Package | Description |
---|---|
org.apache.commons.math3.analysis.polynomials |
Univariate real polynomials implementations, seen as differentiable
univariate real functions.
|
org.apache.commons.math3.linear |
Linear algebra support.
|
org.apache.commons.math3.stat.inference |
Classes providing hypothesis testing and confidence interval
construction.
|
Modifier and Type | Method and Description |
---|---|
protected static double[] |
PolynomialFunction.differentiate(double[] coefficients)
Returns the coefficients of the derivative of the polynomial with the given coefficients.
|
protected static double |
PolynomialFunction.evaluate(double[] coefficients,
double argument)
Uses Horner's Method to evaluate the polynomial with the given coefficients at
the argument.
|
Constructor and Description |
---|
PolynomialFunction(double[] c)
Construct a polynomial with the given coefficients.
|
Modifier and Type | Method and Description |
---|---|
void |
AbstractRealMatrix.setSubMatrix(double[][] subMatrix,
int row,
int column)
Replace the submatrix starting at
row, column using data in
the input subMatrix array. |
void |
BlockRealMatrix.setSubMatrix(double[][] subMatrix,
int row,
int column)
Replace the submatrix starting at
row, column using data in
the input subMatrix array. |
Constructor and Description |
---|
Array2DRowFieldMatrix(Field<T> field,
T[][] d,
boolean copyArray)
Create a new
FieldMatrix<T> using the input array as the underlying
data array. |
Array2DRowRealMatrix(double[][] d)
Create a new
RealMatrix using the input array as the underlying
data array. |
Modifier and Type | Method and Description |
---|---|
double |
MannWhitneyUTest.mannWhitneyU(double[] x,
double[] y)
Computes the Mann-Whitney
U statistic comparing mean for two independent samples possibly of
different length.
|
double |
MannWhitneyUTest.mannWhitneyUTest(double[] x,
double[] y)
Returns the asymptotic observed significance level, or
p-value, associated with a Mann-Whitney
U statistic comparing mean for two independent samples.
|
double |
TTest.pairedT(double[] sample1,
double[] sample2)
Computes a paired, 2-sample t-statistic based on the data in the input
arrays.
|
static double |
TestUtils.pairedT(double[] sample1,
double[] sample2) |
double |
TTest.pairedTTest(double[] sample1,
double[] sample2)
Returns the observed significance level, or
p-value, associated with a paired, two-sample, two-tailed t-test
based on the data in the input arrays.
|
static double |
TestUtils.pairedTTest(double[] sample1,
double[] sample2) |
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) |
double |
WilcoxonSignedRankTest.wilcoxonSignedRank(double[] x,
double[] y)
Computes the
Wilcoxon signed ranked statistic comparing mean for two related
samples or repeated measurements on a single sample.
|
double |
WilcoxonSignedRankTest.wilcoxonSignedRankTest(double[] x,
double[] y,
boolean exactPValue)
Returns the observed significance level, or
p-value, associated with a
Wilcoxon signed ranked statistic comparing mean for two related
samples or repeated measurements on a single sample.
|
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