Package | Description |
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org.apache.commons.math3.analysis.integration |
Numerical integration (quadrature) algorithms for univariate real functions.
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org.apache.commons.math3.distribution |
Implementations of common discrete and continuous distributions.
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org.apache.commons.math3.stat.inference |
Classes providing hypothesis testing and confidence interval
construction.
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Constructor and Description |
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BaseAbstractUnivariateIntegrator(double relativeAccuracy,
double absoluteAccuracy,
int minimalIterationCount,
int maximalIterationCount)
Construct an integrator with given accuracies and iteration counts.
|
BaseAbstractUnivariateIntegrator(int minimalIterationCount,
int maximalIterationCount)
Construct an integrator with given iteration counts.
|
LegendreGaussIntegrator(int n,
double relativeAccuracy,
double absoluteAccuracy,
int minimalIterationCount,
int maximalIterationCount)
Build a Legendre-Gauss integrator with given accuracies and iterations counts.
|
RombergIntegrator(double relativeAccuracy,
double absoluteAccuracy,
int minimalIterationCount,
int maximalIterationCount)
Build a Romberg integrator with given accuracies and iterations counts.
|
RombergIntegrator(int minimalIterationCount,
int maximalIterationCount)
Build a Romberg integrator with given iteration counts.
|
SimpsonIntegrator(double relativeAccuracy,
double absoluteAccuracy,
int minimalIterationCount,
int maximalIterationCount)
Build a Simpson integrator with given accuracies and iterations counts.
|
SimpsonIntegrator(int minimalIterationCount,
int maximalIterationCount)
Build a Simpson integrator with given iteration counts.
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TrapezoidIntegrator(double relativeAccuracy,
double absoluteAccuracy,
int minimalIterationCount,
int maximalIterationCount)
Build a trapezoid integrator with given accuracies and iterations counts.
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TrapezoidIntegrator(int minimalIterationCount,
int maximalIterationCount)
Build a trapezoid integrator with given iteration counts.
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Constructor and Description |
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ExponentialDistribution(double mean,
double inverseCumAccuracy)
Create a exponential distribution with the given mean.
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FDistribution(double numeratorDegreesOfFreedom,
double denominatorDegreesOfFreedom)
Create a F distribution using the given degrees of freedom.
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FDistribution(double numeratorDegreesOfFreedom,
double denominatorDegreesOfFreedom,
double inverseCumAccuracy)
Create an F distribution using the given degrees of freedom
and inverse cumulative probability accuracy.
|
GammaDistribution(double alpha,
double beta,
double inverseCumAccuracy)
Create a new gamma distribution with the given
alpha and
beta values. |
HypergeometricDistribution(int populationSize,
int numberOfSuccesses,
int sampleSize)
Construct a new hypergeometric distribution with the specified population
size, number of successes in the population, and sample size.
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KolmogorovSmirnovDistribution(int n) |
LogNormalDistribution(double scale,
double shape)
Create a log-normal distribution using the specified scale and shape.
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LogNormalDistribution(double scale,
double shape,
double inverseCumAccuracy)
Create a log-normal distribution using the specified scale, shape and
inverse cumulative distribution accuracy.
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NormalDistribution(double mean,
double sd)
Create a normal distribution using the given mean and standard deviation.
|
NormalDistribution(double mean,
double sd,
double inverseCumAccuracy)
Create a normal distribution using the given mean, standard deviation and
inverse cumulative distribution accuracy.
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PascalDistribution(int r,
double p)
Create a Pascal distribution with the given number of successes and
probability of success.
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PoissonDistribution(double p)
Creates a new Poisson distribution with specified mean.
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PoissonDistribution(double p,
double epsilon)
Creates a new Poisson distribution with the specified mean and
convergence criterion.
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PoissonDistribution(double p,
double epsilon,
int maxIterations)
Creates a new Poisson distribution with specified mean, convergence
criterion and maximum number of iterations.
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TDistribution(double degreesOfFreedom)
Create a t distribution using the given degrees of freedom.
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TDistribution(double degreesOfFreedom,
double inverseCumAccuracy)
Create a t distribution using the given degrees of freedom and the
specified inverse cumulative probability absolute accuracy.
|
WeibullDistribution(double alpha,
double beta)
Create a Weibull distribution with the given shape and scale and a
location equal to zero.
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WeibullDistribution(double alpha,
double beta,
double inverseCumAccuracy)
Create a Weibull distribution with the given shape, scale and inverse
cumulative probability accuracy and a location equal to zero.
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ZipfDistribution(int numberOfElements,
double exponent)
Create a new Zipf distribution with the given number of elements and
exponent.
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Modifier and Type | Method and Description |
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static double |
TestUtils.chiSquare(double[] expected,
long[] observed) |
double |
ChiSquareTest.chiSquare(double[] expected,
long[] observed)
|
static double |
TestUtils.chiSquareTest(double[] expected,
long[] observed) |
double |
ChiSquareTest.chiSquareTest(double[] expected,
long[] observed)
Returns the observed significance level, or
p-value, associated with a
Chi-square goodness of fit test comparing the
observed
frequency counts to those in the expected array. |
static boolean |
TestUtils.chiSquareTest(double[] expected,
long[] observed,
double alpha) |
boolean |
ChiSquareTest.chiSquareTest(double[] expected,
long[] observed,
double alpha)
Performs a
Chi-square goodness of fit test evaluating the null hypothesis that the
observed counts conform to the frequency distribution described by the expected
counts, with significance level
alpha . |
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