- calculateAdjustedRSquared() - Method in class org.hipparchus.stat.regression.OLSMultipleLinearRegression
-
Returns the adjusted R-squared statistic, defined by the formula
- calculateBeta() - Method in class org.hipparchus.stat.regression.AbstractMultipleLinearRegression
-
Calculates the beta of multiple linear regression in matrix notation.
- calculateBeta() - Method in class org.hipparchus.stat.regression.GLSMultipleLinearRegression
-
Calculates beta by GLS.
- calculateBeta() - Method in class org.hipparchus.stat.regression.OLSMultipleLinearRegression
-
Calculates the regression coefficients using OLS.
- calculateBetaVariance() - Method in class org.hipparchus.stat.regression.AbstractMultipleLinearRegression
-
Calculates the beta variance of multiple linear regression in matrix
notation.
- calculateBetaVariance() - Method in class org.hipparchus.stat.regression.GLSMultipleLinearRegression
-
Calculates the variance on the beta.
- calculateBetaVariance() - Method in class org.hipparchus.stat.regression.OLSMultipleLinearRegression
-
Calculates the variance-covariance matrix of the regression parameters.
- calculateErrorVariance() - Method in class org.hipparchus.stat.regression.AbstractMultipleLinearRegression
-
Calculates the variance of the error term.
- calculateErrorVariance() - Method in class org.hipparchus.stat.regression.GLSMultipleLinearRegression
-
Calculates the estimated variance of the error term using the formula
- calculateHat() - Method in class org.hipparchus.stat.regression.OLSMultipleLinearRegression
-
Compute the "hat" matrix.
- calculateResiduals() - Method in class org.hipparchus.stat.regression.AbstractMultipleLinearRegression
-
Calculates the residuals of multiple linear regression in matrix
notation.
- calculateResidualSumOfSquares() - Method in class org.hipparchus.stat.regression.OLSMultipleLinearRegression
-
Returns the sum of squared residuals.
- calculateRSquared() - Method in class org.hipparchus.stat.regression.OLSMultipleLinearRegression
-
Returns the R-Squared statistic, defined by the formula
- calculateTotalSumOfSquares() - Method in class org.hipparchus.stat.regression.OLSMultipleLinearRegression
-
Returns the sum of squared deviations of Y from its mean.
- calculateYVariance() - Method in class org.hipparchus.stat.regression.AbstractMultipleLinearRegression
-
Calculates the variance of the y values.
- cdf(double, int) - Method in class org.hipparchus.stat.inference.KolmogorovSmirnovTest
-
Calculates P(D_n < d)
using the method described in [1] with quick decisions for extreme
values given in [2] (see above).
- cdf(double, int, boolean) - Method in class org.hipparchus.stat.inference.KolmogorovSmirnovTest
-
Calculates P(D_n < d)
using method described in [1] with quick decisions for extreme
values given in [2] (see above).
- cdfExact(double, int) - Method in class org.hipparchus.stat.inference.KolmogorovSmirnovTest
-
Calculates P(D_n < d)
.
- chiSquare(double[], long[]) - Method in class org.hipparchus.stat.inference.ChiSquareTest
-
- chiSquare(long[][]) - Method in class org.hipparchus.stat.inference.ChiSquareTest
-
- chiSquare(double[], long[]) - Static method in class org.hipparchus.stat.inference.InferenceTestUtils
-
- chiSquare(long[][]) - Static method in class org.hipparchus.stat.inference.InferenceTestUtils
-
- chiSquareDataSetsComparison(long[], long[]) - Method in class org.hipparchus.stat.inference.ChiSquareTest
-
- chiSquareDataSetsComparison(long[], long[]) - Static method in class org.hipparchus.stat.inference.InferenceTestUtils
-
- ChiSquareTest - Class in org.hipparchus.stat.inference
-
Implements Chi-Square test statistics.
- ChiSquareTest() - Constructor for class org.hipparchus.stat.inference.ChiSquareTest
-
- chiSquareTest(double[], long[]) - Method in class org.hipparchus.stat.inference.ChiSquareTest
-
- chiSquareTest(double[], long[], double) - Method in class org.hipparchus.stat.inference.ChiSquareTest
-
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
.
- chiSquareTest(long[][]) - Method in class org.hipparchus.stat.inference.ChiSquareTest
-
- chiSquareTest(long[][], double) - Method in class org.hipparchus.stat.inference.ChiSquareTest
-
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
.
- chiSquareTest(double[], long[], double) - Static method in class org.hipparchus.stat.inference.InferenceTestUtils
-
- chiSquareTest(double[], long[]) - Static method in class org.hipparchus.stat.inference.InferenceTestUtils
-
- chiSquareTest(long[][], double) - Static method in class org.hipparchus.stat.inference.InferenceTestUtils
-
- chiSquareTest(long[][]) - Static method in class org.hipparchus.stat.inference.InferenceTestUtils
-
- chiSquareTestDataSetsComparison(long[], long[]) - Method in class org.hipparchus.stat.inference.ChiSquareTest
-
Returns the
observed significance level, or
p-value, associated with a Chi-Square two sample test comparing
bin frequency counts in
observed1
and
observed2
.
- chiSquareTestDataSetsComparison(long[], long[], double) - Method in class org.hipparchus.stat.inference.ChiSquareTest
-
Performs a Chi-Square two sample test comparing two binned data
sets.
- chiSquareTestDataSetsComparison(long[], long[]) - Static method in class org.hipparchus.stat.inference.InferenceTestUtils
-
- chiSquareTestDataSetsComparison(long[], long[], double) - Static method in class org.hipparchus.stat.inference.InferenceTestUtils
-
- clear() - Method in class org.hipparchus.stat.descriptive.AbstractStorelessUnivariateStatistic
-
Clears the internal state of the Statistic
- clear() - Method in class org.hipparchus.stat.descriptive.DescriptiveStatistics
-
Resets all statistics and storage.
- clear() - Method in class org.hipparchus.stat.descriptive.moment.GeometricMean
-
Clears the internal state of the Statistic
- clear() - Method in class org.hipparchus.stat.descriptive.moment.Kurtosis
-
Clears the internal state of the Statistic
- clear() - Method in class org.hipparchus.stat.descriptive.moment.Mean
-
Clears the internal state of the Statistic
- clear() - Method in class org.hipparchus.stat.descriptive.moment.SecondMoment
-
Clears the internal state of the Statistic
- clear() - Method in class org.hipparchus.stat.descriptive.moment.Skewness
-
Clears the internal state of the Statistic
- clear() - Method in class org.hipparchus.stat.descriptive.moment.StandardDeviation
-
Clears the internal state of the Statistic
- clear() - Method in class org.hipparchus.stat.descriptive.moment.Variance
-
Clears the internal state of the Statistic
- clear() - Method in class org.hipparchus.stat.descriptive.MultivariateSummaryStatistics
-
Resets all statistics and storage.
- clear() - Method in class org.hipparchus.stat.descriptive.rank.Max
-
Clears the internal state of the Statistic
- clear() - Method in class org.hipparchus.stat.descriptive.rank.Min
-
Clears the internal state of the Statistic
- clear() - Method in class org.hipparchus.stat.descriptive.rank.PSquarePercentile
-
Clears the internal state of the Statistic
- clear() - Method in class org.hipparchus.stat.descriptive.rank.RandomPercentile
-
- clear() - Method in interface org.hipparchus.stat.descriptive.StorelessMultivariateStatistic
-
Clears the internal state of the statistic.
- clear() - Method in interface org.hipparchus.stat.descriptive.StorelessUnivariateStatistic
-
Clears the internal state of the Statistic
- clear() - Method in class org.hipparchus.stat.descriptive.StreamingStatistics
-
Resets all statistics and storage.
- clear() - Method in class org.hipparchus.stat.descriptive.summary.Product
-
Clears the internal state of the Statistic
- clear() - Method in class org.hipparchus.stat.descriptive.summary.Sum
-
Clears the internal state of the Statistic
- clear() - Method in class org.hipparchus.stat.descriptive.summary.SumOfLogs
-
Clears the internal state of the Statistic
- clear() - Method in class org.hipparchus.stat.descriptive.summary.SumOfSquares
-
Clears the internal state of the Statistic
- clear() - Method in class org.hipparchus.stat.descriptive.vector.VectorialCovariance
-
Clears the internal state of the Statistic
- clear() - Method in class org.hipparchus.stat.descriptive.vector.VectorialStorelessStatistic
-
Clears the internal state of the statistic.
- clear() - Method in class org.hipparchus.stat.Frequency
-
Clears the frequency table
- clear() - Method in class org.hipparchus.stat.regression.MillerUpdatingRegression
-
As the name suggests, clear wipes the internals and reorders everything in the
canonical order.
- clear() - Method in class org.hipparchus.stat.regression.SimpleRegression
-
Clears all data from the model.
- clear() - Method in interface org.hipparchus.stat.regression.UpdatingMultipleLinearRegression
-
Clears internal buffers and resets the regression model.
- computeCorrelationMatrix(RealMatrix) - Method in class org.hipparchus.stat.correlation.KendallsCorrelation
-
Computes the Kendall's Tau rank correlation matrix for the columns of
the input matrix.
- computeCorrelationMatrix(double[][]) - Method in class org.hipparchus.stat.correlation.KendallsCorrelation
-
Computes the Kendall's Tau rank correlation matrix for the columns of
the input rectangular array.
- computeCorrelationMatrix(RealMatrix) - Method in class org.hipparchus.stat.correlation.PearsonsCorrelation
-
- computeCorrelationMatrix(double[][]) - Method in class org.hipparchus.stat.correlation.PearsonsCorrelation
-
Computes the correlation matrix for the columns of the
input rectangular array.
- computeCorrelationMatrix(RealMatrix) - Method in class org.hipparchus.stat.correlation.SpearmansCorrelation
-
Computes the Spearman's rank correlation matrix for the columns of the
input matrix.
- computeCorrelationMatrix(double[][]) - Method in class org.hipparchus.stat.correlation.SpearmansCorrelation
-
Computes the Spearman's rank correlation matrix for the columns of the
input rectangular array.
- computeCovarianceMatrix(RealMatrix, boolean) - Method in class org.hipparchus.stat.correlation.Covariance
-
Compute a covariance matrix from a matrix whose columns represent covariates.
- computeCovarianceMatrix(RealMatrix) - Method in class org.hipparchus.stat.correlation.Covariance
-
Create a covariance matrix from a matrix whose columns represent
covariates.
- computeCovarianceMatrix(double[][], boolean) - Method in class org.hipparchus.stat.correlation.Covariance
-
Compute a covariance matrix from a rectangular array whose columns represent covariates.
- computeCovarianceMatrix(double[][]) - Method in class org.hipparchus.stat.correlation.Covariance
-
Create a covariance matrix from a rectangular array whose columns represent
covariates.
- ConfidenceInterval - Class in org.hipparchus.stat.interval
-
Represents an interval estimate of a population parameter.
- ConfidenceInterval(double, double, double) - Constructor for class org.hipparchus.stat.interval.ConfidenceInterval
-
Create a confidence interval with the given bounds and confidence level.
- copy() - Method in class org.hipparchus.stat.descriptive.AbstractStorelessUnivariateStatistic
-
Returns a copy of the statistic with the same internal state.
- copy() - Method in class org.hipparchus.stat.descriptive.AbstractUnivariateStatistic
-
Returns a copy of the statistic with the same internal state.
- copy() - Method in class org.hipparchus.stat.descriptive.DescriptiveStatistics
-
Returns a copy of this DescriptiveStatistics instance with the same internal state.
- copy() - Method in class org.hipparchus.stat.descriptive.moment.GeometricMean
-
Returns a copy of the statistic with the same internal state.
- copy() - Method in class org.hipparchus.stat.descriptive.moment.Kurtosis
-
Returns a copy of the statistic with the same internal state.
- copy() - Method in class org.hipparchus.stat.descriptive.moment.Mean
-
Returns a copy of the statistic with the same internal state.
- copy() - Method in class org.hipparchus.stat.descriptive.moment.SecondMoment
-
Returns a copy of the statistic with the same internal state.
- copy() - Method in class org.hipparchus.stat.descriptive.moment.SemiVariance
-
Returns a copy of the statistic with the same internal state.
- copy() - Method in class org.hipparchus.stat.descriptive.moment.Skewness
-
Returns a copy of the statistic with the same internal state.
- copy() - Method in class org.hipparchus.stat.descriptive.moment.StandardDeviation
-
Returns a copy of the statistic with the same internal state.
- copy() - Method in class org.hipparchus.stat.descriptive.moment.Variance
-
Returns a copy of the statistic with the same internal state.
- copy() - Method in class org.hipparchus.stat.descriptive.rank.Max
-
Returns a copy of the statistic with the same internal state.
- copy() - Method in class org.hipparchus.stat.descriptive.rank.Median
-
Returns a copy of the statistic with the same internal state.
- copy() - Method in class org.hipparchus.stat.descriptive.rank.Min
-
Returns a copy of the statistic with the same internal state.
- copy() - Method in class org.hipparchus.stat.descriptive.rank.Percentile
-
Returns a copy of the statistic with the same internal state.
- copy() - Method in class org.hipparchus.stat.descriptive.rank.PSquarePercentile
-
Returns a copy of the statistic with the same internal state.
- copy() - Method in class org.hipparchus.stat.descriptive.rank.RandomPercentile
-
- copy() - Method in interface org.hipparchus.stat.descriptive.StorelessUnivariateStatistic
-
Returns a copy of the statistic with the same internal state.
- copy() - Method in class org.hipparchus.stat.descriptive.StreamingStatistics
-
Returns a copy of this StreamingStatistics instance with the same internal state.
- copy() - Method in class org.hipparchus.stat.descriptive.summary.Product
-
Returns a copy of the statistic with the same internal state.
- copy() - Method in class org.hipparchus.stat.descriptive.summary.Sum
-
Returns a copy of the statistic with the same internal state.
- copy() - Method in class org.hipparchus.stat.descriptive.summary.SumOfLogs
-
Returns a copy of the statistic with the same internal state.
- copy() - Method in class org.hipparchus.stat.descriptive.summary.SumOfSquares
-
Returns a copy of the statistic with the same internal state.
- copy() - Method in interface org.hipparchus.stat.descriptive.UnivariateStatistic
-
Returns a copy of the statistic with the same internal state.
- copySelf() - Method in interface org.hipparchus.stat.descriptive.rank.PSquarePercentile.PSquareMarkers
-
A deep copy function to clone the current instance.
- correlation(double[], double[]) - Method in class org.hipparchus.stat.correlation.KendallsCorrelation
-
Computes the Kendall's Tau rank correlation coefficient between the two arrays.
- correlation(double[], double[]) - Method in class org.hipparchus.stat.correlation.PearsonsCorrelation
-
Computes the Pearson's product-moment correlation coefficient between two arrays.
- correlation(double[], double[]) - Method in class org.hipparchus.stat.correlation.SpearmansCorrelation
-
Computes the Spearman's rank correlation coefficient between the two arrays.
- Covariance - Class in org.hipparchus.stat.correlation
-
Computes covariances for pairs of arrays or columns of a matrix.
- Covariance() - Constructor for class org.hipparchus.stat.correlation.Covariance
-
Create a Covariance with no data.
- Covariance(double[][], boolean) - Constructor for class org.hipparchus.stat.correlation.Covariance
-
Create a Covariance matrix from a rectangular array
whose columns represent covariates.
- Covariance(double[][]) - Constructor for class org.hipparchus.stat.correlation.Covariance
-
Create a Covariance matrix from a rectangular array
whose columns represent covariates.
- Covariance(RealMatrix, boolean) - Constructor for class org.hipparchus.stat.correlation.Covariance
-
Create a covariance matrix from a matrix whose columns
represent covariates.
- Covariance(RealMatrix) - Constructor for class org.hipparchus.stat.correlation.Covariance
-
Create a covariance matrix from a matrix whose columns
represent covariates.
- covariance(double[], double[], boolean) - Method in class org.hipparchus.stat.correlation.Covariance
-
Computes the covariance between the two arrays.
- covariance(double[], double[]) - Method in class org.hipparchus.stat.correlation.Covariance
-
Computes the covariance between the two arrays, using the bias-corrected
formula.
- covarianceToCorrelation(RealMatrix) - Method in class org.hipparchus.stat.correlation.PearsonsCorrelation
-
Derives a correlation matrix from a covariance matrix.
- cumulativeProbability(double) - Method in class org.hipparchus.stat.fitting.EmpiricalDistribution
- EmpiricalDistribution - Class in org.hipparchus.stat.fitting
-
Represents an
empirical probability distribution -- a probability distribution derived
from observed data without making any assumptions about the functional form
of the population distribution that the data come from.
- EmpiricalDistribution() - Constructor for class org.hipparchus.stat.fitting.EmpiricalDistribution
-
Creates a new EmpiricalDistribution with the default bin count.
- EmpiricalDistribution(int) - Constructor for class org.hipparchus.stat.fitting.EmpiricalDistribution
-
Creates a new EmpiricalDistribution with the specified bin count.
- EmpiricalDistribution(int, RandomGenerator) - Constructor for class org.hipparchus.stat.fitting.EmpiricalDistribution
-
Creates a new EmpiricalDistribution with the specified bin count using the
provided
RandomGenerator
as the source of random data.
- EmpiricalDistribution(RandomGenerator) - Constructor for class org.hipparchus.stat.fitting.EmpiricalDistribution
-
Creates a new EmpiricalDistribution with default bin count using the
provided
RandomGenerator
as the source of random data.
- entrySetIterator() - Method in class org.hipparchus.stat.Frequency
-
Return an Iterator over the set of keys and values that have been added.
- equals(Object) - Method in class org.hipparchus.stat.descriptive.AbstractStorelessUnivariateStatistic
-
Returns true iff
object
is the same type of
StorelessUnivariateStatistic
(the object's class equals this
instance) returning the same values as this for
getResult()
and
getN()
.
- equals(Object) - Method in class org.hipparchus.stat.descriptive.MultivariateSummaryStatistics
-
Returns true iff object
is a MultivariateSummaryStatistics
instance and all statistics have the same values as this.
- equals(Object) - Method in class org.hipparchus.stat.descriptive.rank.PSquarePercentile
-
Returns true iff o
is a PSquarePercentile
returning the
same values as this for getResult()
and getN()
and also
having equal markers
- equals(Object) - Method in class org.hipparchus.stat.descriptive.StatisticalSummaryValues
-
Returns true iff object
is a
StatisticalSummary
instance and all
statistics have the same values as this.
- equals(Object) - Method in class org.hipparchus.stat.descriptive.StreamingStatistics
-
Returns true iff object
is a StreamingStatistics
instance and all statistics have the same values as this.
- equals(Object) - Method in class org.hipparchus.stat.descriptive.vector.VectorialCovariance
- equals(Object) - Method in class org.hipparchus.stat.descriptive.vector.VectorialStorelessStatistic
- equals(Object) - Method in class org.hipparchus.stat.Frequency
- estimate(double[], int[], double, int, KthSelector) - Method in enum org.hipparchus.stat.descriptive.rank.Percentile.EstimationType
-
Estimation based on Kth selection.
- estimate(int) - Method in interface org.hipparchus.stat.descriptive.rank.PSquarePercentile.PSquareMarkers
-
An Estimate of the percentile value of a given Marker
- estimate(double[][], int) - Static method in class org.hipparchus.stat.fitting.MultivariateNormalMixtureExpectationMaximization
-
- estimateErrorVariance() - Method in class org.hipparchus.stat.regression.AbstractMultipleLinearRegression
-
Estimates the variance of the error.
- estimateRegressandVariance() - Method in class org.hipparchus.stat.regression.AbstractMultipleLinearRegression
-
Returns the variance of the regressand, ie Var(y).
- estimateRegressandVariance() - Method in interface org.hipparchus.stat.regression.MultipleLinearRegression
-
Returns the variance of the regressand, ie Var(y).
- estimateRegressionParameters() - Method in class org.hipparchus.stat.regression.AbstractMultipleLinearRegression
-
Estimates the regression parameters b.
- estimateRegressionParameters() - Method in interface org.hipparchus.stat.regression.MultipleLinearRegression
-
Estimates the regression parameters b.
- estimateRegressionParametersStandardErrors() - Method in class org.hipparchus.stat.regression.AbstractMultipleLinearRegression
-
Returns the standard errors of the regression parameters.
- estimateRegressionParametersStandardErrors() - Method in interface org.hipparchus.stat.regression.MultipleLinearRegression
-
Returns the standard errors of the regression parameters.
- estimateRegressionParametersVariance() - Method in class org.hipparchus.stat.regression.AbstractMultipleLinearRegression
-
Estimates the variance of the regression parameters, ie Var(b).
- estimateRegressionParametersVariance() - Method in interface org.hipparchus.stat.regression.MultipleLinearRegression
-
Estimates the variance of the regression parameters, ie Var(b).
- estimateRegressionStandardError() - Method in class org.hipparchus.stat.regression.AbstractMultipleLinearRegression
-
Estimates the standard error of the regression.
- estimateResiduals() - Method in class org.hipparchus.stat.regression.AbstractMultipleLinearRegression
-
Estimates the residuals, ie u = y - X*b.
- estimateResiduals() - Method in interface org.hipparchus.stat.regression.MultipleLinearRegression
-
Estimates the residuals, ie u = y - X*b.
- evaluate(double[], int, int) - Method in class org.hipparchus.stat.descriptive.AbstractUnivariateStatistic
-
Returns the result of evaluating the statistic over the specified entries
in the input array.
- evaluate() - Method in class org.hipparchus.stat.descriptive.AbstractUnivariateStatistic
-
Returns the result of evaluating the statistic over the stored data.
- evaluate(double[], int, int) - Method in class org.hipparchus.stat.descriptive.moment.GeometricMean
-
Returns the geometric mean of the entries in the specified portion
of the input array.
- evaluate(double[], int, int) - Method in class org.hipparchus.stat.descriptive.moment.Kurtosis
-
Returns the kurtosis of the entries in the specified portion of the
input array.
- evaluate(double[], int, int) - Method in class org.hipparchus.stat.descriptive.moment.Mean
-
Returns the arithmetic mean of the entries in the specified portion of
the input array, or Double.NaN
if the designated subarray
is empty.
- evaluate(double[], double[], int, int) - Method in class org.hipparchus.stat.descriptive.moment.Mean
-
Returns the weighted arithmetic mean of the entries in the specified portion of
the input array, or Double.NaN
if the designated subarray
is empty.
- evaluate(double[], int, int) - Method in class org.hipparchus.stat.descriptive.moment.SemiVariance
-
Returns the
SemiVariance
of the designated values against the mean, using
instance properties varianceDirection and biasCorrection.
- evaluate(double[], SemiVariance.Direction) - Method in class org.hipparchus.stat.descriptive.moment.SemiVariance
-
This method calculates
SemiVariance
for the entire array against the mean,
using the current value of the biasCorrection instance property.
- evaluate(double[], double) - Method in class org.hipparchus.stat.descriptive.moment.SemiVariance
-
Returns the
SemiVariance
of the designated values against the cutoff,
using instance properties variancDirection and biasCorrection.
- evaluate(double[], double, SemiVariance.Direction) - Method in class org.hipparchus.stat.descriptive.moment.SemiVariance
-
Returns the
SemiVariance
of the designated values against the cutoff in the
given direction, using the current value of the biasCorrection instance property.
- evaluate(double[], double, SemiVariance.Direction, boolean, int, int) - Method in class org.hipparchus.stat.descriptive.moment.SemiVariance
-
Returns the
SemiVariance
of the designated values against the cutoff
in the given direction with the provided bias correction.
- evaluate(double[], int, int) - Method in class org.hipparchus.stat.descriptive.moment.Skewness
-
Returns the Skewness of the entries in the specified portion of the
input array.
- evaluate(double[], int, int) - Method in class org.hipparchus.stat.descriptive.moment.StandardDeviation
-
Returns the Standard Deviation of the entries in the specified portion of
the input array, or Double.NaN
if the designated subarray
is empty.
- evaluate(double[], double, int, int) - Method in class org.hipparchus.stat.descriptive.moment.StandardDeviation
-
Returns the Standard Deviation of the entries in the specified portion of
the input array, using the precomputed mean value.
- evaluate(double[], double) - Method in class org.hipparchus.stat.descriptive.moment.StandardDeviation
-
Returns the Standard Deviation of the entries in the input array, using
the precomputed mean value.
- evaluate(double[], int, int) - Method in class org.hipparchus.stat.descriptive.moment.Variance
-
Returns the variance of the entries in the specified portion of
the input array, or Double.NaN
if the designated subarray
is empty.
- evaluate(double[], double[], int, int) - Method in class org.hipparchus.stat.descriptive.moment.Variance
-
Returns the weighted variance of the entries in the specified portion of
the input array, or Double.NaN
if the designated subarray
is empty.
- evaluate(double[], double, int, int) - Method in class org.hipparchus.stat.descriptive.moment.Variance
-
Returns the variance of the entries in the specified portion of
the input array, using the precomputed mean value.
- evaluate(double[], double) - Method in class org.hipparchus.stat.descriptive.moment.Variance
-
Returns the variance of the entries in the input array, using the
precomputed mean value.
- evaluate(double[], double[], double, int, int) - Method in class org.hipparchus.stat.descriptive.moment.Variance
-
Returns the weighted variance of the entries in the specified portion of
the input array, using the precomputed weighted mean value.
- evaluate(double[], double[], double) - Method in class org.hipparchus.stat.descriptive.moment.Variance
-
Returns the weighted variance of the values in the input array, using
the precomputed weighted mean value.
- evaluate(double[], int, int) - Method in class org.hipparchus.stat.descriptive.rank.Max
-
Returns the maximum of the entries in the specified portion of
the input array, or Double.NaN
if the designated subarray
is empty.
- evaluate(double[], int, int) - Method in class org.hipparchus.stat.descriptive.rank.Median
-
Returns the result of evaluating the statistic over the specified entries
in the input array.
- evaluate(double[], int, int) - Method in class org.hipparchus.stat.descriptive.rank.Min
-
Returns the minimum of the entries in the specified portion of
the input array, or Double.NaN
if the designated subarray
is empty.
- evaluate(double[], int[], double, KthSelector) - Method in enum org.hipparchus.stat.descriptive.rank.Percentile.EstimationType
-
Evaluate method to compute the percentile for a given bounded array
using earlier computed pivots heap.
This basically calls the
index
and then
estimate
functions to return the estimated percentile value.
- evaluate(double[], double, KthSelector) - Method in enum org.hipparchus.stat.descriptive.rank.Percentile.EstimationType
-
Evaluate method to compute the percentile for a given bounded array.
- evaluate(double) - Method in class org.hipparchus.stat.descriptive.rank.Percentile
-
Returns the result of evaluating the statistic over the stored data.
- evaluate(double[], int, int) - Method in class org.hipparchus.stat.descriptive.rank.Percentile
-
Returns an estimate of the quantile
th percentile of the
designated values in the values
array.
- evaluate(double[], double) - Method in class org.hipparchus.stat.descriptive.rank.Percentile
-
Returns an estimate of the p
th percentile of the values
in the values
array.
- evaluate(double[], int, int, double) - Method in class org.hipparchus.stat.descriptive.rank.Percentile
-
Returns an estimate of the p
th percentile of the values
in the values
array, starting with the element in (0-based)
position begin
in the array and including length
values.
- evaluate(double, double[], int, int) - Method in class org.hipparchus.stat.descriptive.rank.RandomPercentile
-
Returns an estimate of the given percentile, computed using the designated
array segment as input data.
- evaluate(double[], int, int) - Method in class org.hipparchus.stat.descriptive.rank.RandomPercentile
-
Returns an estimate of the median, computed using the designated
array segment as input data.
- evaluate(double, double[]) - Method in class org.hipparchus.stat.descriptive.rank.RandomPercentile
-
Returns an estimate of percentile over the given array.
- evaluate(double[], int, int) - Method in interface org.hipparchus.stat.descriptive.StorelessUnivariateStatistic
-
Returns the result of evaluating the statistic over the specified entries
in the input array.
- evaluate(double[], int, int) - Method in class org.hipparchus.stat.descriptive.summary.Product
-
Returns the product of the entries in the specified portion of
the input array, or Double.NaN
if the designated subarray
is empty.
- evaluate(double[], double[], int, int) - Method in class org.hipparchus.stat.descriptive.summary.Product
-
Returns the weighted product of the entries in the specified portion of
the input array, or Double.NaN
if the designated subarray
is empty.
- evaluate(double[], int, int) - Method in class org.hipparchus.stat.descriptive.summary.Sum
-
The sum of the entries in the specified portion of the input array,
or 0 if the designated subarray is empty.
- evaluate(double[], double[], int, int) - Method in class org.hipparchus.stat.descriptive.summary.Sum
-
The weighted sum of the entries in the specified portion of
the input array, or 0 if the designated subarray
is empty.
- evaluate(double[], int, int) - Method in class org.hipparchus.stat.descriptive.summary.SumOfLogs
-
Returns the sum of the natural logs of the entries in the specified portion of
the input array, or Double.NaN
if the designated subarray
is empty.
- evaluate(double[], int, int) - Method in class org.hipparchus.stat.descriptive.summary.SumOfSquares
-
Returns the sum of the squares of the entries in the specified portion of
the input array, or Double.NaN
if the designated subarray
is empty.
- evaluate(double[]) - Method in interface org.hipparchus.stat.descriptive.UnivariateStatistic
-
Returns the result of evaluating the statistic over the input array.
- evaluate(double[], int, int) - Method in interface org.hipparchus.stat.descriptive.UnivariateStatistic
-
Returns the result of evaluating the statistic over the specified entries
in the input array.
- evaluate(double[], double[]) - Method in interface org.hipparchus.stat.descriptive.WeightedEvaluation
-
Returns the result of evaluating the statistic over the input array,
using the supplied weights.
- evaluate(double[], double[], int, int) - Method in interface org.hipparchus.stat.descriptive.WeightedEvaluation
-
Returns the result of evaluating the statistic over the specified entries
in the input array, using corresponding entries in the supplied weights array.
- exactP(double, int, int, boolean) - Static method in class org.hipparchus.stat.inference.InferenceTestUtils
-
- exactP(double, int, int, boolean) - Method in class org.hipparchus.stat.inference.KolmogorovSmirnovTest
-
Computes \(P(D_{n,m} > d)\) if strict
is true
; otherwise \(P(D_{n,m} \ge
d)\), where \(D_{n,m}\) is the 2-sample Kolmogorov-Smirnov statistic.
- extrema(boolean) - Method in class org.hipparchus.stat.descriptive.StreamingStatistics.StreamingStatisticsBuilder
-
Sets the computeExtrema setting of the factory.
- g(double[], long[]) - Method in class org.hipparchus.stat.inference.GTest
-
- g(double[], long[]) - Static method in class org.hipparchus.stat.inference.InferenceTestUtils
-
- gDataSetsComparison(long[], long[]) - Method in class org.hipparchus.stat.inference.GTest
-
Computes a G (Log-Likelihood Ratio) two sample test statistic for
independence comparing frequency counts in
observed1
and observed2
.
- gDataSetsComparison(long[], long[]) - Static method in class org.hipparchus.stat.inference.InferenceTestUtils
-
- GeometricMean - Class in org.hipparchus.stat.descriptive.moment
-
- GeometricMean() - Constructor for class org.hipparchus.stat.descriptive.moment.GeometricMean
-
Create a GeometricMean instance.
- GeometricMean(SumOfLogs) - Constructor for class org.hipparchus.stat.descriptive.moment.GeometricMean
-
Create a GeometricMean instance using the given SumOfLogs instance.
- GeometricMean(GeometricMean) - Constructor for class org.hipparchus.stat.descriptive.moment.GeometricMean
-
Copy constructor, creates a new GeometricMean
identical
to the original
.
- geometricMean(double...) - Static method in class org.hipparchus.stat.StatUtils
-
Returns the geometric mean of the entries in the input array, or
Double.NaN
if the array is empty.
- geometricMean(double[], int, int) - Static method in class org.hipparchus.stat.StatUtils
-
Returns the geometric mean of the entries in the specified portion of
the input array, or Double.NaN
if the designated subarray
is empty.
- getAdjustedRSquared() - Method in class org.hipparchus.stat.regression.RegressionResults
-
Returns the adjusted R-squared statistic, defined by the formula
- getAggregateN(Collection<RandomPercentile>) - Method in class org.hipparchus.stat.descriptive.rank.RandomPercentile
-
Returns the total number of values that have been consumed by the aggregates.
- getAggregateQuantileRank(double, Collection<RandomPercentile>) - Method in class org.hipparchus.stat.descriptive.rank.RandomPercentile
-
Returns the estimated quantile position of value in the combined dataset of the aggregates.
- getAggregateRank(double, Collection<RandomPercentile>) - Method in class org.hipparchus.stat.descriptive.rank.RandomPercentile
-
Computes the estimated rank of value in the combined dataset of the aggregates.
- getAgrestiCoullInterval(int, double, double) - Static method in class org.hipparchus.stat.interval.BinomialProportion
-
Create an Agresti-Coull binomial confidence interval for the true
probability of success of an unknown binomial distribution with
the given observed number of trials, probability of success and
confidence level.
- getBinCount() - Method in class org.hipparchus.stat.fitting.EmpiricalDistribution
-
Returns the number of bins.
- getBinStats() - Method in class org.hipparchus.stat.fitting.EmpiricalDistribution
-
Returns a List of
StreamingStatistics
instances containing
statistics describing the values in each of the bins.
- getClopperPearsonInterval(int, double, double) - Static method in class org.hipparchus.stat.interval.BinomialProportion
-
Create a Clopper-Pearson binomial confidence interval for the true
probability of success of an unknown binomial distribution with
the given observed number of trials, probability of success and
confidence level.
- getConfidenceLevel() - Method in class org.hipparchus.stat.interval.ConfidenceInterval
-
- getCorrelationMatrix() - Method in class org.hipparchus.stat.correlation.KendallsCorrelation
-
Returns the correlation matrix.
- getCorrelationMatrix() - Method in class org.hipparchus.stat.correlation.PearsonsCorrelation
-
Returns the correlation matrix.
- getCorrelationMatrix() - Method in class org.hipparchus.stat.correlation.SpearmansCorrelation
-
Calculate the Spearman Rank Correlation Matrix.
- getCorrelationPValues() - Method in class org.hipparchus.stat.correlation.PearsonsCorrelation
-
Returns a matrix of p-values associated with the (two-sided) null
hypothesis that the corresponding correlation coefficient is zero.
- getCorrelationStandardErrors() - Method in class org.hipparchus.stat.correlation.PearsonsCorrelation
-
Returns a matrix of standard errors associated with the estimates
in the correlation matrix.
getCorrelationStandardErrors().getEntry(i,j)
is the standard
error associated with getCorrelationMatrix.getEntry(i,j)
- getCount(T) - Method in class org.hipparchus.stat.Frequency
-
Returns the number of values equal to v.
- getCount(int) - Method in class org.hipparchus.stat.LongFrequency
-
Returns the number of values equal to v.
- getCovariance(int, int) - Method in class org.hipparchus.stat.correlation.StorelessCovariance
-
Get the covariance for an individual element of the covariance matrix.
- getCovariance() - Method in class org.hipparchus.stat.descriptive.MultivariateSummaryStatistics
-
Returns the covariance of the available values.
- getCovariance() - Method in interface org.hipparchus.stat.descriptive.StatisticalMultivariateSummary
-
Returns the covariance of the available values.
- getCovarianceMatrix() - Method in class org.hipparchus.stat.correlation.Covariance
-
Returns the covariance matrix
- getCovarianceMatrix() - Method in class org.hipparchus.stat.correlation.StorelessCovariance
-
Returns the covariance matrix
- getCovarianceOfParameters(int, int) - Method in class org.hipparchus.stat.regression.RegressionResults
-
Returns the covariance between regression parameters i and j.
- getCumFreq(T) - Method in class org.hipparchus.stat.Frequency
-
Returns the cumulative frequency of values less than or equal to v.
- getCumFreq(int) - Method in class org.hipparchus.stat.LongFrequency
-
Returns the cumulative frequency of values less than or equal to v.
- getCumPct(T) - Method in class org.hipparchus.stat.Frequency
-
Returns the cumulative percentage of values less than or equal to v
(as a proportion between 0 and 1).
- getCumPct(int) - Method in class org.hipparchus.stat.LongFrequency
-
Returns the cumulative percentage of values less than or equal to v
(as a proportion between 0 and 1).
- getData() - Method in class org.hipparchus.stat.correlation.StorelessCovariance
-
Return the covariance matrix as two-dimensional array.
- getData() - Method in class org.hipparchus.stat.descriptive.AbstractUnivariateStatistic
-
Get a copy of the stored data array.
- getDataRef() - Method in class org.hipparchus.stat.descriptive.AbstractUnivariateStatistic
-
Get a reference to the stored data array.
- getDiagonalOfHatMatrix(double[]) - Method in class org.hipparchus.stat.regression.MillerUpdatingRegression
-
Gets the diagonal of the Hat matrix also known as the leverage matrix.
- getDimension() - Method in class org.hipparchus.stat.descriptive.MultivariateSummaryStatistics
-
Returns the dimension of the data
- getDimension() - Method in interface org.hipparchus.stat.descriptive.StatisticalMultivariateSummary
-
Returns the dimension of the data
- getDimension() - Method in interface org.hipparchus.stat.descriptive.StorelessMultivariateStatistic
-
Returns the dimension of the statistic.
- getDimension() - Method in class org.hipparchus.stat.descriptive.vector.VectorialStorelessStatistic
-
Returns the dimension of the statistic.
- getElement(int) - Method in class org.hipparchus.stat.descriptive.DescriptiveStatistics
-
Returns the element at the specified index
- getErrorSumSquares() - Method in class org.hipparchus.stat.regression.RegressionResults
-
- getEstimationType() - Method in class org.hipparchus.stat.descriptive.rank.Median
-
Get the estimation
type
used for computation.
- getEstimationType() - Method in class org.hipparchus.stat.descriptive.rank.Percentile
-
Get the estimation
type
used for computation.
- getFittedModel() - Method in class org.hipparchus.stat.fitting.MultivariateNormalMixtureExpectationMaximization
-
Gets the fitted model.
- getGeneratorUpperBounds() - Method in class org.hipparchus.stat.fitting.EmpiricalDistribution
-
Returns a fresh copy of the array of upper bounds of the subintervals
of [0,1] used in generating data from the empirical distribution.
- getGeometricMean() - Method in class org.hipparchus.stat.descriptive.DescriptiveStatistics
-
Returns the geometric mean of the available values.
- getGeometricMean() - Method in class org.hipparchus.stat.descriptive.MultivariateSummaryStatistics
-
Returns an array whose ith entry is the
geometric mean of the ith entries of the arrays
that correspond to each multivariate sample
- getGeometricMean() - Method in interface org.hipparchus.stat.descriptive.StatisticalMultivariateSummary
-
Returns an array whose ith entry is the
geometric mean of the ith entries of the arrays
that correspond to each multivariate sample
- getGeometricMean() - Method in class org.hipparchus.stat.descriptive.StreamingStatistics
-
Returns the geometric mean of the values that have been added.
- getIntercept() - Method in class org.hipparchus.stat.regression.SimpleRegression
-
- getInterceptStdErr() - Method in class org.hipparchus.stat.regression.SimpleRegression
-
- getKernel(StreamingStatistics) - Method in class org.hipparchus.stat.fitting.EmpiricalDistribution
-
The within-bin smoothing kernel.
- getKthSelector() - Method in class org.hipparchus.stat.descriptive.rank.Median
-
- getKthSelector() - Method in class org.hipparchus.stat.descriptive.rank.Percentile
-
- getKurtosis() - Method in class org.hipparchus.stat.descriptive.DescriptiveStatistics
-
Returns the Kurtosis of the available values.
- getLocalizedString(Locale) - Method in enum org.hipparchus.stat.LocalizedStatFormats
- getLogLikelihood() - Method in class org.hipparchus.stat.fitting.MultivariateNormalMixtureExpectationMaximization
-
Gets the log likelihood of the data under the fitted model.
- getLowerBound() - Method in class org.hipparchus.stat.interval.ConfidenceInterval
-
- getMax() - Method in class org.hipparchus.stat.descriptive.DescriptiveStatistics
-
Returns the maximum of the available values
- getMax() - Method in class org.hipparchus.stat.descriptive.MultivariateSummaryStatistics
-
Returns an array whose ith entry is the
maximum of the ith entries of the arrays
that correspond to each multivariate sample
- getMax() - Method in interface org.hipparchus.stat.descriptive.StatisticalMultivariateSummary
-
Returns an array whose ith entry is the
maximum of the ith entries of the arrays
that correspond to each multivariate sample
- getMax() - Method in interface org.hipparchus.stat.descriptive.StatisticalSummary
-
Returns the maximum of the available values
- getMax() - Method in class org.hipparchus.stat.descriptive.StatisticalSummaryValues
-
- getMax() - Method in class org.hipparchus.stat.descriptive.StreamingStatistics
-
Returns the maximum of the available values
- getMean() - Method in class org.hipparchus.stat.descriptive.DescriptiveStatistics
-
- getMean() - Method in class org.hipparchus.stat.descriptive.MultivariateSummaryStatistics
-
Returns an array whose ith entry is the
mean of the ith entries of the arrays
that correspond to each multivariate sample
- getMean() - Method in interface org.hipparchus.stat.descriptive.StatisticalMultivariateSummary
-
Returns an array whose ith entry is the
mean of the ith entries of the arrays
that correspond to each multivariate sample
- getMean() - Method in interface org.hipparchus.stat.descriptive.StatisticalSummary
-
- getMean() - Method in class org.hipparchus.stat.descriptive.StatisticalSummaryValues
-
- getMean() - Method in class org.hipparchus.stat.descriptive.StreamingStatistics
-
- getMeanSquareError() - Method in class org.hipparchus.stat.regression.RegressionResults
-
Returns the sum of squared errors divided by the degrees of freedom,
usually abbreviated MSE.
- getMeanSquareError() - Method in class org.hipparchus.stat.regression.SimpleRegression
-
Returns the sum of squared errors divided by the degrees of freedom,
usually abbreviated MSE.
- getMedian() - Method in class org.hipparchus.stat.descriptive.StreamingStatistics
-
Returns an estimate of the median of the values that have been entered.
- getMin() - Method in class org.hipparchus.stat.descriptive.DescriptiveStatistics
-
Returns the minimum of the available values
- getMin() - Method in class org.hipparchus.stat.descriptive.MultivariateSummaryStatistics
-
Returns an array whose ith entry is the
minimum of the ith entries of the arrays
that correspond to each multivariate sample
- getMin() - Method in interface org.hipparchus.stat.descriptive.StatisticalMultivariateSummary
-
Returns an array whose ith entry is the
minimum of the ith entries of the arrays
that correspond to each multivariate sample
- getMin() - Method in interface org.hipparchus.stat.descriptive.StatisticalSummary
-
Returns the minimum of the available values
- getMin() - Method in class org.hipparchus.stat.descriptive.StatisticalSummaryValues
-
- getMin() - Method in class org.hipparchus.stat.descriptive.StreamingStatistics
-
Returns the minimum of the available values
- getMode() - Method in class org.hipparchus.stat.Frequency
-
Returns the mode value(s) in comparator order.
- getN() - Method in class org.hipparchus.stat.correlation.Covariance
-
Returns the number of observations (length of covariate vectors)
- getN() - Method in class org.hipparchus.stat.correlation.StorelessCovariance
-
- getN() - Method in class org.hipparchus.stat.descriptive.DescriptiveStatistics
-
Returns the number of available values
- getN() - Method in class org.hipparchus.stat.descriptive.moment.GeometricMean
-
Returns the number of values that have been added.
- getN() - Method in class org.hipparchus.stat.descriptive.moment.Kurtosis
-
Returns the number of values that have been added.
- getN() - Method in class org.hipparchus.stat.descriptive.moment.Mean
-
Returns the number of values that have been added.
- getN() - Method in class org.hipparchus.stat.descriptive.moment.Skewness
-
Returns the number of values that have been added.
- getN() - Method in class org.hipparchus.stat.descriptive.moment.StandardDeviation
-
Returns the number of values that have been added.
- getN() - Method in class org.hipparchus.stat.descriptive.moment.Variance
-
Returns the number of values that have been added.
- getN() - Method in class org.hipparchus.stat.descriptive.MultivariateSummaryStatistics
-
Returns the number of available values
- getN() - Method in class org.hipparchus.stat.descriptive.rank.Max
-
Returns the number of values that have been added.
- getN() - Method in class org.hipparchus.stat.descriptive.rank.Min
-
Returns the number of values that have been added.
- getN() - Method in class org.hipparchus.stat.descriptive.rank.PSquarePercentile
-
Returns the number of values that have been added.
- getN() - Method in class org.hipparchus.stat.descriptive.rank.RandomPercentile
-
- getN() - Method in interface org.hipparchus.stat.descriptive.StatisticalMultivariateSummary
-
Returns the number of available values
- getN() - Method in interface org.hipparchus.stat.descriptive.StatisticalSummary
-
Returns the number of available values
- getN() - Method in class org.hipparchus.stat.descriptive.StatisticalSummaryValues
-
- getN() - Method in interface org.hipparchus.stat.descriptive.StorelessMultivariateStatistic
-
Returns the number of values that have been added.
- getN() - Method in interface org.hipparchus.stat.descriptive.StorelessUnivariateStatistic
-
Returns the number of values that have been added.
- getN() - Method in class org.hipparchus.stat.descriptive.StreamingStatistics
-
Returns the number of available values
- getN() - Method in class org.hipparchus.stat.descriptive.summary.Product
-
Returns the number of values that have been added.
- getN() - Method in class org.hipparchus.stat.descriptive.summary.Sum
-
Returns the number of values that have been added.
- getN() - Method in class org.hipparchus.stat.descriptive.summary.SumOfLogs
-
Returns the number of values that have been added.
- getN() - Method in class org.hipparchus.stat.descriptive.summary.SumOfSquares
-
Returns the number of values that have been added.
- getN() - Method in class org.hipparchus.stat.descriptive.vector.VectorialCovariance
-
Get the number of vectors in the sample.
- getN() - Method in class org.hipparchus.stat.descriptive.vector.VectorialStorelessStatistic
-
Returns the number of values that have been added.
- getN() - Method in class org.hipparchus.stat.regression.MillerUpdatingRegression
-
Gets the number of observations added to the regression model.
- getN() - Method in class org.hipparchus.stat.regression.RegressionResults
-
Returns the number of observations added to the regression model.
- getN() - Method in class org.hipparchus.stat.regression.SimpleRegression
-
Returns the number of observations that have been added to the model.
- getN() - Method in interface org.hipparchus.stat.regression.UpdatingMultipleLinearRegression
-
Returns the number of observations added to the regression model.
- getNaNStrategy() - Method in class org.hipparchus.stat.descriptive.rank.Median
-
- getNaNStrategy() - Method in class org.hipparchus.stat.descriptive.rank.Percentile
-
- getNanStrategy() - Method in class org.hipparchus.stat.ranking.NaturalRanking
-
Return the NaNStrategy
- getNextValue() - Method in class org.hipparchus.stat.fitting.EmpiricalDistribution
-
Generates a random value from this distribution.
- getNormalApproximationInterval(int, double, double) - Static method in class org.hipparchus.stat.interval.BinomialProportion
-
Create a binomial confidence interval using normal approximation
for the true probability of success of an unknown binomial distribution
with the given observed number of trials, probability of success and
confidence level.
- getNumberOfParameters() - Method in class org.hipparchus.stat.regression.RegressionResults
-
Returns the number of parameters estimated in the model.
- getNumericalMean() - Method in class org.hipparchus.stat.fitting.EmpiricalDistribution
- getNumericalVariance() - Method in class org.hipparchus.stat.fitting.EmpiricalDistribution
- getOmegaInverse() - Method in class org.hipparchus.stat.regression.GLSMultipleLinearRegression
-
Get the inverse of the covariance.
- getOrderOfRegressors() - Method in class org.hipparchus.stat.regression.MillerUpdatingRegression
-
Gets the order of the regressors, useful if some type of reordering
has been called.
- getParameterEstimate(int) - Method in class org.hipparchus.stat.regression.RegressionResults
-
Returns the parameter estimate for the regressor at the given index.
- getParameterEstimates() - Method in class org.hipparchus.stat.regression.RegressionResults
-
Returns a copy of the regression parameters estimates.
- getPartialCorrelations(int) - Method in class org.hipparchus.stat.regression.MillerUpdatingRegression
-
In the original algorithm only the partial correlations of the regressors
is returned to the user.
- getPct(T) - Method in class org.hipparchus.stat.Frequency
-
Returns the percentage of values that are equal to v
(as a proportion between 0 and 1).
- getPct(int) - Method in class org.hipparchus.stat.LongFrequency
-
Returns the percentage of values that are equal to v
(as a proportion between 0 and 1).
- getPercentile(double) - Method in class org.hipparchus.stat.descriptive.DescriptiveStatistics
-
Returns an estimate for the pth percentile of the stored values.
- getPercentile(double) - Method in class org.hipparchus.stat.descriptive.StreamingStatistics
-
Returns an estimate of the given percentile of the values that have been entered.
- getPercentileValue() - Method in interface org.hipparchus.stat.descriptive.rank.PSquarePercentile.PSquareMarkers
-
Returns Percentile value computed thus far.
- getPivotingStrategy() - Method in class org.hipparchus.stat.descriptive.rank.Percentile
-
- getPopulationVariance() - Method in class org.hipparchus.stat.descriptive.DescriptiveStatistics
-
Returns the population variance of the available values.
- getPopulationVariance() - Method in class org.hipparchus.stat.descriptive.StreamingStatistics
-
- getQuadraticMean() - Method in class org.hipparchus.stat.descriptive.DescriptiveStatistics
-
Returns the quadratic mean of the available values.
- getQuadraticMean() - Method in class org.hipparchus.stat.descriptive.StreamingStatistics
-
Returns the quadratic mean, a.k.a.
- getQuantile() - Method in class org.hipparchus.stat.descriptive.rank.Percentile
-
Returns the value of the quantile field (determines what percentile is
computed when evaluate() is called with no quantile argument).
- getQuantile() - Method in class org.hipparchus.stat.descriptive.rank.PSquarePercentile
-
- getQuantileRank(double) - Method in class org.hipparchus.stat.descriptive.rank.RandomPercentile
-
Returns the estimated quantile position of value in the dataset.
- getR() - Method in class org.hipparchus.stat.regression.SimpleRegression
-
- getRank(double) - Method in class org.hipparchus.stat.descriptive.rank.RandomPercentile
-
Gets the estimated rank of value
, i.e.
- getRankCorrelation() - Method in class org.hipparchus.stat.correlation.SpearmansCorrelation
-
- getRegressionSumSquares() - Method in class org.hipparchus.stat.regression.RegressionResults
-
Returns the sum of squared deviations of the predicted y values about
their mean (which equals the mean of y).
- getRegressionSumSquares() - Method in class org.hipparchus.stat.regression.SimpleRegression
-
Returns the sum of squared deviations of the predicted y values about
their mean (which equals the mean of y).
- getResult() - Method in class org.hipparchus.stat.descriptive.AbstractStorelessUnivariateStatistic
-
Returns the current value of the Statistic.
- getResult() - Method in class org.hipparchus.stat.descriptive.moment.GeometricMean
-
Returns the current value of the Statistic.
- getResult() - Method in class org.hipparchus.stat.descriptive.moment.Kurtosis
-
Returns the current value of the Statistic.
- getResult() - Method in class org.hipparchus.stat.descriptive.moment.Mean
-
Returns the current value of the Statistic.
- getResult() - Method in class org.hipparchus.stat.descriptive.moment.SecondMoment
-
Returns the current value of the Statistic.
- getResult() - Method in class org.hipparchus.stat.descriptive.moment.Skewness
-
Returns the value of the statistic based on the values that have been added.
- getResult() - Method in class org.hipparchus.stat.descriptive.moment.StandardDeviation
-
Returns the current value of the Statistic.
- getResult() - Method in class org.hipparchus.stat.descriptive.moment.Variance
-
Returns the current value of the Statistic.
- getResult() - Method in class org.hipparchus.stat.descriptive.rank.Max
-
Returns the current value of the Statistic.
- getResult() - Method in class org.hipparchus.stat.descriptive.rank.Min
-
Returns the current value of the Statistic.
- getResult() - Method in class org.hipparchus.stat.descriptive.rank.PSquarePercentile
-
Returns the current value of the Statistic.
- getResult() - Method in class org.hipparchus.stat.descriptive.rank.RandomPercentile
-
Returns an estimate of the median.
- getResult(double) - Method in class org.hipparchus.stat.descriptive.rank.RandomPercentile
-
Returns an estimate of the given percentile.
- getResult() - Method in interface org.hipparchus.stat.descriptive.StorelessMultivariateStatistic
-
Returns the current value of the Statistic.
- getResult() - Method in interface org.hipparchus.stat.descriptive.StorelessUnivariateStatistic
-
Returns the current value of the Statistic.
- getResult() - Method in class org.hipparchus.stat.descriptive.summary.Product
-
Returns the current value of the Statistic.
- getResult() - Method in class org.hipparchus.stat.descriptive.summary.Sum
-
Returns the current value of the Statistic.
- getResult() - Method in class org.hipparchus.stat.descriptive.summary.SumOfLogs
-
Returns the current value of the Statistic.
- getResult() - Method in class org.hipparchus.stat.descriptive.summary.SumOfSquares
-
Returns the current value of the Statistic.
- getResult() - Method in class org.hipparchus.stat.descriptive.vector.VectorialCovariance
-
Get the covariance matrix.
- getResult() - Method in class org.hipparchus.stat.descriptive.vector.VectorialStorelessStatistic
-
Returns the current value of the Statistic.
- getRSquare() - Method in class org.hipparchus.stat.regression.SimpleRegression
-
- getRSquared() - Method in class org.hipparchus.stat.regression.RegressionResults
-
- getSampleStats() - Method in class org.hipparchus.stat.fitting.EmpiricalDistribution
-
- getSecondMoment() - Method in class org.hipparchus.stat.descriptive.StreamingStatistics
-
Returns a statistic related to the Second Central Moment.
- getSignificance() - Method in class org.hipparchus.stat.regression.SimpleRegression
-
Returns the significance level of the slope (equiv) correlation.
- getSkewness() - Method in class org.hipparchus.stat.descriptive.DescriptiveStatistics
-
Returns the skewness of the available values.
- getSlope() - Method in class org.hipparchus.stat.regression.SimpleRegression
-
Returns the slope of the estimated regression line.
- getSlopeConfidenceInterval() - Method in class org.hipparchus.stat.regression.SimpleRegression
-
Returns the half-width of a 95% confidence interval for the slope
estimate.
- getSlopeConfidenceInterval(double) - Method in class org.hipparchus.stat.regression.SimpleRegression
-
Returns the half-width of a (100-100*alpha)% confidence interval for
the slope estimate.
- getSlopeStdErr() - Method in class org.hipparchus.stat.regression.SimpleRegression
-
- getSortedValues() - Method in class org.hipparchus.stat.descriptive.DescriptiveStatistics
-
Returns the current set of values in an array of double primitives,
sorted in ascending order.
- getSourceString() - Method in enum org.hipparchus.stat.LocalizedStatFormats
- getStandardDeviation() - Method in class org.hipparchus.stat.descriptive.DescriptiveStatistics
-
Returns the standard deviation of the available values.
- getStandardDeviation() - Method in class org.hipparchus.stat.descriptive.MultivariateSummaryStatistics
-
- getStandardDeviation() - Method in interface org.hipparchus.stat.descriptive.StatisticalMultivariateSummary
-
Returns an array whose ith entry is the
standard deviation of the ith entries of the arrays
that correspond to each multivariate sample
- getStandardDeviation() - Method in interface org.hipparchus.stat.descriptive.StatisticalSummary
-
Returns the standard deviation of the available values.
- getStandardDeviation() - Method in class org.hipparchus.stat.descriptive.StatisticalSummaryValues
-
- getStandardDeviation() - Method in class org.hipparchus.stat.descriptive.StreamingStatistics
-
Returns the standard deviation of the values that have been added.
- getStdErrorOfEstimate(int) - Method in class org.hipparchus.stat.regression.RegressionResults
-
- getStdErrorOfEstimates() - Method in class org.hipparchus.stat.regression.RegressionResults
-
- getSum() - Method in class org.hipparchus.stat.descriptive.DescriptiveStatistics
-
Returns the sum of the values that have been added to Univariate.
- getSum() - Method in class org.hipparchus.stat.descriptive.MultivariateSummaryStatistics
-
Returns an array whose ith entry is the
sum of the ith entries of the arrays
that correspond to each multivariate sample
- getSum() - Method in interface org.hipparchus.stat.descriptive.StatisticalMultivariateSummary
-
Returns an array whose ith entry is the
sum of the ith entries of the arrays
that correspond to each multivariate sample
- getSum() - Method in interface org.hipparchus.stat.descriptive.StatisticalSummary
-
Returns the sum of the values that have been added to Univariate.
- getSum() - Method in class org.hipparchus.stat.descriptive.StatisticalSummaryValues
-
- getSum() - Method in class org.hipparchus.stat.descriptive.StreamingStatistics
-
Returns the sum of the values that have been added to Univariate.
- getSumFreq() - Method in class org.hipparchus.stat.Frequency
-
Returns the sum of all frequencies.
- getSumLog() - Method in class org.hipparchus.stat.descriptive.MultivariateSummaryStatistics
-
Returns an array whose ith entry is the
sum of logs of the ith entries of the arrays
that correspond to each multivariate sample
- getSumLog() - Method in interface org.hipparchus.stat.descriptive.StatisticalMultivariateSummary
-
Returns an array whose ith entry is the
sum of logs of the ith entries of the arrays
that correspond to each multivariate sample
- getSummary() - Method in class org.hipparchus.stat.descriptive.StreamingStatistics
-
- getSumOfCrossProducts() - Method in class org.hipparchus.stat.regression.SimpleRegression
-
Returns the sum of crossproducts, xi*yi.
- getSumOfLogs() - Method in class org.hipparchus.stat.descriptive.StreamingStatistics
-
Returns the sum of the logs of the values that have been added.
- getSumOfSquares() - Method in class org.hipparchus.stat.descriptive.DescriptiveStatistics
-
Returns the sum of the squares of the available values.
- getSumOfSquares() - Method in class org.hipparchus.stat.descriptive.StreamingStatistics
-
Returns the sum of the squares of the values that have been added.
- getSumSq() - Method in class org.hipparchus.stat.descriptive.MultivariateSummaryStatistics
-
Returns an array whose ith entry is the
sum of squares of the ith entries of the arrays
that correspond to each multivariate sample
- getSumSq() - Method in interface org.hipparchus.stat.descriptive.StatisticalMultivariateSummary
-
Returns an array whose ith entry is the
sum of squares of the ith entries of the arrays
that correspond to each multivariate sample
- getSumSquaredErrors() - Method in class org.hipparchus.stat.regression.SimpleRegression
-
- getSupportLowerBound() - Method in class org.hipparchus.stat.fitting.EmpiricalDistribution
- getSupportUpperBound() - Method in class org.hipparchus.stat.fitting.EmpiricalDistribution
- getTiesStrategy() - Method in class org.hipparchus.stat.ranking.NaturalRanking
-
Return the TiesStrategy
- getTotalSumSquares() - Method in class org.hipparchus.stat.regression.RegressionResults
-
Returns the sum of squared deviations of the y values about their mean.
- getTotalSumSquares() - Method in class org.hipparchus.stat.regression.SimpleRegression
-
Returns the sum of squared deviations of the y values about their mean.
- getUniqueCount() - Method in class org.hipparchus.stat.Frequency
-
Returns the number of values in the frequency table.
- getUpperBound() - Method in class org.hipparchus.stat.interval.ConfidenceInterval
-
- getUpperBounds() - Method in class org.hipparchus.stat.fitting.EmpiricalDistribution
-
Returns a fresh copy of the array of upper bounds for the bins.
- getValues() - Method in class org.hipparchus.stat.descriptive.DescriptiveStatistics
-
Returns the current set of values in an array of double primitives.
- getVariance() - Method in class org.hipparchus.stat.descriptive.DescriptiveStatistics
-
Returns the variance of the available values.
- getVariance() - Method in interface org.hipparchus.stat.descriptive.StatisticalSummary
-
Returns the variance of the available values.
- getVariance() - Method in class org.hipparchus.stat.descriptive.StatisticalSummaryValues
-
- getVariance() - Method in class org.hipparchus.stat.descriptive.StreamingStatistics
-
Returns the variance of the available values.
- getVarianceDirection() - Method in class org.hipparchus.stat.descriptive.moment.SemiVariance
-
Returns the varianceDirection property.
- getWilsonScoreInterval(int, double, double) - Static method in class org.hipparchus.stat.interval.BinomialProportion
-
Create an Wilson score binomial confidence interval for the true
probability of success of an unknown binomial distribution with
the given observed number of trials, probability of success and
confidence level.
- getWindowSize() - Method in class org.hipparchus.stat.descriptive.DescriptiveStatistics
-
Returns the maximum number of values that can be stored in the
dataset, or INFINITE_WINDOW (-1) if there is no limit.
- getWorkArray(double[], int, int) - Method in class org.hipparchus.stat.descriptive.rank.Percentile
-
Get the work array to operate.
- getX() - Method in class org.hipparchus.stat.regression.AbstractMultipleLinearRegression
-
- getXSumSquares() - Method in class org.hipparchus.stat.regression.SimpleRegression
-
Returns the sum of squared deviations of the x values about their mean.
- getY() - Method in class org.hipparchus.stat.regression.AbstractMultipleLinearRegression
-
- GLSMultipleLinearRegression - Class in org.hipparchus.stat.regression
-
The GLS implementation of multiple linear regression.
- GLSMultipleLinearRegression() - Constructor for class org.hipparchus.stat.regression.GLSMultipleLinearRegression
-
- GTest - Class in org.hipparchus.stat.inference
-
- GTest() - Constructor for class org.hipparchus.stat.inference.GTest
-
- gTest(double[], long[]) - Method in class org.hipparchus.stat.inference.GTest
-
Returns the
observed significance level, or
p-value,
associated with a G-Test for goodness of fit comparing the
observed
frequency counts to those in the
expected
array.
- gTest(double[], long[], double) - Method in class org.hipparchus.stat.inference.GTest
-
Performs a G-Test (Log-Likelihood Ratio Test) for goodness of fit
evaluating the null hypothesis that the observed counts conform to the
frequency distribution described by the expected counts, with
significance level alpha
.
- gTest(double[], long[]) - Static method in class org.hipparchus.stat.inference.InferenceTestUtils
-
- gTest(double[], long[], double) - Static method in class org.hipparchus.stat.inference.InferenceTestUtils
-
- gTestDataSetsComparison(long[], long[]) - Method in class org.hipparchus.stat.inference.GTest
-
Returns the
observed significance level, or
p-value, associated with a G-Value (Log-Likelihood Ratio) for two
sample test comparing bin frequency counts in
observed1
and
observed2
.
- gTestDataSetsComparison(long[], long[], double) - Method in class org.hipparchus.stat.inference.GTest
-
Performs a G-Test (Log-Likelihood Ratio Test) comparing two binned
data sets.
- gTestDataSetsComparison(long[], long[]) - Static method in class org.hipparchus.stat.inference.InferenceTestUtils
-
- gTestDataSetsComparison(long[], long[], double) - Static method in class org.hipparchus.stat.inference.InferenceTestUtils
-
- gTestIntrinsic(double[], long[]) - Method in class org.hipparchus.stat.inference.GTest
-
Returns the intrinsic (Hardy-Weinberg proportions) p-Value, as described
in p64-69 of McDonald, J.H.
- gTestIntrinsic(double[], long[]) - Static method in class org.hipparchus.stat.inference.InferenceTestUtils
-
- m2 - Variable in class org.hipparchus.stat.descriptive.moment.SecondMoment
-
Second moment of values that have been added
- mannWhitneyU(double[], double[]) - Method in class org.hipparchus.stat.inference.MannWhitneyUTest
-
- MannWhitneyUTest - Class in org.hipparchus.stat.inference
-
An implementation of the Mann-Whitney U test.
- MannWhitneyUTest() - Constructor for class org.hipparchus.stat.inference.MannWhitneyUTest
-
Create a test instance using where NaN's are left in place and ties get
the average of applicable ranks.
- MannWhitneyUTest(NaNStrategy, TiesStrategy) - Constructor for class org.hipparchus.stat.inference.MannWhitneyUTest
-
Create a test instance using the given strategies for NaN's and ties.
- mannWhitneyUTest(double[], double[]) - Method in class org.hipparchus.stat.inference.MannWhitneyUTest
-
Returns the asymptotic
observed significance level, or
p-value, associated with a
Mann-Whitney U
Test comparing means for two independent samples.
- mannWhitneyUTest(double[], double[], boolean) - Method in class org.hipparchus.stat.inference.MannWhitneyUTest
-
Returns the asymptotic
observed significance level, or
p-value, associated with a
Mann-Whitney U
Test comparing means for two independent samples.
- Max - Class in org.hipparchus.stat.descriptive.rank
-
Returns the maximum of the available values.
- Max() - Constructor for class org.hipparchus.stat.descriptive.rank.Max
-
Create a Max instance.
- Max(Max) - Constructor for class org.hipparchus.stat.descriptive.rank.Max
-
Copy constructor, creates a new Max
identical
to the original
.
- max(double...) - Static method in class org.hipparchus.stat.StatUtils
-
Returns the maximum of the entries in the input array, or
Double.NaN
if the array is empty.
- max(double[], int, int) - Static method in class org.hipparchus.stat.StatUtils
-
Returns the maximum of the entries in the specified portion of the input array,
or Double.NaN
if the designated subarray is empty.
- MAXIMUM_PARTIAL_SUM_COUNT - Static variable in class org.hipparchus.stat.inference.KolmogorovSmirnovTest
-
- maxValuesRetained(double) - Static method in class org.hipparchus.stat.descriptive.rank.RandomPercentile
-
Returns the maximum number of double
values that a RandomPercentile
instance created with the given epsilon
value will retain in memory.
- Mean - Class in org.hipparchus.stat.descriptive.moment
-
Computes the arithmetic mean of a set of values.
- Mean() - Constructor for class org.hipparchus.stat.descriptive.moment.Mean
-
Constructs a Mean.
- Mean(FirstMoment) - Constructor for class org.hipparchus.stat.descriptive.moment.Mean
-
Constructs a Mean with an External Moment.
- Mean(Mean) - Constructor for class org.hipparchus.stat.descriptive.moment.Mean
-
Copy constructor, creates a new Mean
identical
to the original
.
- mean(double...) - Static method in class org.hipparchus.stat.StatUtils
-
Returns the arithmetic mean of the entries in the input array, or
Double.NaN
if the array is empty.
- mean(double[], int, int) - Static method in class org.hipparchus.stat.StatUtils
-
Returns the arithmetic mean of the entries in the specified portion of
the input array, or Double.NaN
if the designated subarray
is empty.
- meanDifference(double[], double[]) - Static method in class org.hipparchus.stat.StatUtils
-
Returns the mean of the (signed) differences between corresponding elements of the
input arrays -- i.e., sum(sample1[i] - sample2[i]) / sample1.length.
- Median - Class in org.hipparchus.stat.descriptive.rank
-
Returns the median of the available values.
- Median() - Constructor for class org.hipparchus.stat.descriptive.rank.Median
-
Default constructor.
- merge(Frequency<? extends T>) - Method in class org.hipparchus.stat.Frequency
-
Merge another Frequency object's counts into this instance.
- merge(Collection<? extends Frequency<? extends T>>) - Method in class org.hipparchus.stat.Frequency
-
- MillerUpdatingRegression - Class in org.hipparchus.stat.regression
-
- MillerUpdatingRegression(int, boolean, double) - Constructor for class org.hipparchus.stat.regression.MillerUpdatingRegression
-
This is the augmented constructor for the MillerUpdatingRegression class.
- MillerUpdatingRegression(int, boolean) - Constructor for class org.hipparchus.stat.regression.MillerUpdatingRegression
-
Primary constructor for the MillerUpdatingRegression.
- Min - Class in org.hipparchus.stat.descriptive.rank
-
Returns the minimum of the available values.
- Min() - Constructor for class org.hipparchus.stat.descriptive.rank.Min
-
Create a Min instance.
- Min(Min) - Constructor for class org.hipparchus.stat.descriptive.rank.Min
-
Copy constructor, creates a new Min
identical
to the original
.
- min(double...) - Static method in class org.hipparchus.stat.StatUtils
-
Returns the minimum of the entries in the input array, or
Double.NaN
if the array is empty.
- min(double[], int, int) - Static method in class org.hipparchus.stat.StatUtils
-
Returns the minimum of the entries in the specified portion of the input array,
or Double.NaN
if the designated subarray is empty.
- mode(double...) - Static method in class org.hipparchus.stat.StatUtils
-
Returns the sample mode(s).
- mode(double[], int, int) - Static method in class org.hipparchus.stat.StatUtils
-
Returns the sample mode(s).
- moment - Variable in class org.hipparchus.stat.descriptive.moment.Kurtosis
-
Fourth Moment on which this statistic is based
- moment - Variable in class org.hipparchus.stat.descriptive.moment.Mean
-
First moment on which this statistic is based.
- moment - Variable in class org.hipparchus.stat.descriptive.moment.Skewness
-
Third moment on which this statistic is based
- moment - Variable in class org.hipparchus.stat.descriptive.moment.Variance
-
SecondMoment is used in incremental calculation of Variance
- moments(boolean) - Method in class org.hipparchus.stat.descriptive.StreamingStatistics.StreamingStatisticsBuilder
-
Sets the computeMoments setting of the factory
- MultipleLinearRegression - Interface in org.hipparchus.stat.regression
-
The multiple linear regression can be represented in matrix-notation.
- MultivariateNormalMixtureExpectationMaximization - Class in org.hipparchus.stat.fitting
-
Expectation-Maximization algorithm for fitting the parameters of
multivariate normal mixture model distributions.
- MultivariateNormalMixtureExpectationMaximization(double[][]) - Constructor for class org.hipparchus.stat.fitting.MultivariateNormalMixtureExpectationMaximization
-
Creates an object to fit a multivariate normal mixture model to data.
- MultivariateSummaryStatistics - Class in org.hipparchus.stat.descriptive
-
Computes summary statistics for a stream of n-tuples added using the
addValue
method.
- MultivariateSummaryStatistics(int) - Constructor for class org.hipparchus.stat.descriptive.MultivariateSummaryStatistics
-
Construct a MultivariateSummaryStatistics instance for the given
dimension.
- MultivariateSummaryStatistics(int, boolean) - Constructor for class org.hipparchus.stat.descriptive.MultivariateSummaryStatistics
-
Construct a MultivariateSummaryStatistics instance for the given
dimension.
- SecondMoment - Class in org.hipparchus.stat.descriptive.moment
-
Computes a statistic related to the Second Central Moment.
- SecondMoment() - Constructor for class org.hipparchus.stat.descriptive.moment.SecondMoment
-
Create a SecondMoment instance.
- SecondMoment(SecondMoment) - Constructor for class org.hipparchus.stat.descriptive.moment.SecondMoment
-
Copy constructor, creates a new SecondMoment
identical
to the original
.
- SemiVariance - Class in org.hipparchus.stat.descriptive.moment
-
Computes the semivariance of a set of values with respect to a given cutoff value.
- SemiVariance() - Constructor for class org.hipparchus.stat.descriptive.moment.SemiVariance
-
Constructs a SemiVariance with default (true) biasCorrected
property and default (Downside) varianceDirection
property.
- SemiVariance(boolean) - Constructor for class org.hipparchus.stat.descriptive.moment.SemiVariance
-
Constructs a SemiVariance with the specified biasCorrected
property and default (Downside) varianceDirection
property.
- SemiVariance(SemiVariance.Direction) - Constructor for class org.hipparchus.stat.descriptive.moment.SemiVariance
-
Constructs a SemiVariance with the specified Direction
property
and default (true) biasCorrected
property
- SemiVariance(boolean, SemiVariance.Direction) - Constructor for class org.hipparchus.stat.descriptive.moment.SemiVariance
-
Constructs a SemiVariance with the specified isBiasCorrected
property and the specified Direction
property.
- SemiVariance(SemiVariance) - Constructor for class org.hipparchus.stat.descriptive.moment.SemiVariance
-
Copy constructor, creates a new SemiVariance
identical
to the original
.
- SemiVariance.Direction - Enum in org.hipparchus.stat.descriptive.moment
-
The direction of the semivariance - either upside or downside.
- setData(double[]) - Method in class org.hipparchus.stat.descriptive.AbstractUnivariateStatistic
-
Set the data array.
- setData(double[], int, int) - Method in class org.hipparchus.stat.descriptive.AbstractUnivariateStatistic
-
Set the data array.
- setData(double[]) - Method in class org.hipparchus.stat.descriptive.rank.Percentile
-
Set the data array.
- setData(double[], int, int) - Method in class org.hipparchus.stat.descriptive.rank.Percentile
-
Set the data array.
- setNoIntercept(boolean) - Method in class org.hipparchus.stat.regression.AbstractMultipleLinearRegression
-
- setQuantile(double) - Method in class org.hipparchus.stat.descriptive.rank.Percentile
-
Sets the value of the quantile field (determines what percentile is
computed when evaluate() is called with no quantile argument).
- setWindowSize(int) - Method in class org.hipparchus.stat.descriptive.DescriptiveStatistics
-
WindowSize controls the number of values that contribute to the
reported statistics.
- SimpleRegression - Class in org.hipparchus.stat.regression
-
Estimates an ordinary least squares regression model
with one independent variable.
- SimpleRegression() - Constructor for class org.hipparchus.stat.regression.SimpleRegression
-
Create an empty SimpleRegression instance
- SimpleRegression(boolean) - Constructor for class org.hipparchus.stat.regression.SimpleRegression
-
Create a SimpleRegression instance, specifying whether or not to estimate
an intercept.
- Skewness - Class in org.hipparchus.stat.descriptive.moment
-
Computes the skewness of the available values.
- Skewness() - Constructor for class org.hipparchus.stat.descriptive.moment.Skewness
-
Constructs a Skewness.
- Skewness(ThirdMoment) - Constructor for class org.hipparchus.stat.descriptive.moment.Skewness
-
Constructs a Skewness with an external moment.
- Skewness(Skewness) - Constructor for class org.hipparchus.stat.descriptive.moment.Skewness
-
Copy constructor, creates a new Skewness
identical
to the original
.
- SpearmansCorrelation - Class in org.hipparchus.stat.correlation
-
Spearman's rank correlation.
- SpearmansCorrelation() - Constructor for class org.hipparchus.stat.correlation.SpearmansCorrelation
-
Create a SpearmansCorrelation without data.
- SpearmansCorrelation(RankingAlgorithm) - Constructor for class org.hipparchus.stat.correlation.SpearmansCorrelation
-
Create a SpearmansCorrelation with the given ranking algorithm.
- SpearmansCorrelation(RealMatrix) - Constructor for class org.hipparchus.stat.correlation.SpearmansCorrelation
-
Create a SpearmansCorrelation from the given data matrix.
- SpearmansCorrelation(RealMatrix, RankingAlgorithm) - Constructor for class org.hipparchus.stat.correlation.SpearmansCorrelation
-
Create a SpearmansCorrelation with the given input data matrix
and ranking algorithm.
- StandardDeviation - Class in org.hipparchus.stat.descriptive.moment
-
Computes the sample standard deviation.
- StandardDeviation() - Constructor for class org.hipparchus.stat.descriptive.moment.StandardDeviation
-
Constructs a StandardDeviation.
- StandardDeviation(SecondMoment) - Constructor for class org.hipparchus.stat.descriptive.moment.StandardDeviation
-
Constructs a StandardDeviation from an external second moment.
- StandardDeviation(boolean) - Constructor for class org.hipparchus.stat.descriptive.moment.StandardDeviation
-
Constructs a StandardDeviation with the specified value for the
isBiasCorrected
property.
- StandardDeviation(boolean, SecondMoment) - Constructor for class org.hipparchus.stat.descriptive.moment.StandardDeviation
-
Constructs a StandardDeviation with the specified value for the
isBiasCorrected
property and the supplied external moment.
- StandardDeviation(StandardDeviation) - Constructor for class org.hipparchus.stat.descriptive.moment.StandardDeviation
-
Copy constructor, creates a new StandardDeviation
identical
to the original
.
- StatisticalMultivariateSummary - Interface in org.hipparchus.stat.descriptive
-
Reporting interface for basic multivariate statistics.
- StatisticalSummary - Interface in org.hipparchus.stat.descriptive
-
Reporting interface for basic univariate statistics.
- StatisticalSummaryValues - Class in org.hipparchus.stat.descriptive
-
Value object representing the results of a univariate
statistical summary.
- StatisticalSummaryValues(double, double, long, double, double, double) - Constructor for class org.hipparchus.stat.descriptive.StatisticalSummaryValues
-
Constructor.
- StatUtils - Class in org.hipparchus.stat
-
StatUtils provides static methods for computing statistics based on data
stored in double[] arrays.
- StorelessCovariance - Class in org.hipparchus.stat.correlation
-
Covariance implementation that does not require input data to be
stored in memory.
- StorelessCovariance(int) - Constructor for class org.hipparchus.stat.correlation.StorelessCovariance
-
Create a bias corrected covariance matrix with a given dimension.
- StorelessCovariance(int, boolean) - Constructor for class org.hipparchus.stat.correlation.StorelessCovariance
-
Create a covariance matrix with a given number of rows and columns and the
indicated bias correction.
- StorelessMultivariateStatistic - Interface in org.hipparchus.stat.descriptive
-
Base interface implemented by storeless multivariate statistics.
- StorelessUnivariateStatistic - Interface in org.hipparchus.stat.descriptive
-
- StreamingStatistics - Class in org.hipparchus.stat.descriptive
-
Computes summary statistics for a stream of data values added using the
addValue
method.
- StreamingStatistics() - Constructor for class org.hipparchus.stat.descriptive.StreamingStatistics
-
Construct a new StreamingStatistics instance, maintaining all statistics
other than percentiles.
- StreamingStatistics(boolean) - Constructor for class org.hipparchus.stat.descriptive.StreamingStatistics
-
- StreamingStatistics(double, RandomGenerator) - Constructor for class org.hipparchus.stat.descriptive.StreamingStatistics
-
Construct a new StreamingStatistics instance, maintaining all statistics
other than percentiles and with/without percentiles per the arguments.
- StreamingStatistics.StreamingStatisticsBuilder - Class in org.hipparchus.stat.descriptive
-
Builder for StreamingStatistics instances.
- StreamingStatisticsBuilder() - Constructor for class org.hipparchus.stat.descriptive.StreamingStatistics.StreamingStatisticsBuilder
-
Simple constructor.
- Sum - Class in org.hipparchus.stat.descriptive.summary
-
Returns the sum of the available values.
- Sum() - Constructor for class org.hipparchus.stat.descriptive.summary.Sum
-
Create a Sum instance.
- Sum(Sum) - Constructor for class org.hipparchus.stat.descriptive.summary.Sum
-
Copy constructor, creates a new Sum
identical
to the original
.
- sum(double...) - Static method in class org.hipparchus.stat.StatUtils
-
Returns the sum of the values in the input array, or
Double.NaN
if the array is empty.
- sum(double[], int, int) - Static method in class org.hipparchus.stat.StatUtils
-
Returns the sum of the entries in the specified portion of
the input array, or Double.NaN
if the designated subarray is empty.
- sumDifference(double[], double[]) - Static method in class org.hipparchus.stat.StatUtils
-
Returns the sum of the (signed) differences between corresponding elements of the
input arrays -- i.e., sum(sample1[i] - sample2[i]).
- sumLog(double...) - Static method in class org.hipparchus.stat.StatUtils
-
Returns the sum of the natural logs of the entries in the input array, or
Double.NaN
if the array is empty.
- sumLog(double[], int, int) - Static method in class org.hipparchus.stat.StatUtils
-
Returns the sum of the natural logs of the entries in the specified portion of
the input array, or Double.NaN
if the designated subarray is empty.
- sumOfLogs(boolean) - Method in class org.hipparchus.stat.descriptive.StreamingStatistics.StreamingStatisticsBuilder
-
Sets the computeSumOfLogs setting of the factory
- SumOfLogs - Class in org.hipparchus.stat.descriptive.summary
-
Returns the sum of the natural logs for this collection of values.
- SumOfLogs() - Constructor for class org.hipparchus.stat.descriptive.summary.SumOfLogs
-
Create a SumOfLogs instance.
- SumOfLogs(SumOfLogs) - Constructor for class org.hipparchus.stat.descriptive.summary.SumOfLogs
-
Copy constructor, creates a new SumOfLogs
identical
to the original
.
- sumOfSquares(boolean) - Method in class org.hipparchus.stat.descriptive.StreamingStatistics.StreamingStatisticsBuilder
-
Sets the computeSumOfSquares setting of the factory.
- SumOfSquares - Class in org.hipparchus.stat.descriptive.summary
-
Returns the sum of the squares of the available values.
- SumOfSquares() - Constructor for class org.hipparchus.stat.descriptive.summary.SumOfSquares
-
Create a SumOfSquares instance.
- SumOfSquares(SumOfSquares) - Constructor for class org.hipparchus.stat.descriptive.summary.SumOfSquares
-
Copy constructor, creates a new SumOfSquares
identical
to the original
.
- sumSq(double...) - Static method in class org.hipparchus.stat.StatUtils
-
Returns the sum of the squares of the entries in the input array, or
Double.NaN
if the array is empty.
- sumSq(double[], int, int) - Static method in class org.hipparchus.stat.StatUtils
-
Returns the sum of the squares of the entries in the specified portion of
the input array, or Double.NaN
if the designated subarray
is empty.