SSJ
V. labo.
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
 
AbstractDataReader - Class in umontreal.iro.lecuyer.util.io
This abstract class implements shared functionality for data readers.
AbstractDataReader() - Constructor for class umontreal.iro.lecuyer.util.io.AbstractDataReader
 
AbstractDataWriter - Class in umontreal.iro.lecuyer.util.io
This abstract class implements shared functionality for data writers.
AbstractDataWriter() - Constructor for class umontreal.iro.lecuyer.util.io.AbstractDataWriter
 
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
Constructs 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
Constructs-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(int, double[], int) - Method in class umontreal.iro.lecuyer.discrepancy.DiscrepancyContainer
Computes the discrepancies of the first n values contained in points, and adds the values at index i.
add(int, double[][], int, int) - Method in class umontreal.iro.lecuyer.discrepancy.DiscrepancyContainer
Computes the discrepancies of the first n values contained in points using the first s coordinates, and adds the values at index i.
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(double...) - Method in class umontreal.iro.lecuyer.stat.FunctionOfMultipleMeansTally
Adds a new vector x = (X0,…, Xd-1) of observations to this tally.
add(double[], double[]) - Method in class umontreal.iro.lecuyer.stat.list.lincv.ListOfTalliesWithCV
Adds a new observation (X, C) to this list of tallies.
add(double, double[]) - Method in class umontreal.iro.lecuyer.stat.list.lincv.ListOfTalliesWithCV
Variant of the add method that can be used when there is only one output variable.
add(double, double) - Method in class umontreal.iro.lecuyer.stat.list.lincv.ListOfTalliesWithCV
Variant of the add that can be used when p = q = 1.
add(double[][]) - Method in class umontreal.iro.lecuyer.stat.list.ListOfFunctionOfMultipleMeansTallies
For each tally i in this list, adds the vector x[i].
add(DoubleMatrix2D) - Method in class umontreal.iro.lecuyer.stat.list.ListOfFunctionOfMultipleMeansTallies
Equivalent to add (x.toArray()), without copying the elements of x into a temporary 2D array.
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.matrix.MatrixOfFunctionOfMultipleMeansTallies
For each function of multiple means tally with row index r and column index c, adds the vector of observations x[r][c] if collecting is turned ON.
add(DoubleMatrix3D) - Method in class umontreal.iro.lecuyer.stat.matrix.MatrixOfFunctionOfMultipleMeansTallies
Equivalent to add (x.toArray()), without copying the elements of x into a temporary 3D array.
add(DoubleMatrix2D) - Method in class umontreal.iro.lecuyer.stat.matrix.MatrixOfTallies
Adds the observation x.get(r, c) in the tally whose row is r and column is c, for r = 0, ..., rows - 1, and c = 0, ..., columns - 1.
add(double[][]) - Method in class umontreal.iro.lecuyer.stat.matrix.MatrixOfTallies
Same as add for a 2D array.
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.TallyHistogram
Gives a new observation x to the statistical collectors.
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
 
addEffectiveBatchObs(int, int, double) - Method in class umontreal.iro.lecuyer.simexp.BatchMeansSim
Adds an observation to each statistical collector corresponding to an effective batch.
addFaureLemieuxPermutations() - Method in class umontreal.iro.lecuyer.hups.HaltonSequence
Permutes the digits using permutations from for all coordinates.
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
 
addMatrixOfObservationListener(MatrixOfObservationListener) - Method in class umontreal.iro.lecuyer.stat.matrix.MatrixOfStatProbes
Adds the observation listener l to the list of observers of this matrix of statistical probes.
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.CachedPointSet
Add the shift to the contained point set and recaches the points.
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
This method does nothing for this generic class.
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
Deprecated. 
addRandomShift() - Method in class umontreal.iro.lecuyer.hups.RandShiftedPointSet
Deprecated. 
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.
addRandomShift(int, int, RandomStream) - Method in class umontreal.iro.lecuyer.hups.SortedPointSet
Add the shift to the contained point set.
addRandomShift() - Method in class umontreal.iro.lecuyer.markovchain.LeftScrambledSobolSequence
Deprecated.  
addRealBatchObs() - Method in class umontreal.iro.lecuyer.simexp.BatchMeansSim
Collects values of a Vj vector concerning the last simulated real batch.
addReplicationObs(int) - Method in class umontreal.iro.lecuyer.simexp.RepSim
Adds statistical observations for the replication r.
addSameDimension(double[]...) - Method in class umontreal.iro.lecuyer.stat.list.ListOfFunctionOfMultipleMeansTallies
For each element i of this list of tallies, adds the vector of observations x[0][i], ..., x[d-1][i].
addSameDimension(DoubleMatrix1D...) - Method in class umontreal.iro.lecuyer.stat.list.ListOfFunctionOfMultipleMeansTallies
Equivalent to addSameDimension x.toArray(), without copying the elements of x into a temporary 1D array.
addSameDimension(DoubleMatrix2D...) - Method in class umontreal.iro.lecuyer.stat.matrix.MatrixOfFunctionOfMultipleMeansTallies
For each element (r, c) of this matrix of tallies, adds the vector of observations x[0].get (r, c), ..., x[d-1].get (r, c).
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.
addSquare(int, double[], int) - Method in class umontreal.iro.lecuyer.discrepancy.DiscrepancyContainer
Computes the square of the discrepancies of the first n values contained in points, and adds the values at index i.
addSquare(int, double[][], int, int) - Method in class umontreal.iro.lecuyer.discrepancy.DiscrepancyContainer
Computes the square discrepancies of the first n values contained in points using the first s coordinates, and adds the values at index i.
adjustTargetBatches(int) - Method in class umontreal.iro.lecuyer.simexp.BatchMeansSim
Adjusts the target number of real batches to simulate numNewBatches additionnal real batches.
adjustTargetReplications(int) - Method in class umontreal.iro.lecuyer.simexp.RepSim
Adjusts the target number of replications to simulate numNewReplications additional replications.
advanceState(int, int) - Method in class umontreal.iro.lecuyer.rng.RandMrg
Deprecated. 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.
allocateCapacity(int) - Method in class umontreal.iro.lecuyer.simexp.BatchMeansSim
Allocates the necessary memory for storing capacity real batches.
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(DoubleArrayList) - Static method in class umontreal.iro.lecuyer.gof.GofStat
Computes and returns the Anderson-Darling statistic An2 (see method andersonDarling).
andersonDarling(double[]) - Static method in class umontreal.iro.lecuyer.gof.GofStat
Computes and returns the Anderson-Darling statistic An2.
andersonDarling(double[], ContinuousDistribution) - Static method in class umontreal.iro.lecuyer.gof.GofStat
Computes the Anderson-Darling statistic An2 and the corresponding p-value p.
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
 
areAllNumberObsEqual() - Method in class umontreal.iro.lecuyer.stat.list.ListOfFunctionOfMultipleMeansTallies
Tests that every tally in this list contains the same number of observations.
areAllNumberObsEqual() - Method in class umontreal.iro.lecuyer.stat.list.ListOfTallies
Tests that every tally in this list contains the same number of observations.
areAllNumberObsEqual() - Method in class umontreal.iro.lecuyer.stat.matrix.MatrixOfFunctionOfMultipleMeansTallies
Tests that every tally in this matrix contains the same number of observations.
areAllNumberObsEqual() - Method in class umontreal.iro.lecuyer.stat.matrix.MatrixOfTallies
Tests that every tally in this matrix 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.
ArrayOfComparableChains - Class in umontreal.iro.lecuyer.markovchain
Permits one to simulate an array of MarkovChainComparable using the array-RQMC method of[#!vLEC05a!#], where n copies of the chain are simulated in parallel, and sorted using a multi-dimensional sort (see MultiDimSort) at each step of the chain.
ArrayOfComparableChains(MarkovChainComparable, PointSetRandomization, MultiDimSort) - Constructor for class umontreal.iro.lecuyer.markovchain.ArrayOfComparableChains
Creates an array of the comparable chain baseChain.
ArrayOfComparableChains(MarkovChainComparable) - Constructor for class umontreal.iro.lecuyer.markovchain.ArrayOfComparableChains
Same as ArrayOfComparableChains(baseChain, new RandomShift(new MRG32k3a()), new SplitSort(baseChain.stateDim)).
ArrayOfComparableChainsStop - Class in umontreal.iro.lecuyer.markovchain
Deprecated. 
ArrayOfComparableChainsStop(MarkovChainComparable) - Constructor for class umontreal.iro.lecuyer.markovchain.ArrayOfComparableChainsStop
Deprecated.  
ArrayOfDoubleChains - Class in umontreal.iro.lecuyer.markovchain
Similar to ArrayOfComparableChains, except that instead of working with n clones of a MarkovChain, we use a single MarkovChainDouble object for all the chains.
ArrayOfDoubleChains(MarkovChainDouble, PointSetRandomization) - Constructor for class umontreal.iro.lecuyer.markovchain.ArrayOfDoubleChains
Creates a virtual array for the chain baseChain.
ArrayOfDoubleChains(MarkovChainDouble) - Constructor for class umontreal.iro.lecuyer.markovchain.ArrayOfDoubleChains
Same as ArrayOfDoubleChains (baseChain, new RandomShift(new MRG32k3a())).
ArrayOfDoubleChainsStop - Class in umontreal.iro.lecuyer.markovchain
Deprecated. 
ArrayOfDoubleChainsStop(MarkovChainDouble) - Constructor for class umontreal.iro.lecuyer.markovchain.ArrayOfDoubleChainsStop
Deprecated.  
ArrayOfObservationListener - Interface in umontreal.iro.lecuyer.stat.list
Represents an object that can listen to observations broadcast by lists of statistical probes.
asDouble() - Method in class umontreal.iro.lecuyer.util.io.DataField
Returns the value as double or 0 if it is not of type double See isDouble.
asDoubleArray() - Method in class umontreal.iro.lecuyer.util.io.DataField
Returns the value as one-dimensional double array or null if it is not of type double[].
asDoubleArray2D() - Method in class umontreal.iro.lecuyer.util.io.DataField
Returns the value as two-dimensional double array or null if it is not of type double[][].
asFloat() - Method in class umontreal.iro.lecuyer.util.io.DataField
Returns the value as float or 0 if it is not of type float See isFloat.
asFloatArray() - Method in class umontreal.iro.lecuyer.util.io.DataField
Returns the value as one-dimensional float array or null if it is not of type float[].
asFloatArray2D() - Method in class umontreal.iro.lecuyer.util.io.DataField
Returns the value as two-dimensional float array or null if it is not of type float[][].
asInt() - Method in class umontreal.iro.lecuyer.util.io.DataField
Returns the value as int or 0 if it is not of type int See isInt.
asIntArray() - Method in class umontreal.iro.lecuyer.util.io.DataField
Returns the value as one-dimensional int array or null if it is not of type int[].
asIntArray2D() - Method in class umontreal.iro.lecuyer.util.io.DataField
Returns the value as two-dimensional int array or null if it is not of type int[][].
asObject() - Method in class umontreal.iro.lecuyer.util.io.DataField
Returns the value of the field as an Object.
asString() - Method in class umontreal.iro.lecuyer.util.io.DataField
Returns the value as String, or null if it is not of type String.
asStringArray() - Method in class umontreal.iro.lecuyer.util.io.DataField
Returns the value as one-dimensional String array or null if it is not of type String[].
asStringArray2D() - Method in class umontreal.iro.lecuyer.util.io.DataField
Returns the value as two-dimensional String array or null if it is not of type String[][].
average() - Method in class umontreal.iro.lecuyer.simevents.Accumulate
Returns the time-average since the last initialization to the last call to update.
average() - Method in class umontreal.iro.lecuyer.stat.FunctionOfMultipleMeansTally
Computes bar(ν)n = g(bar(X)n), an estimate of the function of means ν.
average(double[]) - Method in class umontreal.iro.lecuyer.stat.list.ListOfFunctionOfMultipleMeansTallies
Computes the function of averages for each tally in this list.
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(DoubleMatrix2D) - Method in class umontreal.iro.lecuyer.stat.matrix.MatrixOfFunctionOfMultipleMeansTallies
Computes the average for each function of multiple means tally in the matrix.
average(DoubleMatrix2D) - Method in class umontreal.iro.lecuyer.stat.matrix.MatrixOfStatProbes
For each statistical probe in the matrix, computes the average by calling average, and stores it into the given matrix m.
average(DoubleMatrix2D) - Method in class umontreal.iro.lecuyer.stat.matrix.MatrixOfTallies
Computes the average for each tally in the matrix.
average() - Method in class umontreal.iro.lecuyer.stat.StatProbe
Returns the average for this collector.
average() - Method in class umontreal.iro.lecuyer.stat.Tally
Returns the average value of the observations since the last initialization.
averageC(double[]) - Method in class umontreal.iro.lecuyer.stat.list.lincv.ListOfTalliesWithCV
Fills the given array with the averages of the control variables.
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.
averageWithCV(int) - Method in class umontreal.iro.lecuyer.stat.list.lincv.ListOfTalliesWithCV
Returns the average of the ith component of XC, denoted XC, i.
averageWithCV(double[]) - Method in class umontreal.iro.lecuyer.stat.list.lincv.ListOfTalliesWithCV
Fills the given array with the controlled averages.
averageX(double[]) - Method in class umontreal.iro.lecuyer.stat.list.lincv.ListOfTalliesWithCV
Fills the given array with the averages without control variables.
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(int) - Method in class umontreal.iro.lecuyer.probdist.BernoulliDist
 
barF(double, int) - Static method in class umontreal.iro.lecuyer.probdist.BernoulliDist
Returns the complementary Bernoulli distribution function bar(F)(x) = P[X >= x] with parameter p.
barF(double) - Method in class umontreal.iro.lecuyer.probdist.BetaDist
 
barF(double, double, int, double) - Static method in class umontreal.iro.lecuyer.probdist.BetaDist
Deprecated. 
barF(double, double, double, double, int, double) - Static method in class umontreal.iro.lecuyer.probdist.BetaDist
Deprecated. 
barF(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.BetaDist
Same as barF (alpha, beta, 0, 1, x).
barF(double, double, double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.BetaDist
Computes the complementary distribution function.
barF(double) - Method in class umontreal.iro.lecuyer.probdist.BetaSymmetricalDist
 
barF(double, int, double) - Static method in class umontreal.iro.lecuyer.probdist.BetaSymmetricalDist
Returns the complementary distribution function.
barF(int) - Method in class umontreal.iro.lecuyer.probdist.BinomialDist
 
barF(int, double, int) - Static method in class umontreal.iro.lecuyer.probdist.BinomialDist
Returns bar(F)(x) = P[X >= x], the complementary distribution function.
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, evaluated at x.
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
Deprecated.  
barF(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.ExtremeValueDist
Deprecated. 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
Deprecated. 
barF(int, int, double) - Static method in class umontreal.iro.lecuyer.probdist.FisherFDist
Computes the complementary distribution function of the Fisher F distribution with parameters n1 and n2, evaluated at x.
barF(double) - Method in class umontreal.iro.lecuyer.probdist.FoldedNormalDist
 
barF(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.FoldedNormalDist
Computes the complementary distribution function.
barF(double) - Method in class umontreal.iro.lecuyer.probdist.FrechetDist
 
barF(double, double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.FrechetDist
Computes and returns the complementary distribution function 1 - F(x).
barF(double) - Method in class umontreal.iro.lecuyer.probdist.GammaDist
 
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.
barF(double) - Method in class umontreal.iro.lecuyer.probdist.GumbelDist
 
barF(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.GumbelDist
Computes and returns the complementary distribution function 1 - F(x).
barF(double) - Method in class umontreal.iro.lecuyer.probdist.HalfNormalDist
 
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.
barF(double) - Method in class umontreal.iro.lecuyer.probdist.HypoExponentialDist
 
barF(double[], double) - Static method in class umontreal.iro.lecuyer.probdist.HypoExponentialDist
Computes the complementary distribution bar(F)(x), with λi = lambda[i - 1], i = 1,…, k.
barF(double) - Method in class umontreal.iro.lecuyer.probdist.HypoExponentialDistEqual
 
barF(int, int, double, double) - Static method in class umontreal.iro.lecuyer.probdist.HypoExponentialDistEqual
Computes the complementary distribution bar(F)(x), as in formula.
barF(double) - Method in class umontreal.iro.lecuyer.probdist.HypoExponentialDistQuick
 
barF(double[], double) - Static method in class umontreal.iro.lecuyer.probdist.HypoExponentialDistQuick
Computes the complementary distribution bar(F)(x), with λi = lambda[i - 1], i = 1,…, k.
barF(double) - Method in class umontreal.iro.lecuyer.probdist.InverseGammaDist
 
barF(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.InverseGammaDist
Computes the complementary distribution function of the inverse gamma distribution with shape parameter α and scale parameter β.
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) - Method in class umontreal.iro.lecuyer.probdist.JohnsonSBDist
 
barF(double, double, double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.JohnsonSBDist
Returns the complementary distribution.
barF(double) - Method in class umontreal.iro.lecuyer.probdist.JohnsonSLDist
 
barF(double, double, double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.JohnsonSLDist
Returns the complementary distribution function 1 - F(x).
barF(double) - Method in class umontreal.iro.lecuyer.probdist.JohnsonSUDist
 
barF(double, double, double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.JohnsonSUDist
Returns 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 P[Dn >= x] with parameter n, in a form that is more precise in the upper tail, using the program described in.
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.
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) - Method in class umontreal.iro.lecuyer.probdist.NakagamiDist
 
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, double, int) - Static method in class umontreal.iro.lecuyer.probdist.NegativeBinomialDist
Returns bar(F)(x) = P[X >= x], the complementary distribution function.
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
Deprecated.  
barF(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.Pearson5Dist
Deprecated. 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, 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(double) - Method in class umontreal.iro.lecuyer.probdist.StudentDist
 
barF(int, double) - Static method in class umontreal.iro.lecuyer.probdist.StudentDist
Computes the complementary distribution function v = bar(F)(x) with n degrees of freedom.
barF(double) - Method in class umontreal.iro.lecuyer.probdist.StudentDistQuick
 
barF(int, double) - Static method in class umontreal.iro.lecuyer.probdist.StudentDistQuick
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.
BatchMeansSim - Class in umontreal.iro.lecuyer.simexp
Performs a simulation experiment on an infinite horizon, for estimating steady-state performance measures, using batch means.
BatchMeansSim(int, double, double) - Constructor for class umontreal.iro.lecuyer.simexp.BatchMeansSim
Constructs a new batch means simulator using at least minBatches batches with size batchSize, with a warmup period of duration warmupTime.
BatchMeansSim(int, int, double, double) - Constructor for class umontreal.iro.lecuyer.simexp.BatchMeansSim
Constructs a batch means simulator with a maximum of maxBatches batches to avoid excessive memory usage and too long simulations when using sequential sampling.
BatchMeansSim(Simulator, int, double, double) - Constructor for class umontreal.iro.lecuyer.simexp.BatchMeansSim
Equivalent to the first constructor, with a user-defined simulator sim.
BatchMeansSim(Simulator, int, int, double, double) - Constructor for class umontreal.iro.lecuyer.simexp.BatchMeansSim
Equivalent to the second constructor, with a user-defined simulator sim.
BatchSort - Class in umontreal.iro.lecuyer.util
This class implements a MultiDimSort that performs a batch sort on the arrays.
BatchSort(int[]) - Constructor for class umontreal.iro.lecuyer.util.BatchSort
Constructs a BatchSort that will use batches.
BatchSort(double[]) - Constructor for class umontreal.iro.lecuyer.util.BatchSort
Constructs a BatchSort that will use batchesExponents.
BernoulliDist - Class in umontreal.iro.lecuyer.probdist
Extends the class DiscreteDistributionInt for the Bernoulli distribution with parameter p, where 0 <= p <= 1.
BernoulliDist(double) - Constructor for class umontreal.iro.lecuyer.probdist.BernoulliDist
Creates a Bernoulli distribution object.
BernoulliGen - Class in umontreal.iro.lecuyer.randvar
This class implements random variate generators for the Bernoulli distribution (see class BernoulliDist).
BernoulliGen(RandomStream, double) - Constructor for class umontreal.iro.lecuyer.randvar.BernoulliGen
Creates a Bernoulli random variate generator with parameter p, using stream s.
BernoulliGen(RandomStream, BernoulliDist) - Constructor for class umontreal.iro.lecuyer.randvar.BernoulliGen
Creates a random variate generator for the Bernoulli distribution dist and the random stream s.
bernoulliPoly(int, double) - Static method in class umontreal.iro.lecuyer.util.Num
Evaluates the Bernoulli polynomial Bn(x) of degree n at x.
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, 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, β = beta and domain [a, b].
BetaDist(double, double, int) - Constructor for class umontreal.iro.lecuyer.probdist.BetaDist
Deprecated. 
BetaDist(double, double, double, double, int) - Constructor for class umontreal.iro.lecuyer.probdist.BetaDist
Deprecated. 
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.
BigDiscrepancy - Class in umontreal.iro.lecuyer.discrepancy
This abstract class is the base class of all discrepancy classes programmed with floating-point numbers with multi-precision.
BigDiscrepancy(double[][], int, int) - Constructor for class umontreal.iro.lecuyer.discrepancy.BigDiscrepancy
Constructor with the n points points[i] in s dimensions.
BigDiscrepancy(double[][], int, int, double[]) - Constructor for class umontreal.iro.lecuyer.discrepancy.BigDiscrepancy
Constructor with the n points points[i] in s dimensions with weight factors gamma.
BigDiscrepancy(int, int, double[]) - Constructor for class umontreal.iro.lecuyer.discrepancy.BigDiscrepancy
The number of points is n, the dimension s, and the s weight factors are gamma[j], j = 0, 1,…,(s - 1).
BigDiscrepancy(PointSet) - Constructor for class umontreal.iro.lecuyer.discrepancy.BigDiscrepancy
Constructor with the point set set.
BigDiscrepancy() - Constructor for class umontreal.iro.lecuyer.discrepancy.BigDiscrepancy
Empty constructor.
BigDiscShiftBaker1 - Class in umontreal.iro.lecuyer.discrepancy
This class computes the same discrepancy as in DiscShiftBaker1 [see eq.], but uses multi-precision real numbers.
BigDiscShiftBaker1(double[][], int, int) - Constructor for class umontreal.iro.lecuyer.discrepancy.BigDiscShiftBaker1
Constructor with the n points points[i] in s dimensions, with all the weights γr = 1.
BigDiscShiftBaker1(double[][], int, int, double[]) - Constructor for class umontreal.iro.lecuyer.discrepancy.BigDiscShiftBaker1
Constructor with the n points points[i] in s dimensions, with weights γr = gamma[r-1].
BigDiscShiftBaker1(int, int, double[]) - Constructor for class umontreal.iro.lecuyer.discrepancy.BigDiscShiftBaker1
Constructor for a lattice of n points in s dimensions, with weights γr = gamma[r-1], r = 1, 2,…, s.
BigDiscShiftBaker1(PointSet) - Constructor for class umontreal.iro.lecuyer.discrepancy.BigDiscShiftBaker1
Constructor with the point set set.
BigDiscShiftBaker1() - Constructor for class umontreal.iro.lecuyer.discrepancy.BigDiscShiftBaker1
Empty constructor.
BigDiscShiftBaker1Lattice - Class in umontreal.iro.lecuyer.discrepancy
This class computes the same discrepancy as in DiscShiftBaker1Lattice [see eq.], but uses multi-precision real numbers.
BigDiscShiftBaker1Lattice(int, int, double[]) - Constructor for class umontreal.iro.lecuyer.discrepancy.BigDiscShiftBaker1Lattice
Constructor for a lattice of n points in at most s dimensions, with weights γr = gamma[r-1], r = 1, 2,…, s.
BigDiscShiftBaker1Lattice(int, int) - Constructor for class umontreal.iro.lecuyer.discrepancy.BigDiscShiftBaker1Lattice
Constructor for a lattice of n points in at most s dimensions, with weights γr = 1.
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.
BinaryDataReader - Class in umontreal.iro.lecuyer.util.io
Binary data reader.
BinaryDataReader(String) - Constructor for class umontreal.iro.lecuyer.util.io.BinaryDataReader
Opens the file with the specified name for reading.
BinaryDataReader(URL) - Constructor for class umontreal.iro.lecuyer.util.io.BinaryDataReader
Opens the file at the specified url for reading.
BinaryDataReader(File) - Constructor for class umontreal.iro.lecuyer.util.io.BinaryDataReader
Opens the specified file for reading.
BinaryDataReader(InputStream) - Constructor for class umontreal.iro.lecuyer.util.io.BinaryDataReader
Opens the specified input stream for reading.
BinaryDataWriter - Class in umontreal.iro.lecuyer.util.io
Binary data writer.
BinaryDataWriter(String, boolean) - Constructor for class umontreal.iro.lecuyer.util.io.BinaryDataWriter
Data will be output to the file with the specified name.
BinaryDataWriter(File, boolean) - Constructor for class umontreal.iro.lecuyer.util.io.BinaryDataWriter
Data will be output to the specified file.
BinaryDataWriter(String) - Constructor for class umontreal.iro.lecuyer.util.io.BinaryDataWriter
Truncates any existing file with the specified name.
BinaryDataWriter(File) - Constructor for class umontreal.iro.lecuyer.util.io.BinaryDataWriter
Truncates any existing file with the specified name.
BinaryDataWriter(OutputStream) - Constructor for class umontreal.iro.lecuyer.util.io.BinaryDataWriter
Constructor.
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
 
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.
bisection(double, double, MathFunction, double) - Static method in class umontreal.iro.lecuyer.util.RootFinder
Computes a root x of the function in f using the bisection method.
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
This class provides tools to create and manage box-and-whisker plots.
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
Constructs a new BrownianMotion with parameters μ = mu, σ = sigma and initial value X(t0) = x0.
BrownianMotion(double, double, double, NormalGen) - Constructor for class umontreal.iro.lecuyer.stochprocess.BrownianMotion
Constructs a new BrownianMotion with parameters μ = mu, σ = sigma and initial value X(t0) = x0.
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
Constructs a new BrownianMotionBridge with parameters μ = mu, σ = sigma and initial value X(t0) = x0.
BrownianMotionBridge(double, double, double, NormalGen) - Constructor for class umontreal.iro.lecuyer.stochprocess.BrownianMotionBridge
Constructs a new BrownianMotionBridge with parameters μ = mu, σ = sigma and initial value X(t0) = x0.
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
Constructs a new BrownianMotionBridge with parameters μ = mu, σ = sigma and initial value X(t0) = x0.
BrownianMotionPCA(double, double, double, NormalGen) - Constructor for class umontreal.iro.lecuyer.stochprocess.BrownianMotionPCA
Constructs a new BrownianMotionBridge with parameters μ = mu, σ = sigma and initial value X(t0) = x0.
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.
BSpline - Class in umontreal.iro.lecuyer.functionfit
Represents a B-spline with control points at (Xi, Yi).
BSpline(double[], double[], int) - Constructor for class umontreal.iro.lecuyer.functionfit.BSpline
Constructs a new uniform B-spline of degree degree with control points at (x[i], y[i]).
BSpline(double[], double[], double[]) - Constructor for class umontreal.iro.lecuyer.functionfit.BSpline
Constructs a new uniform B-spline with control points at (x[i], y[i]), and knot vector given by the array knots.

C

CachedDataWriter - Class in umontreal.iro.lecuyer.util.io
This abstract class implements shared functionality for data writers that store all fields in memory before outputing them with close.
CachedDataWriter() - Constructor for class umontreal.iro.lecuyer.util.io.CachedDataWriter
Class constructor.
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.
calcCoefficients(double[], double[]) - Static method in class umontreal.iro.lecuyer.functionfit.LeastSquares
Computes the regression coefficients using the least squares method.
calcCoefficients(double[], double[], int) - Static method in class umontreal.iro.lecuyer.functionfit.LeastSquares
Computes the regression coefficients using the least squares method.
calcCoefficients(double[][], double[]) - Static method in class umontreal.iro.lecuyer.functionfit.LeastSquares
Computes the regression coefficients using the least squares method.
calcCoefficients0(double[][], double[]) - Static method in class umontreal.iro.lecuyer.functionfit.LeastSquares
Computes the regression coefficients using the least squares method.
calcMatStirling(int, int) - Static method in class umontreal.iro.lecuyer.util.Num
Computes and returns the Stirling numbers of the second kind
calcMeanPerf() - Method in class umontreal.iro.lecuyer.markovchain.ArrayOfComparableChains
Computes and returns the mean performance of the n chains.
calcMeanPerf() - Method in class umontreal.iro.lecuyer.markovchain.ArrayOfDoubleChains
Computes and returns the mean performance of the n chains.
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
Cancels the activating event that was supposed to resume this process, and places 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) at x for sample size n.
cdf(int) - Method in class umontreal.iro.lecuyer.probdist.BernoulliDist
 
cdf(double, int) - Static method in class umontreal.iro.lecuyer.probdist.BernoulliDist
Returns the Bernoulli distribution function F(x) with parameter p (see eq.).
cdf(double) - Method in class umontreal.iro.lecuyer.probdist.BetaDist
 
cdf(double, double, int, double) - Static method in class umontreal.iro.lecuyer.probdist.BetaDist
Deprecated. 
cdf(double, double, double, double, int, double) - Static method in class umontreal.iro.lecuyer.probdist.BetaDist
Deprecated. 
cdf(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.BetaDist
Same as cdf (alpha, beta, 0, 1, x).
cdf(double, double, double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.BetaDist
Computes the distribution function.
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 the chi-square distribution function with n degrees of freedom, evaluated at x.
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
Deprecated.  
cdf(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.ExtremeValueDist
Deprecated. THIS CLASS HAS BEEN REPLACED BY GumbelDist.
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
Deprecated. 
cdf(int, int, double) - Static method in class umontreal.iro.lecuyer.probdist.FisherFDist
Computes the distribution function of the Fisher F distribution with parameters n1 and n2, evaluated at x.
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.FrechetDist
 
cdf(double, double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.FrechetDist
Computes and returns 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.GumbelDist
 
cdf(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.GumbelDist
Computes and returns the distribution function.
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.HypoExponentialDist
 
cdf(double[], double) - Static method in class umontreal.iro.lecuyer.probdist.HypoExponentialDist
Computes the distribution function F(x), with λi = lambda[i - 1], i = 1,…, k.
cdf(double) - Method in class umontreal.iro.lecuyer.probdist.HypoExponentialDistEqual
 
cdf(int, int, double, double) - Static method in class umontreal.iro.lecuyer.probdist.HypoExponentialDistEqual
Computes the distribution function F(x), with arguments as in the constructor.
cdf(double) - Method in class umontreal.iro.lecuyer.probdist.HypoExponentialDistQuick
 
cdf(double[], double) - Static method in class umontreal.iro.lecuyer.probdist.HypoExponentialDistQuick
Computes the distribution function F(x), with λi = lambda[i - 1], i = 1,…, k.
cdf(double) - Method in class umontreal.iro.lecuyer.probdist.InverseDistFromDensity
Computes the distribution function at x.
cdf(double) - Method in class umontreal.iro.lecuyer.probdist.InverseGammaDist
 
cdf(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.InverseGammaDist
Computes the cumulative probability function of the inverse gamma distribution with shape parameter α and scale parameter β.
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
Returns the distribution function.
cdf(double) - Method in class umontreal.iro.lecuyer.probdist.JohnsonSLDist
 
cdf(double, double, double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.JohnsonSLDist
Returns the distribution function F(x).
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
Returns 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 using Durbin's matrix formula.
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 u = P[Dn <= x] with parameter n, using the program described in.
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 NormalDist.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
Deprecated.  
cdf(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.Pearson5Dist
Deprecated. 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
Computes the Student t-distribution function u = F(x) with n degrees of freedom.
cdf(double) - Method in class umontreal.iro.lecuyer.probdist.StudentDistQuick
 
cdf(int, double) - Static method in class umontreal.iro.lecuyer.probdist.StudentDistQuick
Returns an approximation of 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 defined 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 n 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(double[], double) - Static method in class umontreal.iro.lecuyer.probdist.HypoExponentialDist
Computes the distribution function F(x), with λi = lambda[i - 1], i = 1,…, k.
cdf2(int, int, double) - Static method in class umontreal.iro.lecuyer.probdist.StudentDist
Deprecated. 
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(GofStat.OutcomeCategoriesChi2, int[]) - Static method in class umontreal.iro.lecuyer.gof.GofStat
Computes and returns the chi-square statistic for the observations oi in count, for which the corresponding expected values ei are in cat.
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.
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 > 0 degrees of freedom and noncentrality parameter λ > 0.
ChiSquareNoncentralGen(RandomStream, double, double) - Constructor for class umontreal.iro.lecuyer.randvar.ChiSquareNoncentralGen
Creates a noncentral chi square random variate generator with nu = ν > 0 degrees of freedom and noncentrality parameter lambda = 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, ChiSquareNoncentralGen) - Constructor for class umontreal.iro.lecuyer.stochprocess.CIRProcess
The noncentral chi-square variate generator gen is specified directly instead of specifying the stream.
CIRProcessEuler - Class in umontreal.iro.lecuyer.stochprocess
.
CIRProcessEuler(double, double, double, double, RandomStream) - Constructor for class umontreal.iro.lecuyer.stochprocess.CIRProcessEuler
Constructs a new CIRProcessEuler with parameters α = alpha, b, σ = sigma and initial value X(t0) = x0.
CIRProcessEuler(double, double, double, double, NormalGen) - Constructor for class umontreal.iro.lecuyer.stochprocess.CIRProcessEuler
The normal variate generator gen 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.
clearMatrixOfObservationListeners() - Method in class umontreal.iro.lecuyer.stat.matrix.MatrixOfStatProbes
Removes all observation listeners from the list of observers of this matrix of statistical probes.
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.functionfit.PolInterp
 
clone() - Method in class umontreal.iro.lecuyer.functions.Polynomial
 
clone() - Method in class umontreal.iro.lecuyer.markovchain.MarkovChain
Returns a clone of the chain.
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
Deprecated. 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.FunctionOfMultipleMeansTally
Clones this object.
clone() - Method in class umontreal.iro.lecuyer.stat.list.lincv.FunctionOfMultipleMeansTallyWithCV
Clones this object and the function which is stored inside.
clone() - Method in class umontreal.iro.lecuyer.stat.list.lincv.ListOfTalliesWithCV
Clones this object.
clone() - Method in class umontreal.iro.lecuyer.stat.list.ListOfFunctionOfMultipleMeansTallies
Clones 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.matrix.MatrixOfFunctionOfMultipleMeansTallies
Clones this object.
clone() - Method in class umontreal.iro.lecuyer.stat.matrix.MatrixOfStatProbes
Clones this object.
clone() - Method in class umontreal.iro.lecuyer.stat.matrix.MatrixOfTallies
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.TallyHistogram
Clones this object and the array which stores the counters.
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.
close() - Method in class umontreal.iro.lecuyer.util.io.BinaryDataReader
Closes the file.
close() - Method in class umontreal.iro.lecuyer.util.io.BinaryDataWriter
Flushes any pending data and closes the file.
close() - Method in interface umontreal.iro.lecuyer.util.io.DataReader
Closes the input stream.
close() - Method in interface umontreal.iro.lecuyer.util.io.DataWriter
Flushes any pending data and closes the output stream.
close() - Method in class umontreal.iro.lecuyer.util.io.TextDataWriter
Flushes any pending data and closes the file or stream.
CM - Static variable in class umontreal.iro.lecuyer.gof.GofFormat
Cramér-von Mises test
columnReport(int) - Method in class umontreal.iro.lecuyer.stat.matrix.MatrixOfStatProbes
Formats a report for the column c of the statistical probe matrix.
columns() - Method in class umontreal.iro.lecuyer.stat.matrix.MatrixOfStatProbes
Returns the number of columns in this matrix.
combination(int, int) - Static method in class umontreal.iro.lecuyer.util.Num
Returns the number of different combinations of s objects amongst n.
compare(double[], double[]) - Method in class umontreal.iro.lecuyer.util.DoubleArrayComparator
Returns -1, 0, or 1 depending on whether d1[i] is less than, equal to, or greater than d2[i].
compare(T, T) - Method in class umontreal.iro.lecuyer.util.MultiDimComparator
Calls o1.compareTo(o2, i) from class MultiDimComparable.
compareTo(MarkovChainComparable, int) - Method in class umontreal.iro.lecuyer.markovchain.MarkovChainDouble
 
compareTo(Event) - Method in class umontreal.iro.lecuyer.simevents.Event
Compares this object with the specified object e for order.
compareTo(T, int) - Method in interface umontreal.iro.lecuyer.util.MultiDimComparable
Compares objects of type T in the i-th dimension.
compute(double[][], int, int) - Method in class umontreal.iro.lecuyer.discrepancy.BigDiscShiftBaker1
NOT IMPLEMENTED.
compute(double[][], int, int, double[]) - Method in class umontreal.iro.lecuyer.discrepancy.BigDiscShiftBaker1
NOT IMPLEMENTED.
compute(long[], int) - Method in class umontreal.iro.lecuyer.discrepancy.BigDiscShiftBaker1Lattice
Computes the discrepancy for a rank-1 lattice in dimension s.
compute(double[][], int, int) - Method in class umontreal.iro.lecuyer.discrepancy.BigDiscShiftBaker1Lattice
NOT IMPLEMENTED.
compute(double[][], int, int, double[]) - Method in class umontreal.iro.lecuyer.discrepancy.BigDiscShiftBaker1Lattice
 
compute(double[][], int, int, double[]) - Method in class umontreal.iro.lecuyer.discrepancy.DiscL2Hickernell
 
compute(double[][], int, int) - Method in class umontreal.iro.lecuyer.discrepancy.DiscL2Hickernell
Computes the Hickernell L2-discrepancy for the set of n s-dimensional points points.
compute(double[], int) - Method in class umontreal.iro.lecuyer.discrepancy.DiscL2Hickernell
Computes the Hickernell L2-discrepancy for the set of n 1-dimensional points T.
compute(double[][], int, int, double[]) - Method in class umontreal.iro.lecuyer.discrepancy.DiscL2Star
 
compute(double[][], int, int) - Method in class umontreal.iro.lecuyer.discrepancy.DiscL2Star
Computes the traditional L2-star discrepancy for the first n points of points, in dimension s.
compute(double[], int) - Method in class umontreal.iro.lecuyer.discrepancy.DiscL2Star
Computes the traditional L2-star discrepancy for the set of n 1-dimensional points T, using formula above.
compute(double[][], int, int, double[]) - Method in class umontreal.iro.lecuyer.discrepancy.DiscL2Symmetric
 
compute(double[][], int, int) - Method in class umontreal.iro.lecuyer.discrepancy.DiscL2Symmetric
Computes the L2-symmetric discrepancy for the set of n s-dimensional points points, using formula.
compute(double[], int) - Method in class umontreal.iro.lecuyer.discrepancy.DiscL2Symmetric
Computes the L2-symmetric discrepancy for the set of n 1-dimensional points T, using formula.
compute(double[][], int, int, double[]) - Method in class umontreal.iro.lecuyer.discrepancy.DiscL2Unanchored
 
compute(double[][], int, int) - Method in class umontreal.iro.lecuyer.discrepancy.DiscL2Unanchored
Computes the L2-unanchored discrepancy for the set of n s-dimensional points points.
compute(double[], int) - Method in class umontreal.iro.lecuyer.discrepancy.DiscL2Unanchored
Computes the L2-unanchored discrepancy for the 1-dimensional set of n points T, using formula.
compute() - Method in class umontreal.iro.lecuyer.discrepancy.Discrepancy
Computes the discrepancy of all the points in maximal dimension (dimension of the points).
compute(int) - Method in class umontreal.iro.lecuyer.discrepancy.Discrepancy
Computes the discrepancy of all the points in dimension s.
compute(double[][], int, int, double[]) - Method in class umontreal.iro.lecuyer.discrepancy.Discrepancy
Computes the discrepancy of the first n points of points in dimension s with weights gamma.
compute(double[][], int, int) - Method in class umontreal.iro.lecuyer.discrepancy.Discrepancy
Computes the discrepancy of the first n points of points in dimension s with weights = 1.
compute(double[][]) - Method in class umontreal.iro.lecuyer.discrepancy.Discrepancy
Computes the discrepancy of all the points of points in maximum dimension.
compute(double[], int) - Method in class umontreal.iro.lecuyer.discrepancy.Discrepancy
Computes the discrepancy of the first n points of T in 1 dimension.
compute(double[]) - Method in class umontreal.iro.lecuyer.discrepancy.Discrepancy
Computes the discrepancy of all the points of T in 1 dimension.
compute(double[], int, double) - Method in class umontreal.iro.lecuyer.discrepancy.Discrepancy
Computes the discrepancy of the first n points of T in 1 dimension with weight gamma.
compute(PointSet, double[]) - Method in class umontreal.iro.lecuyer.discrepancy.Discrepancy
Computes the discrepancy of all the points in set in the same dimension as the point set and with weights gamma.
compute(PointSet) - Method in class umontreal.iro.lecuyer.discrepancy.Discrepancy
Computes the discrepancy of all the points in set in the same dimension as the point set.
compute(int, double[], int) - Method in class umontreal.iro.lecuyer.discrepancy.DiscrepancyContainer
Computes the discrepancies of the first n values contained in points and sets the values at index i.
compute(int, double[][], int, int) - Method in class umontreal.iro.lecuyer.discrepancy.DiscrepancyContainer
Computes the discrepancies of the first n values contained in points using the first s coordinates and sets the values at index i.
compute(double[][], int, int) - Method in class umontreal.iro.lecuyer.discrepancy.DiscShift1
Computes the discrepancy for the first n points of set points in dimension s.
compute(double[][], int, int, double[]) - Method in class umontreal.iro.lecuyer.discrepancy.DiscShift1
Computes the discrepancy in dimension s with γr = gamma[r-1].
compute(double[], int) - Method in class umontreal.iro.lecuyer.discrepancy.DiscShift1
Computes the discrepancy for the 1-dimensional set of n points T.
compute(double[][], int, int) - Method in class umontreal.iro.lecuyer.discrepancy.DiscShift1Lattice
Computes the discrepancy for the s-dimensional points of lattice points, containing n points.
compute(double[][], int, int, double[]) - Method in class umontreal.iro.lecuyer.discrepancy.DiscShift1Lattice
Computes the discrepancy for the s-dimensional points of lattice points, containing n points, with weights γr = gamma[r-1].
compute(double[], int) - Method in class umontreal.iro.lecuyer.discrepancy.DiscShift1Lattice
Computes the discrepancy for the 1-dimensional lattice of n points T.
compute(double[][], int, int) - Method in class umontreal.iro.lecuyer.discrepancy.DiscShift2
Computes the discrepancy for the first n points of set points in dimension s.
compute(double[][], int, int, double[]) - Method in class umontreal.iro.lecuyer.discrepancy.DiscShift2
Computes the discrepancy for the first n points of set points in dimension s and with weight γr = gamma[r-1].
compute(double[], int) - Method in class umontreal.iro.lecuyer.discrepancy.DiscShift2
Computes the discrepancy for the first n points of T in 1 dimension, with weight γ = 1.
compute(double[], int, double) - Method in class umontreal.iro.lecuyer.discrepancy.DiscShift2
Computes the discrepancy for the first n points of T in 1 dimension, with weight γ = gamma.
compute(double[][], int, int) - Method in class umontreal.iro.lecuyer.discrepancy.DiscShift2Lattice
Computes the discrepancy for the first n s-dimensional points of lattice points.
compute(double[][], int, int, double[]) - Method in class umontreal.iro.lecuyer.discrepancy.DiscShift2Lattice
Computes the discrepancy in dimension s with γr = gamma[r-1].
compute(double[], int) - Method in class umontreal.iro.lecuyer.discrepancy.DiscShift2Lattice
Computes the discrepancy with weight γ = 1 for the 1-dimensional lattice of n points T.
compute(double[], int, double) - Method in class umontreal.iro.lecuyer.discrepancy.DiscShift2Lattice
Computes the discrepancy with weight γ = gamma for the 1-dimensional lattice of n points T.
compute(double[][], int, int) - Method in class umontreal.iro.lecuyer.discrepancy.DiscShiftBaker1
Computes the discrepancy for the s-dimensional points of set points, containing n points.
compute(double[][], int, int, double[]) - Method in class umontreal.iro.lecuyer.discrepancy.DiscShiftBaker1
Computes the discrepancy for the first n points of points in dimension s and with weight γr = gamma[r-1].
compute(double[], int) - Method in class umontreal.iro.lecuyer.discrepancy.DiscShiftBaker1
Computes the discrepancy for the first n points of T in 1 dimension, with weight γ = 1.
compute(double[], int, double) - Method in class umontreal.iro.lecuyer.discrepancy.DiscShiftBaker1
Computes the discrepancy for the first n points of T in 1 dimension, with weight γ = gamma.
compute(double[][], int, int) - Method in class umontreal.iro.lecuyer.discrepancy.DiscShiftBaker1Lattice
Computes the discrepancy for the s-dimensional points of lattice points, containing n points.
compute(double[][], int, int, double[]) - Method in class umontreal.iro.lecuyer.discrepancy.DiscShiftBaker1Lattice
Computes the discrepancy for the s-dimensional points of lattice points, containing n points, with weights γr = gamma[r-1].
compute(double[], int) - Method in class umontreal.iro.lecuyer.discrepancy.DiscShiftBaker1Lattice
Computes the discrepancy with weight γ = 1 for the 1-dimensional lattice of n points T.
compute(double[], int, double) - Method in class umontreal.iro.lecuyer.discrepancy.DiscShiftBaker1Lattice
Computes the discrepancy for the 1-dimensional lattice of n points T, with weight γ = gamma.
compute(double[][], int, int) - Method in class umontreal.iro.lecuyer.discrepancy.Palpha
Computes the discrepancy for the s-dimensional points of lattice points, containing n points.
compute(double[][], int, int, double[]) - Method in class umontreal.iro.lecuyer.discrepancy.Palpha
Computes the discrepancy for the s-dimensional points of lattice points, containing n points, with weights βj = beta[j].
compute(double[][], int, int, int) - Method in class umontreal.iro.lecuyer.discrepancy.Palpha
Computes the discrepancy for the s-dimensional points of lattice points, containing n points, with all weights βj = 1 and α = alpha.
compute(double[][], int, int, double[], int) - Method in class umontreal.iro.lecuyer.discrepancy.Palpha
Computes the discrepancy for the s-dimensional points of lattice points, containing n points, with weights βj = beta[j] and with α = alpha.
computeCorr() - Method in class umontreal.iro.lecuyer.probdistmulti.norta.NI1
Computes and returns the correlation ρZ using the algorithm NI1.
computeCorr() - Method in class umontreal.iro.lecuyer.probdistmulti.norta.NI2a
Computes and returns the correlation ρZ using the algorithm NI2a.
computeCorr() - Method in class umontreal.iro.lecuyer.probdistmulti.norta.NI2b
Computes and returns the correlation ρZ using the algorithm NI2b.
computeCorr() - Method in class umontreal.iro.lecuyer.probdistmulti.norta.NI3
Computes and returns the correlation ρZ using the algorithm NI3.
computeCorr() - Method in class umontreal.iro.lecuyer.probdistmulti.norta.NortaInitDisc
This method computes and returns the correlation ρZ.
computeDensity(EmpiricalDist, ContinuousDistribution, double, double[]) - Static method in class umontreal.iro.lecuyer.gof.KernelDensity
Given the empirical distribution dist, this method computes the kernel density estimate at each of the m points Y[j], j = 0, 1,…,(m - 1), where m is the length of Y, the kernel is kern.density(x), and the bandwidth is h.
computeDensity(EmpiricalDist, ContinuousDistribution, double[]) - Static method in class umontreal.iro.lecuyer.gof.KernelDensity
Similar to method computeDensity , but the bandwidth h is obtained from the method KernelDensityGen.getBaseBandwidth(dist) in package randvar.
computeParams() - Method in class umontreal.iro.lecuyer.probdistmulti.norta.NortaInitDisc
This method computes the following inputs of the two marginal distributions: m1 and m2, mu1, mu2, sd1, sd2, and the vectors p1, p2, z1 and z2.
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.
confidenceIntervalDelta(double, double[]) - Method in class umontreal.iro.lecuyer.stat.FunctionOfMultipleMeansTally
Computes a confidence interval with confidence level level on ν = g(μ), using the delta theorem.
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.
confidenceIntervalStudentWithCV(int, double, double[]) - Method in class umontreal.iro.lecuyer.stat.list.lincv.ListOfTalliesWithCV
Computes a confidence interval for the ith component of XC.
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.
ConstantDist - Class in umontreal.iro.lecuyer.probdist
Represents a constant discrete distribution taking a single real value with probability 1.
ConstantDist(double) - Constructor for class umontreal.iro.lecuyer.probdist.ConstantDist
Constructs a new constant distribution with probability 1 at c.
ConstantGen - Class in umontreal.iro.lecuyer.randvar
This class implements a random variate generator that returns a constant value.
ConstantGen(double) - Constructor for class umontreal.iro.lecuyer.randvar.ConstantGen
Constructs a new constant generator returning the given value val.
ConstantIntDist - Class in umontreal.iro.lecuyer.probdist
Represents a constant discrete distribution taking a single integer value with probability 1.
ConstantIntDist(int) - Constructor for class umontreal.iro.lecuyer.probdist.ConstantIntDist
Constructs a new constant distribution with probability 1 at c.
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 base 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.
copy(double[][], double[][]) - Static method in class umontreal.iro.lecuyer.util.DMatrix
Copies the matrix M into R.
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.
correlationC(DoubleMatrix2D) - Method in class umontreal.iro.lecuyer.stat.list.lincv.ListOfTalliesWithCV
Fills c with the sample correlation matrix of C.
correlationCX(DoubleMatrix2D) - Method in class umontreal.iro.lecuyer.stat.list.lincv.ListOfTalliesWithCV
Fills c with the sample correlation matrix of C and X.
correlationX(DoubleMatrix2D) - Method in class umontreal.iro.lecuyer.stat.list.lincv.ListOfTalliesWithCV
Fills c with the sample correlation matrix of X.
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.
covarianceC(DoubleMatrix2D) - Method in class umontreal.iro.lecuyer.stat.list.lincv.ListOfTalliesWithCV
Fills c with the sample covariance matrix of C.
covarianceCX(DoubleMatrix2D) - Method in class umontreal.iro.lecuyer.stat.list.lincv.ListOfTalliesWithCV
Fills c with the sample covariance matrix of C and X.
covarianceWithCV(DoubleMatrix2D) - Method in class umontreal.iro.lecuyer.stat.list.lincv.ListOfTalliesWithCV
Computes the sample covariance of XC by replacing ΣX, ΣC, and ΣCX with the corresponding matrices of empirical covariances.
covarianceWithCV(int, int) - Method in class umontreal.iro.lecuyer.stat.list.lincv.ListOfTalliesWithCV
Computes the covariance between component i and j of XC.
covarianceX(DoubleMatrix2D) - Method in class umontreal.iro.lecuyer.stat.list.lincv.ListOfTalliesWithCV
Fills c with the sample covariance matrix of X.
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.
create(MultivariateFunction, int, int) - Static method in class umontreal.iro.lecuyer.stat.list.ListOfFunctionOfMultipleMeansTallies
This factory method constructs and returns a list of tallies with size instances of
FunctionOfMultipleMeansTally.
create(MultivariateFunction, int, int, int) - Static method in class umontreal.iro.lecuyer.stat.matrix.MatrixOfFunctionOfMultipleMeansTallies
This factory method constructs and returns a matrix of function of multiple means tallies with numRows rows, numColumns columns, and filled with instances of
FunctionOfMultipleMeansTally.
createApproxBSpline(double[], double[], int, int) - Static method in class umontreal.iro.lecuyer.functionfit.BSpline
Returns a B-spline curve of degree degree smoothing (xi, yi), for i = 0,…, n points.
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.
createInterpBSpline(double[], double[], int) - Static method in class umontreal.iro.lecuyer.functionfit.BSpline
Returns a B-spline curve of degree degree interpolating the (xi, yi) points.
createWithTally(int, int) - Static method in class umontreal.iro.lecuyer.stat.list.lincv.ListOfTalliesWithCV
This factory method constructs and returns a list of tallies with p+q new instances of Tally, q being the number of control variables.
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.
createWithTally(int, int) - Static method in class umontreal.iro.lecuyer.stat.matrix.MatrixOfTallies
This factory method constructs and returns a matrix of tallies with numRows rows, numColumns columns, and filled with new instances of Tally.
createWithTallyStore(int, int) - Static method in class umontreal.iro.lecuyer.stat.list.lincv.ListOfTalliesWithCV
This factory method constructs and returns a list of tallies with p+q new instances of TallyStore, q being the number of control variables.
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.
createWithTallyStore(int, int) - Static method in class umontreal.iro.lecuyer.stat.matrix.MatrixOfTallies
This factory method constructs and returns a matrix of tallies with numRows rows, numColumns columns, and filled with new 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.
CycleBasedLFSR - Class in umontreal.iro.lecuyer.hups
LFSR generators produce numbers by generating a sequence of bits from a linear recurrence modulo 2, and forming fractional numbers by taking blocks of successive bits.
CycleBasedLFSR(int, int, int[]) - Constructor for class umontreal.iro.lecuyer.hups.CycleBasedLFSR
Constructs a point set based on a polynomial in base 2 with 2k1 points.
CycleBasedLFSR(int, int, int, int, int[], int[]) - Constructor for class umontreal.iro.lecuyer.hups.CycleBasedLFSR
Constructs a point set based on a combination of two polynomials in base 2 with 2k1+k2 points.
CycleBasedLFSR(String, int) - Constructor for class umontreal.iro.lecuyer.hups.CycleBasedLFSR
Constructs a point set after reading its parameters from file filename; the parameters associated with number no of filename corresponds to the no-th polynomial.
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.
DataField - Class in umontreal.iro.lecuyer.util.io
This class represents a data field from a file read by an instance of a class implementing DataReader.
DataField(String, Object) - Constructor for class umontreal.iro.lecuyer.util.io.DataField
Constructor.
DataField(String, Object, int) - Constructor for class umontreal.iro.lecuyer.util.io.DataField
Constructor.
dataPending() - Method in class umontreal.iro.lecuyer.util.io.BinaryDataReader
Returns true if there remains data to be read.
dataPending() - Method in interface umontreal.iro.lecuyer.util.io.DataReader
Returns true if there remains data to be read.
DataReader - Interface in umontreal.iro.lecuyer.util.io
Data reader interface.
DataWriter - Interface in umontreal.iro.lecuyer.util.io
Data writer interface.
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 = VΛVt.
decompPCA(DoubleMatrix2D) - Static method in class umontreal.iro.lecuyer.randvarmulti.MultinormalPCAGen
Computes the decomposition sigma = Σ = VΛVt.
decompPCA(double[][]) - Method in class umontreal.iro.lecuyer.stochprocess.BrownianMotionPCA
 
decPrec - Variable in class umontreal.iro.lecuyer.probdist.ContinuousDistribution
Deprecated. 
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.
DEFAULT_COLUMN_SEPARATOR - Variable in class umontreal.iro.lecuyer.util.io.TextDataWriter
Default value for the column separator.
DEFAULT_HEADER_PREFIX - Variable in class umontreal.iro.lecuyer.util.io.TextDataWriter
Default value for the header prefix.
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
Deprecated.  
density(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.ExtremeValueDist
Deprecated. 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 n1 and n2 degrees of freedom, evaluated at x.
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.FrechetDist
 
density(double, double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.FrechetDist
Computes and returns the density function.
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 at x.
density(double) - Method in class umontreal.iro.lecuyer.probdist.GumbelDist
 
density(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.GumbelDist
Computes and returns 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.HypoExponentialDist
 
density(double[], double) - Static method in class umontreal.iro.lecuyer.probdist.HypoExponentialDist
Computes the density function f (x), with λi = lambda[i - 1], i = 1,…, k.
density(double) - Method in class umontreal.iro.lecuyer.probdist.HypoExponentialDistEqual
 
density(int, int, double, double) - Static method in class umontreal.iro.lecuyer.probdist.HypoExponentialDistEqual
Computes the density function f (x), with the same arguments as in the constructor.
density(double) - Method in class umontreal.iro.lecuyer.probdist.HypoExponentialDistQuick
 
density(double[], double) - Static method in class umontreal.iro.lecuyer.probdist.HypoExponentialDistQuick
Computes the density function f (x), with λi = lambda[i - 1], i = 1,…, k.
density(double) - Method in class umontreal.iro.lecuyer.probdist.InverseDistFromDensity
Computes the probability density at x.
density(double) - Method in class umontreal.iro.lecuyer.probdist.InverseGammaDist
 
density(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.InverseGammaDist
Computes the density function of the inverse gamma distribution with shape parameter α and scale parameter β.
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
Returns the density function.
density(double) - Method in class umontreal.iro.lecuyer.probdist.JohnsonSLDist
 
density(double, double, double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.JohnsonSLDist
Returns the density function f (x).
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
Returns 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
Deprecated.  
density(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.Pearson5Dist
Deprecated. 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 of 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
 
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).
deriv(double) - Method in class umontreal.iro.lecuyer.probdistmulti.norta.NortaInitDisc
Computes the first derivative of function gr for each correlation.
derivative(double) - Method in class umontreal.iro.lecuyer.functionfit.BSpline
 
derivative(double, int) - Method in class umontreal.iro.lecuyer.functionfit.BSpline
 
derivative(double) - Method in class umontreal.iro.lecuyer.functionfit.SmoothingCubicSpline
Evaluates and returns the value of the first derivative of the spline at z.
derivative(double, int) - Method in class umontreal.iro.lecuyer.functionfit.SmoothingCubicSpline
Evaluates and returns the value of the n-th derivative of the spline at z.
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.
derivativeBSpline() - Method in class umontreal.iro.lecuyer.functionfit.BSpline
Returns the derivative B-spline object of the current variable.
derivativeBSpline(int) - Method in class umontreal.iro.lecuyer.functionfit.BSpline
Returns the ith derivative B-spline object of the current variable; i must be less than the degree of the original B-spline.
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
 
dimension() - Method in class umontreal.iro.lecuyer.markovchain.MarkovChainComparable
Returns the dimension of the state.
dimension() - Method in class umontreal.iro.lecuyer.markovchain.MarkovChainDouble
 
dimension() - Method in class umontreal.iro.lecuyer.util.BatchSort
 
dimension() - Method in interface umontreal.iro.lecuyer.util.MultiDimComparable
This method returns the number of dimensions for which the method compareTo can be called for this object.
dimension() - Method in interface umontreal.iro.lecuyer.util.MultiDimSort
Returns the greatest dimension used in the sort.
dimension() - Method in class umontreal.iro.lecuyer.util.OneDimSort
Returns the dimension of the states to be sorted.
dimension() - Method in class umontreal.iro.lecuyer.util.SplitSort
Maximum dimension used for the sort.
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
Deprecated. 
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.
DiscL2Hickernell - Class in umontreal.iro.lecuyer.discrepancy
This class computes the Hickernell L2-star discrepancy in for a point set.
DiscL2Hickernell(double[][], int, int) - Constructor for class umontreal.iro.lecuyer.discrepancy.DiscL2Hickernell
Constructor with the n points points[i] in dimension s.
DiscL2Hickernell(int, int) - Constructor for class umontreal.iro.lecuyer.discrepancy.DiscL2Hickernell
Constructor with n points in dimension s.
DiscL2Hickernell(PointSet) - Constructor for class umontreal.iro.lecuyer.discrepancy.DiscL2Hickernell
Constructor with the point set set.
DiscL2Hickernell() - Constructor for class umontreal.iro.lecuyer.discrepancy.DiscL2Hickernell
Empty constructor.
DiscL2Star - Class in umontreal.iro.lecuyer.discrepancy
This class computes the traditional L2-star discrepancy D2*(P) for a set of n points P.
DiscL2Star(double[][], int, int) - Constructor for class umontreal.iro.lecuyer.discrepancy.DiscL2Star
Constructor with the n points points[i] in dimension s.
DiscL2Star(int, int) - Constructor for class umontreal.iro.lecuyer.discrepancy.DiscL2Star
Constructor with n points in dimension s.
DiscL2Star(PointSet) - Constructor for class umontreal.iro.lecuyer.discrepancy.DiscL2Star
Constructor with the point set set.
DiscL2Star() - Constructor for class umontreal.iro.lecuyer.discrepancy.DiscL2Star
Empty constructor.
DiscL2Symmetric - Class in umontreal.iro.lecuyer.discrepancy
COMPLÉTER LA DOC ICI.
DiscL2Symmetric(double[][], int, int) - Constructor for class umontreal.iro.lecuyer.discrepancy.DiscL2Symmetric
Constructor with the n points points[i] in s dimensions.
DiscL2Symmetric(int, int) - Constructor for class umontreal.iro.lecuyer.discrepancy.DiscL2Symmetric
Constructor with n points in dimension s.
DiscL2Symmetric(PointSet) - Constructor for class umontreal.iro.lecuyer.discrepancy.DiscL2Symmetric
Constructor with the point set set.
DiscL2Symmetric() - Constructor for class umontreal.iro.lecuyer.discrepancy.DiscL2Symmetric
Empty constructor.
DiscL2Unanchored - Class in umontreal.iro.lecuyer.discrepancy
A discrepancy is said to be reflection-invariant if it has the same value when the points are reflected through any plane xj = 1/2, passing through the center of the unit hypercube, i.e.
DiscL2Unanchored(double[][], int, int) - Constructor for class umontreal.iro.lecuyer.discrepancy.DiscL2Unanchored
Constructor with the n points points[i] in s dimensions.
DiscL2Unanchored(int, int) - Constructor for class umontreal.iro.lecuyer.discrepancy.DiscL2Unanchored
Constructor with n points in dimension s.
DiscL2Unanchored(PointSet) - Constructor for class umontreal.iro.lecuyer.discrepancy.DiscL2Unanchored
Constructor with the point set set.
DiscL2Unanchored() - Constructor for class umontreal.iro.lecuyer.discrepancy.DiscL2Unanchored
Empty constructor.
Discrepancy - Class in umontreal.iro.lecuyer.discrepancy
This abstract class is the base class of all discrepancy classes.
Discrepancy(double[][], int, int) - Constructor for class umontreal.iro.lecuyer.discrepancy.Discrepancy
Constructor with the n points points[i] in s dimensions.
Discrepancy(double[][], int, int, double[]) - Constructor for class umontreal.iro.lecuyer.discrepancy.Discrepancy
Constructor with the n points points[i] in s dimensions and the s weight factors gamma[j], j = 0, 1,…,(s - 1).
Discrepancy(int, int, double[]) - Constructor for class umontreal.iro.lecuyer.discrepancy.Discrepancy
The number of points is n, the dimension s, and the s weight factors are gamma[j], j = 0, 1,…,(s - 1).
Discrepancy(PointSet) - Constructor for class umontreal.iro.lecuyer.discrepancy.Discrepancy
Constructor with the point set set.
Discrepancy() - Constructor for class umontreal.iro.lecuyer.discrepancy.Discrepancy
Empty constructor.
DiscrepancyContainer - Class in umontreal.iro.lecuyer.discrepancy
This class is used to compute, store and display discrepancies.
DiscrepancyContainer(Discrepancy[]) - Constructor for class umontreal.iro.lecuyer.discrepancy.DiscrepancyContainer
Creates a DiscrepancyContainer for the given discrepancies.
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
This class implements discrete distributions over a finite set of real numbers (also over integers as a particular case).
DiscreteDistribution(double[], double[], int) - Constructor for class umontreal.iro.lecuyer.probdist.DiscreteDistribution
Constructs a discrete distribution over the n values contained in array values, with probabilities given in array prob.
DiscreteDistribution(int[], double[], int) - Constructor for class umontreal.iro.lecuyer.probdist.DiscreteDistribution
Similar to DiscreteDistribution(double[], double[], int).
DiscreteDistribution(double[]) - Constructor for class umontreal.iro.lecuyer.probdist.DiscreteDistribution
Deprecated. 
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
 
DiscShift1 - Class in umontreal.iro.lecuyer.discrepancy
This class computes the discrepancy for randomly shifted points of a set P.
DiscShift1(double[][], int, int) - Constructor for class umontreal.iro.lecuyer.discrepancy.DiscShift1
Constructor with the n points points[i] in s dimensions and with all weights γr = 1.
DiscShift1(double[][], int, int, double[]) - Constructor for class umontreal.iro.lecuyer.discrepancy.DiscShift1
Constructor with the n points points[i] in s dimensions, and with the weights γr = gamma[r-1], r = 1,…, s.
DiscShift1(int, int, double[]) - Constructor for class umontreal.iro.lecuyer.discrepancy.DiscShift1
The number of points is n, the dimension s, and the s weight factors are gamma[j], j = 0, 1,…,(s - 1).
DiscShift1(PointSet) - Constructor for class umontreal.iro.lecuyer.discrepancy.DiscShift1
Constructor with the point set set.
DiscShift1() - Constructor for class umontreal.iro.lecuyer.discrepancy.DiscShift1
Empty constructor.
DiscShift1Lattice - Class in umontreal.iro.lecuyer.discrepancy
This class computes the same discrepancy for randomly shifted points of a set L as given in eq.
DiscShift1Lattice(double[][], int, int) - Constructor for class umontreal.iro.lecuyer.discrepancy.DiscShift1Lattice
Constructor with the n points points[i] in s dimensions with all weights γr = 1.
DiscShift1Lattice(double[][], int, int, double[]) - Constructor for class umontreal.iro.lecuyer.discrepancy.DiscShift1Lattice
Constructor with the n points points[i] in s dimensions with the weights γr = gamma[r-1], r = 1,…, s.
DiscShift1Lattice(int, int, double[]) - Constructor for class umontreal.iro.lecuyer.discrepancy.DiscShift1Lattice
The number of points is n, the dimension s, and the s weight factors are gamma[j], j = 0, 1,…,(s - 1).
DiscShift1Lattice(Rank1Lattice) - Constructor for class umontreal.iro.lecuyer.discrepancy.DiscShift1Lattice
Constructor with the lattice set.
DiscShift1Lattice() - Constructor for class umontreal.iro.lecuyer.discrepancy.DiscShift1Lattice
Empty constructor.
DiscShift2 - Class in umontreal.iro.lecuyer.discrepancy
This class computes the discrepancy in for the randomly shifted points of a set P.
DiscShift2(double[][], int, int) - Constructor for class umontreal.iro.lecuyer.discrepancy.DiscShift2
Constructor with the n points Pi = points[i] in dimension s, with all weights γj = 1.
DiscShift2(double[][], int, int, double[]) - Constructor for class umontreal.iro.lecuyer.discrepancy.DiscShift2
Constructor with the n points Pi = points[i] in dimension s, with the weights γj = gamma[j-1], j = 1,…, s.
DiscShift2(int, int, double[]) - Constructor for class umontreal.iro.lecuyer.discrepancy.DiscShift2
The number of points is n, the dimension s, and the s weight factors are gamma[j], j = 0, 1,…,(s - 1).
DiscShift2(PointSet) - Constructor for class umontreal.iro.lecuyer.discrepancy.DiscShift2
Constructor with the point set set.
DiscShift2() - Constructor for class umontreal.iro.lecuyer.discrepancy.DiscShift2
Empty constructor.
DiscShift2Lattice - Class in umontreal.iro.lecuyer.discrepancy
This class computes the same discrepancy for randomly shifted points of a set L as given in eq.
DiscShift2Lattice(double[][], int, int) - Constructor for class umontreal.iro.lecuyer.discrepancy.DiscShift2Lattice
Constructor with the n points points[i] in dimension s with all weights γr = 1.
DiscShift2Lattice(double[][], int, int, double[]) - Constructor for class umontreal.iro.lecuyer.discrepancy.DiscShift2Lattice
Constructor with the n points points[i] in dimension s with the weights γr = gamma[r-1], r = 1,…, s.
DiscShift2Lattice(int, int, double[]) - Constructor for class umontreal.iro.lecuyer.discrepancy.DiscShift2Lattice
The number of points is n, the dimension s, and the s weight factors are gamma[j], j = 0, 1,…,(s - 1).
DiscShift2Lattice(Rank1Lattice) - Constructor for class umontreal.iro.lecuyer.discrepancy.DiscShift2Lattice
Constructor with the lattice set.
DiscShift2Lattice() - Constructor for class umontreal.iro.lecuyer.discrepancy.DiscShift2Lattice
Empty constructor.
DiscShiftBaker1 - Class in umontreal.iro.lecuyer.discrepancy
This class computes the discrepancy for randomly shifted, then baker folded points of a set P.
DiscShiftBaker1(double[][], int, int) - Constructor for class umontreal.iro.lecuyer.discrepancy.DiscShiftBaker1
Constructor with the n points points[i] in s dimensions, with all the weights γr = 1.
DiscShiftBaker1(double[][], int, int, double[]) - Constructor for class umontreal.iro.lecuyer.discrepancy.DiscShiftBaker1
Constructor with the n points points[i] in s dimensions, with weights γr = gamma[r-1].
DiscShiftBaker1(int, int, double[]) - Constructor for class umontreal.iro.lecuyer.discrepancy.DiscShiftBaker1
The number of points is n, the dimension s, and the s weight factors are gamma[r], r = 0, 1,…,(s - 1).
DiscShiftBaker1(PointSet) - Constructor for class umontreal.iro.lecuyer.discrepancy.DiscShiftBaker1
Constructor with the point set set.
DiscShiftBaker1() - Constructor for class umontreal.iro.lecuyer.discrepancy.DiscShiftBaker1
Empty constructor.
DiscShiftBaker1Lattice - Class in umontreal.iro.lecuyer.discrepancy
This class computes the same discrepancy in for the randomly shifted points of a set L as given in eq.
DiscShiftBaker1Lattice(double[][], int, int) - Constructor for class umontreal.iro.lecuyer.discrepancy.DiscShiftBaker1Lattice
Constructor with the n points points[i] in s dimensions, and with all weights γr = 1.
DiscShiftBaker1Lattice(double[][], int, int, double[]) - Constructor for class umontreal.iro.lecuyer.discrepancy.DiscShiftBaker1Lattice
Constructor with the n points points[i] in s dimensions, with weights γr = gamma[r-1].
DiscShiftBaker1Lattice(int, int, double[]) - Constructor for class umontreal.iro.lecuyer.discrepancy.DiscShiftBaker1Lattice
The number of points is n, the dimension s, and the s weight factors are gamma[r], r = 0, 1,…,(s - 1).
DiscShiftBaker1Lattice(Rank1Lattice) - Constructor for class umontreal.iro.lecuyer.discrepancy.DiscShiftBaker1Lattice
Constructor with the point set set.
DiscShiftBaker1Lattice() - Constructor for class umontreal.iro.lecuyer.discrepancy.DiscShiftBaker1Lattice
Empty constructor.
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.
DoubleArrayComparator - Class in umontreal.iro.lecuyer.util
An implementation of Comparator which compares two double arrays by comparing their i-th element, where i is given in the constructor.
DoubleArrayComparator(int) - Constructor for class umontreal.iro.lecuyer.util.DoubleArrayComparator
Constructs a comparator, where i is the index used for the comparisons.
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.
drawVerticalLine(double, String, double, boolean) - Method in class umontreal.iro.lecuyer.charts.XYChart
Draws a vertical line on the chart at x-coordinate x.
dropFirstRealBatches(int) - Method in class umontreal.iro.lecuyer.simexp.BatchMeansSim
Drops the n first real batches to save memory.
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.
EmptyRandomization - Class in umontreal.iro.lecuyer.hups
This class implements an empty PointSetRandomization.
EmptyRandomization() - Constructor for class umontreal.iro.lecuyer.hups.EmptyRandomization
 
enableAutoCompletion() - Method in class umontreal.iro.lecuyer.charts.XYListSeriesCollection
Enables the auto completion option.
enableCustomLabels() - Method in class umontreal.iro.lecuyer.charts.Axis
Deprecated. 
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 permutations: from now on, the digits will not be 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.
erfcInv(double) - Static method in class umontreal.iro.lecuyer.util.Num
Returns the value of erfc -1(u), the inverse of the complementary error function.
erfInv(double) - Static method in class umontreal.iro.lecuyer.util.Num
Returns the value of erf -1(u), the inverse of the error function.
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.
estimateBeta() - Method in class umontreal.iro.lecuyer.stat.list.lincv.FunctionOfMultipleMeansTallyWithCV
Uses the sample averages and covariances obtained from the internal list of tallies to estimate the βf vector minimizing the variance of h(bar(X)n, C).
estimateBeta() - Method in class umontreal.iro.lecuyer.stat.list.lincv.ListOfTalliesWithCV
Estimates the β* matrix from the observations currently in this list of tallies.
estimateBetaFromMatrix(DoubleMatrix2D) - Method in class umontreal.iro.lecuyer.stat.list.lincv.FunctionOfMultipleMeansTallyWithCV
Multiples the given q×p matrix by the gradient g(bar(X)n) to get an estimate of the βf* vector minimizing the variance.
EULER - Static variable in class umontreal.iro.lecuyer.util.Num
The Euler-Mascheroni constant.
evalCheby(double[], int, double) - Static method in class umontreal.iro.lecuyer.util.Num
Evaluates a series of Chebyshev polynomials Tj at x over the basic interval [- 1,  1].
evalChebyStar(double[], int, double) - Static method in class umontreal.iro.lecuyer.util.Num
Evaluates a series of shifted Chebyshev polynomials Tj* at x over the basic interval [0,  1].
evalPoly(int, double[], double[], double) - Static method in class umontreal.iro.lecuyer.util.Misc
Given n, X and C as described in interpol(n, X, Y, C), this function returns the value of the interpolating polynomial P(z) evaluated at z (see eq.
evalPoly(double[], int, double) - Static method in class umontreal.iro.lecuyer.util.Misc
.
evaluate(double) - Method in class umontreal.iro.lecuyer.functionfit.BSpline
 
evaluate(double) - Method in class umontreal.iro.lecuyer.functionfit.SmoothingCubicSpline
Evaluates and returns the value of the spline at z.
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 interface umontreal.iro.lecuyer.functions.MultiFunction
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
 
evalX(double) - Method in class umontreal.iro.lecuyer.functionfit.BSpline
 
evalY(double) - Method in class umontreal.iro.lecuyer.functionfit.BSpline
 
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.
exhaust(int) - Method in class umontreal.iro.lecuyer.discrepancy.Searcher
Exhaustive search to find the lattice with the best (the smallest) discrepancy in dimension s.
exhaust(int) - Method in class umontreal.iro.lecuyer.discrepancy.SearcherCBC
Exhaustive CBC search to find the lattice with the best (the smallest) discrepancy in dimension s.
exhaust(int) - Method in class umontreal.iro.lecuyer.discrepancy.SearcherKorobov
Exhaustive search to find the best Korobov lattice (i.e.
exhaustPrime(int) - Method in class umontreal.iro.lecuyer.discrepancy.Searcher
Similar to exhaust(s), except that only values of aj relatively prime to n are considered.
exhaustPrime(int) - Method in class umontreal.iro.lecuyer.discrepancy.SearcherCBC
Similar to exhaust(s), except that only values of aj relatively prime to n are considered.
exhaustPrime(int) - Method in class umontreal.iro.lecuyer.discrepancy.SearcherKorobov
Similar to exhaust(s), except that only values of a relatively prime to n are considered.
exp(double[][]) - Static method in class umontreal.iro.lecuyer.util.DMatrix
Similar to exp(A).
exp(DoubleMatrix2D) - Static method in class umontreal.iro.lecuyer.util.DMatrix
Returns eA, the exponential of the square matrix A.
expBesselK1(double, double) - Static method in class umontreal.iro.lecuyer.util.Num
Returns the value of exK1(y), where K1 is the modified Bessel function of the second kind of order 1.
expBidiagonal(DoubleMatrix2D) - Static method in class umontreal.iro.lecuyer.util.DMatrix
Returns eA, the exponential of the bidiagonal square matrix A.
expBidiagonal(DoubleMatrix2D, DoubleMatrix1D) - Static method in class umontreal.iro.lecuyer.util.DMatrix
Computes c = eAb, where eA is the exponential of the bidiagonal square matrix A.
expmiBidiagonal(DoubleMatrix2D) - Static method in class umontreal.iro.lecuyer.util.DMatrix
Computes eA - I, where eA is the exponential of the bidiagonal square matrix A.
expmiBidiagonal(DoubleMatrix2D, DoubleMatrix1D) - Static method in class umontreal.iro.lecuyer.util.DMatrix
Computes c = (eA - I)b, where eA is the exponential of the bidiagonal square matrix A.
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.
ExponentialInverseFromDensityGen - Class in umontreal.iro.lecuyer.randvar
This class implements exponential random variate generators using numerical inversion of the exponential density as described in.
ExponentialInverseFromDensityGen(RandomStream, double, double, int) - Constructor for class umontreal.iro.lecuyer.randvar.ExponentialInverseFromDensityGen
Creates an exponential random variate generator with parameter λ = lambda, using stream stream.
ExponentialInverseFromDensityGen(RandomStream, ExponentialDist, double, int) - Constructor for class umontreal.iro.lecuyer.randvar.ExponentialInverseFromDensityGen
Similar to the above constructor, with the exponential distribution dist.
ExponentialInverseFromDensityGen(RandomStream, InverseDistFromDensity) - Constructor for class umontreal.iro.lecuyer.randvar.ExponentialInverseFromDensityGen
Creates a new exponential generator using the exponential distribution dist and stream stream.
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
Deprecated. 
ExtremeValueDist() - Constructor for class umontreal.iro.lecuyer.probdist.ExtremeValueDist
Deprecated. THIS CLASS HAS BEEN REPLACED BY GumbelDist.
ExtremeValueDist(double, double) - Constructor for class umontreal.iro.lecuyer.probdist.ExtremeValueDist
Deprecated. THIS CLASS HAS BEEN REPLACED BY GumbelDist.
ExtremeValueGen - Class in umontreal.iro.lecuyer.randvar
Deprecated. 
ExtremeValueGen(RandomStream, double, double) - Constructor for class umontreal.iro.lecuyer.randvar.ExtremeValueGen
Deprecated. 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
Deprecated. 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
Deprecated. 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.
factoPow(int) - Static method in class umontreal.iro.lecuyer.util.Num
Returns the value of factorial(n)/nn.
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
This class is similar to FDist, except that it provides static methods to compute or approximate the complementary distribution function of X, which we define as bar(F)(x) = P[X >= x], instead of F(x) = P[X <= x].
FDist - Class in umontreal.iro.lecuyer.gof
This class provides methods to compute (or approximate) the distribution functions of special 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 n1 and n2 degrees of freedom, where n1 and n2 are positive integers.
FisherFDist(int, int) - Constructor for class umontreal.iro.lecuyer.probdist.FisherFDist
Constructs a Fisher F distribution with n1 and n2 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.
FNoncentralGen - Class in umontreal.iro.lecuyer.randvar
This class implements random variate generators for the noncentral F-distribution.
FNoncentralGen(ChiSquareNoncentralGen, ChiSquareGen) - Constructor for class umontreal.iro.lecuyer.randvar.FNoncentralGen
Creates a noncentral-F random variate generator using noncentral chi-square generator ncgen and chi-square generator cgen.
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
Deprecated. 
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.
format(int[][], int, int, int, int, int, int, String) - Static method in class umontreal.iro.lecuyer.util.TableFormat
Similar to the previous method, but for a matrix of int's.
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.
formatBase(int, int, int, double) - Static method in class umontreal.iro.lecuyer.util.PrintfFormat
Converts x to a String representation in base b using formatting similar to the f methods.
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.
formatCIDelta(double, int) - Method in class umontreal.iro.lecuyer.stat.FunctionOfMultipleMeansTally
Similar to confidenceIntervalDelta, but returns the confidence interval in a formatted string of the form ``95% confidence interval for function of means: (32.431, 32.487)'', using d decimal digits of accuracy.
formatCIDelta(double) - Method in class umontreal.iro.lecuyer.stat.FunctionOfMultipleMeansTally
Same as formatCIDelta (level, 3).
formatCINormal(double, int) - Method in class umontreal.iro.lecuyer.stat.Tally
Similar to confidenceIntervalNormal.
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
Similar to confidenceIntervalStudent.
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
Similar to confidenceIntervalVarianceChi2.
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 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 ``p-value of test : '', then calls formatp0 to print p, and adds the marker ``****'' if p is considered suspect (uses the environment variable SUSPECTP 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.discrepancy.Discrepancy
Returns all the points of this class.
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(n, d) with n and d equal to the number of points and the dimension of this object, 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.
formatPoints(PointSetIterator) - Method in class umontreal.iro.lecuyer.hups.PointSet
Same as invoking formatPoints(iter, n, d) with n and d equal to the number of points and the dimension, respectively.
formatPoints(PointSetIterator, int, int) - Method in class umontreal.iro.lecuyer.hups.PointSet
Same as invoking formatPoints(n, d), but prints the points by calling iter repeatedly.
formatPointsBase(int) - Method in class umontreal.iro.lecuyer.hups.PointSet
Similar to formatPoints(), but the points coordinates are printed in base b.
formatPointsBase(int, int, int) - Method in class umontreal.iro.lecuyer.hups.PointSet
Similar to formatPoints(n, d), but the points coordinates are printed in base b.
formatPointsBase(PointSetIterator, int) - Method in class umontreal.iro.lecuyer.hups.PointSet
Similar to formatPoints(iter), but the points coordinates are printed in base b.
formatPointsBase(PointSetIterator, int, int, int) - Method in class umontreal.iro.lecuyer.hups.PointSet
Similar to formatPoints(iter, n, d), but the points coordinates are printed in base b.
formatPointsNumbered() - Method in class umontreal.iro.lecuyer.hups.PointSet
Same as invoking formatPointsNumbered(n, d) with n and d equal to the number of points and the dimension, respectively.
formatPointsNumbered(int, int) - Method in class umontreal.iro.lecuyer.hups.PointSet
Same as invoking formatPoints(n,d), except that the points are numbered.
formatResults(Tally) - Method in class umontreal.iro.lecuyer.markovchain.MarkovChain
Returns a string containing the mean, the variance, and a 90% confidence interval for stat.
formatResultsRQMC(Tally, int) - Method in class umontreal.iro.lecuyer.markovchain.MarkovChain
Returns a string containing the mean, the variance multiplied by numPoints, and a 90% confidence interval for stat.
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.
FrechetDist - Class in umontreal.iro.lecuyer.probdist
Extends the class ContinuousDistribution for the Fréchet distribution, with location parameter δ, scale parameter β > 0, and shape parameter α > 0, where we use the notation z = (x - δ)/β.
FrechetDist(double) - Constructor for class umontreal.iro.lecuyer.probdist.FrechetDist
Constructor for the standard Fréchet distribution with parameters β = 1 and δ = 0.
FrechetDist(double, double, double) - Constructor for class umontreal.iro.lecuyer.probdist.FrechetDist
Constructs a FrechetDist object with parameters α = alpha, β = beta and δ = delta.
FrechetGen - Class in umontreal.iro.lecuyer.randvar
This class implements methods for generating random variates from the Fréchet distribution, with location parameter δ, scale parameter β > 0, and shape parameter α > 0, where we use the notation z = (x - δ)/β.
FrechetGen(RandomStream, double) - Constructor for class umontreal.iro.lecuyer.randvar.FrechetGen
Creates a Fréchet random number generator with α = alpha, β = 1 and δ = 0 using stream s.
FrechetGen(RandomStream, double, double, double) - Constructor for class umontreal.iro.lecuyer.randvar.FrechetGen
Creates a Fréchet random number generator with parameters α = alpha, β = beta and δ = delta using stream s.
FrechetGen(RandomStream, FrechetDist) - Constructor for class umontreal.iro.lecuyer.randvar.FrechetGen
Creates a new generator for the Fréchet distribution dist and stream s.
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.
FunctionOfMultipleMeansTally - Class in umontreal.iro.lecuyer.stat
Represents a statistical collector for estimating a function of multiple means with a confidence interval based on the delta theorem.
FunctionOfMultipleMeansTally(MultivariateFunction, int) - Constructor for class umontreal.iro.lecuyer.stat.FunctionOfMultipleMeansTally
Constructs a function of multiple means tally with dimension d, and function func.
FunctionOfMultipleMeansTally(MultivariateFunction, String, int) - Constructor for class umontreal.iro.lecuyer.stat.FunctionOfMultipleMeansTally
Constructs a function of multiple means tally with name name, dimension d, and function func.
FunctionOfMultipleMeansTally(MultivariateFunction, ListOfTalliesWithCovariance<Tally>) - Constructor for class umontreal.iro.lecuyer.stat.FunctionOfMultipleMeansTally
Constructs a function of multiple means tally using the function func and the list of tallies ta for observation management and covariance estimation.
FunctionOfMultipleMeansTallyWithCV - Class in umontreal.iro.lecuyer.stat.list.lincv
Represents a function of multiple means tally for an estimator with linear control variables.
FunctionOfMultipleMeansTallyWithCV(MultivariateFunction, int, int) - Constructor for class umontreal.iro.lecuyer.stat.list.lincv.FunctionOfMultipleMeansTallyWithCV
Creates a new function of multiple means tally for a function funcNoCV of p variables, and with q control variables.
FunctionOfMultipleMeansTallyWithCV(MultivariateFunction, ListOfTalliesWithCV<Tally>) - Constructor for class umontreal.iro.lecuyer.stat.list.lincv.FunctionOfMultipleMeansTallyWithCV
Constructs a new function of multiple means tally with control variables from the list of tallies l, and the function funcNoCV.

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
Constructs a new GammaProcess with parameters μ = mu, ν = nu and initial value S(t0) = s0.
GammaProcess(double, double, double, GammaGen) - Constructor for class umontreal.iro.lecuyer.stochprocess.GammaProcess
Constructs a new GammaProcess with parameters μ = mu, ν = nu and initial value S(t0) = s0.
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
Constructs a new GammaProcessBridge with parameters μ = mu, ν = nu and initial value S(t0) = s0.
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
Constructs a new GammaProcessPCA with parameters μ = mu, ν = nu and initial value S(t0) = s0.
GammaProcessPCA(double, double, double, GammaGen) - Constructor for class umontreal.iro.lecuyer.stochprocess.GammaProcessPCA
Constructs a new GammaProcessPCA with parameters μ = mu, ν = nu and initial value S(t0) = s0.
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
Constructs a new GammaProcessPCABridge with parameters μ = mu, ν = nu and initial value S(t0) = s0.
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
Constructs a new GammaProcessPCASymmetricalBridge with parameters μ = mu, ν = nu and initial value S(t0) = s0.
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
Constructs a new GammaProcessSymmetricalBridge with parameters μ = mu, ν = nu and initial value S(t0) = s0.
GammaProcessSymmetricalBridge(double, double, double, GammaGen, BetaSymmetricalGen) - Constructor for class umontreal.iro.lecuyer.stochprocess.GammaProcessSymmetricalBridge
Constructs a new GammaProcessSymmetricalBridge with parameters μ = mu, ν = nu and initial value S(t0) = s0.
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.
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.
gaussLobatto(MathFunction, double, double, double) - Static method in class umontreal.iro.lecuyer.functions.MathFunctionUtil
Computes and returns a numerical approximation of the integral of f (x) over [a, b], using Gauss-Lobatto adaptive quadrature with 5 nodes, with tolerance tol.
gaussLobatto(MathFunction, double, double, double, double[][]) - Static method in class umontreal.iro.lecuyer.functions.MathFunctionUtil
Similar to method gaussLobatto(MathFunction, double, double, double), but also returns in T[0] the subintervals of integration, and in T[1], the partial values of the integral over the corresponding subintervals.
gcd(int, int) - Static method in class umontreal.iro.lecuyer.util.Num
Returns the greatest common divisor (gcd) of x and y.
gcd(long, long) - Static method in class umontreal.iro.lecuyer.util.Num
Returns the greatest common divisor (gcd) of x and y.
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.CIRProcessEuler
 
generatePath(RandomStream) - Method in class umontreal.iro.lecuyer.stochprocess.CIRProcessEuler
 
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.MultivariateBrownianMotion
 
generatePath(double[]) - Method in class umontreal.iro.lecuyer.stochprocess.MultivariateBrownianMotion
Same as generatePath() but requires a vector of uniform random numbers which are used to generate the path.
generatePath(RandomStream) - Method in class umontreal.iro.lecuyer.stochprocess.MultivariateBrownianMotion
 
generatePath() - Method in class umontreal.iro.lecuyer.stochprocess.MultivariateBrownianMotionBridge
 
generatePath() - Method in class umontreal.iro.lecuyer.stochprocess.MultivariateBrownianMotionPCA
Sets the parameters
generatePath(double[]) - Method in class umontreal.iro.lecuyer.stochprocess.MultivariateBrownianMotionPCA
 
generatePath() - Method in class umontreal.iro.lecuyer.stochprocess.MultivariateBrownianMotionPCABigSigma
 
generatePath(double[]) - Method in class umontreal.iro.lecuyer.stochprocess.MultivariateBrownianMotionPCABigSigma
 
generatePath() - Method in class umontreal.iro.lecuyer.stochprocess.MultivariateGeometricBrownianMotion
 
generatePath() - Method in class umontreal.iro.lecuyer.stochprocess.MultivariateStochasticProcess
Generates, returns, and saves the sample path {X(t0),X(t1),…,X(td)}, which can then be accessed via getPath, getSubpath, or getObservation.
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.OrnsteinUhlenbeckProcessEuler
Generates a sample path of the process at all observation times, which are provided in array t.
generatePath() - Method in class umontreal.iro.lecuyer.stochprocess.StochasticProcess
Generates, returns, and saves the sample path {X(t0), X(t1),…, X(td)}.
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).
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
Constructs a new GeometricBrownianMotion with parameters μ = mu, σ = sigma, and S(t0) = s0, using bm as the underlying BrownianMotion.
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
Constructs a new GeometricVarianceGammaProcess with parameters θ = theta, σ = sigma, ν = nu, μ = mu and initial value S(t0) = s0.
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.stat.matrix.MatrixOfStatProbes
Returns the statistical probe corresponding to the row r and column c.
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.KorobovLattice
Returns the multiplier a of the lattice.
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.
getA() - Method in class umontreal.iro.lecuyer.stat.TallyHistogram
Returns the left boundary a of interval [a, b].
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
Deprecated. Returns the parameter α of this object.
getAlpha() - Method in class umontreal.iro.lecuyer.probdist.FrechetDist
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.InverseGammaDist
Returns the α parameter of 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
Deprecated. 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
Deprecated. Returns the parameter α of this object.
getAlpha() - Method in class umontreal.iro.lecuyer.randvar.FrechetGen
Returns the parameter α.
getAlpha() - Method in class umontreal.iro.lecuyer.randvar.GammaGen
Returns the parameter α of this object.
getAlpha() - Method in class umontreal.iro.lecuyer.randvar.InverseGammaGen
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
Deprecated. 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.CIRProcessEuler
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 F0(b) - F0(a), the area under the truncated density function.
getArray() - Method in class umontreal.iro.lecuyer.stat.TallyStore
Returns the observations stored in this probe.
getArrayLength() - Method in class umontreal.iro.lecuyer.util.io.DataField
Returns the length of the array contained by the field, or -1 if it is not an array.
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.
getAs() - Method in class umontreal.iro.lecuyer.hups.Rank1Lattice
Returns the generator aj of the lattice.
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.stat.TallyHistogram
Returns the right boundary b of interval [a, b].
getB() - Method in class umontreal.iro.lecuyer.stochprocess.CIRProcess
Returns the value of b.
getB() - Method in class umontreal.iro.lecuyer.stochprocess.CIRProcessEuler
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.
getBatch(double) - Method in class umontreal.iro.lecuyer.simexp.BatchMeansSim
Returns the real batch corresponding to simulation time time when batch lengths are kept.
getBatchAggregation() - Method in class umontreal.iro.lecuyer.simexp.BatchMeansSim
Returns true if the aggregation of batches is turned ON.
getBatchFraction() - Method in class umontreal.iro.lecuyer.simexp.BatchMeansSim
Returns the remaining fraction of batch to be simulated.
getBatchLengthsKeeping() - Method in class umontreal.iro.lecuyer.simexp.BatchMeansSim
Indicates that the length, in simulation time units, of each real batch has to be kept.
getBatchSize() - Method in class umontreal.iro.lecuyer.simexp.BatchMeansSim
Returns the current batch size as defined for this simulator.
getBatchSizeMultiplier() - Method in class umontreal.iro.lecuyer.simexp.BatchMeansSim
Returns the batch size multiplier after the simulation of a new batch.
getBestA() - Method in class umontreal.iro.lecuyer.discrepancy.SearcherKorobov
Returns the generator a of the lattice which gave the best value of the discrepancy in the last search.
getBestAs() - Method in class umontreal.iro.lecuyer.discrepancy.Searcher
Returns the generator of the lattice which gave the best value of the discrepancy in the last search.
getBestVal() - Method in class umontreal.iro.lecuyer.discrepancy.Searcher
Returns the best value of the discrepancy found in the last search.
getBestVals() - Method in class umontreal.iro.lecuyer.discrepancy.SearcherCBC
Returns the best value of the discrepancy found in the last search, in each dimension up to s.
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.FrechetDist
Returns the parameter β of this object.
getBeta() - Method in class umontreal.iro.lecuyer.probdist.GumbelDist
Returns the parameter β of this object.
getBeta() - Method in class umontreal.iro.lecuyer.probdist.InverseGammaDist
Returns the β parameter 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
Deprecated. 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.FrechetGen
Returns the parameter β.
getBeta() - Method in class umontreal.iro.lecuyer.randvar.GumbelGen
Returns the parameter β.
getBeta() - Method in class umontreal.iro.lecuyer.randvar.InverseGammaGen
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
Deprecated. Returns the parameter β of this object.
getBeta() - Method in class umontreal.iro.lecuyer.randvar.Pearson6Gen
Returns the β parameter of this object.
getBeta(int) - Method in class umontreal.iro.lecuyer.stat.list.lincv.FunctionOfMultipleMeansTallyWithCV
Returns the value of βf, i.
getBeta() - Method in class umontreal.iro.lecuyer.stat.list.lincv.FunctionOfMultipleMeansTallyWithCV
Returns the βf vector.
getBeta() - Method in class umontreal.iro.lecuyer.stat.list.lincv.ListOfTalliesWithCV
Returns the current matrix β.
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.MultivariateGeometricBrownianMotion
Returns a reference to the MultivariateBrownianMotion 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.
getChains() - Method in class umontreal.iro.lecuyer.markovchain.ArrayOfComparableChains
Returns the underlying array of n MarkovChainComparable.
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.
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(double[], double[]) - Static method in class umontreal.iro.lecuyer.functionfit.PolInterp
Computes and returns the coefficients the polynomial interpolating through the given points (x[0], y[0]), ..., (x[n], y[n]).
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.
getCompletedRealBatches() - Method in class umontreal.iro.lecuyer.simexp.BatchMeansSim
Returns the number of completed real batches since the beginning of the run.
getCompletedReplications() - Method in class umontreal.iro.lecuyer.simexp.RepSim
Returns the total number of completed replications for the current experiment.
getConfidenceLevel() - Method in class umontreal.iro.lecuyer.stat.FunctionOfMultipleMeansTally
Returns the level of confidence for the intervals on the mean displayed in reports.
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 n and (p1, ..., pd).
getCounters() - Method in class umontreal.iro.lecuyer.stat.TallyHistogram
Returns the bin counters.
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 n 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(double[]) - Method in class umontreal.iro.lecuyer.stochprocess.MultivariateStochasticProcess
Returns the value of the last generated observation X(tj).
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 seriesth 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.FrechetDist
Returns the parameter δ of this object.
getDelta() - Method in class umontreal.iro.lecuyer.probdist.GumbelDist
Returns the parameter δ of 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.FrechetGen
Returns the parameter δ.
getDelta() - Method in class umontreal.iro.lecuyer.randvar.GumbelGen
Returns the parameter δ.
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.discrepancy.Discrepancy
Returns the dimension of the points s.
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.hups.SortedPointSet
Returns the number of coordinates of each point, which is the dimension of the original point set minus the dimension of the sort.
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 class umontreal.iro.lecuyer.stat.FunctionOfMultipleMeansTally
Returns the dimension of this tally, i.e., the size of any vector of observations.
getDimension() - Method in class umontreal.iro.lecuyer.stat.list.ListOfFunctionOfMultipleMeansTallies
Assuming that each tally in this list has the same dimension, returns the dimension of tally 0, or 0 if this list is empty.
getDimension() - Method in class umontreal.iro.lecuyer.stat.matrix.MatrixOfFunctionOfMultipleMeansTallies
Assuming that each tally in this matrix has the same dimension, returns the dimension of tally (0, 0), or 0 if the matrix has no row or column.
getDimension() - Method in class umontreal.iro.lecuyer.stochprocess.MultivariateStochasticProcess
Returns the dimension of X.
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
 
getDimensionWithoutCV() - Method in class umontreal.iro.lecuyer.stat.list.lincv.FunctionOfMultipleMeansTallyWithCV
Returns the dimension of this tally excluding the control variables.
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.
getDroppedRealBatches() - Method in class umontreal.iro.lecuyer.simexp.BatchMeansSim
Returns the number of real batches dropped.
getElement() - Method in class umontreal.iro.lecuyer.simevents.ListWithStat.Node
Returns the element stored into this node.
getEndSimEvent() - Method in class umontreal.iro.lecuyer.simexp.BatchMeansSim
Returns the event used to stop the simulation at the end of the warmup or batches.
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).
getEpsilon() - Method in class umontreal.iro.lecuyer.probdist.InverseDistFromDensity
Returns the u-resolution eps associated with this object.
getEpsilon() - Method in class umontreal.iro.lecuyer.randvar.InverseFromDensityGen
Returns the u-resolution eps.
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.
getExpectedValue(int) - Method in class umontreal.iro.lecuyer.stat.list.lincv.FunctionOfMultipleMeansTallyWithCV
Gets the expected value of the ith component of ν.
getExpectedValue(int) - Method in class umontreal.iro.lecuyer.stat.list.lincv.ListOfTalliesWithCV
Gets the expected value of the ith control variable.
getExpectedValues() - Method in class umontreal.iro.lecuyer.stat.list.lincv.FunctionOfMultipleMeansTallyWithCV
Gets an array containing the vector ν.
getExpectedValues() - Method in class umontreal.iro.lecuyer.stat.list.lincv.ListOfTalliesWithCV
Returns E[C], the expected value of the vector of control variables.
getFa() - Method in class umontreal.iro.lecuyer.probdist.TruncatedDist
Returns the value of F0(a).
getFaureLemieuxPermutation(int, int[]) - Static method in class umontreal.iro.lecuyer.hups.RadicalInverse
Computes the permutations as proposed in σb of the set {0,…, b - 1} and puts it in array pi.
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 F0(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
 
getFitPolynomialIndex(double) - Method in class umontreal.iro.lecuyer.functionfit.SmoothingCubicSpline
Returns the index of P, the Polynomial instance used to evaluate x, in an ArrayList table instance returned by getSplinePolynomials().
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).
getFunction() - Method in class umontreal.iro.lecuyer.stat.FunctionOfMultipleMeansTally
Returns the function of multiple means used by this tally.
getFunctions() - Method in class umontreal.iro.lecuyer.functions.AverageMathFunction
Returns the functions being averaged.
getFunctionWithoutCV() - Method in class umontreal.iro.lecuyer.stat.list.lincv.FunctionOfMultipleMeansTallyWithCV
Returns the implementation computing the function g(μ).
getGamma() - Method in class umontreal.iro.lecuyer.discrepancy.Discrepancy
Returns the weight factors gamma for each dimension up to s.
getGamma() - Method in class umontreal.iro.lecuyer.probdist.FatigueLifeDist
Returns the parameter γ of this object.
getGamma() - Method in class umontreal.iro.lecuyer.probdist.NegativeBinomialDist
Deprecated. 
getGamma() - Method in class umontreal.iro.lecuyer.probdistmulti.NegativeMultinomialDist
Returns the parameter n 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.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 noncentral chi-square random variate generator used.
getGen() - Method in class umontreal.iro.lecuyer.stochprocess.CIRProcessEuler
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.MultivariateBrownianMotion
Returns the normal random variate generator used.
getGen() - Method in class umontreal.iro.lecuyer.stochprocess.MultivariateGeometricBrownianMotion
Returns the normal random variate generator used.
getGen() - Method in class umontreal.iro.lecuyer.stochprocess.OrnsteinUhlenbeckProcess
Returns the normal random variate generator used.
getGen1() - Method in class umontreal.iro.lecuyer.randvarmulti.IIDMultivariateGen
Returns the common one-dimensional generator used in this class.
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.
getHostName() - Static method in class umontreal.iro.lecuyer.util.Systeme
Returns the name of the host computer.
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(int[], int) - Static method in class umontreal.iro.lecuyer.probdist.BernoulliDist
Creates a new instance of a Bernoulli distribution with parameter p estimated using the maximum likelihood method, from the m observations x[i], i = 0, 1,…, m - 1.
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
Deprecated. 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, double) - Static method in class umontreal.iro.lecuyer.probdist.FrechetDist
Given δ = delta, creates a new instance of a Fréchet 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.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.GumbelDist
Creates a new instance of an Gumbel distribution with parameters β and δ estimated using the maximum likelihood method based on the n observations x[i], i = 0, 1,…, n - 1, assuming that β > 0.
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.InverseGammaDist
Creates a new instance of the inverse gamma 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.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, double, double) - Static method in class umontreal.iro.lecuyer.probdist.JohnsonSBDist
Creates a new instance of a JohnsonSBDist object 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.JohnsonSLDist
Creates a new instance of a Johnson SL distribution with parameters 0, δ, ξ and λ over the interval [ξ,∞] 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 n given and hat(p) estimated using the maximum likelihood method, from the m observations x[i], i = 0, 1,…, m - 1.
getInstanceFromMLE(int[], int) - Static method in class umontreal.iro.lecuyer.probdist.NegativeBinomialDist
Creates a new instance of a negative binomial 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.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
Deprecated. 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, double, double) - Static method in class umontreal.iro.lecuyer.probdist.TriangularDist
Creates a new instance of a triangular distribution with parameters a and b.
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.
getInstanceFromMLE1(int[], int, double) - Static method in class umontreal.iro.lecuyer.probdist.NegativeBinomialDist
Creates a new instance of a negative binomial distribution with parameters p given and hat(n) estimated using the maximum likelihood method, from the m observations x[i], i = 0, 1,…, m - 1.
getInstanceFromMLEmin(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.GumbelDist
Similar to getInstanceFromMLE, but for the case β < 0.
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.
getKnots() - Method in class umontreal.iro.lecuyer.functionfit.BSpline
Returns an array containing the knot vector (t0, tm-1).
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.
getLabel() - Method in class umontreal.iro.lecuyer.util.io.DataField
Returns the field label (or name).
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
Deprecated. 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.HypoExponentialDist
Returns the values λi 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.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
Deprecated. 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.HypoExponentialGen
Returns the λi associated with 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.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.
getListOfTallies() - Method in class umontreal.iro.lecuyer.stat.FunctionOfMultipleMeansTally
Returns the (unmodifiable) list of tallies internally used by this object.
getListOfTalliesWithCV() - Method in class umontreal.iro.lecuyer.stat.list.lincv.FunctionOfMultipleMeansTallyWithCV
Returns the list of tallies with control variables used by this object.
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
Deprecated. 
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 seriesth data series.
getMaxBatches() - Method in class umontreal.iro.lecuyer.simexp.BatchMeansSim
Returns M, the maximal number of batches to be used for estimating the steady-state performance measures of interest.
getMaximumLikelihoodEstimate(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.ExtremeValueDist
Deprecated. 
getMaxKnot() - Method in class umontreal.iro.lecuyer.functionfit.BSpline
Returns the knot maximal value.
getMaxReplications() - Method in class umontreal.iro.lecuyer.simexp.RepSim
Returns the maximal number of replications to be simulated before an error check.
getMean() - Method in class umontreal.iro.lecuyer.probdist.BernoulliDist
 
getMean(double) - Static method in class umontreal.iro.lecuyer.probdist.BernoulliDist
Returns the mean E[X] = p of the Bernoulli distribution with parameter p.
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] = ( + )/(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.ConstantDist
Returns the mean E[X] = c.
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] = ∑ipixi 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
Deprecated.  
getMean(double, double) - Static method in class umontreal.iro.lecuyer.probdist.ExtremeValueDist
Deprecated. 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] = n2/(n2 - 2) of the Fisher F distribution with parameters n1 and n2 = n2.
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.FrechetDist
 
getMean(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.FrechetDist
Returns the mean of the Fréchet distribution with parameters α, β and δ.
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.GumbelDist
 
getMean(double, double) - Static method in class umontreal.iro.lecuyer.probdist.GumbelDist
Returns the mean, E[X] = δ + γβ, of the Gumbel distribution with parameters β and δ, where γ = 0.5772156649015329 is the Euler-Mascheroni constant.
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.HypoExponentialDist
 
getMean(double[]) - Static method in class umontreal.iro.lecuyer.probdist.HypoExponentialDist
Returns the mean, E[X] = ∑i=1k1/λi, of the hypoexponential distribution with rates λi = lambda[i - 1], i = 1,…, k.
getMean() - Method in class umontreal.iro.lecuyer.probdist.InverseGammaDist
 
getMean(double, double) - Static method in class umontreal.iro.lecuyer.probdist.InverseGammaDist
Returns the mean E[X] = β/(α - 1) of the inverse gamma distribution with shape parameter α and scale parameter β.
getMean() - Method in class umontreal.iro.lecuyer.probdist.InverseGaussianDist
 
getMean(double, double) - Static method in class umontreal.iro.lecuyer.probdist.InverseGaussianDist
Returns the mean E[X] = μ of the inverse gaussian distribution with parameters μ and λ.
getMean() - Method in class umontreal.iro.lecuyer.probdist.JohnsonSBDist
 
getMean(double, double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.JohnsonSBDist
Returns the mean of the Johnson SB distribution with parameters γ, δ, ξ and λ.
getMean() - Method in class umontreal.iro.lecuyer.probdist.JohnsonSLDist
 
getMean(double, double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.JohnsonSLDist
Returns the mean of the Johnson SL distribution with parameters γ, δ, ξ and λ.
getMean() - Method in class umontreal.iro.lecuyer.probdist.JohnsonSUDist
 
getMean(double, double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.JohnsonSUDist
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] = n(1 - p)/p of the negative binomial distribution with parameters n 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
Deprecated.  
getMean(double, double) - Static method in class umontreal.iro.lecuyer.probdist.Pearson5Dist
Deprecated. 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
Returns an approximation of the mean computed with the Simpson 1/3 numerical integration rule.
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] = npi/p0 of the negative multinomial distribution with parameters n and (p1, ..., pd).
getMedian() - Method in class umontreal.iro.lecuyer.probdist.EmpiricalDist
Returns the median.
getMedian(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.EmpiricalDist
Returns the median.
getMedian(double[], int) - Static method in class umontreal.iro.lecuyer.util.Misc
Returns the median of the first n elements of array A.
getMedian(int[], int) - Static method in class umontreal.iro.lecuyer.util.Misc
Returns the median of the first n elements of array A.
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.
getMinBatches() - Method in class umontreal.iro.lecuyer.simexp.BatchMeansSim
Returns the minimal number of batches required for estimating the steady-state performance measures of interest.
getMinKnot() - Method in class umontreal.iro.lecuyer.functionfit.BSpline
Returns the knot minimal value.
getMinReplications() - Method in class umontreal.iro.lecuyer.simexp.RepSim
Returns the minimal number of replications to be simulated before an error check.
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(int[], int) - Static method in class umontreal.iro.lecuyer.probdist.BernoulliDist
Estimates the parameters p of the Bernoulli 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.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
Deprecated. 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, double) - Static method in class umontreal.iro.lecuyer.probdist.FrechetDist
Given δ = delta, estimates the parameters (α, β) of the Fréchet distribution using the maximum likelihood method with the n observations x[i], i = 0, 1,…, n - 1.
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.GumbelDist
Estimates the parameters (β, δ) of the Gumbel distribution, assuming that β > 0, and using the maximum likelihood method with 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.InverseGammaDist
Estimates the parameters (α, β) of the inverse gamma 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, double, double) - Static method in class umontreal.iro.lecuyer.probdist.JohnsonSBDist
Estimates the parameters (γ, δ) of the Johnson SB 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.JohnsonSLDist
Estimates the parameters (γ, δ, ξ, λ) of the Johnson SL 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 m observations x[i], i = 0, 1,…, m - 1.
getMLE(int[], int) - Static method in class umontreal.iro.lecuyer.probdist.NegativeBinomialDist
Estimates the parameter (n, p) of the negative binomial 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.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
Deprecated. 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, double, double) - Static method in class umontreal.iro.lecuyer.probdist.TriangularDist
Estimates the parameter m of the triangular 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.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(n), hat(p)1, ..., hat(p)d] of the negative multinomial distribution using the maximum likelihood method.
getMLE1(int[], int, double) - Static method in class umontreal.iro.lecuyer.probdist.NegativeBinomialDist
Estimates the parameter n of the negative binomial distribution using the maximum likelihood method, from the m observations x[i], i = 0, 1,…, m - 1.
getMLEmin(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.GumbelDist
Similar to getMLE, but for the case β < 0.
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.
getMLEninv(int[], int) - Static method in class umontreal.iro.lecuyer.probdist.NegativeBinomialDist
Estimates and returns the parameter ν = 1/hat(n) of the negative binomial distribution using the maximum likelihood method, from the m observations x[i], i = 0, 1,…, m - 1.
getMLEninv(int[][], int, int) - Static method in class umontreal.iro.lecuyer.probdistmulti.NegativeMultinomialDist
Estimates and returns the parameter ν = 1/hat(n) of the negative multinomial 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.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.MultivariateBrownianMotion
Returns the vector mu.
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.markovchain.ArrayOfComparableChains
Returns the number n of chains.
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.DiscreteDistribution
Returns the number of possible values xi.
getN() - Method in class umontreal.iro.lecuyer.probdist.EmpiricalDist
Returns n, the number of observations.
getN() - Method in class umontreal.iro.lecuyer.probdist.FisherFDist
Deprecated. 
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.NegativeBinomialDist
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.
getN1() - Method in class umontreal.iro.lecuyer.probdist.FisherFDist
Returns the parameter n1 of this object.
getN1() - Method in class umontreal.iro.lecuyer.probdist.PascalDist
Returns the parameter n of this object.
getN2() - Method in class umontreal.iro.lecuyer.probdist.FisherFDist
Returns the parameter n2 of 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.discrepancy.Discrepancy
Returns the name of the Discrepancy.
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.matrix.MatrixOfStatProbes
Returns the global name of this matrix 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.
getNumAggregates() - Method in class umontreal.iro.lecuyer.simexp.BatchMeansSim
Returns h, the number of real batches contained into an effective batch.
getNumberOfRandomStreams() - Method in class umontreal.iro.lecuyer.stochprocess.InverseGaussianProcess
Returns the number of random streams of this process.
getNumBins() - Method in class umontreal.iro.lecuyer.stat.TallyHistogram
Returns the number of bins s dividing the interval [a, b].
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.
getNumControlVariables() - Method in class umontreal.iro.lecuyer.stat.list.lincv.FunctionOfMultipleMeansTallyWithCV
Returns the number of control variables being used.
getNumControlVariables() - Method in class umontreal.iro.lecuyer.stat.list.lincv.ListOfTalliesWithCV
Returns the number q of control variables.
getNumPoints() - Method in class umontreal.iro.lecuyer.discrepancy.Discrepancy
Returns the number of points n.
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), for i = 0, 1,…, n - 1.
getObs(int) - Method in class umontreal.iro.lecuyer.probdist.PiecewiseLinearEmpiricalDist
Returns the value of X(i).
getObservation(int, double[]) - Method in class umontreal.iro.lecuyer.stochprocess.MultivariateStochasticProcess
Returns X(tj) in the c-dimensional vector obs.
getObservation(int, int) - Method in class umontreal.iro.lecuyer.stochprocess.MultivariateStochasticProcess
Returns Xi(tj) from the current sample path.
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.
getOrder() - Method in class umontreal.iro.lecuyer.probdist.InverseDistFromDensity
Returns the order associated with this object.
getOrder() - Method in class umontreal.iro.lecuyer.randvar.ExponentialInverseFromDensityGen
Returns the order of the interpolating polynomial.
getOrder() - Method in class umontreal.iro.lecuyer.randvar.InverseFromDensityGen
Returns the order of the interpolating polynomial.
getOrder() - Method in class umontreal.iro.lecuyer.randvar.NormalInverseFromDensityGen
Returns the order of the interpolating polynomial.
getOriginalPointSet() - Method in class umontreal.iro.lecuyer.hups.ContainerPointSet
Returns the (untransformed) point set inside this container.
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.BernoulliDist
Returns the parameter p of this object.
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.BernoulliGen
Returns the parameter p 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.BernoulliDist
Returns an array that contains the parameter p of the current distribution: [p].
getParams() - Method in class umontreal.iro.lecuyer.probdist.BetaDist
Return an array containing the parameters of the current distribution as [α, β, a, b].
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
Deprecated. 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.FrechetDist
Return an array containing the parameters of the current object in regular order: [α, β, δ].
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.