Package | Description |
---|---|
org.apache.commons.math3.complex |
Complex number type and implementations of complex transcendental
functions.
|
org.apache.commons.math3.distribution |
Implementations of common discrete and continuous distributions.
|
org.apache.commons.math3.linear |
Linear algebra support.
|
org.apache.commons.math3.stat.inference |
Classes providing hypothesis testing and confidence interval
construction.
|
org.apache.commons.math3.util |
Convenience routines and common data structures used throughout the commons-math library.
|
Modifier and Type | Method and Description |
---|---|
double |
RootsOfUnity.getImaginary(int k)
Get the imaginary part of the
k -th n -th root of unity. |
Modifier and Type | Method and Description |
---|---|
double |
AbstractRealDistribution.inverseCumulativeProbability(double p)
Computes the quantile function of this distribution.
|
double |
ExponentialDistribution.inverseCumulativeProbability(double p)
Computes the quantile function of this distribution.
|
int |
IntegerDistribution.inverseCumulativeProbability(double p)
Computes the quantile function of this distribution.
|
double |
CauchyDistribution.inverseCumulativeProbability(double p)
Computes the quantile function of this distribution.
|
double |
RealDistribution.inverseCumulativeProbability(double p)
Computes the quantile function of this distribution.
|
int |
AbstractIntegerDistribution.inverseCumulativeProbability(double p)
Computes the quantile function of this distribution.
|
double |
TriangularDistribution.inverseCumulativeProbability(double p) |
Constructor and Description |
---|
PascalDistribution(int r,
double p)
Create a Pascal distribution with the given number of successes and
probability of success.
|
Modifier and Type | Method and Description |
---|---|
void |
RealMatrix.setSubMatrix(double[][] subMatrix,
int row,
int column)
Replace the submatrix starting at
row, column using data in
the input subMatrix array. |
Modifier and Type | Method and Description |
---|---|
boolean |
OneWayAnova.anovaTest(Collection<double[]> categoryData,
double alpha)
Performs an ANOVA test, evaluating the null hypothesis that there
is no difference among the means of the data categories.
|
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 . |
static boolean |
TestUtils.chiSquareTest(long[][] counts,
double alpha) |
boolean |
ChiSquareTest.chiSquareTest(long[][] counts,
double alpha)
Performs a
chi-square test of independence evaluating the null hypothesis that the
classifications represented by the counts in the columns of the input 2-way table
are independent of the rows, with significance level
alpha . |
static boolean |
TestUtils.chiSquareTestDataSetsComparison(long[] observed1,
long[] observed2,
double alpha) |
boolean |
ChiSquareTest.chiSquareTestDataSetsComparison(long[] observed1,
long[] observed2,
double alpha)
Performs a Chi-Square two sample test comparing two binned data
sets.
|
boolean |
TTest.homoscedasticTTest(double[] sample1,
double[] sample2,
double alpha)
Performs a
two-sided t-test evaluating the null hypothesis that
sample1
and sample2 are drawn from populations with the same mean,
with significance level alpha , assuming that the
subpopulation variances are equal. |
static boolean |
TestUtils.homoscedasticTTest(double[] sample1,
double[] sample2,
double alpha) |
static boolean |
TestUtils.oneWayAnovaTest(Collection<double[]> categoryData,
double alpha) |
boolean |
TTest.pairedTTest(double[] sample1,
double[] sample2,
double alpha)
Performs a paired t-test evaluating the null hypothesis that the
mean of the paired differences between
sample1 and
sample2 is 0 in favor of the two-sided alternative that the
mean paired difference is not equal to 0, with significance level
alpha . |
static boolean |
TestUtils.pairedTTest(double[] sample1,
double[] sample2,
double alpha) |
boolean |
TTest.tTest(double[] sample1,
double[] sample2,
double alpha)
Performs a
two-sided t-test evaluating the null hypothesis that
sample1
and sample2 are drawn from populations with the same mean,
with significance level alpha . |
static boolean |
TestUtils.tTest(double[] sample1,
double[] sample2,
double alpha) |
boolean |
TTest.tTest(double mu,
double[] sample,
double alpha)
Performs a
two-sided t-test evaluating the null hypothesis that the mean of the population from
which
sample is drawn equals mu . |
static boolean |
TestUtils.tTest(double mu,
double[] sample,
double alpha) |
boolean |
TTest.tTest(double mu,
StatisticalSummary sampleStats,
double alpha)
Performs a
two-sided t-test evaluating the null hypothesis that the mean of the
population from which the dataset described by
stats is
drawn equals mu . |
static boolean |
TestUtils.tTest(double mu,
StatisticalSummary sampleStats,
double alpha) |
boolean |
TTest.tTest(StatisticalSummary sampleStats1,
StatisticalSummary sampleStats2,
double alpha)
Performs a
two-sided t-test evaluating the null hypothesis that
sampleStats1 and sampleStats2 describe
datasets drawn from populations with the same mean, with significance
level alpha . |
static boolean |
TestUtils.tTest(StatisticalSummary sampleStats1,
StatisticalSummary sampleStats2,
double alpha) |
Modifier and Type | Method and Description |
---|---|
int |
MultidimensionalCounter.getCount(int... c)
Convert to unidimensional counter.
|
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