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StatProbe,
 for collecting statistics on a
 variable that evolves in simulation time, with a piecewise-constant trajectory.activeTests, formatActiveTests, etc.
tests, then computes the p-values of those
   that currently belong to activeTests,
 and return these quantities in sVal and pVal, respectively.
add, but adds the new event ev
   immediately after the event other in the list.
add, but adds the new event ev
   immediately before the event other in the list.
addRandomShift(0, dim, stream),
   where dim is the dimension of the digital net.
andersonDarling).
ContinuousDistribution for the
 Anderson-Darling  distribution (see).AndersonDarlingDist for the
   distribution (see).RandomStream to 
 return antithetic variates.s static method to append str to the buffer.
f static method to append x to the buffer.
f static method to append x to the buffer.
d static method to append x to the buffer.
d static method to append x to the buffer.
format
    static method with the same four arguments to append x to the buffer.
isDouble.
isFloat.
isInt.
average, and stores
  the results into the array a.
RandomStream.barF (alpha, beta, 0, 1, x).
barF (alpha, 1.0, d, x).
NormalDist.barF01.
barF (0, 1, x).
barF (0.0, 1.0, x).
RandomStream implementation via the
 newInstance method.DiscreteDistributionInt for the Bernoulli
 distribution with parameter p, where 
 0 <= p <= 1.BernoulliDist).ContinuousDistribution for
 the beta distribution with shape parameters
 
 α > 0 and β > 0, over the interval [a, b], where a < b.BetaRejectionLoglogisticGen (s, s, dist).
BetaStratifiedRejectionGen(s, s, dist).
BetaDist to the case of a symmetrical
 beta distribution over the interval [0, 1],
 with shape parameters 
 α = β.EventList using a binary search tree.DiscreteDistributionInt for the
 binomial distribution with parameters n and p, where
 n is a positive integer and 
 0 <= p <= 1.ContinuousDistribution2Dim for the bivariate 
 normal distribution.BiNormalDist for the bivariate 
   normal distribution
   using a translation of Donnelly's FORTRAN code.BiNormalDonnellyDist (rho, 15).
BiNormalDonnellyDist (mu1, sigma1, mu2, sigma2, rho, 15).
BiNormalDist for the bivariate
 normal distribution
    using Genz's algorithm as described in.ContinuousDistribution2Dim for the  standard  bivariate Student's t distribution.CategoryChart.close.ContinuousDistribution for
 the Cauchy distribution
 with location parameter α
 and scale parameter β > 0.cdf (alpha, beta, 0, 1, x).
cdf (alpha, alpha, d, x).
GumbelDist.
NormalDist.cdf01.
cdf (0, 1, x).
cdf (0.0, 1.0, x).
chi2,
    except that the expected
   number of observations per category is assumed to be the same for
   all categories, and equal to nbExp.
ContinuousDistribution for the chi
   distribution with shape parameter
  v > 0,  where the number of degrees of freedom
  v is a positive integer.ContinuousDistribution for
 the chi-square distribution with n degrees of freedom,
 where n is a positive integer.ChiSquareDist with
 faster but less accurate methods.ContinuousDistribution for
 the noncentral chi-square distribution with ν degrees of freedom
  and noncentrality parameter λ, where ν > 0 and 
 λ > 0.Chrono class extends the 
 AbstractChrono 
 class and computes the CPU time for the current thread only.CloneableRandomStream extends RandomStream and Cloneable.computeDensity ,
 but the bandwidth h is obtained from the method
 KernelDensityGen.getBaseBandwidth(dist) in package randvar.
connectToDatabase (url.openStream()).
connectToDatabase (new FileInputStream (file)).
connectToDatabase (new FileInputStream (fileName)).
connectToDatabase with the stream obtained from
    the resource resource.
covariance for computing
  the sample correlation matrix.
ContinuousDistribution for the
 Cramér-von Mises distribution (see).Event object used for synchronization.
Tally.
Tally.
TallyStore.
TallyStore.
HistogramType.FREQUENCY.
CycleBasedPointSet, except that the successive
 values in the cycles are stored as integers in the range
 
 {0,..., 2k -1}, where 
 1 <= k <= 31.d (0, 1, x).
d (fieldwidth, 1, x).
DataReader.Sim, and the no-argument constructor of Event.
density (alpha, beta, 0, 1, x).
density (0, 1, x).
diff(IntArrayList,IntArrayList,int,int,int,int),
    but for the continuous case.
DigitalNet for the base b = 2.DigitalNetBase2FromFile(filename, r, 31, s1) where
    s1 is the dimension and r is given in data file filename.
DigitalNetFromFile(filename, r, r, s) where
    s is the dimension and  r is given in data file filename.
ContinuousDistributionMulti for the
 Dirichlet distribution with parameters
 (α1,...,αd), 
 αi > 0.RandomMultivariateGen for a
 Dirichlet distribution.DiscreteDistribution(double[], double[], int).
Comparator which compares two
 double arrays by comparing their i-th element,
 where i is given in the constructor.EventList using a doubly linked linear list.String.
String.
E (0, 6, x).
E (fieldwidth, 6, x).
e (0, 6, x).
e (fieldwidth, 6, x).
TallyStore object.
DiscreteDistribution to an empirical
 distribution function,
 based on the observations 
 X(1),..., X(n) (sorted by increasing order).PointSetRandomization.formatp0 to determine
    which p-values are too close to 0 or 1 to be printed explicitly.
GammaDist for the special case
 of the Erlang distribution with
 shape parameter k > 0 and scale parameter 
 λ > 0.interpol(n, X, Y, C), this
 function returns the value of the interpolating polynomial P(z) evaluated
  at z (see eq.
actions method.
exp(A).
ContinuousDistribution for
 the exponential distribution
 with mean 1/λ where 
 λ > 0.ExponentialDist class with a constructor accepting as
 argument the mean 1/λ instead of the rate λ.GumbelDist.
GumbelDist.
f (0, 6, x).
f (fieldwidth, 6, x).
RandomStream interface by using as a backbone
 generator the combination of the WELL607 proposed in
 (and implemented in WELL607) with a nonlinear generator.F2wNetLFSR,
  F2wNetPolyLCG,
  F2wCycleBasedLFSR and
  F2wCycleBasedPolyLCG.ContinuousDistribution for 
 the fatigue life distribution with location
 parameter μ, scale parameter β and shape
 parameter γ.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.
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].ContinuousDistribution for
 the Fisher F distribution with n1 and n2
 degrees of freedom, where n1 and n2 are positive integers.ContinuousDistribution for
 the folded normal distribution with
 parameters μ >=  0 and 
 σ > 0.init for this AbstractChrono to a
   String in  the HH:MM:SS.xx format.
String in the HH:MM:SS.xx format.
d (0, 1, x).
String with a minimum length
 of fieldwidth, the result is right-padded with spaces if
 necessary but it is not truncated.
String containing x.
format, except it formats the given
   value for the locale locale.
String containing the elements n1
   to n2 (inclusive) of table V,
   k elements per line, p positions per element.
formatBase (0, b, x).
String representation in base
 b.
confidenceIntervalNormal.
confidenceIntervalStudent.
confidenceIntervalVarianceChi2.
formatKS,
    but for the KS statistic DN+(a).
formatKS,
   but for DN+(a).
formatp0 to print p, and adds
   the marker ``****'' if p is considered suspect
   (uses the environment variable SUSPECTP for this).
formatp1.
formatPoints(n, d) with n and d equal to the
    number of points and the dimension of this object, respectively.
toString, together with the first d coordinates of the
    first n points.
formatPoints(iter, n, d)
     with n and d equal to the number of points and the dimension, respectively.
formatPoints(n, d), but
    prints the points  by calling iter repeatedly.
formatPoints(), but the
 points coordinates are printed in base b.
formatPoints(n, d), but the
  points coordinates are printed in base b.
formatPoints(iter),
 but the points coordinates are printed in base b.
formatPoints(iter, n, d),
 but the points coordinates are printed in base b.
formatPointsNumbered(n, d)
   with n and d equal to the number of points and the dimension,
    respectively.
formatPoints(n,d), except that the points are numbered.
formatWithError,
   except that it formats the given value and error for the
   locale locale.
formatWithError,
   except that it formats the given value and error for the
   locale locale.
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.G (0, 6, x).
G (fieldwidth, 6, x).
g (0, 6, x).
g (fieldwidth, 6, x).
ContinuousDistribution for
 the gamma distribution with
 shape parameter 
 α > 0 and scale parameter
 
 λ > 0.GammaDist distribution with constructors accepting the
 mean μ and variance σ2 as arguments instead of a shape parameter
 α and a scale parameter λ.GammaProcessPCA, but the generated uniforms
 correspond to a bridge transformation of the BrownianMotionPCA
 instead of a sequential transformation.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.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.
RandomStream's.
RandomStream interface via inheritance
 from RandomStreamBase.BrownianMotion.
DiscreteDistributionInt for
 the geometric distribution with parameter
 p, where 0 < p < 1.BrownianMotionPCA that is included in the
 GammaProcessPCA object.
BrownianMotionPCA.
BrownianMotion object
 used to generate the process.
BrownianMotion.
BrownianMotionPCA.
StringBuffer associated with that object.
ContinuousDistribution
   object by executing the code contained in the string str.
Sim.
getContinuousDistribution, but for discrete distributions
   over the real numbers.
getContinuousDistribution, but for discrete distributions
   over the integers.
Distribution used by this generator.
DiscreteDistributionInt used by this generator.
DoubleArrayList
    object that contains the observations for this probe.
getField,
  except that it can return non-public fields.
Polynomial instance used to evaluate
     x, in an ArrayList table instance returned by
   getSplinePolynomials().
GammaProcess.
NormalGen used.
GammaProcess object gneg
 used to generate the 
 Γ- component of the process.
GammaProcess object gpos
 used to generate the 
 Γ+ component of the process.
init for this AbstractChrono.
initStat was called.
getInstanceFromMLE, but for the case β < 0.
update method (or the initial value if
    update was never called after init).
getMethod, except that it can return non-public methods.
init for this AbstractChrono.
getMLE, but for the case β < 0.
getLambda.
init for this AbstractChrono.
RandomStream.
RandomStream.
RandomStream.
RandomStream.
RandomStream used by this generator.
RandomStream used by this object.
RandomStream stream.
RandomStream
 for the underlying Brownian motion.
RandomStream's are the same.
InverseGaussianProcess.
BrownianMotion process, which should
 be the same as for the GammaProcess.
evaluate in the
  case where the 0/0 function is calculated.
AbstractChrono  class to compute the global CPU time used
 by the Java Virtual Machine.ContinuousDistribution for
 the  Gumbel distribution, with location parameter
 δ and scale parameter 
 β≠ 0.ContinuousDistribution for the half-normal
 distribution with parameters μ and 
 σ > 0.resetStartProcess.
EventList using the doubly-linked
 indexed list of Henriksen (see also).TallyStore object.
TallyHistogram object.
ContinuousDistribution for
 the hyperbolic secant distribution with location
 parameter μ and scale parameter 
 σ > 0.DiscreteDistributionInt for
 the hypergeometric distribution with
 k elements chosen among l, m being
 of one type, and l - m of the other.HypoExponentialDist
 and also implements  the hypoexponential distribution.HypoExponentialDist and
 HypoExponentialDistQuick
 in package probdist for the definition).iBinomialMatrixScramble except that the diagonal
    elements of each matrix 
 Mj are chosen as in
   leftMatrixScrambleFaurePermut.
iBinomialMatrixScrambleFaurePermut except that the
    elements under the diagonal are also
    chosen from the same restricted set as the diagonal elements.
iBinomialMatrixScrambleFaurePermut except that all the
    off-diagonal elements are 0.
RandomMultivariateGen for a vector of independent identically distributed
 (i.i.d.) random variables.init(byte[], int).
init(byte[], int).
init(byte[], int).
init(byte[], int).
init(byte[], int).
init followed by update(x).
init, but also chooses evlist as the
     event list to be used.
init, but also chooses evlist as the
     event list to be used.
SplayTree algorithm
  as EventList.
EventList.
init on each element.
setStatCollecting (true) and makes an update for the
    probe on the list size.
inverseF (alpha, beta, 0, 1, u).
inverseF (alpha, 1, d, u).
NormalDist.inverseF01.
inverseF (0, 1, u).
inverseF (0.0, 1.0, u).
ContinuousDistribution for
 the inverse gamma distribution with shape parameter
 
 α > 0 and scale parameter β > 0.ContinuousDistribution for 
 the inverse Gaussian distribution with location parameter
 μ > 0 and scale parameter 
 λ > 0.InverseGaussianDist.InverseGaussianProcess.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.
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.
iterSpacingsTests, but with the
   GofStat.powerRatios transformation.
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.
ContinuousDistribution for
 the Johnson SB distribution
 with shape parameters γ and 
 δ > 0, location parameter ξ,
 and scale parameter λ > 0.ContinuousDistribution for
 the Johnson SL distribution.JohnsonSLDist
     (gamma, delta, 0, 1).
ContinuousDistribution for
 the Johnson SU distribution.JohnsonSUDist
     (gamma, delta, 0, 1).
KernelDensityGen, but with
 a rescaling of the empirical distribution so that the variance
 of the density used to generate the random variates is equal
 to the empirical variance,
  as suggested by Silverman.kolmogorovSmirnov).
ContinuousDistribution for the
  Kolmogorov-Smirnov distribution with parameter n.KolmogorovSmirnovDist for the  distribution.FDist.kolmogorovSmirnovPlusJumpOne, assuming that F is the
   uniform distribution over [0, 1] and that
   
 U(1),..., U(n) are in sortedData.
ContinuousDistribution for the
 Kolmogorov-Smirnov+  distribution (see).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.
LCGPointSet, but implemented differently.ContinuousDistribution for
 the Laplace distribution.leftMatrixScramble except that all the
    off-diagonal elements of the 
 Mj are 0.
leftMatrixScramble except that the diagonal elements
    of each matrix 
 Mj are chosen from a restricted set of the best
    integers as calculated by Faure.
leftMatrixScrambleFaurePermut except that the
    elements under the diagonal are also
    chosen from the same restricted set as the diagonal elements.
leftMatrixScrambleFaurePermut except that all
    off-diagonal elements are 0.
RandomStreamBase using a composite linear feedback
 shift register (LFSR) (or Tausworthe) RNG as defined in.RandomStreamBase using a  64-bit composite linear feedback
 shift register (LFSR) (or Tausworthe) RNG as defined in.ListWithStat, and
 uses a linked list as the internal data structure.ListOfTallies to add support for the computation
 of the sample covariance between each pair of elements
 in a list, without storing all observations.PointSetRandomization
 that performs a left matrix scrambling and adds a random digital
 shift.DiscreteDistributionInt for
 the logarithmic distribution.ContinuousDistribution for the
 logistic distribution.ContinuousDistribution for the
 Log-Logistic distribution with shape parameter 
 α > 0
 and scale parameter β > 0.ContinuousDistribution for the
 lognormal distribution.LognormalDist class with a constructor accepting the
 mean m and the variance v of the distribution as arguments.derivative.derivative.integral method.matMatModM using double, but with int instead
   of double.
matMatModM using double, but with long instead
   of double.
matPowModM using double, but with int instead
   of double.
matPowModM using double, but with long instead
   of double.
matTwoPowModM using double, but with int instead of
   double.
matTwoPowModM using double, but with long instead of
   double.
matVecModM using double, but with int instead
   of double.
matVecModM using double, but with long instead
   of double.
RandomStreamBase, thus
 implementing the RandomStream interface indirectly.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, except here it is implemented
 with type long instead of double.RandomStream interface via inheritance from
 RandomStreamBase.DiscreteDistributionIntMulti for the
 multinomial distribution with parameters n and
 (p1, ...,pd).MultinormalGen for a multivariate normal distribution, generated via a Cholesky decomposition of the covariance
 matrix.MultinormalCholeskyGen(gen1, mu, new DenseDoubleMatrix2D(sigma)).
ContinuousDistributionMulti for the
 multinormal distribution with mean vector μ and covariance
 matrix 
 Σ.RandomMultivariateGen for a
 multivariate normal (or multinormal) distribution.MultinormalGen for a multivariate normal distribution, generated via the method of principal components analysis
 (PCA) of the covariance matrix.MultinormalPCAGen(gen1, mu, new DenseDoubleMatrix2D(sigma)).
BitVector by a BitMatrix
   and returns the result.
BitVector, by
   a BitMatrix.
ContinuousDistribution for
 the Nakagami distribution with location parameter a,
 scale parameter 
 λ > 0 and shape parameter c > 0.ClassFinder
 when two or more fully qualified class names can be
 associated with a simple class name.DiscreteDistributionInt for
 the negative binomial distribution with real
 parameters n and p, where n > 0 and 
 0 <= p <= 1.DiscreteDistributionIntMulti for the
 negative multinomial distribution with parameters n > 0 and
 (
 p1,…, pd)  such that all 0 < pi < 1 and  
 ∑i=1dpi < 1.nextDouble (s, s, alpha, lambda).
nextDouble (in which the baker transformation is applied).
nextPoint(gen1, mu, new DenseDoubleMatrix2D(sigma), p).
nextPoint(gen1, mu, new DenseDoubleMatrix2D(sigma), p).
ContinuousDistribution for the normal
 distribution (e.g.,).NormalDist (for the normal 
 distribution with mean μ and variance σ2).ContinuousDistribution for
 the normal inverse gaussian distribution with location parameter
 μ,  scale parameter 
 δ > 0, tail heavyness 
 α > 0, and
  asymmetry parameter β such that 
 0 <= | β| < α.InverseGaussianProcess igP, constructs a
 new NormalInverseGaussianProcess.
RandomStream's 
 are set to the same stream, streamAll.
ContinuousDistribution for a distribution
 from the Pareto family, with
 shape parameter 
 α > 0 and location parameter β > 0.InverseGammaDist.
InverseGammaGen.
ContinuousDistribution for
 the Pearson type VI distribution with shape parameters
 
 α1 > 0 and 
 α2 > 0, and scale parameter β > 0.ContinuousDistribution for a piecewise-linear 
 approximation of the empirical distribution function, 
 based on the observations 
 X(1),..., X(n) (sorted by increasing order), 
 and defined as follows (e.g.,).PointSet.DiscreteDistributionInt for the
 Poisson distribution with mean 
 λ >=  0.PoissonGen).ContinuousDistribution for
 the power distribution with shape parameter
 c > 0, over the interval [a, b], where a < b.PPPlot
   (title, XLabel, YLabel, dist, X)
    above, except that only the first numPoints of X
     are plotted.
String containing all the data of
   the BitMatrix.
StringBuffer which defines new types
 of append methods.QQPlot(title, XLabel, YLabel, dist, X) above, except that only the first numPoints of X
     are plotted.
leftMatrixScramble,
    then
    addRandomShift.
addRandomShift (stream).
init.
stripedMatrixScramble,
    then
    addRandomShift.
RandomVariateGen.PointSetRandomization.RandomStream to  stream.
PointSetRandomization
 that randomizes a sequence with a random starting point.newInstance method
 each time a new random stream is needed, instead of invoking
 directly the specific constructor of the desired type.newInstance method.Rank1Lattice with n points and lattice
    vector a of dimension s.
ContinuousDistribution for
 the Rayleigh distribution with
  location parameter a, and scale parameter β > 0.readCSVData to
  obtain a matrix of strings from
  the resource.
readDoubleData2D,
    for reading strings.
readDoubleData2D,
    for reading strings.
readDoubleData to
  obtain an array of double-precision values from
  the resource.
readDoubleData to
  obtain an array of double-precision values from
  the file.
readDoubleData to
  obtain an array of double-precision values from
  the file.
readDoubleData2D to
  obtain a matrix of double-precision values from
  the resource.
readDoubleData2D to
  obtain a matrix of double-precision values from
  the file.
readDoubleData2D to
  obtain a matrix of double-precision values from
  the file.
readDoubleData,
  for reading integers.
readIntData to
  obtain an array of integers from
  the resource.
readDoubleData,
    for reading integers.
readDoubleData,
    for reading integers.
readDoubleData2D,
  for reading integers.
readDoubleData to
  obtain a matrix of integers from
  the resource.
readDoubleData2D,
    for reading integers.
readDoubleData2D,
    for reading integers.
readStringData to
  obtain an array of integers from
  the resource.
readDoubleData,
    for reading strings.
readDoubleData,
    for reading strings.
EventList using a red black tree,
 which is similar to a binary search tree except that
 every node is colored red or black.setStatCollecting (true) has been
    called before for this list.
report, except that
    probes is an Iterable object instead of an array.
report), followed by a confidence interval (as in
     formatCIStudent), using d fractional decimal digits.
reportAndCIStudent (level, 3).
setCurCoordIndex (0).
setCurPointIndex (0).
resetNextSubstream methods
  of all streams in the list.
VarianceGammaProcess object used to generate this process.
BrownianMotion and the GammaProcess objects
 used to generate this process.
GammaProcess objects used to generate this process.
resetStartStream methods
  of all streams in the list.
resetStartSubstream methods
  of all streams in the list.
Process objects.s (0, str).
FBar.scan.
UserRecord objects
   for the processes in the service list for this resource.
nextDouble.
nextDouble.
report, and shortReport.
report and shortReport.
report and shortReport.
nextCoordinate or  nextCoordinates
    will return the values 
 ui, j, ui, j+1,..., where i is the
    index of the current point.
XYChart.
XYChart.
\documentclass (and other) commands in the
 created LATEX files.
setLambda
  with argument 1/mean to change the mean of this distribution.
GammaProcessPCA and the
 BrownianMotionPCA.
InverseGaussianProcessPCA
 and the inner 
 BrownianMotionPCA.
InverseGaussianProcess.
GammaProcess.
GammaProcess'es.
BrownianMotionPCA to ν.
waitList for this bin.
waitList and servList for this resource.
RandomStream to
    stream.
RandomStream to
    stream.
RandomStream to
    stream.
RandomStream to
    stream.
RandomStream used by this generator to stream.
RandomStream used by this object to stream.
RandomStream
 of the GammaGen
 to stream.
RandomStream
 of the GammaGen and
 the BetaGen to stream.
RandomStream
 of the gamma generator and the
 RandomStream of
 the inner BrownianMotionPCA to
 stream.
RandomStream
 for the underlying Brownian motion to stream.
InverseGaussianProcess.
RandomStream's.
RandomStream
 of the two GammaProcess'es to  stream.
DatasetChangeEvent to all
 registered listeners.
evaluate for
  the undefined function 0/0 to zeroOverZero.
shuffle(byte[], RandomStream).
shuffle(byte[], RandomStream).
shuffle(byte[], RandomStream).
shuffle(byte[], RandomStream).
shuffle(byte[], RandomStream).
shuffle(byte[], RandomStream).
shuffle(byte[], RandomStream).
shuffle(Object[], n, k, RandomStream).
shuffle(Object[], n, k, RandomStream).
shuffle(Object[], n, k, RandomStream).
shuffle(Object[], n, k, RandomStream).
shuffle(Object[], n, k, RandomStream).
shuffle(Object[], n, k, RandomStream).
shuffle(Object[], n, k, RandomStream).
shuffle(Object[], n, k, RandomStream).
Simulator static methods.
PointSetRandomization
 that performs a striped matrix scrambling and adds a random
 digital shift.EventList using a splay tree.startInteg, after initializing the variable 
    to val.
UserRecord for this resource.
start.
start.
stripedMatrixScramble except that the
    elements on and under the diagonal of each matrix 
 Mj are
    chosen as in leftMatrixScrambleFaurePermut.
ContinuousDistribution for
 the Student t-distribution
 with n degrees of freedom, where n is a positive integer.StudentDist for
 the Student t-distribution.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, and stores
  the results into the array s.
formatp1 to determine
    which p-values should be marked as suspect when printing test results.
AbstractChrono class to compute
 the total system time using Java's builtin System.nanoTime.Strings in different styles.Tally which gives a more detailed view
 of the observations statistics.Tally for which the individual
 observations are stored in a list implemented as a
 DoubleArrayList.AbstractChrono 
 class to compute the CPU time for a single thread.Thread variable and initializes it to zero.
ThreadProcessSimulator variable.
XYChart.
toLatexCdf, but for the probability density instead
    of the cdf.
toLatexCdf, but for the probability instead
    of the cdf.
String containing all the data of
   the BitMatrix.
String containing all the data of
   the DMatrix.
String.
getLongName.
ContinuousDistribution for
 the triangular distribution with domain [a, b] and mode 
 (or shape parameter) m, where  
 a <= m <= b.ContinuousDistribution for
 the uniform distribution
 over the interval [a, b].DiscreteDistributionInt for
 the discrete uniform distribution over the range [i, j].UnuranContinuous(s, s, genStr).
UnuranDiscreteInt (s, s, genStr).
UnuranEmpirical(s, s, dist, genStr).
UnuranEmpirical(s, aux, genStr), but reading 
   the observations from the empirical distribution dist.
update.
Resource or for Bin tokens, 
 or when a process waits for a Condition.valueOf (cls, name),
  with case insensitive field name look-up.
GammaProcess
 objects for 
 Γ+ and 
 Γ- are
 set to those of and their
 initial values  
 Γ+(t0) and 
 Γ-(t0) are set to t0.
VarianceGammaProcessDiff, but the two inner 
 GammaProcess'es are of PCA type.VarianceGammaProcessDiffPCA with 
 parameters  
 θ = VarianceGammaProcessDiffPCA with 
 parameters  
 θ = VarianceGammaProcessDiff, but the two
 inner GammaProcess'es are of the type PCABridge.VarianceGammaProcessDiffPCABridge with
 parameters  
 θ = VarianceGammaProcessDiff, but the two
 inner GammaProcess'es are of the PCASymmetricalBridge type.VarianceGammaProcessDiffPCASymmetricalBridge with
 parameters  
 θ = viewCdf above.
viewCdf, but for the probability density instead
    of the cdf.
viewProb above.
UserRecord
    for the processes waiting for tokens from this bin.
UserRecord
    for the processes waiting for this condition.
UserRecord objects
   for the processes in the waiting list for this resource.
ContinuousDistribution for the
 Watson G distribution (see).ContinuousDistribution for the
 Watson U  distribution (see).ContinuousDistribution for
 the Weibull distribution with shape parameter
 
 α > 0, location parameter δ, and scale parameter
 
 λ > 0.RandomStream interface via inheritance from
 RandomStreamBase.RandomStream interface via inheritance from
 RandomStreamBase.RandomStream interface via inheritance
 from RandomStreamBase.SSJXYSeriesCollection.XYLineChart.XYListSeriesCollection.YListSeriesCollection(data, numPoints) above,  but the points are 
 (hj, data[j - 1]),
     for 
 j = 1, 2,…,
  | 
SSJ  V. 2.6.  | 
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