lecuyer.stochprocess.VarianceGammaProcessDiff
  Generates, returns and saves the path.
generatePath(double[]) - Method in class umontreal.iro.lecuyer.stochprocess.VarianceGammaProcessDiff
  Similar to the usual `generatePath()`, but here the uniform random numbers used for the simulation must be provided to the method.
generatePath() - Method in class umontreal.iro.lecuyer.stochprocess.VarianceGammaProcessDiffPCA
generatePath(double[]) - Method in class umontreal.iro.lecuyer.stochprocess.VarianceGammaProcessDiffPCA
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`.
GeometricBrownianMotion - Class in umontreal.iro.lecuyer.stochprocess
  .
GeometricBrownianMotion(double, double, double, RandomStream) - Constructor for class umontreal.iro.lecuyer.stochprocess.GeometricBrownianMotion
  Same as `GeometricBrownianMotion (s0, mu, sigma, new BrownianMotion (0.0, 0.0, 1.0, stream))`.
GeometricBrownianMotion(double, double, double, BrownianMotion) - Constructor for class umontreal.iro.lecuyer.stochprocess.GeometricBrownianMotion
  .
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, double) - Constructor for class umontreal.iro.lecuyer.randvar.GeometricGen
  Creates a geometric random variate generator with parameter p, using stream `s`.
GeometricGen(RandomStream, GeometricDist) - Constructor for class umontreal.iro.lecuyer.randvar.GeometricGen
  Creates a new generator for the distribution `dist`, using stream `s`.
GeometricLevyProcess - Class in umontreal.iro.lecuyer.stochprocess
  .
GeometricLevyProcess() - Constructor for class umontreal.iro.lecuyer.stochprocess.GeometricLevyProcess
GeometricNormalInverseGaussianProcess - Class in umontreal.iro.lecuyer.stochprocess
  .
GeometricNormalInverseGaussianProcess(double, double, double, double, double, double, RandomStream, NormalInverseGaussianProcess) - Constructor for class umontreal.iro.lecuyer.stochprocess.GeometricNormalInverseGaussianProcess
  Constructs a new `GeometricNormalInverseGaussianProcess`.
GeometricNormalInverseGaussianProcess(double, double, double, double, double, double, RandomStream, InverseGaussianProcess) - Constructor for class umontreal.iro.lecuyer.stochprocess.GeometricNormalInverseGaussianProcess
  Constructs a new `GeometricNormalInverseGaussianProcess`.
GeometricNormalInverseGaussianProcess(double, double, double, double, double, double, RandomStream, RandomStream, RandomStream, String) - Constructor for class umontreal.iro.lecuyer.stochprocess.GeometricNormalInverseGaussianProcess
  Constructs a new `GeometricNormalInverseGaussianProcess`.
GeometricNormalInverseGaussianProcess(double, double, double, double, double, double, RandomStream, String) - Constructor for class umontreal.iro.lecuyer.stochprocess.GeometricNormalInverseGaussianProcess
  Constructs a new `GeometricNormalInverseGaussianProcess`.
GeometricVarianceGammaProcess - Class in umontreal.iro.lecuyer.stochprocess
  This class represents a geometric variance gamma process S(t) (see).
GeometricVarianceGammaProcess(double, double, double, double, double, RandomStream) - Constructor for class umontreal.iro.lecuyer.stochprocess.GeometricVarianceGammaProcess
  .
GeometricVarianceGammaProcess(double, double, VarianceGammaProcess) - Constructor for class umontreal.iro.lecuyer.stochprocess.GeometricVarianceGammaProcess
  Constructs a new `GeometricVarianceGammaProcess`.
get() - Method in class umontreal.iro.lecuyer.charts.MultipleDatasetChart
  Gets the primary dataset.
get(int) - Method in class umontreal.iro.lecuyer.charts.MultipleDatasetChart
  Gets the element at the specified position in the dataset list.
get(int) - Method in class umontreal.iro.lecuyer.stat.list.ListOfStatProbes
get(int, int) - Method in class umontreal.iro.lecuyer.util.DMatrix
  Returns the matrix element in the specified row and column.
get(int) - Method in class umontreal.iro.lecuyer.util.TransformingList
getA() - Method in class umontreal.iro.lecuyer.functions.PowerMathFunction
  Returns the value of a.
getA() - Method in class umontreal.iro.lecuyer.functions.SquareMathFunction
  Returns the value of a.
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.NakagamiDist
  Returns the location parameter a of this object.
getA() - Method in class umontreal.iro.lecuyer.probdist.PowerDist
  Returns the parameter a.
getA() - Method in class umontreal.iro.lecuyer.probdist.RayleighDist
  Returns the parameter a.
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.
getA() - Method in class umontreal.iro.lecuyer.randvar.BetaGen
  Returns the parameter a of this object.
getA() - Method in class umontreal.iro.lecuyer.randvar.NakagamiGen
  Returns the location parameter a of this object.
getA() - Method in class umontreal.iro.lecuyer.randvar.PowerGen
  Returns the parameter a.
getA() - Method in class umontreal.iro.lecuyer.randvar.RayleighGen
  Returns the parameter a.
getA() - Method in class umontreal.iro.lecuyer.randvar.TriangularGen
  Returns the value of a for this object.
getA() - Method in class umontreal.iro.lecuyer.randvar.UniformGen
  Returns the value of a for this object.
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.NormalInverseGaussianDist
  Returns 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() - Method in class umontreal.iro.lecuyer.randvar.BetaGen
  Returns the parameter α of this object.
getAlpha() - Method in class umontreal.iro.lecuyer.randvar.CauchyGen
  Returns the parameter α of this object.
getAlpha() - Method in class umontreal.iro.lecuyer.randvar.ExtremeValueGen
  Returns the parameter α of this object.
getAlpha() - Method in class umontreal.iro.lecuyer.randvar.GammaGen
  Returns the parameter α of this object.
getAlpha() - Method in class umontreal.iro.lecuyer.randvar.LogisticGen
  Returns the parameter α of this object.
getAlpha() - Method in class umontreal.iro.lecuyer.randvar.LoglogisticGen
  Returns the parameter α of this object.
getAlpha() - Method in class umontreal.iro.lecuyer.randvar.NormalInverseGaussianGen
  Returns the parameter α of this object.
getAlpha() - Method in class umontreal.iro.lecuyer.randvar.ParetoGen
  Returns the parameter α of this object.
getAlpha() - Method in class umontreal.iro.lecuyer.randvar.Pearson5Gen
  Returns the parameter α of this object.
getAlpha() - Method in class umontreal.iro.lecuyer.randvar.WeibullGen
  Returns the parameter α.
getAlpha(int) - Method in class umontreal.iro.lecuyer.randvarmulti.DirichletGen
  Returns the αi+1 parameter for this Dirichlet generator.
getAlpha() - Method in class umontreal.iro.lecuyer.stochprocess.CIRProcess
  Returns the value of α.
getAlpha() - Method in class umontreal.iro.lecuyer.stochprocess.NormalInverseGaussianProcess
  Returns alpha.
getAlpha() - Method in class umontreal.iro.lecuyer.stochprocess.OrnsteinUhlenbeckProcess
  Returns the value of α.
getAlpha1() - Method in class umontreal.iro.lecuyer.probdist.Pearson6Dist
  Returns the α1 parameter of this object.
getAlpha1() - Method in class umontreal.iro.lecuyer.randvar.Pearson6Gen
  Returns the α1 parameter of this object.
getAlpha2() - Method in class umontreal.iro.lecuyer.probdist.Pearson6Dist
  Returns the α2 parameter of this object.
getAlpha2() - Method in class umontreal.iro.lecuyer.randvar.Pearson6Gen
  Returns the α2 parameter of this object.
getAnalyticAverage(double) - Method in class umontreal.iro.lecuyer.stochprocess.InverseGaussianProcess
  Returns the analytic average which is δt/γ, with t = `time`.
getAnalyticAverage(double) - Method in class umontreal.iro.lecuyer.stochprocess.NormalInverseGaussianProcess
  Returns the analytic average, which is μt + δtβ/γ.
getAnalyticVariance(double) - Method in class umontreal.iro.lecuyer.stochprocess.InverseGaussianProcess
  Returns the analytic variance which is (δt)2, with t = `time`.
getAnalyticVariance(double) - Method in class umontreal.iro.lecuyer.stochprocess.NormalInverseGaussianProcess
  Returns the analytic variance, which is δtα2/γ3.
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 observations stored in this probe.
getArrayMappingCounterToIndex() - Method in class umontreal.iro.lecuyer.stochprocess.StochasticProcess
  Returns a reference to an array that maps an integer k to ik, the index of the observation S(tik) corresponding to the k-th observation to be generated for a sample path of this process.
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.functions.PowerMathFunction
  Returns the value of b.
getB() - Method in class umontreal.iro.lecuyer.functions.SquareMathFunction
  Returns the value of b.
getB() - Method in class umontreal.iro.lecuyer.probdist.BetaDist
  Returns the parameter b of this object.
getB() - Method in class umontreal.iro.lecuyer.probdist.PowerDist
  Returns the parameter b.
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.
getB() - Method in class umontreal.iro.lecuyer.randvar.BetaGen
  Returns the parameter b of this object.
getB() - Method in class umontreal.iro.lecuyer.randvar.PowerGen
  Returns the parameter b.
getB() - Method in class umontreal.iro.lecuyer.randvar.TriangularGen
  Returns the value of b for this object.
getB() - Method in class umontreal.iro.lecuyer.randvar.UniformGen
  Returns the value of b for this object.
getB() - Method in class umontreal.iro.lecuyer.stochprocess.CIRProcess
  Returns the value of b.
getB() - Method in class umontreal.iro.lecuyer.stochprocess.OrnsteinUhlenbeckProcess
  Returns the value of 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.LaplaceDist
  Returns the parameter β.
getBeta() - Method in class umontreal.iro.lecuyer.probdist.LoglogisticDist
  Returns the parameter β of this object.
getBeta() - Method in class umontreal.iro.lecuyer.probdist.NormalInverseGaussianDist
  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.
getBeta() - Method in class umontreal.iro.lecuyer.randvar.BetaGen
  Returns the parameter β of this object.
getBeta() - Method in class umontreal.iro.lecuyer.randvar.CauchyGen
  Returns the parameter β of this object.
getBeta() - Method in class umontreal.iro.lecuyer.randvar.FatigueLifeGen
  Returns the parameter β of this object.
getBeta() - Method in class umontreal.iro.lecuyer.randvar.LaplaceGen
  Returns the parameter β.
getBeta() - Method in class umontreal.iro.lecuyer.randvar.LoglogisticGen
  Returns the parameter β of this object.
getBeta() - Method in class umontreal.iro.lecuyer.randvar.NormalInverseGaussianGen
  Returns the parameter β of this object.
getBeta() - Method in class umontreal.iro.lecuyer.randvar.ParetoGen
  Returns the parameter β of this object.
getBeta() - Method in class umontreal.iro.lecuyer.randvar.Pearson5Gen
  Returns the parameter β of this object.
getBeta() - Method in class umontreal.iro.lecuyer.randvar.Pearson6Gen
  Returns the β parameter of this object.
getBeta() - Method in class umontreal.iro.lecuyer.stochprocess.NormalInverseGaussianProcess
  Returns beta.
getBins(int) - Method in class umontreal.iro.lecuyer.charts.CustomHistogramDataset
  Returns the bins for a series.
getBins(int) - Method in class umontreal.iro.lecuyer.charts.HistogramSeriesCollection
  Returns the bins for a series.
getBinWidth(int) - Method in class umontreal.iro.lecuyer.charts.CustomHistogramDataset
  Returns the bin width for a series.
getBMPCA() - Method in class umontreal.iro.lecuyer.stochprocess.GammaProcessPCA
  Returns the `BrownianMotionPCA` that is included in the `GammaProcessPCA` object.
getBMPCA() - Method in class umontreal.iro.lecuyer.stochprocess.GammaProcessPCABridge
  Returns the inner `BrownianMotionPCA`.
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`.
getBrownianMotion() - Method in class umontreal.iro.lecuyer.stochprocess.GeometricBrownianMotion
  Returns a reference to the `BrownianMotion` object used to generate the process.
getBrownianMotion() - Method in class umontreal.iro.lecuyer.stochprocess.VarianceGammaProcess
  Returns a reference to the inner `BrownianMotion`.
getBrownianMotionPCA() - Method in class umontreal.iro.lecuyer.stochprocess.InverseGaussianProcessPCA
  Returns the `BrownianMotionPCA`.
getBuffer() - Method in class umontreal.iro.lecuyer.util.PrintfFormat
  Returns the `StringBuffer` associated with that object.
getC() - Method in class umontreal.iro.lecuyer.probdist.NakagamiDist
  Returns the shape parameter c of this object.
getC() - Method in class umontreal.iro.lecuyer.probdist.PowerDist
  Returns the parameter c.
getC() - Method in class umontreal.iro.lecuyer.randvar.NakagamiGen
  Returns the shape parameter c of this object.
getC() - Method in class umontreal.iro.lecuyer.randvar.PowerGen
  Returns the parameter c.
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.
getCategory(int) - Method in class umontreal.iro.lecuyer.charts.SSJCategorySeriesCollection
  Returns the category-value in the specified series.
getCdf() - Method in class umontreal.iro.lecuyer.charts.DiscreteDistIntChart
  Returns the chart of the cdf.
getChartMargin() - Method in class umontreal.iro.lecuyer.charts.XYChart
  Returns the chart margin, which is the fraction by which the chart is enlarged on its borders.
getCholeskyDecompSigma() - Method in class umontreal.iro.lecuyer.randvarmulti.MultinormalCholeskyGen
  Returns the lower-triangular matrix A in the Cholesky decomposition of Σ.
getCholeskyDecompSigma() - Method in class umontreal.iro.lecuyer.randvarmulti.MultiNormalGen
  Deprecated. Returns the Cholesky decomposition of the covariance matrix used for generating the vectors.
getClassFinder() - Method in exception umontreal.iro.lecuyer.util.NameConflictException
  Returns the class finder associated with this exception.
getCoefficient(int) - Method in class umontreal.iro.lecuyer.functions.Polynomial
  Returns the ith coefficient of the polynomial.
getCoefficients() - Method in class umontreal.iro.lecuyer.functions.Polynomial
  Returns an array containing the coefficients of the polynomial.
getColor(int) - Method in class umontreal.iro.lecuyer.charts.SSJCategorySeriesCollection
  Gets the current plotting color of the selected series.
getColor(int) - Method in class umontreal.iro.lecuyer.charts.SSJXYSeriesCollection
  Gets the current plotting color of the selected series.
getConfidenceLevel() - Method in class umontreal.iro.lecuyer.stat.Tally
  Returns the level of confidence for the intervals on the mean displayed in reports.
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`.
getContinuousVariables() - Method in class umontreal.iro.lecuyer.simevents.ContinuousState
  Returns the list of continuous-time variables currently integrated by the simulator.
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.
getCurrentObservation() - Method in class umontreal.iro.lecuyer.stochprocess.StochasticProcess
  Returns the value of the last generated observation X(tj).
getCurrentObservationIndex() - Method in class umontreal.iro.lecuyer.stochprocess.StochasticProcess
  Returns the value of the index j corresponding to the time tj of the last generated observation.
getCurrentUpperBound() - Method in class umontreal.iro.lecuyer.stochprocess.GeometricVarianceGammaProcess
getDashPattern(int) - Method in class umontreal.iro.lecuyer.charts.EmpiricalSeriesCollection
  Returns the dash pattern associated with the `series`-th data series.
getDashPattern(int) - Method in class umontreal.iro.lecuyer.charts.XYListSeriesCollection
  Returns the dash pattern associated with the `series`th data series.
getDefaultSimulator() - Static method in class umontreal.iro.lecuyer.simevents.Simulator
  Returns the default simulator instance used by the deprecated class `Sim`.
getDegree() - Method in class umontreal.iro.lecuyer.functions.Polynomial
  Returns the degree of this polynomial.
getDelay() - Method in class umontreal.iro.lecuyer.simprocs.SimProcess
  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.functions.ShiftedMathFunction
  Returns the shift δ = `delta`.
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.NormalInverseGaussianDist
  Returns the parameter δ of this object.
getDelta() - Method in class umontreal.iro.lecuyer.probdist.WeibullDist
  Returns the parameter δ.
getDelta() - Method in class umontreal.iro.lecuyer.randvar.JohnsonSBGen
  Returns the δ associated with this object.
getDelta() - Method in class umontreal.iro.lecuyer.randvar.JohnsonSUGen
  Returns the δ associated with this object.
getDelta() - Method in class umontreal.iro.lecuyer.randvar.NormalInverseGaussianGen
  Returns the parameter δ of this object.
getDelta() - Method in class umontreal.iro.lecuyer.randvar.WeibullGen
  Returns the parameter δ.
getDelta() - Method in class umontreal.iro.lecuyer.stochprocess.InverseGaussianProcess
  Returns δ.
getDelta() - Method in class umontreal.iro.lecuyer.stochprocess.NormalInverseGaussianProcess
  Returns delta.
getDimension() - Method in class umontreal.iro.lecuyer.hups.ContainerPointSet
  Returns the dimension of the contained point set.
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 multivariate generator (the dimension of the random points).
getDimension() - Method in interface umontreal.iro.lecuyer.util.MultivariateFunction
  Returns d, the dimension of the function computed by this implementation.
getDimension() - Method in class umontreal.iro.lecuyer.util.RatioFunction
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() - Method in class umontreal.iro.lecuyer.randvar.RandomVariateGen
  Returns the `Distribution` used by this generator.
getDistribution() - Method in class umontreal.iro.lecuyer.randvar.RandomVariateGenInt
  Returns the `DiscreteDistributionInt` 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
getDistributionMLE(String, double[], int) - Static method in class umontreal.iro.lecuyer.probdist.DistributionFactory
getDistributionMLE(String, int[], int) - Static method in class umontreal.iro.lecuyer.probdist.DistributionFactory
getDistributionMLE(Class<T>, double[], int) - Static method in class umontreal.iro.lecuyer.probdist.DistributionFactory
getDistributionMLE(Class<T>, int[], int) - Static method in class umontreal.iro.lecuyer.probdist.DistributionFactory
getDomainBounds() - Method in class umontreal.iro.lecuyer.charts.SSJXYSeriesCollection
  Returns domain (x-coordinates) min and max values.
getDoubleArrayList() - Method in class umontreal.iro.lecuyer.stat.TallyStore
  Returns the `DoubleArrayList` object that contains the observations for this probe.
getElement() - Method in class umontreal.iro.lecuyer.simevents.ListWithStat.Node
  Returns the element stored into this node.
getEndX(int, int) - Method in class umontreal.iro.lecuyer.charts.CustomHistogramDataset
  Returns the end value for a bin.
getEndY(int, int) - Method in class umontreal.iro.lecuyer.charts.CustomHistogramDataset
  Returns the end y-value for a bin (which is the same as the y-value).
getEventList() - Static method in class umontreal.iro.lecuyer.simevents.Sim
  Gets the currently used event list.
getEventList() - Method in class umontreal.iro.lecuyer.simevents.Simulator
  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).
getField(Class<?>, String) - Static method in class umontreal.iro.lecuyer.util.Introspection
  This is like `getField`, except that it can return non-public fields.
getFieldName(Object) - Static method in class umontreal.iro.lecuyer.util.Introspection
  Returns the field name corresponding to the value of an enumerated type `val`.
getFields(Class<?>) - Static method in class umontreal.iro.lecuyer.util.Introspection
  Returns all the fields declared and inherited by a class.
getFilled(int) - Method in class umontreal.iro.lecuyer.charts.HistogramSeriesCollection
  Returns the `filled` flag associated with the `series`-th data series.
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(Class<E>) - Method in class umontreal.iro.lecuyer.simevents.eventlist.BinaryTree
getFirstOfClass(String) - Method in class umontreal.iro.lecuyer.simevents.eventlist.DoublyLinked
getFirstOfClass(Class<E>) - 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(Class<E>) - Method in interface umontreal.iro.lecuyer.simevents.eventlist.EventList
  Returns the first event of the class `E` (a subclass of `Event`) in the event list.
getFirstOfClass(String) - Method in class umontreal.iro.lecuyer.simevents.eventlist.Henriksen
getFirstOfClass(Class<E>) - Method in class umontreal.iro.lecuyer.simevents.eventlist.Henriksen
getFirstOfClass(String) - Method in class umontreal.iro.lecuyer.simevents.eventlist.RedblackTree
getFirstOfClass(Class<E>) - Method in class umontreal.iro.lecuyer.simevents.eventlist.RedblackTree
getFirstOfClass(String) - Method in class umontreal.iro.lecuyer.simevents.eventlist.SplayTree
getFirstOfClass(Class<E>) - Method in class umontreal.iro.lecuyer.simevents.eventlist.SplayTree
getFunction() - Method in class umontreal.iro.lecuyer.functions.PowerMathFunction
  Returns the function f (x).
getFunction() - Method in class umontreal.iro.lecuyer.functions.ShiftedMathFunction
  Returns the function f (x).
getFunction() - Method in class umontreal.iro.lecuyer.functions.SqrtMathFunction
  Returns the function associated with this object.
getFunction() - Method in class umontreal.iro.lecuyer.functions.SquareMathFunction
  Returns the function f (x).
getFunctions() - Method in class umontreal.iro.lecuyer.functions.AverageMathFunction
  Returns the functions being averaged.
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.
getGamma() - Method in class umontreal.iro.lecuyer.randvar.FatigueLifeGen
  Returns the parameter γ of this object.
getGamma() - Method in class umontreal.iro.lecuyer.randvar.JohnsonSBGen
  Returns the γ associated with this object.
getGamma() - Method in class umontreal.iro.lecuyer.randvar.JohnsonSUGen
  Returns the γ associated with this object.
getGamma() - Method in class umontreal.iro.lecuyer.randvar.NegativeBinomialGen
  Returns the parameter γ of this object.
getGamma() - Method in class umontreal.iro.lecuyer.stochprocess.InverseGaussianProcess
  Returns γ.
getGamma() - Method in class umontreal.iro.lecuyer.stochprocess.NormalInverseGaussianProcess
  Returns gamma.
getGammaProcess() - Method in class umontreal.iro.lecuyer.stochprocess.VarianceGammaProcess
  Returns a reference to the inner `GammaProcess`.
getGen() - Method in class umontreal.iro.lecuyer.stochprocess.BrownianMotion
  Returns the normal random variate generator used.
getGen() - Method in class umontreal.iro.lecuyer.stochprocess.CIRProcess
  Returns the normal random variate generator used.
getGen() - Method in class umontreal.iro.lecuyer.stochprocess.GeometricBrownianMotion
  Returns the `NormalGen` used.
getGen() - Method in class umontreal.iro.lecuyer.stochprocess.OrnsteinUhlenbeckProcess
  Returns the normal random variate generator used.
getGneg() - Method in class umontreal.iro.lecuyer.stochprocess.VarianceGammaProcessDiff
  Returns a reference to the `GammaProcess` object `gneg` used to generate the Γ- component of the process.
getGpos() - Method in class umontreal.iro.lecuyer.stochprocess.VarianceGammaProcessDiff
  Returns a reference to the `GammaProcess` object `gpos` used to generate the Γ+ component of the process.
getHours() - Method in class umontreal.iro.lecuyer.util.AbstractChrono
  Returns the CPU time in hours used by the program since the last call to `init` for this `AbstractChrono`.
getHours() - Method in enum umontreal.iro.lecuyer.util.TimeUnit
  Returns this time unit represented in hours.
getI() - Method in class umontreal.iro.lecuyer.probdist.UniformIntDist
  Returns the parameter i.
getI() - Method in class umontreal.iro.lecuyer.randvar.UniformIntGen
  Returns the parameter i.
getImports() - Method in class umontreal.iro.lecuyer.util.ClassFinder
  Returns the current list of import declarations.
getInitTime() - Method in class umontreal.iro.lecuyer.simevents.Accumulate
  Returns the initialization time for this object.
getInitTime() - Method in class umontreal.iro.lecuyer.simevents.ListWithStat
  Returns the last simulation time `initStat` was called.
getInnerList() - Method in class umontreal.iro.lecuyer.util.TransformingList
getInsertionTime() - Method in class umontreal.iro.lecuyer.simevents.ListWithStat.Node
  Returns the insertion time of the element in this node.
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 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 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 n and p estimated using the maximum likelihood method, from 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 (fixed) parameter n, and with parameter p estimated by the maximum likelihood method based on the m observations 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 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 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 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 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 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 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 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 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 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 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 μ and β estimated using the maximum likelihood method based on the n observations 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 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 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 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 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, from the n observations 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 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 x[i], i = 0, 1,…, n - 1.
getInstanceFromMLE(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.NormalInverseGaussianDist
  NOT IMPLEMENTED.
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 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 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 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 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 x[i], i = 0, 1,…, n - 1.
getInstanceFromMLE(double[], int, double, double) - Static method in class umontreal.iro.lecuyer.probdist.PowerDist
  Creates a new instance of a power distribution with parameters a and b, with c estimated using the maximum likelihood method based on the n observations x[i], i = 0,…, n - 1.
getInstanceFromMLE(double[], int, double) - Static method in class umontreal.iro.lecuyer.probdist.RayleighDist
  Creates a new instance of a Rayleigh distribution with parameters a and hat(β).
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 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 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 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 = 0 estimated using the maximum likelihood method based on the n observations 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.
getItemCount(int) - Method in class umontreal.iro.lecuyer.charts.CustomHistogramDataset
  Returns the number of data items for a series.
getJ() - Method in class umontreal.iro.lecuyer.probdist.UniformIntDist
  Returns the parameter j.
getJ() - Method in class umontreal.iro.lecuyer.randvar.UniformIntGen
  Returns the parameter j.
getJFreeChart() - Method in class umontreal.iro.lecuyer.charts.CategoryChart
  Returns the `JFreeChart` object associated with this chart.
getJFreeChart() - Method in class umontreal.iro.lecuyer.charts.MultipleDatasetChart
  Returns the `JFreeChart` variable associated with this chart.
getJFreeChart() - Method in class umontreal.iro.lecuyer.charts.XYChart
  Returns the `JFreeChart` object associated with this chart.
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.
getK() - Method in class umontreal.iro.lecuyer.randvar.ErlangGen
  Returns the parameter k of this object.
getK() - Method in class umontreal.iro.lecuyer.randvar.HypergeometricGen
  Returns the k associated with this object.
getL() - Method in class umontreal.iro.lecuyer.probdist.HypergeometricDist
  Returns the l associated with this object.
getL() - Method in class umontreal.iro.lecuyer.randvar.HypergeometricGen
  Returns the l associated with this object.
getLabel() - Method in class umontreal.iro.lecuyer.charts.Axis
  Returns the axis description.
getLambda() - Method in class umontreal.iro.lecuyer.probdist.ChiSquareNoncentralDist
  Returns the parameter λ of 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.NakagamiDist
  Returns the scale 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.
getLambda() - Method in class umontreal.iro.lecuyer.randvar.ChiSquareNoncentralGen
  Returns the value of λ for this object.
getLambda() - Method in class umontreal.iro.lecuyer.randvar.ExponentialGen
  Returns the λ associated with this object.
getLambda() - Method in class umontreal.iro.lecuyer.randvar.ExtremeValueGen
  Returns the parameter λ of this object.
getLambda() - Method in class umontreal.iro.lecuyer.randvar.GammaGen
  Returns the parameter λ of this object.
getLambda() - Method in class umontreal.iro.lecuyer.randvar.InverseGaussianGen
  Returns the parameter λ of this object.
getLambda() - Method in class umontreal.iro.lecuyer.randvar.JohnsonSBGen
  Returns the λ associated with this object.
getLambda() - Method in class umontreal.iro.lecuyer.randvar.JohnsonSUGen
  Returns the λ associated with this object.
getLambda() - Method in class umontreal.iro.lecuyer.randvar.LogisticGen
  Returns the parameter λ of this object.
getLambda() - Method in class umontreal.iro.lecuyer.randvar.NakagamiGen
  Returns the scale parameter λ of this object.
getLambda() - Method in class umontreal.iro.lecuyer.randvar.PoissonGen
  Returns the λ associated with this object.
getLambda() - Method in class umontreal.iro.lecuyer.randvar.WeibullGen
  Returns the parameter λ.
getLambda(DoubleMatrix2D) - Static method in class umontreal.iro.lecuyer.randvarmulti.MultinormalPCAGen
  Computes and returns the eigenvalues of `sigma` in decreasing order.
getLambda() - Method in class umontreal.iro.lecuyer.randvarmulti.MultinormalPCAGen
  Returns the eigenvalues of Σ in decreasing order.
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`).
getLevyProcess() - Method in class umontreal.iro.lecuyer.stochprocess.GeometricLevyProcess
  Returns the Lévy process.
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.
getList() - Method in class umontreal.iro.lecuyer.charts.MultipleDatasetChart
  Returns the dataset list.
getLongName() - Method in enum umontreal.iro.lecuyer.util.TimeUnit
  Returns the long name of this time unit.
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.
getM() - Method in class umontreal.iro.lecuyer.randvar.FisherFGen
  Returns the parameter p of this object.
getM() - Method in class umontreal.iro.lecuyer.randvar.HypergeometricGen
  Returns the m associated with this object.
getM() - Method in class umontreal.iro.lecuyer.randvar.TriangularGen
  Returns the value of m for this object.
getMargin() - Method in class umontreal.iro.lecuyer.charts.HistogramSeriesCollection
  Returns the margin which is a percentage amount by which the bars are trimmed.
getMarksType(int) - Method in class umontreal.iro.lecuyer.charts.EmpiricalSeriesCollection
  Returns the mark type associated with the `series`-th data series.
getMarksType(int) - Method in class umontreal.iro.lecuyer.charts.XYListSeriesCollection
  Returns the mark type associated with the `series`th data series.
getMaximumLikelihoodEstimate(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.BetaDist
  Deprecated.
getMaximumLikelihoodEstimate(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.BetaSymmetricalDist
  Deprecated.
getMaximumLikelihoodEstimate(int[], int) - Static method in class umontreal.iro.lecuyer.probdist.BinomialDist
  Deprecated.
getMaximumLikelihoodEstimate(int[], int, int) - Static method in class umontreal.iro.lecuyer.probdist.BinomialDist
  Deprecated.
getMaximumLikelihoodEstimate(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.CauchyDist
  Deprecated.
getMaximumLikelihoodEstimate(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.ChiDist
  Deprecated.
getMaximumLikelihoodEstimate(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.ChiSquareDist
  Deprecated.
getMaximumLikelihoodEstimate(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.ErlangDist
  Deprecated.
getMaximumLikelihoodEstimate(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.ExponentialDist
  Deprecated.
getMaximumLikelihoodEstimate(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.ExtremeValueDist
  Deprecated.
getMaximumLikelihoodEstimate(double[], int, double) - Static method in class umontreal.iro.lecuyer.probdist.FatigueLifeDist
  Deprecated.
getMaximumLikelihoodEstimate(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.GammaDist
  Deprecated.
getMaximumLikelihoodEstimate(int[], int) - Static method in class umontreal.iro.lecuyer.probdist.GeometricDist
  Deprecated.
getMaximumLikelihoodEstimate(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.HyperbolicSecantDist
  Deprecated.
getMaximumLikelihoodEstimate(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.InverseGaussianDist
  Deprecated.
getMaximumLikelihoodEstimate(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.LaplaceDist
  Deprecated.
getMaximumLikelihoodEstimate(int[], int) - Static method in class umontreal.iro.lecuyer.probdist.LogarithmicDist
  Deprecated.
getMaximumLikelihoodEstimate(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.LogisticDist
  Deprecated.
getMaximumLikelihoodEstimate(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.LoglogisticDist
  Deprecated.
getMaximumLikelihoodEstimate(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.LognormalDist
  Deprecated.
getMaximumLikelihoodEstimate(int[], int, double) - Static method in class umontreal.iro.lecuyer.probdist.NegativeBinomialDist
  Deprecated.
getMaximumLikelihoodEstimate(int[], int) - Static method in class umontreal.iro.lecuyer.probdist.NegativeBinomialDist
  Deprecated.
getMaximumLikelihoodEstimate(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.NormalDist
  Deprecated.
getMaximumLikelihoodEstimate(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.ParetoDist
  Deprecated.
getMaximumLikelihoodEstimate(int[], int) - Static method in class umontreal.iro.lecuyer.probdist.PascalDist
  Deprecated.
getMaximumLikelihoodEstimate(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.Pearson5Dist
  Deprecated.
getMaximumLikelihoodEstimate(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.Pearson6Dist
  Deprecated.
getMaximumLikelihoodEstimate(int[], int) - Static method in class umontreal.iro.lecuyer.probdist.PoissonDist
  Deprecated.
getMaximumLikelihoodEstimate(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.StudentDist
  Deprecated.
getMaximumLikelihoodEstimate(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.UniformDist
  Deprecated.
getMaximumLikelihoodEstimate(int[], int) - Static method in class umontreal.iro.lecuyer.probdist.UniformIntDist
  Deprecated.
getMaximumLikelihoodEstimate(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.WeibullDist
  Deprecated.
getMaximumLikelihoodEstimate(double[][], int, int) - Static method in class umontreal.iro.lecuyer.probdistmulti.DirichletDist
  Deprecated.
getMaximumLikelihoodEstimate(int[][], int, int, int) - Static method in class umontreal.iro.lecuyer.probdistmulti.MultinomialDist
  Deprecated.
getMaximumLikelihoodEstimate(int[][], int, int) - Static method in class umontreal.iro.lecuyer.probdistmulti.NegativeMultinomialDist
  Deprecated.
getMaximumLikelihoodEstimateMu(double[][], int, int) - Static method in class umontreal.iro.lecuyer.probdistmulti.MultiNormalDist
  Deprecated.
getMaximumLikelihoodEstimateSigma(double[][], int, int) - Static method in class umontreal.iro.lecuyer.probdistmulti.MultiNormalDist
  Deprecated.
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, over the interval [0, 1].
getMean(double, double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.BetaDist
  Computes and returns the mean E[X] = (bα + aβ)/(α + β) of the beta distribution with parameters α and β over the interval [a, b].
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.ChiSquareNoncentralDist
getMean(double, double) - Static method in class umontreal.iro.lecuyer.probdist.ChiSquareNoncentralDist
  Computes and returns the mean E[X] = ν + λ of the noncentral chi-square distribution with parameters ν = `nu` and λ = `lambda`.
getMean() - Method in class umontreal.iro.lecuyer.probdist.ContinuousDistribution
  Returns the mean.
getMean() - Method in class umontreal.iro.lecuyer.probdist.CramerVonMisesDist
getMean(int) - Static method in class umontreal.iro.lecuyer.probdist.CramerVonMisesDist
  Returns the mean of the 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.FoldedNormalDist
getMean(double, double) - Static method in class umontreal.iro.lecuyer.probdist.FoldedNormalDist
  .
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.HalfNormalDist
getMean(double, double) - Static method in class umontreal.iro.lecuyer.probdist.HalfNormalDist
  Computes and returns the mean E[X] = μ + σ(2 / π)1/2.
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.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] = μ of the Laplace distribution with parameters μ and β.
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.NakagamiDist
getMean(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.NakagamiDist
  .
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.NormalInverseGaussianDist
getMean(double, double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.NormalInverseGaussianDist
  Returns the mean E[X] = μ + δβ/γ of the normal inverse gaussian distribution with parameters α, β, μ and δ.
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.PowerDist
getMean(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.PowerDist
  Returns the mean a + (b - a)c/(c + 1) of the power distribution with parameters a, b and c.
getMean() - Method in class umontreal.iro.lecuyer.probdist.RayleighDist
getMean(double, double) - Static method in class umontreal.iro.lecuyer.probdist.RayleighDist
  Returns the mean a + β(π/2)1/2 of the Rayleigh distribution with parameters a and β.
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.WatsonUDist
getMean(int) - Static method in class umontreal.iro.lecuyer.probdist.WatsonUDist
  Returns the mean of the Watson U distribution with parameter n.
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.
getMethod(Class<?>, String, Class[]) - Static method in class umontreal.iro.lecuyer.util.Introspection
  This is like `getMethod`, except that it can return non-public methods.
getMethods(Class<?>) - Static method in class umontreal.iro.lecuyer.util.Introspection
  Returns all the methods declared and inherited by a class.
getMinutes() - Method in class umontreal.iro.lecuyer.util.AbstractChrono
  Returns the CPU time in minutes used by the program since the last call to `init` for this `AbstractChrono`.
getMLE(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.BetaDist
  Estimates the parameters (α, β) of the beta distribution over the interval [0, 1] using the maximum likelihood method, from the n observations x[i], i = 0, 1,…, n - 1.
getMLE(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.BetaSymmetricalDist
  Estimates the parameter α of the symmetrical beta distribution over the interval [0, 1] using the maximum likelihood method, from the n observations x[i], i = 0, 1,…, n - 1.
getMLE(int[], int) - Static method in class umontreal.iro.lecuyer.probdist.BinomialDist
  Estimates the parameters (n, p) of the binomial distribution using the maximum likelihood method, from the m observations x[i], i = 0, 1,…, m - 1.
getMLE(int[], int, int) - Static method in class umontreal.iro.lecuyer.probdist.BinomialDist
  Estimates the parameter p of the binomial distribution with given (fixed) parameter n, by the maximum likelihood method, from the m observations x[i], i = 0, 1,…, m - 1.
getMLE(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.CauchyDist
  Estimates the parameters (α, β) of the Cauchy distribution using the maximum likelihood method, from the n observations x[i], i = 0, 1,…, n - 1.
getMLE(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.ChiDist
  Estimates the parameter ν of the chi distribution using the maximum likelihood method, from the n observations x[i], i = 0, 1,…, n - 1.
getMLE(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.ChiSquareDist
  Estimates the parameter n of the chi-square distribution using the maximum likelihood method, from the m observations x[i], i = 0, 1,…, m - 1.
getMLE(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.ErlangDist
  Estimates the parameters (k, λ) of the Erlang distribution using the maximum likelihood method, from the n observations x[i], i = 0, 1,…, n - 1.
getMLE(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.ExponentialDist
  Estimates the parameter λ of the exponential distribution using the maximum likelihood method, from the n observations x[i], i = 0, 1,…, n - 1.
getMLE(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.ExtremeValueDist
  Estimates the parameters (α, λ) of the extreme value distribution using the maximum likelihood method, from the n observations x[i], i = 0, 1,…, n - 1.
getMLE(double[], int, double) - Static method in class umontreal.iro.lecuyer.probdist.FatigueLifeDist
  Estimates the parameters (μ, β, γ) of the fatigue life distribution using the maximum likelihood method, from the n observations x[i], i = 0, 1,…, n - 1.
getMLE(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.FoldedNormalDist
  NOT IMPLEMENTED.
getMLE(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.GammaDist
  Estimates the parameters (α, λ) of the gamma distribution using the maximum likelihood method, from the n observations x[i], i = 0, 1,…, n - 1.
getMLE(int[], int) - Static method in class umontreal.iro.lecuyer.probdist.GeometricDist
  Estimates the parameter p of the geometric distribution using the maximum likelihood method, from the n observations x[i], i = 0, 1,…, n - 1.
getMLE(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.HalfNormalDist
  Estimates the parameters μ and σ of the half-normal distribution using the maximum likelihood method from the n observations x[i], i = 0, 1,…, n - 1.
getMLE(double[], int, double) - Static method in class umontreal.iro.lecuyer.probdist.HalfNormalDist
  Estimates the parameter σ of the half-normal distribution using the maximum likelihood method from the n observations x[i], i = 0, 1,…, n - 1 and the parameter μ = `mu`.
getMLE(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.HyperbolicSecantDist
  Estimates the parameters (μ, σ) of the hyperbolic secant distribution using the maximum likelihood method, from the n observations x[i], i = 0, 1,…, n - 1.
getMLE(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.InverseGaussianDist
  Estimates the parameters (μ, λ) of the inverse gaussian distribution using the maximum likelihood method, from the n observations x[i], i = 0, 1,…, n - 1.
getMLE(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.LaplaceDist
  Estimates the parameters (μ, β) of the Laplace distribution using the maximum likelihood method, from the n observations x[i], i = 0, 1,…, n - 1.
getMLE(int[], int) - Static method in class umontreal.iro.lecuyer.probdist.LogarithmicDist
  Estimates the parameter θ of the logarithmic distribution using the maximum likelihood method, from the n observations x[i], i = 0, 1,…, n - 1.
getMLE(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.LogisticDist
  Estimates the parameters (α, λ) of the logistic distribution using the maximum likelihood method, from the n observations x[i], i = 0, 1,…, n - 1.
getMLE(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.LoglogisticDist
  Estimates the parameters (α, β) of the log-logistic distribution using the maximum likelihood method, from the n observations x[i], i = 0, 1,…, n - 1.
getMLE(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.LognormalDist
  Estimates the parameters (μ, σ) of the lognormal distribution using the maximum likelihood method, from the n observations x[i], i = 0, 1,…, n - 1.
getMLE(int[], int, double) - Static method in class umontreal.iro.lecuyer.probdist.NegativeBinomialDist
  Estimates the parameter p of the negative binomial distribution using the maximum likelihood method, from the n observations x[i], i = 0, 1,…, n - 1.
getMLE(int[], int) - Static method in class umontreal.iro.lecuyer.probdist.NegativeBinomialDist
  Estimates the parameter (γ, p) of the negative binomial distribution using the maximum likelihood method, from the n observations x[i], i = 0, 1,…, n - 1.
getMLE(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.NormalDist
  Estimates the parameters (μ, σ) of the normal distribution using the maximum likelihood method, from the n observations x[i], i = 0, 1,…, n - 1.
getMLE(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.NormalInverseGaussianDist
  NOT IMPLEMENTED.
getMLE(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.ParetoDist
  Estimates the parameters (α, β) of the Pareto distribution using the maximum likelihood method, from the n observations x[i], i = 0, 1,…, n - 1.
getMLE(int[], int) - Static method in class umontreal.iro.lecuyer.probdist.PascalDist
  Estimates the parameter (n, p) of the Pascal distribution using the maximum likelihood method, from the m observations x[i], i = 0, 1,…, m - 1.
getMLE(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.Pearson5Dist
  Estimates the parameters (α, β) of the Pearson V distribution using the maximum likelihood method, from the n observations x[i], i = 0, 1,…, n - 1.
getMLE(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.Pearson6Dist
  Estimates the parameters (α1, α2, β) of the Pearson VI distribution using the maximum likelihood method, from the n observations x[i], i = 0, 1,…, n - 1.
getMLE(int[], int) - Static method in class umontreal.iro.lecuyer.probdist.PoissonDist
  Estimates the parameter λ of the Poisson distribution using the maximum likelihood method, from the n observations x[i], i = 0, 1,…, n - 1.
getMLE(double[], int, double, double) - Static method in class umontreal.iro.lecuyer.probdist.PowerDist
  Estimates the parameter c of the power distribution from the n observations x[i], i = 0, 1,…, n - 1, using the maximum likelihood method and assuming that a and b are known.
getMLE(double[], int, double) - Static method in class umontreal.iro.lecuyer.probdist.RayleighDist
  Estimates the parameter β of the Rayleigh distribution using the maximum likelihood method, assuming that a is known, from the n observations x[i], i = 0, 1,…, n - 1.
getMLE(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.StudentDist
  Estimates the parameter n of the Student-t distribution using the maximum likelihood method, from the m observations x[i], i = 0, 1,…, m - 1.
getMLE(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.UniformDist
  Estimates the parameter (a, b) of the uniform distribution using the maximum likelihood method, from the n observations x[i], i = 0, 1,…, n - 1.
getMLE(int[], int) - Static method in class umontreal.iro.lecuyer.probdist.UniformIntDist
  Estimates the parameters (i, j) of the uniform distribution over integers using the maximum likelihood method, from the n observations x[k], k = 0, 1,…, n - 1.
getMLE(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.WeibullDist
  Estimates the parameters (α, λ) of the Weibull distribution, assuming that δ = 0, using the maximum likelihood method, from the n observations x[i], i = 0, 1,…, n - 1.
getMLE(double[][], int, int) - Static method in class umontreal.iro.lecuyer.probdistmulti.DirichletDist
  Estimates the parameters [ hat(α_1),…, hat(α_d)] of the Dirichlet distribution using the maximum likelihood method.
getMLE(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.
getMLE(int[][], int, int) - Static method in class umontreal.iro.lecuyer.probdistmulti.NegativeMultinomialDist
  Estimates and returns the parameters [ hat(γ), hat(p_1),..., hat(p_d)] of the negative multinomial distribution using the maximum likelihood method.
getMLEMu(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.
getMLESigma(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.
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.FoldedNormalDist
  Returns the parameter μ of this object.
getMu() - Method in class umontreal.iro.lecuyer.probdist.HalfNormalDist
  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.LaplaceDist
  Returns the parameter μ.
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.probdist.NormalInverseGaussianDist
  Returns the parameter μ of this object.
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.randvar.FatigueLifeGen
  Returns the parameter μ of this object.
getMu() - Method in class umontreal.iro.lecuyer.randvar.FoldedNormalGen
  Returns the parameter μ of this object.
getMu() - Method in class umontreal.iro.lecuyer.randvar.HalfNormalGen
  Returns the parameter μ of this object.
getMu() - Method in class umontreal.iro.lecuyer.randvar.HyperbolicSecantGen
  Returns the parameter μ of this object.
getMu() - Method in class umontreal.iro.lecuyer.randvar.InverseGaussianGen
  Returns the parameter μ of this object.
getMu() - Method in class umontreal.iro.lecuyer.randvar.LaplaceGen
  Returns the parameter μ.
getMu() - Method in class umontreal.iro.lecuyer.randvar.LognormalGen
  Returns the parameter μ of this object.
getMu() - Method in class umontreal.iro.lecuyer.randvar.NormalGen
  Returns the parameter μ of this object.
getMu() - Method in class umontreal.iro.lecuyer.randvar.NormalInverseGaussianGen
  Returns 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.
getMu() - Method in class umontreal.iro.lecuyer.randvarmulti.MultiNormalGen
  Deprecated. Returns the mean vector used by this generator.
getMu(int) - Method in class umontreal.iro.lecuyer.randvarmulti.MultiNormalGen
  Deprecated. Returns the i-th component of the mean vector for this generator.
getMu() - Method in class umontreal.iro.lecuyer.stochprocess.BrownianMotion
  Returns the value of μ.
getMu() - Method in class umontreal.iro.lecuyer.stochprocess.GammaProcess
  Returns the value of the parameter μ.
getMu() - Method in class umontreal.iro.lecuyer.stochprocess.GeometricBrownianMotion
  Returns the value of μ.
getMu() - Method in class umontreal.iro.lecuyer.stochprocess.GeometricVarianceGammaProcess
  Returns the value of the parameter μ.
getMu() - Method in class umontreal.iro.lecuyer.stochprocess.NormalInverseGaussianProcess
  Returns mu.
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.
getMuGeom() - Method in class umontreal.iro.lecuyer.stochprocess.GeometricLevyProcess
  Returns the geometric drift parameter, which is usually the interest rate, r.
getN() - Method in class umontreal.iro.lecuyer.probdist.AndersonDarlingDist
  Returns the parameter n of this object.
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.CramerVonMisesDist
  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.KolmogorovSmirnovDist
  Returns the parameter n of this object.
getN() - Method in class umontreal.iro.lecuyer.probdist.KolmogorovSmirnovPlusDist
  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.probdist.WatsonGDist
  Returns the parameter n of this object.
getN() - Method in class umontreal.iro.lecuyer.probdist.WatsonUDist
  Returns the parameter n of this object.
getN() - Method in class umontreal.iro.lecuyer.probdistmulti.MultinomialDist
  Returns the parameter n of this object.
getN() - Method in class umontreal.iro.lecuyer.randvar.BinomialGen
  Returns the parameter n of this object.
getN() - Method in class umontreal.iro.lecuyer.randvar.ChiSquareGen
  Returns the value of n for this object.
getN() - Method in class umontreal.iro.lecuyer.randvar.FisherFGen
  Returns the parameter n of this object.
getN() - Method in class umontreal.iro.lecuyer.randvar.PascalGen
  Returns the parameter n of this object.
getN() - Method in class umontreal.iro.lecuyer.randvar.StudentGen
  Returns the value of n for this object.
getName(int) - Method in class umontreal.iro.lecuyer.charts.BoxSeriesCollection
  Gets the current name of the selected series.
getName(int) - Method in class umontreal.iro.lecuyer.charts.XYListSeriesCollection
  Gets the current name of the selected series.
getName() - Method in class umontreal.iro.lecuyer.simevents.ListWithStat
  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.list.ListOfStatProbes
  Returns the global name of this list of statistical probes.
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.
getName() - Method in exception umontreal.iro.lecuyer.util.NameConflictException
  Returns the simple name associated with this exception.
getNbObservationTimes() - Method in class umontreal.iro.lecuyer.stochprocess.StochasticProcess
  Returns the number of observation times excluding the time t0.
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.
getNormalGen() - Method in class umontreal.iro.lecuyer.stochprocess.InverseGaussianProcessMSH
  Returns the normal generator.
getNu() - Method in class umontreal.iro.lecuyer.probdist.ChiDist
  Returns the value of ν for this object.
getNu() - Method in class umontreal.iro.lecuyer.probdist.ChiSquareNoncentralDist
  Returns the parameter ν of this object.
getNu() - Method in class umontreal.iro.lecuyer.randvar.ChiGen
  Returns the value of ν for this object.
getNu() - Method in class umontreal.iro.lecuyer.randvar.ChiSquareNoncentralGen
  Returns the value of ν of this object.
getNu() - Method in class umontreal.iro.lecuyer.stochprocess.GammaProcess
  Returns the value of the parameter ν.
getNu() - Method in class umontreal.iro.lecuyer.stochprocess.GeometricVarianceGammaProcess
  Returns the value of the parameter ν.
getNu() - Method in class umontreal.iro.lecuyer.stochprocess.VarianceGammaProcess
  Returns the value of the parameter ν.
getNumberOfRandomStreams() - Method in class umontreal.iro.lecuyer.stochprocess.InverseGaussianProcess
  Returns the number of random streams of this process.
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
  Returns the number of points of the contained point set.
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).
getObservation(int) - Method in class umontreal.iro.lecuyer.stochprocess.StochasticProcess
  Returns X(tj) from the current sample path.
getObservationTimes() - Method in class umontreal.iro.lecuyer.stochprocess.StochasticProcess
  Returns a reference to the array that contains the observation times {t0,..., td}.
getOmega() - Method in class umontreal.iro.lecuyer.stochprocess.GeometricLevyProcess
  Returns the risk neutral correction.
getOmega() - Method in class umontreal.iro.lecuyer.stochprocess.GeometricVarianceGammaProcess
  Returns the value of the quantity ω defined in.
getOtherStream() - Method in class umontreal.iro.lecuyer.stochprocess.InverseGaussianProcessMSH
  Returns the `otherStream`, which is the stream used to choose between the two quadratic roots from the MSH method.
getOutlineWidth(int) - Method in class umontreal.iro.lecuyer.charts.HistogramSeriesCollection
  Returns the outline width in pt.
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.
getP() - Method in class umontreal.iro.lecuyer.randvar.BinomialGen
  Returns the parameter p of this object.
getP() - Method in class umontreal.iro.lecuyer.randvar.GeometricGen
  Returns the parameter p of this object.
getP() - Method in class umontreal.iro.lecuyer.randvar.NegativeBinomialGen
  Returns the parameter p of this object.
getP() - Method in class umontreal.iro.lecuyer.randvar.PascalGen
  Returns the parameter p of this object.
getParams() - Method in class umontreal.iro.lecuyer.probdist.AndersonDarlingDist
  Return an array containing the parameter n of the current distribution.
getParams() - Method in class umontreal.iro.lecuyer.probdist.BetaDist
  Return a table containing parameters of the current distribution.
getParams() - Method in class umontreal.iro.lecuyer.probdist.BetaSymmetricalDist
  Return a table containing the parameter of the current distribution.
getParams() - Method in class umontreal.iro.lecuyer.probdist.BinomialDist
  Returns a table that contains the parameters (n, p) of the current distribution, in regular order: [n, p].
getParams() - Method in class umontreal.iro.lecuyer.probdist.CauchyDist
  Return a table containing parameters of the current distribution.
getParams() - Method in class umontreal.iro.lecuyer.probdist.ChiDist
  Return a table containing parameters of the current distribution.
getParams() - Method in class umontreal.iro.lecuyer.probdist.ChiSquareDist
  Return a table containing the parameters of the current distribution.
getParams() - Method in class umontreal.iro.lecuyer.probdist.ChiSquareNoncentralDist
  Returns a table containing the parameters of the current distribution.
getParams() - Method in class umontreal.iro.lecuyer.probdist.CramerVonMisesDist
  Return an array containing the parameter n of this object.
getParams() - Method in class umontreal.iro.lecuyer.probdist.DiscreteDistribution
  Returns a table containing the parameters of the current distribution.
getParams() - Method in interface umontreal.iro.lecuyer.probdist.Distribution
  Returns the parameters of the distribution function in the same order as in the constructors.
getParams() - Method in class umontreal.iro.lecuyer.probdist.EmpiricalDist
  Return a table containing parameters of the current distribution.
getParams() - Method in class umontreal.iro.lecuyer.probdist.ErlangDist
  Return a table containing parameters of the current distribution.
getParams() - Method in class umontreal.iro.lecuyer.probdist.ExponentialDist
  Return a table containing the parameters of the current distribution.
getParams() - Method in class umontreal.iro.lecuyer.probdist.ExtremeValueDist
  Return a table containing the parameters of the current distribution.
getParams() - Method in class umontreal.iro.lecuyer.probdist.FatigueLifeDist
  Return a table containing the parameters of the current distribution.
getParams() - Method in class umontreal.iro.lecuyer.probdist.FisherFDist
  Return a table containing the parameters of the current distribution.
getParams() - Method in class umontreal.iro.lecuyer.probdist.FoldedNormalDist
  Return a table containing the parameters of the current distribution.
getParams() - Method in class umontreal.iro.lecuyer.probdist.GammaDist
  Return a table containing the parameters of the current distribution.
getParams() - Method in class umontreal.iro.lecuyer.probdist.GeometricDist
  Return a table containing the parameters of the current distribution.
getParams() - Method in class umontreal.iro.lecuyer.probdist.HalfNormalDist
  Return a table containing the parameters of the current distribution.
getParams() - Method in class umontreal.iro.lecuyer.probdist.HyperbolicSecantDist
  Return a table containing the parameters of the current distribution.
getParams() - Method in class umontreal.iro.lecuyer.probdist.HypergeometricDist
  Return a table containing the parameters of the current distribution.
getParams() - Method in class umontreal.iro.lecuyer.probdist.InverseGaussianDist
  Return a table containing the parameters of the current distribution.
getParams() - Method in class umontreal.iro.lecuyer.probdist.JohnsonSBDist
  Return a table containing the parameters of the current distribution.
getParams() - Method in class umontreal.iro.lecuyer.probdist.JohnsonSUDist
  Return a table containing the parameters of the current distribution.
getParams() - Method in class umontreal.iro.lecuyer.probdist.KolmogorovSmirnovDist
  Returns an array containing the parameter n of this object.
getParams() - Method in class umontreal.iro.lecuyer.probdist.KolmogorovSmirnovPlusDist
  Returns an array containing the parameter n of this object.
getParams() - Method in class umontreal.iro.lecuyer.probdist.LaplaceDist
  Return a table containing the parameters of the current distribution.
getParams() - Method in class umontreal.iro.lecuyer.probdist.LogarithmicDist
  Return a table containing the parameters of the current distribution.
getParams() - Method in class umontreal.iro.lecuyer.probdist.LogisticDist
  Return a table containing the parameters of the current distribution.
getParams() - Method in class umontreal.iro.lecuyer.probdist.LoglogisticDist
  Return a table containing the parameters of the current distribution.
getParams() - Method in class umontreal.iro.lecuyer.probdist.LognormalDist
  Return a table containing the parameters of the current distribution.
getParams() - Method in class umontreal.iro.lecuyer.probdist.NakagamiDist
  Return a table containing the parameters of the current distribution.
getParams() - Method in class umontreal.iro.lecuyer.probdist.NegativeBinomialDist
  Return a table containing the parameters of the current distribution.
getParams() - Method in class umontreal.iro.lecuyer.probdist.NormalDist
  Return a table containing the parameters of the current distribution.
getParams() - Method in class umontreal.iro.lecuyer.probdist.NormalInverseGaussianDist
  Returns a table containing the parameters of the current distribution.
getParams() - Method in class umontreal.iro.lecuyer.probdist.ParetoDist
  Return a table containing the parameters of the current distribution.
getParams() - Method in class umontreal.iro.lecuyer.probdist.PascalDist
  Return a table containing the parameters of the current distribution.
getParams() - Method in class umontreal.iro.lecuyer.probdist.Pearson5Dist
  Return a table containing the parameters of the current distribution.
getParams() - Method in class umontreal.iro.lecuyer.probdist.Pearson6Dist
  Return a table containing the parameters of the current distribution.
getParams() - Method in class umontreal.iro.lecuyer.probdist.PiecewiseLinearEmpiricalDist
  Return a table containing parameters of the current distribution.
getParams() - Method in class umontreal.iro.lecuyer.probdist.PoissonDist
  Return a table containing the parameter of the current distribution.
getParams() - Method in class umontreal.iro.lecuyer.probdist.PowerDist
  Return a table containing the parameters of the current distribution.
getParams() - Method in class umontreal.iro.lecuyer.probdist.RayleighDist
  Return an array containing the parameters of the current distribution in the order: [a, β].
getParams() - Method in class umontreal.iro.lecuyer.probdist.StudentDist
  Return a table containing the parameter of the current distribution.
getParams() - Method in class umontreal.iro.lecuyer.probdist.TriangularDist
  Return a table containing the parameters of the current distribution.
getParams() - Method in class umontreal.iro.lecuyer.probdist.TruncatedDist
  Return a table containing the parameters of the current distribution.
getParams() - Method in class umontreal.iro.lecuyer.probdist.UniformDist
  Return a table containing the parameters of the current distribution.
getParams() - Method in class umontreal.iro.lecuyer.probdist.UniformIntDist
  Return a table containing the parameters of the current distribution.
getParams() - Method in class umontreal.iro.lecuyer.probdist.WatsonGDist
  Return an array containing the parameter n of this object.
getParams() - Method in class umontreal.iro.lecuyer.probdist.WatsonUDist
  Return an array containing the parameter n of this object.
getParams() - Method in class umontreal.iro.lecuyer.probdist.WeibullDist
  Return a table containing the parameters of the current distribution.
getPath() - Method in class umontreal.iro.lecuyer.stochprocess.StochasticProcess
  Returns a reference to the last generated sample path {X(t0),..., X(td)}.
getPCADecompSigma() - Method in class umontreal.iro.lecuyer.randvarmulti.MultinormalPCAGen
  Returns the matrix A = V()1/2 of this object.
getPhi() - Method in class umontreal.iro.lecuyer.probdist.LaplaceDist
  Deprecated.
getPlotStyle(int) - Method in class umontreal.iro.lecuyer.charts.XYListSeriesCollection
  Gets the current plot style for the selected series.
getPower() - Method in class umontreal.iro.lecuyer.functions.PowerMathFunction
  Returns the power p.
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.
getProb() - Method in class umontreal.iro.lecuyer.charts.DiscreteDistIntChart
  Returns the chart of the probability.
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.
getRangeBounds() - Method in class umontreal.iro.lecuyer.charts.BoxSeriesCollection
  Returns the range (y-coordinates) min and max values.
getRangeBounds() - Method in class umontreal.iro.lecuyer.charts.SSJCategorySeriesCollection
  Returns range (y-coordinates) min and max values.
getRangeBounds() - Method in class umontreal.iro.lecuyer.charts.SSJXYSeriesCollection
  Returns range (y-coordinates) min and max values.
getRenderer() - Method in class umontreal.iro.lecuyer.charts.SSJCategorySeriesCollection
  Returns the `CategoryItemRenderer` object associated with the current object.
getRenderer() - Method in class umontreal.iro.lecuyer.charts.SSJXYSeriesCollection
  Returns the `XYItemRenderer` object associated with the current object.
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.AbstractChrono
  Returns the CPU time in seconds used by the program since the last call to `init` for this `AbstractChrono`.
getSeriesCollection() - Method in class umontreal.iro.lecuyer.charts.BoxChart
  Returns the chart's dataset.
getSeriesCollection() - Method in class umontreal.iro.lecuyer.charts.EmpiricalChart
  Returns the chart's dataset.
getSeriesCollection() - Method in class umontreal.iro.lecuyer.charts.HistogramChart
  Returns the chart's dataset.
getSeriesCollection() - Method in class umontreal.iro.lecuyer.charts.HistogramSeriesCollection
  Returns the `CustomHistogramDataset` object associated with the current variable.
getSeriesCollection() - Method in class umontreal.iro.lecuyer.charts.ScatterChart
  Returns the chart's dataset.
getSeriesCollection() - Method in class umontreal.iro.lecuyer.charts.SSJCategorySeriesCollection
  Returns the `CategoryDataset` object associated with the current object.
getSeriesCollection() - Method in class umontreal.iro.lecuyer.charts.SSJXYSeriesCollection
  Returns the `XYDataset` object associated with the current object.
getSeriesCollection() - Method in class umontreal.iro.lecuyer.charts.XYLineChart
  Returns the chart's dataset.
getSeriesCount() - Method in class umontreal.iro.lecuyer.charts.CustomHistogramDataset
  Returns the number of series in the dataset.
getSeriesKey(int) - Method in class umontreal.iro.lecuyer.charts.CustomHistogramDataset
  Returns the key for a series.
getShiftDimension() - Method in class umontreal.iro.lecuyer.hups.RandShiftedPointSet
  Returns the number of dimensions of the current random shift.
getShortName() - Method in enum umontreal.iro.lecuyer.util.TimeUnit
  Returns the short name representing this unit in a string specifying a time duration.
getSigma() - Method in class umontreal.iro.lecuyer.probdist.FoldedNormalDist
  Returns the parameter σ of this object.
getSigma() - Method in class umontreal.iro.lecuyer.probdist.HalfNormalDist
  Returns the parameter σ of this object.
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.probdist.RayleighDist
  Returns the parameter β.
getSigma() - Method in class umontreal.iro.lecuyer.probdistmulti.MultiNormalDist
  Returns the parameter Σ of this object.
getSigma() - Method in class umontreal.iro.lecuyer.randvar.FoldedNormalGen
  Returns the parameter σ of this object.
getSigma() - Method in class umontreal.iro.lecuyer.randvar.HalfNormalGen
  Returns the parameter σ of this object.
getSigma() - Method in class umontreal.iro.lecuyer.randvar.HyperbolicSecantGen
  Returns the parameter σ of this object.
getSigma() - Method in class umontreal.iro.lecuyer.randvar.LognormalGen
  Returns the parameter σ of this object.
getSigma() - Method in class umontreal.iro.lecuyer.randvar.NormalGen
  Returns the parameter σ of this object.
getSigma() - Method in class umontreal.iro.lecuyer.randvar.RayleighGen
  Returns the parameter β.
getSigma() - Method in class umontreal.iro.lecuyer.randvarmulti.MultinormalGen
  Returns the covariance matrix Σ used by this generator.
getSigma() - Method in class umontreal.iro.lecuyer.randvarmulti.MultiNormalGen
  Deprecated. Returns the covariance matrix Σ used by this generator.
getSigma() - Method in class umontreal.iro.lecuyer.stochprocess.BrownianMotion
  Returns the value of σ.
getSigma() - Method in class umontreal.iro.lecuyer.stochprocess.CIRProcess
  Returns the value of σ.
getSigma() - Method in class umontreal.iro.lecuyer.stochprocess.GeometricBrownianMotion
  Returns the value of σ.
getSigma() - Method in class umontreal.iro.lecuyer.stochprocess.GeometricVarianceGammaProcess
  Returns the value of the parameter σ.
getSigma() - Method in class umontreal.iro.lecuyer.stochprocess.OrnsteinUhlenbeckProcess
  Returns the value of σ.
getSigma() - Method in class umontreal.iro.lecuyer.stochprocess.VarianceGammaProcess
  Returns the value of the parameter σ.
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.
getSimpleName(Class<?>) - Method in class umontreal.iro.lecuyer.util.ClassFinder
  Returns the simple name of the class `cls` that can be used when the imports contained in this class finder are used.
getSortedEigenvalues() - Method in class umontreal.iro.lecuyer.stochprocess.BrownianMotionPCA
  Returns the sorted eigenvalues obtained in the PCA decomposition.
getSortedEigenvalues() - Method in class umontreal.iro.lecuyer.stochprocess.BrownianMotionPCAEqualSteps
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, over the interval [0, 1].
getStandardDeviation(double, double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.BetaDist
  Computes the standard deviation of the beta distribution with parameters α and β, over the interval [a, b].
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.ChiSquareNoncentralDist
getStandardDeviation(double, double) - Static method in class umontreal.iro.lecuyer.probdist.ChiSquareNoncentralDist
  Computes and returns the standard deviation of the noncentral chi-square distribution with parameters ν = `nu` and λ = `lambda`.
getStandardDeviation() - Method in class umontreal.iro.lecuyer.probdist.ContinuousDistribution
  Returns the standard deviation.
getStandardDeviation() - Method in class umontreal.iro.lecuyer.probdist.CramerVonMisesDist
getStandardDeviation(int) - Static method in class umontreal.iro.lecuyer.probdist.CramerVonMisesDist
  Returns the standard deviation of the 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.FoldedNormalDist
getStandardDeviation(double, double) - Static method in class umontreal.iro.lecuyer.probdist.FoldedNormalDist
  Computes the standard deviation of the folded normal distribution with parameters μ and σ.
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.HalfNormalDist
getStandardDeviation(double, double) - Static method in class umontreal.iro.lecuyer.probdist.HalfNormalDist
  Computes the standard deviation of the half-normal distribution with parameters μ and σ.
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.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 μ and β.
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.NakagamiDist
getStandardDeviation(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.NakagamiDist
  Computes the standard deviation of the Nakagami distribution with parameters a, λ and c.
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.NormalInverseGaussianDist
getStandardDeviation(double, double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.NormalInverseGaussianDist
  Computes and returns the standard deviation of the normal inverse gaussian distribution with parameters α, β, μ and δ.
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.PowerDist
getStandardDeviation(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.PowerDist
  Computes and returns the standard deviation of the power distribution with parameters a, b and c.
getStandardDeviation() - Method in class umontreal.iro.lecuyer.probdist.RayleighDist
getStandardDeviation(double) - Static method in class umontreal.iro.lecuyer.probdist.RayleighDist
  Returns the standard deviation β(2 - π/2)1/2 of the Rayleigh distribution with parameter β.
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.WatsonUDist
getStandardDeviation(int) - Static method in class umontreal.iro.lecuyer.probdist.WatsonUDist
  Returns the standard deviation of the Watson U distribution with parameter n.
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.
getStartX(int, int) - Method in class umontreal.iro.lecuyer.charts.CustomHistogramDataset
  Returns the start value for a bin.
getStartY(int, int) - Method in class umontreal.iro.lecuyer.charts.CustomHistogramDataset
  Returns the start y-value for a bin (which is the same as the y-value).
getStatCollecting() - Method in class umontreal.iro.lecuyer.simevents.ListWithStat
  Returns `true` if the list collects statistics about its size and sojourn times of elements, and `false` otherwise.
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.MRG32k3aL
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.SimProcess
  Returns the state of the process.
getStream() - Method in class umontreal.iro.lecuyer.hups.PointSet
  Returns the random stream used to generate random shifts.
getStream() - Method in interface umontreal.iro.lecuyer.hups.PointSetRandomization
  Returns the internal `RandomStream`.
getStream() - Method in class umontreal.iro.lecuyer.hups.RandomShift
  Returns the internal `RandomStream`.
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.
getStream() - Method in class umontreal.iro.lecuyer.stochprocess.BrownianMotion
  Returns the random stream of the normal generator.
getStream() - Method in class umontreal.iro.lecuyer.stochprocess.CIRProcess
  Returns the random stream of the normal generator.
getStream() - Method in class umontreal.iro.lecuyer.stochprocess.GammaProcess
  Returns the `RandomStream` `stream`.
getStream() - Method in class umontreal.iro.lecuyer.stochprocess.GeometricBrownianMotion
  Returns the `RandomStream` for the underlying Brownian motion.
getStream() - Method in class umontreal.iro.lecuyer.stochprocess.GeometricLevyProcess
  Returns the stream from the underlying Lévy process.
getStream() - Method in class umontreal.iro.lecuyer.stochprocess.GeometricVarianceGammaProcess
getStream() - Method in class umontreal.iro.lecuyer.stochprocess.InverseGaussianProcess
getStream() - Method in class umontreal.iro.lecuyer.stochprocess.InverseGaussianProcessBridge
  Only returns a stream if both inner streams are the same.
getStream() - Method in class umontreal.iro.lecuyer.stochprocess.InverseGaussianProcessMSH
  Only returns a stream if both inner `RandomStream`'s are the same.
getStream() - Method in class umontreal.iro.lecuyer.stochprocess.InverseGaussianProcessPCA
getStream() - Method in class umontreal.iro.lecuyer.stochprocess.NormalInverseGaussianProcess
  Only returns the stream if all streams are equal, including the stream(s) in the underlying `InverseGaussianProcess`.
getStream() - Method in class umontreal.iro.lecuyer.stochprocess.OrnsteinUhlenbeckProcess
  Returns the random stream of the normal generator.
getStream() - Method in class umontreal.iro.lecuyer.stochprocess.StochasticProcess
  Returns the random stream of the underlying generator.
getStream() - Method in class umontreal.iro.lecuyer.stochprocess.VarianceGammaProcess
  Returns the random stream of the `BrownianMotion` process, which should be the same as for the `GammaProcess`.
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.
getSubpath(double[], int[]) - Method in class umontreal.iro.lecuyer.stochprocess.StochasticProcess
  Returns in `subpath` the values of the process at a subset of the observation times, specified as the times tj whose indices j are in the array `pathIndices`.
getTheta() - Method in class umontreal.iro.lecuyer.probdist.LaplaceDist
  Deprecated.
getTheta() - Method in class umontreal.iro.lecuyer.probdist.LogarithmicDist
  Returns the θ associated with this object.
getTheta() - Method in class umontreal.iro.lecuyer.randvar.LogarithmicGen
  Returns the θ associated with this object.
getTheta() - Method in class umontreal.iro.lecuyer.stochprocess.GeometricVarianceGammaProcess
  Returns the value of the parameter θ.
getTheta() - Method in class umontreal.iro.lecuyer.stochprocess.VarianceGammaProcess
  Returns the value of the parameter θ.
getTheta0() - Method in class umontreal.iro.lecuyer.randvar.LogarithmicGen
  Returns the θ0 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`.
getTitle() - Method in class umontreal.iro.lecuyer.charts.CategoryChart
  Gets the current chart title.
getTitle() - Method in class umontreal.iro.lecuyer.charts.MultipleDatasetChart
  Gets the current chart title.
getTitle() - Method in class umontreal.iro.lecuyer.charts.XYChart
  Gets the current chart title.
getTotal(int) - Method in class umontreal.iro.lecuyer.charts.CustomHistogramDataset
  Returns the total number of observations for a series.
getTwinAxisPosition() - Method in class umontreal.iro.lecuyer.charts.Axis
  Returns the drawing position parameter (default equals 0).
getType() - Method in class umontreal.iro.lecuyer.charts.CustomHistogramDataset
  Returns the histogram type.
getValue(int, int) - Method in class umontreal.iro.lecuyer.charts.SSJCategorySeriesCollection
  Returns the y-value at the specified index in the specified series.
getValues(int) - Method in class umontreal.iro.lecuyer.charts.CustomHistogramDataset
  Returns the values for a series.
getValues(int) - Method in class umontreal.iro.lecuyer.charts.HistogramSeriesCollection
  Returns the values for a series.
getValuesList(int) - Method in class umontreal.iro.lecuyer.charts.CustomHistogramDataset
  Returns the values for a series.
getValuesList(int) - Method in class umontreal.iro.lecuyer.charts.HistogramSeriesCollection
  Returns the values for a series.
getVariance() - Method in class umontreal.iro.lecuyer.probdist.BetaDist
getVariance(double, double) - Static method in class umontreal.iro.lecuyer.probdist.BetaDist
  .
getVariance(double, double, 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.ChiSquareNoncentralDist
getVariance(double, double) - Static method in class umontreal.iro.lecuyer.probdist.ChiSquareNoncentralDist
  Computes and returns the variance Var[X] = 2(ν +2λ) of the noncentral chi-square distribution with parameters ν = `nu` and λ = `lambda`.
getVariance() - Method in class umontreal.iro.lecuyer.probdist.ContinuousDistribution
  Returns the variance.
getVariance() - Method in class umontreal.iro.lecuyer.probdist.CramerVonMisesDist
getVariance(int) - Static method in class umontreal.iro.lecuyer.probdist.CramerVonMisesDist
  Returns the variance of the 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.FoldedNormalDist
getVariance(double, double) - Static method in class umontreal.iro.lecuyer.probdist.FoldedNormalDist
  .
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.HalfNormalDist
getVariance(double, double) - Static method in class umontreal.iro.lecuyer.probdist.HalfNormalDist
  Computes and returns the variance Var[X] = (1 - 2/π)σ2.
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.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] = 2β2 of the Laplace distribution with parameters μ and β.
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.NakagamiDist
getVariance(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.NakagamiDist
  .
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.NormalInverseGaussianDist
getVariance(double, double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.NormalInverseGaussianDist
  Computes and returns the variance Var[X] = δα2/γ3 of the normal inverse gaussian distribution with parameters α, β, μ and δ.
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.PowerDist
getVariance(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.PowerDist
  Computes and returns the variance (b - a)2c/[(c + 1)2(c + 2)] of the power distribution with parameters a, b and c.
getVariance() - Method in class umontreal.iro.lecuyer.probdist.RayleighDist
getVariance(double) - Static method in class umontreal.iro.lecuyer.probdist.RayleighDist
  Returns the variance of the Rayleigh distribution with parameter β.
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.WatsonUDist
getVariance(int) - Static method in class umontreal.iro.lecuyer.probdist.WatsonUDist
  Returns the variance of the Watson U distribution with parameter n.
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.
getVarianceGammaProcess() - Method in class umontreal.iro.lecuyer.stochprocess.GeometricVarianceGammaProcess
  Returns a reference to the variance gamma process X defined in the constructor.
getX(int, int) - Method in class umontreal.iro.lecuyer.charts.CustomHistogramDataset
  Returns the X value for a bin.
getX(int, int) - Method in class umontreal.iro.lecuyer.charts.SSJXYSeriesCollection
  Returns the x-value at the specified index in the specified series.
getX() - Method in class umontreal.iro.lecuyer.functions.PiecewiseConstantFunction
  Returns the X coordinates of the function.
getX0() - Method in class umontreal.iro.lecuyer.stochprocess.StochasticProcess
  Returns the initial value X(t0) for this process.
getXAxis() - Method in class umontreal.iro.lecuyer.charts.MultipleDatasetChart
  Returns the chart's domain axis (x-axis) object.
getXAxis() - Method in class umontreal.iro.lecuyer.charts.XYChart
  Returns the chart's domain axis (x-axis) object.
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.
getXi() - Method in class umontreal.iro.lecuyer.randvar.JohnsonSBGen
  Returns the ξ associated with this object.
getXi() - Method in class umontreal.iro.lecuyer.randvar.JohnsonSUGen
  Returns the ξ associated with this object.
getY(int, int) - Method in class umontreal.iro.lecuyer.charts.CustomHistogramDataset
  Returns the y-value for a bin (calculated to take into account the histogram type).
getY(int, int) - Method in class umontreal.iro.lecuyer.charts.SSJXYSeriesCollection
  Returns the y-value at the specified index in the specified series.
getY() - Method in class umontreal.iro.lecuyer.functions.PiecewiseConstantFunction
  Returns the Y coordinates of the function.
getYAxis() - Method in class umontreal.iro.lecuyer.charts.CategoryChart
  Returns the chart's range axis (y-axis) object.
getYAxis() - Method in class umontreal.iro.lecuyer.charts.MultipleDatasetChart
  Returns the chart's range axis (y-axis) object.
getYAxis() - Method in class umontreal.iro.lecuyer.charts.XYChart
  Returns the chart's range axis (y-axis) object.
getZeroOverZeroValue() - Method in class umontreal.iro.lecuyer.util.RatioFunction
  Returns the value returned by `evaluate` in the case where the 0/0 function is calculated.
GlobalCPUTimeChrono - Class in umontreal.iro.lecuyer.util
  Extends the `AbstractChrono` class to compute the global CPU time used by the Java Virtual Machine.
GlobalCPUTimeChrono() - Constructor for class umontreal.iro.lecuyer.util.GlobalCPUTimeChrono
  Constructs a `Chrono` object and initializes it to zero.
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 WILL BE REWRITTEN AND SIMPLIFIED IN THE NEAR FUTURE. 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
  Deprecated.
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

H - Static variable in class umontreal.iro.lecuyer.functions.MathFunctionUtil
HalfNormalDist - Class in umontreal.iro.lecuyer.probdist
  Extends the class `ContinuousDistribution` for the half-normal distribution with parameters μ and σ > 0.
HalfNormalDist(double, double) - Constructor for class umontreal.iro.lecuyer.probdist.HalfNormalDist
  Constructs a `HalfNormalDist` object with parameters μ = `mu` and σ = `sigma`.
HalfNormalGen - Class in umontreal.iro.lecuyer.randvar
  This class implements methods for generating random variates from the half-normal distribution with parameters μ and σ > 0.
HalfNormalGen(RandomStream, double, double) - Constructor for class umontreal.iro.lecuyer.randvar.HalfNormalGen
  Creates a new half-normal generator with parameters μ = `mu` and σ = `sigma`, using stream `s`.
HalfNormalGen(RandomStream, HalfNormalDist) - Constructor for class umontreal.iro.lecuyer.randvar.HalfNormalGen
  Creates a new generator for the distribution `dist`, using stream `s`.
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.
hashCode() - Method in class umontreal.iro.lecuyer.stat.list.ListOfStatProbes
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.
hasNextObservation() - Method in class umontreal.iro.lecuyer.stochprocess.StochasticProcess
  Returns `true` if j < d, where j is the number of observations of the current sample path generated since the last call to `resetStartProcess`.
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
HistogramChart - Class in umontreal.iro.lecuyer.charts
  Extends `XYChart`.
HistogramChart() - Constructor for class umontreal.iro.lecuyer.charts.HistogramChart
  Initializes a new `HistogramChart` instance with an empty data set.
HistogramChart(String, String, String, double[]...) - Constructor for class umontreal.iro.lecuyer.charts.HistogramChart
  Initializes a new `HistogramChart` instance with input `data`.
HistogramChart(String, String, String, double[], int) - Constructor for class umontreal.iro.lecuyer.charts.HistogramChart
  Initializes a new `HistogramChart` instance with input `data`.
HistogramChart(String, String, String, DoubleArrayList...) - Constructor for class umontreal.iro.lecuyer.charts.HistogramChart
  Initializes a new `HistogramChart` instance with data `data`.
HistogramChart(String, String, String, TallyStore...) - Constructor for class umontreal.iro.lecuyer.charts.HistogramChart
  Initializes a new `HistogramChart` instance with data arrays contained in each `TallyStore` object.
HistogramChart(String, String, String, CustomHistogramDataset) - Constructor for class umontreal.iro.lecuyer.charts.HistogramChart
  Initializes a new `HistogramChart` instance with data `data`.
HistogramChart(String, String, String, int[], double[]) - Constructor for class umontreal.iro.lecuyer.charts.HistogramChart
  Initializes a new `HistogramChart` instance with data `count` and `bound`.
HistogramSeriesCollection - Class in umontreal.iro.lecuyer.charts
  Stores data used in a `HistogramChart`.
HistogramSeriesCollection() - Constructor for class umontreal.iro.lecuyer.charts.HistogramSeriesCollection
  Creates a new `HistogramSeriesCollection` instance with empty dataset.
HistogramSeriesCollection(double[]...) - Constructor for class umontreal.iro.lecuyer.charts.HistogramSeriesCollection
  Creates a new `HistogramSeriesCollection` instance with given data series.
HistogramSeriesCollection(double[], int) - Constructor for class umontreal.iro.lecuyer.charts.HistogramSeriesCollection
  Creates a new `HistogramSeriesCollection` instance with the given data series `data`.
HistogramSeriesCollection(DoubleArrayList...) - Constructor for class umontreal.iro.lecuyer.charts.HistogramSeriesCollection
  Creates a new `HistogramSeriesCollection`.
HistogramSeriesCollection(TallyStore...) - Constructor for class umontreal.iro.lecuyer.charts.HistogramSeriesCollection
  Creates a new `HistogramSeriesCollection` instance with default parameters and given data.
HistogramSeriesCollection(CustomHistogramDataset) - Constructor for class umontreal.iro.lecuyer.charts.HistogramSeriesCollection
  Creates a new `HistogramSeriesCollection` instance.
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, double, double) - Constructor for class umontreal.iro.lecuyer.randvar.HyperbolicSecantGen
  Creates a hyperbolic secant random variate generator with parameters μ = `mu` and σ = `sigma`, using stream `s`.
HyperbolicSecantGen(RandomStream) - Constructor for class umontreal.iro.lecuyer.randvar.HyperbolicSecantGen
  Creates a hyperbolic secant random variate generator with parameters μ = 0 and σ = 1, using stream `s`.
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, int, int, int) - Constructor for class umontreal.iro.lecuyer.randvar.HypergeometricGen
  Creates a hypergeometric generator with parameters m = `m`, l = `l` and k = `k`, using stream `s`.
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
IdentityMathFunction - Class in umontreal.iro.lecuyer.functions
  Represents the identity function f (x) = x.
IdentityMathFunction() - Constructor for class umontreal.iro.lecuyer.functions.IdentityMathFunction
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.stat.list.ListOfStatProbes
init(double[]) - Method in class umontreal.iro.lecuyer.hups.HaltonSequence
  Initializes the Halton sequence starting at point `x0`.
init(byte[], int) - Static method in class umontreal.iro.lecuyer.rng.RandomPermutation
  Initializes `array` with the first n positive integers in natural order as `array`[i - 1] = i, for i = 1,..., n.
init(short[], int) - Static method in class umontreal.iro.lecuyer.rng.RandomPermutation
  Similar to `init``(byte[], int)`.
init(int[], int) - Static method in class umontreal.iro.lecuyer.rng.RandomPermutation
  Similar to `init``(byte[], int)`.
init(long[], int) - Static method in class umontreal.iro.lecuyer.rng.RandomPermutation
  Similar to `init``(byte[], int)`.
init(float[], int) - Static method in class umontreal.iro.lecuyer.rng.RandomPermutation
  Similar to `init``(byte[], int)`.
init(double[], int) - Static method in class umontreal.iro.lecuyer.rng.RandomPermutation
  Similar to `init``(byte[], int)`.
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.simevents.Simulator
  Reinitializes the simulation executive by clearing up the event list, and resetting the simulation clock to zero.
init(EventList) - Method in class umontreal.iro.lecuyer.simevents.Simulator
  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() - Method in class umontreal.iro.lecuyer.simprocs.DSOLProcessSimulator
  Initializes the process-driven simulation.
init(EventList) - Method in class umontreal.iro.lecuyer.simprocs.DSOLProcessSimulator
  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
  Deprecated.
init() - Method in class umontreal.iro.lecuyer.simprocs.ThreadProcessSimulator
  Initializes the thread process-driven simulation using `SplayTree` algorithm as `EventList`.
init(EventList) - Method in class umontreal.iro.lecuyer.simprocs.ThreadProcessSimulator
  Initializes the thread process-driven simulation using `evlist` variable as `EventList`.
init() - Method in class umontreal.iro.lecuyer.stat.list.ListOfStatProbes
  Initializes this list of statistical probes by calling `init` on each element.
init() - Method in class umontreal.iro.lecuyer.stat.list.ListOfTalliesWithCovariance
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.AbstractChrono
  Initializes this `AbstractChrono` 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.
initDefault() - Static method in class umontreal.iro.lecuyer.simprocs.ProcessSimulator
  Initializes the default simulator to use processes.
INITIAL - Static variable in class umontreal.iro.lecuyer.simprocs.SimProcess
  The process has been created but not yet scheduled.
initStat() - Method in class umontreal.iro.lecuyer.simevents.ListWithStat
  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.
integMethod() - Method in class umontreal.iro.lecuyer.simevents.ContinuousState
  Return an integer that represent the integration method in use.
integral(double, double) - Method in class umontreal.iro.lecuyer.functions.AverageMathFunction
integral(double, double) - Method in class umontreal.iro.lecuyer.functions.IdentityMathFunction
integral(MathFunction, double, double) - Static method in class umontreal.iro.lecuyer.functions.MathFunctionUtil
  Returns the integral of the function `func` over [a, b].
integral(double, double) - Method in interface umontreal.iro.lecuyer.functions.MathFunctionWithIntegral
  Computes (or estimates) the integral of the function over the interval [a, b].
integral(double, double) - Method in class umontreal.iro.lecuyer.functions.Polynomial
integral(double, double) - Method in class umontreal.iro.lecuyer.functions.ShiftedMathFunction
integralPolynomial(double) - Method in class umontreal.iro.lecuyer.functions.Polynomial
  Returns a polynomial representing the integral of this polynomial.
interpol(int, double[], double[], double[]) - Static method in class umontreal.iro.lecuyer.util.Misc
  Computes the Newton interpolating polynomial.
Introspection - Class in umontreal.iro.lecuyer.util
  Provides utility methods for introspection using Java Reflection API.
inverseBisection(double) - Method in class umontreal.iro.lecuyer.probdist.ContinuousDistribution
  Computes and returns the inverse distribution function x = F-1(u), using bisection.
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.AndersonDarlingDist
inverseF(int, double) - Static method in class umontreal.iro.lecuyer.probdist.AndersonDarlingDist
  Computes the inverse x = Fn-1(u) of the Anderson-Darling distribution with parameter n.
inverseF(double) - Method in class umontreal.iro.lecuyer.probdist.AndersonDarlingDistQuick
inverseF(int, double) - Static method in class umontreal.iro.lecuyer.probdist.AndersonDarlingDistQuick
  Computes the inverse x = Fn-1(u) of the distribution with parameter n.
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.ChiSquareNoncentralDist
inverseF(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.ChiSquareNoncentralDist
  Computes the inverse of the noncentral chi-square distribution with ν = `nu` degrees of freedom and parameter λ = `lambda`.
inverseF(double) - Method in class umontreal.iro.lecuyer.probdist.ContinuousDistribution
  Computes and returns the inverse distribution function x = F-1(u), by calling the method `inverseBrent`.
inverseF(double) - Method in class umontreal.iro.lecuyer.probdist.CramerVonMisesDist
inverseF(int, double) - Static method in class umontreal.iro.lecuyer.probdist.CramerVonMisesDist
  Computes x = Fn-1(u), where Fn is the Cramér-von Mises distribution with parameter n.
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
  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.FoldedNormalDist
inverseF(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.FoldedNormalDist
  Computes the inverse of the distribution function.
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.HalfNormalDist
inverseF(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.HalfNormalDist
  Computes the inverse of the distribution function.
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) - Method in class umontreal.iro.lecuyer.probdist.InverseGaussianDist
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.KolmogorovSmirnovDist
inverseF(int, double) - Static method in class umontreal.iro.lecuyer.probdist.KolmogorovSmirnovDist
  Computes the inverse x = F-1(u) of the distribution F(x) with parameter n.
inverseF(double) - Method in class umontreal.iro.lecuyer.probdist.KolmogorovSmirnovDistQuick
inverseF(int, double) - Static method in class umontreal.iro.lecuyer.probdist.KolmogorovSmirnovDistQuick
  Computes the inverse x = F-1(u) of the distribution F(x) with parameter n.
inverseF(double) - Method in class umontreal.iro.lecuyer.probdist.KolmogorovSmirnovPlusDist
inverseF(int, double) - Static method in class umontreal.iro.lecuyer.probdist.KolmogorovSmirnovPlusDist
  Computes the inverse x = F-1(u) of the distribution with parameter n.
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) - Method in class umontreal.iro.lecuyer.probdist.NakagamiDist
inverseF(double, double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.NakagamiDist
  Computes the inverse of the distribution function.
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, double, double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.NormalInverseGaussianDist
  NOT IMPLEMENTED.
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.PowerDist
inverseF(double, double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.PowerDist
  Computes the inverse of the distribution function.
inverseF(double) - Method in class umontreal.iro.lecuyer.probdist.RayleighDist
inverseF(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.RayleighDist
  Computes the inverse of the distribution function.
inverseF(double, double) - Static method in class umontreal.iro.lecuyer.probdist.RayleighDist
  Same as `inverseF (0, beta, u)`.
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.WatsonGDist
inverseF(int, double) - Static method in class umontreal.iro.lecuyer.probdist.WatsonGDist
  Computes x = Fn-1(u), where Fn is the Watson G distribution with parameter n.
inverseF(double) - Method in class umontreal.iro.lecuyer.probdist.WatsonUDist
inverseF(int, double) - Static method in class umontreal.iro.lecuyer.probdist.WatsonUDist
  Computes x = Fn-1(u), where Fn is the Watson U distribution with parameter n.
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, x)`.
inverseF01(double) - Static method in class umontreal.iro.lecuyer.probdist.NormalDist
  Same as `inverseF` `(0, 1, 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, double, double) - Constructor for class umontreal.iro.lecuyer.randvar.InverseGaussianGen
  Creates an inverse Gaussian random variate generator with parameters μ = `mu` and λ = `lambda`, using stream `s`.
InverseGaussianGen(RandomStream, InverseGaussianDist) - Constructor for class umontreal.iro.lecuyer.randvar.InverseGaussianGen
  Creates a new generator for the distribution `dist`, using stream `s`.
InverseGaussianMSHGen - Class in umontreal.iro.lecuyer.randvar
  This class implements inverse gaussian random variate generators using the many-to-one transformation method of Michael, Schucany and Haas (MHS).
InverseGaussianMSHGen(RandomStream, NormalGen, double, double) - Constructor for class umontreal.iro.lecuyer.randvar.InverseGaussianMSHGen
  Creates an inverse gaussian random variate generator with parameters μ = `mu` and λ = `lambda`, using streams `s` and `sn`.
InverseGaussianMSHGen(RandomStream, NormalGen, InverseGaussianDist) - Constructor for class umontreal.iro.lecuyer.randvar.InverseGaussianMSHGen
  Creates a new generator for the distribution `dist` using streams `s` and `sn`.
InverseGaussianProcess - Class in umontreal.iro.lecuyer.stochprocess
  The inverse Gaussian process is a non-decreasing process where the increments are additive and are given by the inverse gaussian distribution, `InverseGaussianDist`.
InverseGaussianProcess(double, double, double, RandomStream) - Constructor for class umontreal.iro.lecuyer.stochprocess.InverseGaussianProcess
  Constructs a new `InverseGaussianProcess`.
InverseGaussianProcessBridge - Class in umontreal.iro.lecuyer.stochprocess
  Samples the path by bridge sampling: first finding the process value at the final time and then the middle time, etc.
InverseGaussianProcessBridge(double, double, double, RandomStream, RandomStream) - Constructor for class umontreal.iro.lecuyer.stochprocess.InverseGaussianProcessBridge
  Constructs a new `InverseGaussianProcessBridge`.
InverseGaussianProcessMSH - Class in umontreal.iro.lecuyer.stochprocess
  Uses a faster generating method (MSH) than the simple inversion of the distribution function used by `InverseGaussianProcess`.
InverseGaussianProcessMSH(double, double, double, RandomStream, RandomStream) - Constructor for class umontreal.iro.lecuyer.stochprocess.InverseGaussianProcessMSH
  Constructs a new `InverseGaussianProcessMSH`.
InverseGaussianProcessPCA - Class in umontreal.iro.lecuyer.stochprocess
  Approximates a principal component analysis (PCA) decomposition of the `InverseGaussianProcess`.
InverseGaussianProcessPCA(double, double, double, RandomStream) - Constructor for class umontreal.iro.lecuyer.stochprocess.InverseGaussianProcessPCA
  Constructs a new `InverseGaussianProcessPCA`.
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.SimProcess
  Returns `true` if the process is alive, otherwise `false`.
isBroadcasting() - Method in class umontreal.iro.lecuyer.stat.list.ListOfStatProbes
  Determines if this list of statistical probes is broadcasting observations to registered observers.
isBroadcasting() - Method in class umontreal.iro.lecuyer.stat.StatProbe
  Determines if this statistical probe is broadcasting observations to registered observers.
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`.
isCollecting() - Method in class umontreal.iro.lecuyer.stat.list.ListOfStatProbes
  Determines if this list of statistical probes is collecting values.
isCollecting() - Method in class umontreal.iro.lecuyer.stat.StatProbe
  Determines if this statistical probe is collecting values.
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
isEmpty() - Method in class umontreal.iro.lecuyer.stat.list.ListOfStatProbes
isModifiable() - Method in class umontreal.iro.lecuyer.stat.list.ListOfStatProbes
  Determines if this list of statistical probes is modifiable, i.e., if probes can be added or removed.
isSimulating() - Method in class umontreal.iro.lecuyer.simevents.Simulator
  Determines if this simulator is currently running, i.e., executing scheduled events.
isStopped() - Method in class umontreal.iro.lecuyer.simevents.Simulator
  Determines if this simulator was stopped by an event.
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
iterator() - Method in class umontreal.iro.lecuyer.simevents.eventlist.BinaryTree
iterator() - Method in class umontreal.iro.lecuyer.simevents.eventlist.DoublyLinked
iterator() - Method in class umontreal.iro.lecuyer.simevents.eventlist.Henriksen
iterator() - Method in class umontreal.iro.lecuyer.simevents.eventlist.RedblackTree
iterator() - Method in class umontreal.iro.lecuyer.simevents.eventlist.SplayTree
iterator() - Method in class umontreal.iro.lecuyer.simevents.ListWithStat
iterator() - Method in class umontreal.iro.lecuyer.stat.list.ListOfStatProbes
iterator() - Method in class umontreal.iro.lecuyer.util.TransformingList
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.
JohnsonSBGen - Class in umontreal.iro.lecuyer.randvar
  This class implements random variate generators for the Johnson SB distribution.
JohnsonSBGen(RandomStream, double, double, double, double) - Constructor for class umontreal.iro.lecuyer.randvar.JohnsonSBGen
  Creates a JohnsonSB random variate generator.
JohnsonSBGen(RandomStream, JohnsonSBDist) - Constructor for class umontreal.iro.lecuyer.randvar.JohnsonSBGen
  Creates a new generator for the JohnsonSB distribution `dist`, using stream `s`.
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.
JohnsonSUGen - Class in umontreal.iro.lecuyer.randvar
  This class implements random variate generators for the Johnson SU distribution.
JohnsonSUGen(RandomStream, double, double, double, double) - Constructor for class umontreal.iro.lecuyer.randvar.JohnsonSUGen
  Creates a JohnsonSU random variate generator.
JohnsonSUGen(RandomStream, JohnsonSUDist) - Constructor for class umontreal.iro.lecuyer.randvar.JohnsonSUGen
  Creates a new generator for the JohnsonSU distribution `dist`, using stream `s`.

## 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(SimProcess) - Method in class umontreal.iro.lecuyer.simprocs.DSOLProcessSimulator
kill(SimProcess) - Method in class umontreal.iro.lecuyer.simprocs.ProcessSimulator
  Terminates the life of `process` and sets its state to `DEAD`, after canceling its control event if there is one.
kill() - Method in class umontreal.iro.lecuyer.simprocs.SimProcess
  Terminates the life of this process and sets its state to `DEAD`, after canceling its activating event if there is one.
kill(SimProcess) - Method in class umontreal.iro.lecuyer.simprocs.ThreadProcessSimulator
killAll() - Method in class umontreal.iro.lecuyer.simprocs.DSOLProcessSimulator
killAll() - Method in class umontreal.iro.lecuyer.simprocs.ProcessSimulator
  Kills all currently living (active, delayed, or suspended) processes managed by this simulator.
killAll() - Method in class umontreal.iro.lecuyer.simprocs.ThreadProcessSimulator
  Kills all threads linked to the current variable.
kolmogorovSmirnov(int, double) - Static method in class umontreal.iro.lecuyer.gof.FBar
  Deprecated.
kolmogorovSmirnov(int, double) - Static method in class umontreal.iro.lecuyer.gof.FDist
  Deprecated.
kolmogorovSmirnov(DoubleArrayList) - Static method in class umontreal.iro.lecuyer.gof.GofStat
  Computes the Kolmogorov-Smirnov (KS) test statistics DN+, DN-, and DN.
KolmogorovSmirnovDist - Class in umontreal.iro.lecuyer.probdist
  Extends the class `ContinuousDistribution` for the Kolmogorov-Smirnov distribution with parameter n.
KolmogorovSmirnovDist(int) - Constructor for class umontreal.iro.lecuyer.probdist.KolmogorovSmirnovDist
  Constructs a distribution with parameter n.
KolmogorovSmirnovDistQuick - Class in umontreal.iro.lecuyer.probdist
  Extends the class `KolmogorovSmirnovDist` for the distribution.
KolmogorovSmirnovDistQuick(int) - Constructor for class umontreal.iro.lecuyer.probdist.KolmogorovSmirnovDistQuick
  Constructs a distribution with parameter n.
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
  Deprecated.
kolmogorovSmirnovPlus(int, double) - Static method in class umontreal.iro.lecuyer.gof.FDist
  Deprecated.
KolmogorovSmirnovPlusDist - Class in umontreal.iro.lecuyer.probdist
  Extends the class `ContinuousDistribution` for the Kolmogorov-Smirnov+ distribution (see).
KolmogorovSmirnovPlusDist(int) - Constructor for class umontreal.iro.lecuyer.probdist.KolmogorovSmirnovPlusDist
  Constructs an Kolmogorov-Smirnov+ distribution for a sample of size n.
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 μ = 0 and β = 1.
LaplaceDist(double, double) - Constructor for class umontreal.iro.lecuyer.probdist.LaplaceDist
  Constructs a `LaplaceDist` object with parameters μ = `mu` and β = `beta`.
LaplaceGen - Class in umontreal.iro.lecuyer.randvar
  This class implements methods for generating random variates from the Laplace distribution.
LaplaceGen(RandomStream, double, double) - Constructor for class umontreal.iro.lecuyer.randvar.LaplaceGen
  Creates a Laplace random variate generator with parameters μ = `mu` and β = `beta`, using stream `s`.
LaplaceGen(RandomStream) - Constructor for class umontreal.iro.lecuyer.randvar.LaplaceGen
  Creates a Laplace random variate generator with parameters μ = 0 and β = 1, using stream `s`.
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.stat.list.ListOfStatProbes
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
  Deprecated.
length() - Method in class umontreal.iro.lecuyer.util.PrintfFormat
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`.
LINE_SEPARATOR - Static variable in class umontreal.iro.lecuyer.util.PrintfFormat
  End-of-line symbol or line separator.
LinkedListStat<E> - Class in umontreal.iro.lecuyer.simevents
  This class extends `ListWithStat`, and uses a linked list as the internal data structure.
LinkedListStat() - Constructor for class umontreal.iro.lecuyer.simevents.LinkedListStat
  Constructs a new list, initially empty.
LinkedListStat(Simulator) - Constructor for class umontreal.iro.lecuyer.simevents.LinkedListStat
  Constructs a new list, initially empty, and using the default simulator.
LinkedListStat(Collection<? extends E>) - Constructor for class umontreal.iro.lecuyer.simevents.LinkedListStat
  Constructs a list containing the elements of the specified collection, using the default simulator.
LinkedListStat(Simulator, Collection<? extends E>) - 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`, using the default simulator.
LinkedListStat(Simulator, String) - Constructor for class umontreal.iro.lecuyer.simevents.LinkedListStat
  Constructs a new list with name `name`.
LinkedListStat(Collection<? extends E>, 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`, using the default simulator.
LinkedListStat(Simulator, Collection<? extends E>, 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`.
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() - Method in class umontreal.iro.lecuyer.simevents.ListWithStat
listIterator(int) - Method in class umontreal.iro.lecuyer.simevents.ListWithStat
listIterator() - Method in class umontreal.iro.lecuyer.stat.list.ListOfStatProbes
listIterator(int) - Method in class umontreal.iro.lecuyer.stat.list.ListOfStatProbes
listIterator() - Method in class umontreal.iro.lecuyer.util.TransformingList
listIterator(int) - Method in class umontreal.iro.lecuyer.util.TransformingList
ListOfStatProbes<E extends StatProbe> - Class in umontreal.iro.lecuyer.stat.list
  Represents a list of statistical probes that can be managed simultaneously.
ListOfStatProbes() - Constructor for class umontreal.iro.lecuyer.stat.list.ListOfStatProbes
  Constructs an empty list of statistical probes.
ListOfStatProbes(String) - Constructor for class umontreal.iro.lecuyer.stat.list.ListOfStatProbes
  Constructs an empty list of statistical probes with name `name`.
ListOfTallies<E extends Tally> - Class in umontreal.iro.lecuyer.stat.list
  Represents a list of tally statistical collectors.
ListOfTallies() - Constructor for class umontreal.iro.lecuyer.stat.list.ListOfTallies
  Constructs a new empty list of tallies.
ListOfTallies(String) - Constructor for class umontreal.iro.lecuyer.stat.list.ListOfTallies
  Constructs a new empty list of tallies with name `name`.
ListOfTalliesWithCovariance<E extends Tally> - Class in umontreal.iro.lecuyer.stat.list
  Extends `ListOfTallies` to add support for the computation of the sample covariance between each pair of elements in a list, without storing all observations.
ListOfTalliesWithCovariance() - Constructor for class umontreal.iro.lecuyer.stat.list.ListOfTalliesWithCovariance
  Creates an empty list of tallies with covariance support.
ListOfTalliesWithCovariance(String) - Constructor for class umontreal.iro.lecuyer.stat.list.ListOfTalliesWithCovariance
  Creates an empty list of tallies with covariance support and name `name`.
ListWithStat<E> - Class in umontreal.iro.lecuyer.simevents
  Implements a list with integrated statistical probes to provide automatic collection of statistics on the sojourn times of objects in the list and on the size of the list as a function of time given by a simulator.
ListWithStat(List<ListWithStat.Node<E>>) - Constructor for class umontreal.iro.lecuyer.simevents.ListWithStat
  Constructs a new list with internal data structure using the default simulator and implemented by `nodeList`, .
ListWithStat(Simulator, List<ListWithStat.Node<E>>) - Constructor for class umontreal.iro.lecuyer.simevents.ListWithStat
  Constructs a new list with internal data structure implemented by `nodeList`.
ListWithStat(List<ListWithStat.Node<E>>, Collection<? extends E>) - Constructor for class umontreal.iro.lecuyer.simevents.ListWithStat
  Constructs a list containing the elements of the specified collection, whose elements are stored into `nodeList` and using the default simulator.
ListWithStat(Simulator, List<ListWithStat.Node<E>>, Collection<? extends E>) - Constructor for class umontreal.iro.lecuyer.simevents.ListWithStat
  Constructs a list containing the elements of the specified collection, whose elements are stored into `nodeList`.
ListWithStat(List<ListWithStat.Node<E>>, String) - Constructor for class umontreal.iro.lecuyer.simevents.ListWithStat
  Constructs a new list with name `name`, internal list `nodeList`, and using the default simulator.
ListWithStat(Simulator, List<ListWithStat.Node<E>>, String) - Constructor for class umontreal.iro.lecuyer.simevents.ListWithStat
  Constructs a new list with name `name`, and internal list `nodeList`.
ListWithStat(List<ListWithStat.Node<E>>, Collection<? extends E>, String) - Constructor for class umontreal.iro.lecuyer.simevents.ListWithStat
  Constructs a new list containing the elements of the specified collection `c`, with name `name`, internal list `nodeList`, and using the default simulator.
ListWithStat(Simulator, List<ListWithStat.Node<E>>, Collection<? extends E>, String) - Constructor for class umontreal.iro.lecuyer.simevents.ListWithStat
  Constructs a new list containing the elements of the specified collection `c`, with name `name`, and internal list `nodeList`.
ListWithStat.Node<E> - Class in umontreal.iro.lecuyer.simevents
  Represents a node that can be part of a list with statistical collecting.
ListWithStat.Node(E, double) - Constructor for class umontreal.iro.lecuyer.simevents.ListWithStat.Node
  Constructs a new node containing element `element` inserted into the list at time `insertionTime`.
LMScrambleShift - Class in umontreal.iro.lecuyer.hups
  This class implements a `PointSetRandomization` that performs a left matrix scrambling and adds a random digital shift.
LMScrambleShift(RandomStream) - Constructor for class umontreal.iro.lecuyer.hups.LMScrambleShift
  Sets internal variable `stream` to the given `stream`.
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.
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, double) - Constructor for class umontreal.iro.lecuyer.randvar.LogarithmicGen
  Creates a logarithmic random variate generator with parameters θ = `theta` and default value θ0 = 0.96, using stream `s`.
LogarithmicGen(RandomStream, double, double) - Constructor for class umontreal.iro.lecuyer.randvar.LogarithmicGen
  Creates a logarithmic random variate generator with parameters θ = `theta` and θ0 = theta0, using stream `s`.
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, double, double) - Constructor for class umontreal.iro.lecuyer.randvar.LogisticGen
  Creates a logistic random variate generator with parameters α = `alpha` and λ = `lambda`, using stream `s`.
LogisticGen(RandomStream) - Constructor for class umontreal.iro.lecuyer.randvar.LogisticGen
  Creates a logistic random variate generator with parameters α = 0 and λ = 1, using stream `s`.
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, double, double) - Constructor for class umontreal.iro.lecuyer.randvar.LoglogisticGen
  Creates a log-logistic random variate generator with parameters α = `alpha` and β = `beta`, using stream `s`.
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`.
LognormalDistFromMoments - Class in umontreal.iro.lecuyer.probdist
  Extends the `LognormalDist` class with a constructor accepting the mean m and the variance v of the distribution as arguments.
LognormalDistFromMoments(double, double) - Constructor for class umontreal.iro.lecuyer.probdist.LognormalDistFromMoments
LognormalGen - Class in umontreal.iro.lecuyer.randvar
  This class implements methods for generating random variates from the lognormal distribution.
LognormalGen(RandomStream, double, double) - Constructor for class umontreal.iro.lecuyer.randvar.LognormalGen
  Creates a lognormal random variate generator with parameters μ = `mu` and σ = `sigma`, using stream `s`.
LognormalGen(RandomStream) - Constructor for class umontreal.iro.lecuyer.randvar.LognormalGen
  Creates a lognormal random variate generator with parameters μ = 0 and σ = 1, using stream `s`.
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
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
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.functions
  This interface should be implemented by classes which represent univariate mathematical functions.
MathFunctionUtil - Class in umontreal.iro.lecuyer.functions
  Provides utility methods for computing derivatives and integrals of functions.
MathFunctionWithDerivative - Interface in umontreal.iro.lecuyer.functions
  Represents a mathematical function whose nth derivative can be computed using `derivative`.
MathFunctionWithFirstDerivative - Interface in umontreal.iro.lecuyer.functions
  Represents a mathematical function whose derivative can be computed using `derivative`.
MathFunctionWithIntegral - Interface in umontreal.iro.lecuyer.functions
  Represents a mathematical function whose integral can be computed by the `integral` method.
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 `A`c 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 `A`2`e` 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).
MRG32k3aL - Class in umontreal.iro.lecuyer.rng
  The same generator as `MRG32k3a`, except here it is implemented with type `long` instead of `double`.
MRG32k3aL() - Constructor for class umontreal.iro.lecuyer.rng.MRG32k3aL
MRG32k3aL(String) - Constructor for class umontreal.iro.lecuyer.rng.MRG32k3aL
MT19937 - Class in umontreal.iro.lecuyer.rng
  Implements the `RandomStream` interface via inheritance from `RandomStreamBase`.
MT19937(CloneableRandomStream) - Constructor for class umontreal.iro.lecuyer.rng.MT19937
  Constructs a new stream, using `rng` to fill its initial state.
MT19937(CloneableRandomStream, 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.
MultinormalCholeskyGen - Class in umontreal.iro.lecuyer.randvarmulti
  Extends `MultinormalGen` for a multivariate normal distribution, generated via a Cholesky decomposition of the covariance matrix.
MultinormalCholeskyGen(NormalGen, double[], double[][]) - Constructor for class umontreal.iro.lecuyer.randvarmulti.MultinormalCholeskyGen
  Equivalent to `MultinormalCholeskyGen``(gen1, mu, new DenseDoubleMatrix2D(sigma))`.
MultinormalCholeskyGen(NormalGen, double[], DoubleMatrix2D) - Constructor for class umontreal.iro.lecuyer.randvarmulti.MultinormalCholeskyGen
  Constructs a multinormal generator with mean vector `mu` and covariance matrix `sigma`.
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 (or multinormal) distribution.
MultiNormalGen - Class in umontreal.iro.lecuyer.randvarmulti
  Deprecated.
MultinormalGen(NormalGen, int) - Constructor for class umontreal.iro.lecuyer.randvarmulti.MultinormalGen
  Constructs a generator with the standard multinormal distribution (with μ = 0 and Σ = I) in d dimensions.
MultiNormalGen(NormalGen, int) - Constructor for class umontreal.iro.lecuyer.randvarmulti.MultiNormalGen
  Deprecated. 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
  Deprecated. 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
  Deprecated. Equivalent to `MultiNormalGen` `(gen1, mu, new DenseDoubleMatrix2D (sigma))`.
MultinormalPCAGen - Class in umontreal.iro.lecuyer.randvarmulti
  Extends `MultinormalGen` for a multivariate normal distribution, generated via the method of principal components analysis (PCA) of the covariance matrix.
MultinormalPCAGen(NormalGen, double[], double[][]) - Constructor for class umontreal.iro.lecuyer.randvarmulti.MultinormalPCAGen
  Equivalent to `MultinormalPCAGen``(gen1, mu, new DenseDoubleMatrix2D(sigma))`.
MultinormalPCAGen(NormalGen, double[], DoubleMatrix2D) - Constructor for class umontreal.iro.lecuyer.randvarmulti.MultinormalPCAGen
  Constructs a multinormal generator with mean vector `mu` and covariance matrix `sigma`.
MultipleDatasetChart - Class in umontreal.iro.lecuyer.charts
  Provides tools to plot many datasets on the same chart.
MultipleDatasetChart() - Constructor for class umontreal.iro.lecuyer.charts.MultipleDatasetChart
  Initializes a new `MultipleDatasetChart`.
MultipleDatasetChart(String, String, String) - Constructor for class umontreal.iro.lecuyer.charts.MultipleDatasetChart
  Initializes a new `MultipleDatasetChart` instance.
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.
MultivariateFunction - Interface in umontreal.iro.lecuyer.util
  Represents a function of multiple variables.
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

NakagamiDist - Class in umontreal.iro.lecuyer.probdist
  Extends the class `ContinuousDistribution` for the Nakagami distribution with location parameter a, scale parameter λ > 0 and shape parameter c > 0.
NakagamiDist(double, double, double) - Constructor for class umontreal.iro.lecuyer.probdist.NakagamiDist
  Constructs a `NakagamiDist` object with parameters a = `a`, λ = `lambda` and c = `c`.
NakagamiGen - Class in umontreal.iro.lecuyer.randvar
  This class implements random variate generators for the Nakagami distribution.
NakagamiGen(RandomStream, double, double, double) - Constructor for class umontreal.iro.lecuyer.randvar.NakagamiGen
  Creates a new Nakagami generator with parameters a = `a`, λ = `lambda` and c = `c`, using stream `s`.
NakagamiGen(RandomStream, NakagamiDist) - Constructor for class umontreal.iro.lecuyer.randvar.NakagamiGen
  Creates a new generator for the distribution `dist`, using stream `s`.
NameConflictException - Exception in umontreal.iro.lecuyer.util
  This exception is thrown by a `ClassFinder` when two or more fully qualified class names can be associated with a simple class name.
NameConflictException() - Constructor for exception umontreal.iro.lecuyer.util.NameConflictException
  Constructs a new name conflict exception.
NameConflictException(String) - Constructor for exception umontreal.iro.lecuyer.util.NameConflictException
  Constructs a new name conflict exception with message `message`.
NameConflictException(ClassFinder, String, String) - Constructor for exception umontreal.iro.lecuyer.util.NameConflictException
  Constructs a new name conflict exception with class finder `finder`, simple name `name`, and message `message`.
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, double, double) - Constructor for class umontreal.iro.lecuyer.randvar.NegativeBinomialGen
  Creates a negative binomial random variate generator with parameters γ = `gamma` and p, using stream `s`.
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), such that all 0 < pi < 1 and ∑i=1dpi < 1.
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.
newArrayOfObservations(ListOfStatProbes<?>, double[]) - Method in interface umontreal.iro.lecuyer.stat.list.ArrayOfObservationListener
  Receives the new array of observations `x` broadcast by the list of statistical probes `listOfProbes`.
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.
newInstance() - Static method in class umontreal.iro.lecuyer.simprocs.ProcessSimulator
  Constructs and returns a new process-oriented simulator.
newObservation(StatProbe, double) - Method in interface umontreal.iro.lecuyer.stat.ObservationListener
  Receives the new observation `x` broadcast by `probe`.
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(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(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 by Ulrich's polar method using stream `s`.
nextDouble() - Method in class umontreal.iro.lecuyer.randvar.BetaSymmetricalPolarGen
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(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(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.ChiSquareNoncentralGamGen
nextDouble(RandomStream, double, double) - Static method in class umontreal.iro.lecuyer.randvar.ChiSquareNoncentralGamGen
  Generates a variate from the noncentral chi square distribution with parameters ν = `nu` and λ = `lambda` using stream `stream`, as described above.
nextDouble(RandomStream, double, double) - Static method in class umontreal.iro.lecuyer.randvar.ChiSquareNoncentralGen
  Generates a new variate from the noncentral chi square distribution with ν degrees of freedom and noncentrality parameter λ, using stream `s`.
nextDouble() - Method in class umontreal.iro.lecuyer.randvar.ChiSquareNoncentralPoisGen
nextDouble(RandomStream, double, double) - Static method in class umontreal.iro.lecuyer.randvar.ChiSquareNoncentralPoisGen
  Generates a variate from the noncentral chi square distribution with parameters ν = `nu` and λ = `lambda` using stream `stream`, as described above.
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(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(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(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(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, double, double) - Static method in class umontreal.iro.lecuyer.randvar.FoldedNormalGen
  Generates a variate from the folded normal distribution with parameters μ = `mu` and σ = `sigma`, 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(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(RandomStream, double, double) - Static method in class umontreal.iro.lecuyer.randvar.HalfNormalGen
  Generates a variate from the half-normal distribution with parameters μ = `mu` and σ = `sigma`, using stream `s`.
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(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(RandomStream, NormalGen, double, double) - Static method in class umontreal.iro.lecuyer.randvar.InverseGaussianMSHGen
  Generates a new variate from the inverse gaussian distribution with parameters μ = `mu` and λ = `lambda`, using streams `s` and `sn`.
nextDouble() - Method in class umontreal.iro.lecuyer.randvar.InverseGaussianMSHGen
nextDouble(RandomStream, double, double, double, double) - Static method in class umontreal.iro.lecuyer.randvar.JohnsonSBGen
  Uses inversion to generate a new JohnsonSB variate, using stream `s`.
nextDouble(RandomStream, double, double, double, double) - Static method in class umontreal.iro.lecuyer.randvar.JohnsonSUGen
  Uses inversion to generate a new JohnsonSU variate, using stream `s`.
nextDouble() - Method in class umontreal.iro.lecuyer.randvar.KernelDensityGen
nextDouble() - Method in class umontreal.iro.lecuyer.randvar.KernelDensityVarCorrectGen
nextDouble(RandomStream, double, double) - Static method in class umontreal.iro.lecuyer.randvar.LaplaceGen
  Generates a new variate from the Laplace distribution with parameters μ = `mu` and β = `beta`, using stream `s`.
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(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(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(RandomStream, double, double, double) - Static method in class umontreal.iro.lecuyer.randvar.NakagamiGen
  Generates a variate from the Nakagami distribution with parameters a = `a`, λ = `lambda` and c = `c`, using stream `s`.
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(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(RandomStream, double, double, double, double) - Static method in class umontreal.iro.lecuyer.randvar.NormalInverseGaussianGen
  NOT IMPLEMENTED.
nextDouble(InverseGaussianGen, NormalGen, double, double) - Static method in class umontreal.iro.lecuyer.randvar.NormalInverseGaussianIGGen
  Generates a new variate from the distribution with parameters α, β = `beta`, μ = `mu` and δ, using generators `ig` and `ng`, as described in eq..
nextDouble() - Method in class umontreal.iro.lecuyer.randvar.NormalInverseGaussianIGGen
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(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(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(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(RandomStream, double, double, double) - Static method in class umontreal.iro.lecuyer.randvar.PowerGen
  Uses inversion to generate a new variate from the power distribution with parameters a = `a`, b = `b`, and c = `c`, using stream `s`.
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(RandomStream, double, double) - Static method in class umontreal.iro.lecuyer.randvar.RayleighGen
  Uses inversion to generate a new variate from the Rayleigh distribution with parameters a = `a` and β = `beta`, using stream `s`.
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(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(RandomStream, double, double) - Static method in class umontreal.iro.lecuyer.randvar.UniformGen
  Generates a 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(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(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 geometric random variate with parameter p = `p`, using stream `s`, by inversion.
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(RandomStream, double, double) - Static method in class umontreal.iro.lecuyer.randvar.NegativeBinomialGen
  Generates a new variate from the negative binomial distribution, with parameters γ = `gamma` 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(RandomStream, int, double) - Static method in class umontreal.iro.lecuyer.randvar.PascalGen
  Generates a new variate from the Pascal distribution, with parameters n = `n` and p = `p`, using stream `s`.
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(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
nextObservation() - Method in class umontreal.iro.lecuyer.stochprocess.BrownianMotion
nextObservation(double) - Method in class umontreal.iro.lecuyer.stochprocess.BrownianMotion
  .
nextObservation(double, double) - Method in class umontreal.iro.lecuyer.stochprocess.BrownianMotion
  Generates an observation of the process in `dt` time units, assuming that the process has value x at the current time.
nextObservation() - Method in class umontreal.iro.lecuyer.stochprocess.BrownianMotionBridge
nextObservation(double) - Method in class umontreal.iro.lecuyer.stochprocess.BrownianMotionBridge
nextObservation() - Method in class umontreal.iro.lecuyer.stochprocess.BrownianMotionPCA
nextObservation() - Method in class umontreal.iro.lecuyer.stochprocess.BrownianMotionPCAEqualSteps
nextObservation() - Method in class umontreal.iro.lecuyer.stochprocess.CIRProcess
nextObservation(double) - Method in class umontreal.iro.lecuyer.stochprocess.CIRProcess
  Generates and returns the next observation at time tj+1 = nextTime, using the previous observation time tj defined earlier (either by this method or by `setObservationTimes`), as well as the value of the previous observation X(tj).
nextObservation(double, double) - Method in class umontreal.iro.lecuyer.stochprocess.CIRProcess
  Generates an observation of the process in `dt` time units, assuming that the process has value x at the current time.
nextObservation() - Method in class umontreal.iro.lecuyer.stochprocess.GammaProcess
nextObservation(double) - Method in class umontreal.iro.lecuyer.stochprocess.GammaProcess
  .
nextObservation() - Method in class umontreal.iro.lecuyer.stochprocess.GammaProcessBridge
nextObservation(double) - Method in class umontreal.iro.lecuyer.stochprocess.GammaProcessBridge
nextObservation() - Method in class umontreal.iro.lecuyer.stochprocess.GammaProcessPCA
  This method is not implemented in this class since the path cannot be generated sequentially.
nextObservation(double) - Method in class umontreal.iro.lecuyer.stochprocess.GammaProcessPCA
  This method is not implemented in this class since the path cannot be generated sequentially.
nextObservation() - Method in class umontreal.iro.lecuyer.stochprocess.GammaProcessSymmetricalBridge
nextObservation(double) - Method in class umontreal.iro.lecuyer.stochprocess.GammaProcessSymmetricalBridge
nextObservation() - Method in class umontreal.iro.lecuyer.stochprocess.GeometricBrownianMotion
nextObservation() - Method in class umontreal.iro.lecuyer.stochprocess.GeometricLevyProcess
  Returns the next observation.
nextObservation() - Method in class umontreal.iro.lecuyer.stochprocess.GeometricVarianceGammaProcess
nextObservation() - Method in class umontreal.iro.lecuyer.stochprocess.InverseGaussianProcess
nextObservation() - Method in class umontreal.iro.lecuyer.stochprocess.InverseGaussianProcessBridge
  Returns the next observation in the bridge order, not the sequential order.
nextObservation() - Method in class umontreal.iro.lecuyer.stochprocess.InverseGaussianProcessMSH
nextObservation() - Method in class umontreal.iro.lecuyer.stochprocess.InverseGaussianProcessPCA
  Not implementable for PCA.
nextObservation() - Method in class umontreal.iro.lecuyer.stochprocess.NormalInverseGaussianProcess
  Returns the value of the process for the next time step.
nextObservation() - Method in class umontreal.iro.lecuyer.stochprocess.OrnsteinUhlenbeckProcess
nextObservation(double) - Method in class umontreal.iro.lecuyer.stochprocess.OrnsteinUhlenbeckProcess
  .
nextObservation(double, double) - Method in class umontreal.iro.lecuyer.stochprocess.OrnsteinUhlenbeckProcess
  Generates an observation of the process in `dt` time units, assuming that the process has value x at the current time.
nextObservation() - Method in class umontreal.iro.lecuyer.stochprocess.StochasticProcess
  Generates and returns the next observation X(tj) of the stochastic process.
nextObservation() - Method in class umontreal.iro.lecuyer.stochprocess.VarianceGammaProcess
  Generates the observation for the next time.
nextObservation() - Method in class umontreal.iro.lecuyer.stochprocess.VarianceGammaProcessDiff
nextObservation() - Method in class umontreal.iro.lecuyer.stochprocess.VarianceGammaProcessDiffPCA
  This method is not implemented is this class since the path cannot be generated sequentially.
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[], double[][], double[]) - Static method in class umontreal.iro.lecuyer.randvarmulti.MultinormalCholeskyGen
  Equivalent to `nextPoint``(gen1, mu, new DenseDoubleMatrix2D(sigma), p)`.
nextPoint(NormalGen, double[], DoubleMatrix2D, double[]) - Static method in class umontreal.iro.lecuyer.randvarmulti.MultinormalCholeskyGen
  Generates a d-dimensional vector from the multinormal distribution with mean vector `mu` and covariance matrix `sigma`, using the one-dimensional normal generator `gen1` to generate the coordinates of Z, and using the Cholesky decomposition of Σ.
nextPoint(double[]) - Method in class umontreal.iro.lecuyer.randvarmulti.MultinormalCholeskyGen
  Generates a point from this multinormal distribution.
nextPoint(double[]) - Method in class umontreal.iro.lecuyer.randvarmulti.MultinormalGen
  Generates a point from this multinormal distribution.
nextPoint(NormalGen, double[], DoubleMatrix2D, double[]) - Static method in class umontreal.iro.lecuyer.randvarmulti.MultiNormalGen
  Deprecated. 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
  Deprecated. Equivalent to `nextPoint` `(gen1, mu, new DenseDoubleMatrix2D (sigma), p)`.
nextPoint(double[]) - Method in class umontreal.iro.lecuyer.randvarmulti.MultiNormalGen
  Deprecated. Generates a point from the multinormal distribution.
nextPoint(NormalGen, double[], DoubleMatrix2D, double[]) - Static method in class umontreal.iro.lecuyer.randvarmulti.MultinormalPCAGen
  Generates a d-dimensional vector from the multinormal distribution with mean vector `mu` and covariance matrix `sigma`, using the one-dimensional normal generator `gen1` to generate the coordinates of Z, and using the PCA decomposition of Σ.
nextPoint(NormalGen, double[], double[][], double[]) - Static method in class umontreal.iro.lecuyer.randvarmulti.MultinormalPCAGen
  Equivalent to `nextPoint``(gen1, mu, new DenseDoubleMatrix2D(sigma), p)`.
nextPoint(double[]) - Method in class umontreal.iro.lecuyer.randvarmulti.MultinormalPCAGen
  Generates a point from this multinormal distribution.
nextPoint(double[]) - Method in class umontreal.iro.lecuyer.randvarmulti.RandomMultivariateGen
  Generates a random point p using the 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, double, double) - Constructor for class umontreal.iro.lecuyer.randvar.NormalACRGen
  Creates a normal random variate generator with mean `mu` and standard deviation `sigma`, using stream `s`.
NormalACRGen(RandomStream) - Constructor for class umontreal.iro.lecuyer.randvar.NormalACRGen
  Creates a standard normal random variate generator with mean `0` and standard deviation `1`, using stream `s`.
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, double, double) - Constructor for class umontreal.iro.lecuyer.randvar.NormalBoxMullerGen
  Creates a normal random variate generator with mean `mu` and standard deviation `sigma`, using stream `s`.
NormalBoxMullerGen(RandomStream) - Constructor for class umontreal.iro.lecuyer.randvar.NormalBoxMullerGen
  Creates a standard normal random variate generator with mean `0` and standard deviation `1`, using stream `s`.
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 mean μ = `mu` and standard deviation 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 mean μ = `mu` and standard deviation 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, double, double) - Constructor for class umontreal.iro.lecuyer.randvar.NormalGen
  Creates a normal random variate generator with mean `mu` and standard deviation `sigma`, using stream `s`.
NormalGen(RandomStream) - Constructor for class umontreal.iro.lecuyer.randvar.NormalGen
  Creates a standard normal random variate generator with mean `0` and standard deviation `1`, using stream `s`.
NormalGen(RandomStream, NormalDist) - Constructor for class umontreal.iro.lecuyer.randvar.NormalGen
  Creates a random variate generator for the normal distribution `dist` and stream `s`.
NormalInverseGaussianDist - Class in umontreal.iro.lecuyer.probdist
  Extends the class `ContinuousDistribution` for the normal inverse gaussian distribution with location parameter μ, scale parameter δ > 0, tail heavyness α > 0, and asymmetry parameter β such that 0 <= | β| < α.
NormalInverseGaussianDist(double, double, double, double) - Constructor for class umontreal.iro.lecuyer.probdist.NormalInverseGaussianDist
  Constructor for a normal inverse gaussian distribution with parameters α = `alpha`, β = `beta`, μ = `mu` and δ = `delta`.
NormalInverseGaussianGen - Class in umontreal.iro.lecuyer.randvar
  This class implements random variate generators for the normal inverse gaussian (NIG) distribution.
NormalInverseGaussianGen(RandomStream, double, double, double, double) - Constructor for class umontreal.iro.lecuyer.randvar.NormalInverseGaussianGen
  Creates an normal inverse gaussian random variate generator with parameters α = `alpha`, β = `beta`, μ = `mu` and δ = `delta`, using stream `s`.
NormalInverseGaussianGen(RandomStream, NormalInverseGaussianDist) - Constructor for class umontreal.iro.lecuyer.randvar.NormalInverseGaussianGen
  Creates a new generator for the distribution `dist`, using stream `s`.
NormalInverseGaussianIGGen - Class in umontreal.iro.lecuyer.randvar
  .
NormalInverseGaussianIGGen(InverseGaussianGen, NormalGen, double, double) - Constructor for class umontreal.iro.lecuyer.randvar.NormalInverseGaussianIGGen
  Creates a random variate generator with parameters α, β = `beta`, μ = `mu` and δ, using generators `ig` and `ng`, as described above.
NormalInverseGaussianProcess - Class in umontreal.iro.lecuyer.stochprocess
  This class represents a normal inverse gaussian process (NIG).
NormalInverseGaussianProcess(double, double, double, double, double, RandomStream, InverseGaussianProcess) - Constructor for class umontreal.iro.lecuyer.stochprocess.NormalInverseGaussianProcess
  Given an `InverseGaussianProcess` `igP`, constructs a new `NormalInverseGaussianProcess`.
NormalInverseGaussianProcess(double, double, double, double, double, RandomStream, RandomStream, RandomStream, String) - Constructor for class umontreal.iro.lecuyer.stochprocess.NormalInverseGaussianProcess
  Constructs a new `NormalInverseGaussianProcess`.
NormalInverseGaussianProcess(double, double, double, double, double, RandomStream, String) - Constructor for class umontreal.iro.lecuyer.stochprocess.NormalInverseGaussianProcess
  Same as above, but all `RandomStream`'s are set to the same stream, `streamAll`.
NormalKindermannRamageGen - Class in umontreal.iro.lecuyer.randvar
  This class implements normal random variate generators using the Kindermann-Ramage method.
NormalKindermannRamageGen(RandomStream, double, double) - Constructor for class umontreal.iro.lecuyer.randvar.NormalKindermannRamageGen
  Creates a normal random variate generator with mean `mu` and standard deviation `sigma`, using stream `s`.
NormalKindermannRamageGen(RandomStream) - Constructor for class umontreal.iro.lecuyer.randvar.NormalKindermannRamageGen
  Creates a standard normal random variate generator with mean `0` and standard deviation `1`, using stream `s`.
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, double, double) - Constructor for class umontreal.iro.lecuyer.randvar.NormalPolarGen
  Creates a normal random variate generator with mean `mu` and standard deviation `sigma`, using stream `s`.
NormalPolarGen(RandomStream) - Constructor for class umontreal.iro.lecuyer.randvar.NormalPolarGen
  Creates a standard normal random variate generator with μ = 0 and σ = 1, using stream `s`.
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`.
notifyListeners(double[]) - Method in class umontreal.iro.lecuyer.stat.list.ListOfStatProbes
  Notifies the observation `x` to all registered observers if broadcasting is ON.
notifyListeners(double) - Method in class umontreal.iro.lecuyer.stat.StatProbe
  Notifies the observation `x` to all registered observers if broadcasting is ON.
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.list.ListOfTallies
  Assuming that each tally in this list contains the same number of observations, returns the number of observations in tally 0, or 0 if this list is empty.
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`.
numColumns() - Method in class umontreal.iro.lecuyer.util.DMatrix
  Returns the number of columns of the `DMatrix`.
NUMINTERVALS - Static variable in class umontreal.iro.lecuyer.functions.MathFunctionUtil
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`.
numRows() - Method in class umontreal.iro.lecuyer.util.DMatrix
  Returns the number of rows of the `DMatrix`.

## O

ObservationListener - Interface in umontreal.iro.lecuyer.stat
  Represents an object that can listen to observations broadcast by statistical probes.
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.
ORIENTATION_HORIZONTAL - Static variable in class umontreal.iro.lecuyer.charts.Axis
ORIENTATION_VERTICAL - Static variable in class umontreal.iro.lecuyer.charts.Axis
OrnsteinUhlenbeckProcess - Class in umontreal.iro.lecuyer.stochprocess
  This class represents an Ornstein-Uhlenbeck process {X(t) : t >= 0}, sampled at times 0 = t0 < t1 < ...
OrnsteinUhlenbeckProcess(double, double, double, double, RandomStream) - Constructor for class umontreal.iro.lecuyer.stochprocess.OrnsteinUhlenbeckProcess
  .
OrnsteinUhlenbeckProcess(double, double, double, double, NormalGen) - Constructor for class umontreal.iro.lecuyer.stochprocess.OrnsteinUhlenbeckProcess
  Here, the normal variate generator is specified directly instead of specifying the stream.

## 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 (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, double, double) - Constructor for class umontreal.iro.lecuyer.randvar.ParetoGen
  Creates a Pareto random variate generator with parameters α = `alpha` and β = `beta`, using stream `s`.
ParetoGen(RandomStream, double) - Constructor for class umontreal.iro.lecuyer.randvar.ParetoGen
  Creates a Pareto random variate generator with parameters α = `alpha` and β = 1, using stream `s`.
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, int, double) - Constructor for class umontreal.iro.lecuyer.randvar.PascalConvolutionGen
  Creates a Pascal random variate generator with parameters n and p, using stream `s`.
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.
PascalGen - Class in umontreal.iro.lecuyer.randvar
  Implements Pascal random variate generators, which is a special case of the negative binomial generator with parameter γ equal to a positive integer.
PascalGen(RandomStream, int, double) - Constructor for class umontreal.iro.lecuyer.randvar.PascalGen
  Creates a Pascal random variate generator with parameters n and p, using stream `s`.
PascalGen(RandomStream, PascalDist) - Constructor for class umontreal.iro.lecuyer.randvar.PascalGen
  Creates a new generator for the distribution `dist`, using stream `s`.
path - Variable in class umontreal.iro.lecuyer.stochprocess.StochasticProcess
PCADecompose(double[][], double[][], double[]) - Static method in class umontreal.iro.lecuyer.util.DMatrix
  Computes the principal components decomposition M = UΛUt by using the singular value decomposition of matrix M.
PCADecompose(DoubleMatrix2D, double[]) - Static method in class umontreal.iro.lecuyer.util.DMatrix
  Computes the principal components decomposition M = UΛUt by using the singular value decomposition of matrix M.
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, double, double) - Constructor for class umontreal.iro.lecuyer.randvar.Pearson5Gen
  Creates a Pearson5 random variate generator with parameters α = `alpha` and β = `beta`, using stream `s`.
Pearson5Gen(RandomStream, double) - Constructor for class umontreal.iro.lecuyer.randvar.Pearson5Gen
  Creates a Pearson5 random variate generator with parameters α = `alpha` and β = 1, using stream `s`.
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, double, double, double) - Constructor for class umontreal.iro.lecuyer.randvar.Pearson6Gen
  Creates a Pearson6 random variate generator with parameters α1 = `alpha1`, α2 = `alpha2` and β = `beta`, using stream `s`.
Pearson6Gen(RandomStream, double, double) - Constructor for class umontreal.iro.lecuyer.randvar.Pearson6Gen
  Creates a Pearson6 random variate generator with parameters α1 = `alpha1`, α2 = `alpha2` and β = 1, using stream `s`.
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.
PiecewiseConstantFunction - Class in umontreal.iro.lecuyer.functions
  Represents a piecewise-constant function.
PiecewiseConstantFunction(double[], double[]) - Constructor for class umontreal.iro.lecuyer.functions.PiecewiseConstantFunction
  Constructs a new piecewise-constant function with X and Y coordinates given by `x` and `y`.
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
PlotFormat - Class in umontreal.iro.lecuyer.charts
  Provide tools to import and export data set tables to and from GNUPlot, Matlab and Mathematica compatible formats, or customized format.
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.
PointSetRandomization - Interface in umontreal.iro.lecuyer.hups
  This interface is used to randomize a `PointSet`.
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, double) - Constructor for class umontreal.iro.lecuyer.randvar.PoissonGen
  Creates a Poisson random variate generator with parameter λ = `lambda`, using stream `s`.
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, double) - Constructor for class umontreal.iro.lecuyer.randvar.PoissonTIACGen
  Creates a Poisson random variate generator with parameter λ = `lambda`, using stream `s`.
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`.
Polynomial - Class in umontreal.iro.lecuyer.functions
  Represents a polynomial of degree n in power form.
Polynomial(double...) - Constructor for class umontreal.iro.lecuyer.functions.Polynomial
  Constructs a new polynomial with coefficients `coeff`.
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 2`e`.
PowerDist - Class in umontreal.iro.lecuyer.probdist
  Extends the class `ContinuousDistribution` for the power distribution with shape parameter c > 0, over the interval [a, b], where a < b.
PowerDist(double, double, double) - Constructor for class umontreal.iro.lecuyer.probdist.PowerDist
  Constructs a `PowerDist` object with parameters a = `a`, b = `b` and c = `c`.
PowerDist(double, double) - Constructor for class umontreal.iro.lecuyer.probdist.PowerDist
  Constructs a `PowerDist` object with parameters a = 0, b = `b` and c = `c`.
PowerDist(double) - Constructor for class umontreal.iro.lecuyer.probdist.PowerDist
  Constructs a `PowerDist` object with parameters a = 0, b = 1 and c = `c`.
PowerGen - Class in umontreal.iro.lecuyer.randvar
  This class implements random variate generators for the power distribution with shape parameter c > 0, over the interval [a, b].
PowerGen(RandomStream, double, double, double) - Constructor for class umontreal.iro.lecuyer.randvar.PowerGen
  Creates a Power random variate generator with parameters a = `a`, b = `b` and c = `c`, using stream `s`.
PowerGen(RandomStream, double) - Constructor for class umontreal.iro.lecuyer.randvar.PowerGen
  Creates a Power random variate generator with parameters a = 0, b = 1 and c = `c`, using stream `s`.
PowerGen(RandomStream, PowerDist) - Constructor for class umontreal.iro.lecuyer.randvar.PowerGen
  Creates a new generator for the power distribution `dist` and stream `s`.
PowerMathFunction - Class in umontreal.iro.lecuyer.functions
  Represents a function computing (af (x) + b)p for a user-defined function f (x) and power p.
PowerMathFunction(MathFunction, double) - Constructor for class umontreal.iro.lecuyer.functions.PowerMathFunction
  Constructs a new power function for function `func` and power `power`.
PowerMathFunction(MathFunction, double, double, double) - Constructor for class umontreal.iro.lecuyer.functions.PowerMathFunction
  Constructs a new power function for function `func`, power `power`, and constants `a` and `b`.
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.
priority() - Method in class umontreal.iro.lecuyer.simevents.Event
  Returns the priority of this event.
priority() - Method in class umontreal.iro.lecuyer.simprocs.SimProcess
  Return priority of the current variable.
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.
ProcessSimulator - Class in umontreal.iro.lecuyer.simprocs
  Defines a special type of simulator capable of managing processes.
ProcessSimulator() - Constructor for class umontreal.iro.lecuyer.simprocs.ProcessSimulator
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.
quickSort() - Method in class umontreal.iro.lecuyer.stat.TallyStore
  Sorts the elements of this probe using the `quicksort` from Colt.

## 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 implements directly the interface `RandomStream`, with a few additional tools.
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
  Deprecated.
randomize(PointSetRandomization) - Method in class umontreal.iro.lecuyer.hups.CachedPointSet
  Randomizes the underlying point set using `rand` and recaches the points.
randomize(PointSetRandomization) - Method in class umontreal.iro.lecuyer.hups.ContainerPointSet
  Randomizes the contained point set using `rand`.
randomize(PointSet) - Method in class umontreal.iro.lecuyer.hups.LMScrambleShift
  This method calls `leftMatrixScramble`, then `addRandomShift`.
randomize(RandomStream) - Method in class umontreal.iro.lecuyer.hups.PaddedPointSet
randomize(PointSetRandomization) - Method in class umontreal.iro.lecuyer.hups.PointSet
  Randomizes the point set using the given `rand`.
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.
randomize() - Method in class umontreal.iro.lecuyer.hups.PointSet
  Deprecated.
randomize(PointSet) - Method in interface umontreal.iro.lecuyer.hups.PointSetRandomization
  This method must randomize `p`.
randomize(PointSet) - Method in class umontreal.iro.lecuyer.hups.RandomShift
  This method calls `addRandomShift` `(stream)`.
randomize(PointSet) - Method in class umontreal.iro.lecuyer.hups.SMScrambleShift
  This method calls `stripedMatrixScramble`, then `addRandomShift`.
RandomMultivariateGen - Class in umontreal.iro.lecuyer.randvarmulti
  This class is the multivariate counterpart of `RandomVariateGen`.
RandomMultivariateGen() - Constructor for class umontreal.iro.lecuyer.randvarmulti.RandomMultivariateGen
RandomPermutation - Class in umontreal.iro.lecuyer.rng
  Provides methods to randomly shuffle arrays or lists using a random stream.
RandomPermutation() - Constructor for class umontreal.iro.lecuyer.rng.RandomPermutation
RandomShift - Class in umontreal.iro.lecuyer.hups
  This class implements a `PointSetRandomization`.
RandomShift(RandomStream) - Constructor for class umontreal.iro.lecuyer.hups.RandomShift
  Sets the internal `RandomStream` to `stream`.
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.
RatioFunction - Class in umontreal.iro.lecuyer.util
  Represents a function computing a ratio of two values.
RatioFunction() - Constructor for class umontreal.iro.lecuyer.util.RatioFunction
  Constructs a new ratio function.
RatioFunction(double) - Constructor for class umontreal.iro.lecuyer.util.RatioFunction
  Constructs a new ratio function that returns `zeroOverZero` for the special case of 0/0.
RayleighDist - Class in umontreal.iro.lecuyer.probdist
  This class extends the class `ContinuousDistribution` for the Rayleigh distribution with location parameter a, and scale parameter β > 0.
RayleighDist(double) - Constructor for class umontreal.iro.lecuyer.probdist.RayleighDist
  Constructs a `RayleighDist` object with parameters a = 0 and β = `beta`.
RayleighDist(double, double) - Constructor for class umontreal.iro.lecuyer.probdist.RayleighDist
  Constructs a `RayleighDist` object with parameters a = `a`, and β = `beta`.
RayleighGen - Class in umontreal.iro.lecuyer.randvar
  This class implements random variate generators for the Rayleigh distribution.
RayleighGen(RandomStream, double, double) - Constructor for class umontreal.iro.lecuyer.randvar.RayleighGen
  Creates a Rayleigh random variate generator with parameters a = `a` and β = `beta`, using stream `s`.
RayleighGen(RandomStream, double) - Constructor for class umontreal.iro.lecuyer.randvar.RayleighGen
  Creates a Rayleigh random variate generator with parameters a = 0 and β = `beta`, using stream `s`.
RayleighGen(RandomStream, RayleighDist) - Constructor for class umontreal.iro.lecuyer.randvar.RayleighGen
  Creates a new generator for the Rayleigh distribution `dist` and stream `s`.
readCSVData(Reader, char, char) - Static method in class umontreal.iro.lecuyer.util.TextDataReader
  Reads comma-separated values (CSV) from reader `input`, and returns a 2D array of strings corresponding to the read data.
readCSVData(URL, char, char) - Static method in class umontreal.iro.lecuyer.util.TextDataReader
  Connects to the URL referred to by the URL object `url`, and calls `readCSVData` to obtain a matrix of strings from the resource.
readCSVData(File, char, char) - Static method in class umontreal.iro.lecuyer.util.TextDataReader
  This is equivalent to `readDoubleData2D`, for reading strings.
readCSVData(String, char, char) - Static method in class umontreal.iro.lecuyer.util.TextDataReader
  This is equivalent to `readDoubleData2D`, for reading strings.
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(URL) - Static method in class umontreal.iro.lecuyer.util.TextDataReader
  Connects to the URL referred to by the URL object `url`, and calls `readDoubleData` to obtain an array of double-precision values from the resource.
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(URL) - Static method in class umontreal.iro.lecuyer.util.TextDataReader
  Connects to the URL referred to by the URL object `url`, and calls `readDoubleData2D` to obtain a matrix of double-precision values from the resource.
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(URL) - Static method in class umontreal.iro.lecuyer.util.TextDataReader
  Connects to the URL referred to by the URL object `url`, and calls `readIntData` to obtain an array of integers from the resource.
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(URL) - Static method in class umontreal.iro.lecuyer.util.TextDataReader
  Connects to the URL referred to by the URL object `url`, and calls `readDoubleData` to obtain a matrix of integers from the resource.
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`.
readObjectData(Statement, String) - Static method in class umontreal.iro.lecuyer.util.JDBCManager
  Copies the result of the SQL query `query` into an array of objects.
readObjectData(Connection, String) - Static method in class umontreal.iro.lecuyer.util.JDBCManager
  Copies the result of the SQL query `query` into an array of objects.
readObjectData(Statement, String, String) - Static method in class umontreal.iro.lecuyer.util.JDBCManager
  Returns the values of the column `column` of the table `table`.
readObjectData(Connection, String, String) - Static method in class umontreal.iro.lecuyer.util.JDBCManager
  Returns the values of the column `column` of the table `table`.
readObjectData2D(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 objects.
readObjectData2D(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.
readObjectData2DTable(Statement, String) - Static method in class umontreal.iro.lecuyer.util.JDBCManager
  Returns the values of the columns of the table `table`.
readObjectData2DTable(Connection, String) - Static method in class umontreal.iro.lecuyer.util.JDBCManager
  Returns the values of the columns of the table `table`.
readStringData(Reader) - Static method in class umontreal.iro.lecuyer.util.TextDataReader
  Reads an array of strings from the reader `input`.
readStringData(URL) - Static method in class umontreal.iro.lecuyer.util.TextDataReader
  Connects to the URL referred to by the URL object `url`, and calls `readStringData` to obtain an array of integers from the resource.
readStringData(File) - Static method in class umontreal.iro.lecuyer.util.TextDataReader
  This is equivalent to `readDoubleData`, for reading strings.
readStringData(String) - Static method in class umontreal.iro.lecuyer.util.TextDataReader
  This is equivalent to `readDoubleData`, for reading strings.
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(int) - Method in class umontreal.iro.lecuyer.simevents.ListWithStat
remove(int) - Method in class umontreal.iro.lecuyer.stat.list.ListOfStatProbes
remove(Object) - Method in class umontreal.iro.lecuyer.stat.list.ListOfStatProbes
remove(int) - Method in class umontreal.iro.lecuyer.util.TransformingList
removeAll(Collection<?>) - Method in class umontreal.iro.lecuyer.stat.list.ListOfStatProbes
removeArrayOfObservationListener(ArrayOfObservationListener) - Method in class umontreal.iro.lecuyer.stat.list.ListOfStatProbes
  Removes the observation listener `l` from the list of observers of this list of statistical probes.
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
removeLast() - Method in class umontreal.iro.lecuyer.simevents.LinkedListStat
removeNaNs(double[], double[]) - Static method in class umontreal.iro.lecuyer.functions.MathFunctionUtil
  Removes any point `(NaN, y)` or `(x, NaN)` from `x` and `y`, and returns a 2D array containing the filtered points.
removeObservationListener(ObservationListener) - Method in class umontreal.iro.lecuyer.stat.StatProbe
  Removes the observation listener `l` from the list of observers of this statistical probe.
report() - Method in class umontreal.iro.lecuyer.simevents.Accumulate
report() - Method in class umontreal.iro.lecuyer.simevents.ListWithStat
  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.list.ListOfStatProbes
  Formats a report for each probe in the list of statistical probes.
report() - Method in class umontreal.iro.lecuyer.stat.StatProbe
  Returns a string containing a report for this statistical collector.
report(String, StatProbe[]) - Static method in class umontreal.iro.lecuyer.stat.StatProbe
  Formats short reports for each statistical probe in the array `probes` while aligning the probes' names.
report(String, Iterable<? extends StatProbe>) - Static method in class umontreal.iro.lecuyer.stat.StatProbe
  Equivalent to `report`, except that `probes` is an `Iterable` object instead of an array.
report() - Method in class umontreal.iro.lecuyer.stat.Tally
  Returns a formatted string that contains a report on this probe.
report(double, int) - Method in class umontreal.iro.lecuyer.stat.Tally
  Returns a formatted string that contains a report on this probe with a confidence interval level `level` using d fractional decimal digits.
reportAndCIStudent(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 `formatCIStudent`), using d fractional decimal digits.
reportAndCIStudent(double) - Method in class umontreal.iro.lecuyer.stat.Tally
  Same as `reportAndCIStudent` `(level, 3)`.
reportAndConfidenceIntervalStudent(double, int) - Method in class umontreal.iro.lecuyer.stat.Tally
  Deprecated.
reportAndConfidenceIntervalStudent(double) - Method in class umontreal.iro.lecuyer.stat.Tally
  Deprecated.
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.SimProcess
  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.MRG32k3aL
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
resetRiskNeutralCorrection(double) - Method in class umontreal.iro.lecuyer.stochprocess.GeometricLevyProcess
  Changes the value of ωRN.
resetStartProcess() - Method in class umontreal.iro.lecuyer.stochprocess.BrownianMotionBridge
resetStartProcess() - Method in class umontreal.iro.lecuyer.stochprocess.GammaProcessBridge
resetStartProcess() - Method in class umontreal.iro.lecuyer.stochprocess.GeometricBrownianMotion
  Same as in `StochasticProcess`, but also invokes `resetStartProcess` for the underlying `BrownianMotion` object.
resetStartProcess() - Method in class umontreal.iro.lecuyer.stochprocess.GeometricLevyProcess
  Resets the step counter of the geometric process and the underlying Lévy process to the start value.
resetStartProcess() - Method in class umontreal.iro.lecuyer.stochprocess.GeometricVarianceGammaProcess
  Resets the `GeometricaVarianceGammaProcess`, but also applies the `resetStartProcess` method to the `VarianceGammaProcess` object used to generate this process.
resetStartProcess() - Method in class umontreal.iro.lecuyer.stochprocess.InverseGaussianProcessBridge
resetStartProcess() - Method in class umontreal.iro.lecuyer.stochprocess.StochasticProcess
  Resets the observation counter to its initial value j = 0, so that the current observation X(tj) becomes X(t0).
resetStartProcess() - Method in class umontreal.iro.lecuyer.stochprocess.VarianceGammaProcess
  Resets the observation index and counter to 0 and applies the `resetStartProcess` method to the `BrownianMotion` and the `GammaProcess` objects used to generate this process.
resetStartProcess() - Method in class umontreal.iro.lecuyer.stochprocess.VarianceGammaProcessDiff
  Sets the observation times on the `VarianceGammaProcessDiff` as usual, but also applies the `resetStartProcess` method to the two `GammaProcess` objects used to generate this process.
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.MRG32k3aL
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.MRG32k3aL
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 `Process` objects.
Resource(int) - Constructor for class umontreal.iro.lecuyer.simprocs.Resource
  Constructs a new resource linked with the default simulator, with initial capacity `capacity`, and service policy FIFO.
Resource(ProcessSimulator, int) - Constructor for class umontreal.iro.lecuyer.simprocs.Resource
  Constructs a new resource linked with the simulator `sim`, with initial capacity `capacity`, and service policy FIFO.
Resource(int, String) - Constructor for class umontreal.iro.lecuyer.simprocs.Resource
  Constructs a new resource linked with the default simulator, with initial capacity `capacity`, service policy FIFO, and identifier `name`.
Resource(ProcessSimulator, int, String) - Constructor for class umontreal.iro.lecuyer.simprocs.Resource
  Constructs a new resource linked with the simulator `sim`, with initial capacity `capacity`, service policy FIFO, and identifier (or name) `name`.
restoreImports() - Method in class umontreal.iro.lecuyer.util.ClassFinder
  Restores the list of import declarations.
resume() - Method in class umontreal.iro.lecuyer.simprocs.SimProcess
  Places this process at the beginning of the event list to resume its execution.
retainAll(Collection<?>) - Method in class umontreal.iro.lecuyer.stat.list.ListOfStatProbes
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.
sameSignature(Method, Method) - Static method in class umontreal.iro.lecuyer.util.Introspection
  Determines if two methods `m1` and `m2` share the same signature.
saveImports() - Method in class umontreal.iro.lecuyer.util.ClassFinder
  Saves the current import list on the import stack.
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`.
ScatterChart - Class in umontreal.iro.lecuyer.charts
  Extends `XYChart`.
ScatterChart() - Constructor for class umontreal.iro.lecuyer.charts.ScatterChart
  Initializes a new `ScatterChart` instance with an empty data set.
ScatterChart(String, String, String, double[][]...) - Constructor for class umontreal.iro.lecuyer.charts.ScatterChart
  Initializes a new `ScatterChart` instance with data `data`.
ScatterChart(String, String, String, double[][], int) - Constructor for class umontreal.iro.lecuyer.charts.ScatterChart
  Initializes a new `ScatterChart` instance with sets of points `data`.
ScatterChart(String, String, String, double[][], int, int) - Constructor for class umontreal.iro.lecuyer.charts.ScatterChart
  Initializes a new `ScatterChart` instance using subsets of `data`.
ScatterChart(String, String, String, DoubleArrayList...) - Constructor for class umontreal.iro.lecuyer.charts.ScatterChart
  Initializes a new `ScatterChart` instance with data `data`.
ScatterChart(String, String, String, XYSeriesCollection) - Constructor for class umontreal.iro.lecuyer.charts.ScatterChart
  Initializes a new `ScatterChart` instance with data `data`.
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.SimProcess
  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`.
scheduledEvent() - Method in class umontreal.iro.lecuyer.simprocs.SimProcess
  Returns the Event associated with the current variable.
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.SimProcess
  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, for the default simulator.
selectEuler(double) - Method in class umontreal.iro.lecuyer.simevents.ContinuousState
  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`.
selectRungeKutta2(double) - Method in class umontreal.iro.lecuyer.simevents.ContinuousState
  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`.
selectRungeKutta4(double) - Method in class umontreal.iro.lecuyer.simevents.ContinuousState
  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(SSJXYSeriesCollection) - Method in class umontreal.iro.lecuyer.charts.MultipleDatasetChart
  Sets the primary dataset for the plot, replacing the existing dataset if there is one.
set(int, SSJXYSeriesCollection) - Method in class umontreal.iro.lecuyer.charts.MultipleDatasetChart
  Replaces the element at the specified position in the dataset list with the specified element.
set(int, E) - Method in class umontreal.iro.lecuyer.simevents.ListWithStat
set(boolean) - Method in class umontreal.iro.lecuyer.simprocs.Condition
  Sets the condition to `val`.
set(int, E) - Method in class umontreal.iro.lecuyer.stat.list.ListOfStatProbes
set(int, int, double) - Method in class umontreal.iro.lecuyer.util.DMatrix
  Sets the value of the element in the specified row and column.
set(int, OE) - Method in class umontreal.iro.lecuyer.util.TransformingList
setAntithetic(boolean) - Method in class umontreal.iro.lecuyer.rng.RandMrg
setAutoRange() - Method in class umontreal.iro.lecuyer.charts.CategoryChart
  Sets chart y range to automatic values.
setAutoRange(boolean, boolean) - Method in class umontreal.iro.lecuyer.charts.HistogramChart
setAutoRange() - Method in class umontreal.iro.lecuyer.charts.MultipleDatasetChart
  Sets chart range to automatic values.
setAutoRange(boolean, boolean) - Method in class umontreal.iro.lecuyer.charts.XYChart
  The x and the y ranges of the chart are set automatically.
setAutoRange() - Method in class umontreal.iro.lecuyer.charts.XYChart
  The x and the y ranges of the chart are set automatically.
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
setBins(int, int) - Method in class umontreal.iro.lecuyer.charts.CustomHistogramDataset
  Sets the bins for a series.
setBins(int, int, double, double) - Method in class umontreal.iro.lecuyer.charts.CustomHistogramDataset
  Sets the bins for a series.
setBins(int, HistogramBin[]) - Method in class umontreal.iro.lecuyer.charts.CustomHistogramDataset
  Sets the bins for a series.
setBins(int, int) - Method in class umontreal.iro.lecuyer.charts.HistogramSeriesCollection
  Sets `bins` periodic bins from the observation minimum values to the observation maximum value for a series.
setBins(int, int, double, double) - Method in class umontreal.iro.lecuyer.charts.HistogramSeriesCollection
  Sets `bins` periodic bins from `minimum` to `maximum` for a series.
setBins(int, HistogramBin[]) - Method in class umontreal.iro.lecuyer.charts.HistogramSeriesCollection
  Links bins given by table `binsTable` to a series.
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.list.ListOfStatProbes
  Sets the status of the observation broadcasting mechanism to `b`.
setBroadcasting(boolean) - Method in class umontreal.iro.lecuyer.stat.StatProbe
  Instructs the probe to turn its broadcasting ON or OFF.
setBrownianMotionPCA(BrownianMotionPCA) - Method in class umontreal.iro.lecuyer.stochprocess.InverseGaussianProcessPCA
  Sets the brownian motion PCA.
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`.
setChartMargin(double) - Method in class umontreal.iro.lecuyer.charts.XYChart
  Sets the chart margin to `margin`.
setCoefficients(double...) - Method in class umontreal.iro.lecuyer.functions.Polynomial
  Sets the array of coefficients of this polynomial to `coeff`.
setCollecting(boolean) - Method in class umontreal.iro.lecuyer.stat.list.ListOfStatProbes
  Sets the status of the statistical collecting mechanism to `c`.
setCollecting(boolean) - Method in class umontreal.iro.lecuyer.stat.StatProbe
  Turns ON or OFF the collection of statistical observations.
setColor(int, Color) - Method in class umontreal.iro.lecuyer.charts.SSJCategorySeriesCollection
  Sets a new plotting color to the series series.
setColor(int, Color) - Method in class umontreal.iro.lecuyer.charts.SSJXYSeriesCollection
  Sets a new plotting color to the series series.
setConfidenceIntervalNone() - Method in class umontreal.iro.lecuyer.stat.Tally
  Indicates that no confidence interval needs to be printed in reports formatted by `report`, and `shortReport`.
setConfidenceIntervalNormal() - Method in class umontreal.iro.lecuyer.stat.Tally
  Indicates that a confidence interval on the true mean, based on the central limit theorem, needs to be included in reports formatted by `report` and `shortReport`.
setConfidenceIntervalStudent() - Method in class umontreal.iro.lecuyer.stat.Tally
  Indicates that a confidence interval on the true mean, based on the normality assumption, needs to be included in reports formatted by `report` and `shortReport`.
setConfidenceLevel(double) - Method in class umontreal.iro.lecuyer.stat.Tally
  Sets the level of confidence for the intervals on the mean displayed in reports.
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.
setDashPattern(int, String) - Method in class umontreal.iro.lecuyer.charts.EmpiricalSeriesCollection
  Selects dash pattern for a data series.
setDashPattern(int, String) - Method in class umontreal.iro.lecuyer.charts.XYListSeriesCollection
  Selects dash pattern for a data series.
setFillBox(boolean) - Method in class umontreal.iro.lecuyer.charts.BoxChart
  Sets `fill` to `true`, if the boxes are to be filled.
setFilled(int, boolean) - Method in class umontreal.iro.lecuyer.charts.HistogramSeriesCollection
  Sets the `filled` flag.
setLabel(String) - Method in class umontreal.iro.lecuyer.charts.Axis
  Sets the axis description.
setLabels(double) - Method in class umontreal.iro.lecuyer.charts.Axis
  Sets a periodic label display.
setLabels(double[]) - Method in class umontreal.iro.lecuyer.charts.Axis
  Sets the position of each label on this axis.
setLabels(double[], String[]) - Method in class umontreal.iro.lecuyer.charts.Axis
  Assigns custom labels to user-defined positions on the axis.
setLabelsAuto() - Method in class umontreal.iro.lecuyer.charts.Axis
  Calculates and sets an automatic tick unit.
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.
setLatexDocFlag(boolean) - Method in class umontreal.iro.lecuyer.charts.CategoryChart
  Flag to remove the `\documentclass` (and other) commands in the created LATEX files.
setLatexDocFlag(boolean) - Method in class umontreal.iro.lecuyer.charts.MultipleDatasetChart
  Flag to remove the `\documentclass` (and other) commands in the created LATEX files.
setLatexDocFlag(boolean) - Method in class umontreal.iro.lecuyer.charts.XYChart
  Flag to remove the `\documentclass` (and other) commands in the created LATEX files.
setLinearSeed(int[]) - Method in class umontreal.iro.lecuyer.rng.F2NL607
  This method is discouraged for normal use.
setManualRange(double[]) - Method in class umontreal.iro.lecuyer.charts.MultipleDatasetChart
  Sets new x-axis and y-axis bounds, with format: `axisRange` = [xmin, xmax, ymin, ymax].
setManualRange(double[], boolean, boolean) - Method in class umontreal.iro.lecuyer.charts.XYChart
  Sets the x and y ranges of the chart, using the format: `range = [xmin, xmax, ymin, ymax]`.
setManualRange(double[]) - Method in class umontreal.iro.lecuyer.charts.XYChart
  Sets the x and y ranges of the chart, using the format: `range = [xmin, xmax, ymin, ymax]`.
setManuelRange(double[], boolean, boolean) - Method in class umontreal.iro.lecuyer.charts.HistogramChart
setMargin(double) - Method in class umontreal.iro.lecuyer.charts.HistogramSeriesCollection
  Sets the margin which is a percentage amount by which the bars are trimmed for all series.
setMarksType(int, String) - Method in class umontreal.iro.lecuyer.charts.EmpiricalSeriesCollection
  Adds marks on points to a data series.
setMarksType(int, String) - Method in class umontreal.iro.lecuyer.charts.XYListSeriesCollection
  Adds marks on points to a data series.
setMean(double) - Method in class umontreal.iro.lecuyer.probdist.ExponentialDistFromMean
  Calls `setLambda` with argument `1/mean` to change the mean of this distribution.
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`.
setMu(double[]) - Method in class umontreal.iro.lecuyer.randvarmulti.MultiNormalGen
  Deprecated. Sets the mean vector to `mu`.
setMu(int, double) - Method in class umontreal.iro.lecuyer.randvarmulti.MultiNormalGen
  Deprecated. Sets the i-th component of the mean vector to `mui`.
setMuGeom(double) - Method in class umontreal.iro.lecuyer.stochprocess.GeometricLevyProcess
  Sets the drift parameter (interest rate) of the geometric term.
setN(int) - Method in class umontreal.iro.lecuyer.probdist.AndersonDarlingDist
  Sets the parameter n of this object.
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.CramerVonMisesDist
  Sets the parameter n of this object.
setN(int) - Method in class umontreal.iro.lecuyer.probdist.KolmogorovSmirnovDist
  Sets the parameter n of this object.
setN(int) - Method in class umontreal.iro.lecuyer.probdist.KolmogorovSmirnovPlusDist
  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.
setN(int) - Method in class umontreal.iro.lecuyer.probdist.WatsonGDist
  Sets the parameter n of this object.
setN(int) - Method in class umontreal.iro.lecuyer.probdist.WatsonUDist
  Sets the parameter n of this object.
setName(int, String) - Method in class umontreal.iro.lecuyer.charts.XYListSeriesCollection
  Sets the name of the selected series.
setName(String) - Method in class umontreal.iro.lecuyer.stat.list.ListOfStatProbes
  Sets the global name of this list to `name`.
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.
setNormalGen(NormalGen) - Method in class umontreal.iro.lecuyer.stochprocess.InverseGaussianProcessMSH
  Sets the normal generator.
setNu(int) - Method in class umontreal.iro.lecuyer.probdist.ChiDist
  Sets the value of ν for this object.
setObservationTimes(double[], int) - Method in class umontreal.iro.lecuyer.stochprocess.BrownianMotionPCAEqualSteps
setObservationTimes(double, int) - Method in class umontreal.iro.lecuyer.stochprocess.BrownianMotionPCAEqualSteps
setObservationTimes(double[], int) - Method in class umontreal.iro.lecuyer.stochprocess.GammaProcessPCA
  Sets the observation times of the `GammaProcessPCA` and the `BrownianMotionPCA`.
setObservationTimes(double[], int) - Method in class umontreal.iro.lecuyer.stochprocess.GammaProcessPCABridge
setObservationTimes(double[], int) - Method in class umontreal.iro.lecuyer.stochprocess.GeometricBrownianMotion
setObservationTimes(double[], int) - Method in class umontreal.iro.lecuyer.stochprocess.GeometricLevyProcess
  Sets the observation times on the geometric process and the underlying Lévy process.
setObservationTimes(double[], int) - Method in class umontreal.iro.lecuyer.stochprocess.InverseGaussianProcessPCA
  Sets the observation times of both the `InverseGaussianProcessPCA` and the inner
  `BrownianMotionPCA`.
setObservationTimes(double[], int) - Method in class umontreal.iro.lecuyer.stochprocess.NormalInverseGaussianProcess
  Sets the observation times on the NIG process as usual, but also sets the observation times of the underlying `InverseGaussianProcess`.
setObservationTimes(double[], int) - Method in class umontreal.iro.lecuyer.stochprocess.StochasticProcess
  .
setObservationTimes(double, int) - Method in class umontreal.iro.lecuyer.stochprocess.StochasticProcess
  Sets equidistant observation times at tj = jδ, for j = 0,..., d, and `delta` = δ.
setObservationTimes(double[], int) - Method in class umontreal.iro.lecuyer.stochprocess.VarianceGammaProcess
  Sets the observation times on the `VarianceGammaProcess` as usual, but also sets the observation times of the underlying `GammaProcess`.
setObservationTimes(double[], int) - Method in class umontreal.iro.lecuyer.stochprocess.VarianceGammaProcessDiff
  Sets the observation times on the `VarianceGammaProcesDiff` as usual, but also sets the observation times of the underlying `GammaProcess`'es.
setOtherStream(RandomStream) - Method in class umontreal.iro.lecuyer.stochprocess.InverseGaussianProcessMSH
  Sets the `otherStream`, which is the stream used to choose between the two roots in the MSH method.
setOutlineWidth(int, double) - Method in class umontreal.iro.lecuyer.charts.HistogramSeriesCollection
  Sets the outline width in pt.
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.MRG32k3aL
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 this class to the 32 integers of array `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, int) - Method in class umontreal.iro.lecuyer.charts.ContinuousDistChart
  Sets the parameters a, b and m for this object.
setParam(int, int) - Method in class umontreal.iro.lecuyer.charts.DiscreteDistIntChart
  Sets the parameters a and b for this object.
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(double, double) - Method in class umontreal.iro.lecuyer.probdist.ChiSquareNoncentralDist
  Sets the parameters ν = `nu` and λ = `lambda` of 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) - Method in class umontreal.iro.lecuyer.probdist.FoldedNormalDist
  Sets the parameters μ and σ for this object.
setParams(double, double, int) - Method in class umontreal.iro.lecuyer.probdist.GammaDist
setParams(double, double) - Method in class umontreal.iro.lecuyer.probdist.HalfNormalDist
  Sets the parameters μ and σ.
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, double) - Method in class umontreal.iro.lecuyer.probdist.NakagamiDist
  Sets the parameters a, λ and c 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, double, double) - Method in class umontreal.iro.lecuyer.probdist.NormalInverseGaussianDist
  Sets the parameters α, β, μ and δ 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.PowerDist
  Sets the parameters a, b and c for this object.
setParams(double, double) - Method in class umontreal.iro.lecuyer.probdist.RayleighDist
  Sets the parameters a and β for 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.
setParams(double, double, double, double) - Method in class umontreal.iro.lecuyer.randvar.NormalInverseGaussianGen
  Sets the parameters α, β, μ and δ of this object.
setParams(double, double, double) - Method in class umontreal.iro.lecuyer.randvar.Pearson6Gen
  Sets the parameters α1, α2 and β of this object.
setParams(double, double, double) - Method in class umontreal.iro.lecuyer.randvar.PowerGen
  Sets the parameters a, b and c for this object.
setParams(double, double) - Method in class umontreal.iro.lecuyer.randvar.RayleighGen
  Sets the parameters a = `a` and β = `beta` for this object.
setParams(double, double, double) - Method in class umontreal.iro.lecuyer.randvar.WeibullGen
  Sets the parameters α, λ and δ for this object.
setParams(double, double, double) - Method in class umontreal.iro.lecuyer.stochprocess.BrownianMotion
  Resets the parameters X(t0) = [tex2html_wrap_inline288], μ = [tex2html_wrap_inline290] and σ = [tex2html_wrap_inline292] of the process.
setParams(double, double, double) - Method in class umontreal.iro.lecuyer.stochprocess.BrownianMotionPCA
setParams(double, double, double, double) - Method in class umontreal.iro.lecuyer.stochprocess.CIRProcess
  Resets the parameters X(t0) = x0, α = alpha, b = b and σ = sigma of the process.
setParams(double, double, double) - Method in class umontreal.iro.lecuyer.stochprocess.GammaProcess
  Sets the parameters S(t0) = [tex2html_wrap_inline311], μ = [tex2html_wrap_inline313] and ν = [tex2html_wrap_inline315] of the process.
setParams(double, double, double) - Method in class umontreal.iro.lecuyer.stochprocess.GammaProcessPCA
  Sets the parameters `s0`, μ and ν to new values, and sets the variance parameters of the `BrownianMotionPCA` to ν.
setParams(double, double, double) - Method in class umontreal.iro.lecuyer.stochprocess.GammaProcessPCABridge
setParams(double, double, double) - Method in class umontreal.iro.lecuyer.stochprocess.GeometricBrownianMotion
  Sets the parameters S(t0) = [tex2html_wrap_inline213], μ = [tex2html_wrap_inline215] and σ = [tex2html_wrap_inline217] of the process.
setParams(double, double, double, double, double) - Method in class umontreal.iro.lecuyer.stochprocess.GeometricVarianceGammaProcess
  Sets the parameters S(t0) = [tex2html_wrap_inline243], θ = [tex2html_wrap_inline245], σ = [tex2html_wrap_inline247], ν = [tex2html_wrap_inline249] and μ = [tex2html_wrap_inline251] of the process.
setParams(double, double) - Method in class umontreal.iro.lecuyer.stochprocess.InverseGaussianProcess
  Sets the parameters.
setParams(double, double, double, double, double) - Method in class umontreal.iro.lecuyer.stochprocess.NormalInverseGaussianProcess
  Sets the parameters.
setParams(double, double, double, double) - Method in class umontreal.iro.lecuyer.stochprocess.OrnsteinUhlenbeckProcess
  .
setParams(double, double, double, double) - Method in class umontreal.iro.lecuyer.stochprocess.VarianceGammaProcess
  Sets the parameters S(t0) = [tex2html_wrap_inline345], θ = [tex2html_wrap_inline347], σ = [tex2html_wrap_inline349] and ν = [tex2html_wrap_inline351] of the process.
setPlotStyle(int, String) - Method in class umontreal.iro.lecuyer.charts.XYListSeriesCollection
  Selects the plot style for a given series.
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.
setPriority(double) - Method in class umontreal.iro.lecuyer.simevents.Event
  Sets the priority of this event to `inPriority`.
setPriority(double) - Method in class umontreal.iro.lecuyer.simprocs.SimProcess
  Set the priority assigned to the current variable in the simulation.
setRandomStreamClass(Class) - Method in class umontreal.iro.lecuyer.rng.BasicRandomStreamFactory
  Sets the associated random stream class to `rsClass`.
setRenderer(CategoryItemRenderer) - Method in class umontreal.iro.lecuyer.charts.SSJCategorySeriesCollection
  Sets the `CategoryItemRenderer` object associated with the current variable.
setRenderer(XYItemRenderer) - Method in class umontreal.iro.lecuyer.charts.SSJXYSeriesCollection
  Sets the `XYItemRenderer` object associated with the current variable.
setScheduledEvent(Event) - Method in class umontreal.iro.lecuyer.simprocs.SimProcess
  Sets the event associate to the current variable.
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.MRG32k3aL
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.
setSeriesCollection(BoxSeriesCollection) - Method in class umontreal.iro.lecuyer.charts.BoxChart
  Links a new dataset to the current chart.
setSeriesCollection(EmpiricalSeriesCollection) - Method in class umontreal.iro.lecuyer.charts.EmpiricalChart
  Links a new dataset to the current chart.
setSeriesCollection(HistogramSeriesCollection) - Method in class umontreal.iro.lecuyer.charts.HistogramChart
  Links a new dataset to the current chart.
setSeriesCollection(XYListSeriesCollection) - Method in class umontreal.iro.lecuyer.charts.ScatterChart
  Links a new dataset to the current chart.
setSeriesCollection(XYListSeriesCollection) - Method in class umontreal.iro.lecuyer.charts.XYLineChart
  Links a new dataset to the current chart.
setShowNumberObs(boolean) - Method in class umontreal.iro.lecuyer.stat.Tally
  Determines if the number of observations must be displayed in reports.
setSigma(DoubleMatrix2D) - Method in class umontreal.iro.lecuyer.randvarmulti.MultinormalCholeskyGen
  Sets the covariance matrix Σ of this multinormal generator to `sigma` (and recomputes A).
setSigma(DoubleMatrix2D) - Method in class umontreal.iro.lecuyer.randvarmulti.MultiNormalGen
  Deprecated. Sets the covariance matrix of this multinormal generator to `sigma`.
setSigma(DoubleMatrix2D) - Method in class umontreal.iro.lecuyer.randvarmulti.MultinormalPCAGen
  Sets the covariance matrix Σ of this multinormal generator to `sigma` (and recomputes A).
setSimulator(Simulator) - Method in class umontreal.iro.lecuyer.simevents.Accumulate
  Sets the simulator associated with this probe to `sim`.
setSimulator(Simulator) - Method in class umontreal.iro.lecuyer.simevents.Continuous
  Sets the simulator linked to this continuous-time variable.
setSimulator(Simulator) - Method in class umontreal.iro.lecuyer.simevents.Event
  Sets the simulator associated with this event to `sim`.
setSimulator(Simulator) - Method in class umontreal.iro.lecuyer.simevents.ListWithStat
  Sets the simulator associated with this list.
setSimulator(ProcessSimulator) - Method in class umontreal.iro.lecuyer.simprocs.Bin
  Set the current simulator of this continuous-time variable.
setStatCollecting(boolean) - Method in class umontreal.iro.lecuyer.simevents.ListWithStat
  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.hups.PointSet
  Sets the random stream used to generate random shifts to `stream`.
setStream(RandomStream) - Method in interface umontreal.iro.lecuyer.hups.PointSetRandomization
  Sets the internal `RandomStream` to `stream`.
setStream(RandomStream) - Method in class umontreal.iro.lecuyer.hups.RandomShift
  Sets the internal `RandomStream` to `stream`.
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`.
setStream(RandomStream) - Method in class umontreal.iro.lecuyer.stochprocess.BrownianMotion
  Resets the random stream of the normal generator to `stream`.
setStream(RandomStream) - Method in class umontreal.iro.lecuyer.stochprocess.CIRProcess
  Resets the random stream of the normal generator to `stream`.
setStream(RandomStream) - Method in class umontreal.iro.lecuyer.stochprocess.GammaProcess
  Resets the `RandomStream` of the `GammaGen` to `stream`.
setStream(RandomStream) - Method in class umontreal.iro.lecuyer.stochprocess.GammaProcessBridge
  Resets the `RandomStream` of the `GammaGen` and the `BetaGen` to `stream`.
setStream(RandomStream) - Method in class umontreal.iro.lecuyer.stochprocess.GammaProcessPCA
  Resets the `RandomStream` of the gamma generator and the `RandomStream` of the inner `BrownianMotionPCA` to `stream`.
setStream(RandomStream) - Method in class umontreal.iro.lecuyer.stochprocess.GeometricBrownianMotion
  Resets the `RandomStream` for the underlying Brownian motion to `stream`.
setStream(RandomStream) - Method in class umontreal.iro.lecuyer.stochprocess.GeometricLevyProcess
  Resets the stream in the underlying Lévy process.
setStream(RandomStream) - Method in class umontreal.iro.lecuyer.stochprocess.GeometricVarianceGammaProcess
setStream(RandomStream) - Method in class umontreal.iro.lecuyer.stochprocess.InverseGaussianProcess
setStream(RandomStream, RandomStream) - Method in class umontreal.iro.lecuyer.stochprocess.InverseGaussianProcessBridge
  Sets the streams.
setStream(RandomStream) - Method in class umontreal.iro.lecuyer.stochprocess.InverseGaussianProcessBridge
  Sets both inner streams to the same `stream`.
setStream(RandomStream, RandomStream) - Method in class umontreal.iro.lecuyer.stochprocess.InverseGaussianProcessMSH
  Sets the streams.
setStream(RandomStream) - Method in class umontreal.iro.lecuyer.stochprocess.InverseGaussianProcessMSH
  Sets both inner streams to `stream`.
setStream(RandomStream) - Method in class umontreal.iro.lecuyer.stochprocess.InverseGaussianProcessPCA
setStream(RandomStream) - Method in class umontreal.iro.lecuyer.stochprocess.NormalInverseGaussianProcess
  Sets all internal streams to `stream`, including the stream(s) of the underlying `InverseGaussianProcess`.
setStream(RandomStream) - Method in class umontreal.iro.lecuyer.stochprocess.OrnsteinUhlenbeckProcess
  Resets the random stream of the normal generator to `stream`.
setStream(RandomStream) - Method in class umontreal.iro.lecuyer.stochprocess.StochasticProcess
  Resets the random stream of the underlying generator to `stream`.
setStream(RandomStream) - Method in class umontreal.iro.lecuyer.stochprocess.VarianceGammaProcess
  Resets the `RandomStream`'s.
setStream(RandomStream) - Method in class umontreal.iro.lecuyer.stochprocess.VarianceGammaProcessDiff
  Sets the `RandomStream` of the two `GammaProcess`'es to the same `stream`.
setTheta(double) - Method in class umontreal.iro.lecuyer.probdist.LogarithmicDist
  Sets the θ associated with this object.
setTicksSynchro(int) - Method in class umontreal.iro.lecuyer.charts.EmpiricalChart
  Synchronizes x-axis ticks to the s-th series x-values.
setTicksSynchro(int) - Method in class umontreal.iro.lecuyer.charts.HistogramChart
  Synchronizes x-axis ticks to the s-th histogram bins.
setTicksSynchro(int) - Method in class umontreal.iro.lecuyer.charts.ScatterChart
  Synchronizes X-axis ticks to the s-th series x-values.
setTicksSynchro(int) - Method in class umontreal.iro.lecuyer.charts.XYChart
  Synchronizes x-axis ticks to the s-th series x-values.
setTicksSynchro(int) - Method in class umontreal.iro.lecuyer.charts.XYLineChart
  Synchronizes X-axis ticks to the s-th series x-values.
setTime(double) - Method in class umontreal.iro.lecuyer.simevents.Event
  Sets the (planned) time of occurence of this event to `time`.
setTitle(String) - Method in class umontreal.iro.lecuyer.charts.CategoryChart
  Sets a title to this chart.
setTitle(String) - Method in class umontreal.iro.lecuyer.charts.MultipleDatasetChart
  Sets a title to the chart.
setTitle(String) - Method in class umontreal.iro.lecuyer.charts.XYChart
  Sets a title to this chart.
setTwinAxisPosition(double) - Method in class umontreal.iro.lecuyer.charts.Axis
  Defines where the opposite axis must be drawn on the current axis, where it should appear, and on which label.
setType(HistogramType) - Method in class umontreal.iro.lecuyer.charts.CustomHistogramDataset
  Sets the histogram type and sends a `DatasetChangeEvent` to all registered listeners.
setUnmodifiable() - Method in class umontreal.iro.lecuyer.stat.list.ListOfStatProbes
  Forbids any future modification to this list of statistical probes.
setValues(int, List) - Method in class umontreal.iro.lecuyer.charts.CustomHistogramDataset
  Sets the values for a series.
setValues(int, double[]) - Method in class umontreal.iro.lecuyer.charts.CustomHistogramDataset
  Sets the values for a series.
setValues(int, List) - Method in class umontreal.iro.lecuyer.charts.HistogramSeriesCollection
  Sets a new values set to a series from a `List` variable.
setValues(int, double[]) - Method in class umontreal.iro.lecuyer.charts.HistogramSeriesCollection
  Sets a new values set to a series from a table.
setZeroOverZeroValue(double) - Method in class umontreal.iro.lecuyer.util.RatioFunction
  Sets the value returned by `evaluate` for the undefined function 0/0 to `zeroOverZero`.
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.
ShiftedMathFunction - Class in umontreal.iro.lecuyer.functions
  Represents a function computing f (x) - δ for a user-defined function f (x) and shift δ.
ShiftedMathFunction(MathFunction, double) - Constructor for class umontreal.iro.lecuyer.functions.ShiftedMathFunction
  Constructs a new function shifting the function `func` by a shift `delta`.
shortReport() - Method in class umontreal.iro.lecuyer.simevents.Accumulate
shortReport() - Method in class umontreal.iro.lecuyer.stat.StatProbe
  Formats and returns a short, one-line report about this statistical probe.
shortReport() - Method in class umontreal.iro.lecuyer.stat.Tally
  Formats and returns a short statistical report for this tally.
shortReportHeader() - Method in class umontreal.iro.lecuyer.simevents.Accumulate
shortReportHeader() - Method in class umontreal.iro.lecuyer.stat.StatProbe
  Returns a string containing the name of the values returned in the report strings.
shortReportHeader() - Method in class umontreal.iro.lecuyer.stat.Tally
shouldBeInterpreted(Method) - Method in class umontreal.iro.lecuyer.simprocs.SSJInterpretationOracle
shuffle(List<?>, RandomStream) - Static method in class umontreal.iro.lecuyer.rng.RandomPermutation
shuffle(Object[], RandomStream) - Static method in class umontreal.iro.lecuyer.rng.RandomPermutation
  Randomly permutes `array` using `stream`.
shuffle(byte[], RandomStream) - Static method in class umontreal.iro.lecuyer.rng.RandomPermutation
  Randomly permutes `array` using `stream`.
shuffle(short[], RandomStream) - Static method in class umontreal.iro.lecuyer.rng.RandomPermutation
  Similar to `shuffle``(byte[], RandomStream)`.
shuffle(int[], RandomStream) - Static method in class umontreal.iro.lecuyer.rng.RandomPermutation
  Similar to `shuffle``(byte[], RandomStream)`.
shuffle(long[], RandomStream) - Static method in class umontreal.iro.lecuyer.rng.RandomPermutation
  Similar to `shuffle``(byte[], RandomStream)`.
shuffle(char[], RandomStream) - Static method in class umontreal.iro.lecuyer.rng.RandomPermutation
  Similar to `shuffle``(byte[], RandomStream)`.
shuffle(boolean[], RandomStream) - Static method in class umontreal.iro.lecuyer.rng.RandomPermutation
  Similar to `shuffle``(byte[], RandomStream)`.
shuffle(float[], RandomStream) - Static method in class umontreal.iro.lecuyer.rng.RandomPermutation
  Similar to `shuffle``(byte[], RandomStream)`.
shuffle(double[], RandomStream) - Static method in class umontreal.iro.lecuyer.rng.RandomPermutation
  Similar to `shuffle``(byte[], RandomStream)`.
shuffle(List<?>, int, RandomStream) - Static method in class umontreal.iro.lecuyer.rng.RandomPermutation
shuffle(Object[], int, int, RandomStream) - Static method in class umontreal.iro.lecuyer.rng.RandomPermutation
  Partially permutes `array` as follows using `stream`: draws the new k elements, `array[0]` to `array[k-1]`, randomly among the old n elements, `array[0]` to `array[n-1]`, assuming that k <= n <= `array.length`.
shuffle(byte[], int, int, RandomStream) - Static method in class umontreal.iro.lecuyer.rng.RandomPermutation
  Similar to `shuffle``(Object[], n, k, RandomStream)`.
shuffle(short[], int, int, RandomStream) - Static method in class umontreal.iro.lecuyer.rng.RandomPermutation
  Similar to `shuffle``(Object[], n, k, RandomStream)`.
shuffle(int[], int, int, RandomStream) - Static method in class umontreal.iro.lecuyer.rng.RandomPermutation
  Similar to `shuffle``(Object[], n, k, RandomStream)`.
shuffle(long[], int, int, RandomStream) - Static method in class umontreal.iro.lecuyer.rng.RandomPermutation
  Similar to `shuffle``(Object[], n, k, RandomStream)`.
shuffle(char[], int, int, RandomStream) - Static method in class umontreal.iro.lecuyer.rng.RandomPermutation
  Similar to `shuffle``(Object[], n, k, RandomStream)`.
shuffle(boolean[], int, int, RandomStream) - Static method in class umontreal.iro.lecuyer.rng.RandomPermutation
  Similar to `shuffle``(Object[], n, k, RandomStream)`.
shuffle(float[], int, int, RandomStream) - Static method in class umontreal.iro.lecuyer.rng.RandomPermutation
  Similar to `shuffle``(Object[], n, k, RandomStream)`.
shuffle(double[], int, int, RandomStream) - Static method in class umontreal.iro.lecuyer.rng.RandomPermutation
  Similar to `shuffle``(Object[], n, k, RandomStream)`.
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
  This abstract class provides process scheduling tools.
SimProcess() - Constructor for class umontreal.iro.lecuyer.simprocs.SimProcess
  Constructs a new process without scheduling it and associate this new process with the default simulator, you can get additional knowledge with `Simulator` static methods.
SimProcess(ProcessSimulator) - Constructor for class umontreal.iro.lecuyer.simprocs.SimProcess
  Constructs a new process assiciate with `sim` without scheduling it.
simpsonIntegral(MathFunction, double, double, int) - Static method in class umontreal.iro.lecuyer.functions.MathFunctionUtil
  Computes and returns an approximation of the integral of `func` over [a, b], using the Simpsons 1/3 method with `numIntervals` intervals.
simulator() - Method in class umontreal.iro.lecuyer.simevents.Accumulate
  Returns the simulator associated with this statistical probe.
simulator() - Method in class umontreal.iro.lecuyer.simevents.Continuous
  Returns the simulator linked to this continuous-time variable.
simulator() - Method in class umontreal.iro.lecuyer.simevents.Event
  Returns the simulator linked to this event.
simulator() - Method in class umontreal.iro.lecuyer.simevents.ListWithStat
  Returns the simulator associated with this list.
Simulator - Class in umontreal.iro.lecuyer.simevents
  Represents the executive of a discrete-event simulator.
Simulator() - Constructor for class umontreal.iro.lecuyer.simevents.Simulator
  Constructs a new simulator using a splay tree for the event list.
Simulator(EventList) - Constructor for class umontreal.iro.lecuyer.simevents.Simulator
  Constructs a new simulator using `eventList` for the event list.
simulator() - Method in class umontreal.iro.lecuyer.simprocs.Bin
  Returns the current simulator of this continuous-time variable.
size() - Method in class umontreal.iro.lecuyer.stat.list.ListOfStatProbes
size() - Method in class umontreal.iro.lecuyer.util.BitVector
  Returns the length of the `BitVector`.
size() - Method in class umontreal.iro.lecuyer.util.TransformingList
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`.
SMScrambleShift - Class in umontreal.iro.lecuyer.hups
  This class implements a `PointSetRandomization` that performs a striped matrix scrambling and adds a random digital shift.
SMScrambleShift(RandomStream) - Constructor for class umontreal.iro.lecuyer.hups.SMScrambleShift
  Sets internal variable `stream` to the given `stream`.
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 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 and 31 output digits, 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
SqrtMathFunction - Class in umontreal.iro.lecuyer.functions
  Represents a function computing the square root of another function f (x).
SqrtMathFunction(MathFunction) - Constructor for class umontreal.iro.lecuyer.functions.SqrtMathFunction
  Computes and returns the square root of the function `func`.
SquareMathFunction - Class in umontreal.iro.lecuyer.functions
  Represents a function computing (af (x) + b)2 for a user-defined function f (x).
SquareMathFunction(MathFunction) - Constructor for class umontreal.iro.lecuyer.functions.SquareMathFunction
  Constructs a new square function for function `func`.
SquareMathFunction(MathFunction, double, double) - Constructor for class umontreal.iro.lecuyer.functions.SquareMathFunction
  Constructs a new power function for function `func`, and constants `a` and `b`.
SSJCategorySeriesCollection - Class in umontreal.iro.lecuyer.charts
  Stores data used in a `CategoryChart`.
SSJCategorySeriesCollection() - Constructor for class umontreal.iro.lecuyer.charts.SSJCategorySeriesCollection
SSJInterpretationOracle - Class in umontreal.iro.lecuyer.simprocs
  Determines which classes should be interpreted by the DSOL interpreter during process simulation.
SSJInterpretationOracle() - Constructor for class umontreal.iro.lecuyer.simprocs.SSJInterpretationOracle
SSJXYSeriesCollection - Class in umontreal.iro.lecuyer.charts
  Stores data used in a `XYChart`.
SSJXYSeriesCollection() - Constructor for class umontreal.iro.lecuyer.charts.SSJXYSeriesCollection
standardDeviation(double[]) - Method in class umontreal.iro.lecuyer.stat.list.ListOfTallies
  For each tally in this list, computes the sample standard deviation, and stores the standard deviations into the array `std`.
standardDeviation() - Method in class umontreal.iro.lecuyer.stat.Tally
  Returns the sample standard deviation of the observations since the last initialization.
start() - Static method in class umontreal.iro.lecuyer.simevents.Sim
  Starts the simulation executive.
start() - Method in class umontreal.iro.lecuyer.simevents.Simulator
  Starts the simulation executive.
STARTING - Static variable in class umontreal.iro.lecuyer.simprocs.SimProcess
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.ListWithStat
  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.ListWithStat
  Returns the statistical probe on the sojourn times of the objects in the list.
StochasticProcess - Class in umontreal.iro.lecuyer.stochprocess
  Abstract (base) class for a stochastic process {X(t) : t >= 0} sampled (or observed) at a finite number of time points, 0 = t0 < t1 < ...
StochasticProcess() - Constructor for class umontreal.iro.lecuyer.stochprocess.StochasticProcess
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`.
stop() - Method in class umontreal.iro.lecuyer.simevents.Simulator
  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, int) - Constructor for class umontreal.iro.lecuyer.randvar.StudentGen
  Creates a Student random variate generator with n degrees of freedom, using stream `s`.
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, int) - Constructor for class umontreal.iro.lecuyer.randvar.StudentPolarGen
  Creates a Student random variate generator with n degrees of freedom, using stream `s`.
StudentPolarGen(RandomStream, StudentDist) - Constructor for class umontreal.iro.lecuyer.randvar.StudentPolarGen
  Creates a new generator for the Student distribution `dist` and stream `s`.
subList(int, int) - Method in class umontreal.iro.lecuyer.stat.list.ListOfStatProbes
subSequence(int, int) - Method in class umontreal.iro.lecuyer.util.PrintfFormat
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(double[]) - Method in class umontreal.iro.lecuyer.stat.list.ListOfStatProbes
  For each probe in the list, computes the sum by calling `sum`, and stores the results into the array `s`.
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(SimProcess) - Method in class umontreal.iro.lecuyer.simprocs.DSOLProcessSimulator
suspend(SimProcess) - Method in class umontreal.iro.lecuyer.simprocs.ProcessSimulator
  Suspends `process`.
suspend() - Method in class umontreal.iro.lecuyer.simprocs.SimProcess
  This method can only be invoked for the `EXECUTING` or a `DELAYED` process.
suspend(SimProcess) - Method in class umontreal.iro.lecuyer.simprocs.ThreadProcessSimulator
SUSPENDED - Static variable in class umontreal.iro.lecuyer.simprocs.SimProcess
  The process is not executing and will have to be reactivated by another process or event later on.
SystemTimeChrono - Class in umontreal.iro.lecuyer.util
  Extends the `AbstractChrono` class to compute the total system time using Java's builtin `System.nanoTime`.
SystemTimeChrono() - Constructor for class umontreal.iro.lecuyer.util.SystemTimeChrono
  Constructs a new chrono object and initializes it to zero.

## T

TableFormat - Class in umontreal.iro.lecuyer.util
  This class provides methods to format arrays and matrices into `String`s 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 unnamed `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` for which the individual observations are stored in a list implemented as a `DoubleArrayList`.
TallyStore() - Constructor for class umontreal.iro.lecuyer.stat.TallyStore
  Constructs a new `TallyStore` statistical probe.
TallyStore(String) - Constructor for class umontreal.iro.lecuyer.stat.TallyStore
  Constructs a new `TallyStore` statistical probe with name `name`.
TallyStore(int) - Constructor for class umontreal.iro.lecuyer.stat.TallyStore
  Constructs a new `TallyStore` statistical probe with given initial capacity `capacity` for its associated array.
TallyStore(String, int) - Constructor for class umontreal.iro.lecuyer.stat.TallyStore
  Constructs a new `TallyStore` statistical probe with name `name` and given initial capacity `capacity` for its associated array.
TallyStore(DoubleArrayList) - Constructor for class umontreal.iro.lecuyer.stat.TallyStore
  Constructs 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
ThreadCPUTimeChrono - Class in umontreal.iro.lecuyer.util
  Extends the `AbstractChrono` class to compute the CPU time for a single thread.
ThreadCPUTimeChrono() - Constructor for class umontreal.iro.lecuyer.util.ThreadCPUTimeChrono
  Constructs a `ThreadCPUTimeChrono` object associated with current thread and initializes it to zero.
ThreadCPUTimeChrono(Thread) - Constructor for class umontreal.iro.lecuyer.util.ThreadCPUTimeChrono
  Constructs a `ThreadCPUTimeChrono` object associated with the given `Thread` variable and initializes it to zero.
ThreadProcessSimulator - Class in umontreal.iro.lecuyer.simprocs
  Represents a process simulator using Java threads for process synchronization.
ThreadProcessSimulator() - Constructor for class umontreal.iro.lecuyer.simprocs.ThreadProcessSimulator
  Creates a new `ThreadProcessSimulator` variable.
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.
time() - Method in class umontreal.iro.lecuyer.simevents.Simulator
  Returns the current value of the simulation clock.
TimeUnit - Enum in umontreal.iro.lecuyer.util
toArray() - Method in class umontreal.iro.lecuyer.stat.list.ListOfStatProbes
toArray(T[]) - Method in class umontreal.iro.lecuyer.stat.list.ListOfStatProbes
toCSV(double[]...) - Static method in class umontreal.iro.lecuyer.charts.PlotFormat
  Stores data tables `data` into a `String` with format CSV (comma-separated value tabular data).
toCSV(XYSeriesCollection) - Static method in class umontreal.iro.lecuyer.charts.PlotFormat
  Stores series collection `data` into a `String` with format CSV (comma-separated value tabular data).
toCustomizedFormat(String, String, String, String, int, double[]...) - Static method in class umontreal.iro.lecuyer.charts.PlotFormat
  Stores data tables `data` into a `String` with customized format.
toCustomizedFormat(String, String, String, String, int, XYSeriesCollection) - Static method in class umontreal.iro.lecuyer.charts.PlotFormat
  Stores data tables `data` into a `String` with customized format from an `XYSeriesCollection` variable.
toGNUPlot(double[]...) - Static method in class umontreal.iro.lecuyer.charts.PlotFormat
  Stores data tables `data` into a `String`, with format understandable by GNUPlot.
toGNUPlot(XYSeriesCollection) - Static method in class umontreal.iro.lecuyer.charts.PlotFormat
  Stores series collection `data` into a `String`, with format understandable by GNUPlot.
toLatex(double) - Method in class umontreal.iro.lecuyer.charts.Axis
  Formats and returns a string containing a LATEX-compatible source code which represents this axis and its parameters.
toLatex(double, double) - Method in class umontreal.iro.lecuyer.charts.BoxChart
  NOT IMPLEMENTED yet.
toLatex(double, double, double, double) - Method in class umontreal.iro.lecuyer.charts.BoxSeriesCollection
  NOT IMPLEMENTED: To do.
toLatex(double, double) - Method in class umontreal.iro.lecuyer.charts.CategoryChart
  Transforms the chart into LATEX form and returns it as a `String`.
toLatex(double, double) - Method in class umontreal.iro.lecuyer.charts.EmpiricalChart
  Exports the chart to a LATEX source code using PGF/TikZ.
toLatex(double, double, double, double, double, double, double, double) - Method in class umontreal.iro.lecuyer.charts.EmpiricalSeriesCollection
toLatex(double, double) - Method in class umontreal.iro.lecuyer.charts.HistogramChart
  Exports the chart to a LATEX source code using PGF/TikZ.
toLatex(double, double, double, double, double, double, double, double) - Method in class umontreal.iro.lecuyer.charts.HistogramSeriesCollection
toLatex(double, double) - Method in class umontreal.iro.lecuyer.charts.MultipleDatasetChart
  Exports the chart to a LATEX source code using PGF/TikZ.
toLatex(double, double) - Method in class umontreal.iro.lecuyer.charts.ScatterChart
  Exports the chart to a LATEX source code using PGF/TikZ.
toLatex(double, double, double, double) - Method in class umontreal.iro.lecuyer.charts.SSJCategorySeriesCollection
  Formats and returns a string containing a LATEX-compatible source code which represents this data series collection.
toLatex(double, double, double, double, double, double, double, double) - Method in class umontreal.iro.lecuyer.charts.SSJXYSeriesCollection
  Formats and returns a string containing a LATEX-compatible source code which represents this data series collection.
toLatex(double, double) - Method in class umontreal.iro.lecuyer.charts.XYChart
  Transforms the chart into LATEX form and returns it as a `String`.
toLatex(double, double) - Method in class umontreal.iro.lecuyer.charts.XYLineChart
  Exports the chart to a LATEX source code using PGF/TikZ.
toLatex(double, double, double, double, double, double, double, double) - Method in class umontreal.iro.lecuyer.charts.XYListSeriesCollection
toLatexCdf(int, int) - Method in class umontreal.iro.lecuyer.charts.ContinuousDistChart
  Exports a chart of the cdf to a LATEX source code using PGF/TikZ.
toLatexCdf(int, int) - Method in class umontreal.iro.lecuyer.charts.DiscreteDistIntChart
  Exports a chart of the cumulative probability to a LATEX source code using PGF/TikZ.
toLatexDensity(int, int) - Method in class umontreal.iro.lecuyer.charts.ContinuousDistChart
  Exports a chart of the probability density to a LATEX source code using PGF/TikZ.
toLatexProb(int, int) - Method in class umontreal.iro.lecuyer.charts.DiscreteDistIntChart
  Exports a chart of the probability mass to a LATEX source code using PGF/TikZ.
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.charts.BoxSeriesCollection
  Returns in a `String` all data contained in the current object.
toString() - Method in class umontreal.iro.lecuyer.charts.SSJCategorySeriesCollection
  Returns in a `String` all data contained in the current object.
toString() - Method in class umontreal.iro.lecuyer.charts.SSJXYSeriesCollection
  Returns in a `String` all data contained in the current object.
toString() - Method in class umontreal.iro.lecuyer.functions.Polynomial
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.AndersonDarlingDist
toString() - Method in class umontreal.iro.lecuyer.probdist.AndersonDarlingDistQuick
toString() - Method in class umontreal.iro.lecuyer.probdist.BetaDist
toString() - Method in class umontreal.iro.lecuyer.probdist.BetaSymmetricalDist
toString() - Method in class umontreal.iro.lecuyer.probdist.BinomialDist
toString() - Method in class umontreal.iro.lecuyer.probdist.CauchyDist
toString() - Method in class umontreal.iro.lecuyer.probdist.ChiDist
toString() - Method in class umontreal.iro.lecuyer.probdist.ChiSquareDist
toString() - Method in class umontreal.iro.lecuyer.probdist.ChiSquareNoncentralDist
toString() - Method in class umontreal.iro.lecuyer.probdist.CramerVonMisesDist
toString() - Method in class umontreal.iro.lecuyer.probdist.DiscreteDistribution
  Returns a `String` containing information about the current distribution.
toString() - Method in class umontreal.iro.lecuyer.probdist.EmpiricalDist
  Returns a `String` containing information about the current distribution.
toString() - Method in class umontreal.iro.lecuyer.probdist.ErlangDist
toString() - Method in class umontreal.iro.lecuyer.probdist.ExponentialDist
toString() - Method in class umontreal.iro.lecuyer.probdist.ExtremeValueDist
toString() - Method in class umontreal.iro.lecuyer.probdist.FatigueLifeDist
toString() - Method in class umontreal.iro.lecuyer.probdist.FisherFDist
toString() - Method in class umontreal.iro.lecuyer.probdist.FoldedNormalDist
  Returns a `String` containing information about the current distribution.
toString() - Method in class umontreal.iro.lecuyer.probdist.GammaDist
toString() - Method in class umontreal.iro.lecuyer.probdist.GeometricDist
toString() - Method in class umontreal.iro.lecuyer.probdist.HalfNormalDist
  Returns a `String` containing information about the current distribution.
toString() - Method in class umontreal.iro.lecuyer.probdist.HyperbolicSecantDist
toString() - Method in class umontreal.iro.lecuyer.probdist.HypergeometricDist
toString() - Method in class umontreal.iro.lecuyer.probdist.InverseGaussianDist
toString() - Method in class umontreal.iro.lecuyer.probdist.JohnsonSBDist
toString() - Method in class umontreal.iro.lecuyer.probdist.JohnsonSUDist
toString() - Method in class umontreal.iro.lecuyer.probdist.KolmogorovSmirnovDist
toString() - Method in class umontreal.iro.lecuyer.probdist.KolmogorovSmirnovPlusDist
toString() - Method in class umontreal.iro.lecuyer.probdist.LaplaceDist
toString() - Method in class umontreal.iro.lecuyer.probdist.LogarithmicDist
toString() - Method in class umontreal.iro.lecuyer.probdist.LogisticDist
toString() - Method in class umontreal.iro.lecuyer.probdist.LoglogisticDist
toString() - Method in class umontreal.iro.lecuyer.probdist.LognormalDist
toString() - Method in class umontreal.iro.lecuyer.probdist.NakagamiDist
  Returns a `String` containing information about the current distribution.
toString() - Method in class umontreal.iro.lecuyer.probdist.NegativeBinomialDist
toString() - Method in class umontreal.iro.lecuyer.probdist.NormalDist
toString() - Method in class umontreal.iro.lecuyer.probdist.NormalInverseGaussianDist
toString() - Method in class umontreal.iro.lecuyer.probdist.ParetoDist
toString() - Method in class umontreal.iro.lecuyer.probdist.PascalDist
toString() - Method in class umontreal.iro.lecuyer.probdist.Pearson5Dist
toString() - Method in class umontreal.iro.lecuyer.probdist.Pearson6Dist
toString() - Method in class umontreal.iro.lecuyer.probdist.PiecewiseLinearEmpiricalDist
  Returns a `String` containing information about the current distribution.
toString() - Method in class umontreal.iro.lecuyer.probdist.PoissonDist
toString() - Method in class umontreal.iro.lecuyer.probdist.PowerDist
toString() - Method in class umontreal.iro.lecuyer.probdist.RayleighDist
toString() - Method in class umontreal.iro.lecuyer.probdist.StudentDist
toString() - Method in class umontreal.iro.lecuyer.probdist.TriangularDist
  Returns a `String` containing information about the current distribution.
toString() - Method in class umontreal.iro.lecuyer.probdist.TruncatedDist
  Returns a `String` containing information about the current distribution.
toString() - Method in class umontreal.iro.lecuyer.probdist.UniformDist
toString() - Method in class umontreal.iro.lecuyer.probdist.UniformIntDist
toString() - Method in class umontreal.iro.lecuyer.probdist.WatsonGDist
toString() - Method in class umontreal.iro.lecuyer.probdist.WatsonUDist
toString() - Method in class umontreal.iro.lecuyer.probdist.WeibullDist
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.MRG32k3aL
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.simevents.ListWithStat.Node
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(double[][]) - Static method in class umontreal.iro.lecuyer.util.DMatrix
  Returns matrix M as a string.
toString() - Method in class umontreal.iro.lecuyer.util.DMatrix
  Creates a `String` containing all the data of the `DMatrix`.
toString() - Method in class umontreal.iro.lecuyer.util.PrintfFormat
  Converts the buffer into a `String`.
toString() - Method in enum umontreal.iro.lecuyer.util.TimeUnit
  Calls `getLongName`.
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.MRG32k3aL
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.
TransformingList<OE,IE> - Class in umontreal.iro.lecuyer.util
  Represents a list that dynamically transforms the elements of another list.
TransformingList(List<IE>) - Constructor for class umontreal.iro.lecuyer.util.TransformingList
  Creates a new transforming list wrapping the inner list `fromList`.
transpose() - Method in class umontreal.iro.lecuyer.util.BitMatrix
  Returns the transposed matrix.
transpose() - Method in class umontreal.iro.lecuyer.util.DMatrix
  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, double, double, double) - Constructor for class umontreal.iro.lecuyer.randvar.TriangularGen
  Creates a triangular random variate generator over the interval (`a`, `b`), with parameter `m`, using stream `s`.
TriangularGen(RandomStream, double) - Constructor for class umontreal.iro.lecuyer.randvar.TriangularGen
  Creates a triangular random variate generator over the interval (0, 1), with parameter `m`, using stream `s`.
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 dψ(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) instead of in (0, 1), where 0 <= a < b <= 1, by using the contained 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.
type - Variable in class umontreal.iro.lecuyer.charts.CustomHistogramDataset
  The histogram type.

## U

umontreal.iro.lecuyer.charts - package umontreal.iro.lecuyer.charts
  This package contains classes to produce charts used in the Java software developed in the simulation laboratory of the DIRO, at the Université de Montréal.
umontreal.iro.lecuyer.functions - package umontreal.iro.lecuyer.functions
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, very similar to `randvar`, but for multivariate 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.stat - package umontreal.iro.lecuyer.stat
  This package provides elementary tools for collecting statistics and computing confidence intervals.
umontreal.iro.lecuyer.stat.list - package umontreal.iro.lecuyer.stat.list
  Provides support for lists of statistical probes.
umontreal.iro.lecuyer.stochprocess - package umontreal.iro.lecuyer.stochprocess
  This package provides classes to define stochastic processes {X(t), t >= 0}, and to simulate their sample paths at a finite number of (discrete) observation times t0 <= t1 <= ...
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, double, double) - Constructor for class umontreal.iro.lecuyer.randvar.UniformGen
  Creates a uniform random variate generator over the interval (`a`, `b`), using stream `s`.
UniformGen(RandomStream) - Constructor for class umontreal.iro.lecuyer.randvar.UniformGen
  Creates a uniform random variate generator over the interval (0, 1), using stream `s`.
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, int, int) - Constructor for class umontreal.iro.lecuyer.randvar.UniformIntGen
  Creates a uniform random variate generator over the integers in the closed interval [i, j], using stream `s`.
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 i = 0, 1,…, N - 1, 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 i = 0, 1,…, N - 1, 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.
valueOf(String) - Static method in enum umontreal.iro.lecuyer.simevents.ContinuousState.IntegMethod
  Returns the enum constant of this type with the specified name.
valueOf(Class<T>, String) - Static method in class umontreal.iro.lecuyer.util.Introspection
  Returns the field of class `cls` corresponding to the name `name`.
valueOf(String) - Static method in enum umontreal.iro.lecuyer.util.TimeUnit
  Returns the enum constant of this type with the specified name.
valueOfIgnoreCase(Class<T>, String) - Static method in class umontreal.iro.lecuyer.util.Introspection
  Similar to `valueOf` `(cls, name)`, with case insensitive field name look-up.
values() - Static method in enum umontreal.iro.lecuyer.simevents.ContinuousState.IntegMethod
  Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum umontreal.iro.lecuyer.util.TimeUnit
  Returns an array containing the constants of this enum type, in the order they are declared.
variance(double[]) - Method in class umontreal.iro.lecuyer.stat.list.ListOfTallies
  For each tally in this list, computes the sample variance, and stores the variances into the array `v`.
variance() - Method in class umontreal.iro.lecuyer.stat.Tally
  Returns the sample variance of the observations since the last initialization.
VarianceGammaProcess - Class in umontreal.iro.lecuyer.stochprocess
  This class represents a variance gamma (VG) process {S(t) = X(t;θ, σ, ν) : t >= 0}.
VarianceGammaProcess() - Constructor for class umontreal.iro.lecuyer.stochprocess.VarianceGammaProcess
VarianceGammaProcess(double, double, double, double, RandomStream) - Constructor for class umontreal.iro.lecuyer.stochprocess.VarianceGammaProcess
  .
VarianceGammaProcess(double, BrownianMotion, GammaProcess) - Constructor for class umontreal.iro.lecuyer.stochprocess.VarianceGammaProcess
  Constructs a new `VarianceGammaProcess`.
VarianceGammaProcessDiff - Class in umontreal.iro.lecuyer.stochprocess
  This class represents a variance gamma (VG) process {S(t) = X(t;θ, σ, ν) : t >= 0}.
VarianceGammaProcessDiff(double, double, double, double, RandomStream) - Constructor for class umontreal.iro.lecuyer.stochprocess.VarianceGammaProcessDiff
  .
VarianceGammaProcessDiff(double, double, double, double, GammaProcess, GammaProcess) - Constructor for class umontreal.iro.lecuyer.stochprocess.VarianceGammaProcessDiff
  Same as above.
VarianceGammaProcessDiffPCA - Class in umontreal.iro.lecuyer.stochprocess
  Same as `VarianceGammaProcessDiff`, but the two inner `GammaProcess`'es are of PCA type.
VarianceGammaProcessDiffPCA(double, double, double, double, RandomStream) - Constructor for class umontreal.iro.lecuyer.stochprocess.VarianceGammaProcessDiffPCA
  .
VarianceGammaProcessDiffPCA(double, double, double, double, GammaProcessPCA, GammaProcessPCA) - Constructor for class umontreal.iro.lecuyer.stochprocess.VarianceGammaProcessDiffPCA
  Constructs a new `VarianceGammaProcessDiffPCA` with parameters θ = [tex2html_wrap_inline137], σ = [tex2html_wrap_inline139], ν = [tex2html_wrap_inline141] and initial value S(t0) = [tex2html_wrap_inline143].
VarianceGammaProcessDiffPCABridge - Class in umontreal.iro.lecuyer.stochprocess
  Same as `VarianceGammaProcessDiff`, but the two inner `GammaProcess`'es are of the type PCABridge.
VarianceGammaProcessDiffPCABridge(double, double, double, double, RandomStream) - Constructor for class umontreal.iro.lecuyer.stochprocess.VarianceGammaProcessDiffPCABridge
  .
VarianceGammaProcessDiffPCASymmetricalBridge - Class in umontreal.iro.lecuyer.stochprocess
  Same as `VarianceGammaProcessDiff`, but the two inner `GammaProcess`'es are of the PCASymmetricalBridge type.
VarianceGammaProcessDiffPCASymmetricalBridge(double, double, double, double, RandomStream) - Constructor for class umontreal.iro.lecuyer.stochprocess.VarianceGammaProcessDiffPCASymmetricalBridge
  .
view(int, int) - Method in class umontreal.iro.lecuyer.charts.BoxChart
  Displays chart on the screen using Swing.
view(int, int) - Method in class umontreal.iro.lecuyer.charts.CategoryChart
view(int, int) - Method in class umontreal.iro.lecuyer.charts.EmpiricalChart
  Displays chart on the screen using Swing.
view(int, int) - Method in class umontreal.iro.lecuyer.charts.HistogramChart
  Displays chart on the screen using Swing.
view(int, int) - Method in class umontreal.iro.lecuyer.charts.MultipleDatasetChart
  Displays chart on the screen using Swing.
view(int, int) - Method in class umontreal.iro.lecuyer.charts.ScatterChart
  Displays chart on the screen using Swing.
view(int, int) - Method in class umontreal.iro.lecuyer.charts.XYChart
view(int, int) - Method in class umontreal.iro.lecuyer.charts.XYLineChart
  Displays chart on the screen using Swing.
viewBar(int, int) - Method in class umontreal.iro.lecuyer.charts.XYLineChart
  Displays bar chart on the screen using Swing.
viewCdf(int, int) - Method in class umontreal.iro.lecuyer.charts.ContinuousDistChart
  Displays a chart of the cumulative distribution function (cdf) on the screen using Swing.
viewCdf(int, int) - Method in class umontreal.iro.lecuyer.charts.DiscreteDistIntChart
  Displays a chart of the cumulative distribution function (cdf) on the screen using Swing.
viewCdf(int, int, int, int) - Method in class umontreal.iro.lecuyer.charts.DiscreteDistIntChart
  Displays a chart of the cumulative distribution function (cdf) over the interval [a, b] on the screen using Swing.
viewDensity(int, int) - Method in class umontreal.iro.lecuyer.charts.ContinuousDistChart
  Displays a chart of the probability density on the screen using Swing.
viewProb(int, int) - Method in class umontreal.iro.lecuyer.charts.DiscreteDistIntChart
  Displays a chart of the probability mass function on the screen using Swing.
viewProb(int, int, int, int) - Method in class umontreal.iro.lecuyer.charts.DiscreteDistIntChart
  Displays a chart of the probability mass function over the interval [a, b] on the screen using Swing.
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`.
WAITING - Static variable in class umontreal.iro.lecuyer.simprocs.SimProcess
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
  Deprecated.
watsonG(int, double) - Static method in class umontreal.iro.lecuyer.gof.FDist
  Deprecated.
watsonG(DoubleArrayList) - Static method in class umontreal.iro.lecuyer.gof.GofStat
  Computes and returns the Watson statistic GN.
WatsonGDist - Class in umontreal.iro.lecuyer.probdist
  Extends the class `ContinuousDistribution` for the Watson G distribution (see).
WatsonGDist(int) - Constructor for class umontreal.iro.lecuyer.probdist.WatsonGDist
  Constructs a Watson distribution for a sample of size n.
watsonU(int, double) - Static method in class umontreal.iro.lecuyer.gof.FBar
  Deprecated.
watsonU(int, double) - Static method in class umontreal.iro.lecuyer.gof.FDist
  Deprecated.
watsonU(DoubleArrayList) - Static method in class umontreal.iro.lecuyer.gof.GofStat
  Computes and returns the Watson statistic UN2.
WatsonUDist - Class in umontreal.iro.lecuyer.probdist
  Extends the class `ContinuousDistribution` for the Watson U distribution (see).
WatsonUDist(int) - Constructor for class umontreal.iro.lecuyer.probdist.WatsonUDist
  Constructs a Watson U distribution for a sample of size n.
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, double, double, double) - Constructor for class umontreal.iro.lecuyer.randvar.WeibullGen
  Creates a Weibull random variate generator with parameters α = `alpha`, λ = `lambda` and δ = `delta`, using stream `s`.
WeibullGen(RandomStream, double) - Constructor for class umontreal.iro.lecuyer.randvar.WeibullGen
  Creates a Weibull random variate generator with parameters α = `alpha`, λ = 1 and δ = 0, using stream `s`.
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`.
XYChart - Class in umontreal.iro.lecuyer.charts
  This class provides tools to create charts from data in a simple way.
XYChart() - Constructor for class umontreal.iro.lecuyer.charts.XYChart
XYLineChart - Class in umontreal.iro.lecuyer.charts
  Extends `XYChart`.
XYLineChart() - Constructor for class umontreal.iro.lecuyer.charts.XYLineChart
  Initializes a new `XYLineChart` instance with an empty data set.
XYLineChart(String, String, String, double[][]...) - Constructor for class umontreal.iro.lecuyer.charts.XYLineChart
  Initializes a new `XYLineChart` instance with sets of points `data`.
XYLineChart(String, String, String, double[][], int) - Constructor for class umontreal.iro.lecuyer.charts.XYLineChart
  Initializes a new `XYLineChart` instance with sets of points `data`.
XYLineChart(String, String, String, double[][], int, int) - Constructor for class umontreal.iro.lecuyer.charts.XYLineChart
  Initializes a new `XYLineChart` instance using subsets of `data`.
XYLineChart(String, String, String, DoubleArrayList...) - Constructor for class umontreal.iro.lecuyer.charts.XYLineChart
  Initializes a new `XYLineChart` instance with data `data`.
XYLineChart(String, String, String, XYSeriesCollection) - Constructor for class umontreal.iro.lecuyer.charts.XYLineChart
  Initializes a new `XYLineChart` instance with data `data`.
XYListSeriesCollection - Class in umontreal.iro.lecuyer.charts
  This class extends `SSJXYSeriesCollection`.
XYListSeriesCollection() - Constructor for class umontreal.iro.lecuyer.charts.XYListSeriesCollection
  Creates a new `XYListSeriesCollection` instance with an empty dataset.
XYListSeriesCollection(double[][]...) - Constructor for class umontreal.iro.lecuyer.charts.XYListSeriesCollection
  Creates a new `XYListSeriesCollection` instance with default parameters and given data series.
XYListSeriesCollection(double[][], int) - Constructor for class umontreal.iro.lecuyer.charts.XYListSeriesCollection
  Creates a new `XYListSeriesCollection` instance with default parameters and given points `data`.
XYListSeriesCollection(DoubleArrayList...) - Constructor for class umontreal.iro.lecuyer.charts.XYListSeriesCollection
  Creates a new `XYListSeriesCollection` instance with default parameters and given data.
XYListSeriesCollection(XYSeriesCollection) - Constructor for class umontreal.iro.lecuyer.charts.XYListSeriesCollection
  Creates a new `XYListSeriesCollection` instance with default parameters and given data series.

## Y

YListChart - Class in umontreal.iro.lecuyer.charts
  This class extends the class `XYLineChart`.
YListChart(String, String, String, double[]...) - Constructor for class umontreal.iro.lecuyer.charts.YListChart
  Initializes a new `YListChart` instance with set of points `data`.
YListChart(String, String, String, double[], int) - Constructor for class umontreal.iro.lecuyer.charts.YListChart
  Initializes a new `YListChart` instance with a set of points `data`.
YListChart(String, String, String, double[][], int) - Constructor for class umontreal.iro.lecuyer.charts.YListChart
  Initializes a new `YListChart` instance with set of points `data`.
YListSeriesCollection - Class in umontreal.iro.lecuyer.charts
  This class extends `XYListSeriesCollection`.
YListSeriesCollection(double[]...) - Constructor for class umontreal.iro.lecuyer.charts.YListSeriesCollection
  Creates a new `YListSeriesCollection` instance with default parameters and given data series.
YListSeriesCollection(double[], int) - Constructor for class umontreal.iro.lecuyer.charts.YListSeriesCollection
  Creates a new `YListSeriesCollection` instance with default parameters and one data series.
YListSeriesCollection(double[][], int) - Constructor for class umontreal.iro.lecuyer.charts.YListSeriesCollection
  Creates a new `YListSeriesCollection` instance with default parameters and given data series.

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