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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,…,
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SSJ V. 2.6.2. |
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