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
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
.
addRandomShift(int, int, RandomStream) - Method in class umontreal.iro.lecuyer.hups.CachedPointSet
.
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
.
addRandomShift(RandomStream) - Method in class umontreal.iro.lecuyer.hups.DigitalNet
.
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.IndependentPointsCached
.
addRandomShift(int, int, RandomStream) - Method in class umontreal.iro.lecuyer.hups.LatinHypercube
.
addRandomShift(int, int, RandomStream) - Method in class umontreal.iro.lecuyer.hups.PointSet
Deprecated. 
addRandomShift(RandomStream) - Method in class umontreal.iro.lecuyer.hups.PointSet
Deprecated. 
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
.
addRandomShift(RandomStream) - Method in class umontreal.iro.lecuyer.hups.RandShiftedPointSet
.
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
.
addRandomShift(int, int, RandomStream) - Method in class umontreal.iro.lecuyer.hups.StratifiedUnitCube
.
addRandomShift(int, int, RandomStream) - Method in class umontreal.iro.lecuyer.hups.StratifiedUnitCubeAnti
.
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
.
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
.
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
AndersonDarlingDist
AndersonDarlingDist(int) - Constructor for class umontreal.iro.lecuyer.probdist.AndersonDarlingDist
.
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
AntitheticPointSet
AntitheticPointSet(PointSet) - Constructor for class umontreal.iro.lecuyer.hups.AntitheticPointSet
.
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
.
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
.
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
.
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
.
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
.
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
.
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
.
barF(double) - Method in class umontreal.iro.lecuyer.probdist.EmpiricalDist
 
barF(int, double, int, double) - Static method in class umontreal.iro.lecuyer.probdist.ErlangDist
.
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. .
barF(double) - Method in class umontreal.iro.lecuyer.probdist.FatigueLifeDist
 
barF(double, double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.FatigueLifeDist
.
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
.
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
.
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
.
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
.
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
.
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
.
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
.
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
.
barF(int) - Method in class umontreal.iro.lecuyer.probdist.NegativeBinomialDist
 
barF(double, double, int) - Static method in class umontreal.iro.lecuyer.probdist.NegativeBinomialDist
.
barF(double) - Method in class umontreal.iro.lecuyer.probdist.NormalDist
 
barF(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.NormalDist
Computes the complementary normal distribution function bar(F)(x) = 1 - Φ((x - μ)/σ), with mean μ and variance σ2.
barF(double) - Method in class umontreal.iro.lecuyer.probdist.NormalDistQuick
 
barF(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.NormalDistQuick
Returns an approximation of 1 - Φ(x), where Φ is the standard normal distribution function, with mean 0 and variance 1.
barF(double) - Method in class umontreal.iro.lecuyer.probdist.NormalInverseGaussianDist
 
barF(double, double, double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.NormalInverseGaussianDist
.
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
.
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
.
barF(double, double) - Static method in class umontreal.iro.lecuyer.probdist.RayleighDist
.
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
.
barF(double) - Method in class umontreal.iro.lecuyer.probdist.TriangularDist
 
barF(double, double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.TriangularDist
.
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
.
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
.
barF(double, double) - Static method in class umontreal.iro.lecuyer.probdist.WeibullDist
.
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
BasicRandomStreamFactory
BasicRandomStreamFactory(Class) - Constructor for class umontreal.iro.lecuyer.rng.BasicRandomStreamFactory
.
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
BatchSort
BatchSort(int[]) - Constructor for class umontreal.iro.lecuyer.util.BatchSort
.
BatchSort(double[]) - Constructor for class umontreal.iro.lecuyer.util.BatchSort
.
BernoulliDist - Class in umontreal.iro.lecuyer.probdist
BernoulliDist
BernoulliDist(double) - Constructor for class umontreal.iro.lecuyer.probdist.BernoulliDist
.
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
.
besselK025(double) - Static method in class umontreal.iro.lecuyer.util.Num
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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
BetaGen
BetaGen(RandomStream, double, double, double, double) - Constructor for class umontreal.iro.lecuyer.randvar.BetaGen
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BetaGen(RandomStream, double, double) - Constructor for class umontreal.iro.lecuyer.randvar.BetaGen
.
BetaGen(RandomStream, BetaDist) - Constructor for class umontreal.iro.lecuyer.randvar.BetaGen
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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
BetaStratifiedRejectionGen
BetaStratifiedRejectionGen(RandomStream, RandomStream, double, double) - Constructor for class umontreal.iro.lecuyer.randvar.BetaStratifiedRejectionGen
.
BetaStratifiedRejectionGen(RandomStream, double, double) - Constructor for class umontreal.iro.lecuyer.randvar.BetaStratifiedRejectionGen
.
BetaStratifiedRejectionGen(RandomStream, RandomStream, double, double, double, double) - Constructor for class umontreal.iro.lecuyer.randvar.BetaStratifiedRejectionGen
[tabb32]
BetaStratifiedRejectionGen(RandomStream, double, double, double, double) - Constructor for class umontreal.iro.lecuyer.randvar.BetaStratifiedRejectionGen
.
BetaStratifiedRejectionGen(RandomStream, RandomStream, BetaDist) - Constructor for class umontreal.iro.lecuyer.randvar.BetaStratifiedRejectionGen
.
BetaStratifiedRejectionGen(RandomStream, BetaDist) - Constructor for class umontreal.iro.lecuyer.randvar.BetaStratifiedRejectionGen
.
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
BetaSymmetricalGen
BetaSymmetricalGen(RandomStream, double) - Constructor for class umontreal.iro.lecuyer.randvar.BetaSymmetricalGen
.
BetaSymmetricalGen(RandomStream, BetaSymmetricalDist) - Constructor for class umontreal.iro.lecuyer.randvar.BetaSymmetricalGen
.
BetaSymmetricalPolarGen - Class in umontreal.iro.lecuyer.randvar
BetaSymmetricalPolarGen
BetaSymmetricalPolarGen(RandomStream, RandomStream, double) - Constructor for class umontreal.iro.lecuyer.randvar.BetaSymmetricalPolarGen
.
BetaSymmetricalPolarGen(RandomStream, double) - Constructor for class umontreal.iro.lecuyer.randvar.BetaSymmetricalPolarGen
.
BetaSymmetricalPolarGen(RandomStream, RandomStream, BetaSymmetricalDist) - Constructor for class umontreal.iro.lecuyer.randvar.BetaSymmetricalPolarGen
.
BetaSymmetricalPolarGen(RandomStream, BetaSymmetricalDist) - Constructor for class umontreal.iro.lecuyer.randvar.BetaSymmetricalPolarGen
.
BigDiscrepancy - Class in umontreal.iro.lecuyer.discrepancy
BigDiscrepancy 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
.
BigDiscrepancy(double[][], int, int, double[]) - Constructor for class umontreal.iro.lecuyer.discrepancy.BigDiscrepancy
.
BigDiscrepancy(int, int, double[]) - Constructor for class umontreal.iro.lecuyer.discrepancy.BigDiscrepancy
.
BigDiscrepancy(PointSet) - Constructor for class umontreal.iro.lecuyer.discrepancy.BigDiscrepancy
.
BigDiscrepancy() - Constructor for class umontreal.iro.lecuyer.discrepancy.BigDiscrepancy
.
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
BinomialConvolutionGen
BinomialConvolutionGen(RandomStream, int, double) - Constructor for class umontreal.iro.lecuyer.randvar.BinomialConvolutionGen
.
BinomialConvolutionGen(RandomStream, BinomialDist) - Constructor for class umontreal.iro.lecuyer.randvar.BinomialConvolutionGen
.
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
.
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
BitVector
BitVector(int) - Constructor for class umontreal.iro.lecuyer.util.BitVector
.
BitVector(int[], int) - Constructor for class umontreal.iro.lecuyer.util.BitVector
.
BitVector(int[]) - Constructor for class umontreal.iro.lecuyer.util.BitVector
.
BitVector(BitVector) - Constructor for class umontreal.iro.lecuyer.util.BitVector
.
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
.
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
BrownianMotionPCA
BrownianMotionPCA(double, double, double, RandomStream) - Constructor for class umontreal.iro.lecuyer.stochprocess.BrownianMotionPCA
.
BrownianMotionPCA(double, double, double, NormalGen) - Constructor for class umontreal.iro.lecuyer.stochprocess.BrownianMotionPCA
.
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
CachedPointSet
CachedPointSet(PointSet, int, int, int, int) - Constructor for class umontreal.iro.lecuyer.hups.CachedPointSet
.
CachedPointSet(PointSet, int, int) - Constructor for class umontreal.iro.lecuyer.hups.CachedPointSet
.
CachedPointSet(PointSet) - Constructor for class umontreal.iro.lecuyer.hups.CachedPointSet
.
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
.
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
CauchyDist
CauchyDist() - Constructor for class umontreal.iro.lecuyer.probdist.CauchyDist
.
CauchyDist(double, double) - Constructor for class umontreal.iro.lecuyer.probdist.CauchyDist
.
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
.
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
.
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
.
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
.
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
.
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
.
cdf(double) - Method in class umontreal.iro.lecuyer.probdist.EmpiricalDist
 
cdf(int, double, int, double) - Static method in class umontreal.iro.lecuyer.probdist.ErlangDist
.
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. .
cdf(double) - Method in class umontreal.iro.lecuyer.probdist.FatigueLifeDist
 
cdf(double, double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.FatigueLifeDist
.
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
.
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
.
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
.
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
.
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
.
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
.
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
.
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
.
cdf(int) - Method in class umontreal.iro.lecuyer.probdist.NegativeBinomialDist
 
cdf(double, double, int) - Static method in class umontreal.iro.lecuyer.probdist.NegativeBinomialDist
.
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
.
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
.
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
.
cdf(double, double) - Static method in class umontreal.iro.lecuyer.probdist.RayleighDist
.
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
.
cdf(double) - Method in class umontreal.iro.lecuyer.probdist.TriangularDist
 
cdf(double, double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.TriangularDist
.
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
.
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
.
cdf(double, double) - Static method in class umontreal.iro.lecuyer.probdist.WeibullDist
.
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
.
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
ChiGen
ChiGen(RandomStream, int) - Constructor for class umontreal.iro.lecuyer.randvar.ChiGen
.
ChiGen(RandomStream, ChiDist) - Constructor for class umontreal.iro.lecuyer.randvar.ChiGen
.
ChiRatioOfUniformsGen - Class in umontreal.iro.lecuyer.randvar
ChiRatioOfUniformsGen
ChiRatioOfUniformsGen(RandomStream, int) - Constructor for class umontreal.iro.lecuyer.randvar.ChiRatioOfUniformsGen
.
ChiRatioOfUniformsGen(RandomStream, ChiDist) - Constructor for class umontreal.iro.lecuyer.randvar.ChiRatioOfUniformsGen
.
ChiSquareDist - Class in umontreal.iro.lecuyer.probdist
ChiSquareDist
ChiSquareDist(int) - Constructor for class umontreal.iro.lecuyer.probdist.ChiSquareDist
.
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
ChiSquareGen
ChiSquareGen(RandomStream, int) - Constructor for class umontreal.iro.lecuyer.randvar.ChiSquareGen
.
ChiSquareGen(RandomStream, ChiSquareDist) - Constructor for class umontreal.iro.lecuyer.randvar.ChiSquareGen
.
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
ChiSquareNoncentralGamGen
ChiSquareNoncentralGamGen(RandomStream, double, double) - Constructor for class umontreal.iro.lecuyer.randvar.ChiSquareNoncentralGamGen
.
ChiSquareNoncentralGen - Class in umontreal.iro.lecuyer.randvar
ChiSquareNoncentralGen
ChiSquareNoncentralGen(RandomStream, double, double) - Constructor for class umontreal.iro.lecuyer.randvar.ChiSquareNoncentralGen
.
ChiSquareNoncentralGen(RandomStream, ChiSquareNoncentralDist) - Constructor for class umontreal.iro.lecuyer.randvar.ChiSquareNoncentralGen
.
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
.
CholeskyDecompose(DoubleMatrix2D) - Static method in class umontreal.iro.lecuyer.util.DMatrix
[tabb54]
Mthe input matrix the Cholesky lower triangular matrix
Chrono - Class in umontreal.iro.lecuyer.util
Chrono
Chrono() - Constructor for class umontreal.iro.lecuyer.util.Chrono
.
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
.
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
Deprecated. 
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
.
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
.
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
.
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
.
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
.
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
.
compare(double[], double[]) - Method in class umontreal.iro.lecuyer.util.DoubleArrayComparator
.
compare(T, T) - Method in class umontreal.iro.lecuyer.util.MultiDimComparator
.
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
.
compute(double[], int) - Method in class umontreal.iro.lecuyer.discrepancy.DiscL2Unanchored
.
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
.
compute(double[][], int, int, double[]) - Method in class umontreal.iro.lecuyer.discrepancy.DiscShift1Lattice
.
compute(double[], int) - Method in class umontreal.iro.lecuyer.discrepancy.DiscShift1Lattice
.
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
.
compute(double[][], int, int, double[]) - Method in class umontreal.iro.lecuyer.discrepancy.DiscShiftBaker1
.
compute(double[], int) - Method in class umontreal.iro.lecuyer.discrepancy.DiscShiftBaker1
.
compute(double[], int, double) - Method in class umontreal.iro.lecuyer.discrepancy.DiscShiftBaker1
.
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
ConstantGen
ConstantGen(double) - Constructor for class umontreal.iro.lecuyer.randvar.ConstantGen
.
ConstantIntDist - Class in umontreal.iro.lecuyer.probdist
ConstantIntDist
ConstantIntDist(int) - Constructor for class umontreal.iro.lecuyer.probdist.ConstantIntDist
.
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
Continuous
Continuous() - Constructor for class umontreal.iro.lecuyer.simevents.Continuous
.
Continuous(Simulator) - Constructor for class umontreal.iro.lecuyer.simevents.Continuous
.
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
ContinuousDistributionMulti
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
.
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
CramerVonMisesDist
CramerVonMisesDist(int) - Constructor for class umontreal.iro.lecuyer.probdist.CramerVonMisesDist
.
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
.
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
CycleBasedLFSR
CycleBasedLFSR(int, int, int[]) - Constructor for class umontreal.iro.lecuyer.hups.CycleBasedLFSR
.
CycleBasedLFSR(int, int, int, int, int[], int[]) - Constructor for class umontreal.iro.lecuyer.hups.CycleBasedLFSR
.
CycleBasedLFSR(String, int) - Constructor for class umontreal.iro.lecuyer.hups.CycleBasedLFSR
.
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
.
DBL_EPSILON - Static variable in class umontreal.iro.lecuyer.util.Num
.
DBL_MAX_10_EXP - Static variable in class umontreal.iro.lecuyer.util.Num
.
DBL_MAX_EXP - Static variable in class umontreal.iro.lecuyer.util.Num
.
DBL_MIN - Static variable in class umontreal.iro.lecuyer.util.Num
.
DBL_MIN_EXP - Static variable in class umontreal.iro.lecuyer.util.Num
.
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
.
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
.
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
.
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
.
density(int, double, double) - Static method in class umontreal.iro.lecuyer.probdist.ErlangDist
.
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. .
density(double) - Method in class umontreal.iro.lecuyer.probdist.FatigueLifeDist
 
density(double, double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.FatigueLifeDist
.
density(double) - Method in class umontreal.iro.lecuyer.probdist.FisherFDist
 
density(int, int, double) - Static method in class umontreal.iro.lecuyer.probdist.FisherFDist
.
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
.
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
.
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
.
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
.
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
[hide24]
density(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.LoglogisticDist
.
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
.
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
.
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
.
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
.
density(double, double) - Static method in class umontreal.iro.lecuyer.probdist.RayleighDist
.
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
.
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
.
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
.
density(double, double) - Static method in class umontreal.iro.lecuyer.probdist.WeibullDist
.
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
.
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
.
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
.
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
.
DigitalNet - Class in umontreal.iro.lecuyer.hups
DigitalNet
DigitalNet() - Constructor for class umontreal.iro.lecuyer.hups.DigitalNet
.
DigitalNetBase2 - Class in umontreal.iro.lecuyer.hups
DigitalNetBase2
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
DigitalNetFromFile
DigitalNetFromFile(String, int, int, int) - Constructor for class umontreal.iro.lecuyer.hups.DigitalNetFromFile
.
DigitalNetFromFile(String, int) - Constructor for class umontreal.iro.lecuyer.hups.DigitalNetFromFile
.
DigitalSequence - Class in umontreal.iro.lecuyer.hups
DigitalSequence
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 class umontreal.iro.lecuyer.util.HilbertCurveBatchSort
 
dimension() - Method in class umontreal.iro.lecuyer.util.HilbertCurveSort
 
dimension() - Method in class umontreal.iro.lecuyer.util.HilbertCurveSplitSort
 
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
.
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
DiscL2Unanchored
DiscL2Unanchored(double[][], int, int) - Constructor for class umontreal.iro.lecuyer.discrepancy.DiscL2Unanchored
.
DiscL2Unanchored(int, int) - Constructor for class umontreal.iro.lecuyer.discrepancy.DiscL2Unanchored
.
DiscL2Unanchored(PointSet) - Constructor for class umontreal.iro.lecuyer.discrepancy.DiscL2Unanchored
.
DiscL2Unanchored() - Constructor for class umontreal.iro.lecuyer.discrepancy.DiscL2Unanchored
.
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
DiscreteDistIntChart
DiscreteDistIntChart(DiscreteDistributionInt) - Constructor for class umontreal.iro.lecuyer.charts.DiscreteDistIntChart
.
DiscreteDistIntChart(DiscreteDistributionInt, int, int) - Constructor for class umontreal.iro.lecuyer.charts.DiscreteDistIntChart
.
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
DiscShift1Lattice
DiscShift1Lattice(double[][], int, int) - Constructor for class umontreal.iro.lecuyer.discrepancy.DiscShift1Lattice
.
DiscShift1Lattice(double[][], int, int, double[]) - Constructor for class umontreal.iro.lecuyer.discrepancy.DiscShift1Lattice
.
DiscShift1Lattice(int, int, double[]) - Constructor for class umontreal.iro.lecuyer.discrepancy.DiscShift1Lattice
.
DiscShift1Lattice(Rank1Lattice) - Constructor for class umontreal.iro.lecuyer.discrepancy.DiscShift1Lattice
.
DiscShift1Lattice() - Constructor for class umontreal.iro.lecuyer.discrepancy.DiscShift1Lattice
.
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
DiscShiftBaker1
DiscShiftBaker1(double[][], int, int) - Constructor for class umontreal.iro.lecuyer.discrepancy.DiscShiftBaker1
.
DiscShiftBaker1(double[][], int, int, double[]) - Constructor for class umontreal.iro.lecuyer.discrepancy.DiscShiftBaker1
.
DiscShiftBaker1(int, int, double[]) - Constructor for class umontreal.iro.lecuyer.discrepancy.DiscShiftBaker1
.
DiscShiftBaker1(PointSet) - Constructor for class umontreal.iro.lecuyer.discrepancy.DiscShiftBaker1
.
DiscShiftBaker1() - Constructor for class umontreal.iro.lecuyer.discrepancy.DiscShiftBaker1
.
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
Distribution
DistributionFactory - Class in umontreal.iro.lecuyer.probdist
This class implements a string API for the package probdist.
DMatrix - Class in umontreal.iro.lecuyer.util
DMatrix
DMatrix(int, int) - Constructor for class umontreal.iro.lecuyer.util.DMatrix
.
DMatrix(double[][], int, int) - Constructor for class umontreal.iro.lecuyer.util.DMatrix
.
DMatrix(DMatrix) - Constructor for class umontreal.iro.lecuyer.util.DMatrix
.
DoubleArrayComparator - Class in umontreal.iro.lecuyer.util
DoubleArrayComparator
DoubleArrayComparator(int) - Constructor for class umontreal.iro.lecuyer.util.DoubleArrayComparator
.
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
.
EmpiricalChart - Class in umontreal.iro.lecuyer.charts
EmpiricalChart
EmpiricalChart() - Constructor for class umontreal.iro.lecuyer.charts.EmpiricalChart
.
EmpiricalChart(String, String, String, double[]...) - Constructor for class umontreal.iro.lecuyer.charts.EmpiricalChart
.
EmpiricalChart(String, String, String, double[], int) - Constructor for class umontreal.iro.lecuyer.charts.EmpiricalChart
.
EmpiricalChart(String, String, String, DoubleArrayList...) - Constructor for class umontreal.iro.lecuyer.charts.EmpiricalChart
.
EmpiricalChart(String, String, String, TallyStore...) - Constructor for class umontreal.iro.lecuyer.charts.EmpiricalChart
.
EmpiricalChart(String, String, String, XYSeriesCollection) - Constructor for class umontreal.iro.lecuyer.charts.EmpiricalChart
.
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
EmptyRandomization
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
.
enlarge(int) - Method in class umontreal.iro.lecuyer.util.BitVector
.
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
.
eraseOriginalGeneratorMatrices() - Method in class umontreal.iro.lecuyer.hups.DigitalNet
.
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
.
erfc(double) - Static method in class umontreal.iro.lecuyer.util.Num
.
erfcInv(double) - Static method in class umontreal.iro.lecuyer.util.Num
.
erfInv(double) - Static method in class umontreal.iro.lecuyer.util.Num
.
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
ErlangDist
ErlangDist(int) - Constructor for class umontreal.iro.lecuyer.probdist.ErlangDist
.
ErlangDist(int, double) - Constructor for class umontreal.iro.lecuyer.probdist.ErlangDist
.
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
.
evalCheby(double[], int, double) - Static method in class umontreal.iro.lecuyer.util.Num
.
evalChebyStar(double[], int, double) - Static method in class umontreal.iro.lecuyer.util.Num
.
evalPoly(int, double[], double[], double) - Static method in class umontreal.iro.lecuyer.util.Misc
.
evalPoly(double[], int, double) - Static method in class umontreal.iro.lecuyer.util.Misc
\begin{tabb}Evaluates the polynomial $P(x)$
 of degree $n$\ with coefficients $c_...
<DT><A HREF=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
.
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
.
exp(double[][]) - Static method in class umontreal.iro.lecuyer.util.DMatrix
.
exp(DoubleMatrix2D) - Static method in class umontreal.iro.lecuyer.util.DMatrix
.
expBesselK1(double, double) - Static method in class umontreal.iro.lecuyer.util.Num
.
expBidiagonal(DoubleMatrix2D) - Static method in class umontreal.iro.lecuyer.util.DMatrix
.
expBidiagonal(DoubleMatrix2D, DoubleMatrix1D) - Static method in class umontreal.iro.lecuyer.util.DMatrix
.
expmiBidiagonal(DoubleMatrix2D) - Static method in class umontreal.iro.lecuyer.util.DMatrix
.
expmiBidiagonal(DoubleMatrix2D, DoubleMatrix1D) - Static method in class umontreal.iro.lecuyer.util.DMatrix
.
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
ExponentialInverseFromDensityGen
ExponentialInverseFromDensityGen(RandomStream, double, double, int) - Constructor for class umontreal.iro.lecuyer.randvar.ExponentialInverseFromDensityGen
.
ExponentialInverseFromDensityGen(RandomStream, ExponentialDist, double, int) - Constructor for class umontreal.iro.lecuyer.randvar.ExponentialInverseFromDensityGen
.
ExponentialInverseFromDensityGen(RandomStream, InverseDistFromDensity) - Constructor for class umontreal.iro.lecuyer.randvar.ExponentialInverseFromDensityGen
.
extendSequence(int) - Method in class umontreal.iro.lecuyer.hups.DigitalSequence
.
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. .
ExtremeValueDist(double, double) - Constructor for class umontreal.iro.lecuyer.probdist.ExtremeValueDist
Deprecated. .
ExtremeValueGen - Class in umontreal.iro.lecuyer.randvar
Deprecated. 
ExtremeValueGen(RandomStream, double, double) - Constructor for class umontreal.iro.lecuyer.randvar.ExtremeValueGen
Deprecated. .
ExtremeValueGen(RandomStream) - Constructor for class umontreal.iro.lecuyer.randvar.ExtremeValueGen
Deprecated. .
ExtremeValueGen(RandomStream, ExtremeValueDist) - Constructor for class umontreal.iro.lecuyer.randvar.ExtremeValueGen
Deprecated. .

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
F2wStructure
factoPow(int) - Static method in class umontreal.iro.lecuyer.util.Num
.
factorial(int) - Static method in class umontreal.iro.lecuyer.util.Num
.
FatigueLifeDist - Class in umontreal.iro.lecuyer.probdist
FatigueLifeDist
FatigueLifeDist(double, double, double) - Constructor for class umontreal.iro.lecuyer.probdist.FatigueLifeDist
.
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
FisherFDist
FisherFDist(int, int) - Constructor for class umontreal.iro.lecuyer.probdist.FisherFDist
.
FisherFGen - Class in umontreal.iro.lecuyer.randvar
FisherFGen
FisherFGen(RandomStream, int, int) - Constructor for class umontreal.iro.lecuyer.randvar.FisherFGen
.
FisherFGen(RandomStream, FisherFDist) - Constructor for class umontreal.iro.lecuyer.randvar.FisherFGen
.
FNoncentralGen - Class in umontreal.iro.lecuyer.randvar
FNoncentralGen
FNoncentralGen(ChiSquareNoncentralGen, ChiSquareGen) - Constructor for class umontreal.iro.lecuyer.randvar.FNoncentralGen
.
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
.
format(double) - Static method in class umontreal.iro.lecuyer.util.AbstractChrono
.
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
.
format(double[], int, int, int, int, int, int) - Static method in class umontreal.iro.lecuyer.util.TableFormat
.
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
.
format(int[][], int, int, int, int, int, int, String) - Static method in class umontreal.iro.lecuyer.util.TableFormat
.
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
.
formatPoints(int, int) - Method in class umontreal.iro.lecuyer.hups.PointSet
.
formatPoints(PointSetIterator) - Method in class umontreal.iro.lecuyer.hups.PointSet
.
formatPoints(PointSetIterator, int, int) - Method in class umontreal.iro.lecuyer.hups.PointSet
.
formatPointsBase(int) - Method in class umontreal.iro.lecuyer.hups.PointSet
.
formatPointsBase(int, int, int) - Method in class umontreal.iro.lecuyer.hups.PointSet
.
formatPointsBase(PointSetIterator, int) - Method in class umontreal.iro.lecuyer.hups.PointSet
.
formatPointsBase(PointSetIterator, int, int, int) - Method in class umontreal.iro.lecuyer.hups.PointSet
.
formatPointsNumbered() - Method in class umontreal.iro.lecuyer.hups.PointSet
.
formatPointsNumbered(int, int) - Method in class umontreal.iro.lecuyer.hups.PointSet
.
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
FrechetGen
FrechetGen(RandomStream, double) - Constructor for class umontreal.iro.lecuyer.randvar.FrechetGen
.
FrechetGen(RandomStream, double, double, double) - Constructor for class umontreal.iro.lecuyer.randvar.FrechetGen
.
FrechetGen(RandomStream, FrechetDist) - Constructor for class umontreal.iro.lecuyer.randvar.FrechetGen
.
fromCSV(String) - Static method in class umontreal.iro.lecuyer.charts.PlotFormat
.
fromCustomizedFormat(String, String, String) - Static method in class umontreal.iro.lecuyer.charts.PlotFormat
.
fromGNUPlot(String) - Static method in class umontreal.iro.lecuyer.charts.PlotFormat
.
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
GammaDistFromMoments
GammaDistFromMoments(double, double, int) - Constructor for class umontreal.iro.lecuyer.probdist.GammaDistFromMoments
.
GammaDistFromMoments(double, double) - Constructor for class umontreal.iro.lecuyer.probdist.GammaDistFromMoments
.
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
GammaProcess
GammaProcess(double, double, double, RandomStream) - Constructor for class umontreal.iro.lecuyer.stochprocess.GammaProcess
.
GammaProcess(double, double, double, GammaGen) - Constructor for class umontreal.iro.lecuyer.stochprocess.GammaProcess
.
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
GammaProcessPCA
GammaProcessPCA(double, double, double, RandomStream) - Constructor for class umontreal.iro.lecuyer.stochprocess.GammaProcessPCA
.
GammaProcessPCA(double, double, double, GammaGen) - Constructor for class umontreal.iro.lecuyer.stochprocess.GammaProcessPCA
.
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
.
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
.
gcd(long, long) - Static method in class umontreal.iro.lecuyer.util.Num
[tabb83]
xinteger yinteger the 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
.
generatePath(double[]) - Method in class umontreal.iro.lecuyer.stochprocess.GammaProcess
.
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
.
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
.
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
GeometricGen
GeometricGen(RandomStream, double) - Constructor for class umontreal.iro.lecuyer.randvar.GeometricGen
.
GeometricGen(RandomStream, GeometricDist) - Constructor for class umontreal.iro.lecuyer.randvar.GeometricGen
.
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
.
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
.
geta() - Method in class umontreal.iro.lecuyer.hups.LCGPointSet
.
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
.
getA() - Method in class umontreal.iro.lecuyer.probdist.PowerDist
Returns the parameter a.
getA() - Method in class umontreal.iro.lecuyer.probdist.RayleighDist
.
getA() - Method in class umontreal.iro.lecuyer.probdist.TriangularDist
.
getA() - Method in class umontreal.iro.lecuyer.probdist.TruncatedDist
Returns the value of a.
getA() - Method in class umontreal.iro.lecuyer.probdist.UniformDist
.
getA() - Method in class umontreal.iro.lecuyer.randvar.BetaGen
.
getA() - Method in class umontreal.iro.lecuyer.randvar.NakagamiGen
.
getA() - Method in class umontreal.iro.lecuyer.randvar.PowerGen
.
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
.
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
.
getAlpha() - Method in class umontreal.iro.lecuyer.probdist.ExtremeValueDist
Deprecated. .
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
.
getAlpha() - Method in class umontreal.iro.lecuyer.probdist.NormalInverseGaussianDist
.
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
.
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
.
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. .
getAlpha() - Method in class umontreal.iro.lecuyer.randvar.FrechetGen
.
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
.
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
.
getAlpha() - Method in class umontreal.iro.lecuyer.randvar.ParetoGen
.
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
.
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
.
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
.
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
.
getB() - Method in class umontreal.iro.lecuyer.probdist.TruncatedDist
Returns the value of b.
getB() - Method in class umontreal.iro.lecuyer.probdist.UniformDist
.
getB() - Method in class umontreal.iro.lecuyer.randvar.BetaGen
.
getB() - Method in class umontreal.iro.lecuyer.randvar.PowerGen
.
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
.
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
.
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
.
getBeta() - Method in class umontreal.iro.lecuyer.probdist.FatigueLifeDist
.
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
.
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
.
getBeta() - Method in class umontreal.iro.lecuyer.probdist.NormalInverseGaussianDist
.
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
.
getBeta() - Method in class umontreal.iro.lecuyer.randvar.BetaGen
.
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
.
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
.
getBeta() - Method in class umontreal.iro.lecuyer.randvar.ParetoGen
.
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
.
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
.
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
.
getC() - Method in class umontreal.iro.lecuyer.probdist.PowerDist
Returns the parameter c.
getC() - Method in class umontreal.iro.lecuyer.randvar.NakagamiGen
.
getC() - Method in class umontreal.iro.lecuyer.randvar.PowerGen
.
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
.
getCdf() - Method in class umontreal.iro.lecuyer.charts.DiscreteDistIntChart
.
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
.
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
.
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
.
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
.
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
.
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
.
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
.
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
.
getCurPointIndex() - Method in interface umontreal.iro.lecuyer.hups.PointSetIterator
.
getCurrentObservation(double[]) - Method in class umontreal.iro.lecuyer.stochprocess.MultivariateStochasticProcess
.
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
.
getDelta() - Method in class umontreal.iro.lecuyer.probdist.NormalInverseGaussianDist
.
getDelta() - Method in class umontreal.iro.lecuyer.probdist.WeibullDist
.
getDelta() - Method in class umontreal.iro.lecuyer.randvar.FrechetGen
.
getDelta() - Method in class umontreal.iro.lecuyer.randvar.GumbelGen
Returns the parameter δ.
getDelta() - Method in class umontreal.iro.lecuyer.randvar.NormalInverseGaussianGen
.
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
.
getDimension() - Method in class umontreal.iro.lecuyer.hups.SortedPointSet
.
getDimension() - Method in class umontreal.iro.lecuyer.probdistmulti.ContinuousDistributionMulti
.
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
.
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
.
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
.
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
.
getFaurePermutation(int, int[]) - Static method in class umontreal.iro.lecuyer.hups.RadicalInverse
.
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
.
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
.
getHours() - Method in class umontreal.iro.lecuyer.util.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
.
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
.
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
.
getInstanceFromMLE(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.ErlangDist
.
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. .
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
.
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
.
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
.
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
.
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
.
getInstanceFromMLE(int[], int) - Static method in class umontreal.iro.lecuyer.probdist.NegativeBinomialDist
.
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
[tabb51]
xthe list of observations to use to evaluate parameters nthe number of observations to use to evaluate parameters
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
.
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
.
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
.
getInstanceFromMLE(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.UniformDist
.
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
.
getInstanceFromMLE1(int[], int, double) - Static method in class umontreal.iro.lecuyer.probdist.NegativeBinomialDist
.
getInstanceFromMLEmin(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.GumbelDist
.
getInt(int) - Method in class umontreal.iro.lecuyer.util.BitVector
.
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
.
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
.
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
.
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. .
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
.
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
.
getLambda() - Method in class umontreal.iro.lecuyer.randvar.ChiSquareNoncentralGen
.
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. .
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
.
getLambda() - Method in class umontreal.iro.lecuyer.randvar.InverseGaussianGen
.
getLambda() - Method in class umontreal.iro.lecuyer.randvar.LogisticGen
.
getLambda() - Method in class umontreal.iro.lecuyer.randvar.NakagamiGen
.
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
.
getM() - Method in class umontreal.iro.lecuyer.randvar.FisherFGen
.
getM() - Method in class umontreal.iro.lecuyer.randvar.HypergeometricGen
.
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
.
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
.
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
.
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
.
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
.
getMean() - Method in class umontreal.iro.lecuyer.probdist.EmpiricalDist
 
getMean(int, double) - Static method in class umontreal.iro.lecuyer.probdist.ErlangDist
.
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. .
getMean() - Method in class umontreal.iro.lecuyer.probdist.FatigueLifeDist
 
getMean(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.FatigueLifeDist
.
getMean() - Method in class umontreal.iro.lecuyer.probdist.FisherFDist
 
getMean(int, int) - Static method in class umontreal.iro.lecuyer.probdist.FisherFDist
.
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
.
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
.
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
.
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
.
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
.
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
.
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
.
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
.
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
.
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
.
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
.
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
.
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
.
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
.
getMedian(int[], int) - Static method in class umontreal.iro.lecuyer.util.Misc
[tabb47]
Athe array nthe number of used elements the median of 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
.
getMLE(int[], int) - Static method in class umontreal.iro.lecuyer.probdist.BernoulliDist
.
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
.
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
.
getMLE(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.ErlangDist
.
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. .
getMLE(double[], int, double) - Static method in class umontreal.iro.lecuyer.probdist.FatigueLifeDist
.
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
.
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
.
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
.
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
.
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
.
getMLE(int[], int) - Static method in class umontreal.iro.lecuyer.probdist.NegativeBinomialDist
.
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
.
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
.
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
.
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
.
getMLE(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.UniformDist
.
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
.
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
.
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
.
getMLEmin(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.GumbelDist
.
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
.
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
.
getMu() - Method in class umontreal.iro.lecuyer.probdist.FatigueLifeDist
.
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
.
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
.
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
.
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
.
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
.
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
.
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
.
getN() - Method in class umontreal.iro.lecuyer.probdist.CramerVonMisesDist
.
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
.
getN() - Method in class umontreal.iro.lecuyer.probdist.NegativeBinomialDist
.
getN() - Method in class umontreal.iro.lecuyer.probdist.PiecewiseLinearEmpiricalDist
.
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
.
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
.
getN() - Method in class umontreal.iro.lecuyer.randvar.FisherFGen
.
getN() - Method in class umontreal.iro.lecuyer.randvar.PascalGen
.
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
[tabb90]
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
.
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
.
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
.
getNu() - Method in class umontreal.iro.lecuyer.randvar.ChiSquareNoncentralGen
.
getNu() - Method in class umontreal.iro.lecuyer.stochprocess.GammaProcess
.
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
.
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
.
getObservation(int, double[]) - Method in class umontreal.iro.lecuyer.stochprocess.MultivariateStochasticProcess
.
getObservation(int, int) - Method in class umontreal.iro.lecuyer.stochprocess.MultivariateStochasticProcess
.
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
.
getOrder() - Method in class umontreal.iro.lecuyer.randvar.InverseFromDensityGen
.
getOrder() - Method in class umontreal.iro.lecuyer.randvar.NormalInverseFromDensityGen
.
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
.
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
.
getP() - Method in class umontreal.iro.lecuyer.probdistmulti.MultinomialDist
.
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
.
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
.
getParams() - Method in class umontreal.iro.lecuyer.probdist.AndersonDarlingDist
.
getParams() - Method in class umontreal.iro.lecuyer.probdist.BernoulliDist
.
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
.
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
.
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
.
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
.
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
.
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. .
getParams() - Method in class umontreal.iro.lecuyer.probdist.FatigueLifeDist
.
getParams() - Method in class umontreal.iro.lecuyer.probdist.FisherFDist
.
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.GeometricDist
Return a table containing the parameters of the current distribution.
getParams() - Method in class umontreal.iro.lecuyer.probdist.GumbelDist
.
getParams() - Method in class umontreal.iro.lecuyer.probdist.HalfNormalDist
Return a table containing the parameters of the current distribution.
getParams() - Method in class umontreal.iro.lecuyer.probdist.HyperbolicSecantDist
Return a table containing the parameters of the current distribution.
getParams() - Method in class umontreal.iro.lecuyer.probdist.HypergeometricDist
Return a table containing the parameters of the current distribution.
getParams() - Method in class umontreal.iro.lecuyer.probdist.HypoExponentialDist
Same as getLambda.
getParams() - Method in class umontreal.iro.lecuyer.probdist.HypoExponentialDistEqual
Returns the three parameters of this hypoexponential distribution as array (n, k, h).
getParams() - Method in class umontreal.iro.lecuyer.probdist.InverseDistFromDensity
Return a table containing the parameters of the current distribution.
getParams() - Method in class umontreal.iro.lecuyer.probdist.InverseGammaDist
Returns a table containing the parameters of the current distribution.
getParams() - Method in class umontreal.iro.lecuyer.probdist.InverseGaussianDist
Return a table containing the parameters of the current distribution.
getParams() - Method in class umontreal.iro.lecuyer.probdist.KolmogorovSmirnovDist
Returns an array containing the parameter n of this object.
getParams() - Method in class umontreal.iro.lecuyer.probdist.KolmogorovSmirnovPlusDist
.
getParams() - Method in class umontreal.iro.lecuyer.probdist.LaplaceDist
Return a table containing the parameters of the current distribution.
getParams() - Method in class umontreal.iro.lecuyer.probdist.LogarithmicDist
.
getParams() - Method in class umontreal.iro.lecuyer.probdist.LogisticDist
Return a table containing the parameters of the current distribution.
getParams() - Method in class umontreal.iro.lecuyer.probdist.LoglogisticDist
.
getParams() - Method in class umontreal.iro.lecuyer.probdist.LognormalDist
Returns a table containing the parameters of the current distribution, in the order: [μ, σ].
getParams() - Method in class umontreal.iro.lecuyer.probdist.NakagamiDist
.
getParams() - Method in class umontreal.iro.lecuyer.probdist.NegativeBinomialDist
.
getParams() - Method in class umontreal.iro.lecuyer.probdist.NormalDist
Return a table containing the parameters of the current distribution.
getParams() - Method in class umontreal.iro.lecuyer.probdist.NormalInverseGaussianDist
.
getParams() - Method in class umontreal.iro.lecuyer.probdist.ParetoDist
Return a table containing the parameters of the current distribution.
getParams() - Method in class umontreal.iro.lecuyer.probdist.Pearson5Dist
Deprecated. Return a table containing the parameters of the current distribution.
getParams() - Method in class umontreal.iro.lecuyer.probdist.Pearson6Dist
.
getParams() - Method in class umontreal.iro.lecuyer.probdist.PiecewiseLinearEmpiricalDist
.
getParams() - Method in class umontreal.iro.lecuyer.probdist.PoissonDist
Return a table containing the parameter of the current distribution.
getParams() - Method in class umontreal.iro.lecuyer.probdist.PowerDist
Return a table containing the parameters of the current distribution.
getParams() - Method in class umontreal.iro.lecuyer.probdist.RayleighDist
.
getParams() - Method in class umontreal.iro.lecuyer.probdist.StudentDist
Return a table containing the parameter of the current distribution.
getParams() - Method in class umontreal.iro.lecuyer.probdist.TriangularDist
.
getParams() - Method in class umontreal.iro.lecuyer.probdist.TruncatedDist
Return a table containing the parameters of the current distribution.
getParams() - Method in class umontreal.iro.lecuyer.probdist.UniformDist
.
getParams() - Method in class umontreal.iro.lecuyer.probdist.UniformIntDist
Return a table containing the parameters of the current distribution.
getParams() - Method in class umontreal.iro.lecuyer.probdist.WatsonGDist
Return an array containing the parameter n of this object.
getParams() - Method in class umontreal.iro.lecuyer.probdist.WatsonUDist
Return an array containing the parameter n of this object.
getParams() - Method in class umontreal.iro.lecuyer.probdist.WeibullDist
.
getPath() - Method in class umontreal.iro.lecuyer.stochprocess.StochasticProcess
Returns a reference to the last generated sample path {X(t0),..., X(td)}.
getPCADecompSigma() - Method in class umontreal.iro.lecuyer.randvarmulti.MultinormalPCAGen
Returns the matrix A = V()1/2 of this object.
getPerformance() - Method in class umontreal.iro.lecuyer.markovchain.MarkovChain
Returns the performance measure (total or average cost or gain) so far, for the current simulation run.
getPerformance(int) - Method in class umontreal.iro.lecuyer.markovchain.MarkovChainDouble
Returns the performance mesure associated with current state, which may depend on the number of steps numsteps.
getPerformance() - Method in class umontreal.iro.lecuyer.markovchain.MarkovChainDouble
Returns the value of perf which is computed when a chain stops.
getPerformanceDouble(double, int) - Method in class umontreal.iro.lecuyer.markovchain.MarkovChainDouble
Returns the performance measure associated with state state, which may depend on the number of steps numsteps.
getPerformances() - Method in class umontreal.iro.lecuyer.markovchain.ArrayOfComparableChains
Returns the performance of the n chains in an array.
getPlotStyle(int) - Method in class umontreal.iro.lecuyer.charts.XYListSeriesCollection
Gets the current plot style for the selected series.
getPointSet() - Method in class umontreal.iro.lecuyer.hups.RQMCPointSet
Returns the point set associated to this object.
getPower() - Method in class umontreal.iro.lecuyer.functions.PowerMathFunction
Returns the power p.
getPrimes(int) - Static method in class umontreal.iro.lecuyer.hups.RadicalInverse
.
getProb() - Method in class umontreal.iro.lecuyer.charts.DiscreteDistIntChart
.
getProcess() - Method in class umontreal.iro.lecuyer.simprocs.UserRecord
Returns the process object associated with this record.
getProcessInfo() - Static method in class umontreal.iro.lecuyer.util.Systeme
.
getRa() - Method in class umontreal.iro.lecuyer.simevents.Event
 
getRandomization() - Method in class umontreal.iro.lecuyer.hups.RQMCPointSet
Returns the randomization associated to this object.
getRandomization() - Method in class umontreal.iro.lecuyer.markovchain.ArrayOfComparableChains
Returns the internal PointSetRandomization.
getRandomStreamClass() - Method in class umontreal.iro.lecuyer.rng.BasicRandomStreamFactory
.
getRandomStreamFactory() - Method in exception umontreal.iro.lecuyer.rng.RandomStreamInstantiationException
Returns the random stream factory concerned by this exception.
getRangeBounds() - Method in class umontreal.iro.lecuyer.charts.BoxSeriesCollection
Returns the range (y-coordinates) min and max values.
getRangeBounds() - Method in class umontreal.iro.lecuyer.charts.SSJCategorySeriesCollection
.
getRangeBounds() - Method in class umontreal.iro.lecuyer.charts.SSJXYSeriesCollection
Returns range (y-coordinates) min and max values.
getRealBatchEndingTime(int) - Method in class umontreal.iro.lecuyer.simexp.BatchMeansSim
Returns the ending simulation time of batch batch.
getRealBatchLength(int) - Method in class umontreal.iro.lecuyer.simexp.BatchMeansSim
Returns the length, in simulation time units, of the real batch batch.
getRealBatchStartingTime(int) - Method in class umontreal.iro.lecuyer.simexp.BatchMeansSim
Returns the starting simulation time of batch batch.
getRenderer() - Method in class umontreal.iro.lecuyer.charts.SSJCategorySeriesCollection
.
getRenderer() - Method in class umontreal.iro.lecuyer.charts.SSJXYSeriesCollection
Returns the XYItemRenderer object associated with the current object.
getRequestTime() - Method in class umontreal.iro.lecuyer.simprocs.UserRecord
Returns the time of creation of this record.
getRequiredNewBatches() - Method in class umontreal.iro.lecuyer.simexp.BatchMeansSim
Computes the approximate number of required real batches to be simulated before the simulation can be stopped.
getRequiredNewObservations(StatProbe[], double, double) - Static method in class umontreal.iro.lecuyer.simexp.SimExp
Returns the approximate number of additional observations required to reach a relative error smaller than or equal to targetError for each tally in the array a when confidence intervals are computed with confidence level level.
getRequiredNewObservations(Iterable<? extends StatProbe>, double, double) - Static method in class umontreal.iro.lecuyer.simexp.SimExp
Returns the approximate number of additional observations required to reach a relative error smaller than or equal to targetError for each tally enumerated by it when confidence intervals are computed with confidence level level.
getRequiredNewObservations(StatProbe, double, double) - Static method in class umontreal.iro.lecuyer.simexp.SimExp
Calls getRequiredNewObservations with the average, confidence interval radius, and number of observations given by the statistical probe probe.
getRequiredNewObservations(double, double, int, double) - Static method in class umontreal.iro.lecuyer.simexp.SimExp
Returns the approximate number of additional observations needed for the point estimator bar(X)n = center, computed using n = numberObs observations and with a confidence interval having radius δn/(n)1/2 = radius, to have a relative error less than or equal to ε = targetError.
getRequiredNewObservationsTally(Tally, double, double) - Static method in class umontreal.iro.lecuyer.simexp.SimExp
Calls getRequiredNewObservations with the average, confidence interval radius, and number of observations given by the tally ta.
getRequiredNewObservationsTally(FunctionOfMultipleMeansTally, double, double) - Static method in class umontreal.iro.lecuyer.simexp.SimExp
Calls getRequiredNewObservations with the average, confidence interval radius, and number of observations given by the function of multiple means fmmt.
getRequiredNewReplications() - Method in class umontreal.iro.lecuyer.simexp.RepSim
Returns the approximate number of additional replications to meet an experiment-specific stopping criterion.
getRho() - Method in class umontreal.iro.lecuyer.functionfit.SmoothingCubicSpline
Returns the smoothing factor used to construct the spline.
getSampleMean() - Method in class umontreal.iro.lecuyer.probdist.EmpiricalDist
Returns the sample mean of the observations.
getSampleMean() - Method in class umontreal.iro.lecuyer.probdist.PiecewiseLinearEmpiricalDist
.
getSampleStandardDeviation() - Method in class umontreal.iro.lecuyer.probdist.EmpiricalDist
Returns the sample standard deviation of the observations.
getSampleStandardDeviation() - Method in class umontreal.iro.lecuyer.probdist.PiecewiseLinearEmpiricalDist
.
getSampleVariance() - Method in class umontreal.iro.lecuyer.probdist.EmpiricalDist
Returns the sample variance of the observations.
getSampleVariance() - Method in class umontreal.iro.lecuyer.probdist.PiecewiseLinearEmpiricalDist
.
getSeconds() - Method in class umontreal.iro.lecuyer.util.AbstractChrono
.
getSeriesCollection() - Method in class umontreal.iro.lecuyer.charts.BoxChart
Returns the chart's dataset.
getSeriesCollection() - Method in class umontreal.iro.lecuyer.charts.EmpiricalChart
.
getSeriesCollection() - Method in class umontreal.iro.lecuyer.charts.HistogramChart
Returns the chart's dataset.
getSeriesCollection() - Method in class umontreal.iro.lecuyer.charts.HistogramSeriesCollection
Returns the CustomHistogramDataset object associated with the current variable.
getSeriesCollection() - Method in class umontreal.iro.lecuyer.charts.ScatterChart
Returns the chart's dataset.
getSeriesCollection() - Method in class umontreal.iro.lecuyer.charts.SSJCategorySeriesCollection
.
getSeriesCollection() - Method in class umontreal.iro.lecuyer.charts.SSJXYSeriesCollection
Returns the XYDataset object associated with the current object.
getSeriesCollection() - Method in class umontreal.iro.lecuyer.charts.XYLineChart
Returns the chart's dataset.
getSeriesCount() - Method in class umontreal.iro.lecuyer.charts.CustomHistogramDataset
Returns the number of series in the dataset.
getSeriesKey(int) - Method in class umontreal.iro.lecuyer.charts.CustomHistogramDataset
Returns the key for a series.
getShiftDimension() - Method in class umontreal.iro.lecuyer.hups.RandShiftedPointSet
.
getShortName() - Method in enum umontreal.iro.lecuyer.util.TimeUnit
Returns the short name representing this unit in a string specifying a time duration.
getSigma() - Method in class umontreal.iro.lecuyer.probdist.FoldedNormalDist
Returns the parameter σ of this object.
getSigma() - Method in class umontreal.iro.lecuyer.probdist.HalfNormalDist
Returns the parameter σ of this object.
getSigma() - Method in class umontreal.iro.lecuyer.probdist.HyperbolicSecantDist
Returns the parameter σ of this object.
getSigma() - Method in class umontreal.iro.lecuyer.probdist.LognormalDist
Returns the parameter σ of this object.
getSigma() - Method in class umontreal.iro.lecuyer.probdist.NormalDist
Returns the parameter σ.
getSigma() - Method in class umontreal.iro.lecuyer.probdist.RayleighDist
.
getSigma() - Method in class umontreal.iro.lecuyer.probdistmulti.MultiNormalDist
Returns the parameter Σ of this object.
getSigma() - Method in class umontreal.iro.lecuyer.randvar.FoldedNormalGen
Returns the parameter σ of this object.
getSigma() - Method in class umontreal.iro.lecuyer.randvar.HalfNormalGen
.
getSigma() - Method in class umontreal.iro.lecuyer.randvar.HyperbolicSecantGen
Returns the parameter σ of this object.
getSigma() - Method in class umontreal.iro.lecuyer.randvar.LognormalGen
Returns the parameter σ of this object.
getSigma() - Method in class umontreal.iro.lecuyer.randvar.NormalGen
Returns the parameter σ of this object.
getSigma() - Method in class umontreal.iro.lecuyer.randvar.RayleighGen
Returns the parameter β.
getSigma() - Method in class umontreal.iro.lecuyer.randvarmulti.MultinormalGen
Returns the covariance matrix Σ used by this generator.
getSigma() - Method in class umontreal.iro.lecuyer.stochprocess.BrownianMotion
Returns the value of σ.
getSigma() - Method in class umontreal.iro.lecuyer.stochprocess.CIRProcess
Returns the value of σ.
getSigma() - Method in class umontreal.iro.lecuyer.stochprocess.CIRProcessEuler
Returns the value of σ.
getSigma() - Method in class umontreal.iro.lecuyer.stochprocess.GeometricBrownianMotion
Returns the value of σ.
getSigma() - Method in class umontreal.iro.lecuyer.stochprocess.GeometricVarianceGammaProcess
Returns the value of the parameter σ.
getSigma() - Method in class umontreal.iro.lecuyer.stochprocess.OrnsteinUhlenbeckProcess
Returns the value of σ.
getSigma() - Method in class umontreal.iro.lecuyer.stochprocess.VarianceGammaProcess
Returns the value of the parameter σ.
getSigma1() - Method in class umontreal.iro.lecuyer.probdistmulti.BiNormalDist
Returns the parameter σ1.
getSigma2() - Method in class umontreal.iro.lecuyer.probdistmulti.BiNormalDist
Returns the parameter σ2.
getSimpleName(Class<?>) - Method in class umontreal.iro.lecuyer.util.ClassFinder
Returns the simple name of the class cls that can be used when the imports contained in this class finder are used.
getSort() - Method in class umontreal.iro.lecuyer.hups.SortedPointSet
.
getSort() - Method in class umontreal.iro.lecuyer.markovchain.ArrayOfComparableChains
Returns the MultiDimSort used.
getSortCoordinate() - Method in class umontreal.iro.lecuyer.util.OneDimSort
.
getSortedEigenvalues() - Method in class umontreal.iro.lecuyer.stochprocess.BrownianMotionPCA
.
getSortedEigenvalues() - Method in class umontreal.iro.lecuyer.stochprocess.BrownianMotionPCAEqualSteps
 
getSplinePolynomials() - Method in class umontreal.iro.lecuyer.functionfit.SmoothingCubicSpline
Returns a table containing all fitting polynomials.
getStandardDeviation() - Method in class umontreal.iro.lecuyer.probdist.BernoulliDist
 
getStandardDeviation(double) - Static method in class umontreal.iro.lecuyer.probdist.BernoulliDist
.
getStandardDeviation() - Method in class umontreal.iro.lecuyer.probdist.BetaDist
 
getStandardDeviation(double, double) - Static method in class umontreal.iro.lecuyer.probdist.BetaDist
Computes the standard deviation of the beta distribution with parameters α and β, over the interval [0, 1].
getStandardDeviation(double, double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.BetaDist
Computes the standard deviation of the beta distribution with parameters α and β, over the interval [a, b].
getStandardDeviation() - Method in class umontreal.iro.lecuyer.probdist.BetaSymmetricalDist
 
getStandardDeviation(double) - Static method in class umontreal.iro.lecuyer.probdist.BetaSymmetricalDist
Computes and returns the standard deviation of the symmetrical beta distribution with parameter α.
getStandardDeviation() - Method in class umontreal.iro.lecuyer.probdist.BinomialDist
 
getStandardDeviation(int, double) - Static method in class umontreal.iro.lecuyer.probdist.BinomialDist
Computes the standard deviation of the Binomial distribution with parameters n and p.
getStandardDeviation() - Method in class umontreal.iro.lecuyer.probdist.CauchyDist
 
getStandardDeviation(double, double) - Static method in class umontreal.iro.lecuyer.probdist.CauchyDist
.
getStandardDeviation() - Method in class umontreal.iro.lecuyer.probdist.ChiDist
 
getStandardDeviation(int) - Static method in class umontreal.iro.lecuyer.probdist.ChiDist
Computes and returns the standard deviation of the chi distribution with parameter ν.
getStandardDeviation() - Method in class umontreal.iro.lecuyer.probdist.ChiSquareDist
 
getStandardDeviation(int) - Static method in class umontreal.iro.lecuyer.probdist.ChiSquareDist
.
getStandardDeviation() - Method in class umontreal.iro.lecuyer.probdist.ChiSquareNoncentralDist
 
getStandardDeviation(double, double) - Static method in class umontreal.iro.lecuyer.probdist.ChiSquareNoncentralDist
Computes and returns the standard deviation of the noncentral chi-square distribution with parameters ν = nu and λ = lambda.
getStandardDeviation() - Method in class umontreal.iro.lecuyer.probdist.ConstantDist
Returns the standard deviation = 0.
getStandardDeviation() - Method in class umontreal.iro.lecuyer.probdist.ContinuousDistribution
Returns the standard deviation.
getStandardDeviation() - Method in class umontreal.iro.lecuyer.probdist.CramerVonMisesDist
 
getStandardDeviation(int) - Static method in class umontreal.iro.lecuyer.probdist.CramerVonMisesDist
.
getStandardDeviation() - Method in class umontreal.iro.lecuyer.probdist.DiscreteDistribution
Computes the standard deviation of the distribution.
getStandardDeviation() - Method in interface umontreal.iro.lecuyer.probdist.Distribution
.
getStandardDeviation() - Method in class umontreal.iro.lecuyer.probdist.EmpiricalDist
 
getStandardDeviation(int, double) - Static method in class umontreal.iro.lecuyer.probdist.ErlangDist
.
getStandardDeviation() - Method in class umontreal.iro.lecuyer.probdist.ExponentialDist
 
getStandardDeviation(double) - Static method in class umontreal.iro.lecuyer.probdist.ExponentialDist
Computes and returns the standard deviation of the exponential distribution with parameter λ.
getStandardDeviation() - Method in class umontreal.iro.lecuyer.probdist.ExtremeValueDist
Deprecated.  
getStandardDeviation(double, double) - Static method in class umontreal.iro.lecuyer.probdist.ExtremeValueDist
Deprecated. .
getStandardDeviation() - Method in class umontreal.iro.lecuyer.probdist.FatigueLifeDist
 
getStandardDeviation(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.FatigueLifeDist
.
getStandardDeviation() - Method in class umontreal.iro.lecuyer.probdist.FisherFDist
 
getStandardDeviation(int, int) - Static method in class umontreal.iro.lecuyer.probdist.FisherFDist
.
getStandardDeviation() - Method in class umontreal.iro.lecuyer.probdist.FoldedNormalDist
 
getStandardDeviation(double, double) - Static method in class umontreal.iro.lecuyer.probdist.FoldedNormalDist
Computes the standard deviation of the folded normal distribution with parameters μ and σ.
getStandardDeviation() - Method in class umontreal.iro.lecuyer.probdist.FrechetDist
 
getStandardDeviation(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.FrechetDist
Returns the standard deviation of the Fréchet distribution with parameters α, β and δ.
getStandardDeviation() - Method in class umontreal.iro.lecuyer.probdist.GammaDist
 
getStandardDeviation(double, double) - Static method in class umontreal.iro.lecuyer.probdist.GammaDist
Computes and returns the standard deviation of the gamma distribution with parameters α and λ.
getStandardDeviation() - Method in class umontreal.iro.lecuyer.probdist.GeometricDist
 
getStandardDeviation(double) - Static method in class umontreal.iro.lecuyer.probdist.GeometricDist
Computes and returns the standard deviation of the geometric distribution with parameter p.
getStandardDeviation() - Method in class umontreal.iro.lecuyer.probdist.GumbelDist
 
getStandardDeviation(double, double) - Static method in class umontreal.iro.lecuyer.probdist.GumbelDist
.
getStandardDeviation() - Method in class umontreal.iro.lecuyer.probdist.HalfNormalDist
 
getStandardDeviation(double, double) - Static method in class umontreal.iro.lecuyer.probdist.HalfNormalDist
Computes the standard deviation of the half-normal distribution with parameters μ and σ.
getStandardDeviation() - Method in class umontreal.iro.lecuyer.probdist.HyperbolicSecantDist
 
getStandardDeviation(double, double) - Static method in class umontreal.iro.lecuyer.probdist.HyperbolicSecantDist
Computes and returns the standard deviation of the hyperbolic secant distribution with parameters μ and σ.
getStandardDeviation() - Method in class umontreal.iro.lecuyer.probdist.HypergeometricDist
 
getStandardDeviation(int, int, int) - Static method in class umontreal.iro.lecuyer.probdist.HypergeometricDist
Computes and returns the standard deviation of the hypergeometric distribution with parameters m, l and k.
getStandardDeviation() - Method in class umontreal.iro.lecuyer.probdist.HypoExponentialDist
 
getStandardDeviation(double[]) - Static method in class umontreal.iro.lecuyer.probdist.HypoExponentialDist
Returns the standard deviation of the hypoexponential distribution with rates λi = lambda[i - 1], i = 1,…, k.
getStandardDeviation() - Method in class umontreal.iro.lecuyer.probdist.InverseGammaDist
 
getStandardDeviation(double, double) - Static method in class umontreal.iro.lecuyer.probdist.InverseGammaDist
Returns the standard deviation of the inverse gamma distribution with shape parameter α and scale parameter β.
getStandardDeviation() - Method in class umontreal.iro.lecuyer.probdist.InverseGaussianDist
 
getStandardDeviation(double, double) - Static method in class umontreal.iro.lecuyer.probdist.InverseGaussianDist
Computes and returns the standard deviation of the inverse gaussian distribution with parameters μ and λ.
getStandardDeviation() - Method in class umontreal.iro.lecuyer.probdist.JohnsonSBDist
 
getStandardDeviation(double, double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.JohnsonSBDist
.
getStandardDeviation() - Method in class umontreal.iro.lecuyer.probdist.JohnsonSLDist
 
getStandardDeviation(double, double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.JohnsonSLDist
Returns the standard deviation of the Johnson SL distribution with parameters γ, δ, ξ, λ.
getStandardDeviation() - Method in class umontreal.iro.lecuyer.probdist.JohnsonSUDist
 
getStandardDeviation(double, double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.JohnsonSUDist
Returns the standard deviation of the Johnson SU distribution with parameters γ, δ, ξ, λ.
getStandardDeviation() - Method in class umontreal.iro.lecuyer.probdist.LaplaceDist
 
getStandardDeviation(double, double) - Static method in class umontreal.iro.lecuyer.probdist.LaplaceDist
Computes and returns the standard deviation of the Laplace distribution with parameters μ and β.
getStandardDeviation() - Method in class umontreal.iro.lecuyer.probdist.LogarithmicDist
 
getStandardDeviation(double) - Static method in class umontreal.iro.lecuyer.probdist.LogarithmicDist
.
getStandardDeviation() - Method in class umontreal.iro.lecuyer.probdist.LogisticDist
 
getStandardDeviation(double, double) - Static method in class umontreal.iro.lecuyer.probdist.LogisticDist
Computes and returns the standard deviation of the logistic distribution with parameters α and λ.
getStandardDeviation() - Method in class umontreal.iro.lecuyer.probdist.LoglogisticDist
 
getStandardDeviation(double, double) - Static method in class umontreal.iro.lecuyer.probdist.LoglogisticDist
.
getStandardDeviation() - Method in class umontreal.iro.lecuyer.probdist.LognormalDist
 
getStandardDeviation(double, double) - Static method in class umontreal.iro.lecuyer.probdist.LognormalDist
Computes and returns the standard deviation of the lognormal distribution with parameters μ and σ.
getStandardDeviation() - Method in class umontreal.iro.lecuyer.probdist.NakagamiDist
 
getStandardDeviation(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.NakagamiDist
.
getStandardDeviation() - Method in class umontreal.iro.lecuyer.probdist.NegativeBinomialDist
 
getStandardDeviation(double, double) - Static method in class umontreal.iro.lecuyer.probdist.NegativeBinomialDist
.
getStandardDeviation() - Method in class umontreal.iro.lecuyer.probdist.NormalDist
 
getStandardDeviation(double, double) - Static method in class umontreal.iro.lecuyer.probdist.NormalDist
Computes and returns the standard deviation σ of the normal distribution with parameters μ and σ.
getStandardDeviation() - Method in class umontreal.iro.lecuyer.probdist.NormalInverseGaussianDist
 
getStandardDeviation(double, double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.NormalInverseGaussianDist
.
getStandardDeviation() - Method in class umontreal.iro.lecuyer.probdist.ParetoDist
 
getStandardDeviation(double, double) - Static method in class umontreal.iro.lecuyer.probdist.ParetoDist
Computes and returns the standard deviation of the Pareto distribution with parameters α and β.
getStandardDeviation() - Method in class umontreal.iro.lecuyer.probdist.Pearson5Dist
Deprecated.  
getStandardDeviation(double, double) - Static method in class umontreal.iro.lecuyer.probdist.Pearson5Dist
Deprecated. Computes and returns the standard deviation of a Pearson V distribution with shape parameter α and scale parameter β.
getStandardDeviation() - Method in class umontreal.iro.lecuyer.probdist.Pearson6Dist
 
getStandardDeviation(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.Pearson6Dist
.
getStandardDeviation() - Method in class umontreal.iro.lecuyer.probdist.PiecewiseLinearEmpiricalDist
 
getStandardDeviation() - Method in class umontreal.iro.lecuyer.probdist.PoissonDist
 
getStandardDeviation(double) - Static method in class umontreal.iro.lecuyer.probdist.PoissonDist
Computes and returns the standard deviation of the Poisson distribution with parameter λ.
getStandardDeviation() - Method in class umontreal.iro.lecuyer.probdist.PowerDist
 
getStandardDeviation(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.PowerDist
Computes and returns the standard deviation of the power distribution with parameters a, b and c.
getStandardDeviation() - Method in class umontreal.iro.lecuyer.probdist.RayleighDist
 
getStandardDeviation(double) - Static method in class umontreal.iro.lecuyer.probdist.RayleighDist
.
getStandardDeviation() - Method in class umontreal.iro.lecuyer.probdist.StudentDist
 
getStandardDeviation(int) - Static method in class umontreal.iro.lecuyer.probdist.StudentDist
Computes and returns the standard deviation of the Student t-distribution with parameter n.
getStandardDeviation() - Method in class umontreal.iro.lecuyer.probdist.TriangularDist
 
getStandardDeviation(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.TriangularDist
.
getStandardDeviation() - Method in class umontreal.iro.lecuyer.probdist.TruncatedDist
Returns the square root of the approximate variance.
getStandardDeviation() - Method in class umontreal.iro.lecuyer.probdist.UniformDist
 
getStandardDeviation(double, double) - Static method in class umontreal.iro.lecuyer.probdist.UniformDist
.
getStandardDeviation() - Method in class umontreal.iro.lecuyer.probdist.UniformIntDist
 
getStandardDeviation(int, int) - Static method in class umontreal.iro.lecuyer.probdist.UniformIntDist
Computes and returns the standard deviation of the discrete uniform distribution.
getStandardDeviation() - Method in class umontreal.iro.lecuyer.probdist.WatsonUDist
 
getStandardDeviation(int) - Static method in class umontreal.iro.lecuyer.probdist.WatsonUDist
Returns the standard deviation of the Watson U distribution with parameter n.
getStandardDeviation() - Method in class umontreal.iro.lecuyer.probdist.WeibullDist
 
getStandardDeviation(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.WeibullDist
.
getStartX(int, int) - Method in class umontreal.iro.lecuyer.charts.CustomHistogramDataset
Returns the start value for a bin.
getStartY(int, int) - Method in class umontreal.iro.lecuyer.charts.CustomHistogramDataset
Returns the start y-value for a bin (which is the same as the y-value).
getStatCollecting() - Method in class umontreal.iro.lecuyer.simevents.ListWithStat
Returns true if the list collects statistics about its size and sojourn times of elements, and false otherwise.
getState() - Method in class umontreal.iro.lecuyer.rng.GenF2w32
Returns the current state of the stream, represented as an array of 25 integers.
getState() - Method in class umontreal.iro.lecuyer.rng.LFSR113
.
getState() - Method in class umontreal.iro.lecuyer.rng.LFSR258
Returns the current state of the stream, represented as an array of five integers.
getState() - Method in class umontreal.iro.lecuyer.rng.MRG31k3p
.
getState() - Method in class umontreal.iro.lecuyer.rng.MRG32k3a
Returns the current state Cg of this stream.
getState() - Method in class umontreal.iro.lecuyer.rng.MRG32k3aL
 
getState() - Method in class umontreal.iro.lecuyer.rng.RandMrg
Deprecated. Returns the current state Cg of this stream.
getState() - Method in class umontreal.iro.lecuyer.rng.RandRijndael
.
getState() - Method in class umontreal.iro.lecuyer.rng.WELL1024
Returns the current state of the stream, represented as an array of 32 integers.
getState() - Method in class umontreal.iro.lecuyer.rng.WELL512
.
getState() - Method in class umontreal.iro.lecuyer.rng.WELL607
Returns the current state of the stream, represented as an array of 19 integers.
getState() - Method in class umontreal.iro.lecuyer.simprocs.SimProcess
Returns the state of the process.
getStatesDouble() - Method in class umontreal.iro.lecuyer.markovchain.ArrayOfDoubleChains
Returns the array containing the states of the n chains.
getStream() - Method in class umontreal.iro.lecuyer.hups.EmptyRandomization
.
getStream() - Method in class umontreal.iro.lecuyer.hups.PointSet
.
getStream() - Method in interface umontreal.iro.lecuyer.hups.PointSetRandomization
.
getStream() - Method in class umontreal.iro.lecuyer.hups.RandomShift
.
getStream() - Method in class umontreal.iro.lecuyer.hups.RandomStart
.
getStream() - Method in class umontreal.iro.lecuyer.randvar.RandomVariateGen
Returns the RandomStream used by this generator.
getStream() - Method in class umontreal.iro.lecuyer.randvar.RandomVariateGenWithCache
 
getStream() - Method in class umontreal.iro.lecuyer.randvar.UnuranContinuous
 
getStream() - Method in class umontreal.iro.lecuyer.randvar.UnuranDiscreteInt
 
getStream() - Method in class umontreal.iro.lecuyer.randvar.UnuranEmpirical
 
getStream() - Method in class umontreal.iro.lecuyer.randvarmulti.RandomMultivariateGen
Returns the RandomStream used by this object.
getStream() - Method in class umontreal.iro.lecuyer.stochprocess.BrownianMotion
Returns the random stream of the normal generator.
getStream() - Method in class umontreal.iro.lecuyer.stochprocess.CIRProcess
Returns the random stream of the noncentral chi-square generator.
getStream() - Method in class umontreal.iro.lecuyer.stochprocess.CIRProcessEuler
Returns the random stream of the normal generator.
getStream() - Method in class umontreal.iro.lecuyer.stochprocess.GammaProcess
.
getStream() - Method in class umontreal.iro.lecuyer.stochprocess.GeometricBrownianMotion
Returns the RandomStream for the underlying Brownian motion.
getStream() - Method in class umontreal.iro.lecuyer.stochprocess.GeometricLevyProcess
Returns the stream from the underlying Lévy process.
getStream() - Method in class umontreal.iro.lecuyer.stochprocess.GeometricVarianceGammaProcess
 
getStream() - Method in class umontreal.iro.lecuyer.stochprocess.InverseGaussianProcess
 
getStream() - Method in class umontreal.iro.lecuyer.stochprocess.InverseGaussianProcessBridge
Only returns a stream if both inner streams are the same.
getStream() - Method in class umontreal.iro.lecuyer.stochprocess.InverseGaussianProcessMSH
Only returns a stream if both inner RandomStream's are the same.
getStream() - Method in class umontreal.iro.lecuyer.stochprocess.InverseGaussianProcessPCA
 
getStream() - Method in class umontreal.iro.lecuyer.stochprocess.MultivariateBrownianMotion
Returns the random stream of the normal generator.
getStream() - Method in class umontreal.iro.lecuyer.stochprocess.MultivariateGeometricBrownianMotion
Returns the random stream for the underlying Brownian motion.
getStream() - Method in class umontreal.iro.lecuyer.stochprocess.NormalInverseGaussianProcess
Only returns the stream if all streams are equal, including the stream(s) in the underlying InverseGaussianProcess.
getStream() - Method in class umontreal.iro.lecuyer.stochprocess.OrnsteinUhlenbeckProcess
Returns the random stream of the normal generator.
getStream() - Method in class umontreal.iro.lecuyer.stochprocess.StochasticProcess
Returns the random stream of the underlying generator.
getStream() - Method in class umontreal.iro.lecuyer.stochprocess.VarianceGammaProcess
Returns the random stream of the BrownianMotion process, which should be the same as for the GammaProcess.
getStream() - Method in class umontreal.iro.lecuyer.stochprocess.VarianceGammaProcessDiff
Returns the RandomStream of the Γ+ process.
getStream2() - Method in class umontreal.iro.lecuyer.randvar.BetaSymmetricalBestGen
Returns stream s2 associated with this object.
getStream2() - Method in class umontreal.iro.lecuyer.randvar.BetaSymmetricalPolarGen
.
getStream3() - Method in class umontreal.iro.lecuyer.randvar.BetaSymmetricalBestGen
Returns stream s3 associated with this object.
getStreams() - Method in class umontreal.iro.lecuyer.rng.RandomStreamManager
Returns an unmodifiable list containing all the random streams in this random stream manager.
getSubpath(double[], int[]) - Method in class umontreal.iro.lecuyer.stochprocess.MultivariateStochasticProcess
.
getSubpath(double[], int[]) - Method in class umontreal.iro.lecuyer.stochprocess.StochasticProcess
Returns in subpath the values of the process at a subset of the observation times, specified as the times tj whose indices j are in the array pathIndices.
getSum(double[], int, int) - Static method in class umontreal.iro.lecuyer.simexp.BatchMeansSim
Returns the sum of elements start, ..., start + length - 1, in the array a.
getSum(DoubleArrayList, int, int) - Static method in class umontreal.iro.lecuyer.simexp.BatchMeansSim
Returns the sum of elements start, ..., start + length - 1, in the array list l.
getSum(DoubleMatrix1D, int, int) - Static method in class umontreal.iro.lecuyer.simexp.BatchMeansSim
Returns the sum of elements start, ..., start + length - 1, in the 1D matrix m.
getSum(double[][], int, int) - Static method in class umontreal.iro.lecuyer.simexp.BatchMeansSim
Returns an array containing the sum of columns startColumn, ..., startColumn + numColumns - 1, in the 2D matrix represented by the 2D array a.
getSum(DoubleMatrix2D, int, int) - Static method in class umontreal.iro.lecuyer.simexp.BatchMeansSim
Returns an array containing the sum of columns startColumn, ..., startColumn + numColumns - 1, in the 2D matrix m.
getTargetBatches() - Method in class umontreal.iro.lecuyer.simexp.BatchMeansSim
Returns the target number of simulated real batches at the next time the stopping condition is checked.
getTargetReplications() - Method in class umontreal.iro.lecuyer.simexp.RepSim
Returns the actual target number of replications to be simulated before an error check.
getTheta() - Method in class umontreal.iro.lecuyer.probdist.LogarithmicDist
.
getTheta() - Method in class umontreal.iro.lecuyer.randvar.LogarithmicGen
Returns the θ associated with this object.
getTheta() - Method in class umontreal.iro.lecuyer.stochprocess.GeometricVarianceGammaProcess
Returns the value of the parameter θ.
getTheta() - Method in class umontreal.iro.lecuyer.stochprocess.VarianceGammaProcess
Returns the value of the parameter θ.
getTheta0() - Method in class umontreal.iro.lecuyer.randvar.LogarithmicGen
Returns the θ0 associated with this object.
getTimeInterval(double[], int, int, double) - Static method in class umontreal.iro.lecuyer.util.Misc
.
getTitle() - Method in class umontreal.iro.lecuyer.charts.CategoryChart
Gets the current chart title.
getTitle() - Method in class umontreal.iro.lecuyer.charts.MultipleDatasetChart
Gets the current chart title.
getTitle() - Method in class umontreal.iro.lecuyer.charts.XYChart
Gets the current chart title.
getTotal(int) - Method in class umontreal.iro.lecuyer.charts.CustomHistogramDataset
Returns the total number of observations for a series.
getTwinAxisPosition() - Method in class umontreal.iro.lecuyer.charts.Axis
Returns the drawing position parameter (default equals 0).
getType() - Method in class umontreal.iro.lecuyer.charts.CustomHistogramDataset
Returns the histogram type.
getType() - Method in class umontreal.iro.lecuyer.util.io.DataField
Returns the type of the field.
getUepsilon() - Method in class umontreal.iro.lecuyer.randvar.ExponentialInverseFromDensityGen
.
getUepsilon() - Method in class umontreal.iro.lecuyer.randvar.NormalInverseFromDensityGen
.
getValue(int, int) - Method in class umontreal.iro.lecuyer.charts.SSJCategorySeriesCollection
.
getValue(int) - Method in class umontreal.iro.lecuyer.probdist.DiscreteDistribution
Returns the i-th value xi, for 0 <= i < n.
getValues(int) - Method in class umontreal.iro.lecuyer.charts.CustomHistogramDataset
Returns the values for a series.
getValues(int) - Method in class umontreal.iro.lecuyer.charts.HistogramSeriesCollection
Returns the values for a series.
getValuesList(int) - Method in class umontreal.iro.lecuyer.charts.CustomHistogramDataset
Returns the values for a series.
getValuesList(int) - Method in class umontreal.iro.lecuyer.charts.HistogramSeriesCollection
Returns the values for a series.
getVariance() - Method in class umontreal.iro.lecuyer.probdist.BernoulliDist
 
getVariance(double) - Static method in class umontreal.iro.lecuyer.probdist.BernoulliDist
.
getVariance() - Method in class umontreal.iro.lecuyer.probdist.BetaDist
 
getVariance(double, double) - Static method in class umontreal.iro.lecuyer.probdist.BetaDist
.
getVariance(double, double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.BetaDist
.
getVariance() - Method in class umontreal.iro.lecuyer.probdist.BetaSymmetricalDist
 
getVariance(double) - Static method in class umontreal.iro.lecuyer.probdist.BetaSymmetricalDist
Computes and returns the variance, Var[X] = 1/(8α + 4), of the symmetrical beta distribution with parameter α.
getVariance() - Method in class umontreal.iro.lecuyer.probdist.BinomialDist
 
getVariance(int, double) - Static method in class umontreal.iro.lecuyer.probdist.BinomialDist
Computes the variance Var[X] = np(1 - p) of the binomial distribution with parameters n and p.
getVariance() - Method in class umontreal.iro.lecuyer.probdist.CauchyDist
 
getVariance(double, double) - Static method in class umontreal.iro.lecuyer.probdist.CauchyDist
.
getVariance() - Method in class umontreal.iro.lecuyer.probdist.ChiDist
 
getVariance(int) - Static method in class umontreal.iro.lecuyer.probdist.ChiDist
Computes and returns the variance of the chi distribution with parameter ν.
getVariance() - Method in class umontreal.iro.lecuyer.probdist.ChiSquareDist
 
getVariance(int) - Static method in class umontreal.iro.lecuyer.probdist.ChiSquareDist
.
getVariance() - Method in class umontreal.iro.lecuyer.probdist.ChiSquareNoncentralDist
 
getVariance(double, double) - Static method in class umontreal.iro.lecuyer.probdist.ChiSquareNoncentralDist
Computes and returns the variance Var[X] = 2(ν +2λ) of the noncentral chi-square distribution with parameters ν = nu and λ = lambda.
getVariance() - Method in class umontreal.iro.lecuyer.probdist.ConstantDist
Returns the variance Var[X] = 0.
getVariance() - Method in class umontreal.iro.lecuyer.probdist.ContinuousDistribution
Returns the variance.
getVariance() - Method in class umontreal.iro.lecuyer.probdist.CramerVonMisesDist
 
getVariance(int) - Static method in class umontreal.iro.lecuyer.probdist.CramerVonMisesDist
.
getVariance() - Method in class umontreal.iro.lecuyer.probdist.DiscreteDistribution
Computes the variance Var[X] = ∑ipi(xi - E[X])2 of the distribution.
getVariance() - Method in interface umontreal.iro.lecuyer.probdist.Distribution
.
getVariance() - Method in class umontreal.iro.lecuyer.probdist.EmpiricalDist
 
getVariance(int, double) - Static method in class umontreal.iro.lecuyer.probdist.ErlangDist
.
getVariance() - Method in class umontreal.iro.lecuyer.probdist.ExponentialDist
 
getVariance(double) - Static method in class umontreal.iro.lecuyer.probdist.ExponentialDist
Computes and returns the variance, Var[X] = 1/λ2, of the exponential distribution with parameter λ.
getVariance() - Method in class umontreal.iro.lecuyer.probdist.ExtremeValueDist
Deprecated.  
getVariance(double, double) - Static method in class umontreal.iro.lecuyer.probdist.ExtremeValueDist
Deprecated. .
getVariance() - Method in class umontreal.iro.lecuyer.probdist.FatigueLifeDist
 
getVariance(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.FatigueLifeDist
.
getVariance() - Method in class umontreal.iro.lecuyer.probdist.FisherFDist
 
getVariance(int, int) - Static method in class umontreal.iro.lecuyer.probdist.FisherFDist
.
getVariance() - Method in class umontreal.iro.lecuyer.probdist.FoldedNormalDist
 
getVariance(double, double) - Static method in class umontreal.iro.lecuyer.probdist.FoldedNormalDist
.
getVariance() - Method in class umontreal.iro.lecuyer.probdist.FrechetDist
 
getVariance(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.FrechetDist
Returns the variance of the Fréchet distribution with parameters α, β and δ.
getVariance() - Method in class umontreal.iro.lecuyer.probdist.GammaDist
 
getVariance(double, double) - Static method in class umontreal.iro.lecuyer.probdist.GammaDist
Computes and returns the variance Var[X] = α/λ2 of the gamma distribution with parameters α and λ.
getVariance() - Method in class umontreal.iro.lecuyer.probdist.GeometricDist
 
getVariance(double) - Static method in class umontreal.iro.lecuyer.probdist.GeometricDist
Computes and returns the variance Var[X] = (1 - p)/p2 of the geometric distribution with parameter p.
getVariance() - Method in class umontreal.iro.lecuyer.probdist.GumbelDist
 
getVariance(double, double) - Static method in class umontreal.iro.lecuyer.probdist.GumbelDist
.
getVariance() - Method in class umontreal.iro.lecuyer.probdist.HalfNormalDist
 
getVariance(double, double) - Static method in class umontreal.iro.lecuyer.probdist.HalfNormalDist
Computes and returns the variance Var[X] = (1 - 2/π)σ2.
getVariance() - Method in class umontreal.iro.lecuyer.probdist.HyperbolicSecantDist
 
getVariance(double, double) - Static method in class umontreal.iro.lecuyer.probdist.HyperbolicSecantDist
Computes and returns the variance Var[X] = σ2 of the hyperbolic secant distribution with parameters μ and σ.
getVariance() - Method in class umontreal.iro.lecuyer.probdist.HypergeometricDist
 
getVariance(int, int, int) - Static method in class umontreal.iro.lecuyer.probdist.HypergeometricDist
Computes and returns the variance of the hypergeometric distribution with parameters m, l and k.
getVariance() - Method in class umontreal.iro.lecuyer.probdist.HypoExponentialDist
 
getVariance(double[]) - Static method in class umontreal.iro.lecuyer.probdist.HypoExponentialDist
Returns the variance, Var[X] = ∑i=1k1/λi2, of the hypoexponential distribution with rates λi = lambda[i - 1], i = 1,…, k.
getVariance() - Method in class umontreal.iro.lecuyer.probdist.InverseGammaDist
 
getVariance(double, double) - Static method in class umontreal.iro.lecuyer.probdist.InverseGammaDist
Returns the variance Var[X] = β2/((α -1)2(α - 2)) of the inverse gamma distribution with shape parameter α and scale parameter β.
getVariance() - Method in class umontreal.iro.lecuyer.probdist.InverseGaussianDist
 
getVariance(double, double) - Static method in class umontreal.iro.lecuyer.probdist.InverseGaussianDist
Computes and returns the variance Var[X] = μ3/λ of the inverse gaussian distribution with parameters μ and λ.
getVariance() - Method in class umontreal.iro.lecuyer.probdist.JohnsonSBDist
 
getVariance(double, double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.JohnsonSBDist
.
getVariance() - Method in class umontreal.iro.lecuyer.probdist.JohnsonSLDist
 
getVariance(double, double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.JohnsonSLDist
Returns the variance of the Johnson SL distribution with parameters γ, δ, ξ and λ.
getVariance() - Method in class umontreal.iro.lecuyer.probdist.JohnsonSUDist
 
getVariance(double, double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.JohnsonSUDist
Returns the variance of the Johnson SU distribution with parameters γ, δ, ξ and λ.
getVariance() - Method in class umontreal.iro.lecuyer.probdist.LaplaceDist
 
getVariance(double, double) - Static method in class umontreal.iro.lecuyer.probdist.LaplaceDist
Computes and returns the variance Var[X] = 2β2 of the Laplace distribution with parameters μ and β.
getVariance() - Method in class umontreal.iro.lecuyer.probdist.LogarithmicDist
 
getVariance(double) - Static method in class umontreal.iro.lecuyer.probdist.LogarithmicDist
.
getVariance() - Method in class umontreal.iro.lecuyer.probdist.LogisticDist
 
getVariance(double, double) - Static method in class umontreal.iro.lecuyer.probdist.LogisticDist
Computes and returns the variance Var[X] = π2/(3λ2) of the logistic distribution with parameters α and λ.
getVariance() - Method in class umontreal.iro.lecuyer.probdist.LoglogisticDist
 
getVariance(double, double) - Static method in class umontreal.iro.lecuyer.probdist.LoglogisticDist
.
getVariance() - Method in class umontreal.iro.lecuyer.probdist.LognormalDist
 
getVariance(double, double) - Static method in class umontreal.iro.lecuyer.probdist.LognormalDist
Computes and returns the variance Var[X] = e2μ+σ2(eσ2 - 1) of the lognormal distribution with parameters μ and σ.
getVariance() - Method in class umontreal.iro.lecuyer.probdist.NakagamiDist
 
getVariance(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.NakagamiDist
.
getVariance() - Method in class umontreal.iro.lecuyer.probdist.NegativeBinomialDist
 
getVariance(double, double) - Static method in class umontreal.iro.lecuyer.probdist.NegativeBinomialDist
.
getVariance() - Method in class umontreal.iro.lecuyer.probdist.NormalDist
 
getVariance(double, double) - Static method in class umontreal.iro.lecuyer.probdist.NormalDist
Computes and returns the variance Var[X] = σ2 of the normal distribution with parameters μ and σ.
getVariance() - Method in class umontreal.iro.lecuyer.probdist.NormalInverseGaussianDist
 
getVariance(double, double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.NormalInverseGaussianDist
.
getVariance() - Method in class umontreal.iro.lecuyer.probdist.ParetoDist
 
getVariance(double, double) - Static method in class umontreal.iro.lecuyer.probdist.ParetoDist
Computes and returns the variance of the Pareto distribution with parameters α and β.
getVariance() - Method in class umontreal.iro.lecuyer.probdist.Pearson5Dist
Deprecated.  
getVariance(double, double) - Static method in class umontreal.iro.lecuyer.probdist.Pearson5Dist
Deprecated. Computes and returns the variance Var[X] = β2/((α -1)2(α - 2) of a Pearson V distribution with shape parameter α and scale parameter β.
getVariance() - Method in class umontreal.iro.lecuyer.probdist.Pearson6Dist
 
getVariance(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.Pearson6Dist
.
getVariance() - Method in class umontreal.iro.lecuyer.probdist.PiecewiseLinearEmpiricalDist
 
getVariance() - Method in class umontreal.iro.lecuyer.probdist.PoissonDist
 
getVariance(double) - Static method in class umontreal.iro.lecuyer.probdist.PoissonDist
Computes and returns the variance = λ of the Poisson distribution with parameter λ.
getVariance() - Method in class umontreal.iro.lecuyer.probdist.PowerDist
 
getVariance(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.PowerDist
Computes and returns the variance (b - a)2c/[(c + 1)2(c + 2)] of the power distribution with parameters a, b and c.
getVariance() - Method in class umontreal.iro.lecuyer.probdist.RayleighDist
 
getVariance(double) - Static method in class umontreal.iro.lecuyer.probdist.RayleighDist
.
getVariance() - Method in class umontreal.iro.lecuyer.probdist.StudentDist
 
getVariance(int) - Static method in class umontreal.iro.lecuyer.probdist.StudentDist
Computes and returns the variance Var[X] = n/(n - 2) of the Student t-distribution with parameter n.
getVariance() - Method in class umontreal.iro.lecuyer.probdist.TriangularDist
 
getVariance(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.TriangularDist
.
getVariance() - Method in class umontreal.iro.lecuyer.probdist.TruncatedDist
Returns an approximation of the variance computed with the Simpson 1/3 numerical integration rule.
getVariance() - Method in class umontreal.iro.lecuyer.probdist.UniformDist
 
getVariance(double, double) - Static method in class umontreal.iro.lecuyer.probdist.UniformDist
.
getVariance() - Method in class umontreal.iro.lecuyer.probdist.UniformIntDist
 
getVariance(int, int) - Static method in class umontreal.iro.lecuyer.probdist.UniformIntDist
Computes and returns the variance Var[X] = [(j - i + 1)2 -1]/12 of the discrete uniform distribution.
getVariance() - Method in class umontreal.iro.lecuyer.probdist.WatsonUDist
 
getVariance(int) - Static method in class umontreal.iro.lecuyer.probdist.WatsonUDist
Returns the variance of the Watson U distribution with parameter n.
getVariance() - Method in class umontreal.iro.lecuyer.probdist.WeibullDist
 
getVariance(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.WeibullDist
.
getVarianceGammaProcess() - Method in class umontreal.iro.lecuyer.stochprocess.GeometricVarianceGammaProcess
Returns a reference to the variance gamma process X defined in the constructor.
getWarmupTime() - Method in class umontreal.iro.lecuyer.simexp.BatchMeansSim
Returns the duration of the warmup period for the simulation.
getWeights() - Method in class umontreal.iro.lecuyer.functionfit.SmoothingCubicSpline
Returns the weights of the points.
getX(int, int) - Method in class umontreal.iro.lecuyer.charts.CustomHistogramDataset
Returns the X value for a bin.
getX(int, int) - Method in class umontreal.iro.lecuyer.charts.SSJXYSeriesCollection
Returns the x-value at the specified index in the specified series.
getX() - Method in class umontreal.iro.lecuyer.functionfit.BSpline
Returns the Xi coordinates for this spline.
getX() - Method in class umontreal.iro.lecuyer.functionfit.PolInterp
Returns the x coordinates of the interpolated points.
getX() - Method in class umontreal.iro.lecuyer.functionfit.SmoothingCubicSpline
Returns the xi coordinates for this spline.
getX() - Method in class umontreal.iro.lecuyer.functions.PiecewiseConstantFunction
Returns the X coordinates of the function.
getX0(double[]) - Method in class umontreal.iro.lecuyer.stochprocess.MultivariateStochasticProcess
.
getX0() - Method in class umontreal.iro.lecuyer.stochprocess.StochasticProcess
Returns the initial value X(t0) for this process.
getXAxis() - Method in class umontreal.iro.lecuyer.charts.MultipleDatasetChart
Returns the chart's domain axis (x-axis) object.
getXAxis() - Method in class umontreal.iro.lecuyer.charts.XYChart
Returns the chart's domain axis (x-axis) object.
getXc() - Method in class umontreal.iro.lecuyer.probdist.InverseDistFromDensity
Returns the xc given in the constructor.
getXc() - Method in class umontreal.iro.lecuyer.randvar.InverseFromDensityGen
.
getXinf() - Method in class umontreal.iro.lecuyer.probdist.ContinuousDistribution
Returns xa such that the probability density is 0 everywhere outside the interval [xa, xb].
getXinf() - Method in class umontreal.iro.lecuyer.probdist.DiscreteDistribution
Returns the lower limit x0 of the support of the distribution.
getXinf() - Method in class umontreal.iro.lecuyer.probdist.DiscreteDistributionInt
Returns the lower limit xa of the support of the probability mass function.
getXsup() - Method in class umontreal.iro.lecuyer.probdist.ContinuousDistribution
Returns xb such that the probability density is 0 everywhere outside the interval [xa, xb].
getXsup() - Method in class umontreal.iro.lecuyer.probdist.DiscreteDistribution
Returns the upper limit xn-1 of the support of the distribution.
getXsup() - Method in class umontreal.iro.lecuyer.probdist.DiscreteDistributionInt
Returns the upper limit xb of the support of the probability mass function.
getY(int, int) - Method in class umontreal.iro.lecuyer.charts.CustomHistogramDataset
Returns the y-value for a bin (calculated to take into account the histogram type).
getY(int, int) - Method in class umontreal.iro.lecuyer.charts.SSJXYSeriesCollection
Returns the y-value at the specified index in the specified series.
getY() - Method in class umontreal.iro.lecuyer.functionfit.BSpline
Returns the Yi coordinates for this spline.
getY() - Method in class umontreal.iro.lecuyer.functionfit.PolInterp
Returns the y coordinates of the interpolated points.
getY() - Method in class umontreal.iro.lecuyer.functionfit.SmoothingCubicSpline
Returns the yi coordinates for this spline.
getY() - Method in class umontreal.iro.lecuyer.functions.PiecewiseConstantFunction
Returns the Y coordinates of the function.
getYAxis() - Method in class umontreal.iro.lecuyer.charts.CategoryChart
Returns the chart's range axis (y-axis) object.
getYAxis() - Method in class umontreal.iro.lecuyer.charts.MultipleDatasetChart
Returns the chart's range axis (y-axis) object.
getYAxis() - Method in class umontreal.iro.lecuyer.charts.XYChart
Returns the chart's range axis (y-axis) object.
getZeroOverZeroValue() - Method in class umontreal.iro.lecuyer.util.RatioFunction
Returns the value returned by evaluate in the case where the 0/0 function is calculated.
GlobalCPUTimeChrono - Class in umontreal.iro.lecuyer.util
Extends the AbstractChrono class to compute the global CPU time used by the Java Virtual Machine.
GlobalCPUTimeChrono() - Constructor for class umontreal.iro.lecuyer.util.GlobalCPUTimeChrono
Constructs a Chrono object and initializes it to zero.
GNUPLOT - Static variable in class umontreal.iro.lecuyer.gof.GofFormat
Data file format used for plotting functions with Gnuplot.
GofFormat - Class in umontreal.iro.lecuyer.gof
This class contains methods used to format results of GOF test statistics, or to apply a series of tests simultaneously and format the results.
GofStat - Class in umontreal.iro.lecuyer.gof
This class provides methods to compute several types of EDF goodness-of-fit test statistics and to apply certain transformations to a set of observations.
GofStat.OutcomeCategoriesChi2 - Class in umontreal.iro.lecuyer.gof
This class helps managing the partitions of possible outcomes into categories for applying chi-square tests.
GofStat.OutcomeCategoriesChi2(double[]) - Constructor for class umontreal.iro.lecuyer.gof.GofStat.OutcomeCategoriesChi2
Constructs an OutcomeCategoriesChi2 object using the array nbExp for the number of expected observations in each category.
GofStat.OutcomeCategoriesChi2(double[], int, int) - Constructor for class umontreal.iro.lecuyer.gof.GofStat.OutcomeCategoriesChi2
Constructs an OutcomeCategoriesChi2 object using the given nbExp expected observations array.
GofStat.OutcomeCategoriesChi2(double[], int[], int, int, int) - Constructor for class umontreal.iro.lecuyer.gof.GofStat.OutcomeCategoriesChi2
Constructs an OutcomeCategoriesChi2 object.
graphDistUnif(DoubleArrayList, String) - Static method in class umontreal.iro.lecuyer.gof.GofFormat
Formats data to plot the empirical distribution of U(1),..., U(N), which are assumed to be in data[0...N-1], and to compare it with the uniform distribution.
graphSoft - Static variable in class umontreal.iro.lecuyer.gof.GofFormat
Environment variable that selects the type of software to be used for plotting the graphs of functions.
GumbelDist - Class in umontreal.iro.lecuyer.probdist
GumbelDist
GumbelDist() - Constructor for class umontreal.iro.lecuyer.probdist.GumbelDist
.
GumbelDist(double, double) - Constructor for class umontreal.iro.lecuyer.probdist.GumbelDist
.
GumbelGen - Class in umontreal.iro.lecuyer.randvar
This class implements methods for generating random variates from the Gumbel distribution.
GumbelGen(RandomStream) - Constructor for class umontreal.iro.lecuyer.randvar.GumbelGen
Creates a Gumbel random number generator with β = 1 and δ = 0 using stream s.
GumbelGen(RandomStream, double, double) - Constructor for class umontreal.iro.lecuyer.randvar.GumbelGen
Creates a Gumbel random number generator with parameters β = beta and δ = delta using stream s.
GumbelGen(RandomStream, GumbelDist) - Constructor for class umontreal.iro.lecuyer.randvar.GumbelGen
Creates a new generator for the Gumbel distribution dist and stream s.

H

H - Static variable in class umontreal.iro.lecuyer.functions.MathFunctionUtil
Step length in x to compute derivatives.
HalfNormalDist - Class in umontreal.iro.lecuyer.probdist
Extends the class ContinuousDistribution for the half-normal distribution with parameters μ and σ > 0.
HalfNormalDist(double, double) - Constructor for class umontreal.iro.lecuyer.probdist.HalfNormalDist
Constructs a HalfNormalDist object with parameters μ = mu and σ = sigma.
HalfNormalGen - Class in umontreal.iro.lecuyer.randvar
HalfNormalGen
HalfNormalGen(RandomStream, double, double) - Constructor for class umontreal.iro.lecuyer.randvar.HalfNormalGen
.
HalfNormalGen(RandomStream, HalfNormalDist) - Constructor for class umontreal.iro.lecuyer.randvar.HalfNormalGen
.
HaltonSequence - Class in umontreal.iro.lecuyer.hups
This class implements the sequence of Halton, which is essentially a modification of Hammersley nets for producing an infinite sequence of points having low discrepancy.
HaltonSequence(int) - Constructor for class umontreal.iro.lecuyer.hups.HaltonSequence
Constructs a new Halton sequence in dim dimensions.
HammersleyPointSet - Class in umontreal.iro.lecuyer.hups
This class implements Hammersley point sets, which are defined as follows.
HammersleyPointSet(int, int) - Constructor for class umontreal.iro.lecuyer.hups.HammersleyPointSet
Constructs a new Hammersley point set with n points in dim dimensions.
harmonic(long) - Static method in class umontreal.iro.lecuyer.util.Num
.
harmonic2(long) - Static method in class umontreal.iro.lecuyer.util.Num
.
hashCode() - Method in class umontreal.iro.lecuyer.stat.list.ListOfStatProbes
 
hasNextCoordinate() - Method in class umontreal.iro.lecuyer.hups.CycleBasedPointSet.CycleBasedPointSetIterator
 
hasNextCoordinate() - Method in interface umontreal.iro.lecuyer.hups.PointSetIterator
.
hasNextObservation() - Method in class umontreal.iro.lecuyer.stochprocess.StochasticProcess
Returns true if j < d, where j is the number of observations of the current sample path generated since the last call to resetStartProcess.
hasNextPoint() - Method in interface umontreal.iro.lecuyer.hups.PointSetIterator
.
hasStopped() - Method in class umontreal.iro.lecuyer.markovchain.MarkovChain
Tells if the chain has stopped.
hasStopped() - Method in class umontreal.iro.lecuyer.markovchain.MarkovChainDouble
Indicates if the chain has stopped.
Henriksen - Class in umontreal.iro.lecuyer.simevents.eventlist
An implementation of EventList using the doubly-linked indexed list of Henriksen (see also).
Henriksen() - Constructor for class umontreal.iro.lecuyer.simevents.eventlist.Henriksen
 
HilbertCurveBatchSort - Class in umontreal.iro.lecuyer.util
This sort is similar to BatchSort, except that when applying the batch sort, the objects are also given labels that map them to the d-dimensional unit hypercube [0, 1)d as explained below, and then re-ordered by following a Hilbert curve as in the HilbertCurveSort.
HilbertCurveBatchSort(int[]) - Constructor for class umontreal.iro.lecuyer.util.HilbertCurveBatchSort
Constructs a HilbertCurveBatchSort that will use batches.
HilbertCurveBatchSort(double[]) - Constructor for class umontreal.iro.lecuyer.util.HilbertCurveBatchSort
Constructs a HilbertCurveBatchSort that will use batchesExponents.
HilbertCurveSort - Class in umontreal.iro.lecuyer.util
 
HilbertCurveSort(int) - Constructor for class umontreal.iro.lecuyer.util.HilbertCurveSort
 
HilbertCurveSort(int, int) - Constructor for class umontreal.iro.lecuyer.util.HilbertCurveSort
.
HilbertCurveSplitSort - Class in umontreal.iro.lecuyer.util
HilbertCurveSplitSort
HilbertCurveSplitSort(int) - Constructor for class umontreal.iro.lecuyer.util.HilbertCurveSplitSort
.
HistogramChart - Class in umontreal.iro.lecuyer.charts
This class provides tools to create and manage histograms.
HistogramChart() - Constructor for class umontreal.iro.lecuyer.charts.HistogramChart
Initializes a new HistogramChart instance with an empty data set.
HistogramChart(String, String, String, double[]...) - Constructor for class umontreal.iro.lecuyer.charts.HistogramChart
Initializes a new HistogramChart instance with input data.
HistogramChart(String, String, String, double[], int) - Constructor for class umontreal.iro.lecuyer.charts.HistogramChart
Initializes a new HistogramChart instance with input data.
HistogramChart(String, String, String, DoubleArrayList...) - Constructor for class umontreal.iro.lecuyer.charts.HistogramChart
Initializes a new HistogramChart instance with data data.
HistogramChart(String, String, String, TallyStore...) - Constructor for class umontreal.iro.lecuyer.charts.HistogramChart
Initializes a new HistogramChart instance with data arrays contained in each TallyStore object.
HistogramChart(String, String, String, CustomHistogramDataset) - Constructor for class umontreal.iro.lecuyer.charts.HistogramChart
Initializes a new HistogramChart instance with data data.
HistogramChart(String, String, String, int[], double[]) - Constructor for class umontreal.iro.lecuyer.charts.HistogramChart
Initializes a new HistogramChart instance with data count and bound.
HistogramChart(String, String, String, TallyHistogram...) - Constructor for class umontreal.iro.lecuyer.charts.HistogramChart
Initializes a new HistogramChart instance with data arrays contained in each TallyHistogram object.
HistogramSeriesCollection - Class in umontreal.iro.lecuyer.charts
Stores data used in a HistogramChart.
HistogramSeriesCollection() - Constructor for class umontreal.iro.lecuyer.charts.HistogramSeriesCollection
Creates a new HistogramSeriesCollection instance with empty dataset.
HistogramSeriesCollection(double[]...) - Constructor for class umontreal.iro.lecuyer.charts.HistogramSeriesCollection
Creates a new HistogramSeriesCollection instance with given data series.
HistogramSeriesCollection(double[], int) - Constructor for class umontreal.iro.lecuyer.charts.HistogramSeriesCollection
Creates a new HistogramSeriesCollection instance with the given data series data.
HistogramSeriesCollection(DoubleArrayList...) - Constructor for class umontreal.iro.lecuyer.charts.HistogramSeriesCollection
Creates a new HistogramSeriesCollection.
HistogramSeriesCollection(TallyStore...) - Constructor for class umontreal.iro.lecuyer.charts.HistogramSeriesCollection
Creates a new HistogramSeriesCollection instance with default parameters and given data.
HistogramSeriesCollection(TallyHistogram...) - Constructor for class umontreal.iro.lecuyer.charts.HistogramSeriesCollection
Creates a new HistogramSeriesCollection instance with default parameters and given data.
HistogramSeriesCollection(CustomHistogramDataset) - Constructor for class umontreal.iro.lecuyer.charts.HistogramSeriesCollection
Creates a new HistogramSeriesCollection instance.
HyperbolicSecantDist - Class in umontreal.iro.lecuyer.probdist
Extends the class ContinuousDistribution for the hyperbolic secant distribution with location parameter μ and scale parameter σ > 0.
HyperbolicSecantDist(double, double) - Constructor for class umontreal.iro.lecuyer.probdist.HyperbolicSecantDist
Constructs a hyperbolic secant distribution with parameters μ and σ.
HyperbolicSecantGen - Class in umontreal.iro.lecuyer.randvar
This class implements random variate generators for the hyperbolic secant distribution with location parameter μ and scale parameter σ.
HyperbolicSecantGen(RandomStream, double, double) - Constructor for class umontreal.iro.lecuyer.randvar.HyperbolicSecantGen
Creates a hyperbolic secant random variate generator with parameters μ = mu and σ = sigma, using stream s.
HyperbolicSecantGen(RandomStream) - Constructor for class umontreal.iro.lecuyer.randvar.HyperbolicSecantGen
Creates a hyperbolic secant random variate generator with parameters μ = 0 and σ = 1, using stream s.
HyperbolicSecantGen(RandomStream, HyperbolicSecantDist) - Constructor for class umontreal.iro.lecuyer.randvar.HyperbolicSecantGen
Creates a new generator for the distribution dist, using stream s.
HypergeometricDist - Class in umontreal.iro.lecuyer.probdist
Extends the class DiscreteDistributionInt for the hypergeometric distribution with k elements chosen among l, m being of one type, and l - m of the other.
HypergeometricDist(int, int, int) - Constructor for class umontreal.iro.lecuyer.probdist.HypergeometricDist
Constructs an hypergeometric distribution with parameters m, l and k.
HypergeometricGen - Class in umontreal.iro.lecuyer.randvar
HypergeometricGen
HypergeometricGen(RandomStream, int, int, int) - Constructor for class umontreal.iro.lecuyer.randvar.HypergeometricGen
.
HypergeometricGen(RandomStream, HypergeometricDist) - Constructor for class umontreal.iro.lecuyer.randvar.HypergeometricGen
.
HypoExponentialDist - Class in umontreal.iro.lecuyer.probdist
This class implements the hypoexponential distribution, also called the generalized Erlang distribution.
HypoExponentialDist(double[]) - Constructor for class umontreal.iro.lecuyer.probdist.HypoExponentialDist
Constructs a HypoExponentialDist object, with rates λi = lambda[i - 1], i = 1,…, k.
HypoExponentialDistEqual - Class in umontreal.iro.lecuyer.probdist
This class implements the hypoexponential distribution for the case of equidistant λi = (n + 1 - i)h.
HypoExponentialDistEqual(int, int, double) - Constructor for class umontreal.iro.lecuyer.probdist.HypoExponentialDistEqual
Constructor for equidistant rates.
HypoExponentialDistQuick - Class in umontreal.iro.lecuyer.probdist
HypoExponentialDistQuick
HypoExponentialDistQuick(double[]) - Constructor for class umontreal.iro.lecuyer.probdist.HypoExponentialDistQuick
.
HypoExponentialGen - Class in umontreal.iro.lecuyer.randvar
HypoExponentialGen
HypoExponentialGen(RandomStream, double[]) - Constructor for class umontreal.iro.lecuyer.randvar.HypoExponentialGen
.
HypoExponentialGen(RandomStream, HypoExponentialDist) - Constructor for class umontreal.iro.lecuyer.randvar.HypoExponentialGen
.

I

iBinomialMatrixScramble(RandomStream) - Method in class umontreal.iro.lecuyer.hups.DigitalNet
.
iBinomialMatrixScramble(RandomStream) - Method in class umontreal.iro.lecuyer.hups.DigitalNetBase2
 
iBinomialMatrixScrambleFaurePermut(RandomStream, int) - Method in class umontreal.iro.lecuyer.hups.DigitalNet
.
iBinomialMatrixScrambleFaurePermut(RandomStream, int) - Method in class umontreal.iro.lecuyer.hups.DigitalNetBase2
 
iBinomialMatrixScrambleFaurePermutAll(RandomStream, int) - Method in class umontreal.iro.lecuyer.hups.DigitalNet
.
iBinomialMatrixScrambleFaurePermutAll(RandomStream, int) - Method in class umontreal.iro.lecuyer.hups.DigitalNetBase2
 
iBinomialMatrixScrambleFaurePermutDiag(RandomStream, int) - Method in class umontreal.iro.lecuyer.hups.DigitalNet
.
iBinomialMatrixScrambleFaurePermutDiag(RandomStream, int) - Method in class umontreal.iro.lecuyer.hups.DigitalNetBase2
 
IdentityMathFunction - Class in umontreal.iro.lecuyer.functions
Represents the identity function f (x) = x.
IdentityMathFunction() - Constructor for class umontreal.iro.lecuyer.functions.IdentityMathFunction
 
IIDMultivariateGen - Class in umontreal.iro.lecuyer.randvarmulti
Extends RandomMultivariateGen for a vector of independent identically distributed (i.i.d.) random variables.
IIDMultivariateGen(RandomVariateGen, int) - Constructor for class umontreal.iro.lecuyer.randvarmulti.IIDMultivariateGen
Constructs a generator for a d-dimensional vector of i.i.d.
ILN2 - Static variable in class umontreal.iro.lecuyer.util.Num
.
increasedPrecis(boolean) - Method in class umontreal.iro.lecuyer.rng.RandMrg
Deprecated. After calling this method with incp = true, each call to the generator (direct or indirect) for this stream will return a uniform random number with (roughly) 53 bits of resolution instead of 32 bits, and will advance the state of the stream by 2 steps instead of 1.
increasedPrecision(boolean) - Method in class umontreal.iro.lecuyer.rng.RandomStreamBase
After calling this method with incp = true, each call to the RNG (direct or indirect) for this stream will return a uniform random number with more bits of precision than what is returned by nextValue, and will advance the state of the stream by 2 steps instead of 1 (i.e., nextValue will be called twice for each random number).
IndependentPointsCached - Class in umontreal.iro.lecuyer.hups
IndependentPointsCached
IndependentPointsCached(int, int) - Constructor for class umontreal.iro.lecuyer.hups.IndependentPointsCached
.
indexBatchsort(long[][], int, int) - Method in class umontreal.iro.lecuyer.util.HilbertCurveBatchSort
 
indexOf(Object) - Method in class umontreal.iro.lecuyer.stat.list.ListOfStatProbes
 
init(int, String, String, String) - Method in class umontreal.iro.lecuyer.discrepancy.DiscrepancyContainer
Initialize the container with enough space for n values of the parameter and sets the values to 0.
init(int) - Method in class umontreal.iro.lecuyer.discrepancy.DiscrepancyContainer
Calls init(n,"","Parameter", "Discrepancy").
init(double[]) - Method in class umontreal.iro.lecuyer.hups.HaltonSequence
Initializes the Halton sequence starting at point x0.
init(byte[], int) - Static method in class umontreal.iro.lecuyer.rng.RandomPermutation
Deprecated. 
init(short[], int) - Static method in class umontreal.iro.lecuyer.rng.RandomPermutation
Deprecated. 
init(int[], int) - Static method in class umontreal.iro.lecuyer.rng.RandomPermutation
Deprecated. 
init(long[], int) - Static method in class umontreal.iro.lecuyer.rng.RandomPermutation
Deprecated. 
init(float[], int) - Static method in class umontreal.iro.lecuyer.rng.RandomPermutation
Deprecated. 
init(double[], int) - Static method in class umontreal.iro.lecuyer.rng.RandomPermutation
Deprecated. 
init() - Method in class umontreal.iro.lecuyer.simevents.Accumulate
Initializes the statistical collector and puts the current value of the corresponding variable to 0.
init(double) - Method in class umontreal.iro.lecuyer.simevents.Accumulate
Same as init followed by update(x).
init(double) - Method in class umontreal.iro.lecuyer.simevents.Continuous
.
init() - Static method in class umontreal.iro.lecuyer.simevents.Sim
Reinitializes the simulation executive by clearing up the event list, and resetting the simulation clock to zero.
init(EventList) - Static method in class umontreal.iro.lecuyer.simevents.Sim
Same as init, but also chooses evlist as the event list to be used.
init() - Method in class umontreal.iro.lecuyer.simevents.Simulator
Reinitializes the simulation executive by clearing up the event list, and resetting the simulation clock to zero.
init(EventList) - Method in class umontreal.iro.lecuyer.simevents.Simulator
Same as init, but also chooses evlist as the event list to be used.
init() - Method in class umontreal.iro.lecuyer.simexp.BatchMeansSim
Initializes the simulator for a new experiment.
init() - Method in class umontreal.iro.lecuyer.simexp.RepSim
Initializes this simulator for a new experiment.
init() - Method in class umontreal.iro.lecuyer.simprocs.Bin
Reinitializes this bin by clearing up its pile of tokens and its waiting list.
init(boolean) - Method in class umontreal.iro.lecuyer.simprocs.Condition
Reinitializes this Condition by clearing up its waiting list and resetting its state to val.
init() - Method in class umontreal.iro.lecuyer.simprocs.DSOLProcessSimulator
Initializes the process-driven simulation.
init(EventList) - Method in class umontreal.iro.lecuyer.simprocs.DSOLProcessSimulator
Initializes the simulation and sets the given event list evlist to be used by the simulation executive.
init() - Method in class umontreal.iro.lecuyer.simprocs.Resource
Reinitializes this resource by clearing up its waiting list and service list.
init() - Static method in class umontreal.iro.lecuyer.simprocs.SimProcess
This method calls ProcessSimulator.initDefault(), which initializes the default simulator to use processes.
init() - Method in class umontreal.iro.lecuyer.simprocs.ThreadProcessSimulator
Initializes the thread process-driven simulation using SplayTree algorithm as EventList.
init(EventList) - Method in class umontreal.iro.lecuyer.simprocs.ThreadProcessSimulator
Initializes the thread process-driven simulation using evlist variable as EventList.
init() - Method in class umontreal.iro.lecuyer.stat.FunctionOfMultipleMeansTally
 
init() - Method in class umontreal.iro.lecuyer.stat.list.lincv.ListOfTalliesWithCV
 
init() - Method in class umontreal.iro.lecuyer.stat.list.ListOfStatProbes
Initializes this list of statistical probes by calling init on each element.
init() - Method in class umontreal.iro.lecuyer.stat.list.ListOfTalliesWithCovariance
 
init() - Method in class umontreal.iro.lecuyer.stat.matrix.MatrixOfStatProbes
Initializes this matrix of statistical probes by calling StatProbe.init on each element.
init() - Method in class umontreal.iro.lecuyer.stat.StatProbe
.
init() - Method in class umontreal.iro.lecuyer.stat.Tally
 
init(double, double, int) - Method in class umontreal.iro.lecuyer.stat.TallyHistogram
Initializes this object.
init() - Method in class umontreal.iro.lecuyer.stat.TallyStore
 
init() - Method in class umontreal.iro.lecuyer.util.AbstractChrono
.
initBatchStat() - Method in class umontreal.iro.lecuyer.simexp.BatchMeansSim
Resets the counters used for computing observations during the simulation at the beginning of a new batch.
initCache() - Method in class umontreal.iro.lecuyer.randvar.RandomVariateGenWithCache
Resets this generator to recover values from the cache.
initCache() - Method in class umontreal.iro.lecuyer.rng.RandomStreamWithCache
Resets this random stream to recover values from the cache.
initDefault() - Static method in class umontreal.iro.lecuyer.simprocs.ProcessSimulator
Initializes the default simulator to use processes.
initEffectiveBatchProbes() - Method in class umontreal.iro.lecuyer.simexp.BatchMeansSim
Initializes any statistical collector for effective batches.
initGen(int) - Method in class umontreal.iro.lecuyer.discrepancy.Searcher
Initializes the random number generator used in random searches with the starting seed seed.
INITIAL - Static variable in class umontreal.iro.lecuyer.simprocs.SimProcess
The process has been created but not yet scheduled.
initialState() - Method in class umontreal.iro.lecuyer.markovchain.MarkovChain
Sets the Markov chain to its (deterministic) initial state and initializes the collectors for the performance measure, ready to start a simulation.
initialState() - Method in class umontreal.iro.lecuyer.markovchain.MarkovChainDouble
 
initialStateDouble() - Method in class umontreal.iro.lecuyer.markovchain.MarkovChainDouble
Returns the initial (deterministic) state.
initialStates() - Method in class umontreal.iro.lecuyer.markovchain.ArrayOfComparableChains
Initializes the n copies (clones) of the chain baseChain to their initial state by calling initialState() on each chain.
initRealBatchProbes() - Method in class umontreal.iro.lecuyer.simexp.BatchMeansSim
Initializes any statistical collector for real batches.
initReplication(int) - Method in class umontreal.iro.lecuyer.simexp.RepSim
Initializes the simulation model for a new replication r.
initReplicationProbes() - Method in class umontreal.iro.lecuyer.simexp.RepSim
Initializes any statistical collector used to collect values for replications.
initSimulation() - Method in class umontreal.iro.lecuyer.simexp.BatchMeansSim
Initializes the simulator for a new run.
initStat() - Method in class umontreal.iro.lecuyer.simevents.ListWithStat
Reinitializes the two statistical probes created by setStatCollecting (true) and makes an update for the probe on the list size.
initStat() - Method in class umontreal.iro.lecuyer.simprocs.Bin
Reinitializes all the statistical collectors for this bin.
initStat() - Method in class umontreal.iro.lecuyer.simprocs.Resource
Reinitializes all the statistical collectors for this resource.
initStatesDouble() - Method in class umontreal.iro.lecuyer.markovchain.ArrayOfDoubleChains
Initializes the states of the n copies of the base chain.
integ(double) - Method in class umontreal.iro.lecuyer.probdistmulti.norta.NortaInitDisc
Computes the function value gr for each correlation.
integerRadicalInverse(int, int) - Static method in class umontreal.iro.lecuyer.hups.RadicalInverse
.
integMethod() - Method in class umontreal.iro.lecuyer.simevents.ContinuousState
Return an integer that represent the integration method in use.
integral(double, double) - Method in class umontreal.iro.lecuyer.functionfit.BSpline
 
integral(double, double) - Method in class umontreal.iro.lecuyer.functionfit.SmoothingCubicSpline
Evaluates and returns the value of the integral of the spline from a to b.
integral(double, double) - Method in class umontreal.iro.lecuyer.functions.AverageMathFunction
 
integral(double, double) - Method in class umontreal.iro.lecuyer.functions.IdentityMathFunction
 
integral(MathFunction, double, double) - Static method in class umontreal.iro.lecuyer.functions.MathFunctionUtil
Returns the integral of the function func over [a, b].
integral(double, double) - Method in interface umontreal.iro.lecuyer.functions.MathFunctionWithIntegral
Computes (or estimates) the integral of the function over the interval [a, b].
integral(double, double) - Method in class umontreal.iro.lecuyer.functions.Polynomial
 
integral(double, double) - Method in class umontreal.iro.lecuyer.functions.ShiftedMathFunction
 
integralPolynomial(double) - Method in class umontreal.iro.lecuyer.functions.Polynomial
Returns a polynomial representing the integral of this polynomial.
interpol(int, double[], double[], double[]) - Static method in class umontreal.iro.lecuyer.util.Misc
\begin{tabb}Computes the Newton interpolating polynomial.
<DT><A HREF=Introspection - Class in umontreal.iro.lecuyer.util
Provides utility methods for introspection using Java Reflection API.
inverseBisection(double) - Method in class umontreal.iro.lecuyer.probdist.ContinuousDistribution
Computes and returns the inverse distribution function x = F-1(u), using bisection.
inverseBrent(double, double, double, double) - Method in class umontreal.iro.lecuyer.probdist.ContinuousDistribution
Computes the inverse distribution function x = F-1(u), using the Brent-Dekker method.
InverseDistFromDensity - Class in umontreal.iro.lecuyer.probdist
Implements a method for computing the inverse of an arbitrary continuous distribution function when only the probability density is known.
InverseDistFromDensity(ContinuousDistribution, double, double, int) - Constructor for class umontreal.iro.lecuyer.probdist.InverseDistFromDensity
Given a continuous distribution dist with a well-defined density method, this class will compute tables for the numerical inverse of the distribution.
InverseDistFromDensity(MathFunction, double, double, int, double, double) - Constructor for class umontreal.iro.lecuyer.probdist.InverseDistFromDensity
Given a continuous probability density dens, this class will compute tables for the numerical inverse of the distribution.
inverseF(double) - Method in class umontreal.iro.lecuyer.probdist.AndersonDarlingDist
 
inverseF(int, double) - Static method in class umontreal.iro.lecuyer.probdist.AndersonDarlingDist
.
inverseF(double) - Method in class umontreal.iro.lecuyer.probdist.AndersonDarlingDistQuick
 
inverseF(int, double) - Static method in class umontreal.iro.lecuyer.probdist.AndersonDarlingDistQuick
Computes the inverse x = Fn-1(u) of the distribution with parameter n.
inverseF(double, double) - Static method in class umontreal.iro.lecuyer.probdist.BernoulliDist
.
inverseF(double) - Method in class umontreal.iro.lecuyer.probdist.BetaDist
 
inverseF(double, double, int, double) - Static method in class umontreal.iro.lecuyer.probdist.BetaDist
Deprecated. 
inverseF(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.BetaDist
Same as inverseF (alpha, beta, 0, 1, u).
inverseF(double, double, double, double, int, double) - Static method in class umontreal.iro.lecuyer.probdist.BetaDist
Deprecated. 
inverseF(double, double, double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.BetaDist
Returns the inverse beta distribution function using the algorithm implemented in the Cephes math library.
inverseF(double) - Method in class umontreal.iro.lecuyer.probdist.BetaSymmetricalDist
 
inverseF(double, double) - Static method in class umontreal.iro.lecuyer.probdist.BetaSymmetricalDist
Returns the inverse distribution function evaluated at u, for the symmetrical beta distribution over the interval [0, 1], with shape parameters 0 < α = β = alpha.
inverseF(int, double, double) - Static method in class umontreal.iro.lecuyer.probdist.BinomialDist
Computes x = F-1(u), the inverse of the binomial distribution.
inverseF(double) - Method in class umontreal.iro.lecuyer.probdist.CauchyDist
 
inverseF(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.CauchyDist
.
inverseF(double) - Method in class umontreal.iro.lecuyer.probdist.ChiDist
 
inverseF(int, double) - Static method in class umontreal.iro.lecuyer.probdist.ChiDist
Returns the inverse distribution function computed using the gamma inversion.
inverseF(double) - Method in class umontreal.iro.lecuyer.probdist.ChiSquareDist
 
inverseF(int, double) - Static method in class umontreal.iro.lecuyer.probdist.ChiSquareDist
.
inverseF(double) - Method in class umontreal.iro.lecuyer.probdist.ChiSquareDistQuick
 
inverseF(int, double) - Static method in class umontreal.iro.lecuyer.probdist.ChiSquareDistQuick
Computes a quick-and-dirty approximation of F-1(u), where F is the chi-square distribution with n degrees of freedom.
inverseF(double) - Method in class umontreal.iro.lecuyer.probdist.ChiSquareNoncentralDist
 
inverseF(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.ChiSquareNoncentralDist
Computes the inverse of the noncentral chi-square distribution with ν = nu degrees of freedom and parameter λ = lambda.
inverseF(double) - Method in class umontreal.iro.lecuyer.probdist.ConstantDist
Returns the inverse distribution function c = F-1(u).
inverseF(double) - Method in class umontreal.iro.lecuyer.probdist.ContinuousDistribution
Returns the inverse distribution function x = F-1(u).
inverseF(double) - Method in class umontreal.iro.lecuyer.probdist.CramerVonMisesDist
 
inverseF(int, double) - Static method in class umontreal.iro.lecuyer.probdist.CramerVonMisesDist
.
inverseF(double) - Method in class umontreal.iro.lecuyer.probdist.DiscreteDistribution
 
inverseF(double) - Method in class umontreal.iro.lecuyer.probdist.DiscreteDistributionInt
Returns the inverse distribution function F-1(u), where 0 <= u <= 1.
inverseF(double) - Method in interface umontreal.iro.lecuyer.probdist.Distribution
.
inverseF(double) - Method in class umontreal.iro.lecuyer.probdist.EmpiricalDist
 
inverseF(int, double, int, double) - Static method in class umontreal.iro.lecuyer.probdist.ErlangDist
.
inverseF(double) - Method in class umontreal.iro.lecuyer.probdist.ExponentialDist
 
inverseF(double, double) - Static method in class umontreal.iro.lecuyer.probdist.ExponentialDist
Computes the inverse distribution function.
inverseF(double) - Method in class umontreal.iro.lecuyer.probdist.ExtremeValueDist
Deprecated.  
inverseF(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.ExtremeValueDist
Deprecated. .
inverseF(double) - Method in class umontreal.iro.lecuyer.probdist.FatigueLifeDist
 
inverseF(double, double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.FatigueLifeDist
.
inverseF(double) - Method in class umontreal.iro.lecuyer.probdist.FisherFDist
 
inverseF(int, int, int, double) - Static method in class umontreal.iro.lecuyer.probdist.FisherFDist
Deprecated. 
inverseF(int, int, double) - Static method in class umontreal.iro.lecuyer.probdist.FisherFDist
.
inverseF(double) - Method in class umontreal.iro.lecuyer.probdist.FoldedNormalDist
 
inverseF(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.FoldedNormalDist
Computes the inverse of the distribution function.
inverseF(double) - Method in class umontreal.iro.lecuyer.probdist.FrechetDist
 
inverseF(double, double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.FrechetDist
Computes and returns the inverse distribution function.
inverseF(double) - Method in class umontreal.iro.lecuyer.probdist.GammaDist
 
inverseF(double, double, int, double) - Static method in class umontreal.iro.lecuyer.probdist.GammaDist
Computes the inverse distribution function.
inverseF(double, int, double) - Static method in class umontreal.iro.lecuyer.probdist.GammaDist
Same as inverseF (alpha, 1, d, u).
inverseF(double, double) - Static method in class umontreal.iro.lecuyer.probdist.GeometricDist
Computes the inverse of the geometric distribution.
inverseF(double) - Method in class umontreal.iro.lecuyer.probdist.GumbelDist
 
inverseF(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.GumbelDist
.
inverseF(double) - Method in class umontreal.iro.lecuyer.probdist.HalfNormalDist
 
inverseF(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.HalfNormalDist
Computes the inverse of the distribution function.
inverseF(double) - Method in class umontreal.iro.lecuyer.probdist.HyperbolicSecantDist
 
inverseF(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.HyperbolicSecantDist
Computes the inverse of the hyperbolic secant distribution with parameters μ and σ.
inverseF(int, int, int, double) - Static method in class umontreal.iro.lecuyer.probdist.HypergeometricDist
Computes F-1(u) for the hypergeometric distribution without using precomputed tables.
inverseF(double) - Method in class umontreal.iro.lecuyer.probdist.HypoExponentialDist
 
inverseF(double[], double) - Static method in class umontreal.iro.lecuyer.probdist.HypoExponentialDist
Computes the inverse distribution function F-1(u), with λi = lambda[i - 1], i = 1,…, k.
inverseF(double) - Method in class umontreal.iro.lecuyer.probdist.HypoExponentialDistEqual
 
inverseF(int, int, double, double) - Static method in class umontreal.iro.lecuyer.probdist.HypoExponentialDistEqual
Computes the inverse distribution x = F-1(u), with arguments as in the constructor.
inverseF(double) - Method in class umontreal.iro.lecuyer.probdist.HypoExponentialDistQuick
 
inverseF(double[], double) - Static method in class umontreal.iro.lecuyer.probdist.HypoExponentialDistQuick
.
inverseF(double) - Method in class umontreal.iro.lecuyer.probdist.InverseDistFromDensity
Computes the inverse distribution function at u.
inverseF(double) - Method in class umontreal.iro.lecuyer.probdist.InverseGammaDist
 
inverseF(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.InverseGammaDist
Computes the inverse distribution function of the inverse gamma distribution with shape parameter α and scale parameter β.
inverseF(double) - Method in class umontreal.iro.lecuyer.probdist.InverseGaussianDist
 
inverseF(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.InverseGaussianDist
Computes the inverse of the inverse gaussian distribution with parameters μ and λ.
inverseF(double) - Method in class umontreal.iro.lecuyer.probdist.JohnsonSBDist
 
inverseF(double, double, double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.JohnsonSBDist
.
inverseF(double) - Method in class umontreal.iro.lecuyer.probdist.JohnsonSLDist
 
inverseF(double, double, double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.JohnsonSLDist
Returns the inverse distribution function F-1(u).
inverseF(double) - Method in class umontreal.iro.lecuyer.probdist.JohnsonSUDist
 
inverseF(double, double, double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.JohnsonSUDist
Returns the inverse distribution function F-1(u).
inverseF(double) - Method in class umontreal.iro.lecuyer.probdist.KolmogorovSmirnovDist
 
inverseF(int, double) - Static method in class umontreal.iro.lecuyer.probdist.KolmogorovSmirnovDist
Computes the inverse x = F-1(u) of the distribution F(x) with parameter n.
inverseF(double) - Method in class umontreal.iro.lecuyer.probdist.KolmogorovSmirnovDistQuick
 
inverseF(int, double) - Static method in class umontreal.iro.lecuyer.probdist.KolmogorovSmirnovDistQuick
Computes the inverse x = F-1(u) of the distribution F(x) with parameter n.
inverseF(double) - Method in class umontreal.iro.lecuyer.probdist.KolmogorovSmirnovPlusDist
 
inverseF(int, double) - Static method in class umontreal.iro.lecuyer.probdist.KolmogorovSmirnovPlusDist
.
inverseF(double) - Method in class umontreal.iro.lecuyer.probdist.LaplaceDist
 
inverseF(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.LaplaceDist
Computes the inverse Laplace distribution function.
inverseF(double, double) - Static method in class umontreal.iro.lecuyer.probdist.LogarithmicDist
 
inverseF(double) - Method in class umontreal.iro.lecuyer.probdist.LogisticDist
 
inverseF(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.LogisticDist
Computes the inverse distribution function F-1(u).
inverseF(double) - Method in class umontreal.iro.lecuyer.probdist.LoglogisticDist
 
inverseF(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.LoglogisticDist
.
inverseF(double) - Method in class umontreal.iro.lecuyer.probdist.LognormalDist
 
inverseF(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.LognormalDist
Computes the inverse of the lognormal distribution function, using NormalDist.inverseF01.
inverseF(double) - Method in class umontreal.iro.lecuyer.probdist.NakagamiDist
 
inverseF(double, double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.NakagamiDist
.
inverseF(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.NegativeBinomialDist
.
inverseF(double) - Method in class umontreal.iro.lecuyer.probdist.NormalDist
 
inverseF(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.NormalDist
Computes the inverse normal distribution function with mean μ and variance σ2.
inverseF(double) - Method in class umontreal.iro.lecuyer.probdist.NormalDistQuick
 
inverseF(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.NormalDistQuick
Returns an approximation of Φ-1(u), where Φ is the standard normal distribution function, with mean 0 and variance 1.
inverseF(double, double, double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.NormalInverseGaussianDist
.
inverseF(double) - Method in class umontreal.iro.lecuyer.probdist.ParetoDist
 
inverseF(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.ParetoDist
Computes the inverse of the distribution function.
inverseF(double) - Method in class umontreal.iro.lecuyer.probdist.Pearson5Dist
Deprecated.  
inverseF(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.Pearson5Dist
Deprecated. Computes the inverse distribution function of a Pearson V distribution with shape parameter α and scale parameter β.
inverseF(double) - Method in class umontreal.iro.lecuyer.probdist.Pearson6Dist
 
inverseF(double, double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.Pearson6Dist
.
inverseF(double) - Method in class umontreal.iro.lecuyer.probdist.PiecewiseLinearEmpiricalDist
 
inverseF(double, double) - Static method in class umontreal.iro.lecuyer.probdist.PoissonDist
Performs a linear search to get the inverse function without precomputed tables.
inverseF(double) - Method in class umontreal.iro.lecuyer.probdist.PowerDist
 
inverseF(double, double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.PowerDist
Computes the inverse of the distribution function.
inverseF(double) - Method in class umontreal.iro.lecuyer.probdist.RayleighDist
 
inverseF(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.RayleighDist
.
inverseF(double, double) - Static method in class umontreal.iro.lecuyer.probdist.RayleighDist
.
inverseF(double) - Method in class umontreal.iro.lecuyer.probdist.StudentDist
 
inverseF(int, double) - Static method in class umontreal.iro.lecuyer.probdist.StudentDist
Returns the inverse x = F-1(u) of Student t-distribution function with n degrees of freedom.
inverseF(double) - Method in class umontreal.iro.lecuyer.probdist.StudentDistQuick
 
inverseF(int, double) - Static method in class umontreal.iro.lecuyer.probdist.StudentDistQuick
.
inverseF(double) - Method in class umontreal.iro.lecuyer.probdist.TriangularDist
 
inverseF(double, double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.TriangularDist
.
inverseF(double) - Method in class umontreal.iro.lecuyer.probdist.TruncatedDist
 
inverseF(double) - Method in class umontreal.iro.lecuyer.probdist.UniformDist
 
inverseF(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.UniformDist
.
inverseF(int, int, double) - Static method in class umontreal.iro.lecuyer.probdist.UniformIntDist
Computes the inverse of the discrete uniform distribution function.
inverseF(double) - Method in class umontreal.iro.lecuyer.probdist.WatsonGDist
 
inverseF(int, double) - Static method in class umontreal.iro.lecuyer.probdist.WatsonGDist
Computes x = Fn-1(u), where Fn is the Watson G distribution with parameter n.
inverseF(double) - Method in class umontreal.iro.lecuyer.probdist.WatsonUDist
 
inverseF(int, double) - Static method in class umontreal.iro.lecuyer.probdist.WatsonUDist
Computes x = Fn-1(u), where Fn is the Watson U distribution with parameter n.
inverseF(double) - Method in class umontreal.iro.lecuyer.probdist.WeibullDist
 
inverseF(double, double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.WeibullDist
.
inverseF(double, double) - Static method in class umontreal.iro.lecuyer.probdist.WeibullDist
.
inverseF01(double) - Static method in class umontreal.iro.lecuyer.probdist.NormalDist
Same as inverseF (0, 1, u).
inverseF01(double) - Static method in class umontreal.iro.lecuyer.probdist.NormalDistQuick
Same as inverseF (0.0, 1.0, u).
inverseFInt(double) - Method in class umontreal.iro.lecuyer.probdist.BernoulliDist
 
inverseFInt(double) - Method in class umontreal.iro.lecuyer.probdist.BinomialDist
 
inverseFInt(double) - Method in class umontreal.iro.lecuyer.probdist.DiscreteDistributionInt
Returns the inverse distribution function F-1(u), where 0 <= u <= 1.
inverseFInt(double) - Method in class umontreal.iro.lecuyer.probdist.GeometricDist
 
inverseFInt(double) - Method in class umontreal.iro.lecuyer.probdist.HypergeometricDist
 
inverseFInt(double) - Method in class umontreal.iro.lecuyer.probdist.LogarithmicDist
 
inverseFInt(double) - Method in class umontreal.iro.lecuyer.probdist.NegativeBinomialDist
 
inverseFInt(double) - Method in class umontreal.iro.lecuyer.probdist.PoissonDist
 
inverseFInt(double) - Method in class umontreal.iro.lecuyer.probdist.UniformIntDist
 
InverseFromDensityGen - Class in umontreal.iro.lecuyer.randvar
InverseFromDensityGen
InverseFromDensityGen(RandomStream, ContinuousDistribution, double, double, int) - Constructor for class umontreal.iro.lecuyer.randvar.InverseFromDensityGen
.
InverseFromDensityGen(RandomStream, MathFunction, double, double, int, double, double) - Constructor for class umontreal.iro.lecuyer.randvar.InverseFromDensityGen
.
InverseGammaDist - Class in umontreal.iro.lecuyer.probdist
Extends the class ContinuousDistribution for the inverse gamma distribution with shape parameter α > 0 and scale parameter β > 0.
InverseGammaDist(double, double) - Constructor for class umontreal.iro.lecuyer.probdist.InverseGammaDist
Constructs an InverseGammaDist object with parameters α = alpha and β = beta.
InverseGammaGen - Class in umontreal.iro.lecuyer.randvar
This class implements random variate generators for the inverse gamma distribution with shape parameter α > 0 and scale parameter β > 0.
InverseGammaGen(RandomStream, double, double) - Constructor for class umontreal.iro.lecuyer.randvar.InverseGammaGen
Creates an inverse gamma random variate generator with parameters α = alpha and β = beta, using stream s.
InverseGammaGen(RandomStream, double) - Constructor for class umontreal.iro.lecuyer.randvar.InverseGammaGen
Creates an inverse gamma random variate generator with parameters α = alpha and β = 1, using stream s.
InverseGammaGen(RandomStream, InverseGammaDist) - Constructor for class umontreal.iro.lecuyer.randvar.InverseGammaGen
Creates a new generator for the distribution dist, using stream s.
InverseGaussianDist - Class in umontreal.iro.lecuyer.probdist
Extends the class ContinuousDistribution for the inverse Gaussian distribution with location parameter μ > 0 and scale parameter λ > 0.
InverseGaussianDist(double, double) - Constructor for class umontreal.iro.lecuyer.probdist.InverseGaussianDist
Constructs the inverse Gaussian distribution with parameters μ and λ.
InverseGaussianGen - Class in umontreal.iro.lecuyer.randvar
InverseGaussianGen
InverseGaussianGen(RandomStream, double, double) - Constructor for class umontreal.iro.lecuyer.randvar.InverseGaussianGen
.
InverseGaussianGen(RandomStream, InverseGaussianDist) - Constructor for class umontreal.iro.lecuyer.randvar.InverseGaussianGen
.
InverseGaussianMSHGen - Class in umontreal.iro.lecuyer.randvar
This class implements inverse gaussian random variate generators using the many-to-one transformation method of Michael, Schucany and Haas (MHS).
InverseGaussianMSHGen(RandomStream, NormalGen, double, double) - Constructor for class umontreal.iro.lecuyer.randvar.InverseGaussianMSHGen
Creates an inverse gaussian random variate generator with parameters μ = mu and λ = lambda, using streams s and sn.
InverseGaussianMSHGen(RandomStream, NormalGen, InverseGaussianDist) - Constructor for class umontreal.iro.lecuyer.randvar.InverseGaussianMSHGen
Creates a new generator for the distribution dist using streams s and sn.
InverseGaussianProcess - Class in umontreal.iro.lecuyer.stochprocess
The inverse Gaussian process is a non-decreasing process where the increments are additive and are given by the inverse gaussian distribution, InverseGaussianDist.
InverseGaussianProcess(double, double, double, RandomStream) - Constructor for class umontreal.iro.lecuyer.stochprocess.InverseGaussianProcess
Constructs a new InverseGaussianProcess.
InverseGaussianProcessBridge - Class in umontreal.iro.lecuyer.stochprocess
Samples the path by bridge sampling: first finding the process value at the final time and then the middle time, etc.
InverseGaussianProcessBridge(double, double, double, RandomStream, RandomStream) - Constructor for class umontreal.iro.lecuyer.stochprocess.InverseGaussianProcessBridge
Constructs a new InverseGaussianProcessBridge.
InverseGaussianProcessMSH - Class in umontreal.iro.lecuyer.stochprocess
Uses a faster generating method (MSH) than the simple inversion of the distribution function used by InverseGaussianProcess.
InverseGaussianProcessMSH(double, double, double, RandomStream, RandomStream) - Constructor for class umontreal.iro.lecuyer.stochprocess.InverseGaussianProcessMSH
Constructs a new InverseGaussianProcessMSH.
InverseGaussianProcessPCA - Class in umontreal.iro.lecuyer.stochprocess
Approximates a principal component analysis (PCA) decomposition of the InverseGaussianProcess.
InverseGaussianProcessPCA(double, double, double, RandomStream) - Constructor for class umontreal.iro.lecuyer.stochprocess.InverseGaussianProcessPCA
Constructs a new InverseGaussianProcessPCA.
IRAC2 - Static variable in class umontreal.iro.lecuyer.util.Num
.
isAlive() - Method in class umontreal.iro.lecuyer.simprocs.SimProcess
Returns true if the process is alive, otherwise false.
isArray() - Method in class umontreal.iro.lecuyer.util.io.DataField
Returns true if the field contains an array.
isArray2D() - Method in class umontreal.iro.lecuyer.util.io.DataField
Returns true if the field contains a two-dimensional array.
isAtomic() - Method in class umontreal.iro.lecuyer.util.io.DataField
Returns true if the field value is atomic data.
isBroadcasting() - Method in class umontreal.iro.lecuyer.stat.list.ListOfStatProbes
Determines if this list of statistical probes is broadcasting observations to registered observers.
isBroadcasting() - Method in class umontreal.iro.lecuyer.stat.matrix.MatrixOfStatProbes
Determines if this matrix of statistical probes is broadcasting values to registered observers.
isBroadcasting() - Method in class umontreal.iro.lecuyer.stat.StatProbe
.
isCaching() - Method in class umontreal.iro.lecuyer.randvar.RandomVariateGenWithCache
Determines if the random variate generator is caching values, default being true.
isCaching() - Method in class umontreal.iro.lecuyer.rng.RandomStreamWithCache
Determines if the random stream is caching values, default being true.
isCollecting() - Method in class umontreal.iro.lecuyer.stat.list.ListOfStatProbes
Determines if this list of statistical probes is collecting values.
isCollecting() - Method in class umontreal.iro.lecuyer.stat.matrix.MatrixOfStatProbes
Determines if this matrix of statistical probes is collecting values.
isCollecting() - Method in class umontreal.iro.lecuyer.stat.StatProbe
.
isDouble() - Method in class umontreal.iro.lecuyer.util.io.DataField
Returns true if the field value is an atomic double.
isEmpty() - Method in class umontreal.iro.lecuyer.simevents.eventlist.BinaryTree
 
isEmpty() - Method in class umontreal.iro.lecuyer.simevents.eventlist.DoublyLinked
 
isEmpty() - Method in interface umontreal.iro.lecuyer.simevents.eventlist.EventList
Returns true if and only if the event list is empty (no event is scheduled).
isEmpty() - Method in class umontreal.iro.lecuyer.simevents.eventlist.Henriksen
 
isEmpty() - Method in class umontreal.iro.lecuyer.simevents.eventlist.RedblackTree
 
isEmpty() - Method in class umontreal.iro.lecuyer.simevents.eventlist.SplayTree
 
isEmpty() - Method in class umontreal.iro.lecuyer.stat.list.ListOfStatProbes
 
isFloat() - Method in class umontreal.iro.lecuyer.util.io.DataField
Returns true if the field value is an atomic float.
isInt() - Method in class umontreal.iro.lecuyer.util.io.DataField
Returns true if the field value is an atomic int.
isModifiable() - Method in class umontreal.iro.lecuyer.stat.list.ListOfStatProbes
Determines if this list of statistical probes is modifiable, i.e., if probes can be added or removed.
isSimulating() - Method in class umontreal.iro.lecuyer.simevents.Simulator
Determines if this simulator is currently running, i.e., executing scheduled events.
isSimulating() - Method in class umontreal.iro.lecuyer.simexp.SimExp
Determines if the simulation is in progress.
isStopped() - Method in class umontreal.iro.lecuyer.simevents.Simulator
Determines if this simulator was stopped by an event.
isString() - Method in class umontreal.iro.lecuyer.util.io.DataField
Returns true if the field value is an atomic String.
isWarmupDone() - Method in class umontreal.iro.lecuyer.simexp.BatchMeansSim
Determines if the warmup period for the simulation is over.
iterateSpacings(DoubleArrayList, DoubleArrayList) - Static method in class umontreal.iro.lecuyer.gof.GofStat
Applies one iteration of the iterated spacings transformation.
iterator() - Method in class umontreal.iro.lecuyer.hups.AntitheticPointSet
 
iterator() - Method in class umontreal.iro.lecuyer.hups.BakerTransformedPointSet
 
iterator() - Method in class umontreal.iro.lecuyer.hups.ContainerPointSet
 
iterator() - Method in class umontreal.iro.lecuyer.hups.CycleBasedPointSet
 
iterator() - Method in class umontreal.iro.lecuyer.hups.CycleBasedPointSetBase2
 
iterator() - Method in class umontreal.iro.lecuyer.hups.DigitalNet
 
iterator() - Method in class umontreal.iro.lecuyer.hups.DigitalNetBase2
 
iterator() - Method in class umontreal.iro.lecuyer.hups.PaddedPointSet
 
iterator() - Method in class umontreal.iro.lecuyer.hups.PointSet
.
iterator() - Method in class umontreal.iro.lecuyer.hups.RandShiftedPointSet
 
iterator() - Method in class umontreal.iro.lecuyer.hups.Rank1Lattice
 
iterator() - Method in class umontreal.iro.lecuyer.hups.RQMCPointSet
Returns a new point set iterator for the point set associated to this object.
iterator() - Method in class umontreal.iro.lecuyer.hups.SortedPointSet
.
iterator() - Method in class umontreal.iro.lecuyer.hups.SubsetOfPointSet
 
iterator() - Method in class umontreal.iro.lecuyer.simevents.eventlist.BinaryTree
 
iterator() - Method in class umontreal.iro.lecuyer.simevents.eventlist.DoublyLinked
 
iterator() - Method in class umontreal.iro.lecuyer.simevents.eventlist.Henriksen
 
iterator() - Method in class umontreal.iro.lecuyer.simevents.eventlist.RedblackTree
 
iterator() - Method in class umontreal.iro.lecuyer.simevents.eventlist.SplayTree
 
iterator() - Method in class umontreal.iro.lecuyer.simevents.ListWithStat
 
iterator() - Method in class umontreal.iro.lecuyer.stat.list.ListOfStatProbes
 
iterator() - Method in class umontreal.iro.lecuyer.stat.matrix.MatrixOfStatProbes
 
iterator() - Method in class umontreal.iro.lecuyer.util.TransformingList
 
iteratorNoGray() - Method in class umontreal.iro.lecuyer.hups.DigitalNet
.
iteratorNoGray() - Method in class umontreal.iro.lecuyer.hups.DigitalNetBase2
.
iteratorShift() - Method in class umontreal.iro.lecuyer.hups.DigitalSequence
.
iteratorShift() - Method in class umontreal.iro.lecuyer.hups.DigitalSequenceBase2
Similar to iterator, except that the first coordinate of the points is i/n, the second coordinate is obtained via the generating matrix C0, the next one via C1, and so on.
iteratorShiftNoGray() - Method in class umontreal.iro.lecuyer.hups.DigitalSequence
.
iteratorShiftNoGray() - Method in class umontreal.iro.lecuyer.hups.DigitalSequenceBase2
This iterator shifts all coordinates of each point one position to the right and sets the first coordinate of point i to i/n, so that the points enumerated with this iterator have one more dimension.
iterPowRatioTests(DoubleArrayList, int, boolean, boolean, PrintWriter) - Static method in class umontreal.iro.lecuyer.gof.GofFormat
Similar to iterSpacingsTests, but with the GofStat.powerRatios transformation.
iterSpacingsTests(DoubleArrayList, int, boolean, boolean, PrintWriter) - Static method in class umontreal.iro.lecuyer.gof.GofFormat
Repeats the following k times: Applies the GofStat.iterateSpacings transformation to the U(0),..., U(N-1), assuming that these observations are in sortedData, then computes the EDF test statistics and calls activeTests after each transformation.

J

JDBCManager - Class in umontreal.iro.lecuyer.util
This class provides some facilities to connect to a SQL database and to retrieve data stored in it.
JDBCManager() - Constructor for class umontreal.iro.lecuyer.util.JDBCManager
 
JohnsonSBDist - Class in umontreal.iro.lecuyer.probdist
JohnsonSBDist
JohnsonSBDist(double, double, double, double) - Constructor for class umontreal.iro.lecuyer.probdist.JohnsonSBDist
.
JohnsonSBGen - Class in umontreal.iro.lecuyer.randvar
This class implements random variate generators for the Johnson SB distribution.
JohnsonSBGen(RandomStream, double, double, double, double) - Constructor for class umontreal.iro.lecuyer.randvar.JohnsonSBGen
Creates a JohnsonSB random variate generator.
JohnsonSBGen(RandomStream, JohnsonSBDist) - Constructor for class umontreal.iro.lecuyer.randvar.JohnsonSBGen
Creates a new generator for the JohnsonSB distribution dist, using stream s.
JohnsonSLDist - Class in umontreal.iro.lecuyer.probdist
Extends the class ContinuousDistribution for the Johnson SL distribution.
JohnsonSLDist(double, double) - Constructor for class umontreal.iro.lecuyer.probdist.JohnsonSLDist
Same as JohnsonSLDist (gamma, delta, 0, 1).
JohnsonSLDist(double, double, double, double) - Constructor for class umontreal.iro.lecuyer.probdist.JohnsonSLDist
Constructs a JohnsonSLDist object with shape parameters γ and δ, location parameter ξ, and scale parameter λ.
JohnsonSLGen - Class in umontreal.iro.lecuyer.randvar
This class implements random variate generators for the Johnson SL distribution.
JohnsonSLGen(RandomStream, double, double, double, double) - Constructor for class umontreal.iro.lecuyer.randvar.JohnsonSLGen
Creates a JohnsonSL random variate generator.
JohnsonSLGen(RandomStream, JohnsonSLDist) - Constructor for class umontreal.iro.lecuyer.randvar.JohnsonSLGen
Creates a new generator for the JohnsonSL distribution dist, using stream s.
JohnsonSUDist - Class in umontreal.iro.lecuyer.probdist
Extends the class ContinuousDistribution for the Johnson SU distribution.
JohnsonSUDist(double, double) - Constructor for class umontreal.iro.lecuyer.probdist.JohnsonSUDist
Same as JohnsonSUDist (gamma, delta, 0, 1).
JohnsonSUDist(double, double, double, double) - Constructor for class umontreal.iro.lecuyer.probdist.JohnsonSUDist
Constructs a JohnsonSUDist object with shape parameters γ and δ, location parameter ξ, and scale parameter λ.
JohnsonSUGen - Class in umontreal.iro.lecuyer.randvar
This class implements random variate generators for the Johnson SU distribution.
JohnsonSUGen(RandomStream, double, double, double, double) - Constructor for class umontreal.iro.lecuyer.randvar.JohnsonSUGen
Creates a JohnsonSU random variate generator.
JohnsonSUGen(RandomStream, JohnsonSUDist) - Constructor for class umontreal.iro.lecuyer.randvar.JohnsonSUGen
Creates a new generator for the JohnsonSU distribution dist, using stream s.

K

KernelDensity - Class in umontreal.iro.lecuyer.gof
This class provides methods to compute a kernel density estimator from a set of n individual observations x0,…, xn-1, and returns its value at m selected points.
KernelDensity() - Constructor for class umontreal.iro.lecuyer.gof.KernelDensity
 
KernelDensityGen - Class in umontreal.iro.lecuyer.randvar
This class implements random variate generators for distributions obtained via kernel density estimation methods from a set of n individual observations x1,..., xn.
KernelDensityGen(RandomStream, EmpiricalDist, RandomVariateGen, double) - Constructor for class umontreal.iro.lecuyer.randvar.KernelDensityGen
Creates a new generator for a kernel density estimated from the observations given by the empirical distribution dist, using stream s to select the observations, generator kGen to generate the added noise from the kernel density, and bandwidth h.
KernelDensityGen(RandomStream, EmpiricalDist, NormalGen) - Constructor for class umontreal.iro.lecuyer.randvar.KernelDensityGen
This constructor uses a gaussian kernel and the default bandwidth h = αkh0 with the αk suggested in Table  for the gaussian distribution.
KernelDensityVarCorrectGen - Class in umontreal.iro.lecuyer.randvar
KernelDensityVarCorrectGen
KernelDensityVarCorrectGen(RandomStream, EmpiricalDist, RandomVariateGen, double, double) - Constructor for class umontreal.iro.lecuyer.randvar.KernelDensityVarCorrectGen
.
KernelDensityVarCorrectGen(RandomStream, EmpiricalDist, NormalGen) - Constructor for class umontreal.iro.lecuyer.randvar.KernelDensityVarCorrectGen
.
kill(SimProcess) - Method in class umontreal.iro.lecuyer.simprocs.DSOLProcessSimulator
 
kill(SimProcess) - Method in class umontreal.iro.lecuyer.simprocs.ProcessSimulator
Terminates the life of process and sets its state to DEAD, after canceling its control event if there is one.
kill() - Method in class umontreal.iro.lecuyer.simprocs.SimProcess
Terminates the life of this process and sets its state to DEAD, after canceling its activating event if there is one.
kill(SimProcess) - Method in class umontreal.iro.lecuyer.simprocs.ThreadProcessSimulator
 
killAll() - Method in class umontreal.iro.lecuyer.simprocs.DSOLProcessSimulator
 
killAll() - Method in class umontreal.iro.lecuyer.simprocs.ProcessSimulator
Kills all currently living (active, delayed, or suspended) processes managed by this simulator.
killAll() - Method in class umontreal.iro.lecuyer.simprocs.ThreadProcessSimulator
Kills all threads linked to the current variable.
kolmogorovSmirnov(double[]) - Static method in class umontreal.iro.lecuyer.gof.GofStat
Computes the Kolmogorov-Smirnov (KS) test statistics Dn+, Dn-, and Dn (see method kolmogorovSmirnov).
kolmogorovSmirnov(DoubleArrayList) - Static method in class umontreal.iro.lecuyer.gof.GofStat
Computes the Kolmogorov-Smirnov (KS) test statistics Dn+, Dn-, and Dn.
kolmogorovSmirnov(double[], ContinuousDistribution, double[], double[]) - Static method in class umontreal.iro.lecuyer.gof.GofStat
Computes the KolmogorovSmirnov (KS) test statistics and their p-values.
KolmogorovSmirnovDist - Class in umontreal.iro.lecuyer.probdist
Extends the class ContinuousDistribution for the Kolmogorov-Smirnov distribution with parameter n.
KolmogorovSmirnovDist(int) - Constructor for class umontreal.iro.lecuyer.probdist.KolmogorovSmirnovDist
Constructs a distribution with parameter n.
KolmogorovSmirnovDistQuick - Class in umontreal.iro.lecuyer.probdist
Extends the class KolmogorovSmirnovDist for the distribution.
KolmogorovSmirnovDistQuick(int) - Constructor for class umontreal.iro.lecuyer.probdist.KolmogorovSmirnovDistQuick
Constructs a distribution with parameter n.
kolmogorovSmirnovJumpOne(DoubleArrayList, double) - Static method in class umontreal.iro.lecuyer.gof.GofStat
Compute the KS statistics Dn+(a) and Dn-(a) defined in the description of the method FDist.kolmogorovSmirnovPlusJumpOne, assuming that F is the uniform distribution over [0, 1] and that U(1),..., U(n) are in sortedData.
KolmogorovSmirnovPlusDist - Class in umontreal.iro.lecuyer.probdist
KolmogorovSmirnovPlusDist
KolmogorovSmirnovPlusDist(int) - Constructor for class umontreal.iro.lecuyer.probdist.KolmogorovSmirnovPlusDist
.
kolmogorovSmirnovPlusJumpOne(int, double, double) - Static method in class umontreal.iro.lecuyer.gof.FDist
Similar to KolmogorovSmirnovPlusDist but for the case where the distribution function F has a jump of size a at a given point x0, is zero at the left of x0, and is continuous at the right of x0.
KorobovLattice - Class in umontreal.iro.lecuyer.hups
KorobovLattice
KorobovLattice(int, int, int) - Constructor for class umontreal.iro.lecuyer.hups.KorobovLattice
.
KorobovLattice(int, int, int, int) - Constructor for class umontreal.iro.lecuyer.hups.KorobovLattice
.
KorobovLatticeSequence - Class in umontreal.iro.lecuyer.hups
This class implements Korobov lattice sequences, defined as follows.
KorobovLatticeSequence(int, int) - Constructor for class umontreal.iro.lecuyer.hups.KorobovLatticeSequence
Constructs a new lattice sequence with base b and generator = a.
KS - Static variable in class umontreal.iro.lecuyer.gof.GofFormat
Kolmogorov-Smirnov test
KSM - Static variable in class umontreal.iro.lecuyer.gof.GofFormat
Kolmogorov-Smirnov- test
KSP - Static variable in class umontreal.iro.lecuyer.gof.GofFormat
Kolmogorov-Smirnov+ test

L

LaplaceDist - Class in umontreal.iro.lecuyer.probdist
Extends the class ContinuousDistribution for the Laplace distribution.
LaplaceDist() - Constructor for class umontreal.iro.lecuyer.probdist.LaplaceDist
Constructs a LaplaceDist object with default parameters μ = 0 and β = 1.
LaplaceDist(double, double) - Constructor for class umontreal.iro.lecuyer.probdist.LaplaceDist
Constructs a LaplaceDist object with parameters μ = mu and β = beta.
LaplaceGen - Class in umontreal.iro.lecuyer.randvar
This class implements methods for generating random variates from the Laplace distribution.
LaplaceGen(RandomStream, double, double) - Constructor for class umontreal.iro.lecuyer.randvar.LaplaceGen
Creates a Laplace random variate generator with parameters μ = mu and β = beta, using stream s.
LaplaceGen(RandomStream) - Constructor for class umontreal.iro.lecuyer.randvar.LaplaceGen
Creates a Laplace random variate generator with parameters μ = 0 and β = 1, using stream s.
LaplaceGen(RandomStream, LaplaceDist) - Constructor for class umontreal.iro.lecuyer.randvar.LaplaceGen
Creates a new generator for the Laplace distribution dist and stream s.
lastIndexOf(Object) - Method in class umontreal.iro.lecuyer.stat.list.ListOfStatProbes
 
LatinHypercube - Class in umontreal.iro.lecuyer.hups
LatinHypercube
LatinHypercube(int, int) - Constructor for class umontreal.iro.lecuyer.hups.LatinHypercube
.
LCGPointSet - Class in umontreal.iro.lecuyer.hups
LCGPointSet
LCGPointSet(int, int) - Constructor for class umontreal.iro.lecuyer.hups.LCGPointSet
.
LCGPointSet(int, int, int, int) - Constructor for class umontreal.iro.lecuyer.hups.LCGPointSet
.
LeastSquares - Class in umontreal.iro.lecuyer.functionfit
This class implements different linear regression models, using the least squares method to estimate the regression coefficients.
LeastSquares() - Constructor for class umontreal.iro.lecuyer.functionfit.LeastSquares
 
leftMatrixScramble(RandomStream) - Method in class umontreal.iro.lecuyer.hups.DigitalNet
.
leftMatrixScramble(RandomStream) - Method in class umontreal.iro.lecuyer.hups.DigitalNetBase2
 
leftMatrixScrambleDiag(RandomStream) - Method in class umontreal.iro.lecuyer.hups.DigitalNet
.
leftMatrixScrambleDiag(RandomStream) - Method in class umontreal.iro.lecuyer.hups.DigitalNetBase2
 
leftMatrixScrambleFaurePermut(RandomStream, int) - Method in class umontreal.iro.lecuyer.hups.DigitalNet
.
leftMatrixScrambleFaurePermut(RandomStream, int) - Method in class umontreal.iro.lecuyer.hups.DigitalNetBase2
 
leftMatrixScrambleFaurePermutAll(RandomStream, int) - Method in class umontreal.iro.lecuyer.hups.DigitalNet
.
leftMatrixScrambleFaurePermutAll(RandomStream, int) - Method in class umontreal.iro.lecuyer.hups.DigitalNetBase2
 
leftMatrixScrambleFaurePermutDiag(RandomStream, int) - Method in class umontreal.iro.lecuyer.hups.DigitalNet
.
leftMatrixScrambleFaurePermutDiag(RandomStream, int) - Method in class umontreal.iro.lecuyer.hups.DigitalNetBase2
 
LeftScrambledFaureSequence - Class in umontreal.iro.lecuyer.markovchain
Deprecated. 
LeftScrambledFaureSequence(int, int, int, int, int) - Constructor for class umontreal.iro.lecuyer.markovchain.LeftScrambledFaureSequence
Deprecated. Same as FaureSequence(b, k, r, w, dim), except that its randomize method will do a left matrix scramble followed by a random digital shift.
LeftScrambledSobolSequence - Class in umontreal.iro.lecuyer.markovchain
Deprecated. 
LeftScrambledSobolSequence(int, int, int) - Constructor for class umontreal.iro.lecuyer.markovchain.LeftScrambledSobolSequence
Deprecated. Same as SobolSequence(k, w, dim), except that its randomize method will do a left matrix scramble followed by a random digital shift.
length() - Method in class umontreal.iro.lecuyer.util.PrintfFormat
 
LFSR113 - Class in umontreal.iro.lecuyer.rng
LFSR113
LFSR113() - Constructor for class umontreal.iro.lecuyer.rng.LFSR113
.
LFSR113(String) - Constructor for class umontreal.iro.lecuyer.rng.LFSR113
.
LFSR258 - Class in umontreal.iro.lecuyer.rng
Extends RandomStreamBase using a 64-bit composite linear feedback shift register (LFSR) (or Tausworthe) RNG as defined in.
LFSR258() - Constructor for class umontreal.iro.lecuyer.rng.LFSR258
Constructs a new stream.
LFSR258(String) - Constructor for class umontreal.iro.lecuyer.rng.LFSR258
Constructs a new stream with the identifier name.
LINE_SEPARATOR - Static variable in class umontreal.iro.lecuyer.util.PrintfFormat
End-of-line symbol or line separator.
LinkedListStat<E> - Class in umontreal.iro.lecuyer.simevents
This class extends ListWithStat, and uses a linked list as the internal data structure.
LinkedListStat() - Constructor for class umontreal.iro.lecuyer.simevents.LinkedListStat
Constructs a new list, initially empty.
LinkedListStat(Simulator) - Constructor for class umontreal.iro.lecuyer.simevents.LinkedListStat
Constructs a new list, initially empty, and using the default simulator.
LinkedListStat(Collection<? extends E>) - Constructor for class umontreal.iro.lecuyer.simevents.LinkedListStat
Constructs a list containing the elements of the specified collection, using the default simulator.
LinkedListStat(Simulator, Collection<? extends E>) - Constructor for class umontreal.iro.lecuyer.simevents.LinkedListStat
Constructs a list containing the elements of the specified collection.
LinkedListStat(String) - Constructor for class umontreal.iro.lecuyer.simevents.LinkedListStat
Constructs a new list with name name, using the default simulator.
LinkedListStat(Simulator, String) - Constructor for class umontreal.iro.lecuyer.simevents.LinkedListStat
Constructs a new list with name name.
LinkedListStat(Collection<? extends E>, String) - Constructor for class umontreal.iro.lecuyer.simevents.LinkedListStat
Constructs a new list containing the elements of the specified collection c and with name name, using the default simulator.
LinkedListStat(Simulator, Collection<? extends E>, String) - Constructor for class umontreal.iro.lecuyer.simevents.LinkedListStat
Constructs a new list containing the elements of the specified collection c and with name name.
listDir(String) - Static method in class umontreal.iro.lecuyer.hups.DigitalNetBase2FromFile
Lists all files (or directories) in directory dirname.
listDir(String) - Static method in class umontreal.iro.lecuyer.hups.DigitalNetFromFile
.
listDirHTML(String, String) - Static method in class umontreal.iro.lecuyer.hups.DigitalNetFromFile
.
listIterator() - Method in class umontreal.iro.lecuyer.simevents.eventlist.BinaryTree
 
listIterator() - Method in class umontreal.iro.lecuyer.simevents.eventlist.DoublyLinked
 
listIterator() - Method in interface umontreal.iro.lecuyer.simevents.eventlist.EventList
Returns a list iterator over the elements of the class Event in this list.
listIterator() - Method in class umontreal.iro.lecuyer.simevents.eventlist.Henriksen
 
listIterator() - Method in class umontreal.iro.lecuyer.simevents.eventlist.RedblackTree
 
listIterator() - Method in class umontreal.iro.lecuyer.simevents.eventlist.SplayTree
 
listIterator() - Method in class umontreal.iro.lecuyer.simevents.ListWithStat
 
listIterator(int) - Method in class umontreal.iro.lecuyer.simevents.ListWithStat
 
listIterator() - Method in class umontreal.iro.lecuyer.stat.list.ListOfStatProbes
 
listIterator(int) - Method in class umontreal.iro.lecuyer.stat.list.ListOfStatProbes
 
listIterator() - Method in class umontreal.iro.lecuyer.util.TransformingList
 
listIterator(int) - Method in class umontreal.iro.lecuyer.util.TransformingList
 
ListOfFunctionOfMultipleMeansTallies<E extends FunctionOfMultipleMeansTally> - Class in umontreal.iro.lecuyer.stat.list
Represents a list of tally statistical collectors for a vector of functions of multiple means.
ListOfFunctionOfMultipleMeansTallies() - Constructor for class umontreal.iro.lecuyer.stat.list.ListOfFunctionOfMultipleMeansTallies
Constructs a new empty list of tallies.
ListOfFunctionOfMultipleMeansTallies(String) - Constructor for class umontreal.iro.lecuyer.stat.list.ListOfFunctionOfMultipleMeansTallies
Constructs a new empty list of tallies with name name.
ListOfStatProbes<E extends StatProbe> - Class in umontreal.iro.lecuyer.stat.list
Represents a list of statistical probes that can be managed simultaneously.
ListOfStatProbes() - Constructor for class umontreal.iro.lecuyer.stat.list.ListOfStatProbes
Constructs an empty list of statistical probes.
ListOfStatProbes(String) - Constructor for class umontreal.iro.lecuyer.stat.list.ListOfStatProbes
Constructs an empty list of statistical probes with name name.
ListOfTallies<E extends Tally> - Class in umontreal.iro.lecuyer.stat.list
Represents a list of tally statistical collectors.
ListOfTallies() - Constructor for class umontreal.iro.lecuyer.stat.list.ListOfTallies
Constructs a new empty list of tallies.
ListOfTallies(String) - Constructor for class umontreal.iro.lecuyer.stat.list.ListOfTallies
Constructs a new empty list of tallies with name name.
ListOfTalliesWithCovariance<E extends Tally> - Class in umontreal.iro.lecuyer.stat.list
Extends ListOfTallies to add support for the computation of the sample covariance between each pair of elements in a list, without storing all observations.
ListOfTalliesWithCovariance() - Constructor for class umontreal.iro.lecuyer.stat.list.ListOfTalliesWithCovariance
Creates an empty list of tallies with covariance support.
ListOfTalliesWithCovariance(String) - Constructor for class umontreal.iro.lecuyer.stat.list.ListOfTalliesWithCovariance
Creates an empty list of tallies with covariance support and name name.
ListOfTalliesWithCV<E extends Tally> - Class in umontreal.iro.lecuyer.stat.list.lincv
Represents a list of tallies with control variables that inherits the functionalities of a list of tallies, and accepts vectors of length p + q.
ListOfTalliesWithCV() - Constructor for class umontreal.iro.lecuyer.stat.list.lincv.ListOfTalliesWithCV
Constructs a new empty list of tallies with no control variable.
ListOfTalliesWithCV(String) - Constructor for class umontreal.iro.lecuyer.stat.list.lincv.ListOfTalliesWithCV
Constructs a new empty list of tallies with no control variable and name name.
ListWithStat<E> - Class in umontreal.iro.lecuyer.simevents
Implements a list with integrated statistical probes to provide automatic collection of statistics on the sojourn times of objects in the list and on the size of the list as a function of time given by a simulator.
ListWithStat(List<ListWithStat.Node<E>>) - Constructor for class umontreal.iro.lecuyer.simevents.ListWithStat
Constructs a new list with internal data structure using the default simulator and implemented by nodeList.
ListWithStat(Simulator, List<ListWithStat.Node<E>>) - Constructor for class umontreal.iro.lecuyer.simevents.ListWithStat
Constructs a new list with internal data structure implemented by nodeList.
ListWithStat(List<ListWithStat.Node<E>>, Collection<? extends E>) - Constructor for class umontreal.iro.lecuyer.simevents.ListWithStat
Constructs a list containing the elements of the specified collection, whose elements are stored into nodeList and using the default simulator.
ListWithStat(Simulator, List<ListWithStat.Node<E>>, Collection<? extends E>) - Constructor for class umontreal.iro.lecuyer.simevents.ListWithStat
Constructs a list containing the elements of the specified collection, whose elements are stored into nodeList.
ListWithStat(List<ListWithStat.Node<E>>, String) - Constructor for class umontreal.iro.lecuyer.simevents.ListWithStat
Constructs a new list with name name, internal list nodeList, and using the default simulator.
ListWithStat(Simulator, List<ListWithStat.Node<E>>, String) - Constructor for class umontreal.iro.lecuyer.simevents.ListWithStat
Constructs a new list with name name, and internal list nodeList.
ListWithStat(List<ListWithStat.Node<E>>, Collection<? extends E>, String) - Constructor for class umontreal.iro.lecuyer.simevents.ListWithStat
Constructs a new list containing the elements of the specified collection c, with name name, internal list nodeList, and using the default simulator.
ListWithStat(Simulator, List<ListWithStat.Node<E>>, Collection<? extends E>, String) - Constructor for class umontreal.iro.lecuyer.simevents.ListWithStat
Constructs a new list containing the elements of the specified collection c, with name name, and internal list nodeList.
ListWithStat.Node<E> - Class in umontreal.iro.lecuyer.simevents
Represents a node that can be part of a list with statistical collecting.
ListWithStat.Node(E, double) - Constructor for class umontreal.iro.lecuyer.simevents.ListWithStat.Node
Constructs a new node containing element element inserted into the list at time insertionTime.
LMScrambleShift - Class in umontreal.iro.lecuyer.hups
LMScrambleShift
LMScrambleShift() - Constructor for class umontreal.iro.lecuyer.hups.LMScrambleShift
.
LMScrambleShift(RandomStream) - Constructor for class umontreal.iro.lecuyer.hups.LMScrambleShift
.
LN2 - Static variable in class umontreal.iro.lecuyer.util.Num
.
LN_DBL_MIN - Static variable in class umontreal.iro.lecuyer.util.Num
.
lnBeta(double, double) - Static method in class umontreal.iro.lecuyer.util.Num
.
lnCombination(int, int) - Static method in class umontreal.iro.lecuyer.util.Num
.
lnFactorial(int) - Static method in class umontreal.iro.lecuyer.util.Num
.
lnFactorial(long) - Static method in class umontreal.iro.lecuyer.util.Num
[tabb118]
nargument of the log-factorial natural logarithm of n factorial
lnGamma(double) - Static method in class umontreal.iro.lecuyer.util.Num
.
loc - Variable in class umontreal.iro.lecuyer.gof.GofStat.OutcomeCategoriesChi2
loc[i] gives the relocation of the category i in the nbExp array.
log2(int) - Method in class umontreal.iro.lecuyer.discrepancy.DiscrepancyContainer
Takes the logarithm in base 2 of the discrepancy values at index i.
log2(double) - Static method in class umontreal.iro.lecuyer.util.Num
.
LogarithmicDist - Class in umontreal.iro.lecuyer.probdist
LogarithmicDist
LogarithmicDist(double) - Constructor for class umontreal.iro.lecuyer.probdist.LogarithmicDist
.
LogarithmicGen - Class in umontreal.iro.lecuyer.randvar
This class implements random variate generators for the (discrete) logarithmic distribution.
LogarithmicGen(RandomStream, double) - Constructor for class umontreal.iro.lecuyer.randvar.LogarithmicGen
Creates a logarithmic random variate generator with parameters θ = theta and default value θ0 = 0.96, using stream s.
LogarithmicGen(RandomStream, double, double) - Constructor for class umontreal.iro.lecuyer.randvar.LogarithmicGen
Creates a logarithmic random variate generator with parameters θ = theta and θ0 = theta0, using stream s.
LogarithmicGen(RandomStream, LogarithmicDist) - Constructor for class umontreal.iro.lecuyer.randvar.LogarithmicGen
Creates a new generator with distribution dist and stream s, with default value θ0 = 0.96.
LogarithmicGen(RandomStream, LogarithmicDist, double) - Constructor for class umontreal.iro.lecuyer.randvar.LogarithmicGen
Creates a new generator with distribution dist and stream s, with θ0 = theta0.
LogisticDist - Class in umontreal.iro.lecuyer.probdist
Extends the class ContinuousDistribution for the logistic distribution.
LogisticDist() - Constructor for class umontreal.iro.lecuyer.probdist.LogisticDist
Constructs a LogisticDist object with default parameters α = 0 and λ = 1.
LogisticDist(double, double) - Constructor for class umontreal.iro.lecuyer.probdist.LogisticDist
Constructs a LogisticDist object with parameters α = alpha and λ = lambda.
LogisticGen - Class in umontreal.iro.lecuyer.randvar
LogisticGen
LogisticGen(RandomStream, double, double) - Constructor for class umontreal.iro.lecuyer.randvar.LogisticGen
.
LogisticGen(RandomStream) - Constructor for class umontreal.iro.lecuyer.randvar.LogisticGen
.
LogisticGen(RandomStream, LogisticDist) - Constructor for class umontreal.iro.lecuyer.randvar.LogisticGen
.
LoglogisticDist - Class in umontreal.iro.lecuyer.probdist
LoglogisticDist
LoglogisticDist(double, double) - Constructor for class umontreal.iro.lecuyer.probdist.LoglogisticDist
.
LoglogisticGen - Class in umontreal.iro.lecuyer.randvar
This class implements random variate generators for the log-logistic distribution with shape parameter α > 0 and scale parameter β > 0.
LoglogisticGen(RandomStream, double, double) - Constructor for class umontreal.iro.lecuyer.randvar.LoglogisticGen
Creates a log-logistic random variate generator with parameters α = alpha and β = beta, using stream s.
LoglogisticGen(RandomStream, LoglogisticDist) - Constructor for class umontreal.iro.lecuyer.randvar.LoglogisticGen
Creates a new generator for the distribution dist, using stream s.
LognormalDist - Class in umontreal.iro.lecuyer.probdist
Extends the class ContinuousDistribution for the lognormal distribution.
LognormalDist() - Constructor for class umontreal.iro.lecuyer.probdist.LognormalDist
Constructs a LognormalDist object with default parameters μ = 0 and σ = 1.
LognormalDist(double, double) - Constructor for class umontreal.iro.lecuyer.probdist.LognormalDist
Constructs a LognormalDist object with parameters μ = mu and σ = sigma.
LognormalDistFromMoments - Class in umontreal.iro.lecuyer.probdist
Extends the LognormalDist class with a constructor accepting the mean m and the variance v of the distribution as arguments.
LognormalDistFromMoments(double, double) - Constructor for class umontreal.iro.lecuyer.probdist.LognormalDistFromMoments
 
LognormalGen - Class in umontreal.iro.lecuyer.randvar
This class implements methods for generating random variates from the lognormal distribution.
LognormalGen(RandomStream, double, double) - Constructor for class umontreal.iro.lecuyer.randvar.LognormalGen
Creates a lognormal random variate generator with parameters μ = mu and σ = sigma, using stream s.
LognormalGen(RandomStream) - Constructor for class umontreal.iro.lecuyer.randvar.LognormalGen
Creates a lognormal random variate generator with parameters μ = 0 and σ = 1, using stream s.
LognormalGen(RandomStream, LognormalDist) - Constructor for class umontreal.iro.lecuyer.randvar.LognormalGen
Create a random variate generator for the lognormal distribution dist and stream s.
LognormalSpecialGen - Class in umontreal.iro.lecuyer.randvar
LognormalSpecialGen
LognormalSpecialGen(NormalGen) - Constructor for class umontreal.iro.lecuyer.randvar.LognormalSpecialGen
.

M

main(String[]) - Static method in class umontreal.iro.lecuyer.rng.GenF2w32
This method is only meant to be used during the compilation process.
makeCopies(int) - Method in class umontreal.iro.lecuyer.markovchain.ArrayOfComparableChains
Creates n copies (clones) of the chain baseChain and puts them in an array, ready for the array RQMC simulation.
makeCopies(int) - Method in class umontreal.iro.lecuyer.markovchain.ArrayOfDoubleChains
Creates the vector of states for n copies of the base chain.
MarkovChain - Class in umontreal.iro.lecuyer.markovchain
This class defines a generic Markov chain and provides basic tools to simulate it for a given number of steps or until it stops and recover the performance measure.
MarkovChain() - Constructor for class umontreal.iro.lecuyer.markovchain.MarkovChain
 
MarkovChainComparable - Class in umontreal.iro.lecuyer.markovchain
A subclass of Markov chain for which there is a total ordering between the states in each dimension induced by the implementation of the MultiDimComparable interface in package umontreal.iro.lecuyer.util.
MarkovChainComparable() - Constructor for class umontreal.iro.lecuyer.markovchain.MarkovChainComparable
 
MarkovChainComparableStop - Class in umontreal.iro.lecuyer.markovchain
Deprecated. 
MarkovChainComparableStop() - Constructor for class umontreal.iro.lecuyer.markovchain.MarkovChainComparableStop
Deprecated.  
MarkovChainDouble - Class in umontreal.iro.lecuyer.markovchain
A special kind of Markov chain whose state space is a subset of the real numbers.
MarkovChainDouble() - Constructor for class umontreal.iro.lecuyer.markovchain.MarkovChainDouble
 
MarkovChainDoubleStop - Class in umontreal.iro.lecuyer.markovchain
Deprecated. 
MarkovChainDoubleStop() - Constructor for class umontreal.iro.lecuyer.markovchain.MarkovChainDoubleStop
Deprecated.  
MATHEMATICA - Static variable in class umontreal.iro.lecuyer.gof.GofFormat
Data file format used for creating graphics with Mathematica.
MATHEMATICA - Static variable in class umontreal.iro.lecuyer.util.TableFormat
.
MathFunction - Interface in umontreal.iro.lecuyer.functions
This interface should be implemented by classes which represent univariate mathematical functions.
MathFunctionUtil - Class in umontreal.iro.lecuyer.functions
Provides utility methods for computing derivatives and integrals of functions.
MathFunctionWithDerivative - Interface in umontreal.iro.lecuyer.functions
MathFunctionWithDerivative
MathFunctionWithFirstDerivative - Interface in umontreal.iro.lecuyer.functions
Represents a mathematical function whose derivative can be computed using derivative.
MathFunctionWithIntegral - Interface in umontreal.iro.lecuyer.functions
Represents a mathematical function whose integral can be computed by the integral method.
MATLAB - Static variable in class umontreal.iro.lecuyer.util.TableFormat
.
matMatModM(double[][], double[][], double[][], double) - Static method in class umontreal.iro.lecuyer.util.ArithmeticMod
Computes A×B mod m and puts the result in C.
matMatModM(int[][], int[][], int[][], int) - Static method in class umontreal.iro.lecuyer.util.ArithmeticMod
Exactly like matMatModM using double, but with int instead of double.
matMatModM(long[][], long[][], long[][], long) - Static method in class umontreal.iro.lecuyer.util.ArithmeticMod
Exactly like matMatModM using double, but with long instead of double.
matPowModM(double[][], double[][], double, int) - Static method in class umontreal.iro.lecuyer.util.ArithmeticMod
Computes Ac mod m and puts the result in B.
matPowModM(int[][], int[][], int, int) - Static method in class umontreal.iro.lecuyer.util.ArithmeticMod
Exactly like matPowModM using double, but with int instead of double.
matPowModM(long[][], long[][], long, int) - Static method in class umontreal.iro.lecuyer.util.ArithmeticMod
Exactly like matPowModM using double, but with long instead of double.
MatrixOfFunctionOfMultipleMeansTallies<E extends FunctionOfMultipleMeansTally> - Class in umontreal.iro.lecuyer.stat.matrix
Represents a matrix of statistical collectors for functions of multiple means.
MatrixOfFunctionOfMultipleMeansTallies(int, int) - Constructor for class umontreal.iro.lecuyer.stat.matrix.MatrixOfFunctionOfMultipleMeansTallies
Constructs a new unnamed matrix of function of multiple means tallies with numRows rows, and numColumns columns, and filled with null references.
MatrixOfFunctionOfMultipleMeansTallies(String, int, int) - Constructor for class umontreal.iro.lecuyer.stat.matrix.MatrixOfFunctionOfMultipleMeansTallies
Constructs a new empty matrix of function of multiple means tallies with name name, numRows rows, and numColumns columns, and filled with null references.
MatrixOfObservationListener - Interface in umontreal.iro.lecuyer.stat.matrix
Represents an object that can listen to observations broadcast by matrices of statistical probes.
MatrixOfStatProbes<E extends StatProbe> - Class in umontreal.iro.lecuyer.stat.matrix
Represents a matrix of statistical probes that can be managed simultaneously.
MatrixOfStatProbes(int, int) - Constructor for class umontreal.iro.lecuyer.stat.matrix.MatrixOfStatProbes
Constructs a new unnamed matrix of statistical probes with numRows rows, and numColumns columns, and filled with null references.
MatrixOfStatProbes(String, int, int) - Constructor for class umontreal.iro.lecuyer.stat.matrix.MatrixOfStatProbes
Constructs a new matrix of statistical probes with name name, numRows rows, and numColumns columns, and filled with null references.
MatrixOfTallies<E extends Tally> - Class in umontreal.iro.lecuyer.stat.matrix
Represents a matrix of tally statistical collectors.
MatrixOfTallies(int, int) - Constructor for class umontreal.iro.lecuyer.stat.matrix.MatrixOfTallies
Constructs a new unnamed matrix of tallies with numRows rows, and numColumns columns, and filled with null references.
MatrixOfTallies(String, int, int) - Constructor for class umontreal.iro.lecuyer.stat.matrix.MatrixOfTallies
Constructs a new matrix of tallies with name name, numRows rows, and numColumns columns, and filled with null references.
matTwoPowModM(double[][], double[][], double, int) - Static method in class umontreal.iro.lecuyer.util.ArithmeticMod
Computes A2e mod m and puts the result in B.
matTwoPowModM(int[][], int[][], int, int) - Static method in class umontreal.iro.lecuyer.util.ArithmeticMod
Exactly like matTwoPowModM using double, but with int instead of double.
matTwoPowModM(long[][], long[][], long, int) - Static method in class umontreal.iro.lecuyer.util.ArithmeticMod
Exactly like matTwoPowModM using double, but with long instead of double.
matVecModM(double[][], double[], double[], double) - Static method in class umontreal.iro.lecuyer.util.ArithmeticMod
Computes the result of A×s mod m and puts the result in v.
matVecModM(int[][], int[], int[], int) - Static method in class umontreal.iro.lecuyer.util.ArithmeticMod
Exactly like matVecModM using double, but with int instead of double.
matVecModM(long[][], long[], long[], long) - Static method in class umontreal.iro.lecuyer.util.ArithmeticMod
Exactly like matVecModM using double, but with long instead of double.
max() - Method in class umontreal.iro.lecuyer.stat.FunctionOfMultipleMeansTally
Returns Double.NaN.
max() - Method in class umontreal.iro.lecuyer.stat.StatProbe
.
MAXINTDOUBLE - Static variable in class umontreal.iro.lecuyer.util.Num
.
MAXLAMBDA - Static variable in class umontreal.iro.lecuyer.probdist.PoissonDist
 
MAXN - Static variable in class umontreal.iro.lecuyer.probdist.BinomialDist
 
MAXN - Static variable in class umontreal.iro.lecuyer.probdist.HypergeometricDist
 
MAXN - Static variable in class umontreal.iro.lecuyer.probdist.NegativeBinomialDist
.
MAXTWOEXP - Static variable in class umontreal.iro.lecuyer.util.Num
.
MEAN - Static variable in class umontreal.iro.lecuyer.gof.GofFormat
Mean
min() - Method in class umontreal.iro.lecuyer.stat.FunctionOfMultipleMeansTally
Returns Double.NaN.
min() - Method in class umontreal.iro.lecuyer.stat.StatProbe
.
Misc - Class in umontreal.iro.lecuyer.util
Misc
MRG31k3p - Class in umontreal.iro.lecuyer.rng
MRG31k3p
MRG31k3p() - Constructor for class umontreal.iro.lecuyer.rng.MRG31k3p
.
MRG31k3p(String) - Constructor for class umontreal.iro.lecuyer.rng.MRG31k3p
.
MRG32k3a - Class in umontreal.iro.lecuyer.rng
Extends the abstract class RandomStreamBase by using as a backbone (or main) generator the combined multiple recursive generator (CMRG) MRG32k3a proposed by L'Ecuyer, implemented in 64-bit floating-point arithmetic.
MRG32k3a() - Constructor for class umontreal.iro.lecuyer.rng.MRG32k3a
Constructs a new stream, initializes its seed Ig, sets Bg and Cg equal to Ig, and sets its antithetic switch to false.
MRG32k3a(String) - Constructor for class umontreal.iro.lecuyer.rng.MRG32k3a
Constructs a new stream with an identifier name (used when printing the stream state).
MRG32k3aL - Class in umontreal.iro.lecuyer.rng
The same generator as MRG32k3a, except here it is implemented with type long instead of double.
MRG32k3aL() - Constructor for class umontreal.iro.lecuyer.rng.MRG32k3aL
 
MRG32k3aL(String) - Constructor for class umontreal.iro.lecuyer.rng.MRG32k3aL
 
MT19937 - Class in umontreal.iro.lecuyer.rng
Implements the RandomStream interface via inheritance from RandomStreamBase.
MT19937(CloneableRandomStream) - Constructor for class umontreal.iro.lecuyer.rng.MT19937
Constructs a new stream, using rng to fill its initial state.
MT19937(CloneableRandomStream, String) - Constructor for class umontreal.iro.lecuyer.rng.MT19937
Constructs a new stream with the identifier name (used in the toString method).
MultiDimComparable<T> - Interface in umontreal.iro.lecuyer.util
This interface represents an object which can be compared in many dimensions or in many ways.
MultiDimComparator<T extends MultiDimComparable<? super T>> - Class in umontreal.iro.lecuyer.util
MultiDimComparator
MultiDimComparator(int) - Constructor for class umontreal.iro.lecuyer.util.MultiDimComparator
.
MultiDimSort - Interface in umontreal.iro.lecuyer.util
This interface represents a sort on multidimensional arrays.
MultiFunction - Interface in umontreal.iro.lecuyer.functions
This interface should be implemented by classes which represent multivariate mathematical functions.
MultinomialDist - Class in umontreal.iro.lecuyer.probdistmulti
MultinomialDist
MultinomialDist(int, double[]) - Constructor for class umontreal.iro.lecuyer.probdistmulti.MultinomialDist
.
MultinormalCholeskyGen - Class in umontreal.iro.lecuyer.randvarmulti
Extends MultinormalGen for a multivariate normal distribution, generated via a Cholesky decomposition of the covariance matrix.
MultinormalCholeskyGen(NormalGen, double[], double[][]) - Constructor for class umontreal.iro.lecuyer.randvarmulti.MultinormalCholeskyGen
Equivalent to MultinormalCholeskyGen(gen1, mu, new DenseDoubleMatrix2D(sigma)).
MultinormalCholeskyGen(NormalGen, double[], DoubleMatrix2D) - Constructor for class umontreal.iro.lecuyer.randvarmulti.MultinormalCholeskyGen
Constructs a multinormal generator with mean vector mu and covariance matrix sigma.
MultiNormalDist - Class in umontreal.iro.lecuyer.probdistmulti
Implements the abstract class ContinuousDistributionMulti for the multinormal distribution with mean vector μ and covariance matrix Σ.
MultiNormalDist(double[], double[][]) - Constructor for class umontreal.iro.lecuyer.probdistmulti.MultiNormalDist
 
MultinormalGen - Class in umontreal.iro.lecuyer.randvarmulti
Extends RandomMultivariateGen for a multivariate normal (or multinormal) distribution.
MultinormalGen(NormalGen, int) - Constructor for class umontreal.iro.lecuyer.randvarmulti.MultinormalGen
Constructs a generator with the standard multinormal distribution (with μ = 0 and Σ = I) in d dimensions.
MultinormalPCAGen - Class in umontreal.iro.lecuyer.randvarmulti
Extends MultinormalGen for a multivariate normal distribution, generated via the method of principal components analysis (PCA) of the covariance matrix.
MultinormalPCAGen(NormalGen, double[], double[][]) - Constructor for class umontreal.iro.lecuyer.randvarmulti.MultinormalPCAGen
Equivalent to MultinormalPCAGen(gen1, mu, new DenseDoubleMatrix2D(sigma)).
MultinormalPCAGen(NormalGen, double[], DoubleMatrix2D) - Constructor for class umontreal.iro.lecuyer.randvarmulti.MultinormalPCAGen
Constructs a multinormal generator with mean vector mu and covariance matrix sigma.
MultipleDatasetChart - Class in umontreal.iro.lecuyer.charts
Provides tools to plot many datasets on the same chart.
MultipleDatasetChart() - Constructor for class umontreal.iro.lecuyer.charts.MultipleDatasetChart
Initializes a new MultipleDatasetChart.
MultipleDatasetChart(String, String, String) - Constructor for class umontreal.iro.lecuyer.charts.MultipleDatasetChart
Initializes a new MultipleDatasetChart instance.
multiply(BitVector) - Method in class umontreal.iro.lecuyer.util.BitMatrix
Multiplies the column BitVector by a BitMatrix and returns the result.
multiply(int) - Method in class umontreal.iro.lecuyer.util.BitMatrix
Multiplies vect, seen as a column BitVector, by a BitMatrix.
multiply(BitMatrix) - Method in class umontreal.iro.lecuyer.util.BitMatrix
Multiplies two BitMatrix's together.
MultivariateBrownianMotion - Class in umontreal.iro.lecuyer.stochprocess
This class represents a multivariate Brownian motion process {X(t) = (X1(t),..., Xc(t)), t >= 0}, sampled at times 0 = t0 < t1 < ...
MultivariateBrownianMotion(int, double[], double[], double[], double[][], RandomStream) - Constructor for class umontreal.iro.lecuyer.stochprocess.MultivariateBrownianMotion
Constructs a new MultivariateBrownianMotion with parameters μ = mu, σ = sigma, correlation matrix Rz = corrZ, and initial value X(t0) = x0.
MultivariateBrownianMotion(int, double[], double[], double[], double[][], NormalGen) - Constructor for class umontreal.iro.lecuyer.stochprocess.MultivariateBrownianMotion
Constructs a new MultivariateBrownianMotion with parameters μ = mu, σ = sigma, correlation matrix Rz = corrZ, and initial value X(t0) = x0.
MultivariateBrownianMotionBridge - Class in umontreal.iro.lecuyer.stochprocess
A multivariate Brownian motion process {X(t) : t >= 0} sampled via bridge sampling.
MultivariateBrownianMotionBridge(int, double[], double[], double[], double[][], RandomStream) - Constructor for class umontreal.iro.lecuyer.stochprocess.MultivariateBrownianMotionBridge
Constructs a new MultivariateBrownianMotionBridge with parameters μ =mu, σ =sigma, correlation matrix Rz =corrZ, and initial value X(t0) =x0.
MultivariateBrownianMotionBridge(int, double[], double[], double[], double[][], NormalGen) - Constructor for class umontreal.iro.lecuyer.stochprocess.MultivariateBrownianMotionBridge
Constructs a new MultivariateBrownianMotionBridge with parameters μ =mu, σ =sigma, correlation matrix Rz =corrZ, and initial value X(t0) =x0.
MultivariateBrownianMotionPCA - Class in umontreal.iro.lecuyer.stochprocess
.
MultivariateBrownianMotionPCA(int, double[], double[], double[], double[][], RandomStream) - Constructor for class umontreal.iro.lecuyer.stochprocess.MultivariateBrownianMotionPCA
.
MultivariateBrownianMotionPCA(int, double[], double[], double[], double[][], NormalGen) - Constructor for class umontreal.iro.lecuyer.stochprocess.MultivariateBrownianMotionPCA
.
MultivariateBrownianMotionPCABigSigma - Class in umontreal.iro.lecuyer.stochprocess
A multivariate Brownian motion process {X(t) : t >= 0} sampled entirely using the principal component decomposition (PCA).
MultivariateBrownianMotionPCABigSigma(int, double[], double[], double[], double[][], RandomStream) - Constructor for class umontreal.iro.lecuyer.stochprocess.MultivariateBrownianMotionPCABigSigma
Constructs a new MultivariateBrownianMotionPCABigSigma with parameters μ = mu, σ = sigma, correlation matrix Rz = corrZ, and initial value X(t0) = x0.
MultivariateBrownianMotionPCABigSigma(int, double[], double[], double[], double[][], NormalGen) - Constructor for class umontreal.iro.lecuyer.stochprocess.MultivariateBrownianMotionPCABigSigma
Constructs a new MultivariateBrownianMotionPCABigSigma with parameters μ = mu, σ = sigma, correlation matrix Rz = corrZ, and initial value X(t0) = x0.
MultivariateFunction - Interface in umontreal.iro.lecuyer.util
Represents a function of multiple variables.
MultivariateGeometricBrownianMotion - Class in umontreal.iro.lecuyer.stochprocess
This class is a multivariate version of GeometricBrownianMotion.
MultivariateGeometricBrownianMotion(int, double[], double[], double[], MultivariateBrownianMotion) - Constructor for class umontreal.iro.lecuyer.stochprocess.MultivariateGeometricBrownianMotion
Constructs a new MultivariateGeometricBrownianMotion with parameters μ = mu, σ = sigma, and S(t0) = x0, using mbm as the underlying MultivariateBrownianMotion.
MultivariateStochasticProcess - Class in umontreal.iro.lecuyer.stochprocess
MultivariateStochasticProcess
MultivariateStochasticProcess() - Constructor for class umontreal.iro.lecuyer.stochprocess.MultivariateStochasticProcess
 
multModM(double, double, double, double) - Static method in class umontreal.iro.lecuyer.util.ArithmeticMod
Computes (a×s + c) mod m.
multModM(int, int, int, int) - Static method in class umontreal.iro.lecuyer.util.ArithmeticMod
Computes (a×s + c) mod m.
multModM(long, long, long, long) - Static method in class umontreal.iro.lecuyer.util.ArithmeticMod
Computes (a×s + c) mod m.

N

NakagamiDist - Class in umontreal.iro.lecuyer.probdist
NakagamiDist
NakagamiDist(double, double, double) - Constructor for class umontreal.iro.lecuyer.probdist.NakagamiDist
.
NakagamiGen - Class in umontreal.iro.lecuyer.randvar
NakagamiGen
NakagamiGen(RandomStream, double, double, double) - Constructor for class umontreal.iro.lecuyer.randvar.NakagamiGen
.
NakagamiGen(RandomStream, NakagamiDist) - Constructor for class umontreal.iro.lecuyer.randvar.NakagamiGen
.
NameConflictException - Exception in umontreal.iro.lecuyer.util
This exception is thrown by a ClassFinder when two or more fully qualified class names can be associated with a simple class name.
NameConflictException() - Constructor for exception umontreal.iro.lecuyer.util.NameConflictException
Constructs a new name conflict exception.
NameConflictException(String) - Constructor for exception umontreal.iro.lecuyer.util.NameConflictException
Constructs a new name conflict exception with message message.
NameConflictException(ClassFinder, String, String) - Constructor for exception umontreal.iro.lecuyer.util.NameConflictException
Constructs a new name conflict exception with class finder finder, simple name name, and message message.
nbCategories - Variable in class umontreal.iro.lecuyer.gof.GofStat.OutcomeCategoriesChi2
Total number of categories.
nbExp - Variable in class umontreal.iro.lecuyer.gof.GofStat.OutcomeCategoriesChi2
Expected number of observations for each category.
NegativeBinomialDist - Class in umontreal.iro.lecuyer.probdist
.
NegativeBinomialDist(double, double) - Constructor for class umontreal.iro.lecuyer.probdist.NegativeBinomialDist
.
NegativeBinomialGen - Class in umontreal.iro.lecuyer.randvar
This class implements random variate generators having the negative binomial distribution.
NegativeBinomialGen(RandomStream, double, double) - Constructor for class umontreal.iro.lecuyer.randvar.NegativeBinomialGen
Creates a negative binomial random variate generator with parameters γ = gamma and p, using stream s.
NegativeBinomialGen(RandomStream, NegativeBinomialDist) - Constructor for class umontreal.iro.lecuyer.randvar.NegativeBinomialGen
Creates a new generator for the distribution dist, using stream s.
NegativeMultinomialDist - Class in umontreal.iro.lecuyer.probdistmulti
Implements the class DiscreteDistributionIntMulti for the negative multinomial distribution with parameters n > 0 and ( p1,…, pd) such that all 0 < pi < 1 and i=1dpi < 1.
NegativeMultinomialDist(double, double[]) - Constructor for class umontreal.iro.lecuyer.probdistmulti.NegativeMultinomialDist
Creates a NegativeMultinomialDist object with parameters n and (p1, ..., pd) such that i=1dpi < 1, as described above.
newArrayOfObservations(ListOfStatProbes<?>, double[]) - Method in interface umontreal.iro.lecuyer.stat.list.ArrayOfObservationListener
Receives the new array of observations x broadcast by the list of statistical probes listOfProbes.
newInstance() - Method in class umontreal.iro.lecuyer.rng.BasicRandomStreamFactory
 
newInstance() - Method in interface umontreal.iro.lecuyer.rng.RandomStreamFactory
Constructs and returns a new random stream.
newInstance() - Static method in class umontreal.iro.lecuyer.simprocs.ProcessSimulator
Constructs and returns a new process-oriented simulator.
NEWLINE - Static variable in class umontreal.iro.lecuyer.util.PrintfFormat
End-of-line symbol or line separator.
newMatrixOfObservations(MatrixOfStatProbes<?>, DoubleMatrix2D) - Method in interface umontreal.iro.lecuyer.stat.matrix.MatrixOfObservationListener
Receives the new matrix of observations x broadcast by the matrix of statistical probes matrixOfProbes.
newObservation(StatProbe, double) - Method in interface umontreal.iro.lecuyer.stat.ObservationListener
Receives the new observation x broadcast by probe.
nextArrayOfDouble(double[], int, int) - Method in class umontreal.iro.lecuyer.randvar.ConstantGen
 
nextArrayOfDouble(double[], int, int) - Method in class umontreal.iro.lecuyer.randvar.RandomVariateGen
Generates n random numbers from the continuous distribution contained in this object.
nextArrayOfDouble(double[], int, int) - Method in class umontreal.iro.lecuyer.randvar.RandomVariateGenWithCache
 
nextArrayOfDouble(double[], int, int) - Method in class umontreal.iro.lecuyer.randvar.UnuranContinuous
 
nextArrayOfDouble(double[], int, int) - Method in class umontreal.iro.lecuyer.randvar.UnuranEmpirical
 
nextArrayOfDouble(double[], int, int) - Method in class umontreal.iro.lecuyer.rng.AntitheticStream
Calls nextArrayOfDouble (u, start, n) for the base stream, then replaces each u[i] by 1.0 - u[i].
nextArrayOfDouble(double[], int, int) - Method in class umontreal.iro.lecuyer.rng.BakerTransformedStream
Calls nextArrayOfDouble (u, start, n) for the base stream, then applies the baker transformation.
nextArrayOfDouble(double[], int, int) - Method in class umontreal.iro.lecuyer.rng.RandMrg
Deprecated.  
nextArrayOfDouble(double[], int, int) - Method in interface umontreal.iro.lecuyer.rng.RandomStream
Generates n (pseudo)random numbers from the uniform distribution and stores them into the array u starting at index start.
nextArrayOfDouble(double[], int, int) - Method in class umontreal.iro.lecuyer.rng.RandomStreamBase
Calls nextDouble n times to fill the array u.
nextArrayOfDouble(double[], int, int) - Method in class umontreal.iro.lecuyer.rng.RandomStreamWithCache
 
nextArrayOfDouble(double[], int, int) - Method in class umontreal.iro.lecuyer.rng.TruncatedRandomStream
 
nextArrayOfInt(int[], int, int) - Method in class umontreal.iro.lecuyer.randvar.RandomVariateGenInt
.
nextArrayOfInt(int[], int, int) - Method in class umontreal.iro.lecuyer.randvar.UnuranDiscreteInt
 
nextArrayOfInt(int, int, int[], int, int) - Method in class umontreal.iro.lecuyer.rng.AntitheticStream
Calls nextArrayOfInt (i, j, u, start, n) for the base stream, then replaces each u[i] by j - i - u[i].
nextArrayOfInt(int, int, int[], int, int) - Method in class umontreal.iro.lecuyer.rng.BakerTransformedStream
Fills up the array by calling nextInt (i, j).
nextArrayOfInt(int, int, int[], int, int) - Method in class umontreal.iro.lecuyer.rng.RandMrg
Deprecated.  
nextArrayOfInt(int, int, int[], int, int) - Method in interface umontreal.iro.lecuyer.rng.RandomStream
Generates n (pseudo)random numbers from the discrete uniform distribution over the integers {i, i + 1,..., j}, using this stream and stores the result in the array u starting at index start.
nextArrayOfInt(int, int, int[], int, int) - Method in class umontreal.iro.lecuyer.rng.RandomStreamBase
Calls nextInt n times to fill the array u.
nextArrayOfInt(int, int, int[], int, int) - Method in class umontreal.iro.lecuyer.rng.RandomStreamWithCache
 
nextArrayOfInt(int, int, int[], int, int) - Method in class umontreal.iro.lecuyer.rng.TruncatedRandomStream
 
nextArrayOfPoints(double[][], int, int) - Method in class umontreal.iro.lecuyer.randvarmulti.RandomMultivariateGen
Generates n random points.
nextCoordinate() - Method in class umontreal.iro.lecuyer.hups.CycleBasedPointSet.CycleBasedPointSetIterator
 
nextCoordinate() - Method in class umontreal.iro.lecuyer.hups.CycleBasedPointSetBase2.CycleBasedPointSetBase2Iterator
 
nextCoordinate() - Method in interface umontreal.iro.lecuyer.hups.PointSetIterator
.
nextCoordinates(double[], int) - Method in class umontreal.iro.lecuyer.hups.CycleBasedPointSet.CycleBasedPointSetIterator
 
nextCoordinates(double[], int) - Method in class umontreal.iro.lecuyer.hups.CycleBasedPointSetBase2.CycleBasedPointSetBase2Iterator
 
nextCoordinates(double[], int) - Method in interface umontreal.iro.lecuyer.hups.PointSetIterator
.
nextDouble() - Method in class umontreal.iro.lecuyer.hups.CycleBasedPointSet.CycleBasedPointSetIterator
 
nextDouble(RandomStream, double, double, double, double) - Static method in class umontreal.iro.lecuyer.randvar.BetaGen
.
nextDouble() - Method in class umontreal.iro.lecuyer.randvar.BetaRejectionLoglogisticGen
 
nextDouble() - Method in class umontreal.iro.lecuyer.randvar.BetaStratifiedRejectionGen
 
nextDouble(RandomStream, double, double, double, double) - Static method in class umontreal.iro.lecuyer.randvar.BetaStratifiedRejectionGen
 
nextDouble(RandomStream, RandomStream, RandomStream, double) - Static method in class umontreal.iro.lecuyer.randvar.BetaSymmetricalBestGen
Generates a random number using Devroye's one-liner method.
nextDouble(RandomStream, double) - Static method in class umontreal.iro.lecuyer.randvar.BetaSymmetricalBestGen
Generates a random number using Devroye's one-liner method with only one stream s.
nextDouble() - Method in class umontreal.iro.lecuyer.randvar.BetaSymmetricalBestGen
 
nextDouble(RandomStream, double) - Static method in class umontreal.iro.lecuyer.randvar.BetaSymmetricalGen
 
nextDouble(RandomStream, RandomStream, double) - Static method in class umontreal.iro.lecuyer.randvar.BetaSymmetricalPolarGen
.
nextDouble(RandomStream, double) - Static method in class umontreal.iro.lecuyer.randvar.BetaSymmetricalPolarGen
.
nextDouble() - Method in class umontreal.iro.lecuyer.randvar.BetaSymmetricalPolarGen
 
nextDouble(RandomStream, double, double) - Static method in class umontreal.iro.lecuyer.randvar.CauchyGen
Generates a new variate from the Cauchy distribution with parameters α = alpha and β = beta, using stream s.
nextDouble(RandomStream, int) - Static method in class umontreal.iro.lecuyer.randvar.ChiGen
.
nextDouble() - Method in class umontreal.iro.lecuyer.randvar.ChiRatioOfUniformsGen
 
nextDouble(RandomStream, int) - Static method in class umontreal.iro.lecuyer.randvar.ChiRatioOfUniformsGen
 
nextDouble(RandomStream, int) - Static method in class umontreal.iro.lecuyer.randvar.ChiSquareGen
.
nextDouble() - Method in class umontreal.iro.lecuyer.randvar.ChiSquareNoncentralGamGen
 
nextDouble(RandomStream, double, double) - Static method in class umontreal.iro.lecuyer.randvar.ChiSquareNoncentralGamGen
.
nextDouble(RandomStream, double, double) - Static method in class umontreal.iro.lecuyer.randvar.ChiSquareNoncentralGen
.
nextDouble() - Method in class umontreal.iro.lecuyer.randvar.ChiSquareNoncentralPoisGen
 
nextDouble(RandomStream, double, double) - Static method in class umontreal.iro.lecuyer.randvar.ChiSquareNoncentralPoisGen
Generates a variate from the noncentral chi square distribution with parameters ν = nu, and λ = lambda, using stream stream as described above.
nextDouble() - Method in class umontreal.iro.lecuyer.randvar.ConstantGen
 
nextDouble() - Method in class umontreal.iro.lecuyer.randvar.ErlangConvolutionGen
 
nextDouble(RandomStream, int, double) - Static method in class umontreal.iro.lecuyer.randvar.ErlangConvolutionGen
 
nextDouble(RandomStream, int, double) - Static method in class umontreal.iro.lecuyer.randvar.ErlangGen
Generates a new variate from the Erlang distribution with parameters k = k and λ = lambda, using stream s.
nextDouble(RandomStream, double) - Static method in class umontreal.iro.lecuyer.randvar.ExponentialGen
Uses inversion to generate a new exponential variate with parameter λ = lambda, using stream s.
nextDouble(RandomStream, double, double) - Static method in class umontreal.iro.lecuyer.randvar.ExtremeValueGen
Deprecated. .
nextDouble(RandomStream, double, double, double) - Static method in class umontreal.iro.lecuyer.randvar.FatigueLifeGen
Generates a variate from the fatigue life distribution with location parameter μ, scale parameter β and shape parameter γ.
nextDouble(RandomStream, int, int) - Static method in class umontreal.iro.lecuyer.randvar.FisherFGen
.
nextDouble() - Method in class umontreal.iro.lecuyer.randvar.FNoncentralGen
 
nextDouble(RandomStream, double, double) - Static method in class umontreal.iro.lecuyer.randvar.FoldedNormalGen
Generates a variate from the folded normal distribution with parameters μ = mu and σ = sigma, using stream s.
nextDouble(RandomStream, double, double, double) - Static method in class umontreal.iro.lecuyer.randvar.FrechetGen
.
nextDouble(RandomStream, RandomStream, double, double) - Static method in class umontreal.iro.lecuyer.randvar.GammaAcceptanceRejectionGen
Generates a new gamma variate with parameters α = alpha and λ = lambda, using main stream s and auxiliary stream aux.
nextDouble() - Method in class umontreal.iro.lecuyer.randvar.GammaAcceptanceRejectionGen
 
nextDouble(RandomStream, double, double) - Static method in class umontreal.iro.lecuyer.randvar.GammaAcceptanceRejectionGen
Same as nextDouble (s, s, alpha, lambda).
nextDouble(RandomStream, double, double) - Static method in class umontreal.iro.lecuyer.randvar.GammaGen
Generates a new gamma random variate with parameters α = alpha and λ = lambda, using stream s.
nextDouble() - Method in class umontreal.iro.lecuyer.randvar.GammaRejectionLoglogisticGen
 
nextDouble(RandomStream, RandomStream, double, double) - Static method in class umontreal.iro.lecuyer.randvar.GammaRejectionLoglogisticGen
Generates a new gamma variate with parameters α = alpha and λ = lambda, using main stream s and auxiliary stream aux.
nextDouble(RandomStream, double, double) - Static method in class umontreal.iro.lecuyer.randvar.GammaRejectionLoglogisticGen
Same as nextDouble (s, s, alpha, lambda).
nextDouble(RandomStream, double, double) - Static method in class umontreal.iro.lecuyer.randvar.GumbelGen
Generates a new variate from the Gumbel distribution with parameters β = beta and δ = delta using stream s.
nextDouble(RandomStream, double, double) - Static method in class umontreal.iro.lecuyer.randvar.HalfNormalGen
.
nextDouble(RandomStream, double, double) - Static method in class umontreal.iro.lecuyer.randvar.HyperbolicSecantGen
Generates a variate from the hyperbolic secant distribution with location parameter μ and scale parameter σ.
nextDouble(RandomStream, double[]) - Static method in class umontreal.iro.lecuyer.randvar.HypoExponentialGen
.
nextDouble() - Method in class umontreal.iro.lecuyer.randvar.InverseFromDensityGen
.
nextDouble(RandomStream, double, double) - Static method in class umontreal.iro.lecuyer.randvar.InverseGammaGen
Generates a variate from the inverse gamma distribution with shape parameter α > 0 and scale parameter β > 0.
nextDouble(RandomStream, double, double) - Static method in class umontreal.iro.lecuyer.randvar.InverseGaussianGen
.
nextDouble(RandomStream, NormalGen, double, double) - Static method in class umontreal.iro.lecuyer.randvar.InverseGaussianMSHGen
Generates a new variate from the inverse gaussian distribution with parameters μ = mu and λ = lambda, using streams s and sn.
nextDouble() - Method in class umontreal.iro.lecuyer.randvar.InverseGaussianMSHGen
 
nextDouble(RandomStream, double, double, double, double) - Static method in class umontreal.iro.lecuyer.randvar.JohnsonSBGen
Uses inversion to generate a new JohnsonSB variate, using stream s.
nextDouble(RandomStream, double, double, double, double) - Static method in class umontreal.iro.lecuyer.randvar.JohnsonSLGen
Uses inversion to generate a new JohnsonSL variate, using stream s.
nextDouble(RandomStream, double, double, double, double) - Static method in class umontreal.iro.lecuyer.randvar.JohnsonSUGen
Uses inversion to generate a new JohnsonSU variate, using stream s.
nextDouble() - Method in class umontreal.iro.lecuyer.randvar.KernelDensityGen
 
nextDouble() - Method in class umontreal.iro.lecuyer.randvar.KernelDensityVarCorrectGen
 
nextDouble(RandomStream, double, double) - Static method in class umontreal.iro.lecuyer.randvar.LaplaceGen
Generates a new variate from the Laplace distribution with parameters μ = mu and β = beta, using stream s.
nextDouble(RandomStream, double, double) - Static method in class umontreal.iro.lecuyer.randvar.LogisticGen
.
nextDouble(RandomStream, double, double) - Static method in class umontreal.iro.lecuyer.randvar.LoglogisticGen
Generates a variate from the log-logistic distribution with shape parameter α > 0 and scale parameter β > 0.
nextDouble(RandomStream, double, double) - Static method in class umontreal.iro.lecuyer.randvar.LognormalGen
Generates a new variate from the lognormal distribution with parameters μ = mu and σ = sigma, using stream s.
nextDouble() - Method in class umontreal.iro.lecuyer.randvar.LognormalSpecialGen
 
nextDouble(RandomStream, double, double, double) - Static method in class umontreal.iro.lecuyer.randvar.NakagamiGen
.
nextDouble() - Method in class umontreal.iro.lecuyer.randvar.NormalACRGen
 
nextDouble(RandomStream, double, double) - Static method in class umontreal.iro.lecuyer.randvar.NormalACRGen
 
nextDouble() - Method in class umontreal.iro.lecuyer.randvar.NormalBoxMullerGen
 
nextDouble(RandomStream, double, double) - Static method in class umontreal.iro.lecuyer.randvar.NormalBoxMullerGen
 
nextDouble(RandomStream, double, double) - Static method in class umontreal.iro.lecuyer.randvar.NormalGen
Generates a variate from the normal distribution with parameters μ = mu and σ = sigma, using stream s.
nextDouble(RandomStream, double, double, double, double) - Static method in class umontreal.iro.lecuyer.randvar.NormalInverseGaussianGen
.
nextDouble(InverseGaussianGen, NormalGen, double, double) - Static method in class umontreal.iro.lecuyer.randvar.NormalInverseGaussianIGGen
Generates a new variate from the distribution with parameters α, β = beta, μ = mu and δ, using generators ig and ng, as described in eq..
nextDouble() - Method in class umontreal.iro.lecuyer.randvar.NormalInverseGaussianIGGen
 
nextDouble() - Method in class umontreal.iro.lecuyer.randvar.NormalKindermannRamageGen
 
nextDouble(RandomStream, double, double) - Static method in class umontreal.iro.lecuyer.randvar.NormalKindermannRamageGen
 
nextDouble() - Method in class umontreal.iro.lecuyer.randvar.NormalPolarGen
 
nextDouble(RandomStream, double, double) - Static method in class umontreal.iro.lecuyer.randvar.NormalPolarGen
 
nextDouble(RandomStream, double, double) - Static method in class umontreal.iro.lecuyer.randvar.ParetoGen
.
nextDouble(RandomStream, double, double) - Static method in class umontreal.iro.lecuyer.randvar.Pearson5Gen
Deprecated. Generates a variate from the Pearson V distribution with shape parameter α > 0 and scale parameter β > 0.
nextDouble(RandomStream, double, double, double) - Static method in class umontreal.iro.lecuyer.randvar.Pearson6Gen
Generates a variate from the Pearson VI distribution with shape parameters α1 > 0 and α2 > 0, and scale parameter β > 0.
nextDouble(RandomStream, double, double, double) - Static method in class umontreal.iro.lecuyer.randvar.PowerGen
.
nextDouble() - Method in class umontreal.iro.lecuyer.randvar.RandomVariateGen
Generates a random number from the continuous distribution contained in this object.
nextDouble() - Method in class umontreal.iro.lecuyer.randvar.RandomVariateGenWithCache
 
nextDouble(RandomStream, double, double) - Static method in class umontreal.iro.lecuyer.randvar.RayleighGen
Uses inversion to generate a new variate from the Rayleigh distribution with parameters a = a and β = beta, using stream s.
nextDouble(RandomStream, int) - Static method in class umontreal.iro.lecuyer.randvar.StudentGen
Generates a new variate from the Student distribution with n = n degrees of freedom, using stream s.
nextDouble() - Method in class umontreal.iro.lecuyer.randvar.StudentNoncentralGen
 
nextDouble() - Method in class umontreal.iro.lecuyer.randvar.StudentPolarGen
 
nextDouble(RandomStream, int) - Static method in class umontreal.iro.lecuyer.randvar.StudentPolarGen
 
nextDouble(RandomStream, double, double, double) - Static method in class umontreal.iro.lecuyer.randvar.TriangularGen
Generates a new variate from the triangular distribution with parameters a = a, b = b and m = m and stream s, using inversion.
nextDouble(RandomStream, double, double) - Static method in class umontreal.iro.lecuyer.randvar.UniformGen
.
nextDouble() - Method in class umontreal.iro.lecuyer.randvar.UnuranContinuous
 
nextDouble() - Method in class umontreal.iro.lecuyer.randvar.UnuranEmpirical
 
nextDouble(RandomStream, double, double, double) - Static method in class umontreal.iro.lecuyer.randvar.WeibullGen
Uses inversion to generate a new variate from the Weibull distribution with parameters α = alpha, λ = lambda, and δ = delta, using stream s.
nextDouble() - Method in class umontreal.iro.lecuyer.rng.AntitheticStream
Returns 1.0 - s.nextDouble() where s is the base stream.
nextDouble() - Method in class umontreal.iro.lecuyer.rng.BakerTransformedStream
Returns the baker transformation of s.nextDouble() where s is the base stream.
nextDouble() - Method in class umontreal.iro.lecuyer.rng.RandMrg
Deprecated. Returns a (pseudo)random number from the uniform distribution over the interval (0, 1), using this stream, after advancing its state by one step.
nextDouble() - Method in interface umontreal.iro.lecuyer.rng.RandomStream
Returns a (pseudo)random number from the uniform distribution over the interval (0, 1), using this stream, after advancing its state by one step.
nextDouble() - Method in class umontreal.iro.lecuyer.rng.RandomStreamBase
Returns a uniform random number between 0 and 1 from the stream.
nextDouble() - Method in class umontreal.iro.lecuyer.rng.RandomStreamWithCache
 
nextDouble() - Method in class umontreal.iro.lecuyer.rng.TruncatedRandomStream
 
nextDoubleLog() - Method in class umontreal.iro.lecuyer.randvar.GammaAcceptanceRejectionGen
Returns the natural log value of a new gamma variate.
nextDoubleLog(RandomStream, RandomStream, double, double) - Static method in class umontreal.iro.lecuyer.randvar.GammaAcceptanceRejectionGen
Returns the natural log value of a new gamma variate with parameters α = alpha and λ = lambda, using main stream s and auxiliary stream aux.
nextDoubleLog(RandomStream, double, double) - Static method in class umontreal.iro.lecuyer.randvar.GammaAcceptanceRejectionGen
Same as nextDoubleLog (s, s, alpha, lambda).
nextInt(RandomStream, double) - Static method in class umontreal.iro.lecuyer.randvar.BernoulliGen
Generates a new integer from the Bernoulli distribution with parameter p = p, using the given stream s.
nextInt() - Method in class umontreal.iro.lecuyer.randvar.BinomialConvolutionGen
 
nextInt(RandomStream, int, double) - Static method in class umontreal.iro.lecuyer.randvar.BinomialConvolutionGen
 
nextInt(RandomStream, int, double) - Static method in class umontreal.iro.lecuyer.randvar.BinomialGen
Generates a new integer from the binomial distribution with parameters n = n and p = p, using the given stream s.
nextInt() - Method in class umontreal.iro.lecuyer.randvar.GeometricGen
 
nextInt(RandomStream, double) - Static method in class umontreal.iro.lecuyer.randvar.GeometricGen
.
nextInt(RandomStream, int, int, int) - Static method in class umontreal.iro.lecuyer.randvar.HypergeometricGen
.
nextInt() - Method in class umontreal.iro.lecuyer.randvar.LogarithmicGen
 
nextInt(RandomStream, double) - Static method in class umontreal.iro.lecuyer.randvar.LogarithmicGen
Uses stream s to generate a new variate from the logarithmic distribution with parameter θ = theta.
nextInt(RandomStream, double, double) - Static method in class umontreal.iro.lecuyer.randvar.NegativeBinomialGen
Generates a new variate from the negative binomial distribution, with parameters γ = gamma and p = p, using stream s.
nextInt() - Method in class umontreal.iro.lecuyer.randvar.PascalConvolutionGen
 
nextInt(RandomStream, int, double) - Static method in class umontreal.iro.lecuyer.randvar.PascalConvolutionGen
 
nextInt(RandomStream, int, double) - Static method in class umontreal.iro.lecuyer.randvar.PascalGen
.
nextInt(RandomStream, double) - Static method in class umontreal.iro.lecuyer.randvar.PoissonGen
A static method for generating a random variate from a Poisson distribution with parameter λ = lambda.
nextInt() - Method in class umontreal.iro.lecuyer.randvar.PoissonTIACGen
 
nextInt(RandomStream, double) - Static method in class umontreal.iro.lecuyer.randvar.PoissonTIACGen
 
nextInt() - Method in class umontreal.iro.lecuyer.randvar.RandomVariateGenInt
.
nextInt(RandomStream, int, int) - Static method in class umontreal.iro.lecuyer.randvar.UniformIntGen
Generates a new uniform random variate over the interval [i, j], using stream s, by inversion.
nextInt() - Method in class umontreal.iro.lecuyer.randvar.UnuranDiscreteInt
 
nextInt(int, int) - Method in class umontreal.iro.lecuyer.rng.AntitheticStream
Returns j - i - s.nextInt(i, j) where s is the base stream.
nextInt(int, int) - Method in class umontreal.iro.lecuyer.rng.BakerTransformedStream
Generates a random integer in {i,..., j} via nextDouble (in which the baker transformation is applied).
nextInt(int, int) - Method in class umontreal.iro.lecuyer.rng.LFSR113
 
nextInt(int, int) - Method in class umontreal.iro.lecuyer.rng.LFSR258
 
nextInt(int, int) - Method in class umontreal.iro.lecuyer.rng.RandMrg
Deprecated.  
nextInt(int, int) - Method in interface umontreal.iro.lecuyer.rng.RandomStream
Returns a (pseudo)random number from the discrete uniform distribution over the integers {i, i + 1,..., j}, using this stream.
nextInt(int, int) - Method in class umontreal.iro.lecuyer.rng.RandomStreamBase
Calls nextDouble once to create one integer between i and j.
nextInt(int, int) - Method in class umontreal.iro.lecuyer.rng.RandomStreamWithCache
 
nextInt(int, int) - Method in class umontreal.iro.lecuyer.rng.TruncatedRandomStream
 
nextObservation() - Method in class umontreal.iro.lecuyer.stochprocess.BrownianMotion
 
nextObservation(double) - Method in class umontreal.iro.lecuyer.stochprocess.BrownianMotion
Generates and returns the next observation at time tj+1 = nextTime.
nextObservation(double, double) - Method in class umontreal.iro.lecuyer.stochprocess.BrownianMotion
Generates an observation of the process in dt time units, assuming that the process has value x at the current time.
nextObservation() - Method in class umontreal.iro.lecuyer.stochprocess.BrownianMotionBridge
 
nextObservation(double) - Method in class umontreal.iro.lecuyer.stochprocess.BrownianMotionBridge
 
nextObservation() - Method in class umontreal.iro.lecuyer.stochprocess.BrownianMotionPCA
 
nextObservation() - Method in class umontreal.iro.lecuyer.stochprocess.BrownianMotionPCAEqualSteps
 
nextObservation() - Method in class umontreal.iro.lecuyer.stochprocess.CIRProcess
 
nextObservation(double) - Method in class umontreal.iro.lecuyer.stochprocess.CIRProcess
Generates and returns the next observation at time tj+1 = nextTime, using the previous observation time tj defined earlier (either by this method or by setObservationTimes), as well as the value of the previous observation X(tj).
nextObservation(double, double) - Method in class umontreal.iro.lecuyer.stochprocess.CIRProcess
Generates an observation of the process in dt time units, assuming that the process has value x at the current time.
nextObservation() - Method in class umontreal.iro.lecuyer.stochprocess.CIRProcessEuler
 
nextObservation(double) - Method in class umontreal.iro.lecuyer.stochprocess.CIRProcessEuler
Generates and returns the next observation at time tj+1 = nextTime, using the previous observation time tj defined earlier (either by this method or by setObservationTimes), as well as the value of the previous observation X(tj).
nextObservation(double, double) - Method in class umontreal.iro.lecuyer.stochprocess.CIRProcessEuler
Generates an observation of the process in dt time units, assuming that the process has value x at the current time.
nextObservation() - Method in class umontreal.iro.lecuyer.stochprocess.GammaProcess
 
nextObservation(double) - Method in class umontreal.iro.lecuyer.stochprocess.GammaProcess
.
nextObservation() - Method in class umontreal.iro.lecuyer.stochprocess.GammaProcessBridge
 
nextObservation(double) - Method in class umontreal.iro.lecuyer.stochprocess.GammaProcessBridge
 
nextObservation() - Method in class umontreal.iro.lecuyer.stochprocess.GammaProcessPCA
.
nextObservation(double) - Method in class umontreal.iro.lecuyer.stochprocess.GammaProcessPCA
[tabb54]
nextObservation() - Method in class umontreal.iro.lecuyer.stochprocess.GammaProcessSymmetricalBridge
 
nextObservation(double) - Method in class umontreal.iro.lecuyer.stochprocess.GammaProcessSymmetricalBridge
 
nextObservation() - Method in class umontreal.iro.lecuyer.stochprocess.GeometricBrownianMotion
 
nextObservation() - Method in class umontreal.iro.lecuyer.stochprocess.GeometricLevyProcess
Returns the next observation.
nextObservation() - Method in class umontreal.iro.lecuyer.stochprocess.GeometricVarianceGammaProcess
 
nextObservation() - Method in class umontreal.iro.lecuyer.stochprocess.InverseGaussianProcess
 
nextObservation() - Method in class umontreal.iro.lecuyer.stochprocess.InverseGaussianProcessBridge
Returns the next observation in the bridge order, not the sequential order.
nextObservation() - Method in class umontreal.iro.lecuyer.stochprocess.InverseGaussianProcessMSH
 
nextObservation() - Method in class umontreal.iro.lecuyer.stochprocess.InverseGaussianProcessPCA
Not implementable for PCA.
nextObservation() - Method in class umontreal.iro.lecuyer.stochprocess.NormalInverseGaussianProcess
Returns the value of the process for the next time step.
nextObservation() - Method in class umontreal.iro.lecuyer.stochprocess.OrnsteinUhlenbeckProcess
 
nextObservation(double) - Method in class umontreal.iro.lecuyer.stochprocess.OrnsteinUhlenbeckProcess
Generates and returns the next observation at time tj+1 = nextTime, using the previous observation time tj defined earlier (either by this method or by setObservationTimes), as well as the value of the previous observation X(tj).
nextObservation(double, double) - Method in class umontreal.iro.lecuyer.stochprocess.OrnsteinUhlenbeckProcess
Generates an observation of the process in dt time units, assuming that the process has value x at the current time.
nextObservation() - Method in class umontreal.iro.lecuyer.stochprocess.OrnsteinUhlenbeckProcessEuler
Generates and returns the next observation at time tj+1 = nextTime.
nextObservation(double) - Method in class umontreal.iro.lecuyer.stochprocess.OrnsteinUhlenbeckProcessEuler
 
nextObservation(double, double) - Method in class umontreal.iro.lecuyer.stochprocess.OrnsteinUhlenbeckProcessEuler
Generates and returns an observation of the process in dt time units, assuming that the process has value x at the current time.
nextObservation() - Method in class umontreal.iro.lecuyer.stochprocess.StochasticProcess
Generates and returns the next observation X(tj) of the stochastic process.
nextObservation() - Method in class umontreal.iro.lecuyer.stochprocess.VarianceGammaProcess
Generates the observation for the next time.
nextObservation() - Method in class umontreal.iro.lecuyer.stochprocess.VarianceGammaProcessDiff
 
nextObservation() - Method in class umontreal.iro.lecuyer.stochprocess.VarianceGammaProcessDiffPCA
This method is not implemented is this class since the path cannot be generated sequentially.
nextObservationVector(double[]) - Method in class umontreal.iro.lecuyer.stochprocess.MultivariateBrownianMotion
Generates and returns in obs the next observation X(tj) of the multivariate stochastic process.
nextObservationVector() - Method in class umontreal.iro.lecuyer.stochprocess.MultivariateBrownianMotion
Generates and returns the next observation X(tj) of the multivariate stochastic process in a vector created automatically.
nextObservationVector(double, double[]) - Method in class umontreal.iro.lecuyer.stochprocess.MultivariateBrownianMotion
Generates and returns the vector of next observations, at time tj+1 = nextTime, using the previous observation time tj defined earlier (either by this method or by setObservationTimes), as well as the value of the previous observation X(tj).
nextObservationVector(double[], double) - Method in class umontreal.iro.lecuyer.stochprocess.MultivariateBrownianMotion
Generates an observation (vector) of the process in dt time units, assuming that the process has (vector) value x at the current time.
nextObservationVector() - Method in class umontreal.iro.lecuyer.stochprocess.MultivariateBrownianMotionBridge
 
nextObservationVector(double[]) - Method in class umontreal.iro.lecuyer.stochprocess.MultivariateBrownianMotionBridge
 
nextObservationVector() - Method in class umontreal.iro.lecuyer.stochprocess.MultivariateGeometricBrownianMotion
 
nextObservationVector(double[]) - Method in class umontreal.iro.lecuyer.stochprocess.MultivariateGeometricBrownianMotion
Generates and returns the vector of next observations
nextObservationVector(double[]) - Method in class umontreal.iro.lecuyer.stochprocess.MultivariateStochasticProcess
.
nextPoint(double[], int) - Method in class umontreal.iro.lecuyer.hups.CycleBasedPointSet.CycleBasedPointSetIterator
 
nextPoint(double[], int) - Method in class umontreal.iro.lecuyer.hups.CycleBasedPointSetBase2.CycleBasedPointSetBase2Iterator
 
nextPoint(double[], int, int) - Method in interface umontreal.iro.lecuyer.hups.PointSetIterator
.
nextPoint(double[], int) - Method in interface umontreal.iro.lecuyer.hups.PointSetIterator
.
nextPoint(RandomStream, double[], double[]) - Static method in class umontreal.iro.lecuyer.randvarmulti.DirichletGen
Generates a new point from the Dirichlet distribution with parameters alphas, using the stream stream.
nextPoint(double[]) - Method in class umontreal.iro.lecuyer.randvarmulti.DirichletGen
Generates a point from the Dirichlet distribution.
nextPoint(double[]) - Method in class umontreal.iro.lecuyer.randvarmulti.IIDMultivariateGen
Generates a vector of i.i.d.
nextPoint(NormalGen, double[], double[][], double[]) - Static method in class umontreal.iro.lecuyer.randvarmulti.MultinormalCholeskyGen
Equivalent to nextPoint(gen1, mu, new DenseDoubleMatrix2D(sigma), p).
nextPoint(NormalGen, double[], DoubleMatrix2D, double[]) - Static method in class umontreal.iro.lecuyer.randvarmulti.MultinormalCholeskyGen
Generates a d-dimensional vector from the multinormal distribution with mean vector mu and covariance matrix sigma, using the one-dimensional normal generator gen1 to generate the coordinates of Z, and using the Cholesky decomposition of Σ.
nextPoint(double[]) - Method in class umontreal.iro.lecuyer.randvarmulti.MultinormalCholeskyGen
Generates a point from this multinormal distribution.
nextPoint(double[]) - Method in class umontreal.iro.lecuyer.randvarmulti.MultinormalGen
Generates a point from this multinormal distribution.
nextPoint(NormalGen, double[], DoubleMatrix2D, double[]) - Static method in class umontreal.iro.lecuyer.randvarmulti.MultinormalPCAGen
Generates a d-dimensional vector from the multinormal distribution with mean vector mu and covariance matrix sigma, using the one-dimensional normal generator gen1 to generate the coordinates of Z, and using the PCA decomposition of Σ.
nextPoint(NormalGen, double[], double[][], double[]) - Static method in class umontreal.iro.lecuyer.randvarmulti.MultinormalPCAGen
Equivalent to nextPoint(gen1, mu, new DenseDoubleMatrix2D(sigma), p).
nextPoint(double[]) - Method in class umontreal.iro.lecuyer.randvarmulti.MultinormalPCAGen
Generates a point from this multinormal distribution.
nextPoint(double[]) - Method in class umontreal.iro.lecuyer.randvarmulti.RandomMultivariateGen
Generates a random point p using the the stream contained in this object.
nextRadicalInverse(double, double) - Static method in class umontreal.iro.lecuyer.hups.RadicalInverse
.
nextRadicalInverse() - Method in class umontreal.iro.lecuyer.hups.RadicalInverse
.
nextRadicalInverseDigits(int, int, int[]) - Static method in class umontreal.iro.lecuyer.hups.RadicalInverse
.
nextStep(RandomStream) - Method in class umontreal.iro.lecuyer.markovchain.MarkovChain
Simulates one more step of the chain, from its current state, using stream for the randomness.
nextStep(RandomStream) - Method in class umontreal.iro.lecuyer.markovchain.MarkovChainDouble
 
nextStepDouble(int, double, RandomStream) - Method in class umontreal.iro.lecuyer.markovchain.MarkovChainDouble
Simulates one step of the chain, from state s, using stream for the randomness, assuming we are at step step.
NI1 - Class in umontreal.iro.lecuyer.probdistmulti.norta
This class implements the algorithm NI1 based on Brent method for root-finding.
NI1(double, DiscreteDistributionInt, DiscreteDistributionInt, double, double) - Constructor for class umontreal.iro.lecuyer.probdistmulti.norta.NI1
Constructor of the class NI1 with the target rank correlation rX, the two discrete marginals dist1 and dist2, the parameter for truncation tr and the accuracy tolerance, which corresponds to epsilon in the paper (paragraph "Method NI1" of section 3).
NI2a - Class in umontreal.iro.lecuyer.probdistmulti.norta
This class implements the algorithm NI2a wich uses the derivative and simple integration.
NI2a(double, DiscreteDistributionInt, DiscreteDistributionInt, double, double, double) - Constructor for class umontreal.iro.lecuyer.probdistmulti.norta.NI2a
Constructor of the class NI2a with the target rank correlation rX, the two discrete marginals dist1 and dist2, the parameter for truncation tr, and the specific parameters h and delta, which correspond to h and delta in the paper (paragraph "Method NI2" of section 3).
NI2b - Class in umontreal.iro.lecuyer.probdistmulti.norta
This class implements the algorithm NI2b wich uses the derivative and simple integration.
NI2b(double, DiscreteDistributionInt, DiscreteDistributionInt, double, int, double) - Constructor for class umontreal.iro.lecuyer.probdistmulti.norta.NI2b
Constructor of the class NI2b with the target rank correlation rX, the two discrete marginals dist1 and dist2, the parameter for truncation tr, the specific parameters m and delta which correspond to m and delta in the paper (paragraph "Method NI2" of section 3).
NI3 - Class in umontreal.iro.lecuyer.probdistmulti.norta
This class implements the algorithm NI3 (protected Newton-Raphson method).
NI3(double, DiscreteDistributionInt, DiscreteDistributionInt, double, double) - Constructor for class umontreal.iro.lecuyer.probdistmulti.norta.NI3
Constructor of the class NI3 with the target rank correlation rX, the two discrete marginals dist1 and dist2, the parameter for truncation tr and the specific parameter tolerance which corresponds to epsilon in the paper (paragraph "Method NI3" of section 3).
NiedSequenceBase2 - Class in umontreal.iro.lecuyer.hups
This class implements digital sequences constructed from the Niederreiter sequence in base 2.
NiedSequenceBase2(int, int, int) - Constructor for class umontreal.iro.lecuyer.hups.NiedSequenceBase2
Constructs a new digital sequence in base 2 from the first n = 2k points of the Niederreiter sequence, with w output digits, in dim dimensions.
NiedXingSequenceBase2 - Class in umontreal.iro.lecuyer.hups
This class implements digital sequences based on the Niederreiter-Xing sequence in base 2.
NiedXingSequenceBase2(int, int, int) - Constructor for class umontreal.iro.lecuyer.hups.NiedXingSequenceBase2
Constructs a new Niederreiter-Xing digital sequence in base 2 with n = 2k points and w output digits, in dim dimensions.
NormalACRGen - Class in umontreal.iro.lecuyer.randvar
This class implements normal random variate generators using the acceptance-complement ratio method.
NormalACRGen(RandomStream, double, double) - Constructor for class umontreal.iro.lecuyer.randvar.NormalACRGen
Creates a normal random variate generator with mean mu and standard deviation sigma, using stream s.
NormalACRGen(RandomStream) - Constructor for class umontreal.iro.lecuyer.randvar.NormalACRGen
Creates a standard normal random variate generator with mean 0 and standard deviation 1, using stream s.
NormalACRGen(RandomStream, NormalDist) - Constructor for class umontreal.iro.lecuyer.randvar.NormalACRGen
Creates a random variate generator for the normal distribution dist and stream s.
NormalBoxMullerGen - Class in umontreal.iro.lecuyer.randvar
NormalBoxMullerGen
NormalBoxMullerGen(RandomStream, double, double) - Constructor for class umontreal.iro.lecuyer.randvar.NormalBoxMullerGen
.
NormalBoxMullerGen(RandomStream) - Constructor for class umontreal.iro.lecuyer.randvar.NormalBoxMullerGen
.
NormalBoxMullerGen(RandomStream, NormalDist) - Constructor for class umontreal.iro.lecuyer.randvar.NormalBoxMullerGen
.
NormalDist - Class in umontreal.iro.lecuyer.probdist
Extends the class ContinuousDistribution for the normal distribution (e.g.,).
NormalDist() - Constructor for class umontreal.iro.lecuyer.probdist.NormalDist
Constructs a NormalDist object with default parameters μ = 0 and σ = 1.
NormalDist(double, double) - Constructor for class umontreal.iro.lecuyer.probdist.NormalDist
Constructs a NormalDist object with mean μ = mu and standard deviation σ = sigma.
NormalDistQuick - Class in umontreal.iro.lecuyer.probdist
A variant of the class NormalDist (for the normal distribution with mean μ and variance σ2).
NormalDistQuick() - Constructor for class umontreal.iro.lecuyer.probdist.NormalDistQuick
Constructs a NormalDistQuick object with default parameters μ = 0 and σ = 1.
NormalDistQuick(double, double) - Constructor for class umontreal.iro.lecuyer.probdist.NormalDistQuick
Constructs a NormalDistQuick object with mean μ = mu and standard deviation σ = sigma.
NormalGen - Class in umontreal.iro.lecuyer.randvar
This class implements methods for generating random variates from the normal distribution N(μ, σ).
NormalGen(RandomStream, double, double) - Constructor for class umontreal.iro.lecuyer.randvar.NormalGen
Creates a normal random variate generator with mean mu and standard deviation sigma, using stream s.
NormalGen(RandomStream) - Constructor for class umontreal.iro.lecuyer.randvar.NormalGen
Creates a standard normal random variate generator with mean 0 and standard deviation 1, using stream s.
NormalGen(RandomStream, NormalDist) - Constructor for class umontreal.iro.lecuyer.randvar.NormalGen
Creates a random variate generator for the normal distribution dist and stream s.
NormalInverseFromDensityGen - Class in umontreal.iro.lecuyer.randvar
NormalInverseFromDensityGen
NormalInverseFromDensityGen(RandomStream, double, double, double, int) - Constructor for class umontreal.iro.lecuyer.randvar.NormalInverseFromDensityGen
.
NormalInverseFromDensityGen(RandomStream, NormalDist, double, int) - Constructor for class umontreal.iro.lecuyer.randvar.NormalInverseFromDensityGen
.
NormalInverseFromDensityGen(RandomStream, InverseDistFromDensity) - Constructor for class umontreal.iro.lecuyer.randvar.NormalInverseFromDensityGen
.
NormalInverseGaussianDist - Class in umontreal.iro.lecuyer.probdist
NormalInverseGaussianDist
NormalInverseGaussianDist(double, double, double, double) - Constructor for class umontreal.iro.lecuyer.probdist.NormalInverseGaussianDist
.
NormalInverseGaussianGen - Class in umontreal.iro.lecuyer.randvar
NormalInverseGaussianGen
NormalInverseGaussianGen(RandomStream, double, double, double, double) - Constructor for class umontreal.iro.lecuyer.randvar.NormalInverseGaussianGen
.
NormalInverseGaussianGen(RandomStream, NormalInverseGaussianDist) - Constructor for class umontreal.iro.lecuyer.randvar.NormalInverseGaussianGen
.
NormalInverseGaussianIGGen - Class in umontreal.iro.lecuyer.randvar
.
NormalInverseGaussianIGGen(InverseGaussianGen, NormalGen, double, double) - Constructor for class umontreal.iro.lecuyer.randvar.NormalInverseGaussianIGGen
Creates a random variate generator with parameters α, β = beta, μ = mu and δ, using generators ig and ng, as described above.
NormalInverseGaussianProcess - Class in umontreal.iro.lecuyer.stochprocess
This class represents a normal inverse gaussian process (NIG).
NormalInverseGaussianProcess(double, double, double, double, double, RandomStream, InverseGaussianProcess) - Constructor for class umontreal.iro.lecuyer.stochprocess.NormalInverseGaussianProcess
Given an InverseGaussianProcess igP, constructs a new NormalInverseGaussianProcess.
NormalInverseGaussianProcess(double, double, double, double, double, RandomStream, RandomStream, RandomStream, String) - Constructor for class umontreal.iro.lecuyer.stochprocess.NormalInverseGaussianProcess
Constructs a new NormalInverseGaussianProcess.
NormalInverseGaussianProcess(double, double, double, double, double, RandomStream, String) - Constructor for class umontreal.iro.lecuyer.stochprocess.NormalInverseGaussianProcess
Same as above, but all RandomStream's are set to the same stream, streamAll.
NormalKindermannRamageGen - Class in umontreal.iro.lecuyer.randvar
This class implements normal random variate generators using the Kindermann-Ramage method.
NormalKindermannRamageGen(RandomStream, double, double) - Constructor for class umontreal.iro.lecuyer.randvar.NormalKindermannRamageGen
Creates a normal random variate generator with mean mu and standard deviation sigma, using stream s.
NormalKindermannRamageGen(RandomStream) - Constructor for class umontreal.iro.lecuyer.randvar.NormalKindermannRamageGen
Creates a standard normal random variate generator with mean 0 and standard deviation 1, using stream s.
NormalKindermannRamageGen(RandomStream, NormalDist) - Constructor for class umontreal.iro.lecuyer.randvar.NormalKindermannRamageGen
Creates a random variate generator for the normal distribution dist and stream s.
NormalPolarGen - Class in umontreal.iro.lecuyer.randvar
This class implements normal random variate generators using the polar method with rejection.
NormalPolarGen(RandomStream, double, double) - Constructor for class umontreal.iro.lecuyer.randvar.NormalPolarGen
Creates a normal random variate generator with mean mu and standard deviation sigma, using stream s.
NormalPolarGen(RandomStream) - Constructor for class umontreal.iro.lecuyer.randvar.NormalPolarGen
Creates a standard normal random variate generator with μ = 0 and σ = 1, using stream s.
NormalPolarGen(RandomStream, NormalDist) - Constructor for class umontreal.iro.lecuyer.randvar.NormalPolarGen
Creates a random variate generator for the normal distribution dist and stream s.
NortaInitDisc - Class in umontreal.iro.lecuyer.probdistmulti.norta
 
NortaInitDisc(double, DiscreteDistributionInt, DiscreteDistributionInt, double) - Constructor for class umontreal.iro.lecuyer.probdistmulti.norta.NortaInitDisc
Constructor of the abstract class.
not() - Method in class umontreal.iro.lecuyer.util.BitMatrix
Returns the BitMatrix resulting from the application of the not operator on the original BitMatrix.
not() - Method in class umontreal.iro.lecuyer.util.BitVector
.
notifyListeners(double[]) - Method in class umontreal.iro.lecuyer.stat.list.ListOfStatProbes
Notifies the observation x to all registered observers if broadcasting is ON.
notifyListeners(DoubleMatrix2D) - Method in class umontreal.iro.lecuyer.stat.matrix.MatrixOfStatProbes
Notifies the observation x to all registered observers if broadcasting is ON.
notifyListeners(double) - Method in class umontreal.iro.lecuyer.stat.StatProbe
.
NTESTTYPES - Static variable in class umontreal.iro.lecuyer.gof.GofFormat
Total number of test types
Num - Class in umontreal.iro.lecuyer.util
Num
numberObs() - Method in class umontreal.iro.lecuyer.stat.FunctionOfMultipleMeansTally
Returns the number of vectors of observations given to this probe since its last initialization.
numberObs() - Method in class umontreal.iro.lecuyer.stat.list.ListOfFunctionOfMultipleMeansTallies
Assuming that each tally in this list contains the same number of observations, returns the number of observations in tally 0, or 0 if this list is empty.
numberObs() - Method in class umontreal.iro.lecuyer.stat.list.ListOfTallies
Assuming that each tally in this list contains the same number of observations, returns the number of observations in tally 0, or 0 if this list is empty.
numberObs() - Method in class umontreal.iro.lecuyer.stat.matrix.MatrixOfFunctionOfMultipleMeansTallies
Assuming that each tally in this matrix contains the same number of observations, returns the number of observations in tally (0, 0), or 0 if the matrix has no row or column.
numberObs() - Method in class umontreal.iro.lecuyer.stat.matrix.MatrixOfTallies
Assuming that each tally in this matrix contains the same number of observations, returns the number of observations in tally (0, 0), or 0 if this matrix has 0 row or column.
numberObs() - Method in class umontreal.iro.lecuyer.stat.Tally
Returns the number of observations given to this probe since its last initialization.
numColumns() - Method in class umontreal.iro.lecuyer.util.BitMatrix
Returns the number of columns of the BitMatrix.
numColumns() - Method in class umontreal.iro.lecuyer.util.DMatrix
.
NUMINTERVALS - Static variable in class umontreal.iro.lecuyer.functions.MathFunctionUtil
Default number of intervals for Simpson's integral.
NUMINTERVALS - Static variable in class umontreal.iro.lecuyer.probdist.TruncatedDist
 
numRows() - Method in class umontreal.iro.lecuyer.util.BitMatrix
Returns the number of rows of the BitMatrix.
numRows() - Method in class umontreal.iro.lecuyer.util.DMatrix
.

O

ObservationListener - Interface in umontreal.iro.lecuyer.stat
Represents an object that can listen to observations broadcast by statistical probes.
OneDimSort - Class in umontreal.iro.lecuyer.util
OneDimSort
OneDimSort(int, int) - Constructor for class umontreal.iro.lecuyer.util.OneDimSort
.
OneDimSort(int) - Constructor for class umontreal.iro.lecuyer.util.OneDimSort
.
or(BitMatrix) - Method in class umontreal.iro.lecuyer.util.BitMatrix
Returns the BitMatrix resulting from the application of the or operator on the original BitMatrix and that.
or(BitVector) - Method in class umontreal.iro.lecuyer.util.BitVector
.
ORIENTATION_HORIZONTAL - Static variable in class umontreal.iro.lecuyer.charts.Axis
 
ORIENTATION_VERTICAL - Static variable in class umontreal.iro.lecuyer.charts.Axis
 
OrnsteinUhlenbeckProcess - Class in umontreal.iro.lecuyer.stochprocess
This class represents an Ornstein-Uhlenbeck process {X(t) : t >= 0}, sampled at times 0 = t0 < t1 < ...
OrnsteinUhlenbeckProcess(double, double, double, double, RandomStream) - Constructor for class umontreal.iro.lecuyer.stochprocess.OrnsteinUhlenbeckProcess
Constructs a new OrnsteinUhlenbeckProcess with parameters α = alpha, b, σ = sigma and initial value X(t0) = x0.
OrnsteinUhlenbeckProcess(double, double, double, double, NormalGen) - Constructor for class umontreal.iro.lecuyer.stochprocess.OrnsteinUhlenbeckProcess
Here, the normal variate generator is specified directly instead of specifying the stream.
OrnsteinUhlenbeckProcessEuler - Class in umontreal.iro.lecuyer.stochprocess
.
OrnsteinUhlenbeckProcessEuler(double, double, double, double, RandomStream) - Constructor for class umontreal.iro.lecuyer.stochprocess.OrnsteinUhlenbeckProcessEuler
Constructor with parameters α = alpha, b, σ = sigma and initial value X(t0) = x0.
OrnsteinUhlenbeckProcessEuler(double, double, double, double, NormalGen) - Constructor for class umontreal.iro.lecuyer.stochprocess.OrnsteinUhlenbeckProcessEuler
Here, the normal variate generator is specified directly instead of specifying the stream.

P

PaddedPointSet - Class in umontreal.iro.lecuyer.hups
This container class realizes padded point sets, constructed by taking some coordinates from a point set P1, other coordinates from a point set P2, and so on.
PaddedPointSet(int) - Constructor for class umontreal.iro.lecuyer.hups.PaddedPointSet
Constructs a structure for padding at most maxPointSets point sets.
padPointSet(PointSet) - Method in class umontreal.iro.lecuyer.hups.PaddedPointSet
Pads the point set P to the present structure.
padPointSetPermute(PointSet) - Method in class umontreal.iro.lecuyer.hups.PaddedPointSet
Pads the point set P, which is assumed to be finite.
Palpha - Class in umontreal.iro.lecuyer.discrepancy
Extends the class Discrepancy and implements the methods required to compute the Pα figure of merit for a lattice point set Ψs which is the intersection of a lattice L and the unit hypercube [0, 1)s in s dimensions.
Palpha(double[][], int, int, double[], int) - Constructor for class umontreal.iro.lecuyer.discrepancy.Palpha
Constructor with n points in s dimensions and with alpha = α.
Palpha(double[][], int, int, int) - Constructor for class umontreal.iro.lecuyer.discrepancy.Palpha
Constructor with all βj = 1 (see eq.).
Palpha(int, int, double[], int) - Constructor for class umontreal.iro.lecuyer.discrepancy.Palpha
Constructor with n points in s dimensions and with alpha = α.
Palpha(int) - Constructor for class umontreal.iro.lecuyer.discrepancy.Palpha
Constructor with parameter alpha = α.
Palpha(Rank1Lattice, double[], int) - Constructor for class umontreal.iro.lecuyer.discrepancy.Palpha
Constructor with the lattice set with weights beta[j]= βj and parameter alpha = α.
ParetoDist - Class in umontreal.iro.lecuyer.probdist
Extends the class ContinuousDistribution for a distribution from the Pareto family, with shape parameter α > 0 and location parameter β > 0.
ParetoDist(double) - Constructor for class umontreal.iro.lecuyer.probdist.ParetoDist
Constructs a ParetoDist object with parameters α = alpha and β = 1.
ParetoDist(double, double) - Constructor for class umontreal.iro.lecuyer.probdist.ParetoDist
Constructs a ParetoDist object with parameters α = alpha and β = beta.
ParetoGen - Class in umontreal.iro.lecuyer.randvar
ParetoGen
ParetoGen(RandomStream, double, double) - Constructor for class umontreal.iro.lecuyer.randvar.ParetoGen
.
ParetoGen(RandomStream, double) - Constructor for class umontreal.iro.lecuyer.randvar.ParetoGen
.
ParetoGen(RandomStream, ParetoDist) - Constructor for class umontreal.iro.lecuyer.randvar.ParetoGen
.
PascalConvolutionGen - Class in umontreal.iro.lecuyer.randvar
Implements Pascal random variate generators by the convolution method.
PascalConvolutionGen(RandomStream, int, double) - Constructor for class umontreal.iro.lecuyer.randvar.PascalConvolutionGen
Creates a Pascal random variate generator with parameters n and p, using stream s.
PascalConvolutionGen(RandomStream, PascalDist) - Constructor for class umontreal.iro.lecuyer.randvar.PascalConvolutionGen
Creates a new generator for the distribution dist, using stream s.
PascalDist - Class in umontreal.iro.lecuyer.probdist
The Pascal distribution is a special case of the negative binomial distribution with parameters n and p, where n is a positive integer and 0 <= p <= 1.
PascalDist(int, double) - Constructor for class umontreal.iro.lecuyer.probdist.PascalDist
Creates an object that contains the probability terms and the distribution function for the Pascal distribution with parameter n and p.
PascalGen - Class in umontreal.iro.lecuyer.randvar
PascalGen
PascalGen(RandomStream, int, double) - Constructor for class umontreal.iro.lecuyer.randvar.PascalGen
.
PascalGen(RandomStream, PascalDist) - Constructor for class umontreal.iro.lecuyer.randvar.PascalGen
.
PCADecompose(double[][], double[][], double[]) - Static method in class umontreal.iro.lecuyer.util.DMatrix
.
PCADecompose(DoubleMatrix2D, double[]) - Static method in class umontreal.iro.lecuyer.util.DMatrix
.
pDisc(double, double) - Static method in class umontreal.iro.lecuyer.gof.GofStat
Computes a variant of the p-value p whenever a test statistic has a discrete probability distribution.
Pearson5Dist - Class in umontreal.iro.lecuyer.probdist
Deprecated. 
Pearson5Dist(double, double) - Constructor for class umontreal.iro.lecuyer.probdist.Pearson5Dist
Deprecated. THIS CLASS HAS BEEN RENAMED InverseGammaDist.
Pearson5Gen - Class in umontreal.iro.lecuyer.randvar
Deprecated. 
Pearson5Gen(RandomStream, double, double) - Constructor for class umontreal.iro.lecuyer.randvar.Pearson5Gen
Deprecated. THIS CLASS HAS BEEN RENAMED InverseGammaGen.
Pearson5Gen(RandomStream, double) - Constructor for class umontreal.iro.lecuyer.randvar.Pearson5Gen
Deprecated. Creates a Pearson5 random variate generator with parameters α = alpha and β = 1, using stream s.
Pearson5Gen(RandomStream, Pearson5Dist) - Constructor for class umontreal.iro.lecuyer.randvar.Pearson5Gen
Deprecated. Creates a new generator for the distribution dist, using stream s.
Pearson6Dist - Class in umontreal.iro.lecuyer.probdist
Pearson6Dist
Pearson6Dist(double, double, double) - Constructor for class umontreal.iro.lecuyer.probdist.Pearson6Dist
.
Pearson6Gen - Class in umontreal.iro.lecuyer.randvar
This class implements random variate generators for the Pearson type VI distribution with shape parameters α1 > 0 and α2 > 0, and scale parameter β > 0.
Pearson6Gen(RandomStream, double, double, double) - Constructor for class umontreal.iro.lecuyer.randvar.Pearson6Gen
Creates a Pearson6 random variate generator with parameters α1 = alpha1, α2 = alpha2 and β = beta, using stream s.
Pearson6Gen(RandomStream, double, double) - Constructor for class umontreal.iro.lecuyer.randvar.Pearson6Gen
Creates a Pearson6 random variate generator with parameters α1 = alpha1, α2 = alpha2 and β = 1, using stream s.
Pearson6Gen(RandomStream, Pearson6Dist) - Constructor for class umontreal.iro.lecuyer.randvar.Pearson6Gen
Creates a new generator for the distribution dist, using stream s.
performReplication(int) - Method in class umontreal.iro.lecuyer.simexp.RepSim
Contains the necessary logic to perform the rth replication of the simulation.
permutedRadicalInverse(int, int[], long) - Static method in class umontreal.iro.lecuyer.hups.RadicalInverse
.
PiecewiseConstantFunction - Class in umontreal.iro.lecuyer.functions
Represents a piecewise-constant function.
PiecewiseConstantFunction(double[], double[]) - Constructor for class umontreal.iro.lecuyer.functions.PiecewiseConstantFunction
Constructs a new piecewise-constant function with X and Y coordinates given by x and y.
PiecewiseLinearEmpiricalDist - Class in umontreal.iro.lecuyer.probdist
PiecewiseLinearEmpiricalDist
PiecewiseLinearEmpiricalDist(double[]) - Constructor for class umontreal.iro.lecuyer.probdist.PiecewiseLinearEmpiricalDist
.
PiecewiseLinearEmpiricalDist(Reader) - Constructor for class umontreal.iro.lecuyer.probdist.PiecewiseLinearEmpiricalDist
.
PLAIN - Static variable in class umontreal.iro.lecuyer.util.TableFormat
.
PlotFormat - Class in umontreal.iro.lecuyer.charts
PlotFormat
PointSet - Class in umontreal.iro.lecuyer.hups
PointSet
PointSet() - Constructor for class umontreal.iro.lecuyer.hups.PointSet
 
PointSetIterator - Interface in umontreal.iro.lecuyer.hups
PointSetIterator
PointSetRandomization - Interface in umontreal.iro.lecuyer.hups
PointSetRandomization
PoissonDist - Class in umontreal.iro.lecuyer.probdist
Extends the class DiscreteDistributionInt for the Poisson distribution with mean λ >=  0.
PoissonDist(double) - Constructor for class umontreal.iro.lecuyer.probdist.PoissonDist
Creates an object that contains the probability and distribution functions, for the Poisson distribution with parameter lambda, which are computed and stored in dynamic arrays inside that object.
PoissonGen - Class in umontreal.iro.lecuyer.randvar
This class implements random variate generators having the Poisson distribution.
PoissonGen(RandomStream, double) - Constructor for class umontreal.iro.lecuyer.randvar.PoissonGen
Creates a Poisson random variate generator with parameter λ = lambda, using stream s.
PoissonGen(RandomStream, PoissonDist) - Constructor for class umontreal.iro.lecuyer.randvar.PoissonGen
Creates a new random variate generator using the Poisson distribution dist and stream s.
PoissonTIACGen - Class in umontreal.iro.lecuyer.randvar
This class implements random variate generators having the Poisson distribution (see PoissonGen).
PoissonTIACGen(RandomStream, double) - Constructor for class umontreal.iro.lecuyer.randvar.PoissonTIACGen
Creates a Poisson random variate generator with parameter λ = lambda, using stream s.
PoissonTIACGen(RandomStream, PoissonDist) - Constructor for class umontreal.iro.lecuyer.randvar.PoissonTIACGen
Creates a new random variate generator using the Poisson distribution dist and stream s.
PolInterp - Class in umontreal.iro.lecuyer.functionfit
Represents a polynomial that interpolates through a set of points.
PolInterp(double[], double[]) - Constructor for class umontreal.iro.lecuyer.functionfit.PolInterp
Constructs a new polynomial interpolating through the given points (x[0], y[0]), ..., (x[n], y[n]).
Polynomial - Class in umontreal.iro.lecuyer.functions
Represents a polynomial of degree n in power form.
Polynomial(double...) - Constructor for class umontreal.iro.lecuyer.functions.Polynomial
Constructs a new polynomial with coefficients coeff.
power(long) - Method in class umontreal.iro.lecuyer.util.BitMatrix
Raises the BitMatrix to the power p.
power2e(int) - Method in class umontreal.iro.lecuyer.util.BitMatrix
Raises the BitMatrix to power 2e.
PowerDist - Class in umontreal.iro.lecuyer.probdist
Extends the class ContinuousDistribution for the power distribution with shape parameter c > 0, over the interval [a, b], where a < b.
PowerDist(double, double, double) - Constructor for class umontreal.iro.lecuyer.probdist.PowerDist
Constructs a PowerDist object with parameters a = a, b = b and c = c.
PowerDist(double, double) - Constructor for class umontreal.iro.lecuyer.probdist.PowerDist
Constructs a PowerDist object with parameters a = 0, b = b and c = c.
PowerDist(double) - Constructor for class umontreal.iro.lecuyer.probdist.PowerDist
Constructs a PowerDist object with parameters a = 0, b = 1 and c = c.
PowerGen - Class in umontreal.iro.lecuyer.randvar
PowerGen
PowerGen(RandomStream, double, double, double) - Constructor for class umontreal.iro.lecuyer.randvar.PowerGen
.
PowerGen(RandomStream, double) - Constructor for class umontreal.iro.lecuyer.randvar.PowerGen
.
PowerGen(RandomStream, PowerDist) - Constructor for class umontreal.iro.lecuyer.randvar.PowerGen
.
PowerMathFunction - Class in umontreal.iro.lecuyer.functions
Represents a function computing (af (x) + b)p for a user-defined function f (x) and power p.
PowerMathFunction(MathFunction, double) - Constructor for class umontreal.iro.lecuyer.functions.PowerMathFunction
Constructs a new power function for function func and power power.
PowerMathFunction(MathFunction, double, double, double) - Constructor for class umontreal.iro.lecuyer.functions.PowerMathFunction
Constructs a new power function for function func, power power, and constants a and b.
powerRatios(DoubleArrayList) - Static method in class umontreal.iro.lecuyer.gof.GofStat
Applies the power ratios transformation W.
PPPlot - Class in umontreal.iro.lecuyer.charts
This class implements PP-plot (or probability-probability plot) objects that compare two probability distributions.
PPPlot(String, String, String, ContinuousDistribution, double[]) - Constructor for class umontreal.iro.lecuyer.charts.PPPlot
Initializes a new PPPlot instance using the points X.
PPPlot(String, String, String, ContinuousDistribution, double[], int) - Constructor for class umontreal.iro.lecuyer.charts.PPPlot
Similar to the constructor PPPlot (title, XLabel, YLabel, dist, X) above, except that only the first numPoints of X are plotted.
PPPlot(String, String, String, ContinuousDistribution, double[][], int) - Constructor for class umontreal.iro.lecuyer.charts.PPPlot
Initializes a new PPPlot instance.
print(String) - Static method in class umontreal.iro.lecuyer.hups.F2wStructure
.
printData() - Method in class umontreal.iro.lecuyer.util.BitMatrix
Creates a String containing all the data of the BitMatrix.
PrintfFormat - Class in umontreal.iro.lecuyer.util
This class acts like a StringBuffer which defines new types of append methods.
PrintfFormat() - Constructor for class umontreal.iro.lecuyer.util.PrintfFormat
Constructs a new buffer object containing an empty string.
PrintfFormat(int) - Constructor for class umontreal.iro.lecuyer.util.PrintfFormat
Constructs a new buffer object with an initial capacity of length.
PrintfFormat(String) - Constructor for class umontreal.iro.lecuyer.util.PrintfFormat
Constructs a new buffer object containing the initial string str.
printGeneratorMatrices(int) - Method in class umontreal.iro.lecuyer.hups.DigitalNet
.
printGeneratorMatrices(int) - Method in class umontreal.iro.lecuyer.hups.DigitalNetBase2
.
printGeneratorMatricesTrans(int) - Method in class umontreal.iro.lecuyer.hups.DigitalNetBase2
.
priority() - Method in class umontreal.iro.lecuyer.simevents.Event
Returns the priority of this event.
priority() - Method in class umontreal.iro.lecuyer.simprocs.SimProcess
Returns the priority of the current variable.
prob(int) - Method in class umontreal.iro.lecuyer.probdist.BernoulliDist
 
prob(double, int) - Static method in class umontreal.iro.lecuyer.probdist.BernoulliDist
.
prob(int) - Method in class umontreal.iro.lecuyer.probdist.BinomialDist
 
prob(int, double, int) - Static method in class umontreal.iro.lecuyer.probdist.BinomialDist
Computes and returns the binomial probability p(x) in eq..
prob(int, double, double, int) - Static method in class umontreal.iro.lecuyer.probdist.BinomialDist
A generalization of the previous method.
prob(int) - Method in class umontreal.iro.lecuyer.probdist.DiscreteDistribution
Returns pi, the probability of the i-th value, for 0 <= i < n.
prob(int) - Method in class umontreal.iro.lecuyer.probdist.DiscreteDistributionInt
Returns p(x), the probability of x.
prob(int) - Method in class umontreal.iro.lecuyer.probdist.EmpiricalDist
 
prob(int) - Method in class umontreal.iro.lecuyer.probdist.GeometricDist
 
prob(double, int) - Static method in class umontreal.iro.lecuyer.probdist.GeometricDist
Computes the geometric probability p(x).
prob(int) - Method in class umontreal.iro.lecuyer.probdist.HypergeometricDist
 
prob(int, int, int, int) - Static method in class umontreal.iro.lecuyer.probdist.HypergeometricDist
Computes the hypergeometric probability p(x).
prob(int) - Method in class umontreal.iro.lecuyer.probdist.LogarithmicDist
 
prob(double, int) - Static method in class umontreal.iro.lecuyer.probdist.LogarithmicDist
.
prob(int) - Method in class umontreal.iro.lecuyer.probdist.NegativeBinomialDist
 
prob(double, double, int) - Static method in class umontreal.iro.lecuyer.probdist.NegativeBinomialDist
.
prob(int) - Method in class umontreal.iro.lecuyer.probdist.PoissonDist
 
prob(double, int) - Static method in class umontreal.iro.lecuyer.probdist.PoissonDist
Computes and returns the Poisson probability p(x) for λ = lambda..
prob(int) - Method in class umontreal.iro.lecuyer.probdist.UniformIntDist
 
prob(int, int, int) - Static method in class umontreal.iro.lecuyer.probdist.UniformIntDist
Computes the discrete uniform probability p(x).
prob(int[]) - Method in class umontreal.iro.lecuyer.probdistmulti.DiscreteDistributionIntMulti
Returns the probability mass function p(x1, x2,…, xd), which should be a real number in [0, 1].
prob(int[]) - Method in class umontreal.iro.lecuyer.probdistmulti.MultinomialDist
 
prob(int, double[], int[]) - Static method in class umontreal.iro.lecuyer.probdistmulti.MultinomialDist
.
prob(int[]) - Method in class umontreal.iro.lecuyer.probdistmulti.NegativeMultinomialDist
 
prob(double, double[], int[]) - Static method in class umontreal.iro.lecuyer.probdistmulti.NegativeMultinomialDist
Computes the probability mass function of the negative multinomial distribution with parameters n and (p1, ..., pd), evaluated at x.
ProcessSimulator - Class in umontreal.iro.lecuyer.simprocs
Defines a special type of simulator capable of managing processes.
ProcessSimulator() - Constructor for class umontreal.iro.lecuyer.simprocs.ProcessSimulator
 
put(int) - Method in class umontreal.iro.lecuyer.simprocs.Bin
Adds n tokens to this bin.

Q

QQPlot - Class in umontreal.iro.lecuyer.charts
QQPlot
QQPlot(String, String, String, ContinuousDistribution, double[]) - Constructor for class umontreal.iro.lecuyer.charts.QQPlot
.
QQPlot(String, String, String, ContinuousDistribution, double[], int) - Constructor for class umontreal.iro.lecuyer.charts.QQPlot
.
QQPlot(String, String, String, ContinuousDistribution, double[][], int) - Constructor for class umontreal.iro.lecuyer.charts.QQPlot
.
quickSelect(double[], int, int) - Static method in class umontreal.iro.lecuyer.util.Misc
.
quickSelect(int[], int, int) - Static method in class umontreal.iro.lecuyer.util.Misc
[tabb30]
Athe array which contain the items nthe number of items in the array kthe index of the smallest item the kth smallest item of the array A
quickSort() - Method in class umontreal.iro.lecuyer.stat.TallyStore
Sorts the elements of this probe using the quicksort from Colt.

R

RAC2 - Static variable in class umontreal.iro.lecuyer.util.Num
.
RadicalInverse - Class in umontreal.iro.lecuyer.hups
RadicalInverse
RadicalInverse(int, double) - Constructor for class umontreal.iro.lecuyer.hups.RadicalInverse
.
radicalInverse(int, long) - Static method in class umontreal.iro.lecuyer.hups.RadicalInverse
.
radicalInverseInteger(int, double) - Static method in class umontreal.iro.lecuyer.hups.RadicalInverse
.
radicalInverseLong(int, double) - Static method in class umontreal.iro.lecuyer.hups.RadicalInverse
[tabb86]
bbase used for the operation xthe value for which the radical inverse will be computed the radical inverse of x in base b
RandMrg - Class in umontreal.iro.lecuyer.rng
Deprecated. 
RandMrg() - Constructor for class umontreal.iro.lecuyer.rng.RandMrg
Deprecated. Constructs a new stream, initializes its seed Ig, sets Bg and Cg equal to Ig, and sets its antithetic switch to false.
RandMrg(String) - Constructor for class umontreal.iro.lecuyer.rng.RandMrg
Deprecated. Constructs a new stream with an identifier name (can be used when printing the stream state, in error messages, etc.).
random(int, int) - Method in class umontreal.iro.lecuyer.discrepancy.Searcher
Random search to find the lattice with the best (the smallest) discrepancy in dimension s.
random(int, int) - Method in class umontreal.iro.lecuyer.discrepancy.SearcherCBC
Random CBC search to find the lattice with the best (the smallest) discrepancy in dimension s.
random(int, int) - Method in class umontreal.iro.lecuyer.discrepancy.SearcherKorobov
.
randomize(PointSetRandomization) - Method in class umontreal.iro.lecuyer.hups.CachedPointSet
.
randomize(PointSetRandomization) - Method in class umontreal.iro.lecuyer.hups.ContainerPointSet
Randomizes the contained point set using rand.
randomize(PointSet) - Method in class umontreal.iro.lecuyer.hups.EmptyRandomization
.
randomize(RandomStream) - Method in class umontreal.iro.lecuyer.hups.IndependentPointsCached
.
randomize(PointSetRandomization) - Method in class umontreal.iro.lecuyer.hups.IndependentPointsCached
.
randomize(RandomStream) - Method in class umontreal.iro.lecuyer.hups.LatinHypercube
.
randomize(PointSetRandomization) - Method in class umontreal.iro.lecuyer.hups.LatinHypercube
.
randomize(PointSet) - Method in class umontreal.iro.lecuyer.hups.LMScrambleShift
.
randomize(RandomStream) - Method in class umontreal.iro.lecuyer.hups.PaddedPointSet
 
randomize(PointSetRandomization) - Method in class umontreal.iro.lecuyer.hups.PointSet
.
randomize(int, int, RandomStream) - Method in class umontreal.iro.lecuyer.hups.PointSet
.
randomize(RandomStream) - Method in class umontreal.iro.lecuyer.hups.PointSet
.
randomize(int, int) - Method in class umontreal.iro.lecuyer.hups.PointSet
Deprecated. 
randomize() - Method in class umontreal.iro.lecuyer.hups.PointSet
Deprecated. 
randomize(PointSet) - Method in interface umontreal.iro.lecuyer.hups.PointSetRandomization
.
randomize(PointSet) - Method in class umontreal.iro.lecuyer.hups.RandomShift
.
randomize(PointSet) - Method in class umontreal.iro.lecuyer.hups.RandomStart
.
randomize() - Method in class umontreal.iro.lecuyer.hups.RQMCPointSet
Randomizes the point set.
randomize(PointSet) - Method in class umontreal.iro.lecuyer.hups.SMScrambleShift
.
randomize(PointSetRandomization) - Method in class umontreal.iro.lecuyer.hups.SortedPointSet
.
randomize(RandomStream) - Method in class umontreal.iro.lecuyer.hups.StratifiedUnitCube
.
randomize(PointSetRandomization) - Method in class umontreal.iro.lecuyer.hups.StratifiedUnitCube
.
randomize(RandomStream) - Method in class umontreal.iro.lecuyer.hups.StratifiedUnitCubeAnti
.
randomize(PointSetRandomization) - Method in class umontreal.iro.lecuyer.hups.StratifiedUnitCubeAnti
.
randomize(RandomStream) - Method in class umontreal.iro.lecuyer.markovchain.LeftScrambledFaureSequence
Deprecated.  
randomize(int, int, RandomStream) - Method in class umontreal.iro.lecuyer.markovchain.LeftScrambledFaureSequence
Deprecated.  
RandomMultivariateGen - Class in umontreal.iro.lecuyer.randvarmulti
This class is the multivariate counterpart of RandomVariateGen.
RandomMultivariateGen() - Constructor for class umontreal.iro.lecuyer.randvarmulti.RandomMultivariateGen
 
RandomPermutation - Class in umontreal.iro.lecuyer.rng
RandomPermutation
RandomPermutation() - Constructor for class umontreal.iro.lecuyer.rng.RandomPermutation
 
randomPrime(int, int) - Method in class umontreal.iro.lecuyer.discrepancy.Searcher
Similar to random(s, k), except that only values of aj relatively prime to n are considered.
randomPrime(int, int) - Method in class umontreal.iro.lecuyer.discrepancy.SearcherCBC
Similar to random(s, k), except that only values of aj relatively prime to n are considered.
randomPrime(int, int) - Method in class umontreal.iro.lecuyer.discrepancy.SearcherKorobov
.
RandomShift - Class in umontreal.iro.lecuyer.hups
RandomShift
RandomShift() - Constructor for class umontreal.iro.lecuyer.hups.RandomShift
.
RandomShift(RandomStream) - Constructor for class umontreal.iro.lecuyer.hups.RandomShift
.
RandomStart - Class in umontreal.iro.lecuyer.hups
RandomStart
RandomStart() - Constructor for class umontreal.iro.lecuyer.hups.RandomStart
.
RandomStart(RandomStream) - Constructor for class umontreal.iro.lecuyer.hups.RandomStart
.
RandomStream - Interface in umontreal.iro.lecuyer.rng
This interface defines the basic structures to handle multiple streams of uniform (pseudo)random numbers and convenient tools to move around within and across these streams.
RandomStreamBase - Class in umontreal.iro.lecuyer.rng
This class provides a convenient foundation on which RNGs can be built.
RandomStreamBase() - Constructor for class umontreal.iro.lecuyer.rng.RandomStreamBase
 
RandomStreamFactory - Interface in umontreal.iro.lecuyer.rng
Represents a random stream factory capable of constructing instances of a given type of random stream by invoking the newInstance method each time a new random stream is needed, instead of invoking directly the specific constructor of the desired type.
RandomStreamInstantiationException - Exception in umontreal.iro.lecuyer.rng
This exception is thrown when a random stream factory cannot instantiate a stream on a call to its newInstance method.
RandomStreamInstantiationException(RandomStreamFactory) - Constructor for exception umontreal.iro.lecuyer.rng.RandomStreamInstantiationException
Constructs a new random stream instantiation exception with no message, no cause, and thrown by the given factory.
RandomStreamInstantiationException(RandomStreamFactory, String) - Constructor for exception umontreal.iro.lecuyer.rng.RandomStreamInstantiationException
Constructs a new random stream instantiation exception with the given message, no cause, and concerning factory.
RandomStreamInstantiationException(RandomStreamFactory, Throwable) - Constructor for exception umontreal.iro.lecuyer.rng.RandomStreamInstantiationException
Constructs a new random stream instantiation exception with no message, the given cause, and concerning factory.
RandomStreamInstantiationException(RandomStreamFactory, String, Throwable) - Constructor for exception umontreal.iro.lecuyer.rng.RandomStreamInstantiationException
Constructs a new random stream instantiation exception with the given message, the supplied cause, and concerning factory.
RandomStreamManager - Class in umontreal.iro.lecuyer.rng
Manages a list of random streams for more convenient synchronization.
RandomStreamManager() - Constructor for class umontreal.iro.lecuyer.rng.RandomStreamManager
 
RandomStreamWithCache - Class in umontreal.iro.lecuyer.rng
This class represents a random stream whose uniforms are cached for more efficiency when using common random numbers.
RandomStreamWithCache(RandomStream) - Constructor for class umontreal.iro.lecuyer.rng.RandomStreamWithCache
Constructs a new cached random stream with internal stream stream.
RandomStreamWithCache(RandomStream, int) - Constructor for class umontreal.iro.lecuyer.rng.RandomStreamWithCache
Constructs a new cached random stream with internal stream stream.
RandomVariateGen - Class in umontreal.iro.lecuyer.randvar
This is the base class for all random variate generators over the real line.
RandomVariateGen(RandomStream, Distribution) - Constructor for class umontreal.iro.lecuyer.randvar.RandomVariateGen
Creates a new random variate generator from the distribution dist, using stream s.
RandomVariateGenInt - Class in umontreal.iro.lecuyer.randvar
RandomVariateGenInt
RandomVariateGenInt(RandomStream, DiscreteDistributionInt) - Constructor for class umontreal.iro.lecuyer.randvar.RandomVariateGenInt
.
RandomVariateGenWithCache - Class in umontreal.iro.lecuyer.randvar
This class represents a random variate generator whose values are cached for more efficiency when using common random numbers.
RandomVariateGenWithCache(RandomVariateGen) - Constructor for class umontreal.iro.lecuyer.randvar.RandomVariateGenWithCache
Constructs a new cached random variate generator with internal generator rvg.
RandomVariateGenWithCache(RandomVariateGen, int) - Constructor for class umontreal.iro.lecuyer.randvar.RandomVariateGenWithCache
Constructs a new cached random variate generator with internal generator rvg.
RandRijndael - Class in umontreal.iro.lecuyer.rng
RandRijndael
RandRijndael() - Constructor for class umontreal.iro.lecuyer.rng.RandRijndael
.
RandRijndael(String) - Constructor for class umontreal.iro.lecuyer.rng.RandRijndael
.
RandShiftedPointSet - Class in umontreal.iro.lecuyer.hups
RandShiftedPointSet
RandShiftedPointSet(PointSet, int, RandomStream) - Constructor for class umontreal.iro.lecuyer.hups.RandShiftedPointSet
.
Rank1Lattice - Class in umontreal.iro.lecuyer.hups
This class implements point sets specified by integration lattices of rank 1.
Rank1Lattice(int, int[], int) - Constructor for class umontreal.iro.lecuyer.hups.Rank1Lattice
Instantiates a Rank1Lattice with n points and lattice vector a of dimension s.
RatioFunction - Class in umontreal.iro.lecuyer.util
Represents a function computing a ratio of two values.
RatioFunction() - Constructor for class umontreal.iro.lecuyer.util.RatioFunction
Constructs a new ratio function.
RatioFunction(double) - Constructor for class umontreal.iro.lecuyer.util.RatioFunction
Constructs a new ratio function that returns zeroOverZero for the special case of 0/0.
RayleighDist - Class in umontreal.iro.lecuyer.probdist
RayleighDist
RayleighDist(double) - Constructor for class umontreal.iro.lecuyer.probdist.RayleighDist
.
RayleighDist(double, double) - Constructor for class umontreal.iro.lecuyer.probdist.RayleighDist
.
RayleighGen - Class in umontreal.iro.lecuyer.randvar
This class implements random variate generators for the Rayleigh distribution.
RayleighGen(RandomStream, double, double) - Constructor for class umontreal.iro.lecuyer.randvar.RayleighGen
Creates a Rayleigh random variate generator with parameters a = a and β = beta, using stream s.
RayleighGen(RandomStream, double) - Constructor for class umontreal.iro.lecuyer.randvar.RayleighGen
Creates a Rayleigh random variate generator with parameters a = 0 and β = beta, using stream s.
RayleighGen(RandomStream, RayleighDist) - Constructor for class umontreal.iro.lecuyer.randvar.RayleighGen
Creates a new generator for the Rayleigh distribution dist and stream s.
readAllFields() - Method in class umontreal.iro.lecuyer.util.io.AbstractDataReader
Reads all fields in the file and returns a hashmap indexed by field labels.
readAllFields() - Method in interface umontreal.iro.lecuyer.util.io.DataReader
Reads all fields in the file and returns a hashmap indexed by field labels.
readAllNextFields() - Method in class umontreal.iro.lecuyer.util.io.AbstractDataReader
Reads all remaining fields in the file and returns a hashmap indexed by field labels.
readAllNextFields() - Method in interface umontreal.iro.lecuyer.util.io.DataReader
Reads all remaining fields in the file and returns a hashmap indexed by field labels.
readCSVData(Reader, char, char) - Static method in class umontreal.iro.lecuyer.util.TextDataReader
Reads comma-separated values (CSV) from reader input, and returns a 2D array of strings corresponding to the read data.
readCSVData(URL, char, char) - Static method in class umontreal.iro.lecuyer.util.TextDataReader
Connects to the URL referred to by the URL object url, and calls readCSVData to obtain a matrix of strings from the resource.
readCSVData(File, char, char) - Static method in class umontreal.iro.lecuyer.util.TextDataReader
This is equivalent to readDoubleData2D, for reading strings.
readCSVData(String, char, char) - Static method in class umontreal.iro.lecuyer.util.TextDataReader
This is equivalent to readDoubleData2D, for reading strings.
readDouble(String) - Method in class umontreal.iro.lecuyer.util.io.AbstractDataReader
Reads first field labeled as label and returns its double value.
readDouble(String) - Method in interface umontreal.iro.lecuyer.util.io.DataReader
Reads the first field labeled as label and returns its double value.
readDoubleArray(String) - Method in class umontreal.iro.lecuyer.util.io.AbstractDataReader
Reads first field labeled as label and returns its value as a one-dimensional array of double's.
readDoubleArray(String) - Method in interface umontreal.iro.lecuyer.util.io.DataReader
Reads the first field labeled as label and returns its value as a one-dimensional array of double's.
readDoubleArray2D(String) - Method in class umontreal.iro.lecuyer.util.io.AbstractDataReader
Reads first field labeled as label and returns its value as a two-dimensional array of double's.
readDoubleArray2D(String) - Method in interface umontreal.iro.lecuyer.util.io.DataReader
Reads the first field labeled as label and returns its value as a two-dimensional array of double's.
readDoubleData(Statement, String) - Static method in class umontreal.iro.lecuyer.util.JDBCManager
Copies the result of the SQL query query into an array of double-precision values.
readDoubleData(Connection, String) - Static method in class umontreal.iro.lecuyer.util.JDBCManager
Copies the result of the SQL query query into an array of double-precision values.
readDoubleData(Statement, String, String) - Static method in class umontreal.iro.lecuyer.util.JDBCManager
Returns the values of the column column of the table table.
readDoubleData(Connection, String, String) - Static method in class umontreal.iro.lecuyer.util.JDBCManager
Returns the values of the column column of the table table.
readDoubleData(Reader) - Static method in class umontreal.iro.lecuyer.util.TextDataReader
Reads an array of double-precision values from the reader input.
readDoubleData(URL) - Static method in class umontreal.iro.lecuyer.util.TextDataReader
Connects to the URL referred to by the URL object url, and calls readDoubleData to obtain an array of double-precision values from the resource.
readDoubleData(File) - Static method in class umontreal.iro.lecuyer.util.TextDataReader
Opens the file referred to by the file object file, and calls readDoubleData to obtain an array of double-precision values from the file.
readDoubleData(String) - Static method in class umontreal.iro.lecuyer.util.TextDataReader
Opens the file with name file, and calls readDoubleData to obtain an array of double-precision values from the file.
readDoubleData2D(Statement, String) - Static method in class umontreal.iro.lecuyer.util.JDBCManager
Copies the result of the SQL query query into a rectangular 2D array of double-precision values.
readDoubleData2D(Connection, String) - Static method in class umontreal.iro.lecuyer.util.JDBCManager
Copies the result of the SQL query query into a rectangular 2D array of double-precision values.
readDoubleData2D(Reader) - Static method in class umontreal.iro.lecuyer.util.TextDataReader
Uses the reader input to obtain a 2-dimensional array of double-precision values.
readDoubleData2D(URL) - Static method in class umontreal.iro.lecuyer.util.TextDataReader
Connects to the URL referred to by the URL object url, and calls readDoubleData2D to obtain a matrix of double-precision values from the resource.
readDoubleData2D(File) - Static method in class umontreal.iro.lecuyer.util.TextDataReader
Opens the file referred to by the file object file, and calls readDoubleData2D to obtain a matrix of double-precision values from the file.
readDoubleData2D(String) - Static method in class umontreal.iro.lecuyer.util.TextDataReader
Opens the file with name file, and calls readDoubleData2D to obtain a matrix of double-precision values from the file.
readDoubleData2DTable(Statement, String) - Static method in class umontreal.iro.lecuyer.util.JDBCManager
Returns the values of the columns of the table table.
readDoubleData2DTable(Connection, String) - Static method in class umontreal.iro.lecuyer.util.JDBCManager
Returns the values of the columns of the table table.
readField(String) - Method in class umontreal.iro.lecuyer.util.io.BinaryDataReader
Reads the first field labeled as label.
readField(String) - Method in interface umontreal.iro.lecuyer.util.io.DataReader
Reads the first field labeled as label.
readFloat(String) - Method in class umontreal.iro.lecuyer.util.io.AbstractDataReader
Reads first field labeled as label and returns its float value.
readFloat(String) - Method in interface umontreal.iro.lecuyer.util.io.DataReader
Reads the first field labeled as label and returns its float value.
readFloatArray(String) - Method in class umontreal.iro.lecuyer.util.io.AbstractDataReader
Reads first field labeled as label and returns its value as a one-dimensional array of float's.
readFloatArray(String) - Method in interface umontreal.iro.lecuyer.util.io.DataReader
Reads the first field labeled as label and returns its value as a one-dimensional array of float's.
readFloatArray2D(String) - Method in class umontreal.iro.lecuyer.util.io.AbstractDataReader
Reads first field labeled as label and returns its value as a two-dimensional array of float's.
readFloatArray2D(String) - Method in interface umontreal.iro.lecuyer.util.io.DataReader
Reads the first field labeled as label and returns its value as a two-dimensional array of float's.
readInt(String) - Method in class umontreal.iro.lecuyer.util.io.AbstractDataReader
Reads first field labeled as label and returns its int value.
readInt(String) - Method in interface umontreal.iro.lecuyer.util.io.DataReader
Reads the first field labeled as label and returns its int value.
readIntArray(String) - Method in class umontreal.iro.lecuyer.util.io.AbstractDataReader
Reads first field labeled as label and returns its value as a one-dimensional array of int's.
readIntArray(String) - Method in interface umontreal.iro.lecuyer.util.io.DataReader
Reads the first field labeled as label and returns its value as a one-dimensional array of int's.
readIntArray2D(String) - Method in class umontreal.iro.lecuyer.util.io.AbstractDataReader
Reads first field labeled as label and returns its value as a two-dimensional array of int's.
readIntArray2D(String) - Method in interface umontreal.iro.lecuyer.util.io.DataReader
Reads the first field labeled as label and returns its value as a two-dimensional array of int's.
readIntData(Statement, String) - Static method in class umontreal.iro.lecuyer.util.JDBCManager
Copies the result of the SQL query query into an array of integers.
readIntData(Connection, String) - Static method in class umontreal.iro.lecuyer.util.JDBCManager
Copies the result of the SQL query query into an array of integers.
readIntData(Statement, String, String) - Static method in class umontreal.iro.lecuyer.util.JDBCManager
Returns the values of the column column of the table table.
readIntData(Connection, String, String) - Static method in class umontreal.iro.lecuyer.util.JDBCManager
Returns the values of the column column of the table table.
readIntData(Reader) - Static method in class umontreal.iro.lecuyer.util.TextDataReader
This is equivalent to readDoubleData, for reading integers.
readIntData(URL) - Static method in class umontreal.iro.lecuyer.util.TextDataReader
Connects to the URL referred to by the URL object url, and calls readIntData to obtain an array of integers from the resource.
readIntData(File) - Static method in class umontreal.iro.lecuyer.util.TextDataReader
This is equivalent to readDoubleData, for reading integers.
readIntData(String) - Static method in class umontreal.iro.lecuyer.util.TextDataReader
This is equivalent to readDoubleData, for reading integers.
readIntData2D(Statement, String) - Static method in class umontreal.iro.lecuyer.util.JDBCManager
Copies the result of the SQL query query into a rectangular 2D array of integers.
readIntData2D(Connection, String) - Static method in class umontreal.iro.lecuyer.util.JDBCManager
Copies the result of the SQL query query into a rectangular 2D array of integers.
readIntData2D(Reader) - Static method in class umontreal.iro.lecuyer.util.TextDataReader
This is equivalent to readDoubleData2D, for reading integers.
readIntData2D(URL) - Static method in class umontreal.iro.lecuyer.util.TextDataReader
Connects to the URL referred to by the URL object url, and calls readDoubleData to obtain a matrix of integers from the resource.
readIntData2D(File) - Static method in class umontreal.iro.lecuyer.util.TextDataReader
This is equivalent to readDoubleData2D, for reading integers.
readIntData2D(String) - Static method in class umontreal.iro.lecuyer.util.TextDataReader
This is equivalent to readDoubleData2D, for reading integers.
readIntData2DTable(Statement, String) - Static method in class umontreal.iro.lecuyer.util.JDBCManager
Returns the values of the columns of the table table.
readIntData2DTable(Connection, String) - Static method in class umontreal.iro.lecuyer.util.JDBCManager
Returns the values of the columns of the table table.
readNextField() - Method in class umontreal.iro.lecuyer.util.io.BinaryDataReader
Reads the next available field.
readNextField() - Method in interface umontreal.iro.lecuyer.util.io.DataReader
Reads the next available field.
readObjectData(Statement, String) - Static method in class umontreal.iro.lecuyer.util.JDBCManager
Copies the result of the SQL query query into an array of objects.
readObjectData(Connection, String) - Static method in class umontreal.iro.lecuyer.util.JDBCManager
Copies the result of the SQL query query into an array of objects.
readObjectData(Statement, String, String) - Static method in class umontreal.iro.lecuyer.util.JDBCManager
Returns the values of the column column of the table table.
readObjectData(Connection, String, String) - Static method in class umontreal.iro.lecuyer.util.JDBCManager
Returns the values of the column column of the table table.
readObjectData2D(Statement, String) - Static method in class umontreal.iro.lecuyer.util.JDBCManager
Copies the result of the SQL query query into a rectangular 2D array of objects.
readObjectData2D(Connection, String) - Static method in class umontreal.iro.lecuyer.util.JDBCManager
Copies the result of the SQL query query into a rectangular 2D array of integers.
readObjectData2DTable(Statement, String) - Static method in class umontreal.iro.lecuyer.util.JDBCManager
Returns the values of the columns of the table table.
readObjectData2DTable(Connection, String) - Static method in class umontreal.iro.lecuyer.util.JDBCManager
Returns the values of the columns of the table table.
readString(String) - Method in class umontreal.iro.lecuyer.util.io.AbstractDataReader
Reads first field labeled as label and returns its String value.
readString(String) - Method in interface umontreal.iro.lecuyer.util.io.DataReader
Reads the first field labeled as label and returns its String value.
readStringArray(String) - Method in class umontreal.iro.lecuyer.util.io.AbstractDataReader
Reads first field labeled as label and returns its value as a one-dimensional array of String's.
readStringArray(String) - Method in interface umontreal.iro.lecuyer.util.io.DataReader
Reads the first field labeled as label and returns its value as a one-dimensional array of String's.
readStringArray2D(String) - Method in class umontreal.iro.lecuyer.util.io.AbstractDataReader
Reads first field labeled as label and returns its value as a two-dimensional array of String's.
readStringArray2D(String) - Method in interface umontreal.iro.lecuyer.util.io.DataReader
Reads the first field labeled as label and returns its value as a two-dimensional array of String's.
readStringData(Reader) - Static method in class umontreal.iro.lecuyer.util.TextDataReader
Reads an array of strings from the reader input.
readStringData(URL) - Static method in class umontreal.iro.lecuyer.util.TextDataReader
Connects to the URL referred to by the URL object url, and calls readStringData to obtain an array of integers from the resource.
readStringData(File) - Static method in class umontreal.iro.lecuyer.util.TextDataReader
This is equivalent to readDoubleData, for reading strings.
readStringData(String) - Static method in class umontreal.iro.lecuyer.util.TextDataReader
This is equivalent to readDoubleData, for reading strings.
RedblackTree - Class in umontreal.iro.lecuyer.simevents.eventlist
An implementation of EventList using a red black tree, which is similar to a binary search tree except that every node is colored red or black.
RedblackTree() - Constructor for class umontreal.iro.lecuyer.simevents.eventlist.RedblackTree
 
regressionToString() - Method in class umontreal.iro.lecuyer.discrepancy.DiscrepancyContainer
Formats and returns a String containing the linear regression slopes for the discrepancies as function of the parameter.
regroupCategories(double) - Method in class umontreal.iro.lecuyer.gof.GofStat.OutcomeCategoriesChi2
Regroup categories as explained earlier, so that the expected number of observations in each category is at least minExp.
regroupElements(double[], int) - Static method in class umontreal.iro.lecuyer.simexp.BatchMeansSim
Regroups the elements in array a by summing each successive x values.
regroupElements(DoubleArrayList, int) - Static method in class umontreal.iro.lecuyer.simexp.BatchMeansSim
Same as regroupElements for an array list.
regroupElements(DoubleMatrix1D, int) - Static method in class umontreal.iro.lecuyer.simexp.BatchMeansSim
Same as regroupElements for a 1D matrix.
regroupElements(DoubleMatrix2D, int) - Static method in class umontreal.iro.lecuyer.simexp.BatchMeansSim
Same as regroupElements for a 2D matrix.
regroupRealBatches(int) - Method in class umontreal.iro.lecuyer.simexp.BatchMeansSim
Regroups real batches x by x.
release(int) - Method in class umontreal.iro.lecuyer.simprocs.Resource
The executing process that invokes this method releases n units of the resource.
remove(RandomStream) - Method in class umontreal.iro.lecuyer.rng.RandomStreamManager
Removes the given stream from the internal list of this random stream manager.
remove(Event) - Method in class umontreal.iro.lecuyer.simevents.eventlist.BinaryTree
 
remove(Event) - Method in class umontreal.iro.lecuyer.simevents.eventlist.DoublyLinked
 
remove(Event) - Method in interface umontreal.iro.lecuyer.simevents.eventlist.EventList
Removes the event ev from the event list (cancels this event).
remove(Event) - Method in class umontreal.iro.lecuyer.simevents.eventlist.Henriksen
 
remove(Event) - Method in class umontreal.iro.lecuyer.simevents.eventlist.RedblackTree
 
remove(Event) - Method in class umontreal.iro.lecuyer.simevents.eventlist.SplayTree
 
remove(int) - Method in class umontreal.iro.lecuyer.simevents.ListWithStat
 
remove(int) - Method in class umontreal.iro.lecuyer.stat.list.ListOfStatProbes
 
remove(Object) - Method in class umontreal.iro.lecuyer.stat.list.ListOfStatProbes
 
remove(int) - Method in class umontreal.iro.lecuyer.util.TransformingList
 
removeAll(Collection<?>) - Method in class umontreal.iro.lecuyer.stat.list.ListOfStatProbes
 
removeArrayOfObservationListener(ArrayOfObservationListener) - Method in class umontreal.iro.lecuyer.stat.list.ListOfStatProbes
Removes the observation listener l from the list of observers of this list of statistical probes.
removeFirst() - Method in class umontreal.iro.lecuyer.simevents.eventlist.BinaryTree
 
removeFirst() - Method in class umontreal.iro.lecuyer.simevents.eventlist.DoublyLinked
 
removeFirst() - Method in interface umontreal.iro.lecuyer.simevents.eventlist.EventList
Removes the first event from the event list (to cancel or execute this event).
removeFirst() - Method in class umontreal.iro.lecuyer.simevents.eventlist.Henriksen
 
removeFirst() - Method in class umontreal.iro.lecuyer.simevents.eventlist.RedblackTree
 
removeFirst() - Method in class umontreal.iro.lecuyer.simevents.eventlist.SplayTree
 
removeFirst() - Method in class umontreal.iro.lecuyer.simevents.LinkedListStat
 
removeLast() - Method in class umontreal.iro.lecuyer.simevents.LinkedListStat
 
removeMatrixOfObservationListener(MatrixOfObservationListener) - Method in class umontreal.iro.lecuyer.stat.matrix.MatrixOfStatProbes
Removes the observation listener l from the list of observers of this matrix of statistical probes.
removeNaNs(double[], double[]) - Static method in class umontreal.iro.lecuyer.functions.MathFunctionUtil
Removes any point (NaN, y) or (x, NaN) from x and y, and returns a 2D array containing the filtered points.
removeObservationListener(ObservationListener) - Method in class umontreal.iro.lecuyer.stat.StatProbe
.
report() - Method in class umontreal.iro.lecuyer.simevents.Accumulate
 
report() - Method in class umontreal.iro.lecuyer.simevents.ListWithStat
Returns a string containing a statistical report on the list, provided that setStatCollecting (true) has been called before for this list.
report() - Method in class umontreal.iro.lecuyer.simprocs.Bin
Returns a string containing a complete statistical report on this bin.
report() - Method in class umontreal.iro.lecuyer.simprocs.Resource
Returns a string containing a complete statistical report on this resource.
report() - Method in class umontreal.iro.lecuyer.stat.FunctionOfMultipleMeansTally
Returns a string containing a formatted report on this probe.
report() - Method in class umontreal.iro.lecuyer.stat.list.ListOfStatProbes
Formats a report for each probe in the list of statistical probes.
report() - Method in class umontreal.iro.lecuyer.stat.StatProbe
.
report(String, StatProbe[]) - Static method in class umontreal.iro.lecuyer.stat.StatProbe
.
report(String, Iterable<? extends StatProbe>) - Static method in class umontreal.iro.lecuyer.stat.StatProbe
.
report() - Method in class umontreal.iro.lecuyer.stat.Tally
Returns a formatted string that contains a report on this probe.
report(double, int) - Method in class umontreal.iro.lecuyer.stat.Tally
Returns a formatted string that contains a report on this probe with a confidence interval level level using d fractional decimal digits.
reportAndCIDelta(double, int) - Method in class umontreal.iro.lecuyer.stat.FunctionOfMultipleMeansTally
Returns a string containing a formatted report on this probe (as in report), followed by a confidence interval (as in formatCIDelta).
reportAndCIDelta(double) - Method in class umontreal.iro.lecuyer.stat.FunctionOfMultipleMeansTally
Same as reportAndCIDelta (level, 3).
reportAndCIStudent(double, int) - Method in class umontreal.iro.lecuyer.stat.Tally
Returns a formatted string that contains a report on this probe (as in report), followed by a confidence interval (as in formatCIStudent), using d fractional decimal digits.
reportAndCIStudent(double) - Method in class umontreal.iro.lecuyer.stat.Tally
Same as reportAndCIStudent (level, 3).
RepSim - Class in umontreal.iro.lecuyer.simexp
Performs a simulation experiment on a finite horizon, using a certain number of independent runs or replications.
RepSim(int) - Constructor for class umontreal.iro.lecuyer.simexp.RepSim
Constructs a new replications-based simulator with a minimal number of runs, minReps, and no maximal number of runs.
RepSim(int, int) - Constructor for class umontreal.iro.lecuyer.simexp.RepSim
Constructs a new replications-based simulator with a minimal number of runs minReps, and a maximal number of runs maxReps.
RepSim(Simulator, int) - Constructor for class umontreal.iro.lecuyer.simexp.RepSim
Equivalent to the first constructor, with the given simulator sim.
RepSim(Simulator, int, int) - Constructor for class umontreal.iro.lecuyer.simexp.RepSim
Equivalent to the second constructor, with the given simulator sim.
request(int) - Method in class umontreal.iro.lecuyer.simprocs.Resource
The executing process invoking this method requests for n units of this resource.
reschedule(double) - Method in class umontreal.iro.lecuyer.simevents.Event
Cancels this event and reschedules it to happen in delay time units.
reschedule(double) - Method in class umontreal.iro.lecuyer.simprocs.SimProcess
If the process is in the DELAYED state, removes it from the event list and reschedules it in delay units of time.
reset(int) - Method in class umontreal.iro.lecuyer.discrepancy.DiscrepancyContainer
Resets the values of the discrepancies at index i to 0.
reset() - Method in class umontreal.iro.lecuyer.discrepancy.DiscrepancyContainer
Calls reset(i) for all indices i.
reset() - Method in class umontreal.iro.lecuyer.util.io.BinaryDataReader
Reopens the file (does not work with the constructor that takes an input stream).
reset() - Method in interface umontreal.iro.lecuyer.util.io.DataReader
Resets the reader to its initial state, i.e.
resetCurCoordIndex() - Method in class umontreal.iro.lecuyer.hups.CycleBasedPointSet.CycleBasedPointSetIterator
 
resetCurCoordIndex() - Method in interface umontreal.iro.lecuyer.hups.PointSetIterator
.
resetCurCycle(int) - Method in class umontreal.iro.lecuyer.hups.CycleBasedPointSet.CycleBasedPointSetIterator
 
resetCurCycle(int) - Method in class umontreal.iro.lecuyer.hups.CycleBasedPointSetBase2.CycleBasedPointSetBase2Iterator
 
resetCurPointIndex() - Method in class umontreal.iro.lecuyer.hups.CycleBasedPointSet.CycleBasedPointSetIterator
 
resetCurPointIndex() - Method in interface umontreal.iro.lecuyer.hups.PointSetIterator
.
resetGeneratorMatrices() - Method in class umontreal.iro.lecuyer.hups.DigitalNet
.
resetNextSubstream() - Method in class umontreal.iro.lecuyer.rng.AntitheticStream
 
resetNextSubstream() - Method in class umontreal.iro.lecuyer.rng.BakerTransformedStream
 
resetNextSubstream() - Method in class umontreal.iro.lecuyer.rng.F2NL607
 
resetNextSubstream() - Method in class umontreal.iro.lecuyer.rng.GenF2w32
 
resetNextSubstream() - Method in class umontreal.iro.lecuyer.rng.LFSR113
 
resetNextSubstream() - Method in class umontreal.iro.lecuyer.rng.LFSR258
 
resetNextSubstream() - Method in class umontreal.iro.lecuyer.rng.MRG31k3p
 
resetNextSubstream() - Method in class umontreal.iro.lecuyer.rng.MRG32k3a
 
resetNextSubstream() - Method in class umontreal.iro.lecuyer.rng.MRG32k3aL
 
resetNextSubstream() - Method in class umontreal.iro.lecuyer.rng.MT19937
 
resetNextSubstream() - Method in class umontreal.iro.lecuyer.rng.RandMrg
Deprecated.  
resetNextSubstream() - Method in interface umontreal.iro.lecuyer.rng.RandomStream
Reinitializes the stream to the beginning of its next substream: Ng is computed, and Cg and Bg are set to Ng.
resetNextSubstream() - Method in class umontreal.iro.lecuyer.rng.RandomStreamBase
 
resetNextSubstream() - Method in class umontreal.iro.lecuyer.rng.RandomStreamManager
Forwards to the resetNextSubstream methods of all streams in the list.
resetNextSubstream() - Method in class umontreal.iro.lecuyer.rng.RandomStreamWithCache
 
resetNextSubstream() - Method in class umontreal.iro.lecuyer.rng.RandRijndael
 
resetNextSubstream() - Method in class umontreal.iro.lecuyer.rng.TruncatedRandomStream
 
resetNextSubstream() - Method in class umontreal.iro.lecuyer.rng.WELL1024
 
resetNextSubstream() - Method in class umontreal.iro.lecuyer.rng.WELL512
 
resetNextSubstream() - Method in class umontreal.iro.lecuyer.rng.WELL607
 
resetRiskNeutralCorrection(double) - Method in class umontreal.iro.lecuyer.stochprocess.GeometricLevyProcess
Changes the value of ωRN.
resetStartProcess() - Method in class umontreal.iro.lecuyer.stochprocess.BrownianMotionBridge
 
resetStartProcess() - Method in class umontreal.iro.lecuyer.stochprocess.GammaProcessBridge
 
resetStartProcess() - Method in class umontreal.iro.lecuyer.stochprocess.GeometricBrownianMotion
Same as in StochasticProcess, but also invokes resetStartProcess for the underlying BrownianMotion object.
resetStartProcess() - Method in class umontreal.iro.lecuyer.stochprocess.GeometricLevyProcess
Resets the step counter of the geometric process and the underlying Lévy process to the start value.
resetStartProcess() - Method in class umontreal.iro.lecuyer.stochprocess.GeometricVarianceGammaProcess
Resets the GeometricaVarianceGammaProcess, but also applies the resetStartProcess method to the VarianceGammaProcess object used to generate this process.
resetStartProcess() - Method in class umontreal.iro.lecuyer.stochprocess.InverseGaussianProcessBridge
 
resetStartProcess() - Method in class umontreal.iro.lecuyer.stochprocess.MultivariateBrownianMotionBridge
 
resetStartProcess() - Method in class umontreal.iro.lecuyer.stochprocess.MultivariateGeometricBrownianMotion
Same as in StochasticProcess, but also invokes resetStartProcess for the underlying BrownianMotion object.
resetStartProcess() - Method in class umontreal.iro.lecuyer.stochprocess.StochasticProcess
Resets the observation counter to its initial value j = 0, so that the current observation X(tj) becomes X(t0).
resetStartProcess() - Method in class umontreal.iro.lecuyer.stochprocess.VarianceGammaProcess
Resets the observation index and counter to 0 and applies the resetStartProcess method to the BrownianMotion and the GammaProcess objects used to generate this process.
resetStartProcess() - Method in class umontreal.iro.lecuyer.stochprocess.VarianceGammaProcessDiff
Sets the observation times on the VarianceGammaProcessDiff as usual, but also applies the resetStartProcess method to the two GammaProcess objects used to generate this process.
resetStartStream() - Method in class umontreal.iro.lecuyer.rng.AntitheticStream
 
resetStartStream() - Method in class umontreal.iro.lecuyer.rng.BakerTransformedStream
 
resetStartStream() - Method in class umontreal.iro.lecuyer.rng.F2NL607
 
resetStartStream() - Method in class umontreal.iro.lecuyer.rng.GenF2w32
 
resetStartStream() - Method in class umontreal.iro.lecuyer.rng.LFSR113
 
resetStartStream() - Method in class umontreal.iro.lecuyer.rng.LFSR258
 
resetStartStream() - Method in class umontreal.iro.lecuyer.rng.MRG31k3p
 
resetStartStream() - Method in class umontreal.iro.lecuyer.rng.MRG32k3a
 
resetStartStream() - Method in class umontreal.iro.lecuyer.rng.MRG32k3aL
 
resetStartStream() - Method in class umontreal.iro.lecuyer.rng.MT19937
 
resetStartStream() - Method in class umontreal.iro.lecuyer.rng.RandMrg
Deprecated.  
resetStartStream() - Method in interface umontreal.iro.lecuyer.rng.RandomStream
Reinitializes the stream to its initial state Ig: Cg and Bg are set to Ig.
resetStartStream() - Method in class umontreal.iro.lecuyer.rng.RandomStreamBase
 
resetStartStream() - Method in class umontreal.iro.lecuyer.rng.RandomStreamManager
Forwards to the resetStartStream methods of all streams in the list.
resetStartStream() - Method in class umontreal.iro.lecuyer.rng.RandomStreamWithCache
 
resetStartStream() - Method in class umontreal.iro.lecuyer.rng.RandRijndael
 
resetStartStream() - Method in class umontreal.iro.lecuyer.rng.TruncatedRandomStream
 
resetStartStream() - Method in class umontreal.iro.lecuyer.rng.WELL1024
 
resetStartStream() - Method in class umontreal.iro.lecuyer.rng.WELL512
 
resetStartStream() - Method in class umontreal.iro.lecuyer.rng.WELL607
 
resetStartSubstream() - Method in class umontreal.iro.lecuyer.rng.AntitheticStream
 
resetStartSubstream() - Method in class umontreal.iro.lecuyer.rng.BakerTransformedStream
 
resetStartSubstream() - Method in class umontreal.iro.lecuyer.rng.F2NL607
 
resetStartSubstream() - Method in class umontreal.iro.lecuyer.rng.GenF2w32
 
resetStartSubstream() - Method in class umontreal.iro.lecuyer.rng.LFSR113
 
resetStartSubstream() - Method in class umontreal.iro.lecuyer.rng.LFSR258
 
resetStartSubstream() - Method in class umontreal.iro.lecuyer.rng.MRG31k3p
 
resetStartSubstream() - Method in class umontreal.iro.lecuyer.rng.MRG32k3a
 
resetStartSubstream() - Method in class umontreal.iro.lecuyer.rng.MRG32k3aL
 
resetStartSubstream() - Method in class umontreal.iro.lecuyer.rng.MT19937
 
resetStartSubstream() - Method in class umontreal.iro.lecuyer.rng.RandMrg
Deprecated.  
resetStartSubstream() - Method in interface umontreal.iro.lecuyer.rng.RandomStream
Reinitializes the stream to the beginning of its current substream: Cg is set to Bg.
resetStartSubstream() - Method in class umontreal.iro.lecuyer.rng.RandomStreamBase
 
resetStartSubstream() - Method in class umontreal.iro.lecuyer.rng.RandomStreamManager
Forwards to the resetStartSubstream methods of all streams in the list.
resetStartSubstream() - Method in class umontreal.iro.lecuyer.rng.RandomStreamWithCache
 
resetStartSubstream() - Method in class umontreal.iro.lecuyer.rng.RandRijndael
 
resetStartSubstream() - Method in class umontreal.iro.lecuyer.rng.TruncatedRandomStream
 
resetStartSubstream() - Method in class umontreal.iro.lecuyer.rng.WELL1024
 
resetStartSubstream() - Method in class umontreal.iro.lecuyer.rng.WELL512
 
resetStartSubstream() - Method in class umontreal.iro.lecuyer.rng.WELL607
 
resetToNextPoint() - Method in class umontreal.iro.lecuyer.hups.CycleBasedPointSet.CycleBasedPointSetIterator
 
resetToNextPoint() - Method in interface umontreal.iro.lecuyer.hups.PointSetIterator
.
Resource - Class in umontreal.iro.lecuyer.simprocs
Objects of this class are resources having limited capacity, and which can be requested and released by Process objects.
Resource(int) - Constructor for class umontreal.iro.lecuyer.simprocs.Resource
Constructs a new resource linked with the default simulator, with initial capacity capacity, and service policy FIFO.
Resource(ProcessSimulator, int) - Constructor for class umontreal.iro.lecuyer.simprocs.Resource
Constructs a new resource linked with the simulator sim, with initial capacity capacity, and service policy FIFO.
Resource(int, String) - Constructor for class umontreal.iro.lecuyer.simprocs.Resource
Constructs a new resource linked with the default simulator, with initial capacity capacity, service policy FIFO, and identifier name.
Resource(ProcessSimulator, int, String) - Constructor for class umontreal.iro.lecuyer.simprocs.Resource
Constructs a new resource linked with the simulator sim, with initial capacity capacity, service policy FIFO, and identifier (or name) name.
restoreImports() - Method in class umontreal.iro.lecuyer.util.ClassFinder
Restores the list of import declarations.
resume() - Method in class umontreal.iro.lecuyer.simprocs.SimProcess
Places this process at the beginning of the event list to resume its execution.
retainAll(Collection<?>) - Method in class umontreal.iro.lecuyer.stat.list.ListOfStatProbes
 
reverseDigits(int, int[], int[]) - Static method in class umontreal.iro.lecuyer.hups.RadicalInverse
.
rightMatrixScramble(RandomStream) - Method in class umontreal.iro.lecuyer.hups.DigitalNet
.
rightMatrixScramble(RandomStream) - Method in class umontreal.iro.lecuyer.hups.DigitalNetBase2
 
RootFinder - Class in umontreal.iro.lecuyer.util
RootFinder
rowReport(int) - Method in class umontreal.iro.lecuyer.stat.matrix.MatrixOfStatProbes
Formats a report for the row r of the statistical probe matrix.
rows() - Method in class umontreal.iro.lecuyer.stat.matrix.MatrixOfStatProbes
Returns the number of rows in this matrix.
RQMCPointSet - Class in umontreal.iro.lecuyer.hups
This class is used for randomized quasi-Monte Carlo (RQMC) simulations.
RQMCPointSet(PointSet, PointSetRandomization) - Constructor for class umontreal.iro.lecuyer.hups.RQMCPointSet
Constructor with the point set set and the randomization rand.

S

s(String) - Static method in class umontreal.iro.lecuyer.util.PrintfFormat
Same as s (0, str).
s(int, String) - Static method in class umontreal.iro.lecuyer.util.PrintfFormat
Formats the string str like the %s in the C printf function.
sameSignature(Method, Method) - Static method in class umontreal.iro.lecuyer.util.Introspection
Determines if two methods m1 and m2 share the same signature.
saveImports() - Method in class umontreal.iro.lecuyer.util.ClassFinder
Saves the current import list on the import stack.
scalarProduct(BitVector) - Method in class umontreal.iro.lecuyer.util.BitVector
.
scale(int, double) - Method in class umontreal.iro.lecuyer.discrepancy.DiscrepancyContainer
Multiplies all the discrepancies at index i by scale;
scale(double) - Method in class umontreal.iro.lecuyer.discrepancy.DiscrepancyContainer
Calls scale(i,scale) for all indices i.
scan(int, double, int) - Static method in class umontreal.iro.lecuyer.gof.FBar
Return P[SN(d ) >= m], where SN(d ) is the scan statistic.
scan(int, double, int) - Static method in class umontreal.iro.lecuyer.gof.FDist
Returns F(m), the distribution function of the scan statistic with parameters N and d, evaluated at m.
scan(DoubleArrayList, double) - Static method in class umontreal.iro.lecuyer.gof.GofStat
Computes and returns the scan statistic Sn(d ), defined in FBar.scan.
ScatterChart - Class in umontreal.iro.lecuyer.charts
This class provides tools to create and manage scatter plots.
ScatterChart() - Constructor for class umontreal.iro.lecuyer.charts.ScatterChart
Initializes a new ScatterChart instance with an empty data set.
ScatterChart(String, String, String, double[][]...) - Constructor for class umontreal.iro.lecuyer.charts.ScatterChart
Initializes a new ScatterChart instance with data data.
ScatterChart(String, String, String, double[][], int) - Constructor for class umontreal.iro.lecuyer.charts.ScatterChart
Initializes a new ScatterChart instance with sets of points data.
ScatterChart(String, String, String, double[][], int, int) - Constructor for class umontreal.iro.lecuyer.charts.ScatterChart
Initializes a new ScatterChart instance using subsets of data.
ScatterChart(String, String, String, DoubleArrayList...) - Constructor for class umontreal.iro.lecuyer.charts.ScatterChart
Initializes a new ScatterChart instance with data data.
ScatterChart(String, String, String, XYSeriesCollection) - Constructor for class umontreal.iro.lecuyer.charts.ScatterChart
Initializes a new ScatterChart instance with data data.
schedule(double) - Method in class umontreal.iro.lecuyer.simevents.Event
Schedules this event to happen in delay time units, i.e., at time sim.time() + delay, by inserting it in the event list.
schedule(double) - Method in class umontreal.iro.lecuyer.simprocs.SimProcess
Schedules the process to start in delay time units.
scheduleAfter(Event) - Method in class umontreal.iro.lecuyer.simevents.Event
Schedules this event to happen just after, and at the same time, as the event other.
scheduleBefore(Event) - Method in class umontreal.iro.lecuyer.simevents.Event
Schedules this event to happen just before, and at the same time, as the event other.
scheduledEvent() - Method in class umontreal.iro.lecuyer.simprocs.SimProcess
Returns the Event associated with the current variable.
scheduleNext() - Method in class umontreal.iro.lecuyer.simevents.Event
Schedules this event as the first event in the event list, to be executed at the current time (as the next event).
scheduleNext() - Method in class umontreal.iro.lecuyer.simprocs.SimProcess
Schedules this process to start at the current time, by placing it at the beginning of the event list.
Searcher - Class in umontreal.iro.lecuyer.discrepancy
This class implements searches to find the best lattices of rank 1, defined as follows.
Searcher(Discrepancy, boolean) - Constructor for class umontreal.iro.lecuyer.discrepancy.Searcher
The number of points n, the dimension s, and possibly the s weight factors γj must be given in disc.
SearcherCBC - Class in umontreal.iro.lecuyer.discrepancy
This class implements searches to find the best rank-1 lattices with respect to a given discrepancy, using component-by-component (CBC) searches, random or exhaustive for each component.
SearcherCBC(Discrepancy, boolean) - Constructor for class umontreal.iro.lecuyer.discrepancy.SearcherCBC
The number of points n, the dimension s, and possibly the s weight factors γj must be given in disc.
SearcherKorobov - Class in umontreal.iro.lecuyer.discrepancy
SearcherKorobov
SearcherKorobov(Discrepancy, boolean) - Constructor for class umontreal.iro.lecuyer.discrepancy.SearcherKorobov
.
selectCoordinates(int[], int) - Method in class umontreal.iro.lecuyer.hups.SubsetOfPointSet
Selects the numCoord coordinates whose numbers are provided in the array coordIndices.
selectCoordinatesRange(int, int) - Method in class umontreal.iro.lecuyer.hups.SubsetOfPointSet
Selects the coordinates from ``from'' to ``to - 1'' from the original point set.
selectEuler(double) - Static method in class umontreal.iro.lecuyer.simevents.Continuous
.
selectEuler(double) - Method in class umontreal.iro.lecuyer.simevents.ContinuousState
Selects the Euler method as the integration method, with the integration step size h, in time units.
selectPoints(int[], int) - Method in class umontreal.iro.lecuyer.hups.SubsetOfPointSet
Selects the numPoints points whose numbers are provided in the array pointIndices.
selectPointsRange(int, int) - Method in class umontreal.iro.lecuyer.hups.SubsetOfPointSet
Selects the points numbered from ``from'' to ``to - 1'' from the original point set.
selectRungeKutta2(double) - Static method in class umontreal.iro.lecuyer.simevents.Continuous
.
selectRungeKutta2(double) - Method in class umontreal.iro.lecuyer.simevents.ContinuousState
Selects a Runge-Kutta method of order 2 as the integration method to be used, with step size h.
selectRungeKutta4(double) - Static method in class umontreal.iro.lecuyer.simevents.Continuous
.
selectRungeKutta4(double) - Method in class umontreal.iro.lecuyer.simevents.ContinuousState
Selects a Runge-Kutta method of order 4 as the integration method to be used, with step size h.
selfAnd(BitVector) - Method in class umontreal.iro.lecuyer.util.BitVector
.
selfNot() - Method in class umontreal.iro.lecuyer.util.BitVector
.
selfOr(BitVector) - Method in class umontreal.iro.lecuyer.util.BitVector
.
selfShift(int) - Method in class umontreal.iro.lecuyer.util.BitVector
.
selfXor(BitVector) - Method in class umontreal.iro.lecuyer.util.BitVector
.
servList() - Method in class umontreal.iro.lecuyer.simprocs.Resource
Returns the list that contains the UserRecord objects for the processes in the service list for this resource.
set(SSJXYSeriesCollection) - Method in class umontreal.iro.lecuyer.charts.MultipleDatasetChart
Sets the primary dataset for the plot, replacing the existing dataset if there is one.
set(int, SSJXYSeriesCollection) - Method in class umontreal.iro.lecuyer.charts.MultipleDatasetChart
Replaces the element at the specified position in the dataset list with the specified element.
set(int, E) - Method in class umontreal.iro.lecuyer.simevents.ListWithStat
 
set(boolean) - Method in class umontreal.iro.lecuyer.simprocs.Condition
Sets the condition to val.
set(int, E) - Method in class umontreal.iro.lecuyer.stat.list.ListOfStatProbes
 
set(int, int, E) - Method in class umontreal.iro.lecuyer.stat.matrix.MatrixOfStatProbes
Sets the statistical probe corresponding to the row r and column c to probe.
set(int, int, double) - Method in class umontreal.iro.lecuyer.util.DMatrix
.
set(int, OE) - Method in class umontreal.iro.lecuyer.util.TransformingList
 
setAntithetic(boolean) - Method in class umontreal.iro.lecuyer.rng.RandMrg
Deprecated.  
setAutoRange() - Method in class umontreal.iro.lecuyer.charts.CategoryChart
Sets chart y range to automatic values.
setAutoRange(boolean, boolean) - Method in class umontreal.iro.lecuyer.charts.HistogramChart
 
setAutoRange() - Method in class umontreal.iro.lecuyer.charts.MultipleDatasetChart
Sets chart range to automatic values.
setAutoRange() - Method in class umontreal.iro.lecuyer.charts.XYChart
The x and the y ranges of the chart are set automatically.
setAutoRange(boolean, boolean) - Method in class umontreal.iro.lecuyer.charts.XYChart
The x and the y ranges of the chart are set automatically.
setAutoRange00(boolean, boolean) - Method in class umontreal.iro.lecuyer.charts.XYChart
The x and the y ranges of the chart are set automatically.
setBandwidth(double) - Method in class umontreal.iro.lecuyer.randvar.KernelDensityGen
Sets the bandwidth to h.
setBandwidth(double) - Method in class umontreal.iro.lecuyer.randvar.KernelDensityVarCorrectGen
 
setBatchAggregation(boolean) - Method in class umontreal.iro.lecuyer.simexp.BatchMeansSim
Sets the batch aggregation indicator to a.
setBatchLengthsKeeping(boolean) - Method in class umontreal.iro.lecuyer.simexp.BatchMeansSim
Sets the batch lengths keeping indicator to b.
setBatchSize(double) - Method in class umontreal.iro.lecuyer.simexp.BatchMeansSim
Sets the batch size to batchSize.
setBeta(double[]) - Method in class umontreal.iro.lecuyer.discrepancy.Palpha
Sets the values of βj = beta[j], j = 0,…, s, where s is the dimension of the points.
setBeta(int, double) - Method in class umontreal.iro.lecuyer.stat.list.lincv.FunctionOfMultipleMeansTallyWithCV
Sets the value of βf, i.
setBeta(double[]) - Method in class umontreal.iro.lecuyer.stat.list.lincv.FunctionOfMultipleMeansTallyWithCV
Sets the value of βf vector to beta.
setBeta(DoubleMatrix2D) - Method in class umontreal.iro.lecuyer.stat.list.lincv.ListOfTalliesWithCV
Sets the β matrix to beta.
setBins(int, int) - Method in class umontreal.iro.lecuyer.charts.CustomHistogramDataset
Sets the bins for a series.
setBins(int, int, double, double) - Method in class umontreal.iro.lecuyer.charts.CustomHistogramDataset
Sets the bins for a series.
setBins(int, HistogramBin[]) - Method in class umontreal.iro.lecuyer.charts.CustomHistogramDataset
Sets the bins for a series.
setBins(int, int) - Method in class umontreal.iro.lecuyer.charts.HistogramSeriesCollection
Sets bins periodic bins from the observation minimum values to the observation maximum value for a series.
setBins(int, int, double, double) - Method in class umontreal.iro.lecuyer.charts.HistogramSeriesCollection
Sets bins periodic bins from minimum to maximum for a series.
setBins(int, HistogramBin[]) - Method in class umontreal.iro.lecuyer.charts.HistogramSeriesCollection
Links bins given by table binsTable to a series.
setBool(int, int, boolean) - Method in class umontreal.iro.lecuyer.util.BitMatrix
Changes the value of the bit in the specified row and column.
setBool(int, boolean) - Method in class umontreal.iro.lecuyer.util.BitVector
.
setBroadcasting(boolean) - Method in class umontreal.iro.lecuyer.simprocs.Condition
Instructs the condition to start or stop observation broadcasting.
setBroadcasting(boolean) - Method in class umontreal.iro.lecuyer.stat.list.ListOfStatProbes
Sets the status of the observation broadcasting mechanism to b.
setBroadcasting(boolean) - Method in class umontreal.iro.lecuyer.stat.matrix.MatrixOfStatProbes
Sets the status of the observation broadcasting mechanism to b.
setBroadcasting(boolean) - Method in class umontreal.iro.lecuyer.stat.StatProbe
.
setBrownianMotionPCA(BrownianMotionPCA) - Method in class umontreal.iro.lecuyer.stochprocess.InverseGaussianProcessPCA
Sets the brownian motion PCA.
setCachedGen(RandomVariateGen) - Method in class umontreal.iro.lecuyer.randvar.RandomVariateGenWithCache
Sets the random variate generator whose values are cached to rvg.
setCachedStream(RandomStream) - Method in class umontreal.iro.lecuyer.rng.RandomStreamWithCache
Sets the random stream whose values are cached to stream.
setCachedValues(DoubleArrayList) - Method in class umontreal.iro.lecuyer.randvar.RandomVariateGenWithCache
Sets the array list containing the cached values to values.
setCachedValues(DoubleArrayList) - Method in class umontreal.iro.lecuyer.rng.RandomStreamWithCache
Sets the array list containing the cached values to values.
setCacheIndex(int) - Method in class umontreal.iro.lecuyer.randvar.RandomVariateGenWithCache
Sets the index, in the cache, of the next value returned by nextDouble.
setCacheIndex(int) - Method in class umontreal.iro.lecuyer.rng.RandomStreamWithCache
Sets the index, in the cache, of the next value returned by nextDouble.
setCaching(boolean) - Method in class umontreal.iro.lecuyer.randvar.RandomVariateGenWithCache
Sets the caching indicator to caching.
setCaching(boolean) - Method in class umontreal.iro.lecuyer.rng.RandomStreamWithCache
Sets the caching indicator to caching.
setCapacity(int) - Method in class umontreal.iro.lecuyer.simprocs.Resource
Sets the capacity to newcap.
setChartMargin(double) - Method in class umontreal.iro.lecuyer.charts.XYChart
Sets the chart margin to margin.
setChiSquareGen(ChiSquareGen) - Method in class umontreal.iro.lecuyer.randvar.FNoncentralGen
.
setChiSquareGen(ChiSquareGen) - Method in class umontreal.iro.lecuyer.randvar.StudentNoncentralGen
Sets the chi-square generator to cgen.
setChiSquareNoncentralGen(ChiSquareNoncentralGen) - Method in class umontreal.iro.lecuyer.randvar.FNoncentralGen
.
setCoefficients(double...) - Method in class umontreal.iro.lecuyer.functions.Polynomial
Sets the array of coefficients of this polynomial to coeff.
setCollecting(boolean) - Method in class umontreal.iro.lecuyer.stat.list.ListOfStatProbes
Sets the status of the statistical collecting mechanism to c.
setCollecting(boolean) - Method in class umontreal.iro.lecuyer.stat.matrix.MatrixOfStatProbes
Sets the status of the statistical collecting mechanism to c.
setCollecting(boolean) - Method in class umontreal.iro.lecuyer.stat.StatProbe
.
setColor(int, Color) - Method in class umontreal.iro.lecuyer.charts.SSJCategorySeriesCollection
.
setColor(int, Color) - Method in class umontreal.iro.lecuyer.charts.SSJXYSeriesCollection
Sets a new plotting color to the series series.
setColumns(int) - Method in class umontreal.iro.lecuyer.stat.matrix.MatrixOfStatProbes
Similar to setRows, for setting the number of columns.
setColumnSeparator(String) - Method in class umontreal.iro.lecuyer.util.io.TextDataWriter
Changes the column separator.
setConfidenceIntervalDelta() - Method in class umontreal.iro.lecuyer.stat.FunctionOfMultipleMeansTally
Indicates that a confidence interval on the true mean, based on the delta and central limit theorems, needs to be included in reports formatted by report, and shortReport.
setConfidenceIntervalNone() - Method in class umontreal.iro.lecuyer.stat.FunctionOfMultipleMeansTally
Indicates that no confidence interval needs to be printed in reports formatted by report, and shortReport.
setConfidenceIntervalNone() - Method in class umontreal.iro.lecuyer.stat.Tally
Indicates that no confidence interval needs to be printed in reports formatted by report, and shortReport.
setConfidenceIntervalNormal() - Method in class umontreal.iro.lecuyer.stat.Tally
Indicates that a confidence interval on the true mean, based on the central limit theorem, needs to be included in reports formatted by report and shortReport.
setConfidenceIntervalStudent() - Method in class umontreal.iro.lecuyer.stat.Tally
Indicates that a confidence interval on the true mean, based on the normality assumption, needs to be included in reports formatted by report and shortReport.
setConfidenceLevel(double) - Method in class umontreal.iro.lecuyer.stat.FunctionOfMultipleMeansTally
Sets the level of confidence for the intervals on the mean displayed in reports.
setConfidenceLevel(double) - Method in class umontreal.iro.lecuyer.stat.Tally
Sets the level of confidence for the intervals on the mean displayed in reports.
setCurCoordIndex(int) - Method in class umontreal.iro.lecuyer.hups.CycleBasedPointSet.CycleBasedPointSetIterator
 
setCurCoordIndex(int) - Method in interface umontreal.iro.lecuyer.hups.PointSetIterator
.
setCurPointIndex(int) - Method in class umontreal.iro.lecuyer.hups.CycleBasedPointSet.CycleBasedPointSetIterator
 
setCurPointIndex(int) - Method in interface umontreal.iro.lecuyer.hups.PointSetIterator
.
setDashPattern(int, String) - Method in class umontreal.iro.lecuyer.charts.EmpiricalSeriesCollection
Selects dash pattern for a data series.
setDashPattern(int, String) - Method in class umontreal.iro.lecuyer.charts.XYListSeriesCollection
Selects dash pattern for a data series.
setDimension(int) - Method in class umontreal.iro.lecuyer.randvarmulti.IIDMultivariateGen
Changes the dimension of the vector to d.
setExpectedValue(int, double) - Method in class umontreal.iro.lecuyer.stat.list.lincv.FunctionOfMultipleMeansTallyWithCV
Sets the expected value of the ith component of ν to e.
setExpectedValue(int, double) - Method in class umontreal.iro.lecuyer.stat.list.lincv.ListOfTalliesWithCV
Sets the expected value of the ith control variable to e.
setExpectedValues(double[]) - Method in class umontreal.iro.lecuyer.stat.list.lincv.FunctionOfMultipleMeansTallyWithCV
Sets the vector ν to to given array exp.
setExpectedValues(double[]) - Method in class umontreal.iro.lecuyer.stat.list.lincv.ListOfTalliesWithCV
Sets E[C] to exp.
setFillBox(boolean) - Method in class umontreal.iro.lecuyer.charts.BoxChart
Sets fill to true, if the boxes are to be filled.
setFilled(int, boolean) - Method in class umontreal.iro.lecuyer.charts.HistogramSeriesCollection
Sets the filled flag.
setFloatFormatString(String) - Method in class umontreal.iro.lecuyer.util.io.TextDataWriter
Sets the format string used to output floating point numbers.
setFormat(TextDataWriter.Format) - Method in class umontreal.iro.lecuyer.util.io.TextDataWriter
Changes the output format.
setGamma(double[], int) - Method in class umontreal.iro.lecuyer.discrepancy.Discrepancy
Sets the weight factors to gam for each dimension up to s.
setGen1(RandomVariateGen) - Method in class umontreal.iro.lecuyer.randvarmulti.IIDMultivariateGen
Sets the common one-dimensional generator to gen1.
setHeaderPrefix(String) - Method in class umontreal.iro.lecuyer.util.io.TextDataWriter
Changes the header prefix (a string that indicates the beginning of the header line for the COLUMNS format).
setLabel(String) - Method in class umontreal.iro.lecuyer.charts.Axis
Sets the axis description.
setLabels(double) - Method in class umontreal.iro.lecuyer.charts.Axis
Sets a periodic label display.
setLabels(double[]) - Method in class umontreal.iro.lecuyer.charts.Axis
Sets the position of each label on this axis.
setLabels(double[], String[]) - Method in class umontreal.iro.lecuyer.charts.Axis
Assigns custom labels to user-defined positions on the axis.
setLabelsAuto() - Method in class umontreal.iro.lecuyer.charts.Axis
Calculates and sets an automatic tick unit.
setLambda(double) - Method in class umontreal.iro.lecuyer.probdist.ExponentialDist
Sets the value of λ for this object.
setLambda(double[]) - Method in class umontreal.iro.lecuyer.probdist.HypoExponentialDist
Sets the values λi =lambda[i - 1], i = 1,…, k for this object.
setLambda(double[]) - Method in class umontreal.iro.lecuyer.probdist.HypoExponentialDistQuick
 
setLambda(double) - Method in class umontreal.iro.lecuyer.probdist.PoissonDist
Sets the λ associated with this object.
setLambda(double[]) - Method in class umontreal.iro.lecuyer.randvar.HypoExponentialGen
.
setLatexDocFlag(boolean) - Method in class umontreal.iro.lecuyer.charts.CategoryChart
Same as in XYChart.
setLatexDocFlag(boolean) - Method in class umontreal.iro.lecuyer.charts.MultipleDatasetChart
Same as in XYChart.
setLatexDocFlag(boolean) - Method in class umontreal.iro.lecuyer.charts.XYChart
Flag to remove the \documentclass (and other) commands in the created LATEX files.
setLinearSeed(int[]) - Method in class umontreal.iro.lecuyer.rng.F2NL607
This method is discouraged for normal use.
setManualRange(double[]) - Method in class umontreal.iro.lecuyer.charts.MultipleDatasetChart
Sets new x-axis and y-axis bounds, with format: axisRange = [xmin, xmax, ymin, ymax].
setManualRange(double[]) - Method in class umontreal.iro.lecuyer.charts.XYChart
Sets the x and y ranges of the chart using the format: range = [xmin, xmax, ymin, ymax].
setManualRange(double[], boolean, boolean) - Method in class umontreal.iro.lecuyer.charts.XYChart
Sets the x and y ranges of the chart using the format: range = [xmin, xmax, ymin, ymax].
setManualRange00(double[], boolean, boolean) - Method in class umontreal.iro.lecuyer.charts.XYChart
Sets the x and y ranges of the chart using the format: range = [xmin, xmax, ymin, ymax].
setManuelRange(double[], boolean, boolean) - Method in class umontreal.iro.lecuyer.charts.HistogramChart
 
setMargin(double) - Method in class umontreal.iro.lecuyer.charts.HistogramSeriesCollection
Sets the margin which is a percentage amount by which the bars are trimmed for all series.
setMarksType(int, String) - Method in class umontreal.iro.lecuyer.charts.EmpiricalSeriesCollection
Adds marks on points to a data series.
setMarksType(int, String) - Method in class umontreal.iro.lecuyer.charts.XYListSeriesCollection
Adds marks on the points of a data series.
setMaxBatches(int) - Method in class umontreal.iro.lecuyer.simexp.BatchMeansSim
Sets the maximal number of batches to maxBatches.
setMaxReplications(int) - Method in class umontreal.iro.lecuyer.simexp.RepSim
Sets the maximal number of replications required before an error check to maxReps.
setMean(double) - Method in class umontreal.iro.lecuyer.probdist.ExponentialDistFromMean
Calls setLambda with argument 1/mean to change the mean of this distribution.
setMinBatches(int) - Method in class umontreal.iro.lecuyer.simexp.BatchMeansSim
Sets the minimal number of batches to minBatches.
setMinReplications(int) - Method in class umontreal.iro.lecuyer.simexp.RepSim
Sets the minimal number of replications required before an error check to minReps.
setMu(double[]) - Method in class umontreal.iro.lecuyer.randvarmulti.MultinormalGen
Sets the mean vector to mu.
setMu(int, double) - Method in class umontreal.iro.lecuyer.randvarmulti.MultinormalGen
Sets the i-th component of the mean vector to mui.
setMuGeom(double) - Method in class umontreal.iro.lecuyer.stochprocess.GeometricLevyProcess
Sets the drift parameter (interest rate) of the geometric term.
setN(int) - Method in class umontreal.iro.lecuyer.probdist.AndersonDarlingDist
.
setN(int) - Method in class umontreal.iro.lecuyer.probdist.ChiSquareDist
.
setN(int) - Method in class umontreal.iro.lecuyer.probdist.CramerVonMisesDist
.
setN(int) - Method in class umontreal.iro.lecuyer.probdist.KolmogorovSmirnovDist
Sets the parameter n of this object.
setN(int) - Method in class umontreal.iro.lecuyer.probdist.KolmogorovSmirnovPlusDist
.
setN(int) - Method in class umontreal.iro.lecuyer.probdist.StudentDist
Sets the parameter n associated with this object.
setN(int) - Method in class umontreal.iro.lecuyer.probdist.WatsonGDist
Sets the parameter n of this object.
setN(int) - Method in class umontreal.iro.lecuyer.probdist.WatsonUDist
Sets the parameter n of this object.
setName(int, String) - Method in class umontreal.iro.lecuyer.charts.XYListSeriesCollection
Sets the name of the selected series.
setName(String) - Method in class umontreal.iro.lecuyer.stat.FunctionOfMultipleMeansTally
 
setName(String) - Method in class umontreal.iro.lecuyer.stat.list.ListOfStatProbes
Sets the global name of this list to name.
setName(String) - Method in class umontreal.iro.lecuyer.stat.matrix.MatrixOfStatProbes
Sets the global name of this matrix to name.
setName(String) - Method in class umontreal.iro.lecuyer.stat.StatProbe
.
setNonLinearData(int[][]) - Static method in class umontreal.iro.lecuyer.rng.F2NL607
Selects new data for the components of the non-linear generator.
setNonLinearSeed(int[]) - Method in class umontreal.iro.lecuyer.rng.F2NL607
This method is discouraged for normal use.
setNormalGen(NormalGen) - Method in class umontreal.iro.lecuyer.randvar.StudentNoncentralGen
Sets the normal generator to ngen.
setNormalGen(NormalGen) - Method in class umontreal.iro.lecuyer.stochprocess.InverseGaussianProcessMSH
Sets the normal generator.
setNu(int) - Method in class umontreal.iro.lecuyer.probdist.ChiDist
Sets the value of ν for this object.
setNumControlVariables(int) - Method in class umontreal.iro.lecuyer.stat.list.lincv.ListOfTalliesWithCV
Sets the number of control variables to q.
setNumPoints(int) - Method in class umontreal.iro.lecuyer.hups.KorobovLattice
.
setNumPoints(int) - Method in class umontreal.iro.lecuyer.hups.Rank1Lattice
Resets the number of points of the lattice to n.
setObservationTimes(double[], int) - Method in class umontreal.iro.lecuyer.stochprocess.BrownianMotionPCAEqualSteps
 
setObservationTimes(double, int) - Method in class umontreal.iro.lecuyer.stochprocess.BrownianMotionPCAEqualSteps
 
setObservationTimes(double[], int) - Method in class umontreal.iro.lecuyer.stochprocess.GammaProcessPCA
.
setObservationTimes(double[], int) - Method in class umontreal.iro.lecuyer.stochprocess.GammaProcessPCABridge
 
setObservationTimes(double[], int) - Method in class umontreal.iro.lecuyer.stochprocess.GeometricBrownianMotion
 
setObservationTimes(double[], int) - Method in class umontreal.iro.lecuyer.stochprocess.GeometricLevyProcess
Sets the observation times on the geometric process and the underlying Lévy process.
setObservationTimes(double[], int) - Method in class umontreal.iro.lecuyer.stochprocess.InverseGaussianProcessPCA
Sets the observation times of both the InverseGaussianProcessPCA and the inner
BrownianMotionPCA.
setObservationTimes(double[], int) - Method in class umontreal.iro.lecuyer.stochprocess.MultivariateGeometricBrownianMotion
Sets the observation times of the MultivariateGeometricBrownianMotion, but also those of the inner MultivariateBrownianMotion.
setObservationTimes(double[], int) - Method in class umontreal.iro.lecuyer.stochprocess.MultivariateStochasticProcess
.
setObservationTimes(double[], int) - Method in class umontreal.iro.lecuyer.stochprocess.NormalInverseGaussianProcess
Sets the observation times on the NIG process as usual, but also sets the observation times of the underlying InverseGaussianProcess.
setObservationTimes(double[], int) - Method in class umontreal.iro.lecuyer.stochprocess.StochasticProcess
Sets the observation times of the process to a copy of T, with t0 = T[0] and td = T[d].
setObservationTimes(double, int) - Method in class umontreal.iro.lecuyer.stochprocess.StochasticProcess
Sets equidistant observation times at tj = , for j = 0,..., d, and delta = δ.
setObservationTimes(double[], int) - Method in class umontreal.iro.lecuyer.stochprocess.VarianceGammaProcess
Sets the observation times on the VarianceGammaProcess as usual, but also sets the observation times of the underlying GammaProcess.
setObservationTimes(double[], int) - Method in class umontreal.iro.lecuyer.stochprocess.VarianceGammaProcessDiff
Sets the observation times on the VarianceGammaProcesDiff as usual, but also sets the observation times of the underlying GammaProcess'es.
setOtherStream(RandomStream) - Method in class umontreal.iro.lecuyer.stochprocess.InverseGaussianProcessMSH
Sets the otherStream, which is the stream used to choose between the two roots in the MSH method.
setOutlineWidth(int, double) - Method in class umontreal.iro.lecuyer.charts.HistogramSeriesCollection
Sets the outline width in pt.
setP(double) - Method in class umontreal.iro.lecuyer.probdist.GeometricDist
Resets the value of p associated with this object.
setPackageLinearSeed(int[]) - Static method in class umontreal.iro.lecuyer.rng.F2NL607
Sets the initial seed of the linear part of the class F2NL607 to the 19 integers of the vector seed[0..18].
setPackageNonLinearSeed(int[]) - Static method in class umontreal.iro.lecuyer.rng.F2NL607
Sets the non-linear part of the initial seed of the class F2NL607 to the n integers of the vector seed[0..n-1], where n is the number of components of the non-linear part.
setPackageSeed(int[]) - Static method in class umontreal.iro.lecuyer.rng.GenF2w32
Sets the initial seed of the class GenF2w2r32 to the 25 integers of the vector seed[0..24].
setPackageSeed(int[]) - Static method in class umontreal.iro.lecuyer.rng.LFSR113
.
setPackageSeed(long[]) - Static method in class umontreal.iro.lecuyer.rng.LFSR258
Sets the initial seed for the class LFSR258 to the five integers of array seed[0..4].
setPackageSeed(int[]) - Static method in class umontreal.iro.lecuyer.rng.MRG31k3p
.
setPackageSeed(long[]) - Static method in class umontreal.iro.lecuyer.rng.MRG32k3a
Sets the initial seed for the class MRG32k3a to the six integers in the vector seed[0..5].
setPackageSeed(long[]) - Static method in class umontreal.iro.lecuyer.rng.MRG32k3aL
 
setPackageSeed(long[]) - Static method in class umontreal.iro.lecuyer.rng.RandMrg
Deprecated. Sets the initial seed for the class RandMrg to the six integers in the vector seed[0..5].
setPackageSeed(byte[]) - Static method in class umontreal.iro.lecuyer.rng.RandRijndael
.
setPackageSeed(int[]) - Static method in class umontreal.iro.lecuyer.rng.WELL1024
Sets the initial seed of this class to the 32 integers of array seed[0..31].
setPackageSeed(int[]) - Static method in class umontreal.iro.lecuyer.rng.WELL512
.
setPackageSeed(int[]) - Static method in class umontreal.iro.lecuyer.rng.WELL607
Sets the initial seed of the class WELL607 to the 19 integers of the vector seed[0..18].
setParam(double, double, int) - Method in class umontreal.iro.lecuyer.charts.ContinuousDistChart
Sets the parameters a, b and m for this object.
setParam(int, int) - Method in class umontreal.iro.lecuyer.charts.DiscreteDistIntChart
.
setParam(int, double) - Method in class umontreal.iro.lecuyer.discrepancy.DiscrepancyContainer
Sets the parameter value at index i to parmValue.
setParam(double, double) - Method in class umontreal.iro.lecuyer.probdist.InverseGammaDist
Sets the parameters α and β of this object.
setParam(double, double) - Method in class umontreal.iro.lecuyer.probdist.Pearson5Dist
Deprecated. Sets the parameters α and β of this object.
setParam(double, double, double) - Method in class umontreal.iro.lecuyer.probdist.Pearson6Dist
.
setParams(double) - Method in class umontreal.iro.lecuyer.probdist.BernoulliDist
.
setParams(double, double, double, double, int) - Method in class umontreal.iro.lecuyer.probdist.BetaDist
Deprecated. 
setParams(double, double, double, double) - Method in class umontreal.iro.lecuyer.probdist.BetaDist
Sets the parameters of the current distribution.
setParams(double, double, double, double, int) - Method in class umontreal.iro.lecuyer.probdist.BetaSymmetricalDist
 
setParams(int, double) - Method in class umontreal.iro.lecuyer.probdist.BinomialDist
Resets the parameters to these new values and recomputes everything as in the constructor.
setParams(double, double) - Method in class umontreal.iro.lecuyer.probdist.CauchyDist
.
setParams(double, double) - Method in class umontreal.iro.lecuyer.probdist.ChiSquareNoncentralDist
Sets the parameters ν = nu and λ = lambda of this object.
setParams(int, double, int) - Method in class umontreal.iro.lecuyer.probdist.ErlangDist
.
setParams(double, double) - Method in class umontreal.iro.lecuyer.probdist.ExtremeValueDist
Deprecated. .
setParams(double, double, double) - Method in class umontreal.iro.lecuyer.probdist.FatigueLifeDist
.
setParams(int, int) - Method in class umontreal.iro.lecuyer.probdist.FisherFDist
.
setParams(double, double) - Method in class umontreal.iro.lecuyer.probdist.FoldedNormalDist
Sets the parameters μ and σ for this object.
setParams(double, double, double) - Method in class umontreal.iro.lecuyer.probdist.FrechetDist
Sets the parameters α, β and δ of this object.
setParams(double, double, int) - Method in class umontreal.iro.lecuyer.probdist.GammaDist
 
setParams(double, double) - Method in class umontreal.iro.lecuyer.probdist.GumbelDist
.
setParams(double, double) - Method in class umontreal.iro.lecuyer.probdist.HalfNormalDist
Sets the parameters μ and σ.
setParams(double, double) - Method in class umontreal.iro.lecuyer.probdist.HyperbolicSecantDist
Sets the parameters μ and σ of this object.
setParams(int, int, int) - Method in class umontreal.iro.lecuyer.probdist.HypergeometricDist
Resets the parameters of this object to m, l and k.
setParams(int, int, double) - Method in class umontreal.iro.lecuyer.probdist.HypoExponentialDistEqual
 
setParams(double, double) - Method in class umontreal.iro.lecuyer.probdist.InverseGaussianDist
Sets the parameters μ and λ of this object.
setParams(double, double, double, double) - Method in class umontreal.iro.lecuyer.probdist.JohnsonSBDist
.
setParams(double, double, double, double) - Method in class umontreal.iro.lecuyer.probdist.JohnsonSLDist
Sets the value of the parameters γ, δ, ξ and λ for this object.
setParams(double, double, double, double) - Method in class umontreal.iro.lecuyer.probdist.JohnsonSUDist
Sets the value of the parameters γ, δ, ξ and λ for this object.
setParams(double, double) - Method in class umontreal.iro.lecuyer.probdist.LogisticDist
Sets the parameters α and λ of this object.
setParams(double, double) - Method in class umontreal.iro.lecuyer.probdist.LoglogisticDist
.
setParams(double, double) - Method in class umontreal.iro.lecuyer.probdist.LognormalDist
Sets the parameters μ and σ of this object.
setParams(double, double, double) - Method in class umontreal.iro.lecuyer.probdist.NakagamiDist
.
setParams(double, double) - Method in class umontreal.iro.lecuyer.probdist.NegativeBinomialDist
.
setParams(double, double) - Method in class umontreal.iro.lecuyer.probdist.NormalDist
Sets the parameters μ and σ of this object.
setParams(double, double, double, double) - Method in class umontreal.iro.lecuyer.probdist.NormalInverseGaussianDist
.
setParams(double, double) - Method in class umontreal.iro.lecuyer.probdist.ParetoDist
Sets the parameter α and β for this object.
setParams(int, double) - Method in class umontreal.iro.lecuyer.probdist.PascalDist
Sets the parameter n and p of this object.
setParams(double, double, double) - Method in class umontreal.iro.lecuyer.probdist.PowerDist
Sets the parameters a, b and c for this object.
setParams(double, double) - Method in class umontreal.iro.lecuyer.probdist.RayleighDist
.
setParams(double, double, double) - Method in class umontreal.iro.lecuyer.probdist.TriangularDist
.
setParams(ContinuousDistribution, double, double) - Method in class umontreal.iro.lecuyer.probdist.TruncatedDist
Sets the parameters dist, a and b for this object.
setParams(double, double) - Method in class umontreal.iro.lecuyer.probdist.UniformDist
.
setParams(int, int) - Method in class umontreal.iro.lecuyer.probdist.UniformIntDist
Sets the parameters i and j for this object.
setParams(double, double, double) - Method in class umontreal.iro.lecuyer.probdist.WeibullDist
.
setParams(double[]) - Method in class umontreal.iro.lecuyer.probdistmulti.DirichletDist
Sets the parameters (α1, ..., αd) of this object.
setParams(int, double[]) - Method in class umontreal.iro.lecuyer.probdistmulti.MultinomialDist
.
setParams(double[], double[][]) - Method in class umontreal.iro.lecuyer.probdistmulti.MultiNormalDist
Sets the parameters μ and Σ of this object.
setParams(double, double[]) - Method in class umontreal.iro.lecuyer.probdistmulti.NegativeMultinomialDist
Sets the parameters n and (p1, ..., pd) of this object.
setParams(double, double, double, double) - Method in class umontreal.iro.lecuyer.randvar.NormalInverseGaussianGen
.
setParams(double, double, double) - Method in class umontreal.iro.lecuyer.randvar.Pearson6Gen
Sets the parameters α1, α2 and β of this object.
setParams(double, double, double) - Method in class umontreal.iro.lecuyer.randvar.PowerGen
.
setParams(double, double) - Method in class umontreal.iro.lecuyer.randvar.RayleighGen
Sets the parameters a = a and β = beta for this object.
setParams(double, double, double) - Method in class umontreal.iro.lecuyer.randvar.WeibullGen
Sets the parameters α, λ and δ for this object.
setParams(double, double, double) - Method in class umontreal.iro.lecuyer.stochprocess.BrownianMotion
Resets the parameters X(t0) = x0, μ = mu and σ = sigma of the process.
setParams(double, double, double) - Method in class umontreal.iro.lecuyer.stochprocess.BrownianMotionPCA
 
setParams(double, double, double, double) - Method in class umontreal.iro.lecuyer.stochprocess.CIRProcess
Resets the parameters X(t0) = x0, α = alpha, b = b and σ = sigma of the process.
setParams(double, double, double, double) - Method in class umontreal.iro.lecuyer.stochprocess.CIRProcessEuler
Resets the parameters X(t0) = x0, α = alpha, b = b and σ = sigma of the process.
setParams(double, double, double) - Method in class umontreal.iro.lecuyer.stochprocess.GammaProcess
.
setParams(double, double, double) - Method in class umontreal.iro.lecuyer.stochprocess.GammaProcessPCA
.
setParams(double, double, double) - Method in class umontreal.iro.lecuyer.stochprocess.GammaProcessPCABridge
 
setParams(double, double, double) - Method in class umontreal.iro.lecuyer.stochprocess.GeometricBrownianMotion
Sets the parameters S(t0) = s0, μ = mu and σ = sigma of the process.
setParams(double, double, double, double, double) - Method in class umontreal.iro.lecuyer.stochprocess.GeometricVarianceGammaProcess
Sets the parameters S(t0) = s0, θ = theta, σ = sigma, ν = nu and μ = mu of the process.
setParams(double, double) - Method in class umontreal.iro.lecuyer.stochprocess.InverseGaussianProcess
Sets the parameters.
setParams(int, double[], double[], double[], double[][]) - Method in class umontreal.iro.lecuyer.stochprocess.MultivariateBrownianMotion
Sets the dimension c = c, the initial value X(t0) = x0, the average μ = mu, the volatility σ = sigma and the correlation matrix to corrZ.
setParams(double[], double[], double[]) - Method in class umontreal.iro.lecuyer.stochprocess.MultivariateBrownianMotion
Sets the dimension c = c, the initial value X(t0) = x0, the average μ = mu, the volatility σ = sigma.
setParams(int, double[], double[], double[], double[][]) - Method in class umontreal.iro.lecuyer.stochprocess.MultivariateBrownianMotionPCA
 
setParams(int, double[], double[], double[], double[][]) - Method in class umontreal.iro.lecuyer.stochprocess.MultivariateBrownianMotionPCABigSigma
 
setParams(int, double[], double[], double[]) - Method in class umontreal.iro.lecuyer.stochprocess.MultivariateGeometricBrownianMotion
Sets the parameters S(t0) = x0, μ = mu and σ = sigma of the process.
setParams(double, double, double, double, double) - Method in class umontreal.iro.lecuyer.stochprocess.NormalInverseGaussianProcess
Sets the parameters.
setParams(double, double, double, double) - Method in class umontreal.iro.lecuyer.stochprocess.OrnsteinUhlenbeckProcess
Resets the parameters X(t0) = x0, α = alpha, b = b and σ = sigma of the process.
setParams(double, double, double, double) - Method in class umontreal.iro.lecuyer.stochprocess.VarianceGammaProcess
Sets the parameters S(t0) = s0, θ = theta, σ = sigma and ν = nu of the process.
setPlotStyle(int, String) - Method in class umontreal.iro.lecuyer.charts.XYListSeriesCollection
Selects the plot style for a given series.
setPoints(double[][], int, int) - Method in class umontreal.iro.lecuyer.discrepancy.Discrepancy
Sets the points to points and the dimension to s.
setPoints(double[][]) - Method in class umontreal.iro.lecuyer.discrepancy.Discrepancy
Sets the points to points.
setPolicyFIFO() - Method in class umontreal.iro.lecuyer.simprocs.Bin
Sets the service policy for ordering processes waiting for tokens on the bin to FIFO (first in, first out): the processes are placed in the list (and served) according to their order of arrival.
setPolicyFIFO() - Method in class umontreal.iro.lecuyer.simprocs.Resource
Set the service policy to FIFO (first in, first out): the processes are placed in the list (and served) according to their order of arrival.
setPolicyLIFO() - Method in class umontreal.iro.lecuyer.simprocs.Bin
Sets the service policy for ordering processes waiting for tokens on the bin to LIFO (last in, first out): the processes are placed in the list (and served) according to their inverse order of arrival: the last arrived are served first.
setPolicyLIFO() - Method in class umontreal.iro.lecuyer.simprocs.Resource
Set the service policy to LIFO (last in, first out): the processes are placed in the list (and served) according to their inverse order of arrival, the last arrived are served first.
setPositiveReflection(boolean) - Method in class umontreal.iro.lecuyer.randvar.KernelDensityGen
After this method is called with true, the generator will produce only positive values, by using the reflection method: replace all negative values by their absolute values.
setPriority(double) - Method in class umontreal.iro.lecuyer.simevents.Event
Sets the priority of this event to inPriority.
setPriority(double) - Method in class umontreal.iro.lecuyer.simprocs.SimProcess
Sets the priority assigned to the current variable in the simulation.
setprobFlag(boolean) - Method in class umontreal.iro.lecuyer.charts.XYChart
Must be set true when plotting probabilities, false otherwise.
setRa(int) - Method in class umontreal.iro.lecuyer.simevents.Event
 
setRandomization(PointSetRandomization) - Method in class umontreal.iro.lecuyer.markovchain.ArrayOfComparableChains
Sets the internal PointSetRandomization to rand.
setRandomStreamClass(Class) - Method in class umontreal.iro.lecuyer.rng.BasicRandomStreamFactory
.
setRenderer(CategoryItemRenderer) - Method in class umontreal.iro.lecuyer.charts.SSJCategorySeriesCollection
.
setRenderer(XYItemRenderer) - Method in class umontreal.iro.lecuyer.charts.SSJXYSeriesCollection
Sets the XYItemRenderer object associated with the current variable.
setRows(int) - Method in class umontreal.iro.lecuyer.stat.matrix.MatrixOfStatProbes
Sets the number of rows of this matrix of statistical probes to newRows, adding or removing cells as necessary.
setScheduledEvent(Event) - Method in class umontreal.iro.lecuyer.simprocs.SimProcess
Sets the event associated to the current variable.
setScrambleData(RandomStream, int, int[]) - Static method in class umontreal.iro.lecuyer.rng.F2NL607
Selects new data for the components of the non-linear generator.
setSeed(int[]) - Method in class umontreal.iro.lecuyer.rng.GenF2w32
This method is discouraged for normal use.
setSeed(int[]) - Method in class umontreal.iro.lecuyer.rng.LFSR113
.
setSeed(long[]) - Method in class umontreal.iro.lecuyer.rng.LFSR258
This method is discouraged for normal use.
setSeed(int[]) - Method in class umontreal.iro.lecuyer.rng.MRG31k3p
.
setSeed(long[]) - Method in class umontreal.iro.lecuyer.rng.MRG32k3a
Sets the initial seed Ig of this stream to the vector seed[0..5].
setSeed(long[]) - Method in class umontreal.iro.lecuyer.rng.MRG32k3aL
 
setSeed(long[]) - Method in class umontreal.iro.lecuyer.rng.RandMrg
Deprecated. Sets the initial seed Ig of this stream to the vector seed[0..5].
setSeed(byte[]) - Method in class umontreal.iro.lecuyer.rng.RandRijndael
.
setSeed(int[]) - Method in class umontreal.iro.lecuyer.rng.WELL1024
This method is discouraged for normal use.
setSeed(int[]) - Method in class umontreal.iro.lecuyer.rng.WELL512
.
setSeed(int[]) - Method in class umontreal.iro.lecuyer.rng.WELL607
This method is discouraged for normal use.
setSeriesCollection(BoxSeriesCollection) - Method in class umontreal.iro.lecuyer.charts.BoxChart
Links a new dataset to the current chart.
setSeriesCollection(EmpiricalSeriesCollection) - Method in class umontreal.iro.lecuyer.charts.EmpiricalChart
.
setSeriesCollection(HistogramSeriesCollection) - Method in class umontreal.iro.lecuyer.charts.HistogramChart
Links a new dataset to the current chart.
setSeriesCollection(XYListSeriesCollection) - Method in class umontreal.iro.lecuyer.charts.ScatterChart
Links a new dataset to the current chart.
setSeriesCollection(XYListSeriesCollection) - Method in class umontreal.iro.lecuyer.charts.XYLineChart
Links a new dataset to the current chart.
setShowNumberObs(boolean) - Method in class umontreal.iro.lecuyer.stat.FunctionOfMultipleMeansTally
Determines if the number of observations must be displayed in reports.
setShowNumberObs(boolean) - Method in class umontreal.iro.lecuyer.stat.Tally
Determines if the number of observations must be displayed in reports.
setSigma(DoubleMatrix2D) - Method in class umontreal.iro.lecuyer.randvarmulti.MultinormalCholeskyGen
Sets the covariance matrix Σ of this multinormal generator to sigma (and recomputes A).
setSigma(DoubleMatrix2D) - Method in class umontreal.iro.lecuyer.randvarmulti.MultinormalPCAGen
Sets the covariance matrix Σ of this multinormal generator to sigma (and recomputes A).
setSimulator(Simulator) - Method in class umontreal.iro.lecuyer.simevents.Accumulate
Sets the simulator associated with this probe to sim.
setSimulator(Simulator) - Method in class umontreal.iro.lecuyer.simevents.Continuous
.
setSimulator(Simulator) - Method in class umontreal.iro.lecuyer.simevents.Event
Sets the simulator associated with this event to sim.
setSimulator(Simulator) - Method in class umontreal.iro.lecuyer.simevents.ListWithStat
Sets the simulator associated with this list.
setSimulator(Simulator) - Method in class umontreal.iro.lecuyer.simexp.SimExp
Sets the simulator associated with this experiment to sim.
setSimulator(ProcessSimulator) - Method in class umontreal.iro.lecuyer.simprocs.Bin
Set the current simulator of this continuous-time variable.
setSort(MultiDimSort) - Method in class umontreal.iro.lecuyer.markovchain.ArrayOfComparableChains
Sets MultiDimSort to sort.
setStart(double[]) - Method in class umontreal.iro.lecuyer.hups.HaltonSequence
Initializes the Halton sequence starting at point x0.
setStatCollecting(boolean) - Method in class umontreal.iro.lecuyer.simevents.ListWithStat
Starts or stops collecting statistics on this list.
setStatCollecting(boolean) - Method in class umontreal.iro.lecuyer.simprocs.Bin
Starts or stops collecting statistics on the list returned by waitList for this bin.
setStatCollecting(boolean) - Method in class umontreal.iro.lecuyer.simprocs.Resource
Starts or stops collecting statistics on the lists returned by waitList and servList for this resource.
setStatesDouble(double[]) - Method in class umontreal.iro.lecuyer.markovchain.ArrayOfDoubleChains
Sets the states of the n copies of the base chain to S.
setStream(RandomStream) - Method in class umontreal.iro.lecuyer.hups.EmptyRandomization
.
setStream(RandomStream) - Method in class umontreal.iro.lecuyer.hups.PointSet
.
setStream(RandomStream) - Method in interface umontreal.iro.lecuyer.hups.PointSetRandomization
.
setStream(RandomStream) - Method in class umontreal.iro.lecuyer.hups.RandomShift
[tabb47]
streamstream to use in the randomization
setStream(RandomStream) - Method in class umontreal.iro.lecuyer.hups.RandomStart
.
setStream(RandomStream) - Method in class umontreal.iro.lecuyer.randvar.RandomVariateGen
Sets the RandomStream used by this generator to stream.
setStream(RandomStream) - Method in class umontreal.iro.lecuyer.randvarmulti.RandomMultivariateGen
Sets the RandomStream used by this object to stream.
setStream(RandomStream) - Method in class umontreal.iro.lecuyer.stochprocess.BrownianMotion
Resets the random stream of the normal generator to stream.
setStream(RandomStream) - Method in class umontreal.iro.lecuyer.stochprocess.CIRProcess
Resets the random stream of the noncentral chi-square generator to stream.
setStream(RandomStream) - Method in class umontreal.iro.lecuyer.stochprocess.CIRProcessEuler
Resets the random stream of the normal generator to stream.
setStream(RandomStream) - Method in class umontreal.iro.lecuyer.stochprocess.GammaProcess
.
setStream(RandomStream) - Method in class umontreal.iro.lecuyer.stochprocess.GammaProcessBridge
Resets the RandomStream of the GammaGen and the BetaGen to stream.
setStream(RandomStream) - Method in class umontreal.iro.lecuyer.stochprocess.GammaProcessPCA
.
setStream(RandomStream) - Method in class umontreal.iro.lecuyer.stochprocess.GeometricBrownianMotion
Resets the RandomStream for the underlying Brownian motion to stream.
setStream(RandomStream) - Method in class umontreal.iro.lecuyer.stochprocess.GeometricLevyProcess
Resets the stream in the underlying Lévy process.
setStream(RandomStream) - Method in class umontreal.iro.lecuyer.stochprocess.GeometricVarianceGammaProcess
 
setStream(RandomStream) - Method in class umontreal.iro.lecuyer.stochprocess.InverseGaussianProcess
 
setStream(RandomStream, RandomStream) - Method in class umontreal.iro.lecuyer.stochprocess.InverseGaussianProcessBridge
Sets the streams.
setStream(RandomStream) - Method in class umontreal.iro.lecuyer.stochprocess.InverseGaussianProcessBridge
Sets both inner streams to the same stream.
setStream(RandomStream, RandomStream) - Method in class umontreal.iro.lecuyer.stochprocess.InverseGaussianProcessMSH
Sets the streams.
setStream(RandomStream) - Method in class umontreal.iro.lecuyer.stochprocess.InverseGaussianProcessMSH
Sets both inner streams to stream.
setStream(RandomStream) - Method in class umontreal.iro.lecuyer.stochprocess.InverseGaussianProcessPCA
 
setStream(RandomStream) - Method in class umontreal.iro.lecuyer.stochprocess.MultivariateBrownianMotion
Resets the random stream of the normal generator to stream.
setStream(RandomStream) - Method in class umontreal.iro.lecuyer.stochprocess.MultivariateGeometricBrownianMotion
Resets the random stream for the underlying Brownian motion to stream.
setStream(RandomStream) - Method in class umontreal.iro.lecuyer.stochprocess.NormalInverseGaussianProcess
Sets all internal streams to stream, including the stream(s) of the underlying InverseGaussianProcess.
setStream(RandomStream) - Method in class umontreal.iro.lecuyer.stochprocess.OrnsteinUhlenbeckProcess
Resets the random stream of the normal generator to stream.
setStream(RandomStream) - Method in class umontreal.iro.lecuyer.stochprocess.StochasticProcess
Resets the random stream of the underlying generator to stream.
setStream(RandomStream) - Method in class umontreal.iro.lecuyer.stochprocess.VarianceGammaProcess
Resets the RandomStream's.
setStream(RandomStream) - Method in class umontreal.iro.lecuyer.stochprocess.VarianceGammaProcessDiff
Sets the RandomStream of the two GammaProcess'es to stream.
setTargetBatches(int) - Method in class umontreal.iro.lecuyer.simexp.BatchMeansSim
Sets the target number of simulated batches before an error check or the end of the simulation to targetBatches.
setTargetReplications(int) - Method in class umontreal.iro.lecuyer.simexp.RepSim
Sets the target number of simulated replications before an error check to targetReps.
setTheta(double) - Method in class umontreal.iro.lecuyer.probdist.LogarithmicDist
.
setTicksSynchro(int) - Method in class umontreal.iro.lecuyer.charts.EmpiricalChart
.
setTicksSynchro(int) - Method in class umontreal.iro.lecuyer.charts.HistogramChart
Synchronizes x-axis ticks to the s-th histogram bins if the number of bins is not larger than 10; otherwise, choose approximately 10 ticks.
setTicksSynchro(int) - Method in class umontreal.iro.lecuyer.charts.ScatterChart
Synchronizes X-axis ticks to the s-th series x-values.
setTicksSynchro(int) - Method in class umontreal.iro.lecuyer.charts.XYChart
Synchronizes x-axis ticks to the s-th series x-values.
setTicksSynchro(int) - Method in class umontreal.iro.lecuyer.charts.XYLineChart
Synchronizes X-axis ticks to the s-th series x-values.
setTime(double) - Method in class umontreal.iro.lecuyer.simevents.Event
Sets the (planned) time of occurence of this event to time.
setTitle(String) - Method in class umontreal.iro.lecuyer.charts.CategoryChart
Sets a title to this chart.
setTitle(String) - Method in class umontreal.iro.lecuyer.charts.MultipleDatasetChart
Sets a title to the chart.
setTitle(String) - Method in class umontreal.iro.lecuyer.charts.XYChart
Sets a title to this chart.
setTwinAxisPosition(double) - Method in class umontreal.iro.lecuyer.charts.Axis
Defines where the opposite axis must be drawn on the current axis, where it should appear, and on which label.
setType(HistogramType) - Method in class umontreal.iro.lecuyer.charts.CustomHistogramDataset
Sets the histogram type and sends a DatasetChangeEvent to all registered listeners.
setUnmodifiable() - Method in class umontreal.iro.lecuyer.stat.list.ListOfStatProbes
Forbids any future modification to this list of statistical probes.
setValues(int, List) - Method in class umontreal.iro.lecuyer.charts.CustomHistogramDataset
Sets the values for a series.
setValues(int, double[]) - Method in class umontreal.iro.lecuyer.charts.CustomHistogramDataset
Sets the values for a series.
setValues(int, List) - Method in class umontreal.iro.lecuyer.charts.HistogramSeriesCollection
Sets a new values set to a series from a List variable.
setValues(int, double[]) - Method in class umontreal.iro.lecuyer.charts.HistogramSeriesCollection
Sets a new values set to a series from a table.
setWarmupTime(double) - Method in class umontreal.iro.lecuyer.simexp.BatchMeansSim
Sets the warmup time to warmupTime.
setX0(double) - Method in class umontreal.iro.lecuyer.stochprocess.StochasticProcess
Sets the initial value X(t0) for this process to s0, and reinitializes.
setXinf(double) - Method in class umontreal.iro.lecuyer.probdist.ContinuousDistribution
Sets the value xa = xa, such that the probability density is 0 everywhere outside the interval [xa, xb].
setXsup(double) - Method in class umontreal.iro.lecuyer.probdist.ContinuousDistribution
Sets the value xb = xb, such that the probability density is 0 everywhere outside the interval [xa, xb].
setZeroOverZeroValue(double) - Method in class umontreal.iro.lecuyer.util.RatioFunction
Sets the value returned by evaluate for the undefined function 0/0 to zeroOverZero.
shift(int) - Method in class umontreal.iro.lecuyer.util.BitVector
.
ShiftedMathFunction - Class in umontreal.iro.lecuyer.functions
Represents a function computing f (x) - δ for a user-defined function f (x) and shift δ.
ShiftedMathFunction(MathFunction, double) - Constructor for class umontreal.iro.lecuyer.functions.ShiftedMathFunction
Constructs a new function shifting the function func by a shift delta.
shortReport() - Method in class umontreal.iro.lecuyer.simevents.Accumulate
 
shortReport() - Method in class umontreal.iro.lecuyer.stat.FunctionOfMultipleMeansTally
Formats and returns a short statistical report for this function of multiple means tally.
shortReport() - Method in class umontreal.iro.lecuyer.stat.StatProbe
.
shortReport() - Method in class umontreal.iro.lecuyer.stat.Tally
Formats and returns a short statistical report for this tally.
shortReportHeader() - Method in class umontreal.iro.lecuyer.simevents.Accumulate
 
shortReportHeader() - Method in class umontreal.iro.lecuyer.stat.FunctionOfMultipleMeansTally
 
shortReportHeader() - Method in class umontreal.iro.lecuyer.stat.StatProbe
.
shortReportHeader() - Method in class umontreal.iro.lecuyer.stat.Tally
 
shouldBeInterpreted(Method) - Method in class umontreal.iro.lecuyer.simprocs.SSJInterpretationOracle
 
shuffle(List<?>, RandomStream) - Static method in class umontreal.iro.lecuyer.rng.RandomPermutation
 
shuffle(Object[], RandomStream) - Static method in class umontreal.iro.lecuyer.rng.RandomPermutation
.
shuffle(byte[], RandomStream) - Static method in class umontreal.iro.lecuyer.rng.RandomPermutation
[tabb77]
arraythe array being shuffled.
shuffle(short[], RandomStream) - Static method in class umontreal.iro.lecuyer.rng.RandomPermutation
.
shuffle(int[], RandomStream) - Static method in class umontreal.iro.lecuyer.rng.RandomPermutation
[tabb94]
arraythe array being shuffled.
shuffle(long[], RandomStream) - Static method in class umontreal.iro.lecuyer.rng.RandomPermutation
[tabb103]
arraythe array being shuffled.
shuffle(char[], RandomStream) - Static method in class umontreal.iro.lecuyer.rng.RandomPermutation
[tabb112]
arraythe array being shuffled.
shuffle(boolean[], RandomStream) - Static method in class umontreal.iro.lecuyer.rng.RandomPermutation
[tabb121]
arraythe array being shuffled.
shuffle(float[], RandomStream) - Static method in class umontreal.iro.lecuyer.rng.RandomPermutation
[tabb130]
arraythe array being shuffled.
shuffle(double[], RandomStream) - Static method in class umontreal.iro.lecuyer.rng.RandomPermutation
.
shuffle(List<?>, int, RandomStream) - Static method in class umontreal.iro.lecuyer.rng.RandomPermutation
 
shuffle(Object[], int, int, RandomStream) - Static method in class umontreal.iro.lecuyer.rng.RandomPermutation
.
shuffle(byte[], int, int, RandomStream) - Static method in class umontreal.iro.lecuyer.rng.RandomPermutation
.
shuffle(short[], int, int, RandomStream) - Static method in class umontreal.iro.lecuyer.rng.RandomPermutation
[tabb192]
arraythe array being shuffled.
shuffle(int[], int, int, RandomStream) - Static method in class umontreal.iro.lecuyer.rng.RandomPermutation
[tabb205]
arraythe array being shuffled.
shuffle(long[], int, int, RandomStream) - Static method in class umontreal.iro.lecuyer.rng.RandomPermutation
[tabb218]
arraythe array being shuffled.
shuffle(char[], int, int, RandomStream) - Static method in class umontreal.iro.lecuyer.rng.RandomPermutation
[tabb231]
arraythe array being shuffled.
shuffle(boolean[], int, int, RandomStream) - Static method in class umontreal.iro.lecuyer.rng.RandomPermutation
[tabb244]
arraythe array being shuffled.
shuffle(float[], int, int, RandomStream) - Static method in class umontreal.iro.lecuyer.rng.RandomPermutation
[tabb257]
arraythe array being shuffled.
shuffle(double[], int, int, RandomStream) - Static method in class umontreal.iro.lecuyer.rng.RandomPermutation
[tabb270]
arraythe array being shuffled.
Sim - Class in umontreal.iro.lecuyer.simevents
This static class contains the executive of a discrete-event simulation.
SimExp - Class in umontreal.iro.lecuyer.simexp
Represents a framework for performing experiments using simulation.
SimProcess - Class in umontreal.iro.lecuyer.simprocs
This abstract class provides process scheduling tools.
SimProcess() - Constructor for class umontreal.iro.lecuyer.simprocs.SimProcess
Constructs a new process without scheduling it and associates this new process with the default simulator; one can get additional knowledge with Simulator static methods.
SimProcess(ProcessSimulator) - Constructor for class umontreal.iro.lecuyer.simprocs.SimProcess
Constructs a new process associated with sim without scheduling it.
simpsonIntegral(MathFunction, double, double, int) - Static method in class umontreal.iro.lecuyer.functions.MathFunctionUtil
Computes and returns an approximation of the integral of func over [a, b], using the Simpson's 1/3 method with numIntervals intervals.
simulArrayRQMC(PointSet, int) - Method in class umontreal.iro.lecuyer.markovchain.ArrayOfComparableChains
Simulates the n copies of the chain, numSteps steps for each copy, using PointSet p, where n is the current number of copies (clones) of the chain and is assumed to equal the number of points in p.
simulArrayRQMC(PointSet, int) - Method in class umontreal.iro.lecuyer.markovchain.ArrayOfDoubleChains
Simulates the n copies of the chain, numSteps steps for each copy, using point set p, where n is the current number of copies of the chain and is assumed to equal the number of points in p.
simulate() - Method in class umontreal.iro.lecuyer.simexp.BatchMeansSim
Performs a batch means simulation.
simulate() - Method in class umontreal.iro.lecuyer.simexp.RepSim
Simulates several independent simulation replications of a system.
simulate() - Method in class umontreal.iro.lecuyer.simexp.SimExp
Performs an experiment whose logic depends on the used subclass.
simulateBatch() - Method in class umontreal.iro.lecuyer.simexp.BatchMeansSim
Simulate a new batch with default length.
simulateBatch(double) - Method in class umontreal.iro.lecuyer.simexp.BatchMeansSim
Simulates a batch with length batchLength.
simulateBatches() - Method in class umontreal.iro.lecuyer.simexp.BatchMeansSim
Simulates batches until the number of completed real batches corresponds to the target number of batches.
simulator() - Method in class umontreal.iro.lecuyer.simevents.Accumulate
Returns the simulator associated with this statistical probe.
simulator() - Method in class umontreal.iro.lecuyer.simevents.Continuous
.
simulator() - Method in class umontreal.iro.lecuyer.simevents.Event
Returns the simulator linked to this event.
simulator() - Method in class umontreal.iro.lecuyer.simevents.ListWithStat
Returns the simulator associated with this list.
Simulator - Class in umontreal.iro.lecuyer.simevents
Represents the executive of a discrete-event simulator.
Simulator() - Constructor for class umontreal.iro.lecuyer.simevents.Simulator
Constructs a new simulator using a splay tree for the event list.
Simulator(EventList) - Constructor for class umontreal.iro.lecuyer.simevents.Simulator
Constructs a new simulator using eventList for the event list.
simulator() - Method in class umontreal.iro.lecuyer.simexp.SimExp
Returns the simulator linked to this experiment object.
simulator() - Method in class umontreal.iro.lecuyer.simprocs.Bin
Returns the current simulator of this continuous-time variable.
simulOneStepArrayRQMC(PointSet) - Method in class umontreal.iro.lecuyer.markovchain.ArrayOfComparableChains
Simulates the n copies of the chain, one step for each copy, using PointSet p, where n is the current number of copies (clones) of the chain and is assumed to equal the number of points in p.
simulOneStepArrayRQMC(int, PointSet) - Method in class umontreal.iro.lecuyer.markovchain.ArrayOfDoubleChains
Simulate one step for the n copies of the base chain, assuming that we are at step step.
simulReplicatesArrayRQMC(PointSet, int, int, Tally) - Method in class umontreal.iro.lecuyer.markovchain.ArrayOfComparableChains
Performs m independent replications of an array-RQMC simulation as in simulArrayRQMC.
simulRQMC(PointSet, int, int, PointSetRandomization, Tally) - Method in class umontreal.iro.lecuyer.markovchain.MarkovChain
Performs m independent replicates of n simulation runs of the chain using a RQMC point set, each time storing the average of the performance over the n chains.
simulRQMC(PointSet, int, int, RandomStream, Tally) - Method in class umontreal.iro.lecuyer.markovchain.MarkovChain
Deprecated. 
simulRQMCFormat(PointSet, int, int, RandomStream, Tally) - Method in class umontreal.iro.lecuyer.markovchain.MarkovChain
Same as simulRQMC but also returns the results as a formatted string.
simulRuns(int, int, RandomStream, Tally) - Method in class umontreal.iro.lecuyer.markovchain.MarkovChain
Performs n simulation runs of the chain, for numSteps steps per run, using the given stream.
simulRunsFormat(int, int, RandomStream, Tally) - Method in class umontreal.iro.lecuyer.markovchain.MarkovChain
Same as simulRuns but also returns the results as a formatted string.
simulRunsWithSubstreams(int, int, RandomStream, Tally) - Method in class umontreal.iro.lecuyer.markovchain.MarkovChain
Same as simulRuns, except that the stream is first reset to its initial seed and then reset to the first substream at the beginning and to the next substream after each run.
simulRunsWithSubstreams(int, int, RandomStream, Tally) - Method in class umontreal.iro.lecuyer.markovchain.MarkovChainDouble
Same as simulRuns, except that the stream is first reset to its initial seed and then reset to the first substream at the beginning and to the next substream after each run.
simulRunsWithSubstreamsFormat(int, int, RandomStream, Tally) - Method in class umontreal.iro.lecuyer.markovchain.MarkovChain
Same as simulRunsWithSubstreams but also returns the results as a formatted string.
simulSteps(int, RandomStream) - Method in class umontreal.iro.lecuyer.markovchain.MarkovChain
Starts a new simulation run and simulates numSteps steps of the Markov chain or until the chain stops, using the given stream.
simulSteps(RandomStream) - Method in class umontreal.iro.lecuyer.markovchain.MarkovChain
Starts a new simulation run and simulates until the stopping time is reached, using the given stream.
simulStepsDouble(int, RandomStream) - Method in class umontreal.iro.lecuyer.markovchain.MarkovChainDouble
After invoking initialStateDouble, starts a new simulation run, simulates numSteps steps of the Markov chain using the given stream, and returns the final state.
size() - Method in class umontreal.iro.lecuyer.stat.list.ListOfStatProbes
 
size() - Method in class umontreal.iro.lecuyer.util.BitVector
.
size() - Method in class umontreal.iro.lecuyer.util.TransformingList
 
sizeWithoutCV() - Method in class umontreal.iro.lecuyer.stat.list.lincv.ListOfTalliesWithCV
Returns the size of this list excluding the control variables.
smax - Variable in class umontreal.iro.lecuyer.gof.GofStat.OutcomeCategoriesChi2
Maximum index for valid expected numbers in the array nbExp.
smin - Variable in class umontreal.iro.lecuyer.gof.GofStat.OutcomeCategoriesChi2
Minimum index for valid expected numbers in the array nbExp.
SmoothingCubicSpline - Class in umontreal.iro.lecuyer.functionfit
Represents a cubic spline with nodes at (xi, yi) computed with the smoothing cubic spline algorithm of Schoenberg.
SmoothingCubicSpline(double[], double[], double[], double) - Constructor for class umontreal.iro.lecuyer.functionfit.SmoothingCubicSpline
Constructs a spline with nodes at (xi, yi), with weights wi and smoothing factor ρ = rho.
SmoothingCubicSpline(double[], double[], double) - Constructor for class umontreal.iro.lecuyer.functionfit.SmoothingCubicSpline
Constructs a spline with nodes at (xi, yi), with weights = 1 and smoothing factor ρ = rho.
SMScrambleShift - Class in umontreal.iro.lecuyer.hups
SMScrambleShift
SMScrambleShift() - Constructor for class umontreal.iro.lecuyer.hups.SMScrambleShift
.
SMScrambleShift(RandomStream) - Constructor for class umontreal.iro.lecuyer.hups.SMScrambleShift
.
SobolSequence - Class in umontreal.iro.lecuyer.hups
SobolSequence
SobolSequence(int, int, int) - Constructor for class umontreal.iro.lecuyer.hups.SobolSequence
.
SobolSequence(int, int) - Constructor for class umontreal.iro.lecuyer.hups.SobolSequence
.
SobolSequence(String, int, int, int) - Constructor for class umontreal.iro.lecuyer.hups.SobolSequence
.
solveLU(double[][], double[]) - Static method in class umontreal.iro.lecuyer.util.DMatrix
.
solveTriangular(DoubleMatrix2D, DoubleMatrix2D, DoubleMatrix2D) - Static method in class umontreal.iro.lecuyer.util.DMatrix
.
sort(double[], int) - Static method in class umontreal.iro.lecuyer.discrepancy.Discrepancy
Sorts the first n points of T.
sort(T[], int, int) - Method in class umontreal.iro.lecuyer.util.BatchSort
 
sort(T[]) - Method in class umontreal.iro.lecuyer.util.BatchSort
 
sort(double[][], int, int) - Method in class umontreal.iro.lecuyer.util.BatchSort
 
sort(double[][]) - Method in class umontreal.iro.lecuyer.util.BatchSort
 
sort(T[], int, int) - Method in class umontreal.iro.lecuyer.util.HilbertCurveBatchSort
 
sort(T[]) - Method in class umontreal.iro.lecuyer.util.HilbertCurveBatchSort
 
sort(double[][], int, int) - Method in class umontreal.iro.lecuyer.util.HilbertCurveBatchSort
 
sort(double[][]) - Method in class umontreal.iro.lecuyer.util.HilbertCurveBatchSort
 
sort(T[], int, int) - Method in class umontreal.iro.lecuyer.util.HilbertCurveSort
 
sort(T[]) - Method in class umontreal.iro.lecuyer.util.HilbertCurveSort
 
sort(double[][], int, int) - Method in class umontreal.iro.lecuyer.util.HilbertCurveSort
 
sort(double[][]) - Method in class umontreal.iro.lecuyer.util.HilbertCurveSort
 
sort(T[], int, int) - Method in class umontreal.iro.lecuyer.util.HilbertCurveSplitSort
 
sort(T[]) - Method in class umontreal.iro.lecuyer.util.HilbertCurveSplitSort
 
sort(double[][], int, int) - Method in class umontreal.iro.lecuyer.util.HilbertCurveSplitSort
 
sort(double[][]) - Method in class umontreal.iro.lecuyer.util.HilbertCurveSplitSort
 
sort(T[], int, int) - Method in interface umontreal.iro.lecuyer.util.MultiDimSort
Sorts the subarray of a made of the elements with indices from iMin to iMax-1.
sort(T[]) - Method in interface umontreal.iro.lecuyer.util.MultiDimSort
Sorts the entire array.
sort(double[][], int, int) - Method in interface umontreal.iro.lecuyer.util.MultiDimSort
Sorts the subarray of a made of the elements with indices from iMin to iMax-1.
sort(double[][]) - Method in interface umontreal.iro.lecuyer.util.MultiDimSort
Sorts the entire array.
sort(T[], int, int) - Method in class umontreal.iro.lecuyer.util.OneDimSort
 
sort(T[]) - Method in class umontreal.iro.lecuyer.util.OneDimSort
 
sort(double[][], int, int) - Method in class umontreal.iro.lecuyer.util.OneDimSort
 
sort(double[][]) - Method in class umontreal.iro.lecuyer.util.OneDimSort
 
sort(T[], int, int) - Method in class umontreal.iro.lecuyer.util.SplitSort
 
sort(T[]) - Method in class umontreal.iro.lecuyer.util.SplitSort
 
sort(double[][], int, int) - Method in class umontreal.iro.lecuyer.util.SplitSort
 
sort(double[][]) - Method in class umontreal.iro.lecuyer.util.SplitSort
 
sortByCoordinate(int) - Method in class umontreal.iro.lecuyer.hups.CachedPointSet
.
sortChains() - Method in class umontreal.iro.lecuyer.markovchain.ArrayOfComparableChains
Sorts the chains using the stored MultiDimSort.
sortChains() - Method in class umontreal.iro.lecuyer.markovchain.ArrayOfDoubleChains
Sorts the arrays containing the states of the n chains.
SortedPointSet - Class in umontreal.iro.lecuyer.hups
SortedPointSet
SortedPointSet(PointSet, int, int, MultiDimSort) - Constructor for class umontreal.iro.lecuyer.hups.SortedPointSet
.
SortedPointSet(PointSet, MultiDimSort) - Constructor for class umontreal.iro.lecuyer.hups.SortedPointSet
.
sortMultidimBatch(int[], int) - Method in class umontreal.iro.lecuyer.hups.CachedPointSet
.
sortMultidimBatchSplit(int, int) - Method in class umontreal.iro.lecuyer.hups.CachedPointSet
.
sortNotStoppedChains() - Method in class umontreal.iro.lecuyer.markovchain.ArrayOfComparableChains
Sorts the chains that have not stopped yet using the stored MultiDimSort.
SplayTree - Class in umontreal.iro.lecuyer.simevents.eventlist
An implementation of EventList using a splay tree.
SplayTree() - Constructor for class umontreal.iro.lecuyer.simevents.eventlist.SplayTree
 
SplitSort - Class in umontreal.iro.lecuyer.util
This class implements a MultiDimSort that performs a split sort on the arrays.
SplitSort(int) - Constructor for class umontreal.iro.lecuyer.util.SplitSort
Constructs a SplitSort that will use the first d dimensions to sort.
SqrtMathFunction - Class in umontreal.iro.lecuyer.functions
Represents a function computing the square root of another function f (x).
SqrtMathFunction(MathFunction) - Constructor for class umontreal.iro.lecuyer.functions.SqrtMathFunction
Computes and returns the square root of the function func.
square(int) - Method in class umontreal.iro.lecuyer.discrepancy.DiscrepancyContainer
Squares the discrepancy values at index i.
SquareMathFunction - Class in umontreal.iro.lecuyer.functions
Represents a function computing (af (x) + b)2 for a user-defined function f (x).
SquareMathFunction(MathFunction) - Constructor for class umontreal.iro.lecuyer.functions.SquareMathFunction
Constructs a new square function for function func.
SquareMathFunction(MathFunction, double, double) - Constructor for class umontreal.iro.lecuyer.functions.SquareMathFunction
Constructs a new power function for function func, and constants a and b.
SSJCategorySeriesCollection - Class in umontreal.iro.lecuyer.charts
SSJCategorySeriesCollection
SSJCategorySeriesCollection() - Constructor for class umontreal.iro.lecuyer.charts.SSJCategorySeriesCollection
 
SSJInterpretationOracle - Class in umontreal.iro.lecuyer.simprocs
Determines which classes should be interpreted by the DSOL interpreter during process simulation.
SSJInterpretationOracle() - Constructor for class umontreal.iro.lecuyer.simprocs.SSJInterpretationOracle
 
SSJXYSeriesCollection - Class in umontreal.iro.lecuyer.charts
Stores data used in a XYChart.
SSJXYSeriesCollection() - Constructor for class umontreal.iro.lecuyer.charts.SSJXYSeriesCollection
 
standardDeviation() - Method in class umontreal.iro.lecuyer.stat.FunctionOfMultipleMeansTally
Returns the square root of variance.
standardDeviation(double[]) - Method in class umontreal.iro.lecuyer.stat.list.ListOfFunctionOfMultipleMeansTallies
For each tally in this list, computes the standard deviation, and stores it into v.
standardDeviation(double[]) - Method in class umontreal.iro.lecuyer.stat.list.ListOfTallies
For each tally in this list, computes the sample standard deviation, and stores the standard deviations into the array std.
standardDeviation(DoubleMatrix2D) - Method in class umontreal.iro.lecuyer.stat.matrix.MatrixOfFunctionOfMultipleMeansTallies
For each tally in the matrix, computes the standard deviation, and stores it into the matrix m.
standardDeviation(DoubleMatrix2D) - Method in class umontreal.iro.lecuyer.stat.matrix.MatrixOfTallies
For each tally in the matrix, computes the standard deviation, and stores it into the given matrix.
standardDeviation() - Method in class umontreal.iro.lecuyer.stat.Tally
Returns the sample standard deviation of the observations since the last initialization.
standardDeviationWithCV(double[]) - Method in class umontreal.iro.lecuyer.stat.list.lincv.ListOfTalliesWithCV
Fills the given array with the square root of the variance of each component of XC.
start() - Static method in class umontreal.iro.lecuyer.simevents.Sim
Starts the simulation executive.
start() - Method in class umontreal.iro.lecuyer.simevents.Simulator
Starts the simulation executive.
STARTING - Static variable in class umontreal.iro.lecuyer.simprocs.SimProcess
 
startInteg() - Method in class umontreal.iro.lecuyer.simevents.Continuous
.
startInteg(double) - Method in class umontreal.iro.lecuyer.simevents.Continuous
.
state() - Method in class umontreal.iro.lecuyer.simprocs.Condition
Returns the state (true or false) of the condition.
statOnAvail() - Method in class umontreal.iro.lecuyer.simprocs.Bin
Returns the statistical collector for the available tokens on the bin as a function of time.
statOnCapacity() - Method in class umontreal.iro.lecuyer.simprocs.Resource
Returns the statistical collector that measures the evolution of the capacity of the resource as a function of time.
statOnSojourn() - Method in class umontreal.iro.lecuyer.simprocs.Resource
Returns the statistical collector for the sojourn times of the UserRecord for this resource.
statOnUtil() - Method in class umontreal.iro.lecuyer.simprocs.Resource
Returns the statistical collector for the utilization of the resource (number of units busy) as a function of time.
StatProbe - Class in umontreal.iro.lecuyer.stat
StatProbe
StatProbe() - Constructor for class umontreal.iro.lecuyer.stat.StatProbe
 
statSize() - Method in class umontreal.iro.lecuyer.simevents.ListWithStat
Returns the statistical probe on the evolution of the size of the list as a function of the simulation time.
statSojourn() - Method in class umontreal.iro.lecuyer.simevents.ListWithStat
Returns the statistical probe on the sojourn times of the objects in the list.
StochasticProcess - Class in umontreal.iro.lecuyer.stochprocess
Abstract base class for a stochastic process {X(t) : t >= 0} sampled (or observed) at a finite number of time points, 0 = t0 < t1 < ...
StochasticProcess() - Constructor for class umontreal.iro.lecuyer.stochprocess.StochasticProcess
 
stop() - Static method in class umontreal.iro.lecuyer.simevents.Sim
Tells the simulation executive to stop as soon as it takes control, and to return control to the program that called start.
stop() - Method in class umontreal.iro.lecuyer.simevents.Simulator
Tells the simulation executive to stop as soon as it takes control, and to return control to the program that called start.
stopInteg() - Method in class umontreal.iro.lecuyer.simevents.Continuous
.
StratifiedUnitCube - Class in umontreal.iro.lecuyer.hups
StratifiedUnitCube
StratifiedUnitCube(int[], int) - Constructor for class umontreal.iro.lecuyer.hups.StratifiedUnitCube
.
StratifiedUnitCube(int, int) - Constructor for class umontreal.iro.lecuyer.hups.StratifiedUnitCube
.
StratifiedUnitCubeAnti - Class in umontreal.iro.lecuyer.hups
StratifiedUnitCubeAnti
StratifiedUnitCubeAnti(int[], int) - Constructor for class umontreal.iro.lecuyer.hups.StratifiedUnitCubeAnti
.
StratifiedUnitCubeAnti(int, int) - Constructor for class umontreal.iro.lecuyer.hups.StratifiedUnitCubeAnti
.
stripCoordinates(int) - Method in class umontreal.iro.lecuyer.hups.CachedPointSet
.
stripedMatrixScramble(RandomStream) - Method in class umontreal.iro.lecuyer.hups.DigitalNet
.
stripedMatrixScramble(RandomStream) - Method in class umontreal.iro.lecuyer.hups.DigitalNetBase2
 
stripedMatrixScrambleFaurePermutAll(RandomStream, int) - Method in class umontreal.iro.lecuyer.hups.DigitalNet
.
stripedMatrixScrambleFaurePermutAll(RandomStream, int) - Method in class umontreal.iro.lecuyer.hups.DigitalNetBase2
 
StudentDist - Class in umontreal.iro.lecuyer.probdist
Extends the class ContinuousDistribution for the Student t-distribution with n degrees of freedom, where n is a positive integer.
StudentDist(int) - Constructor for class umontreal.iro.lecuyer.probdist.StudentDist
Constructs a StudentDist object with n degrees of freedom.
StudentDistQuick - Class in umontreal.iro.lecuyer.probdist
StudentDistQuick
StudentDistQuick(int) - Constructor for class umontreal.iro.lecuyer.probdist.StudentDistQuick
.
StudentGen - Class in umontreal.iro.lecuyer.randvar
This class implements methods for generating random variates from the Student distribution with n > 0 degrees of freedom.
StudentGen(RandomStream, int) - Constructor for class umontreal.iro.lecuyer.randvar.StudentGen
Creates a Student random variate generator with n degrees of freedom, using stream s.
StudentGen(RandomStream, StudentDist) - Constructor for class umontreal.iro.lecuyer.randvar.StudentGen
Creates a new generator for the Student distribution dist and stream s.
StudentNoncentralGen - Class in umontreal.iro.lecuyer.randvar
This class implements random variate generators for the noncentral Student-t distribution with n > 0 degrees of freedom and noncentrality parameter δ.
StudentNoncentralGen(NormalGen, ChiSquareGen) - Constructor for class umontreal.iro.lecuyer.randvar.StudentNoncentralGen
Creates a noncentral-t random variate generator using normal generator ngen and chi-square generator cgen.
StudentPolarGen - Class in umontreal.iro.lecuyer.randvar
This class implements Student random variate generators using the polar method of.
StudentPolarGen(RandomStream, int) - Constructor for class umontreal.iro.lecuyer.randvar.StudentPolarGen
Creates a Student random variate generator with n degrees of freedom, using stream s.
StudentPolarGen(RandomStream, StudentDist) - Constructor for class umontreal.iro.lecuyer.randvar.StudentPolarGen
Creates a new generator for the Student distribution dist and stream s.
subList(int, int) - Method in class umontreal.iro.lecuyer.stat.list.ListOfStatProbes
 
subSequence(int, int) - Method in class umontreal.iro.lecuyer.util.PrintfFormat
 
SubsetOfPointSet - Class in umontreal.iro.lecuyer.hups
This container class permits one to select a subset of a point set.
SubsetOfPointSet(PointSet) - Constructor for class umontreal.iro.lecuyer.hups.SubsetOfPointSet
Constructs a new PointSet object, initially identical to P, and from which a subset of the points and/or a subset of the coordinates is to be extracted.
sum() - Method in class umontreal.iro.lecuyer.simevents.Accumulate
 
sum() - Method in class umontreal.iro.lecuyer.stat.FunctionOfMultipleMeansTally
Returns Double.NaN.
sum(double[]) - Method in class umontreal.iro.lecuyer.stat.list.ListOfStatProbes
For each probe in the list, computes the sum by calling sum, and stores the results into the array s.
sum(DoubleMatrix2D) - Method in class umontreal.iro.lecuyer.stat.matrix.MatrixOfStatProbes
For each probe in the matrix, computes the sum by calling sum, and stores it into the given matrix m.
sum() - Method in class umontreal.iro.lecuyer.stat.StatProbe
.
sumKahan(double[], int) - Static method in class umontreal.iro.lecuyer.util.Num
.
SUSPECTP - Static variable in class umontreal.iro.lecuyer.gof.GofFormat
Environment variable used in formatp1 to determine which p-values should be marked as suspect when printing test results.
suspend(SimProcess) - Method in class umontreal.iro.lecuyer.simprocs.DSOLProcessSimulator
 
suspend(SimProcess) - Method in class umontreal.iro.lecuyer.simprocs.ProcessSimulator
Suspends process.
suspend() - Method in class umontreal.iro.lecuyer.simprocs.SimProcess
This method can only be invoked for the EXECUTING or a DELAYED process.
suspend(SimProcess) - Method in class umontreal.iro.lecuyer.simprocs.ThreadProcessSimulator
 
SUSPENDED - Static variable in class umontreal.iro.lecuyer.simprocs.SimProcess
The process is not executing and will have to be reactivated by another process or event later on.
Systeme - Class in umontreal.iro.lecuyer.util
Systeme
SystemTimeChrono - Class in umontreal.iro.lecuyer.util
Extends the AbstractChrono class to compute the total system time using Java's builtin System.nanoTime.
SystemTimeChrono() - Constructor for class umontreal.iro.lecuyer.util.SystemTimeChrono
Constructs a new chrono object and initializes it to zero.

T

TableFormat - Class in umontreal.iro.lecuyer.util
TableFormat
take(int) - Method in class umontreal.iro.lecuyer.simprocs.Bin
The executing process invoking this method requests n tokens from this bin.
Tally - Class in umontreal.iro.lecuyer.stat
This type of statistical collector takes a sequence of real-valued observations and can return the average, the variance, a confidence interval for the theoretical mean, etc.
Tally() - Constructor for class umontreal.iro.lecuyer.stat.Tally
Constructs a new unnamed Tally statistical probe.
Tally(String) - Constructor for class umontreal.iro.lecuyer.stat.Tally
Constructs a new Tally statistical probe with name name.
TallyHistogram - Class in umontreal.iro.lecuyer.stat
This class is an extension of Tally which gives a more detailed view of the observations statistics.
TallyHistogram(double, double, int) - Constructor for class umontreal.iro.lecuyer.stat.TallyHistogram
Constructs a TallyHistogram statistical probe.
TallyHistogram(String, double, double, int) - Constructor for class umontreal.iro.lecuyer.stat.TallyHistogram
Constructs a new TallyHistogram statistical probe with name name.
TallyStore - Class in umontreal.iro.lecuyer.stat
This class is a variant of Tally for which the individual observations are stored in a list implemented as a DoubleArrayList.
TallyStore() - Constructor for class umontreal.iro.lecuyer.stat.TallyStore
Constructs a new TallyStore statistical probe.
TallyStore(String) - Constructor for class umontreal.iro.lecuyer.stat.TallyStore
Constructs a new TallyStore statistical probe with name name.
TallyStore(int) - Constructor for class umontreal.iro.lecuyer.stat.TallyStore
Constructs a new TallyStore statistical probe with given initial capacity capacity for its associated array.
TallyStore(String, int) - Constructor for class umontreal.iro.lecuyer.stat.TallyStore
Constructs a new TallyStore statistical probe with name name and given initial capacity capacity for its associated array.
TallyStore(DoubleArrayList) - Constructor for class umontreal.iro.lecuyer.stat.TallyStore
Constructs a new TallyStore statistical probe with given associated array.
TEN_NEG_POW - Static variable in class umontreal.iro.lecuyer.util.Num
.
testImprovementArrayRQMC(PointSet, int, int, double, Tally) - Method in class umontreal.iro.lecuyer.markovchain.ArrayOfComparableChains
Similar to simulReplicatesArrayRQMC, but also gives the variance improvement factor with respect to MC, assuming that varMC gives the variance per run for MC.
testImprovementRQMC(PointSet, int, int, RandomStream, double, Tally) - Method in class umontreal.iro.lecuyer.markovchain.MarkovChain
Similar to simulRQMCFormat, but also gives the variance improvement factor with respect to MC.
TESTNAMES - Static variable in class umontreal.iro.lecuyer.gof.GofFormat
Name of each testType test.
tests(DoubleArrayList, double[]) - Static method in class umontreal.iro.lecuyer.gof.GofFormat
Computes all EDF test statistics to compare the empirical distribution of U(0),..., U(N-1) with the uniform distribution, assuming that these sorted observations are in sortedData.
tests(DoubleArrayList, ContinuousDistribution, double[]) - Static method in class umontreal.iro.lecuyer.gof.GofFormat
The observations V are in data, not necessarily sorted, and their empirical distribution is compared with the continuous distribution dist.
tetragamma(double) - Static method in class umontreal.iro.lecuyer.util.Num
.
TextDataReader - Class in umontreal.iro.lecuyer.util
Provides static methods to read data from text files.
TextDataReader() - Constructor for class umontreal.iro.lecuyer.util.TextDataReader
 
TextDataWriter - Class in umontreal.iro.lecuyer.util.io
Text data writer.
TextDataWriter(String, TextDataWriter.Format, boolean) - Constructor for class umontreal.iro.lecuyer.util.io.TextDataWriter
Class constructor.
TextDataWriter(File, TextDataWriter.Format, boolean) - Constructor for class umontreal.iro.lecuyer.util.io.TextDataWriter
Class constructor.
TextDataWriter(OutputStream, TextDataWriter.Format, boolean) - Constructor for class umontreal.iro.lecuyer.util.io.TextDataWriter
Class constructor.
TextDataWriter.Format - Enum in umontreal.iro.lecuyer.util.io
Output format: organize fields as columns or as rows.
ThreadCPUTimeChrono - Class in umontreal.iro.lecuyer.util
Extends the AbstractChrono class to compute the CPU time for a single thread.
ThreadCPUTimeChrono() - Constructor for class umontreal.iro.lecuyer.util.ThreadCPUTimeChrono
Constructs a ThreadCPUTimeChrono object associated with current thread and initializes it to zero.
ThreadCPUTimeChrono(Thread) - Constructor for class umontreal.iro.lecuyer.util.ThreadCPUTimeChrono
Constructs a ThreadCPUTimeChrono object associated with the given Thread variable and initializes it to zero.
ThreadProcessSimulator - Class in umontreal.iro.lecuyer.simprocs
Represents a process simulator using Java threads for process synchronization.
ThreadProcessSimulator() - Constructor for class umontreal.iro.lecuyer.simprocs.ThreadProcessSimulator
Creates a new ThreadProcessSimulator variable.
time() - Method in class umontreal.iro.lecuyer.simevents.Event
Returns the (planned) time of occurence of this event.
time() - Static method in class umontreal.iro.lecuyer.simevents.Sim
Returns the current value of the simulation clock.
time() - Method in class umontreal.iro.lecuyer.simevents.Simulator
Returns the current value of the simulation clock.
TimeUnit - Enum in umontreal.iro.lecuyer.util
 
toArray(PointSet) - Static method in class umontreal.iro.lecuyer.discrepancy.Discrepancy
Returns all the n points (s-dimensional) of PointSet set as an array points[n][s].
toArray() - Method in class umontreal.iro.lecuyer.stat.list.ListOfStatProbes
 
toArray(T[]) - Method in class umontreal.iro.lecuyer.stat.list.ListOfStatProbes
 
toCSV(double[]...) - Static method in class umontreal.iro.lecuyer.charts.PlotFormat
.
toCSV(XYSeriesCollection) - Static method in class umontreal.iro.lecuyer.charts.PlotFormat
.
toCustomizedFormat(String, String, String, String, int, double[]...) - Static method in class umontreal.iro.lecuyer.charts.PlotFormat
.
toCustomizedFormat(String, String, String, String, int, XYSeriesCollection) - Static method in class umontreal.iro.lecuyer.charts.PlotFormat
.
toDatFile(String, String) - Method in class umontreal.iro.lecuyer.discrepancy.DiscrepancyContainer
Creates a file named filename.dat and writes in it the given header, a table showing the discrepancies for the different values of the parameter and the linear regression slopes.
toGNUPlot(double[]...) - Static method in class umontreal.iro.lecuyer.charts.PlotFormat
.
toGNUPlot(XYSeriesCollection) - Static method in class umontreal.iro.lecuyer.charts.PlotFormat
.
toLatex(double) - Method in class umontreal.iro.lecuyer.charts.Axis
Formats and returns a string containing a LATEX-compatible source code which represents this axis and its parameters.
toLatex(double, double) - Method in class umontreal.iro.lecuyer.charts.BoxChart
NOT IMPLEMENTED.
toLatex(double, double, double, double) - Method in class umontreal.iro.lecuyer.charts.BoxSeriesCollection
NOT IMPLEMENTED: To do.
toLatex(double, double) - Method in class umontreal.iro.lecuyer.charts.CategoryChart
Transforms the chart into LATEX form and returns it as a String.
toLatex(double, double) - Method in class umontreal.iro.lecuyer.charts.EmpiricalChart
 
toLatex(double, double, double, double, double, double, double, double) - Method in class umontreal.iro.lecuyer.charts.EmpiricalSeriesCollection
 
toLatex(double, double) - Method in class umontreal.iro.lecuyer.charts.HistogramChart
 
toLatex(double, double, double, double, double, double, double, double) - Method in class umontreal.iro.lecuyer.charts.HistogramSeriesCollection
 
toLatex(double, double) - Method in class umontreal.iro.lecuyer.charts.MultipleDatasetChart
Same as in XYChart.
toLatex(double, double) - Method in class umontreal.iro.lecuyer.charts.ScatterChart
 
toLatex(double, double, double, double) - Method in class umontreal.iro.lecuyer.charts.SSJCategorySeriesCollection
.
toLatex(double, double, double, double, double, double, double, double) - Method in class umontreal.iro.lecuyer.charts.SSJXYSeriesCollection
Formats and returns a string containing a LATEX-compatible source code which represents this data series collection.
toLatex(double, double) - Method in class umontreal.iro.lecuyer.charts.XYChart
Exports the chart to a LATEX source code using PGF/TikZ.
toLatex(double, double) - Method in class umontreal.iro.lecuyer.charts.XYLineChart
 
toLatex(double, double, double, double, double, double, double, double) - Method in class umontreal.iro.lecuyer.charts.XYListSeriesCollection
 
toLatexCdf(int, int) - Method in class umontreal.iro.lecuyer.charts.ContinuousDistChart
Exports a chart of the cdf to a LATEX source code using PGF/TikZ.
toLatexCdf(int, int) - Method in class umontreal.iro.lecuyer.charts.DiscreteDistIntChart
.
toLatexDensity(int, int) - Method in class umontreal.iro.lecuyer.charts.ContinuousDistChart
Similar to toLatexCdf, but for the probability density instead of the cdf.
toLatexFile(String, double, double) - Method in class umontreal.iro.lecuyer.charts.XYChart
Transforms the chart to LATEX form and writes it in file fileName.
toLatexProb(int, int) - Method in class umontreal.iro.lecuyer.charts.DiscreteDistIntChart
.
toNet() - Method in class umontreal.iro.lecuyer.hups.DigitalSequence
.
toNet() - Method in class umontreal.iro.lecuyer.hups.DigitalSequenceBase2
Transforms this digital sequence into a digital net without changing the coordinates of the points.
toNetShiftCj() - Method in class umontreal.iro.lecuyer.hups.DigitalSequence
.
toNetShiftCj() - Method in class umontreal.iro.lecuyer.hups.DigitalSequenceBase2
Transforms this digital sequence into a digital net by adding one dimension and shifting all coordinates by one position.
toString() - Method in class umontreal.iro.lecuyer.charts.BoxSeriesCollection
Returns in a String all data contained in the current object.
toString() - Method in class umontreal.iro.lecuyer.charts.SSJCategorySeriesCollection
.
toString() - Method in class umontreal.iro.lecuyer.charts.SSJXYSeriesCollection
Returns in a String all data contained in the current object.
toString() - Method in class umontreal.iro.lecuyer.discrepancy.Discrepancy
Returns the parameters of this class.
toString() - Method in class umontreal.iro.lecuyer.discrepancy.DiscrepancyContainer
Returns a String containing a table showing the discrepancies for the different values of the parameter.
toString() - Method in class umontreal.iro.lecuyer.discrepancy.Palpha
 
toString(double[], double[]) - Static method in class umontreal.iro.lecuyer.functionfit.PolInterp
Makes a string representation of a set of points.
toString() - Method in class umontreal.iro.lecuyer.functionfit.PolInterp
 
toString() - Method in class umontreal.iro.lecuyer.functions.Polynomial
 
toString() - Method in class umontreal.iro.lecuyer.gof.GofStat.OutcomeCategoriesChi2
Provides a report on the categories.
toString() - Method in class umontreal.iro.lecuyer.hups.AntitheticPointSet
 
toString() - Method in class umontreal.iro.lecuyer.hups.BakerTransformedPointSet
 
toString() - Method in class umontreal.iro.lecuyer.hups.CachedPointSet
 
toString() - Method in class umontreal.iro.lecuyer.hups.ContainerPointSet
 
toString() - Method in class umontreal.iro.lecuyer.hups.CycleBasedLFSR
 
toString() - Method in class umontreal.iro.lecuyer.hups.CycleBasedPointSet
 
toString() - Method in class umontreal.iro.lecuyer.hups.DigitalNet
 
toString() - Method in class umontreal.iro.lecuyer.hups.DigitalNetBase2
 
toString() - Method in class umontreal.iro.lecuyer.hups.DigitalNetBase2FromFile
 
toString() - Method in class umontreal.iro.lecuyer.hups.DigitalNetFromFile
 
toString() - Method in class umontreal.iro.lecuyer.hups.F2wCycleBasedLFSR
 
toString() - Method in class umontreal.iro.lecuyer.hups.F2wCycleBasedPolyLCG
 
toString() - Method in class umontreal.iro.lecuyer.hups.F2wNetLFSR
 
toString() - Method in class umontreal.iro.lecuyer.hups.F2wNetPolyLCG
 
toString() - Method in class umontreal.iro.lecuyer.hups.F2wStructure
.
toString() - Method in class umontreal.iro.lecuyer.hups.FaureSequence
 
toString() - Method in class umontreal.iro.lecuyer.hups.IndependentPointsCached
 
toString() - Method in class umontreal.iro.lecuyer.hups.KorobovLattice
 
toString() - Method in class umontreal.iro.lecuyer.hups.LatinHypercube
 
toString() - Method in class umontreal.iro.lecuyer.hups.LCGPointSet
 
toString() - Method in class umontreal.iro.lecuyer.hups.NiedSequenceBase2
 
toString() - Method in class umontreal.iro.lecuyer.hups.NiedXingSequenceBase2
 
toString() - Method in class umontreal.iro.lecuyer.hups.PaddedPointSet
 
toString() - Method in class umontreal.iro.lecuyer.hups.PointSet
.
toString() - Method in class umontreal.iro.lecuyer.hups.RandShiftedPointSet
 
toString() - Method in class umontreal.iro.lecuyer.hups.Rank1Lattice
 
toString() - Method in class umontreal.iro.lecuyer.hups.SobolSequence
 
toString() - Method in class umontreal.iro.lecuyer.hups.SortedPointSet
.
toString() - Method in class umontreal.iro.lecuyer.hups.StratifiedUnitCube
 
toString() - Method in class umontreal.iro.lecuyer.hups.StratifiedUnitCubeAnti
 
toString() - Method in class umontreal.iro.lecuyer.hups.SubsetOfPointSet
 
toString() - Method in class umontreal.iro.lecuyer.markovchain.ArrayOfDoubleChains
Creates a String with the states.
toString() - Method in class umontreal.iro.lecuyer.markovchain.LeftScrambledFaureSequence
Deprecated.  
toString() - Method in class umontreal.iro.lecuyer.markovchain.LeftScrambledSobolSequence
Deprecated.  
toString() - Method in class umontreal.iro.lecuyer.probdist.AndersonDarlingDist
.
toString() - Method in class umontreal.iro.lecuyer.probdist.AndersonDarlingDistQuick
 
toString() - Method in class umontreal.iro.lecuyer.probdist.BernoulliDist
.
toString() - Method in class umontreal.iro.lecuyer.probdist.BetaDist
 
toString() - Method in class umontreal.iro.lecuyer.probdist.BetaSymmetricalDist
 
toString() - Method in class umontreal.iro.lecuyer.probdist.BinomialDist
 
toString() - Method in class umontreal.iro.lecuyer.probdist.CauchyDist
.
toString() - Method in class umontreal.iro.lecuyer.probdist.ChiDist
 
toString() - Method in class umontreal.iro.lecuyer.probdist.ChiSquareDist
.
toString() - Method in class umontreal.iro.lecuyer.probdist.ChiSquareNoncentralDist
 
toString() - Method in class umontreal.iro.lecuyer.probdist.ConstantIntDist
.
toString() - Method in class umontreal.iro.lecuyer.probdist.CramerVonMisesDist
.
toString() - Method in class umontreal.iro.lecuyer.probdist.DiscreteDistribution
Returns a String containing information about the current distribution.
toString() - Method in class umontreal.iro.lecuyer.probdist.EmpiricalDist
Returns a String containing information about the current distribution.
toString() - Method in class umontreal.iro.lecuyer.probdist.ErlangDist
.
toString() - Method in class umontreal.iro.lecuyer.probdist.ExponentialDist
 
toString() - Method in class umontreal.iro.lecuyer.probdist.ExtremeValueDist
Deprecated. .
toString() - Method in class umontreal.iro.lecuyer.probdist.FatigueLifeDist
.
toString() - Method in class umontreal.iro.lecuyer.probdist.FisherFDist
.
toString() - Method in class umontreal.iro.lecuyer.probdist.FoldedNormalDist
Returns a String containing information about the current distribution.
toString() - Method in class umontreal.iro.lecuyer.probdist.FrechetDist
 
toString() - Method in class umontreal.iro.lecuyer.probdist.GammaDist
 
toString() - Method in class umontreal.iro.lecuyer.probdist.GeometricDist
 
toString() - Method in class umontreal.iro.lecuyer.probdist.GumbelDist
.
toString() - Method in class umontreal.iro.lecuyer.probdist.HalfNormalDist
Returns a String containing information about the current distribution.
toString() - Method in class umontreal.iro.lecuyer.probdist.HyperbolicSecantDist
 
toString() - Method in class umontreal.iro.lecuyer.probdist.HypergeometricDist
 
toString() - Method in class umontreal.iro.lecuyer.probdist.HypoExponentialDist
 
toString() - Method in class umontreal.iro.lecuyer.probdist.HypoExponentialDistEqual
 
toString() - Method in class umontreal.iro.lecuyer.probdist.HypoExponentialDistQuick
 
toString() - Method in class umontreal.iro.lecuyer.probdist.InverseDistFromDensity
Returns a String containing information about the current distribution.
toString() - Method in class umontreal.iro.lecuyer.probdist.InverseGammaDist
 
toString() - Method in class umontreal.iro.lecuyer.probdist.InverseGaussianDist
 
toString() - Method in class umontreal.iro.lecuyer.probdist.KolmogorovSmirnovDist
 
toString() - Method in class umontreal.iro.lecuyer.probdist.KolmogorovSmirnovPlusDist
.
toString() - Method in class umontreal.iro.lecuyer.probdist.LaplaceDist
 
toString() - Method in class umontreal.iro.lecuyer.probdist.LogarithmicDist
.
toString() - Method in class umontreal.iro.lecuyer.probdist.LogisticDist
 
toString() - Method in class umontreal.iro.lecuyer.probdist.LoglogisticDist
.
toString() - Method in class umontreal.iro.lecuyer.probdist.LognormalDist
 
toString() - Method in class umontreal.iro.lecuyer.probdist.NakagamiDist
.
toString() - Method in class umontreal.iro.lecuyer.probdist.NegativeBinomialDist
.
toString() - Method in class umontreal.iro.lecuyer.probdist.NormalDist
 
toString() - Method in class umontreal.iro.lecuyer.probdist.NormalInverseGaussianDist
.
toString() - Method in class umontreal.iro.lecuyer.probdist.ParetoDist
 
toString() - Method in class umontreal.iro.lecuyer.probdist.PascalDist
 
toString() - Method in class umontreal.iro.lecuyer.probdist.Pearson5Dist
Deprecated.  
toString() - Method in class umontreal.iro.lecuyer.probdist.Pearson6Dist
.
toString() - Method in class umontreal.iro.lecuyer.probdist.PiecewiseLinearEmpiricalDist
.
toString() - Method in class umontreal.iro.lecuyer.probdist.PoissonDist
 
toString() - Method in class umontreal.iro.lecuyer.probdist.PowerDist
 
toString() - Method in class umontreal.iro.lecuyer.probdist.RayleighDist
.
toString() - Method in class umontreal.iro.lecuyer.probdist.StudentDist
 
toString() - Method in class umontreal.iro.lecuyer.probdist.TriangularDist
.
toString() - Method in class umontreal.iro.lecuyer.probdist.TruncatedDist
Returns a String containing information about the current distribution.
toString() - Method in class umontreal.iro.lecuyer.probdist.UniformDist
.
toString() - Method in class umontreal.iro.lecuyer.probdist.UniformIntDist
 
toString() - Method in class umontreal.iro.lecuyer.probdist.WatsonGDist
 
toString() - Method in class umontreal.iro.lecuyer.probdist.WatsonUDist
 
toString() - Method in class umontreal.iro.lecuyer.probdist.WeibullDist
.
toString() - Method in class umontreal.iro.lecuyer.probdistmulti.norta.NI1
 
toString() - Method in class umontreal.iro.lecuyer.probdistmulti.norta.NI2a
 
toString() - Method in class umontreal.iro.lecuyer.probdistmulti.norta.NI2b
 
toString() - Method in class umontreal.iro.lecuyer.probdistmulti.norta.NI3
 
toString() - Method in class umontreal.iro.lecuyer.probdistmulti.norta.NortaInitDisc
 
toString() - Method in class umontreal.iro.lecuyer.randvar.RandomVariateGen
Returns a String containing information about the current generator.
toString() - Method in class umontreal.iro.lecuyer.randvarmulti.IIDMultivariateGen
Returns a string representation of the generator.
toString() - Method in class umontreal.iro.lecuyer.rng.AntitheticStream
Returns a string starting with "Antithetic of " and finishing with the result of the call to the toString method of the generator.
toString() - Method in class umontreal.iro.lecuyer.rng.BakerTransformedStream
Returns a string starting with "Baker transformation of " and finishing with the result of the call to the toString method of the generator.
toString() - Method in class umontreal.iro.lecuyer.rng.BasicRandomStreamFactory
 
toString() - Method in class umontreal.iro.lecuyer.rng.F2NL607
 
toString() - Method in class umontreal.iro.lecuyer.rng.GenF2w32
 
toString() - Method in class umontreal.iro.lecuyer.rng.LFSR113
 
toString() - Method in class umontreal.iro.lecuyer.rng.LFSR258
 
toString() - Method in class umontreal.iro.lecuyer.rng.MRG31k3p
 
toString() - Method in class umontreal.iro.lecuyer.rng.MRG32k3a
Returns a string containing the name and the current state Cg of this stream.
toString() - Method in class umontreal.iro.lecuyer.rng.MRG32k3aL
 
toString() - Method in class umontreal.iro.lecuyer.rng.MT19937
 
toString() - Method in class umontreal.iro.lecuyer.rng.RandMrg
Deprecated.  
toString() - Method in interface umontreal.iro.lecuyer.rng.RandomStream
Returns a string containing the current state of this stream.
toString() - Method in class umontreal.iro.lecuyer.rng.RandomStreamBase
 
toString() - Method in exception umontreal.iro.lecuyer.rng.RandomStreamInstantiationException
Returns a short description of the exception.
toString() - Method in class umontreal.iro.lecuyer.rng.RandomStreamManager
 
toString() - Method in class umontreal.iro.lecuyer.rng.RandRijndael
 
toString() - Method in class umontreal.iro.lecuyer.rng.WELL1024
 
toString() - Method in class umontreal.iro.lecuyer.rng.WELL512
 
toString() - Method in class umontreal.iro.lecuyer.rng.WELL607
 
toString() - Method in class umontreal.iro.lecuyer.simevents.eventlist.BinaryTree
 
toString() - Method in class umontreal.iro.lecuyer.simevents.eventlist.DoublyLinked
 
toString() - Method in class umontreal.iro.lecuyer.simevents.eventlist.Henriksen
 
toString() - Method in class umontreal.iro.lecuyer.simevents.eventlist.RedblackTree
 
toString() - Method in class umontreal.iro.lecuyer.simevents.eventlist.SplayTree
 
toString() - Method in class umontreal.iro.lecuyer.simevents.ListWithStat.Node
 
toString() - Method in class umontreal.iro.lecuyer.simexp.BatchMeansSim
 
toString() - Method in class umontreal.iro.lecuyer.simexp.RepSim
 
toString() - Method in class umontreal.iro.lecuyer.stat.TallyHistogram
Returns the bin counters as a String.
toString() - Method in class umontreal.iro.lecuyer.stat.TallyStore
Returns the observations stored in this object as a String.
toString() - Method in class umontreal.iro.lecuyer.util.BitMatrix
Creates a String containing all the data of the BitMatrix.
toString() - Method in class umontreal.iro.lecuyer.util.BitVector
.
toString(double[][]) - Static method in class umontreal.iro.lecuyer.util.DMatrix
.
toString() - Method in class umontreal.iro.lecuyer.util.DMatrix
.
toString() - Method in class umontreal.iro.lecuyer.util.OneDimSort
.
toString() - Method in class umontreal.iro.lecuyer.util.PrintfFormat
Converts the buffer into a String.
toString() - Method in enum umontreal.iro.lecuyer.util.TimeUnit
Calls getLongName.
toStringDetailed() - Method in class umontreal.iro.lecuyer.hups.DigitalNetBase2FromFile
Writes the parameters and the generating matrices of this digital net to a string.
toStringDetailed() - Method in class umontreal.iro.lecuyer.hups.DigitalNetFromFile
.
toStringFull() - Method in class umontreal.iro.lecuyer.rng.MRG32k3a
Returns a string containing the name of this stream and the values of all its internal variables.
toStringFull() - Method in class umontreal.iro.lecuyer.rng.MRG32k3aL
 
toStringFull() - Method in class umontreal.iro.lecuyer.rng.RandMrg
Deprecated. Returns a string containing the name of this stream and the values of all its internal variables.
toTexFile(String) - Method in class umontreal.iro.lecuyer.discrepancy.DiscrepancyContainer
Creates a file named filename.tex containing LATEX code that can be compiled by pdfLaTeX to a graph of the discrepancies as function of the parameter.
TransformingList<OE,IE> - Class in umontreal.iro.lecuyer.util
Represents a list that dynamically transforms the elements of another list.
TransformingList(List<IE>) - Constructor for class umontreal.iro.lecuyer.util.TransformingList
Creates a new transforming list wrapping the inner list fromList.
transpose() - Method in class umontreal.iro.lecuyer.util.BitMatrix
Returns the transposed matrix.
transpose() - Method in class umontreal.iro.lecuyer.util.DMatrix
.
TriangularDist - Class in umontreal.iro.lecuyer.probdist
TriangularDist
TriangularDist() - Constructor for class umontreal.iro.lecuyer.probdist.TriangularDist
.
TriangularDist(double) - Constructor for class umontreal.iro.lecuyer.probdist.TriangularDist
.
TriangularDist(double, double, double) - Constructor for class umontreal.iro.lecuyer.probdist.TriangularDist
.
TriangularGen - Class in umontreal.iro.lecuyer.randvar
This class implements random variate generators for the triangular distribution.
TriangularGen(RandomStream, double, double, double) - Constructor for class umontreal.iro.lecuyer.randvar.TriangularGen
Creates a triangular random variate generator over the interval (a, b), with parameter m, using stream s.
TriangularGen(RandomStream, double) - Constructor for class umontreal.iro.lecuyer.randvar.TriangularGen
Creates a triangular random variate generator over the interval (0, 1), with parameter m, using stream s.
TriangularGen(RandomStream, TriangularDist) - Constructor for class umontreal.iro.lecuyer.randvar.TriangularGen
Creates a new generator for the triangular distribution dist and stream s.
trigamma(double) - Static method in class umontreal.iro.lecuyer.util.Num
.
TruncatedDist - Class in umontreal.iro.lecuyer.probdist
This container class takes an arbitrary continuous distribution and truncates it to an interval [a, b], where a and b can be finite or infinite.
TruncatedDist(ContinuousDistribution, double, double) - Constructor for class umontreal.iro.lecuyer.probdist.TruncatedDist
Constructs a new distribution by truncating distribution dist to the interval [a, b].
TruncatedRandomStream - Class in umontreal.iro.lecuyer.rng
Represents a container random stream generating numbers in an interval (a, b) instead of in (0, 1), where 0 <= a < b <= 1, by using the contained stream.
TruncatedRandomStream(RandomStream, double, double) - Constructor for class umontreal.iro.lecuyer.rng.TruncatedRandomStream
 
TWOEXP - Static variable in class umontreal.iro.lecuyer.util.Num
.
type - Variable in class umontreal.iro.lecuyer.charts.CustomHistogramDataset
The histogram type.
TYPECHAR_DOUBLE - Static variable in class umontreal.iro.lecuyer.util.io.BinaryDataWriter
Field-type symbol indicating double data.
TYPECHAR_FLOAT - Static variable in class umontreal.iro.lecuyer.util.io.BinaryDataWriter
Field-type symbol indicating float data.
TYPECHAR_INTEGER - Static variable in class umontreal.iro.lecuyer.util.io.BinaryDataWriter
Field-type symbol indicating int data.
TYPECHAR_LABEL - Static variable in class umontreal.iro.lecuyer.util.io.BinaryDataWriter
Field-type symbol indicating a label (it more accurately a field separator symbol).
TYPECHAR_STRING - Static variable in class umontreal.iro.lecuyer.util.io.BinaryDataWriter
Field-type symbol indicating String data.

U

umontreal.iro.lecuyer.charts - package umontreal.iro.lecuyer.charts
This package contains classes to produce charts used in the Java software developed in the simulation laboratory of the DIRO, at the Universite de Montreal.
umontreal.iro.lecuyer.discrepancy - package umontreal.iro.lecuyer.discrepancy
 
umontreal.iro.lecuyer.functionfit - package umontreal.iro.lecuyer.functionfit
 
umontreal.iro.lecuyer.functions - package umontreal.iro.lecuyer.functions
 
umontreal.iro.lecuyer.gof - package umontreal.iro.lecuyer.gof
This package contains tools for performing univariate goodness-of-fit (GOF) statistical tests.
umontreal.iro.lecuyer.hups - package umontreal.iro.lecuyer.hups
Monte Carlo and quasi-Monte Carlo
umontreal.iro.lecuyer.markovchain - package umontreal.iro.lecuyer.markovchain
This package provides tools to implement and use discrete-time Markov chains (DTMC).
umontreal.iro.lecuyer.probdist - package umontreal.iro.lecuyer.probdist
This package contains a set of Java classes providing methods to compute mass, density, distribution, complementary distribution, and inverse distribution functions for some discrete and continuous probability distributions.
umontreal.iro.lecuyer.probdistmulti - package umontreal.iro.lecuyer.probdistmulti
This package contains Java classes providing methods to compute mass, density, distribution and complementary distribution functions for some multi-dimensional discrete and continuous probability distributions.
umontreal.iro.lecuyer.probdistmulti.norta - package umontreal.iro.lecuyer.probdistmulti.norta
 
umontreal.iro.lecuyer.randvar - package umontreal.iro.lecuyer.randvar
This package provides a collection of classes for non-uniform random variate generation, primarily from standard distributions.
umontreal.iro.lecuyer.randvarmulti - package umontreal.iro.lecuyer.randvarmulti
This package provides a collection of classes for non-uniform random variate generation, very similar to randvar, but for multivariate distributions.
umontreal.iro.lecuyer.rng - package umontreal.iro.lecuyer.rng
This package offers the basic facilities for generating uniform random numbers.
umontreal.iro.lecuyer.simevents - package umontreal.iro.lecuyer.simevents
This package provides the simulation clock and tools to manage the future events list.
umontreal.iro.lecuyer.simevents.eventlist - package umontreal.iro.lecuyer.simevents.eventlist
This package provides different kinds of event list implementations.
umontreal.iro.lecuyer.simexp - package umontreal.iro.lecuyer.simexp
Provides some classes to manage simulation experiments.
umontreal.iro.lecuyer.simprocs - package umontreal.iro.lecuyer.simprocs
Process-oriented simulation is managed through this package.
umontreal.iro.lecuyer.stat - package umontreal.iro.lecuyer.stat
This package provides elementary tools for collecting statistics and computing confidence intervals.
umontreal.iro.lecuyer.stat.list - package umontreal.iro.lecuyer.stat.list
Provides support for lists of statistical probes.
umontreal.iro.lecuyer.stat.list.lincv - package umontreal.iro.lecuyer.stat.list.lincv
Provides facilities for computing estimators with linear control variables.
umontreal.iro.lecuyer.stat.matrix - package umontreal.iro.lecuyer.stat.matrix
Provides facilities to construct and manage rectangular 2D arrays of statistical probes.
umontreal.iro.lecuyer.stochprocess - package umontreal.iro.lecuyer.stochprocess
This package provides classes to define stochastic processes {X(t), t≥0}, and to simulate their sample paths at a finite number of (discrete) observation times t0t1 ...
umontreal.iro.lecuyer.util - package umontreal.iro.lecuyer.util
This package contains utility classes used in the Java software developed in the simulation laboratory of the DIRO, at the Universite de Montreal.
umontreal.iro.lecuyer.util.io - package umontreal.iro.lecuyer.util.io
This package provides tools for exporting data to text and binary files, as well as for importing data from files.
UniformDist - Class in umontreal.iro.lecuyer.probdist
UniformDist
UniformDist() - Constructor for class umontreal.iro.lecuyer.probdist.UniformDist
.
UniformDist(double, double) - Constructor for class umontreal.iro.lecuyer.probdist.UniformDist
.
UniformGen - Class in umontreal.iro.lecuyer.randvar
UniformGen
UniformGen(RandomStream, double, double) - Constructor for class umontreal.iro.lecuyer.randvar.UniformGen
.
UniformGen(RandomStream) - Constructor for class umontreal.iro.lecuyer.randvar.UniformGen
.
UniformGen(RandomStream, UniformDist) - Constructor for class umontreal.iro.lecuyer.randvar.UniformGen
.
UniformIntDist - Class in umontreal.iro.lecuyer.probdist
Extends the class DiscreteDistributionInt for the discrete uniform distribution over the range [i, j].
UniformIntDist(int, int) - Constructor for class umontreal.iro.lecuyer.probdist.UniformIntDist
Constructs a discrete uniform distribution over the interval [i, j].
UniformIntGen - Class in umontreal.iro.lecuyer.randvar
This class implements a random variate generator for the uniform distribution over integers, over the interval [i, j].
UniformIntGen(RandomStream, int, int) - Constructor for class umontreal.iro.lecuyer.randvar.UniformIntGen
Creates a uniform random variate generator over the integers in the closed interval [i, j], using stream s.
UniformIntGen(RandomStream, UniformIntDist) - Constructor for class umontreal.iro.lecuyer.randvar.UniformIntGen
Creates a new generator for the distribution dist, using stream s.
unifTransform(DoubleArrayList, ContinuousDistribution) - Static method in class umontreal.iro.lecuyer.gof.GofStat
Applies the probability integral transformation Ui = F(Vi) for i = 0, 1,…, n - 1, where F is a continuous distribution function, and returns the result as an array of length n.
unifTransform(DoubleArrayList, DiscreteDistribution) - Static method in class umontreal.iro.lecuyer.gof.GofStat
Applies the transformation Ui = F(Vi) for i = 0, 1,…, n - 1, where F is a discrete distribution function, and returns the result as an array of length n.
unrandomize() - Method in class umontreal.iro.lecuyer.hups.DigitalNet
.
unrandomize() - Method in class umontreal.iro.lecuyer.hups.PaddedPointSet
 
unrandomize() - Method in class umontreal.iro.lecuyer.hups.PointSet
.
UnuranContinuous - Class in umontreal.iro.lecuyer.randvar
This class permits one to create continuous univariate generators using UNURAN via its string API.
UnuranContinuous(RandomStream, String) - Constructor for class umontreal.iro.lecuyer.randvar.UnuranContinuous
Same as UnuranContinuous(s, s, genStr).
UnuranContinuous(RandomStream, RandomStream, String) - Constructor for class umontreal.iro.lecuyer.randvar.UnuranContinuous
Constructs a new continuous random number generator using the UNURAN generator specification string genStr, main stream s, and auxiliary stream aux.
UnuranDiscreteInt - Class in umontreal.iro.lecuyer.randvar
This class permits one to create a discrete univariate generator using UNURAN via its string API.
UnuranDiscreteInt(RandomStream, String) - Constructor for class umontreal.iro.lecuyer.randvar.UnuranDiscreteInt
Same as UnuranDiscreteInt (s, s, genStr).
UnuranDiscreteInt(RandomStream, RandomStream, String) - Constructor for class umontreal.iro.lecuyer.randvar.UnuranDiscreteInt
Constructs a new discrete random number generator using the UNURAN generator specification string genStr, main stream s, and auxiliary stream aux.
UnuranEmpirical - Class in umontreal.iro.lecuyer.randvar
This class permits one to create generators for empirical and quasi-empirical univariate distributions using UNURAN via its string interface.
UnuranEmpirical(RandomStream, String) - Constructor for class umontreal.iro.lecuyer.randvar.UnuranEmpirical
Constructs a new empirical univariate generator using the specification string genStr and stream s.
UnuranEmpirical(RandomStream, RandomStream, String) - Constructor for class umontreal.iro.lecuyer.randvar.UnuranEmpirical
Constructs a new empirical univariate generator using the specification string genStr, with main stream s and auxiliary stream aux.
UnuranEmpirical(RandomStream, PiecewiseLinearEmpiricalDist, String) - Constructor for class umontreal.iro.lecuyer.randvar.UnuranEmpirical
Same as UnuranEmpirical(s, s, dist, genStr).
UnuranEmpirical(RandomStream, RandomStream, PiecewiseLinearEmpiricalDist, String) - Constructor for class umontreal.iro.lecuyer.randvar.UnuranEmpirical
Same as UnuranEmpirical(s, aux, genStr), but reading the observations from the empirical distribution dist.
UnuranException - Exception in umontreal.iro.lecuyer.randvar
This type of unchecked exception is thrown when an error occurs inside the UNURAN package.
UnuranException() - Constructor for exception umontreal.iro.lecuyer.randvar.UnuranException
Constructs a new generic UNURAN exception.
UnuranException(String) - Constructor for exception umontreal.iro.lecuyer.randvar.UnuranException
Constructs a UNURAN exception with the error message message
update() - Method in class umontreal.iro.lecuyer.simevents.Accumulate
Updates the accumulator using the last value passed to update.
update(double) - Method in class umontreal.iro.lecuyer.simevents.Accumulate
Gives a new observation x to the statistical collector.
UserRecord - Class in umontreal.iro.lecuyer.simprocs
This class represents a record object to store information related to the request of a process for a Resource or for Bin tokens, or when a process waits for a Condition.

V

value() - Method in class umontreal.iro.lecuyer.simevents.Continuous
.
valueOf(String) - Static method in enum umontreal.iro.lecuyer.simevents.ContinuousState.IntegMethod
Returns the enum constant of this type with the specified name.
valueOf(Class<T>, String) - Static method in class umontreal.iro.lecuyer.util.Introspection
Returns the field of class cls corresponding to the name name.
valueOf(String) - Static method in enum umontreal.iro.lecuyer.util.io.TextDataWriter.Format
Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum umontreal.iro.lecuyer.util.TimeUnit
Returns the enum constant of this type with the specified name.
valueOfIgnoreCase(Class<T>, String) - Static method in class umontreal.iro.lecuyer.util.Introspection
Similar to valueOf (cls, name), with case insensitive field name look-up.
values() - Static method in enum umontreal.iro.lecuyer.simevents.ContinuousState.IntegMethod
Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum umontreal.iro.lecuyer.util.io.TextDataWriter.Format
Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum umontreal.iro.lecuyer.util.TimeUnit
Returns an array containing the constants of this enum type, in the order they are declared.
variance() - Method in class umontreal.iro.lecuyer.stat.FunctionOfMultipleMeansTally
Estimates nVar(g(bar(X)n)) where n is the number of vectors of observations given to this collector since the last initialization.
variance(double[]) - Method in class umontreal.iro.lecuyer.stat.list.ListOfFunctionOfMultipleMeansTallies
For each tally in this list, computes the sample variance and stores it into v.
variance(double[]) - Method in class umontreal.iro.lecuyer.stat.list.ListOfTallies
For each tally in this list, computes the sample variance, and stores the variances into the array v.
variance(DoubleMatrix2D) - Method in class umontreal.iro.lecuyer.stat.matrix.MatrixOfFunctionOfMultipleMeansTallies
For each tally in the matrix, computes the sample variance, and stores it into the given matrix.
variance(DoubleMatrix2D) - Method in class umontreal.iro.lecuyer.stat.matrix.MatrixOfTallies
For each tally in the matrix, computes the sample variance, and stores it into the given matrix.
variance() - Method in class umontreal.iro.lecuyer.stat.Tally
Returns the sample variance of the observations since the last initialization.
VarianceGammaProcess - Class in umontreal.iro.lecuyer.stochprocess
This class represents a variance gamma (VG) process {S(t) = X(t;θ, σ, ν) : t >= 0}.
VarianceGammaProcess() - Constructor for class umontreal.iro.lecuyer.stochprocess.VarianceGammaProcess
 
VarianceGammaProcess(double, double, double, double, RandomStream) - Constructor for class umontreal.iro.lecuyer.stochprocess.VarianceGammaProcess
Constructs a new VarianceGammaProcess with parameters θ = theta, σ = sigma, ν = nu and initial value S(t0) = s0.
VarianceGammaProcess(double, BrownianMotion, GammaProcess) - Constructor for class umontreal.iro.lecuyer.stochprocess.VarianceGammaProcess
Constructs a new VarianceGammaProcess.
VarianceGammaProcessDiff - Class in umontreal.iro.lecuyer.stochprocess
This class represents a variance gamma (VG) process {S(t) = X(t;θ, σ, ν) : t >= 0}.
VarianceGammaProcessDiff(double, double, double, double, RandomStream) - Constructor for class umontreal.iro.lecuyer.stochprocess.VarianceGammaProcessDiff
Constructs a new VarianceGammaProcessDiff with parameters θ = theta, σ = sigma, ν = nu and initial value S(t0) = s0.
VarianceGammaProcessDiff(double, double, double, double, GammaProcess, GammaProcess) - Constructor for class umontreal.iro.lecuyer.stochprocess.VarianceGammaProcessDiff
The parameters of the GammaProcess objects for Γ+ and Γ- are set to those of and their initial values Γ+(t0) and Γ-(t0) are set to t0.
VarianceGammaProcessDiffPCA - Class in umontreal.iro.lecuyer.stochprocess
Same as VarianceGammaProcessDiff, but the two inner GammaProcess'es are of PCA type.
VarianceGammaProcessDiffPCA(double, double, double, double, RandomStream) - Constructor for class umontreal.iro.lecuyer.stochprocess.VarianceGammaProcessDiffPCA
Constructs a new VarianceGammaProcessDiffPCA with parameters θ = theta, σ = sigma, ν = nu and initial value S(t0) = s0.
VarianceGammaProcessDiffPCA(double, double, double, double, GammaProcessPCA, GammaProcessPCA) - Constructor for class umontreal.iro.lecuyer.stochprocess.VarianceGammaProcessDiffPCA
Constructs a new VarianceGammaProcessDiffPCA with parameters θ = theta, σ = sigma, ν = nu and initial value S(t0) = s0.
VarianceGammaProcessDiffPCABridge - Class in umontreal.iro.lecuyer.stochprocess
Same as VarianceGammaProcessDiff, but the two inner GammaProcess'es are of the type PCABridge.
VarianceGammaProcessDiffPCABridge(double, double, double, double, RandomStream) - Constructor for class umontreal.iro.lecuyer.stochprocess.VarianceGammaProcessDiffPCABridge
Constructs a new VarianceGammaProcessDiffPCABridge with parameters θ = theta, σ = sigma, ν = nu and initial value S(t0) = s0.
VarianceGammaProcessDiffPCASymmetricalBridge - Class in umontreal.iro.lecuyer.stochprocess
VarianceGammaProcessDiffPCASymmetricalBridge
VarianceGammaProcessDiffPCASymmetricalBridge(double, double, double, double, RandomStream) - Constructor for class umontreal.iro.lecuyer.stochprocess.VarianceGammaProcessDiffPCASymmetricalBridge
.
varianceWithCV(double[]) - Method in class umontreal.iro.lecuyer.stat.list.lincv.ListOfTalliesWithCV
Fills the given array with the variance of each component of XC.
view(int, int) - Method in class umontreal.iro.lecuyer.charts.BoxChart
Displays chart on the screen using Swing.
view(int, int) - Method in class umontreal.iro.lecuyer.charts.CategoryChart
 
view(int, int) - Method in class umontreal.iro.lecuyer.charts.EmpiricalChart
.
view(int, int) - Method in class umontreal.iro.lecuyer.charts.HistogramChart
Displays chart on the screen using Swing.
view(int, int) - Method in class umontreal.iro.lecuyer.charts.MultipleDatasetChart
Displays chart on the screen using Swing.
view(int, int) - Method in class umontreal.iro.lecuyer.charts.ScatterChart
Displays chart on the screen using Swing.
view(int, int) - Method in class umontreal.iro.lecuyer.charts.XYChart
 
view(int, int) - Method in class umontreal.iro.lecuyer.charts.XYLineChart
Displays chart on the screen using Swing.
viewBar(int, int) - Method in class umontreal.iro.lecuyer.charts.XYLineChart
Displays bar chart on the screen using Swing.
viewCdf(int, int) - Method in class umontreal.iro.lecuyer.charts.ContinuousDistChart
Displays a chart of the cumulative distribution function (cdf) on the screen using Swing.
viewCdf(int, int, int, int) - Method in class umontreal.iro.lecuyer.charts.DiscreteDistIntChart
.
viewCdf(int, int) - Method in class umontreal.iro.lecuyer.charts.DiscreteDistIntChart
.
viewColumn(int) - Method in class umontreal.iro.lecuyer.stat.matrix.MatrixOfStatProbes
Returns a list representing a view on column c of this matrix of statistical probe.
viewDensity(int, int) - Method in class umontreal.iro.lecuyer.charts.ContinuousDistChart
Similar to viewCdf, but for the probability density instead of the cdf.
viewProb(int, int, int, int) - Method in class umontreal.iro.lecuyer.charts.DiscreteDistIntChart
.
viewProb(int, int) - Method in class umontreal.iro.lecuyer.charts.DiscreteDistIntChart
.
viewRow(int) - Method in class umontreal.iro.lecuyer.stat.matrix.MatrixOfStatProbes
Returns a list representing a view on row r of this matrix of statistical probe.
volumeSphere(double, int) - Static method in class umontreal.iro.lecuyer.util.Num
.

W

waitFor() - Method in class umontreal.iro.lecuyer.simprocs.Condition
The executing process invoking this method must wait for this condition to be true.
WAITING - Static variable in class umontreal.iro.lecuyer.simprocs.SimProcess
 
waitList() - Method in class umontreal.iro.lecuyer.simprocs.Bin
Returns the list of UserRecord for the processes waiting for tokens from this bin.
waitList() - Method in class umontreal.iro.lecuyer.simprocs.Condition
Returns the list of UserRecord for the processes waiting for this condition.
waitList() - Method in class umontreal.iro.lecuyer.simprocs.Resource
Returns the list that contains the UserRecord objects for the processes in the waiting list for this resource.
warmup() - Method in class umontreal.iro.lecuyer.simexp.BatchMeansSim
Performs a warmup by calling warmup.
warmup(double) - Method in class umontreal.iro.lecuyer.simexp.BatchMeansSim
Performs a warmup of fixed duration warmupTime.
watsonG(DoubleArrayList) - Static method in class umontreal.iro.lecuyer.gof.GofStat
Computes and returns the Watson statistic Gn.
WatsonGDist - Class in umontreal.iro.lecuyer.probdist
Extends the class ContinuousDistribution for the Watson G distribution (see).
WatsonGDist(int) - Constructor for class umontreal.iro.lecuyer.probdist.WatsonGDist
Constructs a Watson distribution for a sample of size n.
watsonU(DoubleArrayList) - Static method in class umontreal.iro.lecuyer.gof.GofStat
Computes and returns the Watson statistic Un2.
WatsonUDist - Class in umontreal.iro.lecuyer.probdist
Extends the class ContinuousDistribution for the Watson U distribution (see).
WatsonUDist(int) - Constructor for class umontreal.iro.lecuyer.probdist.WatsonUDist
Constructs a Watson U distribution for a sample of size n.
WeibullDist - Class in umontreal.iro.lecuyer.probdist
WeibullDist
WeibullDist(double) - Constructor for class umontreal.iro.lecuyer.probdist.WeibullDist
.
WeibullDist(double, double, double) - Constructor for class umontreal.iro.lecuyer.probdist.WeibullDist
.
WeibullGen - Class in umontreal.iro.lecuyer.randvar
This class implements random variate generators for the Weibull distribution.
WeibullGen(RandomStream, double, double, double) - Constructor for class umontreal.iro.lecuyer.randvar.WeibullGen
Creates a Weibull random variate generator with parameters α = alpha, λ = lambda and δ = delta, using stream s.
WeibullGen(RandomStream, double) - Constructor for class umontreal.iro.lecuyer.randvar.WeibullGen
Creates a Weibull random variate generator with parameters α = alpha, λ = 1 and δ = 0, using stream s.
WeibullGen(RandomStream, WeibullDist) - Constructor for class umontreal.iro.lecuyer.randvar.WeibullGen
Creates a new generator for the Weibull distribution dist and stream s.
WELL1024 - Class in umontreal.iro.lecuyer.rng
Implements the RandomStream interface via inheritance from RandomStreamBase.
WELL1024() - Constructor for class umontreal.iro.lecuyer.rng.WELL1024
Constructs a new stream.
WELL1024(String) - Constructor for class umontreal.iro.lecuyer.rng.WELL1024
Constructs a new stream with the identifier name (used in the toString method).
WELL512 - Class in umontreal.iro.lecuyer.rng
WELL512
WELL512() - Constructor for class umontreal.iro.lecuyer.rng.WELL512
.
WELL512(String) - Constructor for class umontreal.iro.lecuyer.rng.WELL512
.
WELL607 - Class in umontreal.iro.lecuyer.rng
This class implements the RandomStream interface via inheritance from RandomStreamBase.
WELL607() - Constructor for class umontreal.iro.lecuyer.rng.WELL607
Constructs a new stream.
WELL607(String) - Constructor for class umontreal.iro.lecuyer.rng.WELL607
Constructs a new stream with the identifier name (used in the toString method).
WG - Static variable in class umontreal.iro.lecuyer.gof.GofFormat
Watson G test
write(String, String[]) - Method in class umontreal.iro.lecuyer.util.io.AbstractDataWriter
Writes a one-dimensional array of strings.
write(String, int[]) - Method in class umontreal.iro.lecuyer.util.io.AbstractDataWriter
Writes a one-dimensional array of 32-bit integers (big endian).
write(String, float[]) - Method in class umontreal.iro.lecuyer.util.io.AbstractDataWriter
Writes a one-dimensional array of 32-bit floats (big endian).
write(String, double[]) - Method in class umontreal.iro.lecuyer.util.io.AbstractDataWriter
Writes a one-dimensional array of 64-bit doubles (big endian).
write(String, String) - Method in class umontreal.iro.lecuyer.util.io.BinaryDataWriter
Writes an atomic string field.
write(String, int) - Method in class umontreal.iro.lecuyer.util.io.BinaryDataWriter
Writes an atomic 32-bit integer (big endian).
write(String, float) - Method in class umontreal.iro.lecuyer.util.io.BinaryDataWriter
Writes an atomic 32-bit float (big endian).
write(String, double) - Method in class umontreal.iro.lecuyer.util.io.BinaryDataWriter
Writes an atomic 64-bit double (big endian).
write(String, String[], int) - Method in class umontreal.iro.lecuyer.util.io.BinaryDataWriter
Writes the first n elements of a one-dimensional array of strings.
write(String, int[], int) - Method in class umontreal.iro.lecuyer.util.io.BinaryDataWriter
Writes the first n elements of a one-dimensional array of 32-bit integers (big endian).
write(String, float[], int) - Method in class umontreal.iro.lecuyer.util.io.BinaryDataWriter
Writes the first n elements of a one-dimensional array of 32-bit floats (big endian).
write(String, double[], int) - Method in class umontreal.iro.lecuyer.util.io.BinaryDataWriter
Writes the first n elements of a one-dimensional array of 64-bit doubles (big endian).
write(String, String[][]) - Method in class umontreal.iro.lecuyer.util.io.BinaryDataWriter
Writes a two-dimensional array of strings.
write(String, int[][]) - Method in class umontreal.iro.lecuyer.util.io.BinaryDataWriter
Writes a two-dimensional array of 32-bit integers (big endian).
write(String, float[][]) - Method in class umontreal.iro.lecuyer.util.io.BinaryDataWriter
Writes a two-dimensional array of 32-bit floats (big endian).
write(String, double[][]) - Method in class umontreal.iro.lecuyer.util.io.BinaryDataWriter
Writes a two-dimensional array of 64-bit doubles (big endian).
write(String, String) - Method in class umontreal.iro.lecuyer.util.io.CachedDataWriter
Writes an atomic string field.
write(String, int) - Method in class umontreal.iro.lecuyer.util.io.CachedDataWriter
Writes an atomic 32-bit integer (big endian).
write(String, float) - Method in class umontreal.iro.lecuyer.util.io.CachedDataWriter
Writes an atomic 32-bit float (big endian).
write(String, double) - Method in class umontreal.iro.lecuyer.util.io.CachedDataWriter
Writes an atomic 64-bit double (big endian).
write(String, String[], int) - Method in class umontreal.iro.lecuyer.util.io.CachedDataWriter
Writes the first n elements of a one-dimensional array of strings.
write(String, int[], int) - Method in class umontreal.iro.lecuyer.util.io.CachedDataWriter
Writes the first n elements of a one-dimensional array of 32-bit integers (big endian).
write(String, float[], int) - Method in class umontreal.iro.lecuyer.util.io.CachedDataWriter
Writes the first n elements of a one-dimensional array of 32-bit floats (big endian).
write(String, double[], int) - Method in class umontreal.iro.lecuyer.util.io.CachedDataWriter
Writes the first n elements of a one-dimensional array of 64-bit doubles (big endian).
write(String, String[][]) - Method in class umontreal.iro.lecuyer.util.io.CachedDataWriter
Writes a two-dimensional array of strings.
write(String, int[][]) - Method in class umontreal.iro.lecuyer.util.io.CachedDataWriter
Writes a two-dimensional array of 32-bit integers (big endian).
write(String, float[][]) - Method in class umontreal.iro.lecuyer.util.io.CachedDataWriter
Writes a two-dimensional array of 32-bit floats (big endian).
write(String, double[][]) - Method in class umontreal.iro.lecuyer.util.io.CachedDataWriter
Writes a two-dimensional array of 64-bit doubles (big endian).
write(String, String) - Method in interface umontreal.iro.lecuyer.util.io.DataWriter
Writes an atomic string field.
write(String, int) - Method in interface umontreal.iro.lecuyer.util.io.DataWriter
Writes an atomic 32-bit integer (big endian).
write(String, float) - Method in interface umontreal.iro.lecuyer.util.io.DataWriter
Writes an atomic 32-bit float (big endian).
write(String, double) - Method in interface umontreal.iro.lecuyer.util.io.DataWriter
Writes an atomic 64-bit double (big endian).
write(String, String[]) - Method in interface umontreal.iro.lecuyer.util.io.DataWriter
Writes a one-dimensional array of strings.
write(String, String[], int) - Method in interface umontreal.iro.lecuyer.util.io.DataWriter
Writes the first n elements of a one-dimensional array of strings.
write(String, int[]) - Method in interface umontreal.iro.lecuyer.util.io.DataWriter
Writes a one-dimensional array of 32-bit integers (big endian).
write(String, int[], int) - Method in interface umontreal.iro.lecuyer.util.io.DataWriter
Writes the first n elements of a one-dimensional array of 32-bit integers (big endian).
write(String, float[]) - Method in interface umontreal.iro.lecuyer.util.io.DataWriter
Writes a one-dimensional array of 32-bit floats (big endian).
write(String, float[], int) - Method in interface umontreal.iro.lecuyer.util.io.DataWriter
Writes the first n elements of a one-dimensional array of 32-bit floats (big endian).
write(String, double[]) - Method in interface umontreal.iro.lecuyer.util.io.DataWriter
Writes a one-dimensional array of 64-bit doubles (big endian).
write(String, double[], int) - Method in interface umontreal.iro.lecuyer.util.io.DataWriter
Writes the first n elements of a one-dimensional array of 64-bit doubles (big endian).
write(String, String[][]) - Method in interface umontreal.iro.lecuyer.util.io.DataWriter
Writes a two-dimensional array of strings.
write(String, int[][]) - Method in interface umontreal.iro.lecuyer.util.io.DataWriter
Writes a two-dimensional array of 32-bit integers (big endian).
write(String, float[][]) - Method in interface umontreal.iro.lecuyer.util.io.DataWriter
Writes a two-dimensional array of 32-bit floats (big endian).
write(String, double[][]) - Method in interface umontreal.iro.lecuyer.util.io.DataWriter
Writes a two-dimensional array of 64-bit doubles (big endian).
WU - Static variable in class umontreal.iro.lecuyer.gof.GofFormat
Watson U test

X

xor(BitMatrix) - Method in class umontreal.iro.lecuyer.util.BitMatrix
Returns the BitMatrix resulting from the application of the xor operator on the original BitMatrix and that.
xor(BitVector) - Method in class umontreal.iro.lecuyer.util.BitVector
.
XYChart - Class in umontreal.iro.lecuyer.charts
This class provides tools to create charts from data in a simple way.
XYChart() - Constructor for class umontreal.iro.lecuyer.charts.XYChart
 
XYLineChart - Class in umontreal.iro.lecuyer.charts
This class provides tools to create and manage curve plots.
XYLineChart() - Constructor for class umontreal.iro.lecuyer.charts.XYLineChart
Initializes a new XYLineChart instance with an empty data set.
XYLineChart(String, String, String, double[][]...) - Constructor for class umontreal.iro.lecuyer.charts.XYLineChart
Initializes a new XYLineChart instance with sets of points data.
XYLineChart(String, String, String, double[][], int) - Constructor for class umontreal.iro.lecuyer.charts.XYLineChart
Initializes a new XYLineChart instance with sets of points data.
XYLineChart(String, String, String, double[][], int, int) - Constructor for class umontreal.iro.lecuyer.charts.XYLineChart
Initializes a new XYLineChart instance using subsets of data.
XYLineChart(String, String, String, DoubleArrayList...) - Constructor for class umontreal.iro.lecuyer.charts.XYLineChart
Initializes a new XYLineChart instance with data data.
XYLineChart(String, String, String, XYSeriesCollection) - Constructor for class umontreal.iro.lecuyer.charts.XYLineChart
Initializes a new XYLineChart instance with data data.
XYListSeriesCollection - Class in umontreal.iro.lecuyer.charts
This class extends SSJXYSeriesCollection.
XYListSeriesCollection() - Constructor for class umontreal.iro.lecuyer.charts.XYListSeriesCollection
Creates a new XYListSeriesCollection instance with an empty dataset.
XYListSeriesCollection(double[][]...) - Constructor for class umontreal.iro.lecuyer.charts.XYListSeriesCollection
Creates a new XYListSeriesCollection instance with default parameters and given data series.
XYListSeriesCollection(double[][], int) - Constructor for class umontreal.iro.lecuyer.charts.XYListSeriesCollection
Creates a new XYListSeriesCollection instance with default parameters and given points data.
XYListSeriesCollection(DoubleArrayList...) - Constructor for class umontreal.iro.lecuyer.charts.XYListSeriesCollection
Creates a new XYListSeriesCollection instance with default parameters and given data.
XYListSeriesCollection(XYSeriesCollection) - Constructor for class umontreal.iro.lecuyer.charts.XYListSeriesCollection
Creates a new XYListSeriesCollection instance with default parameters and given data series.

Y

YListChart - Class in umontreal.iro.lecuyer.charts
This class extends the class XYLineChart.
YListChart() - Constructor for class umontreal.iro.lecuyer.charts.YListChart
Empty constructor.
YListChart(String, String, String, double[]...) - Constructor for class umontreal.iro.lecuyer.charts.YListChart
Initializes a new YListChart instance with set of points data.
YListChart(String, String, String, boolean, double[]...) - Constructor for class umontreal.iro.lecuyer.charts.YListChart
Similar to the constructor above.
YListChart(String, String, String, double[], int) - Constructor for class umontreal.iro.lecuyer.charts.YListChart
Initializes a new YListChart instance with a set of points data.
YListChart(String, String, String, double, double[], int) - Constructor for class umontreal.iro.lecuyer.charts.YListChart
Similar to the constructor above, but the points are (h(j + 1), data[j]).
YListChart(String, String, String, double[][], int) - Constructor for class umontreal.iro.lecuyer.charts.YListChart
Initializes a new YListChart instance with set of points data.
YListSeriesCollection - Class in umontreal.iro.lecuyer.charts
YListSeriesCollection
YListSeriesCollection(double[]...) - Constructor for class umontreal.iro.lecuyer.charts.YListSeriesCollection
.
YListSeriesCollection(boolean, double[]...) - Constructor for class umontreal.iro.lecuyer.charts.YListSeriesCollection
.
YListSeriesCollection(double[], int) - Constructor for class umontreal.iro.lecuyer.charts.YListSeriesCollection
.
YListSeriesCollection(double, double[], int) - Constructor for class umontreal.iro.lecuyer.charts.YListSeriesCollection
.
YListSeriesCollection(double[][], int) - Constructor for class umontreal.iro.lecuyer.charts.YListSeriesCollection
.

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
SSJ
V. labo.

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