## Uses of Class
org.apache.commons.math3.exception.DimensionMismatchException

- Packages that use DimensionMismatchException
  Package | Description
  --- | ---
  org.apache.commons.math3.analysis.interpolation | Univariate real functions interpolation algorithms.
  org.apache.commons.math3.geometry.euclidean.threed | This package provides basic 3D geometry components.
  org.apache.commons.math3.geometry.euclidean.twod | This package provides basic 2D geometry components.
  org.apache.commons.math3.linear | Linear algebra support.
  org.apache.commons.math3.ode | This package provides classes to solve Ordinary Differential Equations problems.
  org.apache.commons.math3.ode.nonstiff | This package provides classes to solve non-stiff Ordinary Differential Equations problems.
  org.apache.commons.math3.stat.correlation | Correlations/Covariance computations.
  org.apache.commons.math3.stat.descriptive.moment | Summary statistics based on moments.
  org.apache.commons.math3.stat.inference | Classes providing hypothesis testing and confidence interval construction.
  org.apache.commons.math3.transform | Implementations of transform methods, including Fast Fourier transforms.

- - ### Uses of DimensionMismatchException in org.apache.commons.math3.analysis.interpolation

    Constructors in org.apache.commons.math3.analysis.interpolation that throw DimensionMismatchException
    Constructor and Description
    `BicubicSplineInterpolatingFunction(double[] x, double[] y, double[][] f, double[][] dFdX, double[][] dFdY, double[][] d2FdXdY)`

  - ### Uses of DimensionMismatchException in org.apache.commons.math3.geometry.euclidean.threed

    Constructors in org.apache.commons.math3.geometry.euclidean.threed that throw DimensionMismatchException
    Constructor and Description
    `Vector3D(double[] v)` Simple constructor.

  - ### Uses of DimensionMismatchException in org.apache.commons.math3.geometry.euclidean.twod

    Constructors in org.apache.commons.math3.geometry.euclidean.twod that throw DimensionMismatchException
    Constructor and Description
    `Vector2D(double[] v)` Simple constructor.

  - ### Uses of DimensionMismatchException in org.apache.commons.math3.linear

    Subclasses of DimensionMismatchException in org.apache.commons.math3.linear
    Modifier and Type | Class and Description
    --- | ---
    `class` | `NonSquareMatrixException` Exception to be thrown when a square matrix is expected.
    `class` | `NonSquareOperatorException` Exception to be thrown when a square linear operator is expected.

    Methods in org.apache.commons.math3.linear that throw DimensionMismatchException
    Modifier and Type | Method and Description
    --- | ---
    `protected static void` | PreconditionedIterativeLinearSolver.`checkParameters(RealLinearOperator a, RealLinearOperator minv, RealVector b, RealVector x0)` Performs all dimension checks on the parameters of `solve` and `solveInPlace`, and throws an exception if one of the checks fails.
    `protected static void` | IterativeLinearSolver.`checkParameters(RealLinearOperator a, RealVector b, RealVector x0)` Performs all dimension checks on the parameters of `solve` and `solveInPlace`, and throws an exception if one of the checks fails.
    `RealVector` | RealLinearOperator.`operateTranspose(RealVector x)` Returns the result of multiplying the transpose of `this` operator by the vector `x` (optional operation).
    `void` | AbstractRealMatrix.`setSubMatrix(double[][] subMatrix, int row, int column)` Replace the submatrix starting at `row, column` using data in the input `subMatrix` array.
    `void` | RealMatrix.`setSubMatrix(double[][] subMatrix, int row, int column)` Replace the submatrix starting at `row, column` using data in the input `subMatrix` array.
    `RealVector` | PreconditionedIterativeLinearSolver.`solve(RealLinearOperator a, RealLinearOperator minv, RealVector b)` Returns an estimate of the solution to the linear system A · x = b.
    `RealVector` | SymmLQ.`solve(RealLinearOperator a, RealLinearOperator minv, RealVector b)` Returns an estimate of the solution to the linear system A · x = b.
    `RealVector` | SymmLQ.`solve(RealLinearOperator a, RealLinearOperator minv, RealVector b, boolean goodb, double shift)` Returns an estimate of the solution to the linear system (A - shift · I) · x = b.
    `RealVector` | PreconditionedIterativeLinearSolver.`solve(RealLinearOperator a, RealLinearOperator minv, RealVector b, RealVector x0)` Returns an estimate of the solution to the linear system A · x = b.
    `RealVector` | SymmLQ.`solve(RealLinearOperator a, RealLinearOperator minv, RealVector b, RealVector x)` Returns an estimate of the solution to the linear system A · x = b.
    `RealVector` | IterativeLinearSolver.`solve(RealLinearOperator a, RealVector b)` Returns an estimate of the solution to the linear system A · x = b.
    `RealVector` | PreconditionedIterativeLinearSolver.`solve(RealLinearOperator a, RealVector b)` Returns an estimate of the solution to the linear system A · x = b.
    `RealVector` | SymmLQ.`solve(RealLinearOperator a, RealVector b)` Returns an estimate of the solution to the linear system A · x = b.
    `RealVector` | SymmLQ.`solve(RealLinearOperator a, RealVector b, boolean goodb, double shift)` Returns the solution to the system (A - shift · I) · x = b.
    `RealVector` | IterativeLinearSolver.`solve(RealLinearOperator a, RealVector b, RealVector x0)` Returns an estimate of the solution to the linear system A · x = b.
    `RealVector` | PreconditionedIterativeLinearSolver.`solve(RealLinearOperator a, RealVector b, RealVector x0)` Returns an estimate of the solution to the linear system A · x = b.
    `RealVector` | SymmLQ.`solve(RealLinearOperator a, RealVector b, RealVector x)` Returns an estimate of the solution to the linear system A · x = b.
    `abstract RealVector` | PreconditionedIterativeLinearSolver.`solveInPlace(RealLinearOperator a, RealLinearOperator minv, RealVector b, RealVector x0)` Returns an estimate of the solution to the linear system A · x = b.
    `RealVector` | SymmLQ.`solveInPlace(RealLinearOperator a, RealLinearOperator minv, RealVector b, RealVector x)` Returns an estimate of the solution to the linear system A · x = b.
    `RealVector` | ConjugateGradient.`solveInPlace(RealLinearOperator a, RealLinearOperator minv, RealVector b, RealVector x0)` Returns an estimate of the solution to the linear system A · x = b.
    `RealVector` | SymmLQ.`solveInPlace(RealLinearOperator a, RealLinearOperator minv, RealVector b, RealVector x, boolean goodb, double shift)` Returns an estimate of the solution to the linear system (A - shift · I) · x = b.
    `abstract RealVector` | IterativeLinearSolver.`solveInPlace(RealLinearOperator a, RealVector b, RealVector x0)` Returns an estimate of the solution to the linear system A · x = b.
    `RealVector` | PreconditionedIterativeLinearSolver.`solveInPlace(RealLinearOperator a, RealVector b, RealVector x0)` Returns an estimate of the solution to the linear system A · x = b.
    `RealVector` | SymmLQ.`solveInPlace(RealLinearOperator a, RealVector b, RealVector x)` Returns an estimate of the solution to the linear system A · x = b.

    Constructors in org.apache.commons.math3.linear that throw DimensionMismatchException
    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.

  - ### Uses of DimensionMismatchException in org.apache.commons.math3.ode

    Methods in org.apache.commons.math3.ode that throw DimensionMismatchException
    Modifier and Type | Method and Description
    --- | ---
    `void` | EquationsMapper.`extractEquationData(double[] complete, double[] equationData)` Extract equation data from a complete state or derivative array.
    `void` | EquationsMapper.`insertEquationData(double[] equationData, double[] complete)` Insert equation data into a complete state or derivative array.
    `void` | ExpandableStatefulODE.`setCompleteState(double[] completeState)` Set the complete current state.
    `void` | JacobianMatrices.`setInitialMainStateJacobian(double[][] dYdY0)` Set the initial value of the Jacobian matrix with respect to state.
    `void` | ExpandableStatefulODE.`setPrimaryState(double[] primaryState)` Set primary part of the current state.
    `void` | ExpandableStatefulODE.`setSecondaryState(int index, double[] secondaryState)` Set secondary part of the current state.

  - ### Uses of DimensionMismatchException in org.apache.commons.math3.ode.nonstiff

    Methods in org.apache.commons.math3.ode.nonstiff that throw DimensionMismatchException
    Modifier and Type | Method and Description
    --- | ---
    `protected void` | AdaptiveStepsizeIntegrator.`sanityChecks(ExpandableStatefulODE equations, double t)` Check the integration span.

  - ### Uses of DimensionMismatchException in org.apache.commons.math3.stat.correlation

    Methods in org.apache.commons.math3.stat.correlation that throw DimensionMismatchException
    Modifier and Type | Method and Description
    --- | ---
    `void` | StorelessCovariance.`increment(double[] data)` Increment the covariance matrix with one row of data.

  - ### Uses of DimensionMismatchException in org.apache.commons.math3.stat.descriptive.moment

    Methods in org.apache.commons.math3.stat.descriptive.moment that throw DimensionMismatchException
    Modifier and Type | Method and Description
    --- | ---
    `void` | VectorialCovariance.`increment(double[] v)` Add a new vector to the sample.

  - ### Uses of DimensionMismatchException in org.apache.commons.math3.stat.inference

    Methods in org.apache.commons.math3.stat.inference that throw DimensionMismatchException
    Modifier and Type | Method and Description
    --- | ---
    `double` | OneWayAnova.`anovaFValue(Collection<double[]> categoryData)` Computes the ANOVA F-value for a collection of `double[]` arrays.
    `double` | OneWayAnova.`anovaPValue(Collection<double[]> categoryData)` Computes the ANOVA P-value for a collection of `double[]` arrays.
    `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.
    `double` | ChiSquareTest.`chiSquare(double[] expected, long[] observed)` Computes the Chi-Square statistic comparing `observed` and `expected` frequency counts.
    `static double` | TestUtils.`chiSquare(double[] expected, long[] observed)`
    `double` | ChiSquareTest.`chiSquare(long[][] counts)` Computes the Chi-Square statistic associated with a chi-square test of independence based on the input `counts` array, viewed as a two-way table.
    `static double` | TestUtils.`chiSquare(long[][] counts)`
    `double` | ChiSquareTest.`chiSquareDataSetsComparison(long[] observed1, long[] observed2)` Computes a Chi-Square two sample test statistic comparing bin frequency counts in `observed1` and `observed2`.
    `static double` | TestUtils.`chiSquareDataSetsComparison(long[] observed1, long[] observed2)`
    `double` | ChiSquareTest.`chiSquareTest(double[] expected, long[] observed)` Returns the observed significance level, or p-value, associated with a Chi-square goodness of fit test comparing the `observed` frequency counts to those in the `expected` array.
    `static double` | TestUtils.`chiSquareTest(double[] expected, long[] observed)`
    `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(double[] expected, long[] observed, double alpha)`
    `double` | ChiSquareTest.`chiSquareTest(long[][] counts)` Returns the observed significance level, or p-value, associated with a chi-square test of independence based on the input `counts` array, viewed as a two-way table.
    `static double` | TestUtils.`chiSquareTest(long[][] counts)`
    `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.`chiSquareTest(long[][] counts, double alpha)`
    `double` | ChiSquareTest.`chiSquareTestDataSetsComparison(long[] observed1, long[] observed2)` Returns the observed significance level, or p-value, associated with a Chi-Square two sample test comparing bin frequency counts in `observed1` and `observed2`.
    `static double` | TestUtils.`chiSquareTestDataSetsComparison(long[] observed1, long[] observed2)`
    `boolean` | ChiSquareTest.`chiSquareTestDataSetsComparison(long[] observed1, long[] observed2, double alpha)` Performs a Chi-Square two sample test comparing two binned data sets.
    `static boolean` | TestUtils.`chiSquareTestDataSetsComparison(long[] observed1, long[] observed2, double alpha)`
    `static double` | TestUtils.`oneWayAnovaFValue(Collection<double[]> categoryData)`
    `static double` | TestUtils.`oneWayAnovaPValue(Collection<double[]> categoryData)`
    `static boolean` | TestUtils.`oneWayAnovaTest(Collection<double[]> categoryData, double alpha)`
    `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.

  - ### Uses of DimensionMismatchException in org.apache.commons.math3.transform

    Methods in org.apache.commons.math3.transform that throw DimensionMismatchException
    Modifier and Type | Method and Description
    --- | ---
    `static Complex[]` | TransformUtils.`createComplexArray(double[][] dataRI)` Builds a new array of `Complex` from the specified two dimensional array of real and imaginary parts.

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