Index (JMSL Numerical Library)

A B C D E F G H I J K L M N O P Q R S T U V W X Y Z

A

abs(Complex) - Static method in class com.imsl.math.Complex: Returns the absolute value (modulus) of a Complex, |z|.
abs(int) - Static method in class com.imsl.math.JMath: Returns the absolute value of an int.
abs(long) - Static method in class com.imsl.math.JMath: Returns the absolute value of a long.
abs(float) - Static method in class com.imsl.math.JMath: Returns the absolute value of a float.
abs(double) - Static method in class com.imsl.math.JMath: Returns the absolute value of a double.
ABS_CORRELATION_COEFFICIENT - Static variable in class com.imsl.stat.Dissimilarities: Indicates the absolute value of the correlation coefficient distance method.
ABS_COSINE - Static variable in class com.imsl.stat.Dissimilarities: Indicates the absolute value of the cosine of the angle between the vectors distance method.
ABS_DIFF - Static variable in class com.imsl.stat.TimeSeriesOperations.CombineMethod: Takes the absolute difference |ts1-ts2| between the two values.
absolute(int) - Method in class com.imsl.io.AbstractFlatFile: Moves the cursor to the given row number in this ResultSet object.
AbstractChartNode - Class in com.imsl.chart: The base class of all of the nodes in both the 2D and 3D chart trees.
AbstractChartNode(AbstractChartNode) - Constructor for class com.imsl.chart.AbstractChartNode
AbstractFlatFile - Class in com.imsl.io: Reads a text or binary file as a ResultSet.
AbstractFlatFile() - Constructor for class com.imsl.io.AbstractFlatFile: Initializes an AbstractFlatFile.
AbstractFlatFile.FlatFileSQLException - Exception in com.imsl.io: A SQLException thrown by the AbstractFlatFile class.
AbstractFlatFile.FlatFileSQLFeatureNotSupportedException - Exception in com.imsl.io: A SQLFeatureNotSupportedException thrown by the AbstractFlatFile class.
accrint(GregorianCalendar, GregorianCalendar, GregorianCalendar, double, double, int, DayCountBasis) - Static method in class com.imsl.finance.Bond: Returns the interest which has accrued on a security that pays interest periodically.
accrintm(GregorianCalendar, GregorianCalendar, double, double, DayCountBasis) - Static method in class com.imsl.finance.Bond: Returns the interest which has accrued on a security that pays interest at maturity.
ACCUMULATE - Static variable in class com.imsl.math.NumericalDerivatives: Indicates the accumulation of the result from whatever type of differences have been specified previously into initial values of the Jacobian.
acos(Complex) - Static method in class com.imsl.math.Complex: Returns the inverse cosine (arc cosine) of a Complex, with branch cuts outside the interval [-1,1] along the real axis.
acos(double) - Static method in class com.imsl.math.JMath: Returns the inverse (arc) cosine of a double.
acosh(Complex) - Static method in class com.imsl.math.Complex: Returns the inverse hyperbolic cosine (arc cosh) of a Complex, with a branch cut at values less than one along the real axis.
acosh(double) - Static method in class com.imsl.math.Hyperbolic: Returns the inverse hyperbolic cosine of its argument.
Activation - Interface in com.imsl.datamining.neural: Interface implemented by perceptron activation functions.
ADABOOST - Static variable in class com.imsl.datamining.GradientBoosting.LossFunctionType: The loss criteria is the AdaBoost.M1 criterion.
add(Complex, Complex) - Static method in class com.imsl.math.Complex: Returns the sum of two Complex objects, x+y.
add(Complex, double) - Static method in class com.imsl.math.Complex: Returns the sum of a Complex and a double, x+y.
add(double, Complex) - Static method in class com.imsl.math.Complex: Returns the sum of a double and a Complex, x+y.
add(Complex[][], Complex[][]) - Static method in class com.imsl.math.ComplexMatrix: Add two rectangular Complex arrays, a + b.
add(Complex, Complex, ComplexSparseMatrix, ComplexSparseMatrix) - Static method in class com.imsl.math.ComplexSparseMatrix: Performs element-wise addition of two complex sparse matrices A, B of type ComplexSparseMatrix, $C leftarrow alpha A + beta B.$
add(double[][], double[][]) - Static method in class com.imsl.math.Matrix: Add two rectangular arrays, a + b.
add(Physical, Physical) - Static method in class com.imsl.math.Physical: Add two compatible Physical objects.
add(double, double, SparseMatrix, SparseMatrix) - Static method in class com.imsl.math.SparseMatrix: Performs element-wise addition of two real sparse matrices A, B of type SparseMatrix, $C leftarrow alpha A + beta B.$
addCenterLine() - Method in class com.imsl.chart.qc.ShewhartControlChart: Adds the center line to the control chart and returns the newly added line.
addControlLimit() - Method in class com.imsl.chart.qc.ShewhartControlChart: Adds a control limit to the chart.
addCumulativeLine(AxisXY) - Method in class com.imsl.chart.qc.ParetoChart: Adds a cumulative line to the specified axis.
addCumulativeLine() - Method in class com.imsl.chart.qc.ParetoChart: Creates a new right-side axis and adds a cumulative line to it.
addDataMarkers() - Method in class com.imsl.chart.qc.CuSumStatus: Adds the original data to the chart on a newly created axis.
addDataMarkers(AxisXY) - Method in class com.imsl.chart.qc.CuSumStatus: Adds the original data to the chart.
ADDITIVE - Static variable in class com.imsl.stat.ARMAOutlierIdentification: Indicates detection of an additive outlier.
ADDITIVE - Static variable in class com.imsl.stat.AutoARIMA: Indicates detection of an additive outlier.
addLegendItem(int, ChartNode) - Method in class com.imsl.chart.Chart: Adds a legend to this ChartNode.
addLowerControlLimit() - Method in class com.imsl.chart.qc.ShewhartControlChart: Creates lower ControlLimit, adds it to the control chart, and returns the newly created object.
addMouseListener(MouseListener) - Method in class com.imsl.chart.Chart: Adds a MouseListener to the component associated with this chart.
addMouseMotionListener(MouseMotionListener) - Method in class com.imsl.chart.Chart: Adds a MouseMotionListener to the component associated with this chart.
addNode(Node) - Method in class com.imsl.datamining.neural.Layer: Associates a Perceptron with this Layer.
addPickListener(PickListener) - Method in class com.imsl.chart.ChartNode: Adds a PickListener to this node.
addPostRenderPaint(Canvas3DChart.Paint) - Method in class com.imsl.chart3d.Canvas3DChart: Adds a Paint object to draw to the canvas after the the 3D image is rendered.
addPreRenderPaint(Canvas3DChart.Paint) - Method in class com.imsl.chart3d.Canvas3DChart: Adds a Paint object to draw to the canvas before the the 3D image is rendered.
addSurrogates(Tree, double[]) - Method in class com.imsl.datamining.decisionTree.ALACART: Adds the surrogate information to the tree.
addSurrogates(Tree, double[]) - Method in interface com.imsl.datamining.decisionTree.DecisionTreeSurrogateMethod: Adds the surrogate information to the tree.
addToSceneGraph(Group) - Method in class com.imsl.chart3d.AmbientLight
addToSceneGraph(Group) - Method in class com.imsl.chart3d.Axis3D
addToSceneGraph(Group) - Method in class com.imsl.chart3d.AxisBox
addToSceneGraph(Group) - Method in class com.imsl.chart3d.AxisLabel
addToSceneGraph(Group) - Method in class com.imsl.chart3d.AxisLine
addToSceneGraph(Group) - Method in class com.imsl.chart3d.AxisTitle
addToSceneGraph(Group) - Method in class com.imsl.chart3d.AxisXYZ
addToSceneGraph(Group) - Method in class com.imsl.chart3d.Background
addToSceneGraph(Group) - Method in class com.imsl.chart3d.Chart3D
addToSceneGraph(Group) - Method in class com.imsl.chart3d.ChartLights
addToSceneGraph(Group) - Method in class com.imsl.chart3d.ChartNode3D: Called to add this object to the scene graph.
addToSceneGraph(Group) - Method in class com.imsl.chart3d.ColormapLegend
addToSceneGraph(Group) - Method in class com.imsl.chart3d.Data
addToSceneGraph(Group) - Method in class com.imsl.chart3d.DirectionalLight
addToSceneGraph(Group) - Method in class com.imsl.chart3d.MajorTick
addToSceneGraph(Group) - Method in class com.imsl.chart3d.PointLight
addToSceneGraph(Group) - Method in class com.imsl.chart3d.Surface
addUpperControlLimit() - Method in class com.imsl.chart.qc.ShewhartControlChart: Creates upper ControlLimit, adds it to the control chart, and returns the newly created object.
addWecoLimits() - Method in class com.imsl.chart.qc.ShewhartControlChart: Adds lines for the Western Electric Company Rules.
ADJUSTED_R_SQUARED_CRITERION - Static variable in class com.imsl.stat.SelectionRegression: Indicates (adjusted ) criterion regression.
AFTER_SUCCESSFUL_STEP - Static variable in class com.imsl.math.ODE: Used by method examineStep to indicate examining after a successful step
AFTER_UNSUCCESSFUL_STEP - Static variable in class com.imsl.math.ODE: Used by method examineStep to indicate examining after an unsuccessful step
afterLast() - Method in class com.imsl.io.AbstractFlatFile: Moves the cursor to the end of this ResultSet object, just after the last row.
aggregate() - Method in class com.imsl.datamining.BootstrapAggregation: Performs the bootstrap aggregation.
AIC - Static variable in class com.imsl.stat.AutoARIMA: Indicates that Akaike's information criterion (AIC) is used in the optimum model determination.
AICC - Static variable in class com.imsl.stat.AutoARIMA: Indicates that Akaike's corrected information criterion (AICC) is used in the optimum model determination.
ALACART - Class in com.imsl.datamining.decisionTree: Generates a decision tree using the CART^TM method of Breiman, Friedman, Olshen and Stone (1984).
ALACART(double[][], int, PredictiveModel.VariableType[]) - Constructor for class com.imsl.datamining.decisionTree.ALACART: Constructs an ALACART decision tree for a single response variable and multiple predictor variables.
ALL - Static variable in class com.imsl.stat.RegressorsForGLM: The n indicator variables are the dummy variables.
allConverged() - Method in class com.imsl.math.ZerosFunction: Returns true if the iterations for all of the roots have converged.
ALPHA_FACTOR_ANALYSIS - Static variable in class com.imsl.stat.FactorAnalysis: Indicates alpha factor analysis.
AmbientLight - Class in com.imsl.chart3d: An ambient light.
AmbientLight(Chart3D) - Constructor for class com.imsl.chart3d.AmbientLight: Creates an ambient light.
amordegrc(double, GregorianCalendar, GregorianCalendar, double, int, double, DayCountBasis) - Static method in class com.imsl.finance.Bond: Returns the depreciation for each accounting period.
amorlinc(double, GregorianCalendar, GregorianCalendar, double, int, double, DayCountBasis) - Static method in class com.imsl.finance.Bond: Returns the depreciation for each accounting period.
amultp(double[], double[]) - Method in interface com.imsl.math.ConjugateGradient.Function: A user-supplied function which computes z=Ap.
amultp(double[], double[]) - Method in interface com.imsl.math.GenMinRes.Function: Used to compute $z = Ap$ where A is the matrix of coefficients to solve and p and z are arrays of length n, the order of matrix A.
ANCOVA - Class in com.imsl.stat: Analyzes a one-way classification model with covariates.
ANCOVA(double[][], double[][][]) - Constructor for class com.imsl.stat.ANCOVA: Constructs a one-way classification model with covariates.
ANGLE_IN_RADIANS - Static variable in class com.imsl.stat.Dissimilarities: Indicates the angle in radians (0, ) between the lines through the origin defined by the vectors distance method.
Annotation - Class in com.imsl.chart: Draws an annotation.
Annotation(ChartNode, Text, double, double) - Constructor for class com.imsl.chart.Annotation: Creates a Text object at the specific x,y location in chart coordinates.
Annotation(ChartNode, String, double, double) - Constructor for class com.imsl.chart.Annotation: Draws a String at the specific x,y location in chart coordinates.
Annotation(ChartNode, Image, double, double) - Constructor for class com.imsl.chart.Annotation: Renders an Image object centered at an x,y location in chart coordinates.
ANNUAL - Static variable in class com.imsl.finance.Bond: Coupon payments are made annually.
ANOVA - Class in com.imsl.stat: Analysis of Variance table and related statistics.
ANOVA(double[][]) - Constructor for class com.imsl.stat.ANOVA: /** Analyzes a one-way classification model.
ANOVA(double, double, double, double, double) - Constructor for class com.imsl.stat.ANOVA: Construct an analysis of variance table and related statistics.
ANOVAFactorial - Class in com.imsl.stat: Analyzes a balanced factorial design with fixed effects.
ANOVAFactorial(int, int[], double[]) - Constructor for class com.imsl.stat.ANOVAFactorial: Constructor for ANOVAFactorial.
Apriori - Class in com.imsl.datamining: Performs the Apriori algorithm for association rule discovery.
AR_1 - Static variable in class com.imsl.stat.ARMAEstimateMissing: Indicates that missing values should be estimated using an autoregressive time series with 1 lag.
AR_P - Static variable in class com.imsl.stat.ARMAEstimateMissing: Indicates that missing values should be estimated using an autoregressive time series with a maximum lag of maxLag.
ARAutoUnivariate - Class in com.imsl.stat: Automatically determines the best autoregressive time series model using Akaike's Information Criterion.
ARAutoUnivariate(int, double[]) - Constructor for class com.imsl.stat.ARAutoUnivariate: ARAutoUnivariate constructor.
ARAutoUnivariate.TriangularMatrixSingularException - Exception in com.imsl.stat: The input triangular matrix is singular.
ARAutoUnivariate.TriangularMatrixSingularException(String) - Constructor for exception com.imsl.stat.ARAutoUnivariate.TriangularMatrixSingularException: Constructs a TriangularMatrixSingularException object.
ARAutoUnivariate.TriangularMatrixSingularException(String, Object[]) - Constructor for exception com.imsl.stat.ARAutoUnivariate.TriangularMatrixSingularException: Constructs a TriangularMatrixSingularException object.
argument(Complex) - Static method in class com.imsl.math.Complex: Returns the argument (phase) of a Complex, in radians, with a branch cut along the negative real axis.
ARMA - Class in com.imsl.stat: Computes least-square estimates of parameters for an ARMA model.
ARMA(int, int, double[]) - Constructor for class com.imsl.stat.ARMA: Constructor for ARMA.
ARMA.IllConditionedException - Exception in com.imsl.stat: The problem is ill-conditioned.
ARMA.IllConditionedException(String) - Constructor for exception com.imsl.stat.ARMA.IllConditionedException: Constructs an IllConditionedException with the specified detail message.
ARMA.IllConditionedException(String, Object[]) - Constructor for exception com.imsl.stat.ARMA.IllConditionedException: Constructs an IllConditionedException with the specified detail message.
ARMA.IncreaseErrRelException - Exception in com.imsl.stat: The bound for the relative error is too small.
ARMA.IncreaseErrRelException(String) - Constructor for exception com.imsl.stat.ARMA.IncreaseErrRelException: Constructs an IncreaseErrRelException with the specified detail message.
ARMA.IncreaseErrRelException(String, Object[]) - Constructor for exception com.imsl.stat.ARMA.IncreaseErrRelException: Constructs an IncreaseErrRelException with the specified detail message.
ARMA.MatrixSingularException - Exception in com.imsl.stat: The input matrix is singular.
ARMA.MatrixSingularException(String) - Constructor for exception com.imsl.stat.ARMA.MatrixSingularException: Constructs an MatrixSingularException with the specified detail message.
ARMA.MatrixSingularException(String, Object[]) - Constructor for exception com.imsl.stat.ARMA.MatrixSingularException: Constructs an MatrixSingularException with the specified detail message.
ARMA.NewInitialGuessException - Exception in com.imsl.stat: The iteration has not made good progress.
ARMA.NewInitialGuessException(String) - Constructor for exception com.imsl.stat.ARMA.NewInitialGuessException: Constructs an NewInitialGuessException with the specified detail message.
ARMA.NewInitialGuessException(String, Object[]) - Constructor for exception com.imsl.stat.ARMA.NewInitialGuessException: Constructs an NewInitialGuessException with the specified detail message.
ARMA.TooManyCallsException - Exception in com.imsl.stat: The number of calls to the function has exceeded the maximum number of iterations times the number of moving average (MA) parameters + 1.
ARMA.TooManyCallsException(String) - Constructor for exception com.imsl.stat.ARMA.TooManyCallsException: Constructs an TooManyCallsException with the specified detail message.
ARMA.TooManyCallsException(String, Object[]) - Constructor for exception com.imsl.stat.ARMA.TooManyCallsException: Constructs an TooManyCallsException with the specified detail message.
ARMA.TooManyFcnEvalException - Exception in com.imsl.stat: Maximum number of function evaluations exceeded.
ARMA.TooManyFcnEvalException(String) - Constructor for exception com.imsl.stat.ARMA.TooManyFcnEvalException: Constructs an TooManyFcnEvalException with the specified detail message.
ARMA.TooManyFcnEvalException(String, Object[]) - Constructor for exception com.imsl.stat.ARMA.TooManyFcnEvalException: Constructs an TooManyFcnEvalException with the specified detail message.
ARMA.TooManyITNException - Exception in com.imsl.stat: Maximum number of iterations exceeded.
ARMA.TooManyITNException(String) - Constructor for exception com.imsl.stat.ARMA.TooManyITNException: Constructs an TooManyITNException with the specified detail message.
ARMA.TooManyITNException(String, Object[]) - Constructor for exception com.imsl.stat.ARMA.TooManyITNException: Constructs an TooManyITNException with the specified detail message.
ARMA.TooManyJacobianEvalException - Exception in com.imsl.stat: Maximum number of Jacobian evaluations exceeded.
ARMA.TooManyJacobianEvalException(String) - Constructor for exception com.imsl.stat.ARMA.TooManyJacobianEvalException: Constructs an TooManyJacobianEvalException with the specified detail message.
ARMA.TooManyJacobianEvalException(String, Object[]) - Constructor for exception com.imsl.stat.ARMA.TooManyJacobianEvalException: Constructs an TooManyJacobianEvalException with the specified detail message.
ARMAEstimateMissing - Class in com.imsl.stat: Estimates missing values in a time series collected with equal spacing.
ARMAEstimateMissing(int[], double[]) - Constructor for class com.imsl.stat.ARMAEstimateMissing: Constructor for ARMAEstimateMissing.
ARMAMaxLikelihood - Class in com.imsl.stat: Computes maximum likelihood estimates of parameters for an ARMA model with p and q autoregressive and moving average terms respectively.
ARMAMaxLikelihood(int, int, double[]) - Constructor for class com.imsl.stat.ARMAMaxLikelihood: Constructor for ARMAMaxLikelihood.
ARMAMaxLikelihood.InitialMAException - Exception in com.imsl.stat: The initial values for the moving average parameters are not invertible.
ARMAMaxLikelihood.InitialMAException(String) - Constructor for exception com.imsl.stat.ARMAMaxLikelihood.InitialMAException: Constructs an InitialMAException with the specified detail message.
ARMAMaxLikelihood.InitialMAException(String, Object[]) - Constructor for exception com.imsl.stat.ARMAMaxLikelihood.InitialMAException: Constructs an InitialMAException with the specified detail message.
ARMAMaxLikelihood.NonInvertibleException - Exception in com.imsl.stat: The solution is noninvertible.
ARMAMaxLikelihood.NonInvertibleException(String) - Constructor for exception com.imsl.stat.ARMAMaxLikelihood.NonInvertibleException: Constructs a NonInvertible exception with the specified detail message.
ARMAMaxLikelihood.NonInvertibleException(String, Object[]) - Constructor for exception com.imsl.stat.ARMAMaxLikelihood.NonInvertibleException: Constructs a NonInvertibleException exception with the specified detail message.
ARMAMaxLikelihood.NonStationaryException - Exception in com.imsl.stat: The solution is nonstationary.
ARMAMaxLikelihood.NonStationaryException(String) - Constructor for exception com.imsl.stat.ARMAMaxLikelihood.NonStationaryException: Constructs a NonStationary exception with the specified detail message.
ARMAMaxLikelihood.NonStationaryException(String, Object[]) - Constructor for exception com.imsl.stat.ARMAMaxLikelihood.NonStationaryException: Constructs a NonStationary exception with the specified detail message.
ARMAOutlierIdentification - Class in com.imsl.stat: Detects and determines outliers and simultaneously estimates the model parameters in a time series whose underlying outlier free series follows a general seasonal or nonseasonal ARMA model.
ARMAOutlierIdentification(double[]) - Constructor for class com.imsl.stat.ARMAOutlierIdentification: Constructor for ARMAOutlierIdentification.
ARSeasonalFit - Class in com.imsl.stat: Estimates the optimum seasonality parameters for a time series using an autoregressive model, AR(p), to represent the time series.
ARSeasonalFit(int, int[][], double[]) - Constructor for class com.imsl.stat.ARSeasonalFit: Constructor for ARSeasonalFit.
ascending(int[]) - Static method in class com.imsl.stat.Sort: Sorts an integer array into ascending order.
ascending(int[], int[]) - Static method in class com.imsl.stat.Sort: Sorts an integer array into ascending order and returns the permutation vector.
ascending(double[]) - Static method in class com.imsl.stat.Sort: Sorts an array into ascending order.
ascending(double[], int[]) - Static method in class com.imsl.stat.Sort: Sorts an array into ascending order and returns the permutation vector.
ascending(double[][], int) - Static method in class com.imsl.stat.Sort: Sorts a matrix into ascending order by the first nKeys.
ascending(double[][], int[]) - Static method in class com.imsl.stat.Sort: Sorts a matrix into ascending order by specified keys.
ascending(double[][], int, int[]) - Static method in class com.imsl.stat.Sort: Sorts a matrix into ascending order according to the first nKeys keys and returns the permutation vector.
ascending(int[][], int) - Static method in class com.imsl.stat.Sort: Sorts a matrix into ascending order by the first nKeys.
ascending(int[][], int, int[]) - Static method in class com.imsl.stat.Sort: Sorts a matrix into ascending order according to the first nKeys keys and returns the permutation vector.
ascending(double[][], int[], int[]) - Static method in class com.imsl.stat.Sort: Sorts a matrix into ascending order by specified keys and returns the permutation vector.
ascending(int[][], int[], int[]) - Static method in class com.imsl.stat.Sort: Sorts a matrix into ascending order by specified keys and returns the permutation vector.
asin(Complex) - Static method in class com.imsl.math.Complex: Returns the inverse sine (arc sine) of a Complex, with branch cuts outside the interval [-1,1] along the real axis.
asin(double) - Static method in class com.imsl.math.JMath: Returns the inverse (arc) sine of a double.
asinh(Complex) - Static method in class com.imsl.math.Complex: Returns the inverse hyperbolic sine (arc sinh) of a Complex, with branch cuts outside the interval [-i,i].
asinh(double) - Static method in class com.imsl.math.Hyperbolic: Returns the inverse hyperbolic sine of its argument.
AssociationRule - Class in com.imsl.datamining: Association rule of the form .
AT_BEGINNING_OF_PERIOD - Static variable in class com.imsl.finance.Finance: Flag used to indicate that payment is made at the beginning of each period.
AT_END_OF_PERIOD - Static variable in class com.imsl.finance.Finance: Flag used to indicate that payment is made at the end of each period.
atan(Complex) - Static method in class com.imsl.math.Complex: Returns the inverse tangent (arc tangent) of a Complex, with branch cuts outside the interval [-i,i] along the imaginary axis.
atan(double) - Static method in class com.imsl.math.JMath: Returns the inverse (arc) tangent of a double.
atan2(double, double) - Static method in class com.imsl.math.JMath: Returns the angle corresponding to a Cartesian point.
atanh(Complex) - Static method in class com.imsl.math.Complex: Returns the inverse hyperbolic tangent (arc tanh) of a Complex, with branch cuts outside the interval [-1,1] on the real axis.
atanh(double) - Static method in class com.imsl.math.Hyperbolic: Returns the inverse hyperbolic tangent of its argument.
AutoARIMA - Class in com.imsl.stat: Automatically identifies time series outliers, determines parameters of a multiplicative seasonal $text{ARIMA}(p,0,q)times(0,d,0)_s$ model and produces forecasts that incorporate the effects of outliers whose effects persist beyond the end of the series.
AutoARIMA(int[], double[]) - Constructor for class com.imsl.stat.AutoARIMA: Constructor for AutoARIMA.
AutoARIMA.NoAcceptableModelFoundException - Exception in com.imsl.stat: No appropriate ARIMA model could be found.
AutoARIMA.NoAcceptableModelFoundException(String) - Constructor for exception com.imsl.stat.AutoARIMA.NoAcceptableModelFoundException: Constructs a NoAcceptableModelFoundException exception with the specified detail message.
AutoARIMA.NoAcceptableModelFoundException(String, Object[]) - Constructor for exception com.imsl.stat.AutoARIMA.NoAcceptableModelFoundException: Constructs a NoAcceptableModelFoundException exception with the specified detail message.
AutoCorrelation - Class in com.imsl.stat: Computes the sample autocorrelation function of a stationary time series.
AutoCorrelation(double[], int) - Constructor for class com.imsl.stat.AutoCorrelation: Constructor to compute the sample autocorrelation function of a stationary time series.
AutoCorrelation.NonPosVariancesException - Exception in com.imsl.stat: The problem is ill-conditioned.
AutoCorrelation.NonPosVariancesException(String) - Constructor for exception com.imsl.stat.AutoCorrelation.NonPosVariancesException: Constructs an NonPosVariancesException with the specified detail message.
AutoCorrelation.NonPosVariancesException(String, Object[]) - Constructor for exception com.imsl.stat.AutoCorrelation.NonPosVariancesException: Constructs an NonPosVariancesException with the specified detail message.
AUTOMATIC - Static variable in class com.imsl.chart.Treemap: Flag to set the treemap orientation automatically.
AUTOSCALE_DATA - Static variable in class com.imsl.chart.AbstractChartNode: Flag used to indicate that autoscaling is to be done by scanning the data nodes.
AUTOSCALE_DENSITY - Static variable in class com.imsl.chart.AbstractChartNode: Flag used to indicate that autoscaling is to adjust the "Density" attribute.
AUTOSCALE_NUMBER - Static variable in class com.imsl.chart.AbstractChartNode: Flag used to indicate that autoscaling is to adjust the "Number" attribute.
AUTOSCALE_OFF - Static variable in class com.imsl.chart.AbstractChartNode: Flag used to indicate that autoscaling is turned off.
AUTOSCALE_WINDOW - Static variable in class com.imsl.chart.AbstractChartNode: Flag used to indicate that autoscaling is to be done by using the "Window" attribute.
AVERAGE - Static variable in class com.imsl.stat.TimeSeriesOperations.CombineMethod: Takes the average of the two values.
Axis - Class in com.imsl.chart: The Axis node provides the mapping for all of its children from the user coordinate space to the device (screen) space.
Axis(Chart) - Constructor for class com.imsl.chart.Axis: Contructs an Axis node.
Axis1D - Class in com.imsl.chart: An x-axis or a y-axis.
Axis3D - Class in com.imsl.chart3d: An x-axis, y-axis or a z-axis.
AXIS_TITLE_AT_END - Static variable in class com.imsl.chart3d.ChartNode3D: Value for attribute "AxisTitlePosition" indicating that the axis title should be placed at the end of the axis.
AXIS_TITLE_PARALLEL - Static variable in class com.imsl.chart3d.ChartNode3D: Value for attribute "AxisTitlePosition" indicating that the axis title should be placed parallel to the axis.
AXIS_X - Static variable in class com.imsl.chart.AbstractChartNode: Flag to indicate x-axis.
AXIS_X_TOP - Static variable in class com.imsl.chart.ChartNode: Flag to indicate x-axis placed on top of the chart.
AXIS_Y - Static variable in class com.imsl.chart.AbstractChartNode: Flag to indicate y-axis.
AXIS_Y_RIGHT - Static variable in class com.imsl.chart.ChartNode: Flag to indicate y-axis placed to the right of the chart.
AXIS_Z - Static variable in class com.imsl.chart.AbstractChartNode: Flag to indicate z-axis.
AxisBox - Class in com.imsl.chart3d: Box behind the axis.
AxisLabel - Class in com.imsl.chart: The labels on an axis.
AxisLabel - Class in com.imsl.chart3d: The labels on an axis.
AxisLine - Class in com.imsl.chart: The axis line.
AxisLine - Class in com.imsl.chart3d: The axis line.
AxisR - Class in com.imsl.chart: The R-axis in a polar plot.
AxisRLabel - Class in com.imsl.chart: The labels on an axis.
AxisRLine - Class in com.imsl.chart: The radius axis line in a polar plot.
AxisRMajorTick - Class in com.imsl.chart: The major tick marks for the radius axis in a polar plot.
AxisTheta - Class in com.imsl.chart: The angular axis in a polar plot.
AxisTitle - Class in com.imsl.chart: The title on an axis.
AxisTitle - Class in com.imsl.chart3d: Axis title.
AxisUnit - Class in com.imsl.chart: The unit title on an axis.
AxisXY - Class in com.imsl.chart: The axes for an x-y chart.
AxisXY(Chart) - Constructor for class com.imsl.chart.AxisXY: Create an AxisXY.
AxisXYZ - Class in com.imsl.chart3d: The axes for an x-y-z chart.
AxisXYZ(Chart3D) - Constructor for class com.imsl.chart3d.AxisXYZ: Create an AxisXYZ.

B

Background - Class in com.imsl.chart: The background of a chart.
Background - Class in com.imsl.chart3d: Background of the chart.
backshift(TimeSeries, int) - Method in class com.imsl.stat.TimeSeriesOperations: Returns the backshifted version of the time series.
backward(Complex[]) - Method in class com.imsl.math.ComplexFFT: Compute the complex periodic sequence from its Fourier coefficients.
backward(double[]) - Method in class com.imsl.math.FFT: Compute the real periodic sequence from its Fourier coefficients.
BACKWARD_REGRESSION - Static variable in class com.imsl.stat.StepwiseRegression: Indicates backward regression.
Bar - Class in com.imsl.chart: A bar chart.
Bar(AxisXY) - Constructor for class com.imsl.chart.Bar: Constructs a bar chart.
Bar(AxisXY, double[]) - Constructor for class com.imsl.chart.Bar: Constructs a simple bar chart using supplied y data.
Bar(AxisXY, double[], double[]) - Constructor for class com.imsl.chart.Bar: Constructs a simple bar chart using supplied x and y data.
Bar(AxisXY, double[][]) - Constructor for class com.imsl.chart.Bar: Constructs a grouped bar chart using supplied x and y data.
Bar(AxisXY, double[], double[][]) - Constructor for class com.imsl.chart.Bar: Constructs a grouped bar chart using supplied x and y data.
Bar(AxisXY, double[][][]) - Constructor for class com.imsl.chart.Bar: Constructs a stacked, grouped bar chart using supplied y data.
Bar(AxisXY, double[], double[][][]) - Constructor for class com.imsl.chart.Bar: Constructs a stacked, grouped bar chart using supplied x and y data.
BAR_TYPE_HORIZONTAL - Static variable in class com.imsl.chart.ChartNode: Flag to indicate a horizontal bar chart.
BAR_TYPE_VERTICAL - Static variable in class com.imsl.chart.ChartNode: Flag to indicate a vertical bar chart.
BarItem - Class in com.imsl.chart: A single bar in a bar chart.
BarSet - Class in com.imsl.chart: A set of bars in a bar chart.
BARTLETTS_FORMULA - Static variable in class com.imsl.stat.AutoCorrelation: Indicates standard error computation using Bartlett's formula.
BARTLETTS_FORMULA - Static variable in class com.imsl.stat.CrossCorrelation: Indicates standard error computation using Bartlett's formula.
BARTLETTS_FORMULA_NOCC - Static variable in class com.imsl.stat.CrossCorrelation: Indicates standard error computation using Bartlett's formula with the assumption of no cross-correlation.
basis(int, double) - Method in interface com.imsl.stat.RegressionBasis: Public interface for the nonlinear least-squares function.
Basis30e360 - Static variable in class com.imsl.finance.DayCountBasis: Computations are based on the assumption of 30 days per month and 360 days per year.
BasisActual360 - Static variable in class com.imsl.finance.DayCountBasis: Computations are based on the number of days in a month based on the actual calendar value and the number of days, but assuming 360 days per year.
BasisActual365 - Static variable in class com.imsl.finance.DayCountBasis: Computations are based on the number of days in a month based on the actual calendar value and the number of days, but assuming 365 days per year.
BasisActualActual - Static variable in class com.imsl.finance.DayCountBasis: Computations are based on the actual calendar.
BasisNASD - Static variable in class com.imsl.finance.DayCountBasis: Computations are based on the assumption of 30 days per month and 360 days per year.
BasisPart - Interface in com.imsl.finance: Component of DayCountBasis.
BasisPart30E360 - Static variable in class com.imsl.finance.DayCountBasis: Computations are based on the assumption of 30 days per month and 360 days per year.
BasisPart365 - Static variable in class com.imsl.finance.DayCountBasis: Computations are based on the assumption of 365 days per year.
BasisPartActual - Static variable in class com.imsl.finance.DayCountBasis: Computations are are based on the actual calendar.
BasisPartNASD - Static variable in class com.imsl.finance.DayCountBasis: Computations based on the assumption of 30 days per month and 360 days per year.
BEFORE_STEP - Static variable in class com.imsl.math.ODE: Used by method examineStep to indicate examining before the next step
beforeFirst() - Method in class com.imsl.io.AbstractFlatFile: Moves the cursor to the front of this ResultSet object, just before the first row.
BEGIN_COLUMN_LABEL - Static variable in class com.imsl.math.PrintMatrixFormat: This flag as the type argument to format, indicates that the formatting string for ending a column label is to be returned.
BEGIN_COLUMN_LABELS - Static variable in class com.imsl.math.PrintMatrixFormat: This flag as the type argument to format, indicates that the formatting string for beginning a column label row is to be returned.
BEGIN_ENTRY - Static variable in class com.imsl.math.PrintMatrixFormat: This flag as the type argument to format, indicates that the formatted string for beginning an entry is to be returned.
BEGIN_MATRIX - Static variable in class com.imsl.math.PrintMatrixFormat: This flag as the type argument to format, indicates that the formatting string for beginning a matrix is to be returned.
BEGIN_ROW - Static variable in class com.imsl.math.PrintMatrixFormat: This flag as the type argument to format, indicates that the formatting string for beginning a row is to be returned.
BEGIN_ROW_LABEL - Static variable in class com.imsl.math.PrintMatrixFormat: This flag as the type argument to format, indicates that the formatting string for beginning a row label is to be returned.
beginGet() - Method in class com.imsl.io.AbstractFlatFile: This method should be called at the start of every get Type method.
BERNOULLI - Static variable in class com.imsl.datamining.GradientBoosting.LossFunctionType: The loss criteria is the binomial or Bernoulli negative log-likelihood, or deviance.
Bessel - Class in com.imsl.math: Collection of Bessel functions.
beta(double, double) - Static method in class com.imsl.math.Sfun: Returns the value of the beta function.
beta(double, double, double) - Static method in class com.imsl.stat.Cdf: Evaluates the beta cumulative probability distribution function.
beta(double, double, double) - Static method in class com.imsl.stat.InvCdf: Evaluates the inverse of the beta cumulative probability distribution function.
beta(double, double, double) - Static method in class com.imsl.stat.Pdf: Evaluates the beta probability density function.
betaIncomplete(double, double, double) - Static method in class com.imsl.math.Sfun: Returns the incomplete beta function ratio.
betaMean(double, double) - Static method in class com.imsl.stat.Cdf: Evaluates the mean of the beta cumulative probability distribution function
BetaPD - Class in com.imsl.stat.distributions: The beta probability distribution
BetaPD() - Constructor for class com.imsl.stat.distributions.BetaPD: Constructor for the beta probability distribution
betaVariance(double, double) - Static method in class com.imsl.stat.Cdf: Evaluates the variance of the beta cumulative probability distribution function
BIC - Static variable in class com.imsl.stat.AutoARIMA: Indicates that the Bayesian information criterion (BIC) is used in the optimum model determination.
BIMONTHLY - Static variable in class com.imsl.finance.Bond: Coupon payments are made bimonthly (6 times per year).
BINARY_VARIABLE - Static variable in class com.imsl.io.MPSReader: Variable must be either 0 or 1.
BinaryClassification - Class in com.imsl.datamining.neural: Classifies patterns into two classes.
BinaryClassification(Network) - Constructor for class com.imsl.datamining.neural.BinaryClassification: Creates a binary classifier.
binomial(int, int, double) - Static method in class com.imsl.stat.Cdf: Evaluates the binomial cumulative probability distribution function.
binomial(int, int, double) - Static method in class com.imsl.stat.Pdf: Evaluates the binomial probability density function.
bivariateNormal(double, double, double) - Static method in class com.imsl.stat.Cdf: Evaluates the bivariate normal cumulative probability distribution function.
BLUE - Static variable in interface com.imsl.chart.Colormap: Linear blue colormap.
BLUE_GREEN_RED_YELLOW - Static variable in interface com.imsl.chart.Colormap: Blue/green/red/yellow colormap.
BLUE_RED - Static variable in interface com.imsl.chart.Colormap: Blue/red colormap.
BLUE_WHITE - Static variable in interface com.imsl.chart.Colormap: Blue/white colormap.
Bond - Class in com.imsl.finance: Collection of bond functions.
BONFERRONI - Static variable in class com.imsl.stat.ANOVA: The Bonferroni method
BootstrapAggregation - Class in com.imsl.datamining: Performs bootstrap aggregation to generate predictions using predictive models.
BootstrapAggregation(PredictiveModel) - Constructor for class com.imsl.datamining.BootstrapAggregation: Constructs a BootstrapAggregation class in order to generate predictions of a PredictiveModel using bootstrap aggregation.
BOUNDED_SCALING - Static variable in class com.imsl.datamining.neural.ScaleFilter: Flag to indicate bounded scaling.
BOUNDED_Z_SCORE_SCALING_MEAN_STDEV - Static variable in class com.imsl.datamining.neural.ScaleFilter: Flag to indicate bounded z-score scaling using the mean and standard deviation.
BOUNDED_Z_SCORE_SCALING_MEDIAN_MAD - Static variable in class com.imsl.datamining.neural.ScaleFilter: Flag to indicate bounded z-score scaling using the median and mean absolute difference.
BoundedLeastSquares - Class in com.imsl.math: Solves a nonlinear least-squares problem subject to bounds on the variables using a modified Levenberg-Marquardt algorithm.
BoundedLeastSquares(BoundedLeastSquares.Function, int, int, int, double[], double[]) - Constructor for class com.imsl.math.BoundedLeastSquares: Constructor for BoundedLeastSquares.
BoundedLeastSquares.FalseConvergenceException - Exception in com.imsl.math: False convergence - The iterates appear to be converging to a noncritical point.
BoundedLeastSquares.FalseConvergenceException(String) - Constructor for exception com.imsl.math.BoundedLeastSquares.FalseConvergenceException: Constructs an FalseConvergenceException with the specified detail message.
BoundedLeastSquares.FalseConvergenceException(String, Object[]) - Constructor for exception com.imsl.math.BoundedLeastSquares.FalseConvergenceException: Constructs an FalseConvergenceException with the specified detail message.
BoundedLeastSquares.Function - Interface in com.imsl.math: Public interface for the user-supplied function to evaluate the function that defines the least-squares problem.
BoundedLeastSquares.Jacobian - Interface in com.imsl.math: Public interface for the user-supplied function to compute the Jacobian.
BoundedVariableLeastSquares - Class in com.imsl.math: Solve a linear least-squares problem with bounds on the variables.
BoundedVariableLeastSquares(double[][], double[], double[], double[]) - Constructor for class com.imsl.math.BoundedVariableLeastSquares: Construct a new BoundedVariableLeastSquares instance to solve Ax-b subject to bounds on the variables.
BoundedVariableLeastSquares.TooManyIterException - Exception in com.imsl.math: Maximum number of iterations exceeded.
BoundedVariableLeastSquares.TooManyIterException(String) - Constructor for exception com.imsl.math.BoundedVariableLeastSquares.TooManyIterException: The maximum number of iterations has exceeded.
BoundedVariableLeastSquares.TooManyIterException(String, Object[]) - Constructor for exception com.imsl.math.BoundedVariableLeastSquares.TooManyIterException: The maximum number of iterations has exceeded.
BoxPlot - Class in com.imsl.chart: Draws a multiple-group Box plot.
BoxPlot(AxisXY, double[], double[][]) - Constructor for class com.imsl.chart.BoxPlot: Constructs a box plot chart node with specified x values.
BoxPlot(AxisXY, double[], BoxPlot.Statistics[]) - Constructor for class com.imsl.chart.BoxPlot: Constructs a box plot chart node with specified x values.
BoxPlot(AxisXY, double[][]) - Constructor for class com.imsl.chart.BoxPlot: Constructs a box plot chart.
BoxPlot.Statistics - Class in com.imsl.chart: Computes the statistics for one set of observations in a Boxplot.
BoxPlot.Statistics(double[]) - Constructor for class com.imsl.chart.BoxPlot.Statistics: Creates a new instance of BoxPlot.Statistics.
BOXPLOT_TYPE_HORIZONTAL - Static variable in class com.imsl.chart.BoxPlot: Value for attribute "BoxPlotType" indicating that this is a horizontal box plot.
BOXPLOT_TYPE_VERTICAL - Static variable in class com.imsl.chart.BoxPlot: Value for attribute "BoxPlotType" indicating that this is a horizontal box plot.
breakPoint - Variable in class com.imsl.math.Spline: The breakpoint array of length n, where n is the number of piecewise polynomials.
BRESLOWS_APPROXIMATE - Static variable in class com.imsl.stat.ProportionalHazards: Breslows approximate method of handling ties.
BsInterpolate - Class in com.imsl.math: Extension of the BSpline class to interpolate data points.
BsInterpolate(double[], double[]) - Constructor for class com.imsl.math.BsInterpolate: Constructs a B-spline that interpolates the given data points.
BsInterpolate(double[], double[], int) - Constructor for class com.imsl.math.BsInterpolate: Constructs a B-spline that interpolates the given data points and order, using a default "not-a-knot" spline knot sequence.
BsInterpolate(double[], double[], int, double[]) - Constructor for class com.imsl.math.BsInterpolate: Constructs a B-spline that interpolates the given data points, using the specified order and knots.
BsLeastSquares - Class in com.imsl.math: Extension of the BSpline class to compute a least squares spline approximation to data points.
BsLeastSquares(double[], double[], int) - Constructor for class com.imsl.math.BsLeastSquares: Constructs a least squares B-spline approximation to the given data points.
BsLeastSquares(double[], double[], int, int) - Constructor for class com.imsl.math.BsLeastSquares: Constructs a least squares B-spline approximation to the given data points.
BsLeastSquares(double[], double[], int, int, double[], double[]) - Constructor for class com.imsl.math.BsLeastSquares: Constructs a least squares B-spline approximation to the given data points.
BSpline - Class in com.imsl.math: BSpline represents and evaluates univariate B-splines.
BSpline() - Constructor for class com.imsl.math.BSpline
BufferedPaint - Class in com.imsl.chart3d: A Paint object cached into an image.
BufferedPaint(Canvas3DChart.Paint, int, int, int, int, Component) - Constructor for class com.imsl.chart3d.BufferedPaint: The paint method in Canvas3DChart.Paint is written into an image of size width by height.
BW_LINEAR - Static variable in interface com.imsl.chart.Colormap: Black and white (grayscale) colormap.
byteValue() - Method in class com.imsl.math.Complex: Returns the value of the real part as a byte.

C

C45 - Class in com.imsl.datamining.decisionTree: Generates a decision tree using the C4.5 algorithm for a categorical response variable and categorical or quantitative predictor variables.
C45(double[][], int, PredictiveModel.VariableType[]) - Constructor for class com.imsl.datamining.decisionTree.C45: Constructs a C45 object for a single response variable and multiple predictor variables.
cancelRowUpdates() - Method in class com.imsl.io.AbstractFlatFile: Cancels the updates made to the current row in this ResultSet object.
Candlestick - Class in com.imsl.chart: Candlestick plot of stock data.
Candlestick(AxisXY, Date, double[], double[], double[], double[]) - Constructor for class com.imsl.chart.Candlestick: Constructs a candlestick chart node beginning with specified start date.
Candlestick(AxisXY, double[], double[], double[], double[], double[]) - Constructor for class com.imsl.chart.Candlestick: Constructs a candlestick chart node at specified axis points.
CandlestickItem - Class in com.imsl.chart: A candlestick for the up days or the down days.
canonicalCorrelation(double[][]) - Method in class com.imsl.stat.Random: Method canonicalCorrelation generates a canonical correlation matrix from an arbitrarily distributed multivariate deviate sequence with nvar deviate variables, nseq steps in the sequence, and a Gaussian Copula dependence structure.
Canvas3DChart - Class in com.imsl.chart3d: A canvas for rendering a 3D chart.
Canvas3DChart() - Constructor for class com.imsl.chart3d.Canvas3DChart: Creates a Canvas3DChart with a new Chart3D object.
Canvas3DChart(Chart3D) - Constructor for class com.imsl.chart3d.Canvas3DChart: Creates a Canvas3DChart with a given Chart3D object.
Canvas3DChart.Paint - Interface in com.imsl.chart3d: Interface for 2D drawing on the canvas before or after the the 3D image is drawn.
capabilityIndexCp(double, double) - Method in class com.imsl.chart.qc.XbarR: Returns the capability index
$c_p=frac{mathrm{USL}-mathrm{LSL}}{6sigma}$
capabilityIndexCp(double, double) - Method in class com.imsl.chart.qc.XbarS: Returns the capability index
$c_p=frac{mathrm{USL}-mathrm{LSL}}{6sigma}$
capabilityIndexCpk(double, double) - Method in class com.imsl.chart.qc.XbarR: Returns the capability index $c_{pk}$
$c_{pk}=minleft[frac{mathrm{USL}-bar{x}}{3sigma},frac{bar{x}-mathrm{LSL}}{3sigma}right]$
capabilityIndexCpk(double, double) - Method in class com.imsl.chart.qc.XbarS: Returns the capability index $c_{pk}$
$c_{pk}=minleft[frac{mathrm{USL}-bar{x}}{3sigma},frac{bar{x}-mathrm{LSL}}{3sigma}right]$
CATEGORICAL - Static variable in class com.imsl.datamining.PredictiveModel.VariableType: The associated variable can take on one of a limited number of values (levels).
CategoricalGenLinModel - Class in com.imsl.stat: Analyzes categorical data using logistic, probit, Poisson, and other linear models.
CategoricalGenLinModel(double[][], int) - Constructor for class com.imsl.stat.CategoricalGenLinModel: Constructs a new CategoricalGenLinModel.
CategoricalGenLinModel.ClassificationVariableException - Exception in com.imsl.stat: The ClassificationVariable vector has not been initialized.
CategoricalGenLinModel.ClassificationVariableException() - Constructor for exception com.imsl.stat.CategoricalGenLinModel.ClassificationVariableException: Constructs a ClassificationVariableException.
CategoricalGenLinModel.ClassificationVariableLimitException - Exception in com.imsl.stat: The Classification Variable limit set by the user through setUpperBound has been exceeded.
CategoricalGenLinModel.ClassificationVariableLimitException(int) - Constructor for exception com.imsl.stat.CategoricalGenLinModel.ClassificationVariableLimitException: Constructs a ClassificationVariableLimitException.
CategoricalGenLinModel.ClassificationVariableValueException - Exception in com.imsl.stat: The number of distinct values for each Classification Variable must be greater than 1.
CategoricalGenLinModel.ClassificationVariableValueException(int, int) - Constructor for exception com.imsl.stat.CategoricalGenLinModel.ClassificationVariableValueException: Constructs a ClassificationVariableValueException.
CategoricalGenLinModel.DeleteObservationsException - Exception in com.imsl.stat: The number of observations to be deleted (set by setObservationMax) has grown too large.
CategoricalGenLinModel.DeleteObservationsException(int) - Constructor for exception com.imsl.stat.CategoricalGenLinModel.DeleteObservationsException: Constructs a DeleteObservationsException.
CategoricalGenLinModel.RankDeficientException - Exception in com.imsl.stat: The model has been determined to be rank deficient.
CategoricalGenLinModel.RankDeficientException(int) - Constructor for exception com.imsl.stat.CategoricalGenLinModel.RankDeficientException: Constructs a RankDeficientException.
CChart - Class in com.imsl.chart.qc: CChart is a c-chart for monitoring the count of the number of defects when defects are rare.
CChart(AxisXY, int[]) - Constructor for class com.imsl.chart.qc.CChart: Creates a C-chart given the number of defects in a series of samples.
Cdf - Class in com.imsl.stat: Cumulative probability distribution functions.
cdf(double) - Method in interface com.imsl.stat.CdfFunction: Public interface for the user-supplied cumulative distribution function to be used by InverseCdf.
CdfFunction - Interface in com.imsl.stat: Public interface for the user-supplied cumulative distribution function to be used by InverseCdf and ChiSquaredTest.
ceil(double) - Static method in class com.imsl.math.JMath: Returns the value of a double rounded toward positive infinity to an integral value.
CENTER_MEAN - Static variable in class com.imsl.stat.ARSeasonalFit: Indicates the transformed series should be centered using the average of the differenced series.
CENTER_MEDIAN - Static variable in class com.imsl.stat.ARSeasonalFit: Indicates the transformed series should be centered using the median of the differenced series.
CENTRAL - Static variable in class com.imsl.math.NumericalDerivatives: Indicates central differences.
CHAID - Class in com.imsl.datamining.decisionTree: Generates a decision tree using CHAID for categorical or discrete ordered predictor variables.
CHAID(double[][], int, PredictiveModel.VariableType[]) - Constructor for class com.imsl.datamining.decisionTree.CHAID: Constructs a CHAID object for a single response variable and multiple predictor variables.
Chart - Class in com.imsl.chart: The root node of the chart tree.
Chart() - Constructor for class com.imsl.chart.Chart: This is the root of our tree, it has no parent.
Chart(Component) - Constructor for class com.imsl.chart.Chart: This is the root of our tree, it has no parent.
Chart(Image) - Constructor for class com.imsl.chart.Chart: This is the root of our tree, it has no parent.
chart - Variable in class com.imsl.chart.JPanelChart: The embedded chart.
Chart3D - Class in com.imsl.chart3d: Root node of a 3d chart tree.
Chart3D() - Constructor for class com.imsl.chart3d.Chart3D: Creates a new instance of Chart3D
ChartFunction - Interface in com.imsl.chart: An interface that allows a function to be plotted.
ChartLights - Class in com.imsl.chart3d: Default set of lights.
ChartNode - Class in com.imsl.chart: The base class of all of the nodes in the 2D chart tree.
ChartNode(ChartNode) - Constructor for class com.imsl.chart.ChartNode: Construct a ChartNode object.
ChartNode3D - Class in com.imsl.chart3d: The base class of all of the nodes in the 3D chart tree.
ChartNode3D(ChartNode3D) - Constructor for class com.imsl.chart3d.ChartNode3D: Construct a ChartNode3D object.
ChartServlet - Class in com.imsl.chart: The base class for chart servlets.
ChartServlet() - Constructor for class com.imsl.chart.ChartServlet
ChartSpline - Class in com.imsl.chart: Wrap a spline into a ChartFunction to be plotted.
ChartSpline(Spline) - Constructor for class com.imsl.chart.ChartSpline: Creates a ChartSpline from a Spline.
ChartSpline(Spline, int) - Constructor for class com.imsl.chart.ChartSpline: Creates a ChartSpline from the derivative of a Spline.
ChartTitle - Class in com.imsl.chart: The main title of a chart.
ChartXML - Class in com.imsl.chart.xml: Create a Chart from an XML file.
ChartXML(String) - Constructor for class com.imsl.chart.xml.ChartXML: Creates a ChartXML from an XML file.
ChartXML(String, boolean) - Constructor for class com.imsl.chart.xml.ChartXML: Creates a ChartXML from an XML file.
ChartXML(InputSource, boolean) - Constructor for class com.imsl.chart.xml.ChartXML: Creates a ChartXML from an XML source.
ChartXML(Document) - Constructor for class com.imsl.chart.xml.ChartXML: Creates a ChartXML from a DOM tree.
check(int) - Method in class com.imsl.chart.Draw
checkCompatibility(Physical, Physical) - Static method in class com.imsl.math.Physical: Checks the compatibility of two Physical objects.
checkerboard(int, Color, Color) - Static method in class com.imsl.chart.FillPaint: Returns a checkerboard pattern.
checkMatrix(Complex[][]) - Static method in class com.imsl.math.ComplexMatrix: Check that all of the rows in the Complex matrix have the same length.
checkMatrix(double[][]) - Static method in class com.imsl.math.Matrix: Check that all of the rows in the matrix have the same length.
checkSquareMatrix(Complex[][]) - Static method in class com.imsl.math.ComplexMatrix: Check that the Complex matrix is square.
checkSquareMatrix() - Method in class com.imsl.math.ComplexSparseMatrix: Check that the matrix is square.
checkSquareMatrix(double[][]) - Static method in class com.imsl.math.Matrix: Check that the matrix is square.
checkSquareMatrix() - Method in class com.imsl.math.SparseMatrix: Check that the matrix is square.
chi(double, double) - Static method in class com.imsl.stat.Cdf: Evaluates the chi-squared cumulative distribution function.
chi(double, double) - Static method in class com.imsl.stat.InvCdf: Evaluates the inverse of the chi-squared cumulative probability distribution function.
chi(double, double) - Static method in class com.imsl.stat.Pdf: Evaluates the chi-squared probability density function
chiMean(double) - Static method in class com.imsl.stat.Cdf: Evaluates the mean of the chi-squared cumulative probability distribution function
ChiSquaredTest - Class in com.imsl.stat: Chi-squared goodness-of-fit test.
ChiSquaredTest(CdfFunction, double[], int) - Constructor for class com.imsl.stat.ChiSquaredTest: Constructor for the Chi-squared goodness-of-fit test.
ChiSquaredTest(CdfFunction, int, int) - Constructor for class com.imsl.stat.ChiSquaredTest: Constructor for the Chi-squared goodness-of-fit test
ChiSquaredTest(int) - Method in class com.imsl.stat.NormalityTest: Performs the chi-squared goodness-of-fit test.
ChiSquaredTest.DidNotConvergeException - Exception in com.imsl.stat: The iteration did not converge
ChiSquaredTest.DidNotConvergeException(String) - Constructor for exception com.imsl.stat.ChiSquaredTest.DidNotConvergeException: Constructs a DidNotConvergeException object.
ChiSquaredTest.DidNotConvergeException(String, Object[]) - Constructor for exception com.imsl.stat.ChiSquaredTest.DidNotConvergeException: Constructs a DidNotConvergeException object.
ChiSquaredTest.NoObservationsException - Exception in com.imsl.stat: There are no observations.
ChiSquaredTest.NoObservationsException(String, Object[]) - Constructor for exception com.imsl.stat.ChiSquaredTest.NoObservationsException: Constructs a NoObservationsException object.
ChiSquaredTest.NotCDFException - Exception in com.imsl.stat: The function is not a Cumulative Distribution Function (CDF).
ChiSquaredTest.NotCDFException(String, Object[]) - Constructor for exception com.imsl.stat.ChiSquaredTest.NotCDFException: Constructs a NotCDFException object.
chiVariance(double) - Static method in class com.imsl.stat.Cdf: Evaluates the variance of the chi-squared cumulative probability distribution function
Cholesky - Class in com.imsl.math: Cholesky factorization of a matrix of type double.
Cholesky(double[][]) - Constructor for class com.imsl.math.Cholesky: Create the Cholesky factorization of a symmetric positive definite matrix of type double.
Cholesky.NotSPDException - Exception in com.imsl.math: The matrix is not symmetric, positive definite.
Cholesky.NotSPDException() - Constructor for exception com.imsl.math.Cholesky.NotSPDException: Constructs a NotSPDException object.
circle(int, int, int) - Method in class com.imsl.chart.DrawMap: Sets a circle as the target.
classError(double[], int[], int) - Method in class com.imsl.datamining.NaiveBayesClassifier: Returns the classification probability error for the input pattern and known target classification.
classify(double[], int) - Method in class com.imsl.stat.ClusterKNN: Classify an observation using k nearest neighbors.
classify(double[][], int) - Method in class com.imsl.stat.ClusterKNN: Classify a set of observations using k nearest neighbors.
classify(double[][]) - Method in class com.imsl.stat.DiscriminantAnalysis: Classify a set of observations using the linear or quadratic discriminant functions generated during the training process.
classify(double[][], int[]) - Method in class com.imsl.stat.DiscriminantAnalysis: Classify a set of observations using the linear or quadratic discriminant functions generated during the training process.
classify(double[][], int[], double[]) - Method in class com.imsl.stat.DiscriminantAnalysis: Classify a set of observations and associated frequencies and weights using the linear or quadratic discriminant functions generated during the training process.
classify(double[][], int[], int[], double[]) - Method in class com.imsl.stat.DiscriminantAnalysis: Classify a set of observations and associated frequencies and weights using the linear or quadratic discriminant functions generated during the training process.
classify(double[][], int[], int[]) - Method in class com.imsl.stat.DiscriminantAnalysis: Classify a set of observations and compare against known groups using the linear or quadratic discriminant functions generated during the training process.
classify(double[][], int[], int[], int[], double[]) - Method in class com.imsl.stat.DiscriminantAnalysis: Classify a set of observations, associated frequencies and weights, and compare against known groups using the linear or quadratic discriminant functions generated during the training process.
cleanup() - Method in class com.imsl.chart3d.Chart3D: Cleanup memory use and references used by the chart.
clearWarnings() - Method in class com.imsl.io.AbstractFlatFile: Clears all warnings reported on this ResultSet object.
clone(Map) - Method in class com.imsl.chart.AbstractChartNode: Returns a deep-copy clone of this node.
clone(Object, Map) - Method in class com.imsl.chart.AbstractChartNode: Returns a deep copy of an Object.
clone(Map, Map) - Method in class com.imsl.chart.AbstractChartNode: Returns a deep copy of a Hashtable.
clone(List, Map) - Method in class com.imsl.chart.AbstractChartNode: Returns a deep copy of a vector of ChartNode's.
clone() - Method in class com.imsl.chart.Chart: Returns a clone of the graphics tree.
clone(Map) - Method in class com.imsl.chart.Chart: Returns a clone of this node.
clone() - Method in class com.imsl.chart3d.BufferedPaint
clone() - Method in class com.imsl.chart3d.Chart3D: Returns a clone of the graphics tree.
clone(Map) - Method in class com.imsl.chart3d.Chart3D: Returns a clone of this node.
clone() - Method in class com.imsl.datamining.decisionTree.Tree: Returns a clone of this object.
clone() - Method in class com.imsl.datamining.decisionTree.TreeNode: Returns a clone of a TreeNode.
clone() - Method in class com.imsl.datamining.neural.LeastSquaresTrainer: Clones a copy of the trainer.
clone() - Method in class com.imsl.datamining.neural.QuasiNewtonTrainer: Clones a copy of the trainer.
clone() - Method in class com.imsl.math.DenseLP: Creates and returns a copy of this object.
clone() - Method in class com.imsl.stat.FaureSequence: Returns a copy of this object.
clone() - Method in class com.imsl.stat.MersenneTwister: Returns a clone of this object.
clone() - Method in class com.imsl.stat.MersenneTwister64: Returns a clone of this object.
close() - Method in class com.imsl.io.AbstractFlatFile: Releases this ResultSet object's database and JDBC resources immediately instead of waiting for this to happen when it is automatically closed.
ClosedFormMaximumLikelihoodInterface - Interface in com.imsl.stat.distributions: A public interface for probability distributions that provide a method for a closed form solution of the maximum likelihood function
ClusterHierarchical - Class in com.imsl.stat: Performs a hierarchical cluster analysis from a distance matrix.
ClusterHierarchical(double[][]) - Constructor for class com.imsl.stat.ClusterHierarchical: Constructor for ClusterHierarchical.
ClusterKMeans - Class in com.imsl.stat: Perform a K-means (centroid) cluster analysis.
ClusterKMeans(double[][], double[][]) - Constructor for class com.imsl.stat.ClusterKMeans: Constructor for ClusterKMeans.
ClusterKMeans.ClusterNoPointsException - Exception in com.imsl.stat: There is a cluster with no points
ClusterKMeans.ClusterNoPointsException(String) - Constructor for exception com.imsl.stat.ClusterKMeans.ClusterNoPointsException: Constructs a ClusterNoPointsException object.
ClusterKMeans.ClusterNoPointsException(String, Object[]) - Constructor for exception com.imsl.stat.ClusterKMeans.ClusterNoPointsException: Constructs a ClusterNoPointsException object.
ClusterKMeans.NoConvergenceException - Exception in com.imsl.stat: Convergence did not occur within the maximum number of iterations.
ClusterKMeans.NoConvergenceException(String) - Constructor for exception com.imsl.stat.ClusterKMeans.NoConvergenceException: Constructs a NoConvergenceException object.
ClusterKMeans.NoConvergenceException(String, Object[]) - Constructor for exception com.imsl.stat.ClusterKMeans.NoConvergenceException: Constructs a NoConvergenceException object.
ClusterKNN - Class in com.imsl.stat: Perform a k-Nearest Neighbor classification.
ClusterKNN(double[][], int[]) - Constructor for class com.imsl.stat.ClusterKNN: Constructor for ClusterKNN.
coef - Variable in class com.imsl.math.BSpline: The B-spline coefficient array.
coef - Variable in class com.imsl.math.Spline: Coefficients of the piecewise polynomials.
color(double) - Method in interface com.imsl.chart.Colormap: Maps the parameterization interval [0,1] into Colors.
color(double, double, double) - Method in interface com.imsl.chart3d.ColorFunction
ColorFunction - Interface in com.imsl.chart3d: Interface to define value dependent colors.
Colormap - Interface in com.imsl.chart: Colormaps are mappings from the unit interval to Colors.
ColormapLegend - Class in com.imsl.chart3d: Adds a legend for a Colormap gradient to the background of the canvas.
ColormapLegend(Chart3D, Colormap, double[]) - Constructor for class com.imsl.chart3d.ColormapLegend: Creates a legend for a Colormap and adds it to the canvas.
ColormapLegend(Chart3D, Colormap, double, double) - Constructor for class com.imsl.chart3d.ColormapLegend
COLUMN_APPROXIMATE_MINIMUM_DEGREE - Static variable in class com.imsl.math.ComplexSuperLU: For column ordering, use column approximate minimum degree ordering.
COLUMN_APPROXIMATE_MINIMUM_DEGREE - Static variable in class com.imsl.math.SuperLU: For column ordering, use column approximate minimum degree ordering.
COLUMN_LABEL - Static variable in class com.imsl.math.PrintMatrixFormat: This flag as the type argument to format, indicates that the formatted string for a given column label is to be returned.
COLUMN_SCALING - Static variable in class com.imsl.math.ComplexSuperLU: Indicates that input matrix A was column scaled before factorization.
COLUMN_SCALING - Static variable in class com.imsl.math.SuperLU: Indicates that input matrix A was column scaled before factorization.
COLUMNFIRST - Static variable in class com.imsl.chart.Treemap: Flag to set the treemap orientation drawing columns first.
com.imsl - package com.imsl
com.imsl.chart - package com.imsl.chart
com.imsl.chart.qc - package com.imsl.chart.qc
com.imsl.chart.xml - package com.imsl.chart.xml
com.imsl.chart3d - package com.imsl.chart3d
com.imsl.datamining - package com.imsl.datamining
com.imsl.datamining.decisionTree - package com.imsl.datamining.decisionTree
com.imsl.datamining.neural - package com.imsl.datamining.neural
com.imsl.finance - package com.imsl.finance
com.imsl.io - package com.imsl.io
com.imsl.math - package com.imsl.math
com.imsl.stat - package com.imsl.stat
com.imsl.stat.distributions - package com.imsl.stat.distributions
compareTo(Object) - Method in class com.imsl.math.Complex: Compares this Complex to another Object.
compareTo(Complex) - Method in class com.imsl.math.Complex: Compares two Complex objects.
complementaryChi(double, double) - Static method in class com.imsl.stat.Cdf: Calculates the complement of the chi-squared cumulative distribution function.
complementaryF(double, double, double) - Static method in class com.imsl.stat.Cdf: Calculates the complement of the F distribution function.
complementaryF2(double, double, double) - Static method in class com.imsl.stat.Cdf
complementaryNoncentralF(double, double, double, double) - Static method in class com.imsl.stat.Cdf: Calculates the complement of the noncentral F cumulative distribution function.
complementaryStudentsT(double, double) - Static method in class com.imsl.stat.Cdf: Calculates the complement of the Student's t distribution.
Complex - Class in com.imsl.math: Set of mathematical functions for complex numbers.
Complex(Complex) - Constructor for class com.imsl.math.Complex: Constructs a Complex equal to the argument.
Complex(double, double) - Constructor for class com.imsl.math.Complex: Constructs a Complex with real and imaginary parts given by the input arguments.
Complex(double) - Constructor for class com.imsl.math.Complex: Constructs a Complex with a zero imaginary part.
Complex() - Constructor for class com.imsl.math.Complex: Constructs a Complex equal to zero.
ComplexFFT - Class in com.imsl.math: Complex FFT.
ComplexFFT(int) - Constructor for class com.imsl.math.ComplexFFT: Constructs a complex FFT object.
ComplexLU - Class in com.imsl.math: LU factorization of a matrix of type Complex.
ComplexLU(Complex[][]) - Constructor for class com.imsl.math.ComplexLU: Creates the LU factorization of a square matrix of type Complex.
ComplexMatrix - Class in com.imsl.math: Complex matrix manipulation functions.
ComplexSparseCholesky - Class in com.imsl.math: Sparse Cholesky factorization of a matrix of type ComplexSparseMatrix.
ComplexSparseCholesky(ComplexSparseMatrix) - Constructor for class com.imsl.math.ComplexSparseCholesky: Constructs the matrix structure for the Cholesky factorization of a sparse Hermitian positive definite matrix of type ComplexSparseMatrix.
ComplexSparseCholesky.NotSPDException - Exception in com.imsl.math: The matrix is not Hermitian, positive definite.
ComplexSparseCholesky.NotSPDException() - Constructor for exception com.imsl.math.ComplexSparseCholesky.NotSPDException: Constructs a NotSPDException object.
ComplexSparseCholesky.NumericFactor - Class in com.imsl.math: Data structures and functions for the numeric Cholesky factor.
ComplexSparseCholesky.SymbolicFactor - Class in com.imsl.math: Data structures and functions for the symbolic Cholesky factor.
ComplexSparseMatrix - Class in com.imsl.math: Sparse matrix of type Complex.
ComplexSparseMatrix(int, int) - Constructor for class com.imsl.math.ComplexSparseMatrix: Creates a new instance of ComplexSparseMatrix.
ComplexSparseMatrix(ComplexSparseMatrix) - Constructor for class com.imsl.math.ComplexSparseMatrix: Creates a new instance of ComplexSparseMatrix which is a copy of another ComplexSparseMatrix.
ComplexSparseMatrix(ComplexSparseMatrix.SparseArray) - Constructor for class com.imsl.math.ComplexSparseMatrix: Constructs a complex sparse matrix from a SparseArray object.
ComplexSparseMatrix(int, int, int[][], Complex[][]) - Constructor for class com.imsl.math.ComplexSparseMatrix: Constructs a sparse matrix from SparseArray (Java Sparse Array) data.
ComplexSparseMatrix.SparseArray - Class in com.imsl.math: The SparseArray class uses public fields to hold the data for a sparse matrix in the Java Sparse Array format.
ComplexSparseMatrix.SparseArray() - Constructor for class com.imsl.math.ComplexSparseMatrix.SparseArray
ComplexSuperLU - Class in com.imsl.math: Computes the LU factorization of a general sparse matrix of type ComplexSparseMatrix by a column method and solves a sparse linear system of equations $Ax=b$ .
ComplexSuperLU(ComplexSparseMatrix) - Constructor for class com.imsl.math.ComplexSuperLU: Constructor for ComplexSuperLU.
compute(double[], double[]) - Method in interface com.imsl.math.BoundedLeastSquares.Function: Public interface for the user-supplied function to evaluate the function that defines the least-squares problem.
compute(double[], double[]) - Method in interface com.imsl.math.BoundedLeastSquares.Jacobian: Public interface for the user-supplied function to compute the Jacobian.
compute() - Method in class com.imsl.math.CsTCB: Computes the tension-continuity-bias (TCB) cubic spline interpolant.
compute() - Method in class com.imsl.math.Spline2DLeastSquares: Computes a two-dimensional, tensor-product spline approximant using least squares.
compute() - Method in class com.imsl.stat.ANCOVA: Performs one-way analysis of covariance assuming parallelism and returns an array containing the parallelism tests for the one-way analysis of covariance.
compute() - Method in class com.imsl.stat.ANOVAFactorial: Analyzes a balanced factorial design with fixed effects.
compute() - Method in class com.imsl.stat.ARAutoUnivariate: Determines the autoregressive model with the minimum AIC by fitting autoregressive models from 0 to maxlag lags using the method of moments or an estimation method specified by the user through setEstimationMethod.
compute() - Method in class com.imsl.stat.ARMA: Computes least-square estimates of parameters for an ARMA model.
compute() - Method in class com.imsl.stat.ARMAMaxLikelihood: Computes the exact maximum likelihood estimates for the autoregressive and moving average parameters of an ARMA time series
compute(int[]) - Method in class com.imsl.stat.ARMAOutlierIdentification: Detects and determines outliers and simultaneously estimates the model parameters for the given time series.
compute() - Method in class com.imsl.stat.ARSeasonalFit: Computes the minimum AIC and optimum values for s and d based upon the candidates provided in sInitial and dInitial, and computes the values for the transformed series, .
compute(int) - Method in class com.imsl.stat.AutoARIMA: Estimates potential missing values, detects and determines outliers and simultaneously fits an optimum model from a set of different $text{ARIMA}(p,0,0)times(0,d,0)_s$ models to the outlier free time series.
compute(int[], int[]) - Method in class com.imsl.stat.AutoARIMA: Estimates potential missing values, detects and determines outliers and simultaneously fits an optimum model from a set of different $text{ARIMA}(p,0,q)times(0,d,0)_s$ models to the outlier free time series.
compute(int, int, int, int) - Method in class com.imsl.stat.AutoARIMA: Estimates potential missing values, detects and determines outliers and simultaneously fits an $text{ARIMA}(p,0,q)times(0,d,0)_s$ model to the outlier free time series.
compute() - Method in class com.imsl.stat.ClusterHierarchical: Performs a hierarchical cluster analysis.
compute() - Method in class com.imsl.stat.ClusterKMeans: Computes the cluster means.
compute(int) - Method in class com.imsl.stat.Covariances: Computes the matrix.
compute(double[], int[]) - Method in class com.imsl.stat.Difference: Computes a Difference series.
compute() - Method in class com.imsl.stat.Dissimilarities: Computes a matrix of dissimilarities (or similarities) between the columns (or rows) of a matrix.
compute() - Method in class com.imsl.stat.distributions.MaximumLikelihoodEstimation: Computes the maximum likelihood estimates.
compute() - Method in class com.imsl.stat.GARCH: Computes estimates of the parameters of a GARCH(p,q) model.
compute() - Method in class com.imsl.stat.HoltWintersExponentialSmoothing: Computes the values of the smoothing parameters.
compute(int, double[], int, int) - Static method in class com.imsl.stat.LackOfFit: Performs lack-of-fit test for a univariate time series or transfer function given the appropriate correlation function using a minimum lag of 1.
compute(int, double[], int, int, int) - Static method in class com.imsl.stat.LackOfFit: Performs lack-of-fit test for a univariate time series or transfer function given the appropriate correlation function.
compute() - Method in class com.imsl.stat.MultipleComparisons: Performs Student-Newman-Keuls multiple comparisons test.
compute(double[][], double[]) - Method in class com.imsl.stat.SelectionRegression: Computes the best multiple linear regression models.
compute(double[][], double[], double[]) - Method in class com.imsl.stat.SelectionRegression: Computes the best weighted multiple linear regression models.
compute(double[][], double[], double[], double[]) - Method in class com.imsl.stat.SelectionRegression: Computes the best weighted multiple linear regression models using frequencies for each observation.
compute(double[][], int) - Method in class com.imsl.stat.SelectionRegression: Computes the best multiple linear regression models using a user-supplied covariance matrix.
compute() - Method in class com.imsl.stat.SignTest: Performs a sign test.
compute() - Method in class com.imsl.stat.StepwiseRegression: Builds the multiple linear regression models using forward selection, backward selection, or stepwise selection.
compute(double, double) - Method in interface com.imsl.stat.TimeSeriesOperations.Function: Public interface for the user-supplied function to combine two time series values that occur at the same date and time.
compute() - Method in class com.imsl.stat.WilcoxonRankSum: Performs a Wilcoxon rank sum test using an approximate p-value calculation.
computeCoefficients(int, int, FeynmanKac.Boundaries, double[], double[]) - Method in class com.imsl.math.FeynmanKac: Determines the coefficients of the Hermite quintic splines that represent an approximate solution for the Feynman-Kac PDE.
computeExactPValues() - Method in class com.imsl.stat.WilcoxonRankSum: Performs a Wilcoxon rank sum test using exact p-value calculations.
computeForecasts(int) - Method in class com.imsl.stat.ARMAOutlierIdentification: Computes forecasts, associated probability limits and weights for an outlier contaminated time series whose underlying outlier free series obeys a general seasonal or non-seasonal ARMA model.
computeLags(int[], int[], double[]) - Method in class com.imsl.datamining.neural.TimeSeriesClassFilter: Computes lags of an array sorted first by class designations and then descending chronological order.
computeLags(int, double[][]) - Method in class com.imsl.datamining.neural.TimeSeriesFilter: Lags time series data to a format used for input to a neural network.
computeMin(MinUncon.Function) - Method in class com.imsl.math.MinUncon: Return the minimum of a smooth function of a single variable of type double using function values only or using function values and derivatives.
computeMin(MinUnconMultiVar.Function) - Method in class com.imsl.math.MinUnconMultiVar: Return the minimum point of a function of n variables of type double using a finite-difference gradient or using a user-supplied gradient.
computeRoots(double[]) - Method in class com.imsl.math.ZeroPolynomial: Computes the roots of the polynomial with real coefficients.
computeRoots(Complex[]) - Method in class com.imsl.math.ZeroPolynomial: Computes the roots of the polynomial with Complex coefficients.
computeStatistics(double[][], int[]) - Method in class com.imsl.datamining.neural.BinaryClassification: Computes the classification error statistics for the supplied network patterns and their associated classifications.
computeStatistics(double[][], int[]) - Method in class com.imsl.datamining.neural.MultiClassification: Computes classification statistics for the supplied network patterns and their associated classifications.
computeStatistics(double[][], double[][]) - Method in class com.imsl.datamining.neural.Network: Computes error statistics.
computeZeros(ZerosFunction.Function) - Method in class com.imsl.math.ZerosFunction: Returns the zeros of a univariate function.
condition(Complex[][]) - Method in class com.imsl.math.ComplexLU: Return an estimate of the reciprocal of the $L_1$ condition number.
condition(double[][]) - Method in class com.imsl.math.LU: Return an estimate of the reciprocal of the $L_1$ condition number of a matrix.
confidenceMean(double) - Method in class com.imsl.stat.Summary: Returns the confidence interval for the mean (assuming normality).
confidenceVariance(double) - Method in class com.imsl.stat.Summary: Returns the confidence interval for the variance (assuming normality).
conjugate(Complex) - Static method in class com.imsl.math.Complex: Returns the complex conjugate of a Complex object.
ConjugateGradient - Class in com.imsl.math: Solves a real symmetric definite linear system using the conjugate gradient method with optional preconditioning.
ConjugateGradient(int, ConjugateGradient.Function) - Constructor for class com.imsl.math.ConjugateGradient: Conjugate gradient constructor.
ConjugateGradient.Function - Interface in com.imsl.math: Public interface for the user supplied function to ConjugateGradient.
ConjugateGradient.NoConvergenceException - Exception in com.imsl.math: The conjugate gradient method did not converge within the allowed maximum number of iterations.
ConjugateGradient.NoConvergenceException(String) - Constructor for exception com.imsl.math.ConjugateGradient.NoConvergenceException: Constructs a NoConvergenceException object.
ConjugateGradient.NoConvergenceException(String, Object[]) - Constructor for exception com.imsl.math.ConjugateGradient.NoConvergenceException: Constructs a NoConvergenceException object.
ConjugateGradient.NotDefiniteAMatrixException - Exception in com.imsl.math: The input matrix A is indefinite, that is the matrix is not positive or negative definite.
ConjugateGradient.NotDefiniteAMatrixException(String) - Constructor for exception com.imsl.math.ConjugateGradient.NotDefiniteAMatrixException: Constructs a NotDefiniteAMatrixException object.
ConjugateGradient.NotDefiniteAMatrixException(String, Object[]) - Constructor for exception com.imsl.math.ConjugateGradient.NotDefiniteAMatrixException: Constructs a NotDefiniteAMatrixException object.
ConjugateGradient.NotDefiniteJacobiPreconditionerException - Exception in com.imsl.math: The Jacobi preconditioner is not strictly positive or negative definite.
ConjugateGradient.NotDefiniteJacobiPreconditionerException(String) - Constructor for exception com.imsl.math.ConjugateGradient.NotDefiniteJacobiPreconditionerException: Constructs a NotDefiniteJacobiPreconditionerException object.
ConjugateGradient.NotDefiniteJacobiPreconditionerException(String, Object[]) - Constructor for exception com.imsl.math.ConjugateGradient.NotDefiniteJacobiPreconditionerException: Constructs a NotDefiniteJacobiPreconditionerException object.
ConjugateGradient.NotDefinitePreconditionMatrixException - Exception in com.imsl.math: The Precondition matrix is indefinite.
ConjugateGradient.NotDefinitePreconditionMatrixException(String) - Constructor for exception com.imsl.math.ConjugateGradient.NotDefinitePreconditionMatrixException: Constructs a NotDefinitePreconditionMatrixException object.
ConjugateGradient.NotDefinitePreconditionMatrixException(String, Object[]) - Constructor for exception com.imsl.math.ConjugateGradient.NotDefinitePreconditionMatrixException: Constructs a NotDefinitePreconditionMatrixException object.
ConjugateGradient.Preconditioner - Interface in com.imsl.math: Public interface for the user supplied function to ConjugateGradient used for preconditioning.
ConjugateGradient.SingularPreconditionMatrixException - Exception in com.imsl.math: The Precondition matrix is singular.
ConjugateGradient.SingularPreconditionMatrixException(String) - Constructor for exception com.imsl.math.ConjugateGradient.SingularPreconditionMatrixException: Constructs a SingularPreconditionMatrixException object.
ConjugateGradient.SingularPreconditionMatrixException(String, Object[]) - Constructor for exception com.imsl.math.ConjugateGradient.SingularPreconditionMatrixException: Constructs a SingularPreconditionMatrixException object.
conjugateTranspose() - Method in class com.imsl.math.ComplexSparseMatrix: Returns the conjugate transpose of the matrix.
constant(String) - Static method in class com.imsl.math.Physical: Returns the value of a constant, given its name.
constant(String, String) - Static method in class com.imsl.math.Physical: Returns the value of a constant, given its name, in the specified units.
ContingencyTable - Class in com.imsl.stat: Performs a chi-squared analysis of a two-way contingency table.
ContingencyTable(double[][]) - Constructor for class com.imsl.stat.ContingencyTable: Constructs and performs a chi-squared analysis of a two-way contingency table.
CONTINUOUS_VARIABLE - Static variable in class com.imsl.io.MPSReader: Variable is a real number.
Contour - Class in com.imsl.chart: A Contour chart shows level curves of a two-dimensional function.
Contour(AxisXY, double[], double[], double[][], double[]) - Constructor for class com.imsl.chart.Contour: Create a Contour chart from rectangularly gridded data.
Contour(AxisXY, double[], double[], double[][]) - Constructor for class com.imsl.chart.Contour: Create a Contour chart from rectangularly gridded data with computed contour levels.
Contour(AxisXY, double[], double[], double[]) - Constructor for class com.imsl.chart.Contour: Create a Contour chart from scattered data with computed contour levels.
Contour(AxisXY, double[], double[], double[], double[], int) - Constructor for class com.imsl.chart.Contour: Create a Contour chart from scattered data.
Contour.Legend - Class in com.imsl.chart: A legend for a contour chart.
ContourLevel - Class in com.imsl.chart: ContourLevel draws a level curve line and the fill area between the level curve and the next smaller level curve.
ControlLimit - Class in com.imsl.chart.qc: ControlLimit is a control limit line on a process control chart.
convert(Physical, String) - Static method in class com.imsl.math.Physical: Converts a value to a different set of units.
convexity(GregorianCalendar, GregorianCalendar, double, double, int, DayCountBasis) - Static method in class com.imsl.finance.Bond: Returns the convexity for a security.
copy() - Method in class com.imsl.chart.Chart: Copy the chart to the clipboard.
copyAndSortData(double[], double[]) - Method in class com.imsl.math.Spline: Copy and sort xData into breakPoint and yData into the first column of coef.
copyAndSortData(double[], double[], double[]) - Method in class com.imsl.math.Spline: Copy and sort xData into breakPoint and yData into the first column of coef.
copysign(double, double) - Static method in class com.imsl.math.IEEE: Returns a value with the magnitude of x and with the sign bit of y.
CORRECTED_SSCP_MATRIX - Static variable in class com.imsl.stat.Covariances: Indicates corrected sums of squares and crossproducts matrix.
CORRELATION_COEFFICIENT - Static variable in class com.imsl.stat.Dissimilarities: Indicates the correlation coefficient distance method.
CORRELATION_MATRIX - Static variable in class com.imsl.stat.Covariances: Indicates correlation matrix.
CORRELATION_MATRIX - Static variable in class com.imsl.stat.FactorAnalysis: Indicates correlation matrix.
cos(Complex) - Static method in class com.imsl.math.Complex: Returns the cosine of a Complex.
cos(double) - Static method in class com.imsl.math.JMath: Returns the cosine of a double.
cosh(Complex) - Static method in class com.imsl.math.Complex: Returns the hyperbolic cosh of a Complex.
cosh(double) - Static method in class com.imsl.math.Hyperbolic: Returns the hyperbolic cosine of its argument.
cot(double) - Static method in class com.imsl.math.Sfun: Returns the cotangent of a double.
countFrequency(Itemsets, int[][]) - Static method in class com.imsl.datamining.Apriori: Returns the frequency of each itemset in the transaction data set, x.
countFrequency(Itemsets, int[][], int[]) - Static method in class com.imsl.datamining.Apriori: Returns the frequency of each itemset in the transaction data set, x, added to the previous frequencies.
countTokens() - Method in class com.imsl.io.Tokenizer: Returns the number of times that the nextToken method can be called without generating an exception.
coupdaybs(GregorianCalendar, GregorianCalendar, int, DayCountBasis) - Static method in class com.imsl.finance.Bond: Returns the number of days starting with the beginning of the coupon period and ending with the settlement date.
coupdays(GregorianCalendar, GregorianCalendar, int, DayCountBasis) - Static method in class com.imsl.finance.Bond: Returns the number of days in the coupon period containing the settlement date.
coupdaysnc(GregorianCalendar, GregorianCalendar, int, DayCountBasis) - Static method in class com.imsl.finance.Bond: Returns the number of days starting with the settlement date and ending with the next coupon date.
coupncd(GregorianCalendar, GregorianCalendar, int, DayCountBasis) - Static method in class com.imsl.finance.Bond: Returns the first coupon date which follows the settlement date.
coupnum(GregorianCalendar, GregorianCalendar, int, DayCountBasis) - Static method in class com.imsl.finance.Bond: Returns the number of coupons payable between the settlement date and the maturity date.
couppcd(GregorianCalendar, GregorianCalendar, int, DayCountBasis) - Static method in class com.imsl.finance.Bond: Returns the coupon date which immediately precedes the settlement date.
Covariances - Class in com.imsl.stat: Computes the sample variance-covariance or correlation matrix.
Covariances(double[][]) - Constructor for class com.imsl.stat.Covariances: Constructor for Covariances.
Covariances.NonnegativeFreqException - Exception in com.imsl.stat: Frequencies must be nonnegative.
Covariances.NonnegativeFreqException(String) - Constructor for exception com.imsl.stat.Covariances.NonnegativeFreqException: Constructs a NonnegativeFreqException object.
Covariances.NonnegativeFreqException(String, Object[]) - Constructor for exception com.imsl.stat.Covariances.NonnegativeFreqException: Constructs a NonnegativeFreqException object.
Covariances.NonnegativeWeightException - Exception in com.imsl.stat: Weights must be nonnegative.
Covariances.NonnegativeWeightException(String) - Constructor for exception com.imsl.stat.Covariances.NonnegativeWeightException: Constructs a NonnegativeWeightException object.
Covariances.NonnegativeWeightException(String, Object[]) - Constructor for exception com.imsl.stat.Covariances.NonnegativeWeightException: Constructs a NonnegativeWeightException object.
createCharts(Chart, double[][]) - Static method in class com.imsl.chart.qc.XbarR: Creates a combined XbarR chart and RChart from data.
createCharts(Chart, int, double[], double[]) - Static method in class com.imsl.chart.qc.XbarR: Creates a combined XbarR chart and RChart given the means and ranges for a series of equally sized samples.
createCharts(Chart, int[], double[], double[]) - Static method in class com.imsl.chart.qc.XbarR: Creates a combined XbarR chart and RChart given the means and ranges for a series of unequally sized samples.
createCharts(Chart, double[][]) - Static method in class com.imsl.chart.qc.XbarS: Creates a combined XbarS chart and SChart from data.
createCharts(Chart, int, double[], double[]) - Static method in class com.imsl.chart.qc.XbarS: Creates a combined XbarS chart and SChart given the means and in sample standard deviations for a series of equally sized samples.
createCharts(Chart, int[], double[], double[]) - Static method in class com.imsl.chart.qc.XbarS: Creates a combined X-bar chart and S-chart given the means and in sample standard deviations for a series of unequally sized samples.
createContinuousAttribute(ProbabilityDistribution) - Method in class com.imsl.datamining.NaiveBayesClassifier: Create a continuous variable and the associated distribution function.
createContinuousAttribute(ProbabilityDistribution[]) - Method in class com.imsl.datamining.NaiveBayesClassifier: Create a continuous variable and the associated distribution functions for each target classification.
createCumulativeLineAxis() - Method in class com.imsl.chart.qc.ParetoChart: Creates a new axis to hold a cumulative line.
createCustomMarker() - Method in interface com.imsl.chart3d.Data.CustomMarkerFactory: Returns a custom marker.
createDataAxis() - Method in class com.imsl.chart.qc.CuSumStatus: Creates a new axis to hold a cummulative line.
createHiddenLayer() - Method in class com.imsl.datamining.neural.FeedForwardNetwork: Creates a HiddenLayer.
createHiddenLayer() - Method in class com.imsl.datamining.neural.Network: Creates the next HiddenLayer in the Network.
createInput() - Method in class com.imsl.datamining.neural.InputLayer: Creates an InputNode in the InputLayer of the neural network.
createInputs(int) - Method in class com.imsl.datamining.neural.InputLayer: Creates a number of InputNodes in this Layer of the neural network.
createNominalAttribute(int) - Method in class com.imsl.datamining.NaiveBayesClassifier: Create a nominal attribute and the number of categories
createPerceptron(Activation, double) - Method in class com.imsl.datamining.neural.HiddenLayer: Creates a Perceptron in this Layer with a specified activation function and bias.
createPerceptron() - Method in class com.imsl.datamining.neural.HiddenLayer: Creates a Perceptron in this Layer of the neural network.
createPerceptron(Activation, double) - Method in class com.imsl.datamining.neural.OutputLayer: Creates a Perceptron in this Layer with a specified Activation and bias.
createPerceptron() - Method in class com.imsl.datamining.neural.OutputLayer: Creates a Perceptron in this Layer of the neural network.
createPerceptrons(int) - Method in class com.imsl.datamining.neural.HiddenLayer: Creates a number of Perceptrons in this Layer of the neural network.
createPerceptrons(int, Activation, double) - Method in class com.imsl.datamining.neural.HiddenLayer: Creates a number of Perceptrons in this Layer with the specified bias.
createPerceptrons(int) - Method in class com.imsl.datamining.neural.OutputLayer: Creates a number of Perceptrons in this Layer of the neural network.
createPerceptrons(int, Activation, double) - Method in class com.imsl.datamining.neural.OutputLayer: Creates a number of Perceptrons in this Layer with specified activation and bias.
CrossCorrelation - Class in com.imsl.stat: Computes the sample cross-correlation function of two stationary time series.
CrossCorrelation(double[], double[], int) - Constructor for class com.imsl.stat.CrossCorrelation: Constructor to compute the sample cross-correlation function of two stationary time series.
CrossCorrelation.NonPosVariancesException - Exception in com.imsl.stat: The problem is ill-conditioned.
CrossCorrelation.NonPosVariancesException(String) - Constructor for exception com.imsl.stat.CrossCorrelation.NonPosVariancesException: Constructs a NonPosVariancesException object.
CrossCorrelation.NonPosVariancesException(String, Object[]) - Constructor for exception com.imsl.stat.CrossCorrelation.NonPosVariancesException: Constructs a NonPosVariancesException object.
crosshatch(int, int, Color, Color) - Static method in class com.imsl.chart.FillPaint: Returns a horizonal and vertical crosshatch pattern.
crossValidate() - Method in class com.imsl.datamining.CrossValidation: Performs V-Fold cross-validation.
CrossValidation - Class in com.imsl.datamining: Performs V-Fold cross-validation for predictive models.
CrossValidation(PredictiveModel) - Constructor for class com.imsl.datamining.CrossValidation: Creates a CrossValidation object.
CsAkima - Class in com.imsl.math: Extension of the Spline class to handle the Akima cubic spline.
CsAkima(double[], double[]) - Constructor for class com.imsl.math.CsAkima: Constructs the Akima cubic spline interpolant to the given data points.
CsInterpolate - Class in com.imsl.math: Extension of the Spline class to interpolate data points.
CsInterpolate(double[], double[]) - Constructor for class com.imsl.math.CsInterpolate: Constructs a cubic spline that interpolates the given data points.
CsInterpolate(double[], double[], int, double, int, double) - Constructor for class com.imsl.math.CsInterpolate: Constructs a cubic spline that interpolates the given data points with specified derivative endpoint conditions.
CsPeriodic - Class in com.imsl.math: Extension of the Spline class to interpolate data points with periodic boundary conditions.
CsPeriodic(double[], double[]) - Constructor for class com.imsl.math.CsPeriodic: Constructs a cubic spline that interpolates the given data points with periodic boundary conditions.
CsShape - Class in com.imsl.math: Extension of the Spline class to interpolate data points consistent with the concavity of the data.
CsShape(double[], double[]) - Constructor for class com.imsl.math.CsShape: Construct a cubic spline interpolant which is consistent with the concavity of the data.
CsShape.TooManyIterationsException - Exception in com.imsl.math: Too many iterations.
CsShape.TooManyIterationsException() - Constructor for exception com.imsl.math.CsShape.TooManyIterationsException: Constructs a TooManyIterationsException object.
CsShape.TooManyIterationsException(String, Object[]) - Constructor for exception com.imsl.math.CsShape.TooManyIterationsException: Constructs a TooManyIterationsException object.
CsShape.TooManyIterationsException(Object[]) - Constructor for exception com.imsl.math.CsShape.TooManyIterationsException: Constructs a TooManyIterationsException object.
CsSmooth - Class in com.imsl.math: Extension of the Spline class to construct a smooth cubic spline from noisy data points.
CsSmooth(double[], double[]) - Constructor for class com.imsl.math.CsSmooth: Constructs a smooth cubic spline from noisy data using cross-validation to estimate the smoothing parameter.
CsSmooth(double[], double[], double[]) - Constructor for class com.imsl.math.CsSmooth: Constructs a smooth cubic spline from noisy data using cross-validation to estimate the smoothing parameter.
CsSmoothC2 - Class in com.imsl.math: Extension of the Spline class used to construct a spline for noisy data points using an alternate method.
CsSmoothC2(double[], double[], double) - Constructor for class com.imsl.math.CsSmoothC2: Constructs a smooth cubic spline from noisy data using an algorithm based on Reinsch (1967).
CsSmoothC2(double[], double[], double[], double) - Constructor for class com.imsl.math.CsSmoothC2: Constructs a smooth cubic spline from noisy data using an algorithm based on Reinsch (1967) with weights supplied by the user.
CsTCB - Class in com.imsl.math: Extension of the Spline class to handle a tension-continuity-bias (TCB) cubic spline, also known as a Kochanek-Bartels spline and is a generalization of the Catmull-Rom spline.
CsTCB(double[], double[]) - Constructor for class com.imsl.math.CsTCB: Constructs the tension-continuity-bias (TCB) cubic spline interpolant to the given data points.
CUBIC_SPLINE - Static variable in class com.imsl.stat.ARMAEstimateMissing: Indicates that missing values should be estimated using cublic spline interpolation.
cumipmt(double, int, double, int, int, int) - Static method in class com.imsl.finance.Finance: Returns the cumulative interest paid between two periods.
cumprinc(double, int, double, int, int, int) - Static method in class com.imsl.finance.Finance: Returns the cumulative principal paid between two periods.
CURRENT - Static variable in class com.imsl.math.Physical
currentType - Variable in class com.imsl.chart.Draw
CUSTOM - Static variable in class com.imsl.stat.TimeSeriesOperations.CombineMethod: Uses a custom combine function that is provided by the user.
CuSum - Class in com.imsl.chart.qc: CuSum is a cumulative sum chart.
CuSum(AxisXY, double[]) - Constructor for class com.imsl.chart.qc.CuSum: Creates a CuSum chart given the means of a series of samples.
CuSumStatus - Class in com.imsl.chart.qc: CuSumStatus is a cumulative sum status chart.
CuSumStatus(AxisXY, double[], double, double) - Constructor for class com.imsl.chart.qc.CuSumStatus: Creates a CuSumStatus chart.

D

d2 - Static variable in class com.imsl.chart.qc.ShewhartControlChart: This field contains $d_{2,n}$ the mean of the ranges of n samples from a Gaussian distribution.
d3 - Static variable in class com.imsl.chart.qc.ShewhartControlChart: This field contains $d_{3,n}$ the standard deviation of the ranges of n samples from a Gaussian distribution.
DASH_PATTERN_DASH - Static variable in class com.imsl.chart.ChartNode: Flag to draw a dashed line.
DASH_PATTERN_DASH_DOT - Static variable in class com.imsl.chart.ChartNode: Flag to draw a dash-dot pattern line.
DASH_PATTERN_DOT - Static variable in class com.imsl.chart.ChartNode: Flag to draw a dotted line.
DASH_PATTERN_SOLID - Static variable in class com.imsl.chart.ChartNode: Flag to draw solid line.
Data - Class in com.imsl.chart: Draws a data node.
Data(ChartNode) - Constructor for class com.imsl.chart.Data: Creates a data node.
Data(ChartNode, double[]) - Constructor for class com.imsl.chart.Data: Creates a data node with y values.
Data(ChartNode, ChartFunction, double, double) - Constructor for class com.imsl.chart.Data: Creates a data node with y values.
Data(ChartNode, double[], double[]) - Constructor for class com.imsl.chart.Data: Creates a data node with x and y values.
Data - Class in com.imsl.chart3d: Draws a 3D data node.
Data(AxisXYZ) - Constructor for class com.imsl.chart3d.Data: Creates a data node.
Data(AxisXYZ, double[], double[], double[]) - Constructor for class com.imsl.chart3d.Data: Creates a data node with x, y and z values.
Data.CustomMarkerFactory - Interface in com.imsl.chart3d: Factory to create customized markers.
DATA_TYPE_ERROR_X - Static variable in class com.imsl.chart.ErrorBar: Value for attribute "DataType" indicating that this is a horizontal error bar.
DATA_TYPE_ERROR_Y - Static variable in class com.imsl.chart.ErrorBar: Value for attribute "DataType" indicating that this is a vertical error bar.
DATA_TYPE_FILL - Static variable in class com.imsl.chart.ChartNode: Value for attribute "DataType" indicating that the area between the lines connecting the data points and the horizontal reference line (y = attribute "Reference") should be filled.
DATA_TYPE_LINE - Static variable in class com.imsl.chart.ChartNode: Value for attribute "DataType" indicating that the data points should be connected with line segments.
DATA_TYPE_LINE - Static variable in class com.imsl.chart3d.ChartNode3D: Value for attribute "DataType" indicating that the data points should be connected with line segments.
DATA_TYPE_MARKER - Static variable in class com.imsl.chart.ChartNode: Value for attribute "DataType" indicating that a marker should be drawn at each data point.
DATA_TYPE_MARKER - Static variable in class com.imsl.chart3d.ChartNode3D: Value for attribute "DataType" indicating that a marker should be drawn at each data point.
DATA_TYPE_PICTURE - Static variable in class com.imsl.chart.ChartNode: Value for attribute "DataType" indicating that an image (attribute "Image") should be drawn at each data point.
DATA_TYPE_PICTURE - Static variable in class com.imsl.chart3d.ChartNode3D: Value for attribute "DataType" indicating that an image (attribute "Image") should be drawn at each data point.
DATA_TYPE_TUBE - Static variable in class com.imsl.chart.ChartNode: Value for attribute "DataType" indicating that an a tube connecting the data points should be drawn.
DATA_TYPE_TUBE - Static variable in class com.imsl.chart3d.ChartNode3D: Value for attribute "DataType" indicating that a tube connecting the data points should be drawn.
dataRange(double[]) - Method in class com.imsl.chart.Bar: Update the data range, range = {xmin,xmax,ymin,ymax}.
dataRange(double[]) - Method in class com.imsl.chart.BarItem: Update the data range, range = {xmin,xmax,ymin,ymax}.
dataRange(double[]) - Method in class com.imsl.chart.BarSet: Update the data range, range = {xmin,xmax,ymin,ymax}.
dataRange(double[]) - Method in class com.imsl.chart.BoxPlot: Update the data range, range = {xmin,xmax,ymin,ymax}.
dataRange(double[]) - Method in class com.imsl.chart.Contour: Update the data range, range = {xmin,xmax,ymin,ymax}.
dataRange(double[]) - Method in class com.imsl.chart.Data: Update the data range, range = {xmin,xmax,ymin,ymax}.
dataRange(double[]) - Method in class com.imsl.chart.Dendrogram: Update the data range, range = {xmin,xmax,ymin,ymax}.
dataRange(double[]) - Method in class com.imsl.chart.ErrorBar: Update the data range, range = {xmin,xmax,ymin,ymax}.
dataRange(double[]) - Method in class com.imsl.chart.Heatmap: Update the data range, range = {xmin,xmax,ymin,ymax}.
dataRange(double[]) - Method in class com.imsl.chart.HighLowClose: Update the data range, range = {xmin,xmax,ymin,ymax}.
dataRange(double[]) - Method in class com.imsl.chart.qc.ControlLimit: Update the data range, range = {xmin,xmax,ymin,ymax}.
dataRange(double[]) - Method in class com.imsl.chart.qc.CuSum: Update the data range, range = {xmin,xmax,ymin,ymax}.
dataRange(double[]) - Method in class com.imsl.chart.qc.ShewhartControlChart: Update the data range, range = {xmin,xmax,ymin,ymax}.
dataRange(double[]) - Method in class com.imsl.chart.Treemap: Updates the data range, range = {xmin,xmax,ymin,ymax} or range = {xmin,xmax,ymin,ymax,zmin,zmax}.
dataRange(double[]) - Method in class com.imsl.chart3d.Data: Update the data range, range = {xmin,xmax,ymin,ymax}.
dataRange(double[]) - Method in class com.imsl.chart3d.Surface: Update the data range, range = {xmin,xmax,ymin,ymax}.
DAY - Static variable in class com.imsl.chart.HighLowClose: Milliseconds per day
DayCountBasis - Class in com.imsl.finance: The Day Count Basis.
DayCountBasis(BasisPart, BasisPart) - Constructor for class com.imsl.finance.DayCountBasis: Creates a new DayCountBasis.
daysBetween(GregorianCalendar, GregorianCalendar) - Method in interface com.imsl.finance.BasisPart: Returns the number of days from date1 to date2.
daysInPeriod(GregorianCalendar, int) - Method in interface com.imsl.finance.BasisPart: Returns the number of days in a coupon period.
db(double, double, int, int, int) - Static method in class com.imsl.finance.Finance: Returns the depreciation of an asset using the fixed-declining balance method.
ddb(double, double, int, int, double) - Static method in class com.imsl.finance.Finance: Returns the depreciation of an asset using the double-declining balance method.
DecisionTree - Class in com.imsl.datamining.decisionTree: Abstract class for generating a decision tree for a single response variable and one or more predictor variables.
DecisionTree(double[][], int, PredictiveModel.VariableType[]) - Constructor for class com.imsl.datamining.decisionTree.DecisionTree: Constructs a DecisionTree object for a single response variable and multiple predictor variables.
DecisionTree.MaxTreeSizeExceededException - Exception in com.imsl.datamining.decisionTree: Exception thrown when the maximum tree size has been exceeded.
DecisionTree.MaxTreeSizeExceededException(String) - Constructor for exception com.imsl.datamining.decisionTree.DecisionTree.MaxTreeSizeExceededException: Constructs a MaxTreeSizeExceededException and issues the specified message.
DecisionTree.MaxTreeSizeExceededException(String, Object[]) - Constructor for exception com.imsl.datamining.decisionTree.DecisionTree.MaxTreeSizeExceededException: Constructs a MaxTreeSizeExceededException with the specified detail message.
DecisionTree.PruningFailedToConvergeException - Exception in com.imsl.datamining.decisionTree: Exception thrown when pruning fails to converge.
DecisionTree.PruningFailedToConvergeException(String) - Constructor for exception com.imsl.datamining.decisionTree.DecisionTree.PruningFailedToConvergeException: Constructs a PruningFailedToConvergeException with the specified detail message.
DecisionTree.PureNodeException - Exception in com.imsl.datamining.decisionTree: Exception thrown when attempting to split a node that is already pure (response variable is constant).
DecisionTree.PureNodeException(String) - Constructor for exception com.imsl.datamining.decisionTree.DecisionTree.PureNodeException: Constructs a PureNodeException with the specified detail message.
DecisionTreeInfoGain - Class in com.imsl.datamining.decisionTree: Abstract class that extends DecisionTree for classes that use an information gain criteria.
DecisionTreeInfoGain(double[][], int, PredictiveModel.VariableType[]) - Constructor for class com.imsl.datamining.decisionTree.DecisionTreeInfoGain: Constructs a DecisionTree object for a single response variable and multiple predictor variables.
DecisionTreeInfoGain.GainCriteria - Class in com.imsl.datamining.decisionTree: Specifies which information gain criteria to use in determining the best split at each node.
DecisionTreeSurrogateMethod - Interface in com.imsl.datamining.decisionTree: Methods to account for missing values in predictor variables.
decode(double) - Method in class com.imsl.datamining.neural.ScaleFilter: Unscales a value.
decode(double[]) - Method in class com.imsl.datamining.neural.ScaleFilter: Unscales an array of values.
decode(int, double[][]) - Method in class com.imsl.datamining.neural.ScaleFilter: Unscales a single column of a two dimensional array of values.
decode(int[]) - Method in class com.imsl.datamining.neural.UnsupervisedNominalFilter: Decodes a binary encoded array into its nominal category.
decode(int[][]) - Method in class com.imsl.datamining.neural.UnsupervisedNominalFilter: Decodes a matrix representing the binary encoded columns of the nominal variable.
decode(double) - Method in class com.imsl.datamining.neural.UnsupervisedOrdinalFilter: Decodes an encoded ordinal variable.
decode(double[]) - Method in class com.imsl.datamining.neural.UnsupervisedOrdinalFilter: Decodes an array of encoded ordinal values.
defineConstant(String, Physical) - Static method in class com.imsl.math.Physical: Defines a new constant.
definePrefix(String, double) - Static method in class com.imsl.math.Physical: Defines a new prefix.
defineUnit(String, Physical) - Static method in class com.imsl.math.Physical: Defines a new unit.
deleteRow() - Method in class com.imsl.io.AbstractFlatFile: Deletes the current row from this ResultSet object and from the underlying database.
Dendrogram - Class in com.imsl.chart: A Dendrogram chart for cluster analysis.
Dendrogram(AxisXY, ClusterHierarchical) - Constructor for class com.imsl.chart.Dendrogram: Constructs a vertical dendrogram chart using supplied ClusterHierarchical object.
Dendrogram(AxisXY, double[], int[], int[]) - Constructor for class com.imsl.chart.Dendrogram: Constructs a vertical dendrogram chart using supplied data.
Dendrogram(AxisXY, ClusterHierarchical, int) - Constructor for class com.imsl.chart.Dendrogram: Constructs a dendrogram chart using supplied ClusterHierarchical object.
Dendrogram(AxisXY, double[], int[], int[], int) - Constructor for class com.imsl.chart.Dendrogram: Constructs a dendrogram chart using supplied data.
DENDROGRAM_TYPE_HORIZONTAL - Static variable in class com.imsl.chart.ChartNode: Flag to indicate a horizontal dendrogram.
DENDROGRAM_TYPE_VERTICAL - Static variable in class com.imsl.chart.ChartNode: Flag to indicate a vertical dendrogram.
DenseLP - Class in com.imsl.math: Solves a linear programming problem using an active set strategy.
DenseLP(MPSReader) - Constructor for class com.imsl.math.DenseLP: Constructor using an MPSReader object.
DenseLP(double[][], double[], double[]) - Constructor for class com.imsl.math.DenseLP: Constructor variables of type double.
DenseLP.AllConstraintsNotSatisfiedException - Exception in com.imsl.math: All constraints are not satisfied.
DenseLP.AllConstraintsNotSatisfiedException() - Constructor for exception com.imsl.math.DenseLP.AllConstraintsNotSatisfiedException: All constraints are not satisfied.
DenseLP.AllConstraintsNotSatisfiedException(String) - Constructor for exception com.imsl.math.DenseLP.AllConstraintsNotSatisfiedException: All constraints are not satisfied.
DenseLP.AllConstraintsNotSatisfiedException(String, Object[]) - Constructor for exception com.imsl.math.DenseLP.AllConstraintsNotSatisfiedException: All constraints are not satisfied.
DenseLP.BoundsInconsistentException - Exception in com.imsl.math: The bounds given are inconsistent.
DenseLP.BoundsInconsistentException(String) - Constructor for exception com.imsl.math.DenseLP.BoundsInconsistentException: The bounds given are inconsistent.
DenseLP.BoundsInconsistentException(String, Object[]) - Constructor for exception com.imsl.math.DenseLP.BoundsInconsistentException: The bounds given are inconsistent.
DenseLP.CyclingOccurringException - Exception in com.imsl.math: The algorithm appears to be cycling.
DenseLP.CyclingOccurringException() - Constructor for exception com.imsl.math.DenseLP.CyclingOccurringException: The algorithm appears to be cycling.
DenseLP.CyclingOccurringException(String) - Constructor for exception com.imsl.math.DenseLP.CyclingOccurringException: The algorithm appears to be cycling.
DenseLP.CyclingOccurringException(String, Object[]) - Constructor for exception com.imsl.math.DenseLP.CyclingOccurringException: The algorithm appears to be cycling.
DenseLP.MultipleSolutionsException - Exception in com.imsl.math: The problem has multiple solutions giving essentially the same minimum.
DenseLP.MultipleSolutionsException() - Constructor for exception com.imsl.math.DenseLP.MultipleSolutionsException: The problem has multiple solutions giving essentially the same minimum.
DenseLP.MultipleSolutionsException(String) - Constructor for exception com.imsl.math.DenseLP.MultipleSolutionsException: The problem has multiple solutions giving essentially the same minimum.
DenseLP.MultipleSolutionsException(String, Object[]) - Constructor for exception com.imsl.math.DenseLP.MultipleSolutionsException: The problem has multiple solutions giving essentially the same minimum.
DenseLP.NoAcceptablePivotException - Exception in com.imsl.math: No acceptable pivot could be found.
DenseLP.NoAcceptablePivotException() - Constructor for exception com.imsl.math.DenseLP.NoAcceptablePivotException: No acceptable pivot could be found.
DenseLP.NoAcceptablePivotException(String) - Constructor for exception com.imsl.math.DenseLP.NoAcceptablePivotException: No acceptable pivot could be found.
DenseLP.NoAcceptablePivotException(String, Object[]) - Constructor for exception com.imsl.math.DenseLP.NoAcceptablePivotException: No acceptable pivot could be found.
DenseLP.ProblemUnboundedException - Exception in com.imsl.math: The problem is unbounded.
DenseLP.ProblemUnboundedException() - Constructor for exception com.imsl.math.DenseLP.ProblemUnboundedException: The problem is unbounded.
DenseLP.ProblemUnboundedException(String) - Constructor for exception com.imsl.math.DenseLP.ProblemUnboundedException: The problem is unbounded.
DenseLP.ProblemUnboundedException(String, Object[]) - Constructor for exception com.imsl.math.DenseLP.ProblemUnboundedException: The problem is unbounded.
DenseLP.ProblemVacuousException - Exception in com.imsl.math: The problem is vaxuous.
DenseLP.ProblemVacuousException() - Constructor for exception com.imsl.math.DenseLP.ProblemVacuousException: The problem is vaxuous.
DenseLP.ProblemVacuousException(String) - Constructor for exception com.imsl.math.DenseLP.ProblemVacuousException: The problem is vaxuous.
DenseLP.ProblemVacuousException(String, Object[]) - Constructor for exception com.imsl.math.DenseLP.ProblemVacuousException: The problem is vaxuous.
DenseLP.SomeConstraintsDiscardedException - Exception in com.imsl.math: Some constraints were discarded because they were too linearly dependent on other active constraints.
DenseLP.SomeConstraintsDiscardedException() - Constructor for exception com.imsl.math.DenseLP.SomeConstraintsDiscardedException: Some constraints were discarded because they were too linearly dependent on other active constraints.
DenseLP.SomeConstraintsDiscardedException(String) - Constructor for exception com.imsl.math.DenseLP.SomeConstraintsDiscardedException: Some constraints were discarded because they were too linearly dependent on other active constraints.
DenseLP.SomeConstraintsDiscardedException(String, Object[]) - Constructor for exception com.imsl.math.DenseLP.SomeConstraintsDiscardedException: Some constraints were discarded because they were too linearly dependent on other active constraints.
derivative(double, double) - Method in interface com.imsl.datamining.neural.Activation: Returns the value of the derivative of the activation function.
derivative(double) - Method in class com.imsl.math.BSpline: Returns the value of the first derivative of the B-spline at a point.
derivative(double, int) - Method in class com.imsl.math.BSpline: Returns the value of the derivative of the B-spline at a point.
derivative(double[], int) - Method in class com.imsl.math.BSpline: Returns the value of the derivative of the B-spline at each point of an array.
derivative(double) - Method in class com.imsl.math.Spline: Returns the value of the first derivative of the spline at a point.
derivative(double, int) - Method in class com.imsl.math.Spline: Returns the value of the derivative of the spline at a point.
derivative(double[], int) - Method in class com.imsl.math.Spline: Returns the value of the derivative of the spline at each point of an array.
derivative(double, double, int, int) - Method in class com.imsl.math.Spline2D: Returns the value of the partial derivative of the tensor-product spline at the point (x, y).
derivative(double[], double[], int, int) - Method in class com.imsl.math.Spline2D: Returns the values of the partial derivative of the tensor-product spline of an array of points.
derivative(double[], int, double[], double[], double[]) - Method in interface com.imsl.stat.NonlinearRegression.Derivative: Computes the weight, frequency, and partial derivatives of the residual given the parameter vector theta for a single observation.
descending(int[]) - Static method in class com.imsl.stat.Sort: Sorts an integer array into descending order.
descending(int[], int[]) - Static method in class com.imsl.stat.Sort: Sorts an integer array into descending order and returns the permutation vector.
descending(double[]) - Static method in class com.imsl.stat.Sort: Sorts an array into descending order.
descending(double[], int[]) - Static method in class com.imsl.stat.Sort: Sorts an array into descending order and returns the permutation vector.
descending(double[][], int) - Static method in class com.imsl.stat.Sort: Sorts a matrix into descending order by the first nkeys.
descending(double[][], int[]) - Static method in class com.imsl.stat.Sort: Sorts a matrix into descending order by specified keys.
descending(double[][], int, int[]) - Static method in class com.imsl.stat.Sort: Sorts a matrix into descending order by the first nkeys and returns the permutation vector.
descending(double[][], int[], int[]) - Static method in class com.imsl.stat.Sort: Sorts a matrix into descending order by specified keys and return the permutation vector.
determinant() - Method in class com.imsl.math.ComplexLU: Return the determinant of the matrix used to construct this instance.
determinant() - Method in class com.imsl.math.LU: Return the determinant of the matrix used to construct this instance.
DEVIANCE - Static variable in class com.imsl.datamining.decisionTree.DecisionTreeInfoGain.GainCriteria: A measure of the quality of fit.
diagonal(int, Color, Color) - Static method in class com.imsl.chart.FillPaint: Returns a diagonal pattern.
diamond(int, int, Color, Color) - Static method in class com.imsl.chart.FillPaint: Returns a diamond pattern (a checkerboard rotated 45 degrees).
diamondHatch(int, int, Color, Color) - Static method in class com.imsl.chart.FillPaint: Returns a crosshatch on a 45 degree angle.
DIFF - Static variable in class com.imsl.stat.TimeSeriesOperations.CombineMethod: Takes the difference (ts1-ts2) of the two values.
Difference - Class in com.imsl.stat: Differences a seasonal or nonseasonal time series.
Difference() - Constructor for class com.imsl.stat.Difference: Constructor for Difference.
dim - Variable in class com.imsl.math.Physical
DIRECT_AT_RESTART_AND_TERMINATION - Static variable in class com.imsl.math.GenMinRes: Indicates residual updating is to be done by direct evaluation upon restarting and at termination.
DIRECT_AT_RESTART_ONLY - Static variable in class com.imsl.math.GenMinRes: Indicates residual updating is to be done by direct evaluation upon restarting only.
DirectionalLight - Class in com.imsl.chart3d: A directional light.
DirectionalLight(Chart3D) - Constructor for class com.imsl.chart3d.DirectionalLight: Creates a directional light pointing in the negative z direction.
DirectionalLight(Chart3D, double, double, double) - Constructor for class com.imsl.chart3d.DirectionalLight: Creates a directional light pointing with a specified direction.
disc(GregorianCalendar, GregorianCalendar, double, double, DayCountBasis) - Static method in class com.imsl.finance.Bond: Returns the implied interest rate of a discount bond.
discreteUniform(int, int) - Static method in class com.imsl.stat.Cdf: Evaluates the discrete uniform cumulative probability distribution function.
discreteUniform(double, int) - Static method in class com.imsl.stat.InvCdf: Returns the inverse of the discrete uniform cumulative probability distribution function.
discreteUniform(int, int) - Static method in class com.imsl.stat.Pdf: Evaluates the discrete uniform probability density function.
DiscriminantAnalysis - Class in com.imsl.stat: Performs a linear or a quadratic discriminant function analysis among several known groups.
DiscriminantAnalysis(int, int) - Constructor for class com.imsl.stat.DiscriminantAnalysis: Constructs a DiscriminantAnalysis.
DiscriminantAnalysis.CovarianceSingularException - Exception in com.imsl.stat: The variance-covariance matrix is singular.
DiscriminantAnalysis.CovarianceSingularException(String) - Constructor for exception com.imsl.stat.DiscriminantAnalysis.CovarianceSingularException: The variance-covariance matrix is singular.
DiscriminantAnalysis.CovarianceSingularException(String, Object[]) - Constructor for exception com.imsl.stat.DiscriminantAnalysis.CovarianceSingularException: The variance-covariance matrix is singular.
DiscriminantAnalysis.EmptyGroupException - Exception in com.imsl.stat: There are no observations in a group.
DiscriminantAnalysis.EmptyGroupException(String) - Constructor for exception com.imsl.stat.DiscriminantAnalysis.EmptyGroupException: There are no observations in a group.
DiscriminantAnalysis.EmptyGroupException(String, Object[]) - Constructor for exception com.imsl.stat.DiscriminantAnalysis.EmptyGroupException: There are no observations in a group.
DiscriminantAnalysis.SumOfWeightsNegException - Exception in com.imsl.stat: The sum of the weights have become negative.
DiscriminantAnalysis.SumOfWeightsNegException(String) - Constructor for exception com.imsl.stat.DiscriminantAnalysis.SumOfWeightsNegException: The sum of the weights have become negative.
DiscriminantAnalysis.SumOfWeightsNegException(String, Object[]) - Constructor for exception com.imsl.stat.DiscriminantAnalysis.SumOfWeightsNegException: The sum of the weights have become negative.
Dissimilarities - Class in com.imsl.stat: Computes a matrix of dissimilarities (or similarities) between the columns (or rows) of a matrix.
Dissimilarities(double[][]) - Constructor for class com.imsl.stat.Dissimilarities: Constructor for Dissimilarities.
Dissimilarities.NoPositiveVarianceException - Exception in com.imsl.stat: No variable has positive variance.
Dissimilarities.NoPositiveVarianceException() - Constructor for exception com.imsl.stat.Dissimilarities.NoPositiveVarianceException: Constructs a NoPositiveVarianceException.
Dissimilarities.ScaleFactorZeroException - Exception in com.imsl.stat: The computations cannot continue because a scale factor is zero.
Dissimilarities.ScaleFactorZeroException(int) - Constructor for exception com.imsl.stat.Dissimilarities.ScaleFactorZeroException: Constructs a ScaleFactorZeroException.
Dissimilarities.ZeroNormException - Exception in com.imsl.stat: The computations cannot continue because the Euclidean norm of the column is equal to zero.
Dissimilarities.ZeroNormException(int) - Constructor for exception com.imsl.stat.Dissimilarities.ZeroNormException: Constructs a ZeroNormException.
Distribution - Interface in com.imsl.stat: Public interface for the user-supplied distribution function.
divide(Complex, Complex) - Static method in class com.imsl.math.Complex: Returns the result of a Complex object divided by a Complex object, x/y.
divide(Complex, double) - Static method in class com.imsl.math.Complex: Returns the result of a Complex object divided by a double, x/y.
divide(double, Complex) - Static method in class com.imsl.math.Complex: Returns the result of a double divided by a Complex object, x/y.
divide(Physical, Physical) - Static method in class com.imsl.math.Physical: Divide two Physical objects.
divide(Physical, double) - Static method in class com.imsl.math.Physical: Divide a Physical object by a double.
divide(double, Physical) - Static method in class com.imsl.math.Physical: Divide a double by a Physical object.
doGet(HttpServletRequest, HttpServletResponse) - Method in class com.imsl.chart.ChartServlet: Returns the chart as a PNG image.
doGetBytes(int) - Method in class com.imsl.io.AbstractFlatFile: Implements the actual getBytes().
doGetBytes(int) - Method in class com.imsl.io.FlatFile: Gets the value of the designated column in the current row as a byte array.
dollarde(double, int) - Static method in class com.imsl.finance.Finance: Converts a fractional price to a decimal price.
dollarfr(double, int) - Static method in class com.imsl.finance.Finance: Converts a decimal price to a fractional price.
doNext() - Method in class com.imsl.io.AbstractFlatFile: Implements the operations on the file required by the method next().
doNext() - Method in class com.imsl.io.FlatFile: Moves the cursor down one row from its current position.
dot(int, int, Color, Color) - Static method in class com.imsl.chart.FillPaint: Returns a pattern that is an array of circles.
doubleValue() - Method in class com.imsl.math.Complex: Returns the value of the real part as a double.
doubleValue() - Method in class com.imsl.math.Physical: Returns the value of this dimensionless object.
downdate(double[]) - Method in class com.imsl.math.Cholesky: Downdates the factorization by subtracting a rank-1 matrix.
downdate(double[][], int[]) - Method in class com.imsl.stat.DiscriminantAnalysis: Removes a set of observations from the discriminant functions.
downdate(double[][], int[], int[]) - Method in class com.imsl.stat.DiscriminantAnalysis: Removes a set of observations from the discriminant functions.
downdate(double[][], int[], int[], double[]) - Method in class com.imsl.stat.DiscriminantAnalysis: Removes a set of observations and associated frequencies and weights from the discriminant functions.
downdate(double[][], int[], int[], int[], double[]) - Method in class com.imsl.stat.DiscriminantAnalysis: Removes a set of observations and associated frequencies and weights from the discriminant functions.
downdateX(double[]) - Method in class com.imsl.stat.NormTwoSample: Removes the observations in x from the first sample.
downdateY(double[]) - Method in class com.imsl.stat.NormTwoSample: Removes the observations in y from the second sample.
Draw - Class in com.imsl.chart: Chart tree renderer.
Draw(Graphics, Dimension) - Constructor for class com.imsl.chart.Draw: Contructs a Draw object.
drawArc(int, int, int, int, int, int) - Method in class com.imsl.chart.Draw: Draws the outline of a circular or elliptical arc covering the specified rectangle.
drawArc(int, int, int, int, int, int) - Method in class com.imsl.chart.DrawMap: Draws the outline of a circular or elliptical arc covering the specified rectangle.
drawArc(int, int, int, int, int, int) - Method in class com.imsl.chart.DrawPick: Draw an arc.
drawClippedImage(Image, int, int) - Method in class com.imsl.chart.Draw: Draws an image such that any portion of the image beyond the axis range is clipped.
drawErrorBar(int, int, int, int, int) - Method in class com.imsl.chart.Draw: Draw an error bar.
drawErrorBar(int, int, int, int, int) - Method in class com.imsl.chart.DrawMap: Draw an error bar.
drawErrorBar(int, int, int, int, int) - Method in class com.imsl.chart.DrawPick: Draw ErrorBar
drawImage(Image, int, int) - Method in class com.imsl.chart.Draw: Draw an image.
drawImage(Image, int, int) - Method in class com.imsl.chart.DrawMap: Draw Image
drawImage(Image, int, int) - Method in class com.imsl.chart.DrawPick: Draw Image
drawLine(int, int, int, int) - Method in class com.imsl.chart.Draw: Draw a line from (x0,y0) to (x1,y1).
drawLine(int, int, int, int) - Method in class com.imsl.chart.DrawMap: Draw a line from (x0,y0) to (x1,y1).
drawLine(int, int, int, int) - Method in class com.imsl.chart.DrawPick: Draw a line from (x0,y0) to (x1,y1).
DrawMap - Class in com.imsl.chart: Creates an HTML client-side imagemap from a chart tree.
DrawMap(Graphics, Dimension) - Constructor for class com.imsl.chart.DrawMap: Contructs a DrawMap object.
drawMarker(int, int) - Method in class com.imsl.chart.Draw: Draw a marker.
drawMarker(int, int) - Method in class com.imsl.chart.DrawMap: Draw a marker.
drawMarker(int, int) - Method in class com.imsl.chart.DrawPick: Draw a marker.
DrawPick - Class in com.imsl.chart: The DrawPick class.
DrawPick(MouseEvent, Graphics, Dimension) - Constructor for class com.imsl.chart.DrawPick: Contructs a DrawPick object.
drawRotatedText(Text, int, int, float) - Method in class com.imsl.chart.Draw: Draws a text object, at the specified angle, with its lower left point being at (x,y).
drawText(Text, int, int) - Method in class com.imsl.chart.Draw: Draws a text object.
drawText(Text, int, int, boolean) - Method in class com.imsl.chart.Draw: Draws a text object.
drawText(Graphics, Text) - Method in class com.imsl.chart.Draw: Draws the text.
drawText(Text, int, int, boolean) - Method in class com.imsl.chart.DrawMap
drawText(Text, int, int) - Method in class com.imsl.chart.DrawPick
DUNN_SIDAK - Static variable in class com.imsl.stat.ANOVA: The Dunn-Sidak method
duration(GregorianCalendar, GregorianCalendar, double, double, int, DayCountBasis) - Static method in class com.imsl.finance.Bond: Returns the Macaulay duration of a security where the security has periodic interest payments.

E

E - Static variable in class com.imsl.math.JMath
effect(double, int) - Static method in class com.imsl.finance.Finance: Returns the effective annual interest rate.
Eigen - Class in com.imsl.math: Collection of Eigen System functions.
Eigen() - Constructor for class com.imsl.math.Eigen: Constructs the eigenvalues and the eigenvectors of a real square matrix.
Eigen.DidNotConvergeException - Exception in com.imsl.math: The iteration did not converge
Eigen.DidNotConvergeException(String) - Constructor for exception com.imsl.math.Eigen.DidNotConvergeException: Constructs a DidNotConvergeException object.
Eigen.DidNotConvergeException(String, Object[]) - Constructor for exception com.imsl.math.Eigen.DidNotConvergeException: Constructs a DidNotConvergeException object.
EmpiricalQuantiles - Class in com.imsl.stat: Computes empirical quantiles.
EmpiricalQuantiles(double[], double[]) - Constructor for class com.imsl.stat.EmpiricalQuantiles: Constructor for EmpiricalQuantiles.
EmpiricalQuantiles.ScaleFactorZeroException - Exception in com.imsl.stat: The computations cannot continue because a scale factor is zero.
EmpiricalQuantiles.ScaleFactorZeroException(int) - Constructor for exception com.imsl.stat.EmpiricalQuantiles.ScaleFactorZeroException: Constructs a ScaleFactorZeroException.
encode(double) - Method in class com.imsl.datamining.neural.ScaleFilter: Scales a value.
encode(double[]) - Method in class com.imsl.datamining.neural.ScaleFilter: Scales an array of values.
encode(int, double[][]) - Method in class com.imsl.datamining.neural.ScaleFilter: Scales a single column of a two dimensional array of values.
encode(int[]) - Method in class com.imsl.datamining.neural.UnsupervisedNominalFilter: Encodes class data prior to its use in neural network training.
encode(int) - Method in class com.imsl.datamining.neural.UnsupervisedNominalFilter: Apply forward encoding to a value.
encode(int[]) - Method in class com.imsl.datamining.neural.UnsupervisedOrdinalFilter: Encodes an array of ordinal categories into an array of transformed percentages.
encode(int) - Method in class com.imsl.datamining.neural.UnsupervisedOrdinalFilter: Encodes an ordinal category.
END_COLUMN_LABEL - Static variable in class com.imsl.math.PrintMatrixFormat: This flag as the type argument to format, indicates that the formatting string for ending a column label is to be returned.
END_COLUMN_LABELS - Static variable in class com.imsl.math.PrintMatrixFormat: This flag as the type argument to format, indicates that the formatting string for ending a column label row is to be returned.
END_ENTRY - Static variable in class com.imsl.math.PrintMatrixFormat: This flag as the type argument to format, indicates that the formatted string for ending an entry is to be returned.
END_MATRIX - Static variable in class com.imsl.math.PrintMatrixFormat: This flag as the type argument to format, indicates that the formatting string for ending a matrix is to be returned.
END_ROW - Static variable in class com.imsl.math.PrintMatrixFormat: This flag as the type argument to format, indicates that the formatting string for ending a row is to be returned.
END_ROW_LABEL - Static variable in class com.imsl.math.PrintMatrixFormat: This flag as the type argument to format, indicates that the formatting string for ending a row label is to be returned.
endErrorBar() - Method in class com.imsl.chart.Draw: Stop drawing an error bar.
endErrorBar() - Method in class com.imsl.chart.DrawMap
endErrorBar() - Method in class com.imsl.chart.DrawPick: End ErrorBar
endFill() - Method in class com.imsl.chart.Draw: Stop drawing a filled region.
endFill() - Method in class com.imsl.chart.DrawMap
endFill() - Method in class com.imsl.chart.DrawPick: End fill
endImage() - Method in class com.imsl.chart.Draw: Stop drawing an image.
endImage() - Method in class com.imsl.chart.DrawMap
endImage() - Method in class com.imsl.chart.DrawPick: End Image
endLine() - Method in class com.imsl.chart.Draw: Finish drawing lines.
endLine() - Method in class com.imsl.chart.DrawMap
endLine() - Method in class com.imsl.chart.DrawPick: Finish drawing lines.
endMarker() - Method in class com.imsl.chart.Draw: Finish drawing markers.
endMarker() - Method in class com.imsl.chart.DrawMap
endMarker() - Method in class com.imsl.chart.DrawPick: Finish drawing markers.
endText() - Method in class com.imsl.chart.Draw: Stop drawing text.
endText() - Method in class com.imsl.chart.DrawMap
endText() - Method in class com.imsl.chart.DrawPick: End Text
ENTRY - Static variable in class com.imsl.math.PrintMatrixFormat: This flag as the type argument to format, indicates that the formatted string for a given entry is to be returned.
EpochTrainer - Class in com.imsl.datamining.neural: Two-stage training using randomly selected training patterns in stage I.
EpochTrainer(Trainer) - Constructor for class com.imsl.datamining.neural.EpochTrainer: Creates a single stage EpochTrainer.
EpochTrainer(Trainer, Trainer) - Constructor for class com.imsl.datamining.neural.EpochTrainer: Creates an two-stage EpochTrainer.
EPSILON_LARGE - Static variable in class com.imsl.math.Sfun: The largest relative spacing for doubles.
EPSILON_LARGE - Static variable in class com.imsl.math.Spline: The largest relative spacing for double.
EPSILON_SMALL - Static variable in class com.imsl.math.QuadraticProgramming: The smallest relative spacing for doubles.
EPSILON_SMALL - Static variable in class com.imsl.math.Sfun: The smallest relative spacing for doubles.
EPSILON_SMALL - Static variable in class com.imsl.math.ZeroPolynomial: The smallest relative spacing for doubles.
EpsilonAlgorithm - Class in com.imsl.math: The class is used to determine the limit of a sequence of approximations, by means of the Epsilon algorithm of P.
EpsilonAlgorithm() - Constructor for class com.imsl.math.EpsilonAlgorithm: Initializes an EpsilonAlgorithm with a maximum table size of 50.
EpsilonAlgorithm(int) - Constructor for class com.imsl.math.EpsilonAlgorithm: Initializes an EpsilonAlgorithm.
equals(Complex) - Method in class com.imsl.math.Complex: Compares with another Complex.
equals(Object) - Method in class com.imsl.math.Complex: Compares this object against the specified object.
erf(double) - Static method in class com.imsl.math.Sfun: Returns the error function of a double.
erfc(double) - Static method in class com.imsl.math.Sfun: Returns the complementary error function of a double.
erfce(double) - Static method in class com.imsl.math.Sfun: Returns the exponentially scaled complementary error function.
erfcInverse(double) - Static method in class com.imsl.math.Sfun: Returns the inverse of the complementary error function.
erfInverse(double) - Static method in class com.imsl.math.Sfun: Returns the inverse of the error function.
error(String, Object[]) - Method in class com.imsl.chart.xml.ChartXML: Handles error messages.
error(SAXParseException) - Method in class com.imsl.chart.xml.ChartXML: Receive notification of a recoverable error.
error(double[], double[]) - Method in interface com.imsl.datamining.neural.QuasiNewtonTrainer.Error: Returns the contribution to the error from a single training output target.
ERROR_BAR - Static variable in class com.imsl.chart.Draw
ERROR_NORM_ABS - Static variable in class com.imsl.math.ODE: Used by method setNorm to indicate that the error norm to be used is to be the absolute error, equals $max(|e_i|)$
ERROR_NORM_EUCLIDEAN - Static variable in class com.imsl.math.ODE: Used by method setNorm to indicate that the error norm to be used is to be the scaled Euclidean norm defined as
${s = sqrt {sum_{i=1}^{neq}{frac{{e_i}^2}{{w_i}^2}}}}$
ERROR_NORM_MAX - Static variable in class com.imsl.math.ODE: Used by method setNorm to indicate that the error norm to be used is to be the maximum of $e_i/max(|y_i(t)|, floor)$ where floor is set via setFloor
ERROR_NORM_MINABSREL - Static variable in class com.imsl.math.ODE: Used by method setNorm to indicate that the error norm to be used is to be the minimum of the absolute error and the relative error, equals the maximum of $e_i/max(|y_i(t)|, 1)$
ErrorBar - Class in com.imsl.chart: Data points with error bars.
ErrorBar(AxisXY, double[], double[], double[], double[]) - Constructor for class com.imsl.chart.ErrorBar: Creates a set of error bars centered at (x[k],y[k]) and with extents low[k],high[k].
errorGradient(double[], double[]) - Method in interface com.imsl.datamining.neural.QuasiNewtonTrainer.Error: Returns the derivative of the error function with respect to the forecast output.
eval(HyperRectangleQuadrature.Function) - Method in class com.imsl.math.HyperRectangleQuadrature: Returns the value of the integral over the unit cube.
eval(HyperRectangleQuadrature.Function, double[], double[]) - Method in class com.imsl.math.HyperRectangleQuadrature: Returns the value of the integral over a cube.
eval(Quadrature.Function, double, double) - Method in class com.imsl.math.Quadrature: Returns the value of the integral from a to b.
eval(double[]) - Method in interface com.imsl.stat.Distribution: Evaluation method to fit the user-supplied probability density function to input data
eval(double[]) - Method in class com.imsl.stat.GammaDistribution: Fits a gamma probability distribution to xData and returns the probability density at each value.
eval(double[], Object[]) - Method in class com.imsl.stat.GammaDistribution: Evaluates a gamma probability distribution with a given set of parameters at each point in xData and returns the probability density at each value.
eval(double, Object[]) - Method in class com.imsl.stat.GammaDistribution: Evaluates a gamma probability density at a given point xData.
eval(double, double) - Method in class com.imsl.stat.InverseCdf: Evaluates the inverse CDF function.
eval(double[]) - Method in class com.imsl.stat.LogNormalDistribution: Fits a lognormal probability distribution to xData and returns the probability density at each value.
eval(double[], Object[]) - Method in class com.imsl.stat.LogNormalDistribution: Evaluates a lognormal probability distribution with a given set of parameters at each point in xData and returns the probability density at each value.
eval(double, Object[]) - Method in class com.imsl.stat.LogNormalDistribution: Evaluates a lognormal probability density function at a given point xData.
eval(double[]) - Method in class com.imsl.stat.NormalDistribution: Fits a normal (Gaussian) probability distribution to xData and returns the probability density at each value.
eval(double[], Object[]) - Method in class com.imsl.stat.NormalDistribution: Evaluates a normal (Gaussian) probability distribution with the given parameters at each point in xData and returns the probability density at each value.
eval(double, Object[]) - Method in class com.imsl.stat.NormalDistribution: Evaluates a normal (Gaussian) probability density at a given point xData.
eval(double[]) - Method in class com.imsl.stat.PoissonDistribution: Fits a Poisson probability distribution to xData and returns the probability density at each value.
eval(double[], Object[]) - Method in class com.imsl.stat.PoissonDistribution: Evaluates a Poisson probability distribution with a given set of parameters at each point in xData and returns the probability density at each value.
eval(double, Object[]) - Method in class com.imsl.stat.PoissonDistribution: Evaluates a Poisson probability density function at a given point xData.
eval(double[], Object[]) - Method in interface com.imsl.stat.ProbabilityDistribution: Evaluates the user-supplied probability density of each value in xData using the supplied probability distribution parameters.
eval(double, Object[]) - Method in interface com.imsl.stat.ProbabilityDistribution: Evaluation method for the user-supplied distribution function and parameters.
evaluateCDF() - Method in class com.imsl.stat.KaplanMeierECDF: Computes the empirical CDF and returns the CDF values up to, but not including the time values returned by getTimes.
evaluateF(int, double[]) - Method in class com.imsl.math.NumericalDerivatives: This method is provided by the user to compute the function values at the current independent variable values y.
evaluateJ(double[]) - Method in class com.imsl.math.NumericalDerivatives: Evaluates the Jacobian for a system of (m) equations in (n) variables.
EWMA - Class in com.imsl.chart.qc: EWMA is an exponentially weighted moving average control chart.
EWMA(AxisXY, double[], double) - Constructor for class com.imsl.chart.qc.EWMA: Creates an exponentially weighted moving average chart.
EWMA(AxisXY, double[], double, double, double) - Constructor for class com.imsl.chart.qc.EWMA: Creates an exponentially weighted moving average chart using the given values for the expected mean and standard deviation.
EXACT_MATCHES - Static variable in class com.imsl.stat.Dissimilarities: Indicates the number of exact matches distance method.
examineStep(int, double, double[]) - Method in class com.imsl.math.ODE: Called before and after each internal step.
excludeFirst(boolean) - Method in class com.imsl.stat.Difference: If set to true, the observations lost due to differencing will be excluded.
exp(Complex) - Static method in class com.imsl.math.Complex: Returns the exponential of a Complex z, exp(z).
exp(double) - Static method in class com.imsl.math.JMath: Returns the exponential of a double.
expectedNormalOrderStatistic(int, int) - Static method in class com.imsl.stat.Ranks: Returns the expected value of a normal order statistic.
expm1(double) - Static method in class com.imsl.math.Hyperbolic: Returns exp(x)-1, the exponential of x minus 1.
exponential(double, double) - Static method in class com.imsl.stat.Cdf: Evaluates the exponential cumulative probability distribution function.
exponential(double, double) - Static method in class com.imsl.stat.InvCdf: Evaluates the inverse of the exponential cumulative probability distribution function.
exponential(double, double) - Static method in class com.imsl.stat.Pdf: Evaluates the exponential probability density function
extrapolate(double) - Method in class com.imsl.math.EpsilonAlgorithm: Extrapolates the convergence limit of a sequence.
extremeValue(double, double, double) - Static method in class com.imsl.stat.Cdf: Evaluates the extreme value cumulative probability distribution function.
extremeValue(double, double, double) - Static method in class com.imsl.stat.InvCdf: Returns the inverse of the extreme value cumulative probability distribution function.
extremeValue(double, double, double) - Static method in class com.imsl.stat.Pdf: Evaluates the extreme value probability density function.

F

f(double) - Method in interface com.imsl.chart.ChartFunction: Function to be charted.
f(double) - Method in class com.imsl.chart.ChartSpline: Function to be charted.
f(double, double) - Method in interface com.imsl.chart3d.Surface.ZFunction: Define the surface function.
f(double[]) - Method in class com.imsl.datamining.neural.QuasiNewtonTrainer.BlockGradObjective
f(double[]) - Method in class com.imsl.datamining.neural.QuasiNewtonTrainer.BlockObjective
f(double[]) - Method in class com.imsl.datamining.neural.QuasiNewtonTrainer.Objective
f(double[]) - Method in interface com.imsl.math.HyperRectangleQuadrature.Function: Returns the value of the function at the given point.
f(double[]) - Method in interface com.imsl.math.MinConGenLin.Function: Public interface for the function to be minimized.
f(double[], int, boolean[]) - Method in interface com.imsl.math.MinConNLP.Function: Compute the value of the function at the given point.
f(double) - Method in interface com.imsl.math.MinUncon.Function: Public interface for the smooth function of a single variable to be minimized.
f(double[]) - Method in interface com.imsl.math.MinUnconMultiVar.Function: Public interface for the multivariate function to be minimized.
f(double[], double[]) - Method in interface com.imsl.math.NonlinLeastSquares.Function: Public interface for the nonlinear least-squares function.
f(int, double[]) - Method in interface com.imsl.math.NumericalDerivatives.Function: Returns the equations evaluated at the point y.
f(double, double[]) - Method in interface com.imsl.math.OdeAdamsGear.Function: Computes the value of the function $y^{'} = f(t,y)$ at the given point.
f(double, double[], double[]) - Method in interface com.imsl.math.OdeRungeKutta.Function: Returns the value of the function at the given point.
f(double) - Method in interface com.imsl.math.Quadrature.Function: Returns the value of the function at the given point.
f(double) - Method in interface com.imsl.math.RadialBasis.Function: A radial basis function.
f(double) - Method in class com.imsl.math.RadialBasis.Gaussian: A Gaussian basis function.
f(double) - Method in class com.imsl.math.RadialBasis.HardyMultiquadric: A Hardy multiquadric basis function.
f(double) - Method in interface com.imsl.math.ZerosFunction.Function: Returns the value of the function at the given point.
f(double[], double[]) - Method in interface com.imsl.math.ZeroSystem.Function: Returns the value of the function at the given point.
F(double, double, double) - Static method in class com.imsl.stat.Cdf: Evaluates the F cumulative probability distribution function.
F(double, double, double) - Static method in class com.imsl.stat.InvCdf: Returns the inverse of the F cumulative probability distribution function.
f(double[], int, double[], double[], double[]) - Method in interface com.imsl.stat.NonlinearRegression.Function: Computes the weight, frequency, and residual given the parameter vector theta for a single observation.
F(double, double, double) - Static method in class com.imsl.stat.Pdf: Evaluates the F probability density function.
FACE_XA - Static variable in class com.imsl.chart3d.AxisBox: Show the x = a face of the box.
FACE_XB - Static variable in class com.imsl.chart3d.AxisBox: Show the x = b face of the box.
FACE_YA - Static variable in class com.imsl.chart3d.AxisBox: Show the y = a face of the box.
FACE_YB - Static variable in class com.imsl.chart3d.AxisBox: Show the y = b face of the box.
FACE_ZA - Static variable in class com.imsl.chart3d.AxisBox: Show the z = a face of the box.
FACE_ZB - Static variable in class com.imsl.chart3d.AxisBox: Show the z = b face of the box.
fact(int) - Static method in class com.imsl.math.Sfun: Returns the factorial of an integer.
factor - Variable in class com.imsl.math.ComplexLU: This is an n by n Complex matrix containing the LU factorization of the matrix A.
factor - Variable in class com.imsl.math.LU: This is an n by n matrix containing the LU factorization of the matrix A.
FactorAnalysis - Class in com.imsl.stat: Performs Principal Component Analysis or Factor Analysis on a covariance or correlation matrix.
FactorAnalysis(double[][], int, int) - Constructor for class com.imsl.stat.FactorAnalysis: Constructor for FactorAnalysis.
FactorAnalysis.BadVarianceException - Exception in com.imsl.stat: Bad variance error.
FactorAnalysis.BadVarianceException(String) - Constructor for exception com.imsl.stat.FactorAnalysis.BadVarianceException: Constructs a BadVarianceException object.
FactorAnalysis.BadVarianceException(String, Object[]) - Constructor for exception com.imsl.stat.FactorAnalysis.BadVarianceException: Constructs a BadVarianceException object.
FactorAnalysis.EigenvalueException - Exception in com.imsl.stat: Eigenvalue error.
FactorAnalysis.EigenvalueException(String) - Constructor for exception com.imsl.stat.FactorAnalysis.EigenvalueException: Constructs a EigenvalueException object.
FactorAnalysis.EigenvalueException(String, Object[]) - Constructor for exception com.imsl.stat.FactorAnalysis.EigenvalueException: Constructs a EigenvalueException object.
FactorAnalysis.NonPositiveEigenvalueException - Exception in com.imsl.stat: Non positive eigenvalue error.
FactorAnalysis.NonPositiveEigenvalueException(String) - Constructor for exception com.imsl.stat.FactorAnalysis.NonPositiveEigenvalueException: Constructs a NonPositiveEigenvalueException object.
FactorAnalysis.NonPositiveEigenvalueException(String, Object[]) - Constructor for exception com.imsl.stat.FactorAnalysis.NonPositiveEigenvalueException: Constructs a NonPositiveEigenvalueException object.
FactorAnalysis.NotPositiveSemiDefiniteException - Exception in com.imsl.stat: Covariance matrix not positive semi-definite.
FactorAnalysis.NotPositiveSemiDefiniteException(String) - Constructor for exception com.imsl.stat.FactorAnalysis.NotPositiveSemiDefiniteException: Constructs a NotPositiveSemiDefiniteException object.
FactorAnalysis.NotPositiveSemiDefiniteException(String, Object[]) - Constructor for exception com.imsl.stat.FactorAnalysis.NotPositiveSemiDefiniteException: Constructs a NotPositiveSemiDefiniteException object.
FactorAnalysis.NotSemiDefiniteException - Exception in com.imsl.stat: Hessian matrix not semi-definite.
FactorAnalysis.NotSemiDefiniteException(String) - Constructor for exception com.imsl.stat.FactorAnalysis.NotSemiDefiniteException: Constructs a NotSemiDefiniteException object.
FactorAnalysis.NotSemiDefiniteException(String, Object[]) - Constructor for exception com.imsl.stat.FactorAnalysis.NotSemiDefiniteException: Constructs a NotSemiDefiniteException object.
FactorAnalysis.RankException - Exception in com.imsl.stat: Rank of covariance matrix error.
FactorAnalysis.RankException(String) - Constructor for exception com.imsl.stat.FactorAnalysis.RankException: Constructs a RankException object.
FactorAnalysis.RankException(String, Object[]) - Constructor for exception com.imsl.stat.FactorAnalysis.RankException: Constructs a RankException object.
FactorAnalysis.SingularException - Exception in com.imsl.stat: Covariance matrix singular error.
FactorAnalysis.SingularException(String) - Constructor for exception com.imsl.stat.FactorAnalysis.SingularException: Constructs a SingularException object.
FactorAnalysis.SingularException(String, Object[]) - Constructor for exception com.imsl.stat.FactorAnalysis.SingularException: Constructs a SingularException object.
factorNumerically() - Method in class com.imsl.math.ComplexSparseCholesky: Computes the numeric factorization of a sparse Hermitian positive definite matrix.
factorNumerically() - Method in class com.imsl.math.SparseCholesky: Computes the numeric factorization of a sparse real symmetric positive definite matrix.
factorSymbolically() - Method in class com.imsl.math.ComplexSparseCholesky: Computes the symbolic factorization of a sparse Hermitian positive definite matrix.
factorSymbolically() - Method in class com.imsl.math.SparseCholesky: Computes the symbolic factorization of a sparse real symmetric positive definite matrix.
fatalError(SAXParseException) - Method in class com.imsl.chart.xml.ChartXML: Receive notification of a non-recoverable error.
FaureSequence - Class in com.imsl.stat: Generates the low-discrepancy Faure sequence.
FaureSequence(int) - Constructor for class com.imsl.stat.FaureSequence: Creates a Faure sequence with the default base.
FaureSequence(int, int, int) - Constructor for class com.imsl.stat.FaureSequence: Creates a Faure sequence.
FeedForwardNetwork - Class in com.imsl.datamining.neural: A representation of a feed forward neural network.
FeedForwardNetwork() - Constructor for class com.imsl.datamining.neural.FeedForwardNetwork: Creates a new instance of FeedForwardNetwork.
FeynmanKac - Class in com.imsl.math: Solves the generalized Feynman-Kac PDE.
FeynmanKac(FeynmanKac.PdeCoefficients) - Constructor for class com.imsl.math.FeynmanKac: Constructs a PDE solver to solve the Feynman-Kac PDE.
FeynmanKac.Boundaries - Interface in com.imsl.math: Public interface for user supplied boundary coefficients and terminal condition the PDE must satisfy.
FeynmanKac.BoundaryInconsistentException - Exception in com.imsl.math: The boundary conditions are inconsistent.
FeynmanKac.BoundaryInconsistentException(String, Object[]) - Constructor for exception com.imsl.math.FeynmanKac.BoundaryInconsistentException: The boundary conditions are inconsistent.
FeynmanKac.ConstraintsInconsistentException - Exception in com.imsl.math: The constraints are inconsistent.
FeynmanKac.ConstraintsInconsistentException(String, Object[]) - Constructor for exception com.imsl.math.FeynmanKac.ConstraintsInconsistentException: The constraints are inconsistent.
FeynmanKac.CorrectorConvergenceException - Exception in com.imsl.math: Corrector failed to converge.
FeynmanKac.CorrectorConvergenceException(String, Object[]) - Constructor for exception com.imsl.math.FeynmanKac.CorrectorConvergenceException: Corrector failed to converge.
FeynmanKac.ErrorTestException - Exception in com.imsl.math: Error test failure detected.
FeynmanKac.ErrorTestException(String, Object[]) - Constructor for exception com.imsl.math.FeynmanKac.ErrorTestException: Error test failure detected.
FeynmanKac.ForcingTerm - Interface in com.imsl.math: Public interface for non-zero forcing term in the Feynman-Kac equation.
FeynmanKac.InitialConstraintsException - Exception in com.imsl.math: The constraints at the initial point are inconsistent.
FeynmanKac.InitialConstraintsException(String, Object[]) - Constructor for exception com.imsl.math.FeynmanKac.InitialConstraintsException: The constraints at the initial point are inconsistent.
FeynmanKac.InitialData - Interface in com.imsl.math: Public interface for adjustment of initial data or as an opportunity for output during the integration steps.
FeynmanKac.IterationMatrixSingularException - Exception in com.imsl.math: Iteration matrix is singular.
FeynmanKac.IterationMatrixSingularException(String, Object[]) - Constructor for exception com.imsl.math.FeynmanKac.IterationMatrixSingularException: Iteration matrix is singular.
FeynmanKac.PdeCoefficients - Interface in com.imsl.math: Public interface for user supplied PDE coefficients in the Feynman-Kac PDE.
FeynmanKac.TcurrentTstopInconsistentException - Exception in com.imsl.math: The end value for the integration in time, tout, is not consistent with the current time value, t.
FeynmanKac.TcurrentTstopInconsistentException(String, Object[]) - Constructor for exception com.imsl.math.FeynmanKac.TcurrentTstopInconsistentException: The end value for the integration in time, tout, is not consistent with the current time value, t.
FeynmanKac.TEqualsToutException - Exception in com.imsl.math: The current integration point in time and the end point are equal.
FeynmanKac.TEqualsToutException(String, Object[]) - Constructor for exception com.imsl.math.FeynmanKac.TEqualsToutException: The current integration point in time and the end point are equal.
FeynmanKac.TimeIntervalTooSmallException - Exception in com.imsl.math: Distance between starting time point and end point for the integration is too small.
FeynmanKac.TimeIntervalTooSmallException(String, Object[]) - Constructor for exception com.imsl.math.FeynmanKac.TimeIntervalTooSmallException: Distance between starting time point and end point for the integration is too small.
FeynmanKac.ToleranceTooSmallException - Exception in com.imsl.math: Tolerance is too small.
FeynmanKac.ToleranceTooSmallException(String, Object[]) - Constructor for exception com.imsl.math.FeynmanKac.ToleranceTooSmallException: Tolerance is too small.
FeynmanKac.TooManyIterationsException - Exception in com.imsl.math: Too many iterations required by the DAE solver.
FeynmanKac.TooManyIterationsException(String, Object[]) - Constructor for exception com.imsl.math.FeynmanKac.TooManyIterationsException: Too many iterations required by the DAE solver.
FFT - Class in com.imsl.math: FFT functions.
FFT(int) - Constructor for class com.imsl.math.FFT: Constructs an FFT object.
FILL - Static variable in class com.imsl.chart.Draw
FILL_FACTOR - Static variable in class com.imsl.math.ComplexSuperLU: A performance tuning parameter which can be adjusted via method setPerformanceTuningParameters.
FILL_FACTOR - Static variable in class com.imsl.math.SuperLU: A performance tuning parameter which can be adjusted via method setPerformanceTuningParameters.
FILL_TYPE_GRADIENT - Static variable in class com.imsl.chart.ChartNode: Value for attribute "FillType" indicating that the region is to be drawn in a color gradient as specified by the attribute Gradient.
FILL_TYPE_NONE - Static variable in class com.imsl.chart.ChartNode: Value for attribute "FillType" and "FillOutlineType" indicating that the region is not to be drawn.
FILL_TYPE_PAINT - Static variable in class com.imsl.chart.ChartNode: Value for attribute "FillType" indicating that the region is to be drawn using the texture specified by the attribute FillPaint.
FILL_TYPE_SOLID - Static variable in class com.imsl.chart.ChartNode: Value for attribute "FillType" and "FillOutlineType" indicating that the region is to be drawn using the solid color specified by the attribute FillColor or FillOutlineColor.
fillArc(int, int, int, int, int, int) - Method in class com.imsl.chart.Draw: Fills a circular or elliptical arc covering the specified rectangle.
fillArc(int, int, int, int, int, int) - Method in class com.imsl.chart.DrawMap: Fills a circular or elliptical arc covering the specified rectangle.
fillArc(int, int, int, int, int, int) - Method in class com.imsl.chart.DrawPick: Fills a circular or elliptical arc covering the specified rectangle.
fillColor - Variable in class com.imsl.chart.Draw
fillOutlineColor - Variable in class com.imsl.chart.Draw
fillOutlineType - Variable in class com.imsl.chart.Draw
fillPaint - Variable in class com.imsl.chart.Draw
FillPaint - Class in com.imsl.chart: A collection of methods to create Paint objects for fill areas.
fillPolygon(int[], int[], int) - Method in class com.imsl.chart.Draw: Fill a polygon.
fillPolygon(Polygon) - Method in class com.imsl.chart.Draw: Fill a polygon defined by a Polygon object.
fillPolygon(int[], int[], int) - Method in class com.imsl.chart.DrawMap: Fill a polygon.
fillPolygon(Polygon) - Method in class com.imsl.chart.DrawMap: Fill a polygon defined by a Polygon object.
fillPolygon(int[], int[], int) - Method in class com.imsl.chart.DrawPick: Fill a polygon.
fillPolygon(Polygon) - Method in class com.imsl.chart.DrawPick: Fill a polygon defined by a Polygon object.
fillRectangle(int, int, int, int) - Method in class com.imsl.chart.Draw: Fill a rectangle.
fillRectangle(int, int, int, int) - Method in class com.imsl.chart.DrawMap: Fill a rectangle.
fillRectangle(int, int, int, int) - Method in class com.imsl.chart.DrawPick: Fill a rectangle.
fillType - Variable in class com.imsl.chart.Draw
filter() - Method in class com.imsl.stat.KalmanFilter: Performs Kalman filtering and evaluates the likelihood function for the state-space model.
finalize() - Method in class com.imsl.chart.Chart
finalize() - Method in class com.imsl.chart3d.Chart3D
Finance - Class in com.imsl.finance: Collection of finance functions.
findColumn(String) - Method in class com.imsl.io.AbstractFlatFile: Maps the given ResultSet column name to its ResultSet column index.
findColumnName(int) - Method in class com.imsl.io.AbstractFlatFile: Maps the given columnIndex into its column name.
findLink(Node, Node) - Method in class com.imsl.datamining.neural.FeedForwardNetwork: Returns the Link between two Nodes.
findLinks(Node) - Method in class com.imsl.datamining.neural.FeedForwardNetwork: Returns all of the Links to a given Node.
finite(double) - Static method in class com.imsl.math.IEEE: Finite number test on an argument of type double.
fire() - Method in class com.imsl.chart.DrawPick: Fires the pickListeners for all of the picked nodes.
firePickListeners(MouseEvent) - Method in class com.imsl.chart.ChartNode: Fires the pick listeners defined at this node and at all of its ancestors, if the event "hits" the node.
first() - Method in class com.imsl.io.AbstractFlatFile: Moves the cursor to the first row in this ResultSet object.
FIRST - Static variable in class com.imsl.stat.TimeSeriesOperations.CombineMethod: Uses the value from ts1, the first series in the call.
FIRST_DERIVATIVE - Static variable in class com.imsl.math.CsInterpolate
FIRST_GRAM_SCHMIDT - Static variable in class com.imsl.math.GenMinRes: Indicates the first Gram-Schmidt implementation method is to be used.
FIRST_HOUSEHOLDER - Static variable in class com.imsl.math.GenMinRes: Indicates the first Householder implementation method is to be used.
fitModel() - Method in class com.imsl.datamining.decisionTree.DecisionTree: Fits the decision tree.
fitModel() - Method in class com.imsl.datamining.GradientBoosting: Performs the gradient boosting on the training data.
fitModel() - Method in class com.imsl.datamining.PredictiveModel: Fits the predictive model to the training data (estimates the model using the training data and current configuration settings).
FlatFile - Class in com.imsl.io: Reads a text file as a ResultSet.
FlatFile(BufferedReader, Tokenizer) - Constructor for class com.imsl.io.FlatFile: Creates a FlatFile from a BufferedReader.
FlatFile(BufferedReader) - Constructor for class com.imsl.io.FlatFile: Creates a FlatFile with the CSV tokenizer.
FlatFile(String) - Constructor for class com.imsl.io.FlatFile: Creates a FlatFile from a CSV file.
FlatFile(String, Tokenizer) - Constructor for class com.imsl.io.FlatFile: Creates a FlatFile from a file.
FlatFile.Parser - Interface in com.imsl.io: Defines a method that parses a String into an Object.
floatValue() - Method in class com.imsl.math.Complex: Returns the value of the real part as a float.
floatValue() - Method in class com.imsl.math.Physical: Returns the value of this dimensionless object.
floor(double) - Static method in class com.imsl.math.JMath: Returns the value of a double rounded toward negative infinity to an integral value.
force(int, double[], double, double, double[], double[], double[][], double[], double[][]) - Method in interface com.imsl.math.FeynmanKac.ForcingTerm: Computes approximations to the forcing term $phi(f,x,t)$ and its derivative $partial phi/partial y$ .
forecast(double[]) - Method in class com.imsl.datamining.KohonenSOM: Returns a forecast computed using the KohonenSOM object.
forecast(double[][]) - Method in class com.imsl.datamining.KohonenSOM: Returns forecasts computed using the KohonenSOM object.
forecast(double[]) - Method in class com.imsl.datamining.neural.FeedForwardNetwork: Computes a forecast using the Network.
forecast(double[]) - Method in class com.imsl.datamining.neural.Network: Returns a forecast for each of the Network's outputs computed from the trained Network.
forecast(int) - Method in class com.imsl.stat.ARAutoUnivariate: Returns forecasts and associated confidence interval offsets.
forecast(int) - Method in class com.imsl.stat.ARMA: Computes forecasts and their associated probability limits for an ARMA model.
forecast(int) - Method in class com.imsl.stat.ARMAMaxLikelihood: Returns forecasts for lead times at origins z.length-backwardOrigin-1+j where .
forecast(int) - Method in class com.imsl.stat.AutoARIMA: Computes forecasts, associated probability limits and weights for the given outlier contaminated time series.
format(LogRecord) - Method in class com.imsl.IMSLFormatter: Format the given log record and return the formatted string.
format(int, Object, int, int, ParsePosition) - Method in class com.imsl.math.PrintMatrixFormat: Returns a formatted string.
formatLabel(double, double) - Method in class com.imsl.chart.Data
formatMessage(String, String, Object[]) - Static method in class com.imsl.Messages: A message is formatted using a MessageFormat string retrieved from the named resource bundle using the given key.
formatMessage(String, String) - Static method in class com.imsl.Messages: A message is formatted, without arguments, using a MessageFormat string retrieved from the named resource bundle using the given key.
forward(Complex[]) - Method in class com.imsl.math.ComplexFFT: Compute the Fourier coefficients of a complex periodic sequence.
forward(double[]) - Method in class com.imsl.math.FFT: Compute the Fourier coefficients of a real periodic sequence.
FORWARD_REGRESSION - Static variable in class com.imsl.stat.StepwiseRegression: Indicates forward regression.
frobeniusNorm(Complex[][]) - Static method in class com.imsl.math.ComplexMatrix: Return the Frobenius norm of a Complex matrix.
frobeniusNorm() - Method in class com.imsl.math.ComplexSparseMatrix: Returns the Frobenius norm of the matrix.
frobeniusNorm(double[][]) - Static method in class com.imsl.math.Matrix: Return the Frobenius norm of a matrix.
frobeniusNorm() - Method in class com.imsl.math.SparseMatrix: Returns the Frobenius norm of the matrix.
FULL - Static variable in class com.imsl.math.PrintMatrix: This flag as the argument to setMatrixType, indicates that the full matrix is to be printed.
fv(double, int, double, double, int) - Static method in class com.imsl.finance.Finance: Returns the future value of an investment.
fvschedule(double, double[]) - Static method in class com.imsl.finance.Finance: Returns the future value of an initial principal taking into consideration a schedule of compound interest rates.

G

g(double) - Method in interface com.imsl.datamining.neural.Activation: Returns the value of the activation function.
g(double) - Method in interface com.imsl.math.MinUncon.Derivative: Public interface for the smooth function of a single variable to be minimized.
g(double) - Method in interface com.imsl.math.RadialBasis.Function: The derivative of the radial basis function used to calculate the gradient of the radial basis approximation.
g(double) - Method in class com.imsl.math.RadialBasis.Gaussian: The derivative of the Gaussian basis function used to calculate the gradient of the radial basis approximation.
g(double) - Method in class com.imsl.math.RadialBasis.HardyMultiquadric: The derivative of the Hardy multiquadric basis function used to calculate the gradient of the radial basis approximation.
gamma(double) - Static method in class com.imsl.math.Sfun: Returns the Gamma function of a double.
gamma(double, double) - Static method in class com.imsl.stat.Cdf: Evaluates the gamma cumulative probability distribution function.
gamma(double, double) - Static method in class com.imsl.stat.InvCdf: Evaluates the inverse of the gamma cumulative probability distribution function.
gamma(double, double, double) - Static method in class com.imsl.stat.Pdf: Evaluates the gamma probability density function.
GammaDistribution - Class in com.imsl.stat: Evaluates a gamma probability density for a given set of data.
GammaDistribution() - Constructor for class com.imsl.stat.GammaDistribution
gammaIncomplete(double, double) - Static method in class com.imsl.math.Sfun: Evaluates the incomplete gamma function.
GammaPD - Class in com.imsl.stat.distributions: The gamma probability distribution
GammaPD() - Constructor for class com.imsl.stat.distributions.GammaPD: Constructor for the gamma probability distribution
GARCH - Class in com.imsl.stat: Computes estimates of the parameters of a GARCH(p,q) model.
GARCH(int, int, double[], double[]) - Constructor for class com.imsl.stat.GARCH: Constructor for GARCH.
GARCH.ConstrInconsistentException - Exception in com.imsl.stat: The equality constraints are inconsistent.
GARCH.ConstrInconsistentException(String) - Constructor for exception com.imsl.stat.GARCH.ConstrInconsistentException: Constructs a ConstrInconsistentException object.
GARCH.ConstrInconsistentException(String, Object[]) - Constructor for exception com.imsl.stat.GARCH.ConstrInconsistentException: Constructs a ConstrInconsistentException object.
GARCH.EqConstrInconsistentException - Exception in com.imsl.stat: The equality constraints and the bounds on the variables are found to be inconsistent.
GARCH.EqConstrInconsistentException(String) - Constructor for exception com.imsl.stat.GARCH.EqConstrInconsistentException: Constructs a EqConstrInconsistentException object.
GARCH.EqConstrInconsistentException(String, Object[]) - Constructor for exception com.imsl.stat.GARCH.EqConstrInconsistentException: Constructs a EqConstrInconsistentException object.
GARCH.NoVectorXException - Exception in com.imsl.stat: No vector X satisfies all of the constraints.
GARCH.NoVectorXException(String) - Constructor for exception com.imsl.stat.GARCH.NoVectorXException: Constructs a NoVectorXException object.
GARCH.NoVectorXException(String, Object[]) - Constructor for exception com.imsl.stat.GARCH.NoVectorXException: Constructs a NoVectorXException object.
GARCH.TooManyIterationsException - Exception in com.imsl.stat: Number of function evaluations exceeded 1000.
GARCH.TooManyIterationsException(String) - Constructor for exception com.imsl.stat.GARCH.TooManyIterationsException: Constructs a TooManyIterationsException object.
GARCH.TooManyIterationsException(String, Object[]) - Constructor for exception com.imsl.stat.GARCH.TooManyIterationsException: Constructs a TooManyIterationsException object.
GARCH.VarsDeterminedException - Exception in com.imsl.stat: The variables are determined by the equality constraints.
GARCH.VarsDeterminedException(String) - Constructor for exception com.imsl.stat.GARCH.VarsDeterminedException: Constructs a VarsDeterminedException object.
GARCH.VarsDeterminedException(String, Object[]) - Constructor for exception com.imsl.stat.GARCH.VarsDeterminedException: Constructs a VarsDeterminedException object.
GENERALIZED_LEAST_SQUARES - Static variable in class com.imsl.stat.FactorAnalysis: Indicates generalized least squares method.
GenMinRes - Class in com.imsl.math: Linear system solver using the restarted Generalized Minimum Residual (GMRES) method.
GenMinRes(int, GenMinRes.Function) - Constructor for class com.imsl.math.GenMinRes: GMRES linear system solver constructor.
GenMinRes.Function - Interface in com.imsl.math: Public interface for the user supplied function to GenMinRes.
GenMinRes.Norm - Interface in com.imsl.math: Public interface for the user supplied function to the GenMinRes object used for the norm $Vert X Vert$ when the Gram-Schmidt implementation is used.
GenMinRes.Preconditioner - Interface in com.imsl.math: Public interface for the user supplied function to GenMinRes used for preconditioning.
GenMinRes.TooManyIterationsException - Exception in com.imsl.math: Maximum number of iterations exceeded.
GenMinRes.TooManyIterationsException(String) - Constructor for exception com.imsl.math.GenMinRes.TooManyIterationsException: Constructs a TooManyIterationsException object.
GenMinRes.TooManyIterationsException(String, Object[]) - Constructor for exception com.imsl.math.GenMinRes.TooManyIterationsException: Constructs a TooManyIterationsException object.
GenMinRes.VectorProducts - Interface in com.imsl.math: Public interface for the user supplied function to the GenMinRes object used for the inner product when the Gram-Schmidt implementation is used.
geometric(int, double) - Static method in class com.imsl.stat.Cdf: Evaluates the discrete geometric cumulative probability distribution function.
geometric(double, double) - Static method in class com.imsl.stat.InvCdf: Returns the inverse of the discrete geometric cumulative probability distribution function.
geometric(int, double) - Static method in class com.imsl.stat.Pdf: Evaluates the discrete geometric probability density function.
get(String) - Method in class com.imsl.chart.xml.ChartXML: Returns a generated object given the id attribute in the XML tag that created the object.
get(int, int) - Method in class com.imsl.math.ComplexSparseMatrix: Returns the value of an element in the matrix.
get(int, int) - Method in class com.imsl.math.SparseMatrix: Returns the value of an element in the matrix.
getAbsoluteErrorTolerances() - Method in class com.imsl.math.FeynmanKac: Returns absolute error tolerances.
getAbsoluteH() - Method in class com.imsl.chart.qc.CuSumStatus: Returns the value for H used for setting limits.
getAbstractParent() - Method in class com.imsl.chart.AbstractChartNode: Returns the parent of this node.
getActivation() - Method in class com.imsl.datamining.neural.Perceptron: Returns the activation function.
getAdjustedANOVA() - Method in class com.imsl.stat.ANCOVA: Returns the partial sum of squares for the one-way analysis of covariance.
getAdjustedRSquared() - Method in class com.imsl.stat.ANOVA: Returns the adjusted R-squared (in percent).
getAIC() - Method in class com.imsl.stat.ARAutoUnivariate: Returns the final estimate for Akaike's Information Criterion (AIC) at the optimum.
getAIC() - Method in class com.imsl.stat.ARMAOutlierIdentification: Returns Akaike's information criterion (AIC).
getAIC() - Method in class com.imsl.stat.ARSeasonalFit: Returns the final estimate for Akaike's Information Criterion (AIC) at the optimum.
getAIC() - Method in class com.imsl.stat.AutoARIMA: Returns Akaike's information criterion (AIC) for the optimum model.
getAICC() - Method in class com.imsl.stat.ARMAOutlierIdentification: Returns Akaike's Corrected Information Criterion (AICC).
getAICC() - Method in class com.imsl.stat.AutoARIMA: Returns Akaike's Corrected Information Criterion (AICC) for the optimum model.
getAkaike() - Method in class com.imsl.stat.GARCH: Returns the value of Akaike Information Criterion evaluated at the estimated parameter array.
getAlignment() - Method in class com.imsl.chart.Text: Gets the alignment for this Text object.
getALT() - Method in class com.imsl.chart.ChartNode: Returns the value of the "ALT" attribute.
getALT() - Method in class com.imsl.chart.DrawMap: Returns the current ALT string.
getANCOVA() - Method in class com.imsl.stat.ANCOVA: Returns an array containing the one-way analysis of covariance assuming parallelism.
getANOVA() - Method in class com.imsl.math.RadialBasis: Returns the ANOVA statistics from the linear regression.
getANOVA() - Method in class com.imsl.stat.LinearRegression: Get an analysis of variance table and related statistics.
getANOVA() - Method in class com.imsl.stat.StepwiseRegression: Get an analysis of variance table and related statistics.
getANOVA() - Method in class com.imsl.stat.UserBasisRegression: Get an analysis of variance table and related statistics.
getANOVATable() - Method in class com.imsl.stat.ANOVAFactorial: Returns the analysis of variance table.
getANOVATables() - Method in class com.imsl.stat.ANCOVA: Returns a matrix of size ngroup by 15 containing the analysis of variance tables for each linear regression model fitted separately to each treatment group.
getAR() - Method in class com.imsl.stat.ARAutoUnivariate: Returns the final auto regressive parameter estimates at the optimum AIC using the estimation method specified in setEstimationMethod.
getAR() - Method in class com.imsl.stat.ARMA: Returns the final autoregressive parameter estimates.
getAR() - Method in class com.imsl.stat.ARMAMaxLikelihood: Returns the final autoregressive parameter estimates.
getAR() - Method in class com.imsl.stat.ARMAOutlierIdentification: Returns the final autoregressive parameter estimates.
getAR() - Method in class com.imsl.stat.ARSeasonalFit: Returns the final autoregressive parameter estimates at the optimum in the transformed series .
getAR() - Method in class com.imsl.stat.AutoARIMA: Returns the final autoregressive parameter estimates of the optimum model.
getARCH() - Method in class com.imsl.stat.GARCH: Returns the estimated values of the ARCH coefficients.
getARConstants() - Method in class com.imsl.stat.VectorAutoregression: Returns the current settings of the constants used in the autoregression model.
getARModel() - Method in class com.imsl.stat.VectorAutoregression: Returns the autoregressive model configuration.
getAROrder() - Method in class com.imsl.stat.ARSeasonalFit: Returns optimum number of lags, p, for the optimum autoregressive AR(p) model.
getArray(String) - Method in class com.imsl.io.AbstractFlatFile: Returns the value of the designated column in the current row of this ResultSet object as an Array object in the Java programming language.
getArray() - Method in class com.imsl.stat.ANOVA: Returns the ANOVA values as an array.
getAsciiStream(int) - Method in class com.imsl.io.AbstractFlatFile: Gets the value of the designated column in the current row of this ResultSet object as a stream of ASCII characters.
getAsciiStream(String) - Method in class com.imsl.io.AbstractFlatFile: Gets the value of the designated column in the current row of this ResultSet object as a stream of ASCII characters.
getAssociationRules(Itemsets, double, double) - Static method in class com.imsl.datamining.Apriori: Returns strong association rules among the itemsets in itemsets.
getAttribute(String) - Method in class com.imsl.chart.AbstractChartNode: Gets an attribute.
getAutoCorrelations() - Method in class com.imsl.stat.AutoCorrelation: Returns the autocorrelations of the time series x.
getAutoCorrelationX() - Method in class com.imsl.stat.CrossCorrelation: Returns the autocorrelations of the time series x.
getAutoCorrelationY() - Method in class com.imsl.stat.CrossCorrelation: Returns the autocorrelations of the time series y.
getAutoCovariance() - Method in class com.imsl.stat.ARMA: Returns the autocovariances of the time series z.
getAutoCovariances() - Method in class com.imsl.stat.AutoCorrelation: Returns the variance and autocovariances of the time series x.
getAutoCovarianceX() - Method in class com.imsl.stat.CrossCorrelation: Returns the autocovariances of the time series x.
getAutoCovarianceY() - Method in class com.imsl.stat.CrossCorrelation: Returns the autocovariances of the time series y.
getAutoscaleInput() - Method in class com.imsl.chart.AbstractChartNode: Returns the value of the "AutoscaleInput" attribute.
getAutoscaleMinimumTimeInterval() - Method in class com.imsl.chart.AbstractChartNode: Returns the value of the "AutoscaleMinimumTimeInterval" attribute.
getAutoscaleOutput() - Method in class com.imsl.chart.AbstractChartNode: Returns the value of the "AutoscaleOutput" attribute.
getAxis() - Method in class com.imsl.chart.ChartNode: Returns the value of the "Axis" attribute.
getAxisBox() - Method in class com.imsl.chart3d.AxisXYZ: Return the axis box node.
getAxisLabel() - Method in class com.imsl.chart.Axis1D: Returns the label node associated with this axis.
getAxisLabel() - Method in class com.imsl.chart3d.Axis3D: Returns the label node associated with this axis.
getAxisLine() - Method in class com.imsl.chart.Axis1D: Returns the axis line node associated with this axis.
getAxisLine() - Method in class com.imsl.chart3d.Axis3D: Returns the axis line node associated with this axis.
getAxisR() - Method in class com.imsl.chart.Polar: Return the radius axis node.
getAxisRLabel() - Method in class com.imsl.chart.AxisR: Returns the AxisRLabel node.
getAxisRLine() - Method in class com.imsl.chart.AxisR: Returns the AxisRLine node.
getAxisRMajorTick() - Method in class com.imsl.chart.AxisR: Returns the major tick node associated with this axis.
getAxisTheta() - Method in class com.imsl.chart.Polar: Return the angular axis node.
getAxisTitle() - Method in class com.imsl.chart.Axis1D: Returns the title node associated with this axis.
getAxisTitle() - Method in class com.imsl.chart3d.Axis3D: Returns the title node associated with this axis.
getAxisTitlePosition() - Method in class com.imsl.chart3d.ChartNode3D: Returns the value of the "AxisTitlePosition" attribute.
getAxisUnit() - Method in class com.imsl.chart.Axis1D: Returns the unit node associated with this axis.
getAxisX() - Method in class com.imsl.chart.AxisXY: Return the x-axis node.
getAxisX() - Method in class com.imsl.chart3d.AxisXYZ: Return the x-axis node.
getAxisY() - Method in class com.imsl.chart.AxisXY: Return the y-axis node.
getAxisY() - Method in class com.imsl.chart3d.AxisXYZ: Return the y-axis node.
getAxisZ() - Method in class com.imsl.chart3d.AxisXYZ: Return the z-axis node.
getBackground() - Method in class com.imsl.chart.ChartNode: Returns the value of the "Background" attribute.
getBackground() - Method in class com.imsl.chart3d.Chart3D: Returns the value of the "Background" attribute.
getBackwardOrigin() - Method in class com.imsl.stat.ARAutoUnivariate: Returns the maximum backward origin.
getBackwardOrigin() - Method in class com.imsl.stat.ARMA: Returns the user-specified backward origin
getBackwardOrigin() - Method in class com.imsl.stat.ARMAMaxLikelihood: Returns the current value for forecasting backward origin.
getBalancedTable() - Method in class com.imsl.stat.TableMultiWay: Returns an object containing the balanced table.
getBarData() - Method in class com.imsl.chart.Bar: Returns the "BarData" attribute.
getBarGap() - Method in class com.imsl.chart.ChartNode: Returns the value of the "BarGap" attribute.
getBarItem() - Method in class com.imsl.chart.BarSet: Returns an array of BarItems.
getBarItem(int) - Method in class com.imsl.chart.BarSet: Returns the BarItem given the index.
getBarMinus() - Method in class com.imsl.chart.qc.CuSumStatus: Returns the value of the attribute "BarMinus" containing the $C^{-}$ bars.
getBarPlus() - Method in class com.imsl.chart.qc.CuSumStatus: Returns the value of the attribute "BarPlus" containing the $C^{+}$ bars.
getBarSet() - Method in class com.imsl.chart.Bar: Returns the BarSet object.
getBarSet(int, int) - Method in class com.imsl.chart.Bar: Returns the BarSet object.
getBarSet(int) - Method in class com.imsl.chart.Bar: Returns the BarSet object.
getBarType() - Method in class com.imsl.chart.ChartNode: Returns the value of the "BarType" attribute.
getBarWidth() - Method in class com.imsl.chart.ChartNode: Returns the value of the "BarWidth" attribute.
getBase() - Method in class com.imsl.stat.FaureSequence: Returns the base.
getBias() - Method in class com.imsl.datamining.neural.Perceptron: Returns the bias for this Perceptron.
getBIC() - Method in class com.imsl.stat.ARMAOutlierIdentification: Returns the Bayesian Information Criterion (BIC).
getBIC() - Method in class com.imsl.stat.AutoARIMA: Returns the Bayesian Information Criterion (BIC) for the optimum model.
getBigDecimal(int) - Method in class com.imsl.io.AbstractFlatFile: Gets the value of the designated column in the current row of this ResultSet object as a java.math.BigDecimal with full precision.
getBigDecimal(String) - Method in class com.imsl.io.AbstractFlatFile: Gets the value of the designated column in the current row of this ResultSet object as a java.math.BigDecimal with full precision.
getBinaryStream(int) - Method in class com.imsl.io.AbstractFlatFile: Gets the value of the designated column in the current row of this ResultSet object as a binary stream of uninterpreted bytes.
getBinaryStream(String) - Method in class com.imsl.io.AbstractFlatFile: Gets the value of the designated column in the current row of this ResultSet object as a stream of uninterpreted bytes.
getBlob(int) - Method in class com.imsl.io.AbstractFlatFile: Returns the value of the designated column in the current row of this ResultSet object as a Blob object in the Java programming language.
getBlob(String) - Method in class com.imsl.io.AbstractFlatFile: Returns the value of the designated column in the current row of this ResultSet object as a Blob object in the Java programming language.
getBlomScores(double[]) - Method in class com.imsl.stat.Ranks: Gets the Blom version of normal scores for each observation.
getBodies() - Method in class com.imsl.chart.BoxPlot: Returns a node containing the body elements in the Box plot.
getBoolean(int) - Method in class com.imsl.io.AbstractFlatFile: Gets the value of the designated column in the current row of this ResultSet object as a boolean in the Java programming language.
getBoolean(String) - Method in class com.imsl.io.AbstractFlatFile: Gets the value of the designated column in the current row of this ResultSet object as a boolean in the Java programming language.
getBooleanAttribute(String, boolean) - Method in class com.imsl.chart.AbstractChartNode: Convenience routine to get a Boolean-valued attribute.
getBoundingSphere() - Method in class com.imsl.chart3d.ChartNode3D: Gets the spherical bounding region object BoundingSphere.
getBounds() - Method in class com.imsl.datamining.neural.ScaleFilter: Retrieves bounds used during bounded scaling.
getBoxPlotType() - Method in class com.imsl.chart.BoxPlot: Returns the value of the "BoxPlotType" attribute.
getBreakpoints() - Method in class com.imsl.math.Spline: Returns a copy of the breakpoints.
getByte(int) - Method in class com.imsl.io.AbstractFlatFile: Gets the value of the designated column in the current row of this ResultSet object as a byte in the Java programming language.
getByte(String) - Method in class com.imsl.io.AbstractFlatFile: Gets the value of the designated column in the current row of this ResultSet object as a byte in the Java programming language.
getBytes(int) - Method in class com.imsl.io.AbstractFlatFile: Gets the value of the designated column in the current row of this ResultSet object as a byte array in the Java programming language.
getBytes(String) - Method in class com.imsl.io.AbstractFlatFile: Gets the value of the designated column in the current row of this ResultSet object as a byte array in the Java programming language.
getCanvas() - Method in class com.imsl.chart3d.Chart3D
getCanvas() - Method in class com.imsl.chart3d.JFrameChart3D: Returns the Canvas3DChart into which the chart is drawn.
getCaseAnalysis() - Method in class com.imsl.stat.CategoricalGenLinModel: Returns the case analysis.
getCaseStatistics(double[], double) - Method in class com.imsl.stat.LinearRegression: Returns the case statistics for an observation.
getCaseStatistics(double[], double, double) - Method in class com.imsl.stat.LinearRegression: Returns the case statistics for an observation and a weight.
getCaseStatistics(double[], double, int) - Method in class com.imsl.stat.LinearRegression: Returns the case statistics for an observation and future response count for the desired prediction interval.
getCaseStatistics(double[], double, double, int) - Method in class com.imsl.stat.LinearRegression: Returns the case statistics for an observation, weight, and future response count for the desired prediction interval.
getCaseStatistics() - Method in class com.imsl.stat.ProportionalHazards: Returns the case statistics for each observation.
getCellCounts() - Method in class com.imsl.stat.ChiSquaredTest: Returns the cell counts.
getCensorColumn() - Method in class com.imsl.stat.KaplanMeierEstimates: Returns the column index of x containing the optional censoring code for each observation.
getCensorColumn() - Method in class com.imsl.stat.ProportionalHazards: Returns the column index of x containing the optional censoring code for each observation.
getCenter() - Method in class com.imsl.chart.qc.ShewhartControlChart: Returns the value of the attribute "Center".
getCenter() - Method in class com.imsl.datamining.neural.ScaleFilter: Retrieves the measure of center to be used during z-score scaling.
getCenter() - Method in class com.imsl.stat.ARSeasonalFit: Returns the current setting for centering the input time series.
getCenterLine() - Method in class com.imsl.chart.qc.ShewhartControlChart: Returns the center line.
getCharacterStream(int) - Method in class com.imsl.io.AbstractFlatFile: Gets the value of the designated column in the current row of this ResultSet object as a java.io.Reader object.
getCharacterStream(String) - Method in class com.imsl.io.AbstractFlatFile: Gets the value of the designated column in the current row of this ResultSet object as a java.io.Reader object.
getChart() - Method in class com.imsl.chart.ChartNode: Returns the value of the "Chart" attribute.
getChart(HttpServletRequest) - Method in class com.imsl.chart.ChartServlet: Returns the chart found in the session saved with the key "chart"+id, where id is the value of the "id" parameter in the request.
getChart() - Method in class com.imsl.chart.JFrameChart: Return the Chart object.
getChart() - Method in class com.imsl.chart.JPanelChart: Return the Chart object.
getChart() - Method in class com.imsl.chart.xml.ChartXML: Returns the root node of the chart tree.
getChart3D() - Method in class com.imsl.chart3d.Canvas3DChart: Returns the Chart3D associated with this canvas.
getChart3D() - Method in class com.imsl.chart3d.JFrameChart3D: Return the Chart object.
getChartServletName() - Method in class com.imsl.chart.JspBean: Returns the URL of the servlet used to render the chart.
getChartTitle() - Method in class com.imsl.chart.ChartNode: Returns the value of the "ChartTitle" attribute.
getChildId(int) - Method in class com.imsl.datamining.decisionTree.TreeNode: Returns the id of a child node.
getChildList() - Method in class com.imsl.chart.AbstractChartNode: Returns the children of this node.
getChildren() - Method in class com.imsl.chart.ChartNode: Returns an array of the children of this node.
getChildren() - Method in class com.imsl.chart3d.ChartNode3D: Returns an array of the children of this node.
getChildrenIds() - Method in class com.imsl.datamining.decisionTree.TreeNode: Returns the array of child node id's.
getChiSquared() - Method in class com.imsl.stat.ChiSquaredTest: Returns the chi-squared statistic.
getChiSquared() - Method in class com.imsl.stat.ContingencyTable: Returns the Pearson chi-squared test statistic.
getChiSquared() - Method in class com.imsl.stat.NormalityTest: Returns the chi-square statistic for the chi-squared goodness-of-fit test.
getChiSquaredTest() - Method in class com.imsl.stat.NormOneSample: Returns the test statistic associated with the chi-squared test for variances.
getChiSquaredTest() - Method in class com.imsl.stat.NormTwoSample: Returns the test statistic associated with the chi-squared test for common, or pooled, variances.
getChiSquaredTestDF() - Method in class com.imsl.stat.NormOneSample: Returns the degrees of freedom associated with the chi-squared test for variances.
getChiSquaredTestDF() - Method in class com.imsl.stat.NormTwoSample: Returns the degrees of freedom associated with the chi-squared test for the common, or pooled, variances.
getChiSquaredTestP() - Method in class com.imsl.stat.NormOneSample: Returns the probability of a larger chi-squared associated with the chi-squared test for variances.
getChiSquaredTestP() - Method in class com.imsl.stat.NormTwoSample: Returns the probability of a larger chi-squared associated with the chi-squared test for common, or pooled, variances.
getClassCounts(int[]) - Method in class com.imsl.datamining.NaiveBayesClassifier: Returns the number of patterns for each target classification.
getClassCounts() - Method in class com.imsl.datamining.PredictiveModel: Returns the counts of each class (level) of the categorical response variable.
getClassFittedValues() - Method in class com.imsl.datamining.GradientBoosting: Returns the fitted values ${f(x_i)}$ for a categorical response variable with two or more levels.
getClassificationErrors() - Method in class com.imsl.datamining.NaiveBayesClassifier: Returns the classification probability errors for each pattern in the training data.
getClassificationVariableCounts() - Method in class com.imsl.stat.CategoricalGenLinModel: Returns the number of values taken by each classification variable.
getClassificationVariableValues() - Method in class com.imsl.stat.CategoricalGenLinModel: Returns the distinct values of the classification variables in ascending order.
getClassMembership() - Method in class com.imsl.stat.DiscriminantAnalysis: Returns the group number to which the observation was classified.
getClassProbabilities() - Method in class com.imsl.datamining.GradientBoosting: Returns the predicted probabilities on the training data for a categorical response variable.
getClassTable() - Method in class com.imsl.stat.DiscriminantAnalysis: Returns the classification table.
getClassValueCounts() - Method in class com.imsl.stat.ProportionalHazards: Returns the number of values taken by each classification variable.
getClassValues() - Method in class com.imsl.stat.ProportionalHazards: Returns the class values taken by each classification variable.
getClipBounds() - Method in class com.imsl.chart.Draw: Get the clipping rectangle.
getClipData() - Method in class com.imsl.chart.ChartNode: Returns the value of the "ClipData" attribute.
getClob(int) - Method in class com.imsl.io.AbstractFlatFile: Returns the value of the designated column in the current row of this ResultSet object as a Clob object in the Java programming language.
getClob(String) - Method in class com.imsl.io.AbstractFlatFile: Returns the value of the designated column in the current row of this ResultSet object as a Clob object in the Java programming language.
getClose() - Method in class com.imsl.chart.HighLowClose: Gets the value of the attribute "Close".
getClosedFormMLE(double[]) - Method in interface com.imsl.stat.distributions.ClosedFormMaximumLikelihoodInterface: Returns the maximum likelihood estimates (MLEs).
getClosedFormMlStandardError(double[]) - Method in interface com.imsl.stat.distributions.ClosedFormMaximumLikelihoodInterface: Returns the standard error based on the closed form solution of the maximum liklihood for the sample data.
getClusterCounts() - Method in class com.imsl.stat.ClusterKMeans: Returns the number of observations in each cluster.
getClusterLeftSons() - Method in class com.imsl.stat.ClusterHierarchical: Returns the left sons of each merged cluster.
getClusterLevel() - Method in class com.imsl.stat.ClusterHierarchical: Returns the level at which the clusters are joined.
getClusterMembership(int) - Method in class com.imsl.stat.ClusterHierarchical: Returns the cluster membership of each observation.
getClusterMembership() - Method in class com.imsl.stat.ClusterKMeans: Returns the cluster membership for each observation.
getClusterRightSons() - Method in class com.imsl.stat.ClusterHierarchical: Returns the right sons of each merged cluster.
getClusterSSQ() - Method in class com.imsl.stat.ClusterKMeans: Returns the within sum of squares for each cluster.
getCMinus() - Method in class com.imsl.chart.qc.CuSumStatus: Returns the $C^{-}$ values.
getCoefficient(int) - Method in class com.imsl.stat.LinearRegression.CoefficientTTests: Returns the estimate for a coefficient.
getCoefficient(int) - Method in class com.imsl.stat.NonlinearRegression: Returns the estimate for a coefficient.
getCoefficient(int) - Method in class com.imsl.stat.StepwiseRegression.CoefficientTTests: Returns the estimate for a coefficient of the independent variable.
getCoefficientOfVariation() - Method in class com.imsl.stat.ANOVA: Returns the coefficient of variation (in percent).
getCoefficients() - Method in class com.imsl.io.MPSReader.Row: Returns the coeffients of this row as a dense array.
getCoefficients() - Method in class com.imsl.math.Spline2D: Returns the coefficients for the tensor-product spline.
getCoefficients() - Method in class com.imsl.stat.DiscriminantAnalysis: Returns the linear discriminant function coefficients.
getCoefficients() - Method in class com.imsl.stat.LinearRegression: Returns the regression coefficients.
getCoefficients() - Method in class com.imsl.stat.NonlinearRegression: Returns the regression coefficients.
getCoefficients() - Method in class com.imsl.stat.UserBasisRegression: Returns the regression coefficients.
getCoefficientStatistics(int) - Method in class com.imsl.stat.SelectionRegression.Statistics: Returns the coefficients statistics for each of the best regressions found for each subset considered.
getCoefficientTable(int) - Method in class com.imsl.stat.ANCOVA: Returns a matrix of size ncov + 1 by 4 containing statistics for a linear regression model fitted separately for each of the ngroup treatment groups.
getCoefficientTables() - Method in class com.imsl.stat.ANCOVA: Returns an array containing statistics for a linear regression model fitted separately for all ngroup treatments.
getCoefficientTTests() - Method in class com.imsl.stat.LinearRegression: Returns statistics relating to the regression coefficients.
getCoefficientTTests() - Method in class com.imsl.stat.StepwiseRegression: Returns the student-t test statistics for the regression coefficients.
getCoefficientVIF() - Method in class com.imsl.stat.StepwiseRegression: Returns the variance inflation factors for the final model in this invocation.
getColorAttribute(String) - Method in class com.imsl.chart.AbstractChartNode: Convenience routine to get a Color-valued attribute.
getColorFunction() - Method in class com.imsl.chart3d.ChartNode3D: Returns the value of the "ColorFunction" attribute.
getColormap() - Method in class com.imsl.chart.Heatmap: Returns the value of the "Colormap" attribute.
getColormap() - Method in class com.imsl.chart.Treemap: Returns the value of the "Colormap" attribute.
getColumn() - Method in class com.imsl.io.MPSReader.Element: Returns the column index.
getColumnClass(int) - Method in class com.imsl.io.AbstractFlatFile: Returns the class of the items in the specified column.
getColumnCount() - Method in class com.imsl.io.AbstractFlatFile: Returns the number of columns in this ResultSet object.
getColumnCount() - Method in class com.imsl.io.FlatFile: Returns the number of columns in this ResultSet object.
getColumnPermutationMethod() - Method in class com.imsl.math.ComplexSuperLU: Returns the method that will be used to permute the columns of the input matrix.
getColumnPermutationMethod() - Method in class com.imsl.math.SuperLU: Returns the method that will be used to permute the columns of the input matrix.
getCompleteTimes() - Method in class com.imsl.stat.ARMAEstimateMissing: Returns an int array of all time points, including values for times with missing values in z.
getCompleteTimes() - Method in class com.imsl.stat.AutoARIMA: Returns all time points at which the original series was observed, including values for times with missing values in x.
getCompleteTimeSeries() - Method in class com.imsl.stat.ARMAEstimateMissing: Returns a double precision vector of length tpoints[tpoints.length-1]-tpoints[0]+1 containing the observed values in the time series z plus estimates for missing values in gaps identified in tpoints.
getCompleteTimeSeries() - Method in class com.imsl.stat.AutoARIMA: Returns the original series with potentially missing values replaced by estimates.
getComponent() - Method in class com.imsl.chart.ChartNode: Returns the value of the "Component" attribute.
getConcatenatedViewport() - Method in class com.imsl.chart.ChartNode: Returns the value of the "Viewport" attribute concatenated with the "Viewport" attributes set in its ancestor nodes.
getConcatenatedViewport() - Method in class com.imsl.chart3d.ChartNode3D: Returns the value of the "Viewport" attribute concatenated with the "Viewport" attributes set in its ancestor nodes.
getConcurrency() - Method in class com.imsl.io.AbstractFlatFile: Returns the concurrency mode of this ResultSet object.
getConditionNumber() - Method in class com.imsl.math.ComplexSuperLU: Returns the estimate of the reciprocal condition number of the matrix A.
getConditionNumber() - Method in class com.imsl.math.SuperLU: Returns the estimate of the reciprocal condition number of the matrix A.
getConfidence() - Method in class com.imsl.datamining.AssociationRule: The confidence measure of the association rule.
getConfidence() - Method in class com.imsl.stat.ARAutoUnivariate: Returns the confidence level for calculating confidence limit deviations returned from getDeviations.
getConfidence() - Method in class com.imsl.stat.ARMAMaxLikelihood: Returns the confidence level used for calculating deviations in getDeviations.
getConfidenceInterval(double, int, int, int) - Method in class com.imsl.stat.ANOVA: Computes the confidence interval associated with the difference of means between two groups using a specified method.
getConfidenceInterval() - Method in class com.imsl.stat.LinearRegression.CaseStatistics: Returns the Confidence Interval of the population mean for an observation.
getConstant() - Method in class com.imsl.math.SparseLP: Returns the value of the constant term in the objective function.
getConstant() - Method in class com.imsl.stat.ARAutoUnivariate: Returns the estimate for the constant parameter in the ARMA series.
getConstant() - Method in class com.imsl.stat.ARMA: Returns the constant parameter estimate.
getConstant() - Method in class com.imsl.stat.ARMAMaxLikelihood: Returns the estimate for the constant parameter in the ARMA series.
getConstant() - Method in class com.imsl.stat.ARMAOutlierIdentification: Returns the constant parameter estimate.
getConstant() - Method in class com.imsl.stat.AutoARIMA: Returns the constant parameter estimate for the optimum model.
getConstantColumn() - Method in class com.imsl.stat.ProportionalHazards: Returns the column index of x containing the constant to be added to the linear response.
getConstraintResiduals() - Method in class com.imsl.math.MinConNLP: Returns the constraint residuals.
getConstraintType() - Method in class com.imsl.math.SparseLP: Returns the types of general constraints in the matrix A.
getContingencyCoef() - Method in class com.imsl.stat.ContingencyTable: Returns contingency coefficient.
getContourLegend() - Method in class com.imsl.chart.Contour: Returns the contour chart legend.
getContourLevel() - Method in class com.imsl.chart.Contour: Returns all of the contour levels.
getContourLevel(int) - Method in class com.imsl.chart.Contour: Returns a ContourLevel.
getContributions() - Method in class com.imsl.stat.ContingencyTable: Returns the contributions to chi-squared for each cell in the table.
getControlData() - Method in class com.imsl.chart.qc.ShewhartControlChart: Returns the Data object for the control data.
getControlLimit() - Method in class com.imsl.chart.qc.ControlLimit: Returns the value of the attribute "ControlLimit".
getConvergenceTol() - Method in class com.imsl.stat.ProportionalHazards: Returns the convergence tolerance used.
getConvergenceTolerance() - Method in class com.imsl.stat.ARAutoUnivariate: Returns the tolerance level used to determine convergence of the nonlinearleast-squares and maximum likelihood algorithms.
getConvergenceTolerance() - Method in class com.imsl.stat.ARMAEstimateMissing: Returns the current value of convergence tolerance used by the AR_1 and AR_P estimation methods.
getCooksDistance() - Method in class com.imsl.stat.LinearRegression.CaseStatistics: Returns Cook's Distance for an observation.
getCoordinates() - Method in class com.imsl.chart.Dendrogram: Convenience routine to get the "Coordinates" attribute.
getCorrelations() - Method in class com.imsl.stat.FactorAnalysis: Returns the correlations of the principal components.
getCost() - Method in class com.imsl.datamining.decisionTree.TreeNode: Returns the misclassification cost of a node (in-sample cost measure at the current node).
getCostComplexityValues() - Method in class com.imsl.datamining.decisionTree.DecisionTree: Returns an array containing cost-complexity values.
getCostMatrix() - Method in class com.imsl.datamining.PredictiveModel: Returns the cost matrix for a categorical response variable.
getCount() - Method in class com.imsl.stat.FaureSequence
getCovariance() - Method in class com.imsl.stat.DiscriminantAnalysis: Returns the array of covariances.
getCovarianceMatrix() - Method in class com.imsl.stat.CategoricalGenLinModel: Returns the estimated asymptotic covariance matrix of the coefficients.
getCovariancesSwept() - Method in class com.imsl.stat.StepwiseRegression: Returns the results after cov has been swept for the columns corresponding to the variables in the model.
getCovB() - Method in class com.imsl.stat.KalmanFilter: Returns the mean squared error matrix for b divided by sigma squared.
getCovV() - Method in class com.imsl.stat.KalmanFilter: Returns the variance-covariance matrix of v divided by sigma squared.
getCPlus() - Method in class com.imsl.chart.qc.CuSumStatus: Returns the $C^{+}$ values.
getCramersV() - Method in class com.imsl.stat.ContingencyTable: Returns Cramer's V.
getCreateImageMap() - Method in class com.imsl.chart.JspBean: Returns true if a client-side imagemap is to be created.
getCriterionOption() - Method in class com.imsl.stat.SelectionRegression: Returns the criterion option used to calculate the regression estimates.
getCriterionValues(int) - Method in class com.imsl.stat.SelectionRegression.Statistics: Returns an array containing the values of the best criterion for the number of variables considered.
getCross() - Method in class com.imsl.chart.AxisXY: Returns the value of the "Cross" attribute.
getCrossCorrelation() - Method in class com.imsl.stat.CrossCorrelation: Returns the cross-correlations between the time series x and y.
getCrossCorrelation() - Method in class com.imsl.stat.MultiCrossCorrelation: Returns the cross-correlations between the channels of x and y.
getCrossCovariance() - Method in class com.imsl.stat.CrossCorrelation: Returns the cross-covariances between the time series x and y.
getCrossCovariance() - Method in class com.imsl.stat.MultiCrossCorrelation: Returns the cross-covariances between the channels of x and y.
getCrossValidatedError() - Method in class com.imsl.datamining.CrossValidation: Returns the cross-validated error.
getCumulativeAxis() - Method in class com.imsl.chart.qc.ParetoChart: Returns the "CumulativeAxis" attribute.
getCumulativeLine() - Method in class com.imsl.chart.qc.ParetoChart: Returns the "CumulativeLine" attribute.
getCursorName() - Method in class com.imsl.io.AbstractFlatFile: Gets the name of the SQL cursor used by this ResultSet object.
getCustomMarkerFactory() - Method in class com.imsl.chart3d.Data: Returns a custom marker factory.
getCustomTransform() - Method in class com.imsl.chart.AbstractChartNode: Returns the value of the "CustomTransform" attribute.
getCutpoints() - Method in class com.imsl.stat.ChiSquaredTest: Returns the cutpoints.
getDataMarkers() - Method in class com.imsl.chart.qc.CuSumStatus: Returns the "DataMarkers" attribute containing the data markers.
getDataMarkersAxis() - Method in class com.imsl.chart.qc.CuSumStatus: Returns the "DataMarkersAxis" attribute containing the axis associated with the data markers.
getDataType() - Method in class com.imsl.chart.ChartNode: Returns the value of the "DataType" attribute.
getDataType() - Method in class com.imsl.chart3d.ChartNode3D: Returns the value of the "DataType" attribute.
getDate(int) - Method in class com.imsl.io.AbstractFlatFile: Gets the value of the designated column in the current row of this ResultSet object as a java.sql.Date object in the Java programming language.
getDate(String) - Method in class com.imsl.io.AbstractFlatFile: Gets the value of the designated column in the current row of this ResultSet object as a java.sql.Date object in the Java programming language.
getDate(int, Calendar) - Method in class com.imsl.io.AbstractFlatFile: Returns the value of the designated column in the current row of this ResultSet object as a java.sql.Date object in the Java programming language.
getDate(String, Calendar) - Method in class com.imsl.io.AbstractFlatFile: Returns the value of the designated column in the current row of this ResultSet object as a java.sql.Date object in the Java programming language.
getDateIncrement() - Method in class com.imsl.stat.TimeSeries: Returns the date increment for this TimeSeries object.
getDates() - Method in class com.imsl.stat.TimeSeries: Returns the date array associated with the time series.
getDaysInYear(GregorianCalendar) - Method in interface com.imsl.finance.BasisPart: Returns the number of days in the year.
getDecisionTree() - Method in class com.imsl.datamining.decisionTree.DecisionTree: Returns a Tree object.
getDegreesOfFreedom() - Method in class com.imsl.stat.ChiSquaredTest: Returns the degrees of freedom in chi-squared.
getDegreesOfFreedom() - Method in class com.imsl.stat.ContingencyTable: Returns the degrees of freedom for the chi-squared tests associated with the table.
getDegreesOfFreedom() - Method in class com.imsl.stat.NormalityTest: Returns the degrees of freedom for the chi-squared goodness-of-fit test.
getDegreesOfFreedomForError() - Method in class com.imsl.stat.ANOVA: Returns the degrees of freedom for error.
getDegreesOfFreedomForModel() - Method in class com.imsl.stat.ANOVA: Returns the degrees of freedom for model.
getDensity() - Method in class com.imsl.chart.AbstractChartNode: Returns the value of the "Density" attribute.
getDesignVariableMeans() - Method in class com.imsl.stat.CategoricalGenLinModel: Returns the means of the design variables.
getDeviations() - Method in class com.imsl.stat.ARAutoUnivariate: Returns the deviations used for calculating the forecast confidence limits.
getDeviations() - Method in class com.imsl.stat.ARMA: Returns the deviations used for calculating the forecast confidence limits.
getDeviations() - Method in class com.imsl.stat.ARMAMaxLikelihood: Returns the deviations from each forecast used for calculating the forecast confidence limits.
getDeviations() - Method in class com.imsl.stat.ARMAOutlierIdentification: Returns the deviations used for calculating the forecast confidence limits.
getDeviations() - Method in class com.imsl.stat.AutoARIMA: Returns the deviations used for calculating the forecast confidence limits.
getDeviceMarkerSize() - Method in class com.imsl.chart.Draw: Returns the marker size in device corrdinates.
getDFError() - Method in class com.imsl.stat.NonlinearRegression: Returns the degrees of freedom for error.
getDFFITS() - Method in class com.imsl.stat.LinearRegression.CaseStatistics: Returns DFFITS for an observation.
getDiagonalPivotThreshold() - Method in class com.imsl.math.ComplexSuperLU: Returns the threshold used for a diagonal entry to be an acceptable pivot.
getDiagonalPivotThreshold() - Method in class com.imsl.math.SuperLU: Returns the threshold used for a diagonal entry to be an acceptable pivot.
getDiffMean() - Method in class com.imsl.stat.NormTwoSample: Returns the difference in means, mean of x - mean of y.
getDimension() - Method in class com.imsl.datamining.KohonenSOM: Returns the number of weights for each node.
getDimension() - Method in class com.imsl.stat.FaureSequence: Returns the dimension of the sequence.
getDimension() - Method in interface com.imsl.stat.RandomSequence: Returns the dimension of the sequence.
getDInitial() - Method in class com.imsl.stat.ARSeasonalFit: Returns the candidate values for d to evaluate.
getDirection() - Method in class com.imsl.chart3d.DirectionalLight: Returns the value of the "Direction" attribute.
getDistanceMatrix() - Method in class com.imsl.stat.Dissimilarities: Returns the distance matrix.
getDistanceMethod() - Method in class com.imsl.stat.Dissimilarities: Returns the method used in computing the dissimilarities or similarities.
getDouble(int) - Method in class com.imsl.io.AbstractFlatFile: Gets the value of the designated column in the current row of this ResultSet object as a double in the Java programming language.
getDouble(String) - Method in class com.imsl.io.AbstractFlatFile: Gets the value of the designated column in the current row of this ResultSet object as a double in the Java programming language.
getDoubleAttribute(String, double) - Method in class com.imsl.chart.AbstractChartNode: Convenience routine to get a Double-valued attribute.
getDoubleBuffering() - Method in class com.imsl.chart.ChartNode: Returns the value of the "DoubleBuffering" attribute.
getDown() - Method in class com.imsl.chart.Candlestick: Returns the CandlestickItem for down days.
getDual() - Method in class com.imsl.math.QuadraticProgramming: Returns the dual (Lagrange multipliers).
getDualInfeasibility() - Method in class com.imsl.math.SparseLP: Returns the dual infeasibility of the solution.
getDualInfeasibilityTolerance() - Method in class com.imsl.math.SparseLP: Returns the dual infeasibility tolerance.
getDualSolution() - Method in class com.imsl.math.BoundedVariableLeastSquares: Returns the dual solution vector, w.
getDualSolution() - Method in class com.imsl.math.DenseLP: Returns the dual solution.
getDualSolution() - Method in class com.imsl.math.NonNegativeLeastSquares: Returns the dual solution vector, w.
getDualSolution() - Method in class com.imsl.math.SparseLP: Returns the dual solution.
getDummyMethod() - Method in class com.imsl.stat.RegressorsForGLM: Returns the dummy method.
getEffects() - Method in class com.imsl.stat.RegressorsForGLM: Returns the effects.
getEffectsColumns() - Method in class com.imsl.stat.RegressorsForGLM: Returns a mapping of effects to regressor columns.
getEnumValue(String) - Static method in class com.imsl.chart.xml.ChartXML: Returns the int corresponding to an enumeration.
getEpochSize() - Method in class com.imsl.datamining.neural.EpochTrainer: Returns the number of sample training patterns in each stage 1 epoch.
getEquilibrate() - Method in class com.imsl.math.ComplexSuperLU: Returns the equilibration flag.
getEquilibrate() - Method in class com.imsl.math.SuperLU: Returns the equilibration flag.
getEquilibrationMethod() - Method in class com.imsl.math.ComplexSuperLU: Returns information on the type of equilibration used before matrix factorization.
getEquilibrationMethod() - Method in class com.imsl.math.SuperLU: Returns information on the type of equilibration used before matrix factorization.
getError() - Method in class com.imsl.datamining.neural.BinaryClassification: Returns the error function for use by QuasiNewtonTrainer for training a binary classification network.
getError() - Method in class com.imsl.datamining.neural.MultiClassification: Returns the error function for use by QuasiNewtonTrainer for training a classification network.
getError() - Method in class com.imsl.datamining.neural.QuasiNewtonTrainer: Returns the function used to compute the error to be minimized.
getErrorEstimate() - Method in class com.imsl.math.EpsilonAlgorithm: Returns the current error estimate.
getErrorEstimate() - Method in class com.imsl.math.HyperRectangleQuadrature: Returns an estimate of the relative error in the computed result.
getErrorEstimate() - Method in class com.imsl.math.Quadrature: Returns an estimate of the relative error in the computed result.
getErrorGradient() - Method in class com.imsl.datamining.neural.EpochTrainer: Returns the value of the gradient of the error function with respect to the weights.
getErrorGradient() - Method in class com.imsl.datamining.neural.LeastSquaresTrainer: Returns the value of the gradient of the error function with respect to the weights.
getErrorGradient() - Method in class com.imsl.datamining.neural.QuasiNewtonTrainer: Returns the value of the gradient of the error function with respect to the weights.
getErrorGradient() - Method in interface com.imsl.datamining.neural.Trainer: Returns the value of the gradient of the error function with respect to the weights.
getErrorMeanSquare() - Method in class com.imsl.stat.ANOVA: Returns the error mean square.
getErrorNumber() - Method in exception com.imsl.LicenseManagerException: Returns the error number for this exception.
getErrorStatus() - Method in class com.imsl.datamining.neural.EpochTrainer: Returns the training error status.
getErrorStatus() - Method in class com.imsl.datamining.neural.LeastSquaresTrainer: Returns the error status from the trainer.
getErrorStatus() - Method in class com.imsl.datamining.neural.QuasiNewtonTrainer: Returns the error status from the trainer.
getErrorStatus() - Method in interface com.imsl.datamining.neural.Trainer: Returns the error status.
getErrorStatus() - Method in class com.imsl.math.MinUnconMultiVar: Returns the non-fatal error status.
getErrorStatus() - Method in class com.imsl.math.NonlinLeastSquares: Get information about the performance of NonlinLeastSquares.
getErrorStatus() - Method in class com.imsl.math.Quadrature: Returns the non-fatal error status.
getErrorStatus() - Method in class com.imsl.stat.NonlinearRegression: Gets information about the performance of NonlinearRegression.
getErrorSumOfSquares() - Method in class com.imsl.math.Spline2DLeastSquares: Returns the weighted error sum of squares.
getErrorValue() - Method in class com.imsl.datamining.neural.EpochTrainer: Returns the value of the error function.
getErrorValue() - Method in class com.imsl.datamining.neural.LeastSquaresTrainer: Returns the final value of the error function.
getErrorValue() - Method in class com.imsl.datamining.neural.QuasiNewtonTrainer: Returns the final value of the error function.
getErrorValue() - Method in interface com.imsl.datamining.neural.Trainer: Returns the value of the error function minimized by the trainer.
getEstimates() - Method in class com.imsl.stat.distributions.MaximumLikelihoodEstimation: Returns the parameter estimates.
getEstimates() - Method in class com.imsl.stat.VectorAutoregression: Returns the parameter estimates (coefficients) of the vector autoregression model.
getEstimationMethod() - Method in class com.imsl.stat.ARAutoUnivariate: Returns the estimation method used for estimating the autoregressive coefficients.
getEstimationMethod() - Method in class com.imsl.stat.ARMAEstimateMissing: Returns the method used for estimating the final autoregressive coefficients for missing value estimation methods AR_1 and AR_P.
getExactMean() - Method in class com.imsl.stat.ContingencyTable: Returns exact mean.
getExactStdev() - Method in class com.imsl.stat.ContingencyTable: Returns exact standard deviation.
getExclude() - Method in class com.imsl.stat.ARSeasonalFit: Returns the current setting for excluding or replacing the inital values in the transformed series.
getExpectedCounts() - Method in class com.imsl.stat.ChiSquaredTest: Returns the expected counts.
getExpectedMean() - Method in class com.imsl.chart.qc.CuSum: Returns the expected mean of all of the data from all of the samples.
getExpectedMean() - Method in class com.imsl.chart.qc.CuSumStatus: Returns the expected (target) mean value.
getExpectedValues() - Method in class com.imsl.stat.ContingencyTable: Returns the expected values of each cell in the table.
getExplode() - Method in class com.imsl.chart.ChartNode: Returns the value of the "Explode" attribute.
getExtendedLikelihoodObservations() - Method in class com.imsl.stat.CategoricalGenLinModel: Returns a vector indicating which observations are included in the extended likelihood.
getF() - Method in class com.imsl.stat.ANOVA: Returns the F statistic.
getFactorLoadings() - Method in class com.imsl.stat.FactorAnalysis: Returns the unrotated factor loadings.
getFarMarkers() - Method in class com.imsl.chart.BoxPlot: Returns the FarMarkers node.
getFarMarkers() - Method in class com.imsl.chart.BoxPlot.Statistics: Returns the array of far markers.
getFeature() - Method in exception com.imsl.LicenseManagerException: Returns the name of the feature that could not be licensed.
getFetchDirection() - Method in class com.imsl.io.AbstractFlatFile: Returns the fetch direction for this ResultSet object.
getFetchSize() - Method in class com.imsl.io.AbstractFlatFile: Returns the fetch size for this ResultSet object.
getFillColor() - Method in class com.imsl.chart.AbstractChartNode: Returns the value of the "FillColor" attribute.
getFillOutlineColor() - Method in class com.imsl.chart.ChartNode: Returns the value of the "FillOutlineColor" attribute.
getFillOutlineType() - Method in class com.imsl.chart.ChartNode: Returns the value of the "FillOutlineType" attribute.
getFillPaint() - Method in class com.imsl.chart.ChartNode: Returns the value of the "FillPaint" attribute.
getFillType() - Method in class com.imsl.chart.ChartNode: Returns the value of the "FillType" attribute.
getFinalActiveConstraints() - Method in class com.imsl.math.MinConGenLin: Returns the indices of the final active constraints.
getFinalActiveConstraintsNum() - Method in class com.imsl.math.MinConGenLin: Returns the final number of active constraints.
getFirstTick() - Method in class com.imsl.chart.Axis1D: Convenience routine to get the "FirstTick" attribute.
getFirstTick() - Method in class com.imsl.chart3d.Axis3D: Convenience routine to get the "FirstTick" attribute.
getFittedMeanSquaredError() - Method in class com.imsl.datamining.decisionTree.DecisionTree: Returns the mean squared error on the training data.
getFittedValues() - Method in class com.imsl.datamining.GradientBoosting: Returns the fitted values ${f(x_i)}$ for a continuous response variable after gradient boosting.
getFloat(int) - Method in class com.imsl.io.AbstractFlatFile: Gets the value of the designated column in the current row of this ResultSet object as a float in the Java programming language.
getFloat(String) - Method in class com.imsl.io.AbstractFlatFile: Gets the value of the designated column in the current row of this ResultSet object as a float in the Java programming language.
getFloor() - Method in class com.imsl.math.ODE: Returns the value used in the norm computation.
getFont() - Method in class com.imsl.chart.AbstractChartNode: Convenience routine which gets a Font object based on the "FontName", "FontStyle" and "FontSize" attributes.
getFontName() - Method in class com.imsl.chart.AbstractChartNode: Returns the value of the "FontName" attribute.
getFontSize() - Method in class com.imsl.chart.AbstractChartNode: Returns the value of the "FontSize" attribute.
getFontStyle() - Method in class com.imsl.chart.AbstractChartNode: Returns the value of the "FontStyle" attribute.
getForecast(int) - Method in class com.imsl.stat.ARAutoUnivariate: Returns forecasts
getForecast(int) - Method in class com.imsl.stat.ARMA: Returns forecasts
getForecast(int) - Method in class com.imsl.stat.ARMAMaxLikelihood: Returns forecasts
getForecast() - Method in class com.imsl.stat.ARMAOutlierIdentification: Returns forecasts for the original outlier contaminated series.
getForecast() - Method in class com.imsl.stat.AutoARIMA: Returns forecasts for the original outlier contaminated series.
getForecastGradient(double[]) - Method in class com.imsl.datamining.neural.FeedForwardNetwork: Returns the derivatives of the outputs with respect to the weights.
getForecastGradient(double[]) - Method in class com.imsl.datamining.neural.Network: Returns the derivatives of the outputs with respect to the weights.
getForecasts() - Method in class com.imsl.stat.HoltWintersExponentialSmoothing: Returns the forecasts past the series data.
getForecasts() - Method in class com.imsl.stat.VectorAutoregression: Returns the h-step ahead forecast at times t=nT, nT+1, ..., T, where h=1,2, ..., maxStepsAhead.
getFormatter() - Static method in class com.imsl.datamining.neural.EpochTrainer: Returns the logging Formatter object.
getFormatter() - Static method in class com.imsl.datamining.neural.LeastSquaresTrainer: Returns the logging Formatter object.
getFormatter() - Static method in class com.imsl.datamining.neural.QuasiNewtonTrainer: Returns the logging formatter object.
getForwardErrorBound() - Method in class com.imsl.math.ComplexSuperLU: Returns the estimated forward error bound for each solution vector.
getForwardErrorBound() - Method in class com.imsl.math.SuperLU: Returns the estimated forward error bound for the solution vector.
getFrequencyColumn() - Method in class com.imsl.stat.KaplanMeierEstimates: Returns the column index of x containing the frequency of response for each observation.
getFrequencyColumn() - Method in class com.imsl.stat.ProportionalHazards: Returns the column index of x containing the frequency of response for each observation.
getFrequencyTable() - Method in class com.imsl.stat.TableOneWay: Returns the one-way frequency table.
getFrequencyTable(double, double) - Method in class com.imsl.stat.TableOneWay: Returns a one-way frequency table using known bounds.
getFrequencyTable() - Method in class com.imsl.stat.TableTwoWay: Returns the two-way frequency table.
getFrequencyTable(double, double, double, double) - Method in class com.imsl.stat.TableTwoWay: Compute a two-way frequency table using intervals of equal length and user supplied upper and lower bounds, xLowerBound, xUpperBound, yLowerBound, yUpperBound.
getFrequencyTableUsingClassmarks(double[]) - Method in class com.imsl.stat.TableOneWay: Returns the one-way frequency table using class marks.
getFrequencyTableUsingClassmarks(double[], double[]) - Method in class com.imsl.stat.TableTwoWay: Returns the two-way frequency table using class marks.
getFrequencyTableUsingCutpoints(double[]) - Method in class com.imsl.stat.TableOneWay: Returns the one-way frequency table using cutpoints.
getFrequencyTableUsingCutpoints(double[], double[]) - Method in class com.imsl.stat.TableTwoWay: Returns the two-way frequency table using cutpoints.
getFrequentItemsets(int[][], int, int, double) - Static method in class com.imsl.datamining.Apriori: Computes the frequent itemsets in the transaction set x.
getFrom() - Method in class com.imsl.datamining.neural.Link: Returns the origination Node for this Link.
getFTest() - Method in class com.imsl.stat.NormTwoSample: Returns the F test value of the F test for equality of variances.
getFTestDFdenominator() - Method in class com.imsl.stat.NormTwoSample: Returns the denominator degrees of freedom of the F test for equality of variances.
getFTestDFnumerator() - Method in class com.imsl.stat.NormTwoSample: Returns the numerator degrees of freedom of the F test for equality of variances.
getFTestP() - Method in class com.imsl.stat.NormTwoSample: Returns the probability of a larger F in absolute value for the F test for equality of variances, assuming equal variances.
getGARCH() - Method in class com.imsl.stat.GARCH: Returns the estimated values of the GARCH coefficients.
getGaussLegendreDegree() - Method in class com.imsl.math.FeynmanKac: Returns the number of quadrature points used in the Gauss-Legendre quadrature formula.
getGradient() - Method in class com.imsl.chart.ChartNode: Returns the value of the "Gradient" attribute.
getGradient() - Method in class com.imsl.stat.ProportionalHazards: Returns the inverse of the Hessian times the gradient vector, computed at the initial estimates.
getGradients() - Method in class com.imsl.stat.ARMAMaxLikelihood: Returns the gradients for the final parameter estimates.
getGradientTolerance() - Method in class com.imsl.stat.ARMAMaxLikelihood: Returns the gradient tolerance for the convergence algorithm.
getGrid() - Method in class com.imsl.chart.Axis1D: Returns the grid node associated with this axis.
getGridPolar() - Method in class com.imsl.chart.Polar: Returns the grid.
getGridType() - Method in class com.imsl.datamining.KohonenSOM: Returns the grid type.
getGroupCounts() - Method in class com.imsl.stat.DiscriminantAnalysis: Returns the group counts.
getGroupInformation() - Method in class com.imsl.stat.ANOVA: Returns information concerning the groups.
getGroups() - Method in class com.imsl.stat.TableMultiWay: Returns the number of observations (rows) in each group.
getGroupTotal(double) - Method in class com.imsl.stat.KaplanMeierEstimates: Returns the total number in the group for the specified group value.
getGSquared() - Method in class com.imsl.stat.ContingencyTable: Returns the likelihood ratio G² (chi-squared).
getGSquaredP() - Method in class com.imsl.stat.ContingencyTable: Returns the probability of a larger G² (chi-squared).
getGuess() - Method in class com.imsl.math.GenMinRes: Returns the initial guess of the solution.
getHeatmapLabels() - Method in class com.imsl.chart.Heatmap: Returns the value of the "HeatmapLabels" attribute.
getHeatmapLegend() - Method in class com.imsl.chart.Heatmap: Returns the heatmap legend.
getHessian() - Method in class com.imsl.stat.CategoricalGenLinModel: Returns the Hessian computed at the initial parameter estimates.
getHessian() - Method in class com.imsl.stat.distributions.MaximumLikelihoodEstimation: Returns the Hessian of the log-likelihood function evaluated at the current parameter estimates.
getHessian() - Method in class com.imsl.stat.ProportionalHazards: Returns the inverse of the Hessian of the negative of the log-likelihood, computed at the initial estimates.
getHessianOption() - Method in class com.imsl.stat.ProportionalHazards: Returns the boolean used to indicate whether or not to compute the Hessian and gradient at the initial estimates.
getHiddenLayers() - Method in class com.imsl.datamining.neural.FeedForwardNetwork: Returns the HiddenLayers in this network.
getHigh() - Method in class com.imsl.chart.ErrorBar: Convenience routine to get the "High" attribute.
getHigh() - Method in class com.imsl.chart.HighLowClose: Convenience routine to get the "High" attribute.
getHistory() - Method in class com.imsl.stat.StepwiseRegression: Returns the stepwise regression history for the independent variables.
getHoldability() - Method in class com.imsl.io.FlatFile: Retrieves the holdability of this ResultSet object.
getHREF() - Method in class com.imsl.chart.ChartNode: Returns the value of the "HREF" attribute.
getHREF() - Method in class com.imsl.chart.DrawMap: Returns the current HREF string.
getId() - Method in class com.imsl.chart.JspBean: Returns the identifier number for the chart.
getImage() - Method in class com.imsl.chart.AbstractChartNode: Returns the value of the "Image" attribute.
getImageMap() - Method in class com.imsl.chart.JspBean: Returns an HTML for the client-side imagemap.
getImageTag() - Method in class com.imsl.chart.JspBean: Returns an HTML image tag.
getIncidenceMatrix() - Method in class com.imsl.stat.Covariances: Returns the incidence matrix.
getIndependentVariables(int) - Method in class com.imsl.stat.SelectionRegression.Statistics: Returns the identification numbers for the independent variables for the number of variables considered and in the same order as the criteria returned by SelectionRegression.Statistics.getCriterionValues(int).
getIndex() - Method in class com.imsl.datamining.neural.Layer: Returns the index of this Layer.
getIndex() - Method in class com.imsl.stat.Dissimilarities: Returns the indices of the rows (columns) used in computing the distance measure.
getInfo() - Method in class com.imsl.math.SVD: Returns convergence information about S, U, and V.
getInitialCMinus() - Method in class com.imsl.chart.qc.CuSumStatus: Returns the initial value of $C^{-}$ .
getInitialCPlus() - Method in class com.imsl.chart.qc.CuSumStatus: Returns the initial value of $C^{+}$ .
getInitialEstimates() - Method in class com.imsl.stat.ProportionalHazards: Gets the initial parameter estimates.
getInitialStepsize() - Method in class com.imsl.math.FeynmanKac: Returns the starting step size for the integration.
getInitialStepsize() - Method in class com.imsl.math.ODE: Returns the initial internal step size.
getInnovationVariance() - Method in class com.imsl.stat.ARAutoUnivariate: Returns the final estimate for the innovation variance.
getInnovationVariance() - Method in class com.imsl.stat.ARMA: Returns the variance of the random shock.
getInnovationVariance() - Method in class com.imsl.stat.ARMAMaxLikelihood: Returns the estimated innovation variance of this series.
getInputLayer() - Method in class com.imsl.datamining.neural.FeedForwardNetwork: Returns the InputLayer.
getInputLayer() - Method in class com.imsl.datamining.neural.Network: Returns the InputLayer object.
getInt(int) - Method in class com.imsl.io.AbstractFlatFile: Gets the value of the designated column in the current row of this ResultSet object as an int in the Java programming language.
getInt(String) - Method in class com.imsl.io.AbstractFlatFile: Gets the value of the designated column in the current row of this ResultSet object as an int in the Java programming language.
getIntegerAttribute(String, int) - Method in class com.imsl.chart.AbstractChartNode: Convenience routine to get an Integer-valued attribute.
getIntegrationMethod() - Method in class com.imsl.math.OdeAdamsGear: Returns the integration method used.
getIntercept() - Method in class com.imsl.stat.StepwiseRegression: Returns the intercept.
getItemset(int) - Method in class com.imsl.datamining.Itemsets: Returns a particular itemset.
getItemsetsMatrix() - Method in class com.imsl.datamining.Itemsets: Returns the set of Itemsets as an integer matrix.
getIterationCount() - Method in class com.imsl.math.DenseLP: Returns the iteration count.
getIterationCount() - Method in class com.imsl.math.SparseLP: Returns the number of iterations used by the primal-dual solver.
getIterations() - Method in class com.imsl.datamining.KohonenSOMTrainer: Returns the number of iterations used for training.
getIterations() - Method in class com.imsl.math.BoundedVariableLeastSquares: Returns the number of iterations used to find the solution.
getIterations() - Method in class com.imsl.math.ConjugateGradient: Returns the number of iterations needed by the conjugate gradient algorithm.
getIterations() - Method in class com.imsl.math.GenMinRes: Returns the actual number of GMRES iterations used.
getIterations() - Method in class com.imsl.math.MinConNLP: Returns the actual number of iterations used.
getIterations() - Method in class com.imsl.math.MinUnconMultiVar: Returns the number of iterations used to compute a minimum.
getIterations() - Method in class com.imsl.math.NonNegativeLeastSquares: Returns the number of iterations used to find the solution.
getIterationsArray() - Method in class com.imsl.datamining.GradientBoosting: Returns the array of different values for the number of iterations.
getIterativeRefinement() - Method in class com.imsl.math.ComplexSuperLU: Returns a value specifying whether iterative refinement is to be performed or not.
getIterativeRefinement() - Method in class com.imsl.math.SuperLU: Returns a value specifying whether iterative refinement is to be performed or not.
getJackknifeResidual() - Method in class com.imsl.stat.LinearRegression.CaseStatistics: Returns the Jackknife Residual for an observation.
getJacobi() - Method in class com.imsl.math.ConjugateGradient: Returns the Jacobi preconditioning matrix.
getJacobian() - Method in class com.imsl.math.BoundedLeastSquares: Returns the Jacobian at the approximate solution.
getKeyboard() - Method in class com.imsl.chart3d.Chart3D: Returns the value of the "Keyboard" attribute.
getKnots() - Method in class com.imsl.math.BSpline: Returns a copy of the knot sequence.
getKurtosis() - Method in class com.imsl.stat.Summary: Returns the kurtosis.
getL() - Method in class com.imsl.math.ComplexLU: Returns the lower triangular portion of the LU factorization of A.
getL() - Method in class com.imsl.math.LU: Returns the lower triangular portion of the LU factorization of A.
getLabels() - Method in class com.imsl.chart.AxisLabel: Returns the "Labels" attribute.
getLabels() - Method in class com.imsl.chart.AxisRLabel: Returns the "Labels" attribute.
getLabels() - Method in class com.imsl.chart.Treemap: Returns the value of the "TreemapLabels" attribute.
getLabels() - Method in class com.imsl.chart3d.AxisLabel: Returns the "Labels" attribute.
getLabelType() - Method in class com.imsl.chart.AbstractChartNode: Returns the value of the "LabelType" attribute.
getLagrangeMultiplierEst() - Method in class com.imsl.math.MinConGenLin: Returns the Lagrange multiplier estimates of the final active constraints.
getLagrangeMultiplierEst() - Method in class com.imsl.math.MinConNLP: Returns the Lagrange multiplier estimates of the constraints.
getLambda() - Method in class com.imsl.chart.qc.EWMA: Returns the value of the attribute "Lambda".
getLargestCPRatio() - Method in class com.imsl.math.SparseLP: Returns the ratio of the largest complementarity product to the average.
getLargestDiagonalElement() - Method in class com.imsl.math.ComplexSparseCholesky: Returns the largest diagonal element of the Cholesky factor.
getLargestDiagonalElement() - Method in class com.imsl.math.SparseCholesky: Returns the largest diagonal element of the Cholesky factor.
getLastParameterUpdates() - Method in class com.imsl.stat.CategoricalGenLinModel: Returns the last parameter updates (excluding step halvings).
getLastUpdates() - Method in class com.imsl.stat.ProportionalHazards: Gets the last parameter updates.
getLayer() - Method in class com.imsl.datamining.neural.Node: Returns the Layer in which this Node exists.
getLearningCoefficient(int) - Method in class com.imsl.datamining.KohonenSOMTrainer: Returns the learning coefficient.
getLeftEndTangent() - Method in class com.imsl.math.CsTCB: Returns the value of the tangent at the leftmost endpoint.
getLeftSons() - Method in class com.imsl.chart.Dendrogram: Convenience routine to get the "LeftSons" attribute.
getLegend() - Method in class com.imsl.chart.ChartNode: Returns the value of the "Legend" attribute.
getLength() - Method in class com.imsl.stat.TimeSeries: Returns the length of the time series.
getLevels() - Method in class com.imsl.chart.Dendrogram: Convenience routine to get the "Levels" attribute.
getLeverage() - Method in class com.imsl.stat.LinearRegression.CaseStatistics: Returns the Leverage for an observation.
getLicensePath() - Method in exception com.imsl.LicenseManagerException: Returns the license file path for this exception.
getLifeTable() - Method in class com.imsl.stat.LifeTables: Compute a cohort table.
getLift() - Method in class com.imsl.datamining.AssociationRule: The lift measure of the association rule.
getLightColor() - Method in class com.imsl.chart.AbstractChartNode: Returns the value of the "LightColor" attribute.
getLightingEnabled() - Method in class com.imsl.chart3d.ChartNode3D: Returns the value of the "LightingEnabled" attribute.
getLikelihood() - Method in class com.imsl.stat.ARAutoUnivariate: Returns the final estimate for $L=e^{-(mbox{AIC} - 2p)/2}$ , where p is the AR order, AIC is Akaike's Information Criterion, and L is the likelihood function evaluated for the optimum autoregressive model.
getLikelihood() - Method in class com.imsl.stat.ARMAMaxLikelihood: Returns the final estimate for , where is equal to the likelihood function evaluated using the final parameter estimates.
getLineColor() - Method in class com.imsl.chart.AbstractChartNode: Returns the value of the "LineColor" attribute.
getLineDashPattern() - Method in class com.imsl.chart.ChartNode: Returns the value of the "LineDashPattern" attribute.
getLineWidth() - Method in class com.imsl.chart.AbstractChartNode: Returns the value of the "LineWidth" attribute.
getLinks() - Method in class com.imsl.datamining.neural.FeedForwardNetwork: Return all of the Links in this Network.
getLinks() - Method in class com.imsl.datamining.neural.Network: Returns an array containing the Link objects in the Network.
getListCells() - Method in class com.imsl.stat.TableMultiWay.UnbalancedTable: Returns for each row, a list of the levels of nKeys corresponding classification variables that describe a cell.
getLocale() - Method in class com.imsl.chart.AbstractChartNode: Returns the value of the "Locale" attribute.
getLocalizedMessage() - Method in exception com.imsl.LicenseManagerException: Returns the localized error message for this exception.
getLogDeterminant() - Method in class com.imsl.stat.KalmanFilter: Returns the natural log of the product of the nonzero eigenvalues of P where P * sigma² is the variance-covariance matrix of the observations.
getLogger() - Static method in class com.imsl.datamining.neural.EpochTrainer: Returns the Logger object.
getLogger() - Static method in class com.imsl.datamining.neural.LeastSquaresTrainer: Returns the Logger object.
getLogger() - Static method in class com.imsl.datamining.neural.QuasiNewtonTrainer: Returns the Logger object.
getLogger() - Method in class com.imsl.math.GenMinRes: Returns the logger object.
getLogger() - Method in class com.imsl.math.MinConNLP: Returns the logger object.
getLogger() - Method in class com.imsl.math.ZeroSystem: Returns the logger object.
getLogger() - Method in class com.imsl.stat.ARAutoUnivariate: Returns the logger object.
getLogger() - Method in class com.imsl.stat.ProportionalHazards: Returns the logger object and enables logging.
getLogLikelihood(double[], double[]) - Method in class com.imsl.stat.distributions.MaximumLikelihoodEstimation: Returns the log-likelihood.
getLogLikelihood() - Method in class com.imsl.stat.GARCH: Returns the value of Log-likelihood function evaluated at the estimated parameter array.
getLogLikelihood(double) - Method in class com.imsl.stat.KaplanMeierEstimates: Returns the Kaplan-Meier log-likelihood of the group with the specified group value.
getLong(int) - Method in class com.imsl.io.AbstractFlatFile: Gets the value of the designated column in the current row of this ResultSet object as a long in the Java programming language.
getLong(String) - Method in class com.imsl.io.AbstractFlatFile: Gets the value of the designated column in the current row of this ResultSet object as a long in the Java programming language.
getLossType() - Method in class com.imsl.datamining.GradientBoosting: Returns the current loss function type.
getLossValue() - Method in class com.imsl.datamining.GradientBoosting: Returns the loss function value.
getLow() - Method in class com.imsl.chart.ErrorBar: Convenience routine to get the "Low" attribute.
getLow() - Method in class com.imsl.chart.HighLowClose: Convenience routine to get the "Low" attribute.
getLowerAdjacentValue() - Method in class com.imsl.chart.BoxPlot.Statistics: Returns the lower adjacent value.
getLowerBound(int) - Method in class com.imsl.io.MPSReader: Returns the lower bound for a variable.
getLowerBound() - Method in class com.imsl.math.SparseLP: Returns the lower bound on the variables.
getLowerCICommonVariance() - Method in class com.imsl.stat.NormTwoSample: Returns the lower confidence limits for the common, or pooled, variance.
getLowerCIDiff() - Method in class com.imsl.stat.NormTwoSample: Returns the lower confidence limit for the mean of the first population minus the mean of the second for equal or unequal variances depending on the value set by setUnequalVariances.
getLowerCIMean() - Method in class com.imsl.stat.NormOneSample: Returns the lower confidence limit for the mean.
getLowerCIRatioVariance() - Method in class com.imsl.stat.NormTwoSample: Returns the approximate lower confidence limit for the ratio of the variance of the first population to the second.
getLowerCIVariance() - Method in class com.imsl.stat.NormOneSample: Returns the lower confidence limits for the variance.
getLowerControlLimit() - Method in class com.imsl.chart.qc.ShewhartControlChart: Returns the lower control limit.
getLowerQuartile() - Method in class com.imsl.chart.BoxPlot.Statistics: Returns the lower quartile value.
getLowerRange(int) - Method in class com.imsl.io.MPSReader: Returns the lower range value for a constraint equation.
getMA() - Method in class com.imsl.stat.ARMA: Returns the final moving average parameter estimates.
getMA() - Method in class com.imsl.stat.ARMAMaxLikelihood: Returns the final moving average parameter estimates.
getMA() - Method in class com.imsl.stat.ARMAOutlierIdentification: Returns the final moving average parameter estimates.
getMA() - Method in class com.imsl.stat.AutoARIMA: Returns the final moving average parameter estimates of the optimum model.
getMahalanobis() - Method in class com.imsl.stat.DiscriminantAnalysis: Returns the Mahalanobis distances between the group means.
getMajorTick() - Method in class com.imsl.chart.Axis1D: Returns the major tick node associated with this axis.
getMajorTick() - Method in class com.imsl.chart3d.Axis3D: Returns the major tick node associated with this axis.
getMannWhitney() - Method in class com.imsl.stat.WilcoxonRankSum: Returns the Mann-Whitney test statistic.
getMap() - Method in class com.imsl.chart.DrawMap: Returns the body of the HTML imagemap.
getMapName() - Method in class com.imsl.chart.JspBean: Returns the name of the client-size imagemap.
getMarkerColor() - Method in class com.imsl.chart.AbstractChartNode: Returns the value of the "MarkerColor" attribute.
getMarkerDashPattern() - Method in class com.imsl.chart.ChartNode: Returns the value of the "MarkerPattern" attribute.
getMarkerPulsingCycle() - Method in class com.imsl.chart3d.ChartNode3D: Returns the value of the "MarkerPulsingCycle" attribute.
getMarkerPulsingCycleOffset() - Method in class com.imsl.chart3d.ChartNode3D: Returns the value of the "MarkerPulsingCycleOffset" attribute.
getMarkerPulsingMaximumScale() - Method in class com.imsl.chart3d.ChartNode3D: Returns the value of the "MarkerPulsingMaximumScale" attribute.
getMarkerPulsingMinimumScale() - Method in class com.imsl.chart3d.ChartNode3D: Returns the value of the "MarkerPulsingMinimumScale" attribute.
getMarkerRotatingAxis() - Method in class com.imsl.chart3d.ChartNode3D: Returns the value of the "MarkerRotatingAxis" attribute.
getMarkerRotatingCycle() - Method in class com.imsl.chart3d.ChartNode3D: Returns the value of the "MarkerRotatingCycle" attribute.
getMarkerRotatingCycleOffset() - Method in class com.imsl.chart3d.ChartNode3D: Returns the value of the "MarkerRotatingCycleOffset" attribute.
getMarkerSize() - Method in class com.imsl.chart.AbstractChartNode: Returns the value of the "MarkerSize" attribute.
getMarkerThickness() - Method in class com.imsl.chart.ChartNode: Returns the value of the "MarkerThickness" attribute.
getMarkerType() - Method in class com.imsl.chart.ChartNode: Returns the value of the "MarkerType" attribute.
getMarkerType() - Method in class com.imsl.chart3d.ChartNode3D: Returns the value of the "MarkerType" attribute.
getMaterial() - Method in class com.imsl.chart3d.ChartNode3D: Returns the value of the "Material" attribute.
getMaxClass() - Method in class com.imsl.stat.ProportionalHazards: Returns the upper bound used on the sum of the number of distinct values found among the classification variables in x.
getMaxDepth() - Method in class com.imsl.datamining.decisionTree.DecisionTree: Returns the maximum depth a tree is allowed to have.
getMaxDifference() - Method in class com.imsl.stat.NormalityTest: Returns the maximum absolute difference between the empirical and the theoretical distributions for the Lilliefors test.
getMaxEvaluations() - Method in class com.imsl.math.ZerosFunction: Returns the maximum number of function evaluations allowed.
getMaximum() - Method in class com.imsl.stat.Summary: Returns the maximum.
getMaximum() - Method in class com.imsl.stat.TableOneWay: Returns maximum value of x.
getMaximumBDFOrder() - Method in class com.imsl.math.FeynmanKac: Returns the maximum order of the BDF formulas.
getMaximumDifference() - Method in class com.imsl.stat.KolmogorovOneSample: Returns $D^{+}$ , the maximum difference between the theoretical and empirical CDF's.
getMaximumDifference() - Method in class com.imsl.stat.KolmogorovTwoSample: Returns $D^{+}$ , the maximum difference between the theoretical and empirical CDF's.
getMaximumFunctionEvaluations() - Method in class com.imsl.math.OdeAdamsGear: Returns the maximum number of function evaluations of $y'$ allowed.
getMaximumLikelihood() - Method in class com.imsl.stat.ProportionalHazards: Returns the maximized log-likelihood.
getMaximumStepsize() - Method in class com.imsl.math.FeynmanKac: Returns the maximum internal step size used by the integrator.
getMaximumStepsize() - Method in class com.imsl.math.ODE: Returns the maximum internal step size.
getMaximumTime() - Method in class com.imsl.math.MinConNLP: Returns the maximum time allowed for the solve step.
getMaximumValue() - Method in class com.imsl.chart.BoxPlot.Statistics: Returns the maximum value of the observations.
getMaximumValue() - Method in class com.imsl.chart.qc.ControlLimit: Returns the maximum value of this control limit line.
getMaximumX() - Method in class com.imsl.stat.TableTwoWay: Returns the maximum value of x.
getMaximumY() - Method in class com.imsl.stat.TableTwoWay: Returns the maximum value of y.
getMaxIterations() - Method in class com.imsl.math.ConjugateGradient: Returns the maximum number of iterations allowed.
getMaxIterations() - Method in class com.imsl.math.Eigen: Returns the maximum number of iterations.
getMaxIterations() - Method in class com.imsl.math.GenMinRes: Returns the maximum number of iterations allowed.
getMaxIterations() - Method in class com.imsl.math.SparseLP: Returns the maximum number of iterations allowed for the primal-dual solver.
getMaxIterations() - Method in class com.imsl.stat.ARAutoUnivariate: Returns the value currently being used as the maximum number of iterations allowed in the nonlinear equation solver used in both the method of moments and least-squares algorithms.
getMaxIterations() - Method in class com.imsl.stat.ARMAEstimateMissing: Returns the maximum number of estimation iterations used by missing value estimation methods AR_1 and AR_P.
getMaxIterations() - Method in class com.imsl.stat.ARMAMaxLikelihood: Returns the maximum number of iterations.
getMaxIterations() - Method in class com.imsl.stat.ProportionalHazards: Return the maximum number of iterations allowed.
getMaxKrylovDim() - Method in class com.imsl.math.GenMinRes: Returns the maximum Krylov subspace dimension, i.e., the maximum allowable number of GMRES iterations allowed before restarting.
getMaxlag() - Method in class com.imsl.stat.ARAutoUnivariate: Returns the current value used to represent the maximum number of autoregressive lags to achieve the minimum AIC.
getMaxlag() - Method in class com.imsl.stat.ARMAEstimateMissing: Returns the current value of autoregressive lags used in the AR_P estimation method.
getMaxlag() - Method in class com.imsl.stat.ARSeasonalFit: Returns the maximum lag used to fit the AR(p) model.
getMaxNodes() - Method in class com.imsl.datamining.decisionTree.DecisionTree: Returns the maximum number of TreeNode instances allowed in a tree.
getMaxNumberOfCategories() - Method in class com.imsl.datamining.PredictiveModel: Returns the maximum number of categorical variables allowed.
getMaxOrder() - Method in class com.imsl.math.OdeAdamsGear: Returns the highest order formula to use of implicit METHOD_ADAMS type or METHOD_BDF type.
getMaxSteps() - Method in class com.imsl.math.FeynmanKac: Returns the maximum number of internal steps allowed.
getMaxSteps() - Method in class com.imsl.math.ODE: Returns the maximum number of internal steps allowed.
getMean() - Method in class com.imsl.stat.ARAutoUnivariate: Returns the mean used to center the time series z.
getMean() - Method in class com.imsl.stat.ARMA: Returns an update of the mean of the time series z.
getMean() - Method in class com.imsl.stat.ARMAEstimateMissing: Returns the mean value used to center the series.
getMean() - Method in class com.imsl.stat.ARMAMaxLikelihood: Returns the mean used to center the time series.
getMean() - Method in class com.imsl.stat.AutoCorrelation: Returns the mean of the time series x.
getMean() - Method in class com.imsl.stat.LogNormalDistribution: Returns the lognormal probability distribution mean parameter.
getMean() - Method in class com.imsl.stat.NormalDistribution: Returns the population mean of xData.
getMean() - Method in class com.imsl.stat.NormOneSample: Returns the mean of the sample.
getMean() - Method in class com.imsl.stat.Summary: Returns the population mean.
getMeanOfY() - Method in class com.imsl.stat.ANOVA: Returns the mean of the response (dependent variable).
getMeans(double[][], int[]) - Method in class com.imsl.datamining.NaiveBayesClassifier: Returns a table of means for each continuous attribute in continuousData segmented by the target classes in classificationData.
getMeans() - Method in class com.imsl.stat.ANCOVA: Returns a matrix containing the unadjusted means for the covariates and the response variate and the means for the response variate adjusted for the covariates.
getMeans() - Method in class com.imsl.stat.ANOVAFactorial: Returns the subgroup means.
getMeans() - Method in class com.imsl.stat.Covariances: Returns the means of the variables in x.
getMeans() - Method in class com.imsl.stat.DiscriminantAnalysis: Returns the variable means.
getMeans() - Method in class com.imsl.stat.ProportionalHazards: Returns the means of the design variables.
getMeanSampleSize() - Method in class com.imsl.chart.qc.ShewhartControlChart: Returns the value of the attribute "MeanSampleSize".
getMeanSquaredPredictionError() - Method in class com.imsl.datamining.BootstrapAggregation: Returns the mean squared prediction error.
getMeanSquaredPredictionError() - Method in class com.imsl.datamining.decisionTree.DecisionTree: Returns the mean squared error.
getMeanX() - Method in class com.imsl.stat.CrossCorrelation: Returns the mean of the time series x.
getMeanX() - Method in class com.imsl.stat.MultiCrossCorrelation: Returns the mean of each channel of x.
getMeanX() - Method in class com.imsl.stat.NormTwoSample: Returns the mean of the first sample, x.
getMeanY() - Method in class com.imsl.stat.CrossCorrelation: Returns the mean of the time series y.
getMeanY() - Method in class com.imsl.stat.MultiCrossCorrelation: Returns the mean of each channel of y.
getMeanY() - Method in class com.imsl.stat.NormTwoSample: Returns the mean of the second sample, y.
getMedian() - Method in class com.imsl.chart.BoxPlot.Statistics: Returns the median value.
getMedianLowerConfidenceInterval() - Method in class com.imsl.chart.BoxPlot.Statistics: Returns the lower confidence interval for the median.
getMedianUpperConfidenceInterval() - Method in class com.imsl.chart.BoxPlot.Statistics: Returns the upper confidence interval for the median.
getMergeCategoriesSigLevel() - Method in class com.imsl.datamining.decisionTree.CHAID: Returns the significance level for merging categories.
getMetaData() - Method in class com.imsl.io.AbstractFlatFile: Retrieves the number, types and properties of this ResultSet object's columns.
getMethod() - Method in class com.imsl.math.GenMinRes: Returns the implementation method to be used.
getMethod() - Method in class com.imsl.stat.ClusterHierarchical: Returns the clustering method used.
getMinimum() - Method in class com.imsl.stat.Summary: Returns the minimum.
getMinimum() - Method in class com.imsl.stat.TableOneWay: Returns the minimum value of x.
getMinimumDifference() - Method in class com.imsl.stat.KolmogorovOneSample: Returns $D^{-}$ , the minimum difference between the theoretical and empirical CDF's.
getMinimumDifference() - Method in class com.imsl.stat.KolmogorovTwoSample: Returns $D^{-}$ , the minimum difference between the theoretical and empirical CDF's.
getMinimumStepsize() - Method in class com.imsl.math.ODE: Returns the minimum internal step size.
getMinimumValue() - Method in class com.imsl.chart.BoxPlot.Statistics: Returns the minimum value of the observations.
getMinimumValue() - Method in class com.imsl.chart.qc.ControlLimit: Returns the minimum value of this control limit line.
getMinimumX() - Method in class com.imsl.stat.TableTwoWay: Returns the minimum value of x.
getMinimumY() - Method in class com.imsl.stat.TableTwoWay: Returns the minimum value of y.
getMinObsPerChildNode() - Method in class com.imsl.datamining.decisionTree.DecisionTree: Returns the minimum number of observations that are required for any child node before performing a split.
getMinObsPerNode() - Method in class com.imsl.datamining.decisionTree.DecisionTree: Returns the minimum number of observations that are required in a node before performing a split.
getMinorTick() - Method in class com.imsl.chart.Axis1D: Returns the minor tick node associated with this axis.
getMinusLogLikelihood() - Method in class com.imsl.stat.distributions.MaximumLikelihoodEstimation: Returns minus the log-likelihood evaluated at the parameter estimates.
getMissingIndicator() - Method in class com.imsl.stat.TimeSeries: Returns an array of missing value indicators.
getMissingTestYFlag() - Method in class com.imsl.datamining.GradientBoosting: Returns the flag that sets whether the test data is missing the response variable data.
getMissingTimes() - Method in class com.imsl.stat.ARMAEstimateMissing: Returns an int array of the times with missing values.
getMissingValueMethod() - Method in class com.imsl.stat.ARMAEstimateMissing: Returns the current missing value estimation method.
getMLEs(double[]) - Method in class com.imsl.stat.distributions.NormalPD: Returns the mean and standard deviation of the sample data.
getModelCoefficients() - Method in class com.imsl.stat.ANCOVA: Returns a matrix containing statistics for the regression coefficients for the model assuming parallelism.
getModelErrorStdev() - Method in class com.imsl.stat.ANOVA: Returns the estimated standard deviation of the model error.
getModelMeanSquare() - Method in class com.imsl.stat.ANOVA: Returns the model mean square.
getMonthBasis() - Method in class com.imsl.finance.DayCountBasis: Returns the (days in month) portion of the Day Count Basis.
getMRBar() - Method in class com.imsl.chart.qc.XmR: Returns the expected mean of of all of the moving ranges of two observations.
getMultinomialResponse() - Method in class com.imsl.datamining.GradientBoosting: Returns the multinomial representation of the response variable.
getName() - Method in class com.imsl.chart.AbstractChartNode: Returns the value of the "Name" attribute.
getName() - Method in class com.imsl.io.MPSReader: Returns the name of the MPS problem.
getName() - Method in class com.imsl.io.MPSReader.Row: Returns the name of this row.
getNameBounds() - Method in class com.imsl.io.MPSReader: Returns the name of the BOUNDS set.
getNameColumn(int) - Method in class com.imsl.io.MPSReader: Returns the name of a constraint column.
getNameObjective() - Method in class com.imsl.io.MPSReader: Returns the name of the free row containing the objective.
getNameRanges() - Method in class com.imsl.io.MPSReader: Returns the name of the RANGES set.
getNameRHS() - Method in class com.imsl.io.MPSReader: Returns the name of the RHS section.
getNameRow(int) - Method in class com.imsl.io.MPSReader: Returns the name of a contraint row.
getNCells() - Method in class com.imsl.stat.TableMultiWay.UnbalancedTable: Returns the number of non-empty cells.
getNCharacterStream(int) - Method in class com.imsl.io.FlatFile: Retrieves the value of the designated column in the current row of this ResultSet object as a java.io.Reader object.
getNCharacterStream(String) - Method in class com.imsl.io.FlatFile: Retrieves the value of the designated column in the current row of this ResultSet object as a java.io.Reader object.
getNClob(int) - Method in class com.imsl.io.FlatFile: Retrieves the value of the designated column in the current row of this ResultSet object as a NClob object in the Java programming language.
getNClob(String) - Method in class com.imsl.io.FlatFile: Retrieves the value of the designated column in the current row of this ResultSet object as a NClob object in the Java programming language.
getNeighborhoodType() - Method in class com.imsl.datamining.KohonenSOM: Returns the neighborhood type for the rectangular grid.
getNeighborhoodValue(int, double) - Method in class com.imsl.datamining.KohonenSOMTrainer: Returns the neighborhood function value.
getNetwork() - Method in class com.imsl.datamining.neural.BinaryClassification: Returns the network being used for classification.
getNetwork() - Method in class com.imsl.datamining.neural.MultiClassification: Returns the network being used for classification.
getNLost() - Method in class com.imsl.stat.ARSeasonalFit: Returns the number of values in the initial part of the series lost to differencing.
getNMinus() - Method in class com.imsl.chart.qc.CuSumStatus: Returns $N^{-}$ , the number of consecutive periods that the cusums $C_i^{-}$ have been nonzero.
getNode() - Method in class com.imsl.chart.PickEvent: Gets this ChartNode.
getNode(int) - Method in class com.imsl.datamining.decisionTree.Tree: Returns a copy of the specified node of the decision tree.
getNodeAssigments(double[][]) - Method in class com.imsl.datamining.decisionTree.DecisionTree: Returns the terminal node assignments for each row of the test data.
getNodeId() - Method in class com.imsl.datamining.decisionTree.TreeNode: Returns the id of the current node.
getNodes() - Method in class com.imsl.datamining.decisionTree.Tree: Returns nodes within a decision tree.
getNodes() - Method in class com.imsl.datamining.neural.InputLayer: Return the Perceptrons in the InputLayer.
getNodes() - Method in class com.imsl.datamining.neural.Layer: Return a list of the Perceptrons in this Layer.
getNodes() - Method in class com.imsl.datamining.neural.OutputLayer: Return the Perceptrons in the OutputLayer.
getNodeSplitValue() - Method in class com.imsl.datamining.decisionTree.TreeNode: Returns the value around which the node may be split, if the split variable is of a continuous type.
getNodeValueIndicator(int) - Method in class com.imsl.datamining.decisionTree.TreeNode: Returns the indicator value for the i-th value of the split variable in the current node, if the split variable is of discrete type.
getNodeValuesIndicator() - Method in class com.imsl.datamining.decisionTree.TreeNode: Returns the array indicating which values of the split variable apply in the current node, if the split variable is of discrete type.
getNodeVariableId() - Method in class com.imsl.datamining.decisionTree.TreeNode: Returns the id of the variable that defines the split in the current node.
getNorm() - Method in class com.imsl.math.ODE: Returns the switch for determining the error norm.
getNormalScores(double[]) - Method in class com.imsl.stat.Ranks: Gets the expected value of normal order statistics (for tied observations, the average of the expected normal scores).
getNotch() - Method in class com.imsl.chart.BoxPlot: Gets the "Notch" attribute value.
getNPlus() - Method in class com.imsl.chart.qc.CuSumStatus: Returns $N^{+}$ , the number of consecutive periods that the cusums $C_i^{+}$ have been nonzero.
getNRowsMissing() - Method in class com.imsl.stat.CategoricalGenLinModel: Returns the number of rows of data in x that contain missing values in one or more specific columns of x.
getNString(int) - Method in class com.imsl.io.FlatFile: Retrieves the value of the designated column in the current row of this ResultSet object as a String in the Java programming language.
getNString(String) - Method in class com.imsl.io.FlatFile: Retrieves the value of the designated column in the current row of this ResultSet object as a String in the Java programming language.
getNumber() - Method in class com.imsl.chart.AbstractChartNode: Returns the value of the "Number" attribute.
getNumberAtRisk() - Method in class com.imsl.stat.KaplanMeierEstimates: Returns the number of individuals at risk at each failure point.
getNumberFormat() - Method in class com.imsl.math.PrintMatrixFormat: Returns the NumberFormat to be used in formatting double and Complex entries.
getNumberGridPointsX() - Method in class com.imsl.chart3d.Surface: Returns the value of the "NumberGridPointsX" attribute.
getNumberGridPointsY() - Method in class com.imsl.chart3d.Surface: Returns the value of the "NumberGridPointsY" attribute.
getNumberMissing() - Method in class com.imsl.stat.ARMAEstimateMissing: Returns the number of missing values in the original series
getNumberMissing() - Method in class com.imsl.stat.KolmogorovOneSample: Returns the number of missing values in the data.
getNumberMissingX() - Method in class com.imsl.stat.KolmogorovTwoSample: Returns the number of missing values in the x sample.
getNumberMissingX() - Method in class com.imsl.stat.WilcoxonRankSum: Returns the number of missing observations detected in x.
getNumberMissingY() - Method in class com.imsl.stat.KolmogorovTwoSample: Returns the number of missing values in the y sample.
getNumberMissingY() - Method in class com.imsl.stat.WilcoxonRankSum: Returns the number of missing observations detected in y.
getNumberObservations() - Method in class com.imsl.chart.BoxPlot.Statistics: Returns the number of observations.
getNumberOfBackcasts() - Method in class com.imsl.stat.ARMA: Returns the number of backcasts used to calculate the AR coefficients for the time series z.
getNumberOfBinaryConstraints() - Method in class com.imsl.io.MPSReader: Returns the number of binary constraints.
getNumberOfCases() - Method in class com.imsl.datamining.decisionTree.TreeNode: Returns the number of cases in the training data that fall into the current node.
getNumberOfChildren() - Method in class com.imsl.datamining.decisionTree.TreeNode: Returns the number of child nodes associated with the current node.
getNumberOfClasses() - Method in class com.imsl.datamining.decisionTree.Tree: Returns the number of classes assumed by the response variable, if the response variable is categorical.
getNumberOfClasses() - Method in class com.imsl.datamining.neural.UnsupervisedNominalFilter: Retrieves the number of classes in the nominal variable.
getNumberOfClasses() - Method in class com.imsl.datamining.neural.UnsupervisedOrdinalFilter: Retrieves the number of categories associated with this ordinal variable.
getNumberOfClasses() - Method in class com.imsl.datamining.PredictiveModel: Returns the number of unique classes found in the categorical response data.
getNumberOfCoefficients() - Method in class com.imsl.stat.ProportionalHazards: Returns the number of estimated coefficients in the model.
getNumberOfColumns() - Method in class com.imsl.datamining.KohonenSOM: Returns the number of columns of the node grid.
getNumberOfColumns() - Method in class com.imsl.datamining.PredictiveModel: Returns the number of columns in xy.
getNumberOfColumns() - Method in class com.imsl.io.MPSReader: Returns the number of columns in the constraint matrix.
getNumberOfColumns() - Method in class com.imsl.math.ComplexSparseMatrix: Returns the number of columns in the matrix.
getNumberOfColumns() - Method in class com.imsl.math.SparseMatrix: Returns the number of columns in the matrix.
getNumberOfComplexityValues() - Method in class com.imsl.datamining.decisionTree.DecisionTree: Return the number of cost complexity values determined.
getNumberOfEpochs() - Method in class com.imsl.datamining.neural.EpochTrainer: Returns the number of epochs used during stage I training.
getNumberOfEvaluations() - Method in class com.imsl.math.ZerosFunction: Returns the actual number of function evaluations performed.
getNumberOfFailures() - Method in class com.imsl.stat.KaplanMeierEstimates: Returns the number of failures which occurred at each failure point.
getNumberOfFcnEvals() - Method in class com.imsl.math.OdeAdamsGear: Returns the number of function evaluations of $y'$ made.
getNumberOfInputs() - Method in class com.imsl.datamining.neural.FeedForwardNetwork: Returns the number of inputs to the Network.
getNumberOfInputs() - Method in class com.imsl.datamining.neural.Network: Returns the number of Network inputs.
getNumberOfIntegerConstraints() - Method in class com.imsl.io.MPSReader: Returns the number of integer constraints.
getNumberOfItemsets() - Method in class com.imsl.datamining.Itemsets: Returns the number of itemsets in this Itemsets.
getNumberOfIterations() - Method in class com.imsl.datamining.GradientBoosting: Returns the current setting for the number of iterations to use in the gradient boosting algorithm.
getNumberOfJacobianEvals() - Method in class com.imsl.math.OdeAdamsGear: Returns the number of Jacobian matrix evaluations used.
getNumberOfLevels() - Method in class com.imsl.datamining.decisionTree.Tree: Returns the number of levels or depth of a tree.
getNumberOfLinks() - Method in class com.imsl.datamining.neural.FeedForwardNetwork: Returns the number of Links in the Network.
getNumberOfLinks() - Method in class com.imsl.datamining.neural.Network: Returns the number of Network Links among the nodes.
getNumberOfMissing() - Method in class com.imsl.datamining.PredictiveModel: Returns the number of missing values of the response variable found in the data xy.
getNumberOfMissing() - Method in class com.imsl.stat.ANCOVA: Returns the number of cases with missing values in covariates or responses.
getNumberOfMissingRows() - Method in class com.imsl.stat.RegressorsForGLM: Returns the number of rows in the regressors matrix containing NaN (not a number).
getNumberOfNodes() - Method in class com.imsl.datamining.decisionTree.Tree: Returns the number of nodes (size of a tree).
getNumberOfNonZeros() - Method in class com.imsl.io.MPSReader: Returns the number of nonzeros in the constraint matrix.
getNumberOfNonZeros() - Method in class com.imsl.io.MPSReader.Row: Returns the number of nonzero elements in this row.
getNumberOfNonzeros() - Method in class com.imsl.math.ComplexSparseCholesky: Returns the number of nonzeros in the Cholesky factor.
getNumberOfNonZeros() - Method in class com.imsl.math.ComplexSparseMatrix: Returns the number of nonzeros in the matrix.
getNumberOfNonzeros() - Method in class com.imsl.math.SparseCholesky: Returns the number of nonzeros in the Cholesky factor.
getNumberOfNonZeros() - Method in class com.imsl.math.SparseMatrix: Returns the number of nonzeros in the matrix.
getNumberOfObservations() - Method in class com.imsl.stat.Summary: Returns the number of non-missing observations.
getNumberOfOutliers() - Method in class com.imsl.stat.ARMAOutlierIdentification: Returns the number of outliers detected.
getNumberOfOutliers() - Method in class com.imsl.stat.AutoARIMA: Returns the number of outliers detected.
getNumberOfOutputs() - Method in class com.imsl.datamining.neural.FeedForwardNetwork: Returns the number of outputs from the Network.
getNumberOfOutputs() - Method in class com.imsl.datamining.neural.Network: Returns the number of Network output Perceptrons.
getNumberOfParameters() - Method in class com.imsl.stat.distributions.ProbabilityDistribution: Returns the number of parameters of the probability distribution.
getNumberOfPoints() - Method in class com.imsl.stat.KaplanMeierECDF: Retrieves the number of points in the empirical CDF
getNumberOfPredictors() - Method in class com.imsl.datamining.decisionTree.Tree: Returns the number of predictors used in the model.
getNumberOfPredictors() - Method in class com.imsl.datamining.PredictiveModel: Returns the number of predictors.
getNumberOfRegressors() - Method in class com.imsl.stat.RegressorsForGLM: Returns the number regressors.
getNumberOfRoots() - Method in class com.imsl.math.ZerosFunction: Returns the requested number of roots to be found.
getNumberOfRootsFound() - Method in class com.imsl.math.ZerosFunction: Returns the number of zeros found.
getNumberOfRows() - Method in class com.imsl.datamining.KohonenSOM: Returns the number of rows of the node grid.
getNumberOfRows() - Method in class com.imsl.datamining.PredictiveModel: Returns the number of rows in xy (observations).
getNumberOfRows() - Method in class com.imsl.io.MPSReader: Returns the number of rows in the constraint matrix.
getNumberOfRows() - Method in class com.imsl.math.ComplexSparseMatrix: Returns the number of rows in the matrix.
getNumberOfRows() - Method in class com.imsl.math.SparseMatrix: Returns the number of rows in the matrix.
getNumberOfRowsMissing() - Method in class com.imsl.stat.DiscriminantAnalysis: Returns the number of rows of data encountered containing missing values (Double.NaN).
getNumberOfRowsMissing() - Method in class com.imsl.stat.KaplanMeierEstimates: Returns the number of rows of data in x that contain missing values in one or more specific columns of x.
getNumberOfSampleFolds() - Method in class com.imsl.datamining.CrossValidation: Returns the number of folds set for the cross validation selection.
getNumberOfSeries() - Method in class com.imsl.stat.TimeSeries: Returns the number of series stored in this TimeSeries object.
getNumberOfSets(double[], int[]) - Method in class com.imsl.datamining.decisionTree.DecisionTree: Returns the number of sets for a split.
getNumberOfSteps() - Method in class com.imsl.math.OdeAdamsGear: Returns the number of internal steps taken.
getNumberOfSurrogateSplits() - Method in class com.imsl.datamining.decisionTree.ALACART: Returns the number of surrogate splits.
getNumberOfSurrogateSplits() - Method in interface com.imsl.datamining.decisionTree.DecisionTreeSurrogateMethod: Indicates the number of surrogate splits.
getNumberOfSurrogateSplits() - Method in class com.imsl.datamining.decisionTree.Tree: Returns the number of surrogate splits searched for at each tree node.
getNumberOfThreads() - Method in class com.imsl.datamining.BootstrapAggregation: Returns the maximum number of java.lang.Thread instances that may be used for parallel processing.
getNumberOfThreads() - Method in class com.imsl.datamining.CrossValidation: Returns the maximum number of java.lang.Thread instances that may be used for parallel processing.
getNumberOfThreads() - Method in class com.imsl.datamining.KohonenSOMTrainer: Returns the number of java.lang.Thread instances used for parallel processing.
getNumberOfThreads() - Method in class com.imsl.datamining.neural.EpochTrainer: Gets the number of java.lang.Thread instances to use during stage I training.
getNumberOfThreads() - Method in class com.imsl.math.BoundedLeastSquares: Returns the number of java.lang.Thread instances used for parallel processing.
getNumberOfThreads() - Method in class com.imsl.math.MinConGenLin: Returns the number of java.lang.Thread instances used for parallel processing.
getNumberOfThreads() - Method in class com.imsl.math.MinConNLP: Returns the number of java.lang.Thread instances used for parallel processing.
getNumberOfThreads() - Method in class com.imsl.math.MinUnconMultiVar: Returns the number of java.lang.Thread instances used for parallel processing.
getNumberOfThreads() - Method in class com.imsl.math.NonlinLeastSquares: Returns the number of java.lang.Thread instances used for parallel processing.
getNumberOfThreads() - Method in class com.imsl.stat.AutoCorrelation: Returns the number of java.lang.Thread instances used for parallel processing.
getNumberOfTies() - Method in class com.imsl.stat.KolmogorovOneSample: Returns the number of ties in the data.
getNumberOfTransactions() - Method in class com.imsl.datamining.Itemsets: Returns an int indicating the number of transactions used to construct the itemsets.
getNumberOfUniquePredictorValues() - Method in class com.imsl.datamining.PredictiveModel: Returns an array containing the number of distinct values of each categorical or ordinal predictor found in the input data.
getNumberOfWeights() - Method in class com.imsl.datamining.neural.FeedForwardNetwork: Returns the number of weights in the Network.
getNumberOfWeights() - Method in class com.imsl.datamining.neural.Network: Returns the number of weights in the Network.
getNumberRowsMissing() - Method in class com.imsl.stat.ProportionalHazards: Returns the number of rows of data in x that contain missing values in one or more specific columns of x.
getNumericFactor() - Method in class com.imsl.math.ComplexSparseCholesky: Returns the numeric Cholesky factor.
getNumericFactor() - Method in class com.imsl.math.SparseCholesky: Returns the numeric Cholesky factor.
getNumericFactorizationMethod() - Method in class com.imsl.math.ComplexSparseCholesky: Returns the method used in the numerical factorization of the permuted input matrix.
getNumericFactorizationMethod() - Method in class com.imsl.math.SparseCholesky: Returns the method used in the numerical factorization of the permuted input matrix.
getNumMissing() - Method in class com.imsl.stat.TimeSeries: Returns the number of missing values.
getNumPositiveDev() - Method in class com.imsl.stat.SignTest: Returns the number of positive differences.
getNumRowMissing() - Method in class com.imsl.stat.Covariances: Returns the total number of observations that contain any missing values (Double.NaN).
getNumZeroDev() - Method in class com.imsl.stat.SignTest: Returns the number of zero differences.
getNvalues() - Method in class com.imsl.stat.TableMultiWay.BalancedTable: Returns an array of length nKeys containing in its i-th element (i=0,1,...nKeys-1), the number of levels or categories of the i-th classification variable (column).
getObject(int) - Method in class com.imsl.io.AbstractFlatFile: Gets the value of the designated column in the current row of this ResultSet object as an Object in the Java programming language.
getObject(String) - Method in class com.imsl.io.AbstractFlatFile: Gets the value of the designated column in the current row of this ResultSet object as an Object in the Java programming language.
getObject(int, Map) - Method in class com.imsl.io.AbstractFlatFile: Returns the value of the designated column in the current row of this ResultSet object as an Object in the Java programming language.
getObject(String, Class) - Method in class com.imsl.io.AbstractFlatFile: Returns the value of the designated column in the current row of this ResultSet object converted to the requested data type in the Java programming language.
getObject(int, Class) - Method in class com.imsl.io.AbstractFlatFile: Returns the value of the designated column in the current row of this ResultSet object converted to the requested data type in the Java programming language.
getObject(String, Map) - Method in class com.imsl.io.AbstractFlatFile: Returns the value of the designated column in the current row of this ResultSet object as an Object in the Java programming language.
getObject(int) - Method in class com.imsl.io.FlatFile: Gets the value of the designated column in the current row of this ResultSet object as an Object in the Java programming language.
getObjective() - Method in class com.imsl.io.MPSReader: Returns the objective as a Row.
getObjectiveCoefficients() - Method in class com.imsl.io.MPSReader: Returns the coefficents of the objective row.
getObjectiveValue() - Method in class com.imsl.math.MinConGenLin: Returns the value of the objective function.
getObservations() - Method in class com.imsl.stat.Covariances: Returns the sum of the frequencies.
getObservationsLost() - Method in class com.imsl.stat.Difference: Returns the number of observations lost because of differencing the time series.
getObservedResponse() - Method in class com.imsl.stat.LinearRegression.CaseStatistics: Returns the observed response for an observation.
getObsPerCluster(int) - Method in class com.imsl.stat.ClusterHierarchical: Returns the number of observations in each cluster.
getOffset() - Method in class com.imsl.chart.Text: Returns the offset.
getOmegaWeights() - Method in class com.imsl.stat.ARMAOutlierIdentification: Returns the weights for the detected outliers.
getOneSidedPValue() - Method in class com.imsl.stat.KolmogorovOneSample: Probability of the statistic exceeding D under the null hypothesis of equality and against the one-sided alternative.
getOneSidedPValue() - Method in class com.imsl.stat.KolmogorovTwoSample: Probability of the statistic exceeding D under the null hypothesis of equality and against the one-sided alternative.
getOpen() - Method in class com.imsl.chart.HighLowClose: Gets the value of the attribute "Open".
getOptimalValue() - Method in class com.imsl.math.DenseLP: Returns the optimal value of the objective function.
getOptimalValue() - Method in class com.imsl.math.MinConNLP: Returns the value of the objective function.
getOptimalValue() - Method in class com.imsl.math.QuadraticProgramming: Returns the optimal value.
getOptimalValue() - Method in class com.imsl.math.SparseLP: Returns the optimal value of the objective function.
getOptimizedCriterion() - Method in class com.imsl.stat.CategoricalGenLinModel: Returns the optimized criterion.
getOptimumD() - Method in class com.imsl.stat.ARSeasonalFit: Returns the optimum values for d selected among the candidates in dInitial.
getOptimumModelOrder() - Method in class com.imsl.stat.AutoARIMA: Returns the order of the optimum model.
getOptimumS() - Method in class com.imsl.stat.ARSeasonalFit: Returns the optimum values for s selected among the candidates in sInitial.
getOrbit() - Method in class com.imsl.chart3d.Chart3D: Returns the value of the "Orbit" attribute.
getOrder() - Method in class com.imsl.chart.Dendrogram: Convenience routine to get the "Order" attribute.
getOrder() - Method in class com.imsl.stat.ARAutoUnivariate: Returns the order of the AR model selected with the minimum AIC.
getOrientation() - Method in class com.imsl.chart.Treemap: Gets the value of the "Orientation" attribute.
getOutlierFreeForecast() - Method in class com.imsl.stat.ARMAOutlierIdentification: Returns forecasts for the outlier free series.
getOutlierFreeForecast() - Method in class com.imsl.stat.AutoARIMA: Returns forecasts for the outlier free series.
getOutlierFreeSeries() - Method in class com.imsl.stat.ARMAOutlierIdentification: Returns the outlier free series.
getOutlierFreeSeries() - Method in class com.imsl.stat.AutoARIMA: Returns the outlier free series.
getOutlierStatistics() - Method in class com.imsl.stat.ARMAOutlierIdentification: Returns the outlier statistics.
getOutlierStatistics() - Method in class com.imsl.stat.AutoARIMA: Returns the outlier statistics.
getOutputLayer() - Method in class com.imsl.datamining.neural.FeedForwardNetwork: Returns the OutputLayer.
getOutputLayer() - Method in class com.imsl.datamining.neural.Network: Returns the OutputLayer.
getOutsideMarkers() - Method in class com.imsl.chart.BoxPlot: Returns the OutsideMarkers node.
getOutsideMarkers() - Method in class com.imsl.chart.BoxPlot.Statistics: Returns the array of outside markers.
getP() - Method in class com.imsl.stat.ANOVA: Returns the p-value.
getP() - Method in class com.imsl.stat.ARMAMaxLikelihood: Returns the number of autoregressive terms in the ARMA model
getP() - Method in class com.imsl.stat.ChiSquaredTest: Returns the p-value for the chi-squared statistic.
getP() - Method in class com.imsl.stat.ContingencyTable: Returns the Pearson chi-squared p-value for independence of rows and columns.
getPaint() - Method in class com.imsl.chart.AbstractChartNode: Returns the value of the "Paint" attribute.
getPanel() - Method in class com.imsl.chart.JFrameChart: Returns the JPanelChart into which the chart is drawn.
getParamEstimatesCovariance() - Method in class com.imsl.stat.ARMA: Returns the covariances of parameter estimates.
getParameterLowerBounds() - Method in class com.imsl.stat.distributions.BetaPD: Returns the lower bounds for the two shape parameters of the beta distribution.
getParameterLowerBounds() - Method in class com.imsl.stat.distributions.GammaPD: Returns the lower bounds for the shape and scale parameters of the gamma distribution.
getParameterLowerBounds() - Method in class com.imsl.stat.distributions.NormalPD: Returns the lower bounds for the mean and standard deviation .
getParameterLowerBounds() - Method in class com.imsl.stat.distributions.ProbabilityDistribution: Returns the lower bounds of the parameters.
getParameters() - Method in class com.imsl.stat.CategoricalGenLinModel: Returns the parameter estimates and associated statistics.
getParameters() - Method in class com.imsl.stat.GammaDistribution: Returns the current parameters of the gamma probability density function.
getParameters() - Method in class com.imsl.stat.LogNormalDistribution: Returns the current parameters of the lognormal probability density function
getParameters() - Method in class com.imsl.stat.NormalDistribution: Returns the current parameters of the normal probability density function.
getParameters() - Method in class com.imsl.stat.PoissonDistribution: Returns the current parameters of the Poisson probability density function.
getParameters() - Method in interface com.imsl.stat.ProbabilityDistribution: Returns the current parameters of the probability density function.
getParameterStatistics() - Method in class com.imsl.stat.ProportionalHazards: Returns the parameter estimates and associated statistics.
getParameterUpdates() - Method in class com.imsl.stat.FactorAnalysis: Returns the parameter updates.
getParameterUpperBounds() - Method in class com.imsl.stat.distributions.BetaPD: Returns the upper bounds for the two shape parameters of the beta distribution.
getParameterUpperBounds() - Method in class com.imsl.stat.distributions.GammaPD: Returns the upper bounds for the shape and scale parameters of the gamma distribution.
getParameterUpperBounds() - Method in class com.imsl.stat.distributions.NormalPD: Returns the upper bounds for the mean and standard deviation .
getParameterUpperBounds() - Method in class com.imsl.stat.distributions.ProbabilityDistribution: Returns the upper bounds of the parameters.
getParent() - Method in class com.imsl.chart.ChartNode: Returns the parent of this node.
getParent() - Method in class com.imsl.chart3d.ChartNode3D: Returns the parent of this node.
getParentId() - Method in class com.imsl.datamining.decisionTree.TreeNode: Returns the id of the parent node of the current node.
getPartialAutoCorrelations() - Method in class com.imsl.stat.AutoCorrelation: Returns the sample partial autocorrelation function of the stationary time series x.
getPartialCorrelationMatrix() - Method in class com.imsl.stat.PartialCovariances: Returns the partial correlation matrix.
getPartialCovarianceMatrix() - Method in class com.imsl.stat.PartialCovariances: Returns the partial covariance matrix.
getPartialDegreesOfFreedom() - Method in class com.imsl.stat.PartialCovariances: Returns the degrees of freedom in the test that the partial correlation (covariance) is zero.
getPDFGradient(double, double[]) - Method in class com.imsl.stat.distributions.NormalPD: Returns the analytic gradient of the normal pdf.
getPDFGradient(double, double[]) - Method in interface com.imsl.stat.distributions.PDFGradientInterface: Returns the gradient of the probability density function.
getPDFGradientApproximation(double, double[]) - Method in class com.imsl.stat.distributions.ProbabilityDistribution: Returns the approximate gradient of the probability density function, pdf.
getPDFHessian(double, double[]) - Method in class com.imsl.stat.distributions.NormalPD: Returns the analytic hessian matrix of the normal pdf evaluated at a point, x.
getPDFHessian(double, double[]) - Method in interface com.imsl.stat.distributions.PDFHessianInterface: Returns the hessian of the probability density function.
getPDFHessianApproximation(double, double[]) - Method in class com.imsl.stat.distributions.ProbabilityDistribution: Returns the approximate hessian of the probability density function, pdf.
getPercentageFactor() - Method in class com.imsl.math.NumericalDerivatives: Returns the percentage factor for differencing.
getPercentages() - Method in class com.imsl.datamining.neural.UnsupervisedOrdinalFilter: Retrieves the cumulative percentages used for encoding and decoding.
getPercents() - Method in class com.imsl.stat.FactorAnalysis: Returns the cumulative percent of the total variance explained by each principal component.
getPerceptrons() - Method in class com.imsl.datamining.neural.FeedForwardNetwork: Returns the Perceptrons in this Network.
getPerceptrons() - Method in class com.imsl.datamining.neural.Network: Returns an array containing the Perceptrons in the Network.
getPerformanceTuningParameters(int) - Method in class com.imsl.math.ComplexSuperLU: Returns a performance tuning parameter value.
getPerformanceTuningParameters(int) - Method in class com.imsl.math.SuperLU: Returns a performance tuning parameter value.
getPermutationMatrix() - Method in class com.imsl.math.ComplexLU: Returns the permutation matrix which results from the LU factorization of A.
getPermutationMatrix() - Method in class com.imsl.math.LU: Returns the permutation matrix which results from the LU factorization of A.
getPermute() - Method in class com.imsl.math.QR: Returns an integer vector containing information about the permutation of the elements of the matrix during pivoting.
getPhi() - Method in class com.imsl.stat.ContingencyTable: Returns phi.
getPieSlice() - Method in class com.imsl.chart.Pie: Returns the PieSlice objects.
getPieSlice(int) - Method in class com.imsl.chart.Pie: Returns a specified PieSlice.
getPivotGrowth() - Method in class com.imsl.math.ComplexSuperLU: Returns the reciprocal pivot growth factor flag.
getPivotGrowth() - Method in class com.imsl.math.SuperLU: Returns the reciprocal pivot growth factor flag.
getPooledVariance() - Method in class com.imsl.stat.NormTwoSample: Returns the Pooled variance for the two samples.
getPopulationTable(int[]) - Method in class com.imsl.stat.LifeTables: Compute a population table.
getPosition() - Method in class com.imsl.chart3d.ColormapLegend: Returns the position of the legend.
getPosition() - Method in class com.imsl.chart3d.PointLight: Returns the value of the "Position" attribute.
getPreconditionerSolves() - Method in class com.imsl.math.GenMinRes: Returns the total number of GMRES right preconditioner solves.
getPredictedClass() - Method in class com.imsl.datamining.decisionTree.TreeNode: Returns the predicted class at the current node, for response variables of categorical type.
getPredictedClass() - Method in class com.imsl.datamining.NaiveBayesClassifier: Returns the predicted classification for each training pattern.
getPredictedResponse() - Method in class com.imsl.stat.LinearRegression.CaseStatistics: Returns the predicted response for an observation.
getPredictedVal() - Method in class com.imsl.datamining.decisionTree.TreeNode: Returns the predicted value at the current node, for response variables of continuous type.
getPredictionError() - Method in class com.imsl.stat.KalmanFilter: Returns the one-step-ahead prediction error.
getPredictionInterval() - Method in class com.imsl.stat.LinearRegression.CaseStatistics: Returns the Prediction Interval of the predicted response for an observation.
getPredictions() - Method in class com.imsl.datamining.BootstrapAggregation: Returns the predicted values.
getPredictorIndexes() - Method in class com.imsl.datamining.PredictiveModel: Returns an array of indices into xy where the predictor variables reside.
getPredictorNumberOfValues() - Method in class com.imsl.datamining.decisionTree.Tree: Returns the number of distinct values of each predictor variable.
getPredictorType(int) - Method in class com.imsl.datamining.decisionTree.Tree: Returns the PredictiveModel.VariableType of a predictor variable.
getPredictorTypes() - Method in class com.imsl.datamining.PredictiveModel: Returns an array of VariableType objects that correspond to the predictor data types in xy.
getPreordering() - Method in class com.imsl.math.SparseLP: Returns the variant of the Minimum Degree Ordering (MDO) algorithm used in the preordering of the normal equations or augmented system matrix.
getPresolve() - Method in class com.imsl.math.SparseLP: Returns the presolve option.
getPrimalInfeasibility() - Method in class com.imsl.math.SparseLP: Returns the primal infeasibility of the solution.
getPrimalInfeasibilityTolerance() - Method in class com.imsl.math.SparseLP: Returns the primal infeasibility tolerance.
getPrimalSolution() - Method in class com.imsl.math.DenseLP: Returns the solution x of the linear programming problem.
getPrintLevel() - Method in class com.imsl.datamining.BootstrapAggregation: Returns the current print level.
getPrintLevel() - Method in class com.imsl.datamining.PredictiveModel: Returns the current print level.
getPrintLevel() - Method in class com.imsl.math.SparseLP: Returns the print level.
getPrior() - Method in class com.imsl.stat.DiscriminantAnalysis: Returns the prior probabilities.
getPriorProbabilities() - Method in class com.imsl.datamining.PredictiveModel: Returns an array containing the prior probabilities.
getProbabilities() - Method in class com.imsl.datamining.NaiveBayesClassifier: Returns the predicted classification probabilities for each target class.
getProbability() - Method in class com.imsl.stat.DiscriminantAnalysis: Returns the posterior probabilities for each observation.
getProduct() - Method in class com.imsl.stat.CategoricalGenLinModel: Returns the inverse of the Hessian times the gradient vector computed at the input parameter estimates.
getProducts() - Method in class com.imsl.math.GenMinRes: Returns the total number of GMRES matrix-vector products used.
getPsiWeights() - Method in class com.imsl.stat.ARMA: Returns the psi weights of the infinite order moving average form of the model.
getPsiWeights() - Method in class com.imsl.stat.ARMAMaxLikelihood: Returns the psi weights used for calculating forecasts from the infinite order moving average form of the ARMA model.
getPsiWeights() - Method in class com.imsl.stat.ARMAOutlierIdentification: Returns the weights of the infinite order moving average form of the model.
getPsiWeights() - Method in class com.imsl.stat.AutoARIMA: Returns the weights of the infinite order moving average form of the model.
getPValue(int) - Method in class com.imsl.stat.LinearRegression.CoefficientTTests: Returns the p-value for the two-sided test.
getPValue(int) - Method in class com.imsl.stat.StepwiseRegression.CoefficientTTests: Returns the p-value for the two-sided test $H_0 : { beta} = 0$ vs.
getPValues() - Method in class com.imsl.stat.PartialCovariances: Calculates the p-values for testing the null hypothesis that the associated partial covariance/correlation is zero.
getQ() - Method in class com.imsl.math.QR: Returns the orthogonal or unitary matrix Q.
getQ() - Method in class com.imsl.stat.ARMAMaxLikelihood: Returns the number of moving average terms in the ARMA model
getQ() - Method in class com.imsl.stat.EmpiricalQuantiles: Returns the empirical quantiles.
getR() - Method in class com.imsl.math.Cholesky: Returns the R matrix that results from the Cholesky factorization.
getR() - Method in class com.imsl.math.QR: Returns the upper trapezoidal matrix R.
getR() - Method in class com.imsl.stat.ANCOVA: Returns the R matrix from the QR decomposition.
getR() - Method in class com.imsl.stat.LinearRegression: Returns a copy of the R matrix.
getR() - Method in class com.imsl.stat.NonlinearRegression: Returns a copy of the R matrix.
getRadialFunction() - Method in class com.imsl.math.RadialBasis: Returns the radial function.
getRadius(int) - Method in class com.imsl.math.ZeroPolynomial: Returns an a-posteriori absolute error bound on the root.
getRandom() - Method in class com.imsl.datamining.neural.EpochTrainer: Returns the random number generator used to perturb the stage 1 guesses.
getRandomObject() - Method in class com.imsl.datamining.PredictiveModel: Returns the random object being used in the permutation of the observations.
getRandomSampleIndicies() - Method in class com.imsl.datamining.neural.EpochTrainer: Gets the random number generators used to select random training patterns in stage 1.
getRangeOfX() - Method in class com.imsl.stat.distributions.ProbabilityDistribution: Returns the proper range of the random variable having the current probability distribution.
getRank() - Method in class com.imsl.math.QR: Returns the rank of the matrix used to construct this instance.
getRank() - Method in class com.imsl.math.SVD: Returns the rank of the matrix used to construct this instance.
getRank() - Method in class com.imsl.stat.KalmanFilter: Returns the rank of the variance-covariance matrix for all the observations.
getRank() - Method in class com.imsl.stat.LinearRegression: Returns the rank of the matrix.
getRank() - Method in class com.imsl.stat.NonlinearRegression: Returns the rank of the matrix.
getRanks(double[]) - Method in class com.imsl.stat.Ranks: Gets the rank for each observation.
getRbar() - Method in class com.imsl.chart.qc.XbarR: Returns the value of the "Rbar" attribute, the mean of the ranges for a series of samples.
getReciprocalPivotGrowthFactor() - Method in class com.imsl.math.ComplexSuperLU: Returns the reciprocal pivot growth factor.
getReciprocalPivotGrowthFactor() - Method in class com.imsl.math.SuperLU: Returns the reciprocal pivot growth factor.
getRef(int) - Method in class com.imsl.io.AbstractFlatFile: Returns the value of the designated column in the current row of this ResultSet object as a Ref object in the Java programming language.
getRef(String) - Method in class com.imsl.io.AbstractFlatFile: Returns the value of the designated column in the current row of this ResultSet object as a Ref object in the Java programming language.
getReference() - Method in class com.imsl.chart.ChartNode: Returns the value of the "Reference" attribute.
getRegressors() - Method in class com.imsl.stat.RegressorsForGLM: Returns the regressor array.
getRelativeBackwardError() - Method in class com.imsl.math.ComplexSuperLU: Returns the componentwise relative backward error of the solution vector.
getRelativeBackwardError() - Method in class com.imsl.math.SuperLU: Returns the componentwise relative backward error of the solution vector.
getRelativeError(double) - Method in class com.imsl.math.ConjugateGradient: Returns the relative error used for stopping the algorithm.
getRelativeError() - Method in class com.imsl.math.GenMinRes: Returns the stopping tolerance.
getRelativeError() - Method in class com.imsl.stat.ARMAEstimateMissing: Returns the relative error used for the METHOD_OF_MOMENTS and LEAST_SQARES estimation methods.
getRelativeErrorTolerances() - Method in class com.imsl.math.FeynmanKac: Returns relative error tolerances.
getRelativeH() - Method in class com.imsl.chart.qc.CuSumStatus: Returns the value for relative h.
getRelativeOptimalityTolerance() - Method in class com.imsl.math.SparseLP: Returns the relative optimality tolerance.
getResidual() - Method in class com.imsl.stat.ARMA: Returns the residuals.
getResidual() - Method in class com.imsl.stat.ARMAOutlierIdentification: Returns the residuals.
getResidual() - Method in class com.imsl.stat.LinearRegression.CaseStatistics: Returns the Residual for an observation.
getResidualNorm() - Method in class com.imsl.math.BoundedVariableLeastSquares: Returns the euclidean norm of the residual vector, $|Ax-b|^2$ .
getResidualNorm() - Method in class com.imsl.math.GenMinRes: Returns the final residual norm, ${Vert b-Ax Vert}_2$ .
getResidualNorm() - Method in class com.imsl.math.NonNegativeLeastSquares: Returns the euclidean norm of the residual vector, $|Ax-b|^2$ .
getResiduals() - Method in class com.imsl.math.BoundedLeastSquares: Returns the residuals at the approximate solution.
getResiduals() - Method in class com.imsl.stat.ARAutoUnivariate: Returns the current values of the vector of residuals.
getResiduals() - Method in class com.imsl.stat.ARMAMaxLikelihood: Returns the current values of the vector of residuals.
getResiduals() - Method in class com.imsl.stat.AutoARIMA: Returns the residuals.
getResidualStandardError() - Method in class com.imsl.stat.ARMAOutlierIdentification: Returns the residual standard error of the outlier free series.
getResidualStandardError() - Method in class com.imsl.stat.AutoARIMA: Returns the residual standard error of the outlier free series.
getResidualUpdating() - Method in class com.imsl.math.GenMinRes: Returns the residual updating method to be used.
getResponseColumn() - Method in class com.imsl.stat.KaplanMeierEstimates: Returns the column index of x containing the response time for each observation.
getResponseColumn() - Method in class com.imsl.stat.ProportionalHazards: Returns the column index of x containing the response time for each observation.
getResponseColumnIndex() - Method in class com.imsl.datamining.PredictiveModel: Returns the column index in xy containing the response variable.
getResponseType() - Method in class com.imsl.datamining.decisionTree.Tree: Returns the PredictiveModel.VariableType of the response variable.
getResponseVariableAverage() - Method in class com.imsl.datamining.PredictiveModel: Returns the weighted average value of the response variable.
getResponseVariableMostFrequentClass() - Method in class com.imsl.datamining.PredictiveModel: Returns the most frequent value of the response variable.
getResponseVariableType() - Method in class com.imsl.datamining.PredictiveModel: Returns the variable type of the response variable.
getRightEndTangent() - Method in class com.imsl.math.CsTCB: Returns the value of the tangent at the rightmost endpoint.
getRightSons() - Method in class com.imsl.chart.Dendrogram: Convenience routine to get the "RightSons" attribute.
getRiskStandardErrors() - Method in class com.imsl.datamining.CrossValidation: Returns the estimated standard errors for the risk values.
getRiskValues() - Method in class com.imsl.datamining.CrossValidation: Returns the vector of risk values.
getRoot(int) - Method in class com.imsl.math.ZeroPolynomial: Returns a zero of the polynomial.
getRoots() - Method in class com.imsl.math.ZeroPolynomial: Returns the zeros of the polynomial.
getRow() - Method in class com.imsl.io.AbstractFlatFile: Retrieves the current row number.
getRow(int) - Method in class com.imsl.io.MPSReader: Returns a row of the constraint matrix or a free row.
getRow() - Method in class com.imsl.stat.Dissimilarities: Returns a boolean indicating whether distances are computed between rows or columns of x.
getRowCoefficients(int) - Method in class com.imsl.io.MPSReader: Returns the coefficents of a row.
getRowId(int) - Method in class com.imsl.io.FlatFile: Retrieves the value of the designated column in the current row of this ResultSet object as a java.sql.RowId object in the Java programming language.
getRowId(String) - Method in class com.imsl.io.FlatFile: Retrieves the value of the designated column in the current row of this ResultSet object as a java.sql.RowId object in the Java programming language.
getRSquared() - Method in class com.imsl.stat.ANOVA: Returns the R-squared (in percent).
getS() - Method in class com.imsl.math.SVD: Returns the singular values.
getSafePixel(Color) - Static method in class com.imsl.chart.WebSafeImageFilter: Returns the Web-safe color nearest the given color.
getSampleSize() - Method in class com.imsl.chart.qc.ShewhartControlChart: Returns the value of the attribute "SampleSize".
getSampleSizeProportion() - Method in class com.imsl.datamining.GradientBoosting: Returns the current setting of the sample size proportion.
getSampleStandardDeviation() - Method in class com.imsl.stat.Summary: Returns the sample standard deviation.
getSampleVariance() - Method in class com.imsl.stat.Summary: Returns the sample variance.
getSavageScores(double[]) - Method in class com.imsl.stat.Ranks: Gets the Savage scores (the expected value of exponential order statistics).
getScale() - Method in class com.imsl.math.ODE: Returns the scaling factor.
getScaleFont() - Method in class com.imsl.chart.Draw: Returns the factor by which fonts are to be scaled.
getScaleParameter() - Method in class com.imsl.stat.GammaDistribution: Returns the maximum-likelihood estimate found for the gamma scale parameter.
getScalingFactors() - Method in class com.imsl.math.NumericalDerivatives: Returns the scaling factors for the y values.
getScalingOption() - Method in class com.imsl.stat.Dissimilarities: Returns the scaling option.
getScreenAxis() - Method in class com.imsl.chart.ChartNode: Returns the value of the "ScreenAxis" attribute.
getScreenSize() - Method in class com.imsl.chart.ChartNode: Returns the value of the "ScreenSize" attribute.
getScreenViewport() - Method in class com.imsl.chart.ChartNode: Returns the value of the "Viewport" attribute scaled by the screen size.
getSeriesValues() - Method in class com.imsl.stat.TimeSeries: Returns the values associated with the time series.
getSeriesValues(int) - Method in class com.imsl.stat.TimeSeries
getShapeParameter() - Method in class com.imsl.stat.GammaDistribution: Returns the maximum-likelihood estimate found for the gamma shape parameter.
getShapiroWilkW() - Method in class com.imsl.stat.NormalityTest: Returns the Shapiro-Wilk W statistic for the Shapiro-Wilk W test.
getShort(int) - Method in class com.imsl.io.AbstractFlatFile: Gets the value of the designated column in the current row of this ResultSet object as a short in the Java programming language.
getShort(String) - Method in class com.imsl.io.AbstractFlatFile: Gets the value of the designated column in the current row of this ResultSet object as a short in the Java programming language.
getShrinkageParameter() - Method in class com.imsl.datamining.GradientBoosting: Returns the current shrinkage parameter.
getSigma() - Method in class com.imsl.chart.qc.CuSumStatus: Returns the standard deviation of the data.
getSigma() - Method in class com.imsl.stat.GARCH: Returns the estimated value of sigma squared.
getSInitial() - Method in class com.imsl.stat.ARSeasonalFit: Returns the the candidate values for s to evaluate.
getSize() - Method in class com.imsl.chart.ChartNode: Returns the value of the "Size" attribute.
getSize(Text) - Method in class com.imsl.chart.Draw: Returns the size of the bounding box for a text object.
getSize() - Method in class com.imsl.chart.JspBean: Returns the size of the generated image.
getSkewness() - Method in class com.imsl.stat.Summary: Returns the skewness.
getSkip() - Method in class com.imsl.stat.FaureSequence: Returns the number of points skipped at the beginning of the sequence.
getSkipWeekends() - Method in class com.imsl.chart.ChartNode: Returns the value of the "SkipWeekends" attribute.
getSlackValue() - Method in class com.imsl.chart.qc.CuSumStatus: Returns the slack value.
getSmallestCPRatio() - Method in class com.imsl.math.SparseLP: Returns the ratio of the smallest complementarity product to the average.
getSmallestDiagonalElement() - Method in class com.imsl.math.ComplexSparseCholesky: Returns the smallest diagonal element of the Cholesky factor.
getSmallestDiagonalElement() - Method in class com.imsl.math.SparseCholesky: Returns the smallest diagonal element of the Cholesky factor.
getSmoothedSeries() - Method in class com.imsl.stat.HoltWintersExponentialSmoothing: Returns the fitted series values.
getSolution() - Method in class com.imsl.math.BoundedLeastSquares: Returns the solution.
getSolution() - Method in class com.imsl.math.BoundedVariableLeastSquares: Returns the solution to the problem.
getSolution() - Method in class com.imsl.math.MinConGenLin: Returns the computed solution.
getSolution() - Method in class com.imsl.math.MinConNLP: Returns the last computed solution.
getSolution() - Method in class com.imsl.math.NonNegativeLeastSquares: Returns the solution to the problem, x.
getSolution() - Method in class com.imsl.math.QuadraticProgramming: Returns the solution.
getSolution() - Method in class com.imsl.math.SparseLP: Returns the solution x of the linear programming problem.
getSolveMethod() - Method in class com.imsl.math.OdeAdamsGear: Returns the method for solving the formula equations.
getSpline() - Method in class com.imsl.math.BSpline: Returns a Spline representation of the B-spline.
getSplineCoefficients() - Method in class com.imsl.math.FeynmanKac: Returns the coefficients of the Hermite quintic splines that represent an approximate solution of the Feynman-Kac PDE.
getSplineCoefficientsPrime() - Method in class com.imsl.math.FeynmanKac: Returns the first derivatives of the Hermite quintic spline coefficients that represent an approximate solution of the Feynman-Kac PDE.
getSplineValue(double[], double[], int) - Method in class com.imsl.math.FeynmanKac: Evaluates for time value 0 or a time value in tGrid the derivative of the Hermite quintic spline interpolant at evaluation points within the range of xGrid.
getSplitMergedCategoriesSigLevel() - Method in class com.imsl.datamining.decisionTree.CHAID: Returns the significance level for splitting previously merged categories.
getSplitVariableSelectionCriterion() - Method in class com.imsl.datamining.decisionTree.QUEST: Returns the significance level for split variable selection.
getSplitVariableSignificanceLevel() - Method in class com.imsl.datamining.decisionTree.CHAID: Returns the significance level for split variable selection.
getSpread() - Method in class com.imsl.datamining.neural.ScaleFilter: Retrieves the measure of spread to be used during scaling.
getSQLXML(int) - Method in class com.imsl.io.FlatFile: Retrieves the value of the designated column in the current row of this ResultSet as a java.sql.SQLXML object in the Java programming language.
getSQLXML(String) - Method in class com.imsl.io.FlatFile: Retrieves the value of the designated column in the current row of this ResultSet as a java.sql.SQLXML object in the Java programming language.
getSSE() - Method in class com.imsl.stat.NonlinearRegression: Returns the sums of squares for error.
getSSResidual() - Method in class com.imsl.stat.ARMA: Returns the sum of squares of the random shock.
getStage1Trainer() - Method in class com.imsl.datamining.neural.EpochTrainer: Returns the stage 1 trainer.
getStage2Trainer() - Method in class com.imsl.datamining.neural.EpochTrainer: Returns the stage 1 trainer.
getStandardDeviation() - Method in class com.imsl.stat.LogNormalDistribution: Returns the lognormal probability distribution standard deviation parameter.
getStandardDeviation() - Method in class com.imsl.stat.NormalDistribution: Returns the population standard deviation.
getStandardDeviation() - Method in class com.imsl.stat.Summary: Returns the population standard deviation.
getStandardDeviations(double[][], int[]) - Method in class com.imsl.datamining.NaiveBayesClassifier: Returns a table of standard deviations for each continuous attribute in continuousData segmented by the target classes in classificationData.
getStandardError(int) - Method in class com.imsl.stat.LinearRegression.CoefficientTTests: Returns the estimated standard error for a coefficient estimate.
getStandardError(int) - Method in class com.imsl.stat.StepwiseRegression.CoefficientTTests: Returns the estimated standard error for a coefficient estimate.
getStandardErrors(int) - Method in class com.imsl.stat.AutoCorrelation: Returns the standard errors of the autocorrelations of the time series x.
getStandardErrors(int) - Method in class com.imsl.stat.CrossCorrelation: Returns the standard errors of the cross-correlations between the time series x and y.
getStandardErrors() - Method in class com.imsl.stat.distributions.MaximumLikelihoodEstimation: Returns the approximate standard errors of the maximum likelihood estimates.
getStandardErrors() - Method in class com.imsl.stat.FactorAnalysis: Returns the estimated asymptotic standard errors of the eigenvalues.
getStandardErrors() - Method in class com.imsl.stat.KaplanMeierEstimates: Returns Greenwood's estimated standard errors.
getStandardizedResidual() - Method in class com.imsl.stat.LinearRegression.CaseStatistics: Returns the Standardized Residual for an observation.
getStartDate() - Method in class com.imsl.stat.TimeSeries: Returns the starting date of the time series.
getStatement() - Method in class com.imsl.io.AbstractFlatFile: Returns the Statement object that produced this ResultSet object.
getStateVector() - Method in class com.imsl.stat.KalmanFilter: Returns the estimated state vector at time k + 1 given the observations through time k.
getStatistics() - Method in class com.imsl.chart.BoxPlot: Returns an array of BoxPlot.Statistics objects, one for each set of observations.
getStatistics(int) - Method in class com.imsl.chart.BoxPlot: Returns a BoxPlot.Statistics for a set of observations.
getStatistics() - Method in class com.imsl.stat.ContingencyTable: Returns the statistics associated with this table.
getStatistics() - Method in class com.imsl.stat.DiscriminantAnalysis: Returns statistics.
getStatistics() - Method in class com.imsl.stat.FactorAnalysis: Returns statistics.
getStatistics() - Method in class com.imsl.stat.SelectionRegression: Returns a new Statistics object.
getStatistics() - Method in class com.imsl.stat.WilcoxonRankSum: Returns the statistics.
getStatus() - Method in class com.imsl.math.NumericalDerivatives: Returns status information.
getStatus(int) - Method in class com.imsl.math.ZeroPolynomial: Returns the error status of a root.
getStdDev() - Method in class com.imsl.stat.NormOneSample: Returns the standard deviation of the sample.
getStdDevX() - Method in class com.imsl.stat.NormTwoSample: Returns the standard deviation of the first sample.
getStdDevY() - Method in class com.imsl.stat.NormTwoSample: Returns the standard deviation of the second sample.
getStepControlMethod() - Method in class com.imsl.math.FeynmanKac: Returns the step control method used in the integration of the Feynman-Kac PDE.
getStratumColumn() - Method in class com.imsl.stat.KaplanMeierEstimates: Returns the column index of x containing the stratum number for each observation.
getStratumColumn() - Method in class com.imsl.stat.ProportionalHazards: Returns the column index of x containing the stratum number for each observation.
getStratumNumbers() - Method in class com.imsl.stat.ProportionalHazards: Returns the stratum number used for each observation.
getStratumRatio() - Method in class com.imsl.stat.ProportionalHazards: Returns the ratio at which a stratum is split into two strata.
getString() - Method in class com.imsl.chart.Text: Gets the string for this Text object.
getString(int) - Method in class com.imsl.io.AbstractFlatFile: Gets the value of the designated column in the current row of this ResultSet object as a String in the Java programming language.
getString(String) - Method in class com.imsl.io.AbstractFlatFile: Gets the value of the designated column in the current row of this ResultSet object as a String in the Java programming language.
getStringAttribute(String) - Method in class com.imsl.chart.AbstractChartNode: Convenience routine to get a String-valued attribute.
getSumOfSquares() - Method in class com.imsl.stat.HoltWintersExponentialSmoothing: Returns the sum of squares of the one step ahead forecast errors.
getSumOfSquares() - Method in class com.imsl.stat.KalmanFilter: Returns the generalized sum of squares.
getSumOfSquaresForError() - Method in class com.imsl.stat.ANOVA: Returns the sum of squares for error.
getSumOfSquaresForModel() - Method in class com.imsl.stat.ANOVA: Returns the sum of squares for model.
getSumOfWeights() - Method in class com.imsl.stat.Covariances: Returns the sum of the weights of all observations.
getSupport() - Method in class com.imsl.datamining.AssociationRule: Support for the Z, X, and Y components of the association rule.
getSupport(int) - Method in class com.imsl.datamining.Itemsets: Returns the support determined for a particular itemset.
getSurfaceType() - Method in class com.imsl.chart3d.Surface: Returns the attribute "SurfaceType".
getSurrogateInfo() - Method in class com.imsl.datamining.decisionTree.ALACART: Returns the surrogate split information.
getSurrogateInfo() - Method in interface com.imsl.datamining.decisionTree.DecisionTreeSurrogateMethod: Returns the surrogate split information.
getSurrogateInfo(int) - Method in class com.imsl.datamining.decisionTree.TreeNode: Returns a value from the surrogate split information array.
getSurrogateInfo() - Method in class com.imsl.datamining.decisionTree.TreeNode: Returns the surrogate split information array.
getSurvivalProbabilities() - Method in class com.imsl.stat.KaplanMeierEstimates: Returns the estimated survival probabilities.
getSwept() - Method in class com.imsl.stat.StepwiseRegression: Returns an array containing information indicating whether or not a particular variable is in the model.
getSymbolicFactor() - Method in class com.imsl.math.ComplexSparseCholesky: Returns the symbolic Cholesky factor.
getSymbolicFactor() - Method in class com.imsl.math.SparseCholesky: Returns the symbolic Cholesky factor.
getSymmetricMode() - Method in class com.imsl.math.ComplexSuperLU: Returns the symmetric mode flag.
getSymmetricMode() - Method in class com.imsl.math.SuperLU: Returns the symmetric mode flag.
getTable() - Method in class com.imsl.stat.TableMultiWay.BalancedTable: Returns an array containing the frequencies for each variable.
getTable() - Method in class com.imsl.stat.TableMultiWay.UnbalancedTable: Returns the frequency for each cell.
getTauStatistics() - Method in class com.imsl.stat.ARMAOutlierIdentification: Returns the t value for each detected outlier.
getTerminalNodeIndicators() - Method in class com.imsl.datamining.decisionTree.Tree: Returns the terminal node indicator array.
getTerminationCriterion() - Method in class com.imsl.math.MinConNLP: Returns the reason the solve step terminated.
getTerminationStatus() - Method in class com.imsl.math.SparseLP: Returns the termination status for the problem.
getTestClassFittedValues() - Method in class com.imsl.datamining.GradientBoosting: Returns the fitted values ${f(x_i)}$ for a categorical response variable with two or more levels on the test data.
getTestClassProbabilities() - Method in class com.imsl.datamining.GradientBoosting: Returns the predicted probabilities on the test data for a categorical response variable.
getTestEffects() - Method in class com.imsl.stat.ANOVAFactorial: Returns statistics relating to the sums of squares for the effects in the model.
getTestFittedValues() - Method in class com.imsl.datamining.GradientBoosting: Returns the fitted values ${f(x_i)}$ for a continuous response variable after gradient boosting on the test data.
getTestLossValue() - Method in class com.imsl.datamining.GradientBoosting: Returns the loss function value on the test data.
getTestStatistic() - Method in class com.imsl.stat.KolmogorovOneSample: Returns $D = max(D^{+}, D^{-})$ .
getTestStatistic() - Method in class com.imsl.stat.KolmogorovTwoSample: Returns $D = max(D^{+}, D^{-})$ .
getText() - Method in class com.imsl.chart.Annotation: Gets the Text for this Annotation object.
getTextAngle() - Method in class com.imsl.chart.ChartNode: Returns the value of the "TextAngle" attribute.
getTextColor() - Method in class com.imsl.chart.AbstractChartNode: Returns the value of the "TextColor" attribute.
getTextColor() - Method in class com.imsl.chart.ChartNode: Returns the value of the "TextColor" attribute.
getTextFormat() - Method in class com.imsl.chart.AbstractChartNode: Returns the value of the "TextFormat" attribute.
getTheta() - Method in class com.imsl.stat.PoissonDistribution: Returns the mean number of successes in a given time period of the Poisson probability distribution.
getTickInterval() - Method in class com.imsl.chart.Axis1D: Retrieves the tick interval.
getTickInterval() - Method in class com.imsl.chart.AxisR: Retrieves the tick interval.
getTickInterval() - Method in class com.imsl.chart3d.Axis3D: Retrieves the tick interval.
getTickLength() - Method in class com.imsl.chart.AbstractChartNode: Returns the value of the "TickLength" attribute.
getTicks() - Method in class com.imsl.chart.Axis1D: Returns the value of the "Ticks" attribute, if set.
getTicks() - Method in class com.imsl.chart.AxisR: Returns the value of the "Ticks" attribute, if set.
getTicks() - Method in class com.imsl.chart.AxisTheta: Returns the value of the "Ticks" attribute, if set.
getTicks() - Method in class com.imsl.chart3d.Axis3D: Returns the value of the "Ticks" attribute, if set.
getTicks() - Method in class com.imsl.chart3d.ColormapLegend: Returns the value of the "Ticks" attribute, if set.
getTiesOption() - Method in class com.imsl.stat.ProportionalHazards: Returns the method used for handling ties.
getTime(int) - Method in class com.imsl.io.AbstractFlatFile: Gets the value of the designated column in the current row of this ResultSet object as a java.sql.Time object in the Java programming language.
getTime(String) - Method in class com.imsl.io.AbstractFlatFile: Gets the value of the designated column in the current row of this ResultSet object as a java.sql.Time object in the Java programming language.
getTime(int, Calendar) - Method in class com.imsl.io.AbstractFlatFile: Returns the value of the designated column in the current row of this ResultSet object as a java.sql.Time object in the Java programming language.
getTime(String, Calendar) - Method in class com.imsl.io.AbstractFlatFile: Returns the value of the designated column in the current row of this ResultSet object as a java.sql.Time object in the Java programming language.
getTimeBarrier() - Method in class com.imsl.math.FeynmanKac: Returns the barrier set for integration in the time direction.
getTimes() - Method in class com.imsl.stat.KaplanMeierECDF: Retrieves the time values where the step function CDF jumps to a greater value.
getTimeSeries() - Method in class com.imsl.stat.ARAutoUnivariate: Returns the time series used for estimating the minimum AIC and the autoregressive coefficients.
getTimeSeries() - Method in class com.imsl.stat.ARMAMaxLikelihood: Returns the time series used to construct ARMAMaxLikelihood.
getTimeSeries() - Method in class com.imsl.stat.ARSeasonalFit: Returns the time series.
getTimestamp(int) - Method in class com.imsl.io.AbstractFlatFile: Gets the value of the designated column in the current row of this ResultSet object as a java.sql.Timestamp object in the Java programming language.
getTimestamp(String) - Method in class com.imsl.io.AbstractFlatFile: Gets the value of the designated column in the current row of this ResultSet object as a java.sql.Timestamp object.
getTimestamp(int, Calendar) - Method in class com.imsl.io.AbstractFlatFile: Returns the value of the designated column in the current row of this ResultSet object as a java.sql.Timestamp object in the Java programming language.
getTimestamp(String, Calendar) - Method in class com.imsl.io.AbstractFlatFile: Returns the value of the designated column in the current row of this ResultSet object as a java.sql.Timestamp object in the Java programming language.
getTimeZone() - Method in class com.imsl.stat.TimeSeries: Returns the time zone of the time series.
getTimeZoneOffset() - Method in class com.imsl.stat.TimeSeries: Returns the number of hours (+ or -) from GMT of the time zone associated with the time series.
getTimsacAR() - Method in class com.imsl.stat.ARAutoUnivariate: Returns the final auto regressive parameter estimates at the optimum AIC estimated by the original TIMSAC routine (UNIMAR).
getTimsacConstant() - Method in class com.imsl.stat.ARAutoUnivariate: Returns the estimate for the constant parameter in the ARMA series.
getTimsacVariance() - Method in class com.imsl.stat.ARAutoUnivariate: Returns the final estimate for the innovation variance calculated by the TIMSAC automatic AR modeling routine (UNIMAR).
getTitle() - Method in class com.imsl.chart.ChartNode: Returns the value of the "Title" attribute.
getTitle() - Method in class com.imsl.chart3d.ChartNode3D: Returns the value of the "Title" attribute.
getTo() - Method in class com.imsl.datamining.neural.Link: Returns the destination Node for this Link.
getTolerance() - Method in class com.imsl.chart.DrawMap: Get the minimum distance that an event can be from a point or a line and still be considered a hit.
getTolerance() - Method in class com.imsl.chart.DrawPick: Get the minimum distance that an event can be from a point or a line and still be considered a hit.
getTolerance() - Method in class com.imsl.math.MinConNLP: Returns the desired precision of the solution.
getTolerance() - Method in class com.imsl.math.ODE: Returns the error tolerance.
getTolerance() - Method in class com.imsl.stat.ARMAMaxLikelihood: Returns the tolerance for the convergence algorithm.
getToolTip() - Method in class com.imsl.chart.ChartNode: Returns the value of the "ToolTip" attribute.
getTotalDegreesOfFreedom() - Method in class com.imsl.stat.ANOVA: Returns the total degrees of freedom.
getTotalMissing() - Method in class com.imsl.stat.ANOVA: Returns the total number of missing values.
getTotalMissing() - Method in class com.imsl.stat.EmpiricalQuantiles: Returns the total number of missing values.
getTotalNumberOfFailures(double) - Method in class com.imsl.stat.KaplanMeierEstimates: Returns the total number failing in the group for the specified group value.
getTotalSumOfSquares() - Method in class com.imsl.stat.ANOVA: Returns the total sum of squares.
getTotalWeight() - Method in class com.imsl.datamining.PredictiveModel: Returns the sum of the active case weights.
getTrainingErrors() - Method in class com.imsl.datamining.NaiveBayesClassifier: Returns a table of classification errors of non-missing classifications for each target classification plus the overall total of classification errors.
getTrainingIterations() - Method in class com.imsl.datamining.neural.QuasiNewtonTrainer: Returns the number of iterations used during training.
getTransform() - Method in class com.imsl.chart.AbstractChartNode: Returns the value of the "Transform" attribute.
getTransform() - Method in class com.imsl.datamining.neural.UnsupervisedOrdinalFilter: Retrieves the transform flag used for encoding and decoding.
getTransformedTimeSeries() - Method in class com.imsl.stat.ARSeasonalFit: Returns the transformed series, .
getTransformType() - Method in class com.imsl.stat.ClusterHierarchical: Returns the type of transformation.
getTreemapLegend() - Method in class com.imsl.chart.Treemap: Returns the treemap legend.
getTStatistic(int) - Method in class com.imsl.stat.LinearRegression.CoefficientTTests: Returns the t-statistic for the test that the i-th coefficient is zero.
getTStatistic(int) - Method in class com.imsl.stat.StepwiseRegression.CoefficientTTests: Returns the student-t test statistic for testing the i-th coefficient equal to zero ( ${beta}_{index} = 0$ ).
getTTest() - Method in class com.imsl.stat.NormOneSample: Returns the test statistic associated with the t test.
getTTest() - Method in class com.imsl.stat.NormTwoSample: Returns the test statistic for the Satterthwaite's approximation.
getTTestDF() - Method in class com.imsl.stat.NormOneSample: Returns the degrees of freedom associated with the t test for the mean.
getTTestDF() - Method in class com.imsl.stat.NormTwoSample: Returns the degrees of freedom for the Satterthwaite's approximation for t-test for either equal or unequal variances, depending on the value set by setUnequalVariances.
getTTestP() - Method in class com.imsl.stat.NormOneSample: Returns the probability associated with the t test of a larger t in absolute value.
getTTestP() - Method in class com.imsl.stat.NormTwoSample: Returns the approximate probability of a larger t for the Satterthwaite's approximation for equal or unequal variances.
getTukeyScores(double[]) - Method in class com.imsl.stat.Ranks: Gets the Tukey version of normal scores for each observation.
getTwoSidedPValue() - Method in class com.imsl.stat.KolmogorovOneSample: Probability of the statistic exceeding D under the null hypothesis of equality and against the two-sided alternative.
getTwoSidedPValue() - Method in class com.imsl.stat.KolmogorovTwoSample: Probability of the statistic exceeding D under the null hypothesis of equality and against the two-sided alternative.
getType() - Method in class com.imsl.chart.Axis1D: Returns the axis type.
getType() - Method in class com.imsl.chart.Grid: Returns the axis type.
getType() - Method in class com.imsl.chart3d.Axis3D: Returns the axis type.
getType() - Method in class com.imsl.io.AbstractFlatFile: Returns the type of this ResultSet object.
getTypeVariable(int) - Method in class com.imsl.io.MPSReader: Returns the type of a variable.
getU() - Method in class com.imsl.math.ComplexLU: Returns the unit upper triangular portion of the LU factorization of A.
getU() - Method in class com.imsl.math.LU: Returns the unit upper triangular portion of the LU factorization of A.
getU() - Method in class com.imsl.math.SVD: Returns the left singular vectors.
getUnbalancedTable() - Method in class com.imsl.stat.TableMultiWay: Returns an object containing the unbalanced table.
getUnion(Itemsets, Itemsets) - Static method in class com.imsl.datamining.Apriori: Return the union of two sets of itemsets.
getUp() - Method in class com.imsl.chart.Candlestick: Returns the CandlestickItem for up days.
getUpperAdjacentValue() - Method in class com.imsl.chart.BoxPlot.Statistics: Returns the lower adjacent value.
getUpperBound(int) - Method in class com.imsl.io.MPSReader: Returns the upper bound for a variable.
getUpperBound() - Method in class com.imsl.math.SparseLP: Returns the upper bound on the variables.
getUpperCICommonVariance() - Method in class com.imsl.stat.NormTwoSample: Returns the upper confidence limits for the common, or pooled, variance.
getUpperCIDiff() - Method in class com.imsl.stat.NormTwoSample: Returns the upper confidence limit for the mean of the first population minus the mean of the second for equal or unequal variances depending on the value set by setUnequalVariances.
getUpperCIMean() - Method in class com.imsl.stat.NormOneSample: Returns the upper confidence limit for the mean.
getUpperCIRatioVariance() - Method in class com.imsl.stat.NormTwoSample: Returns the approximate upper confidence limit for the ratio of the variance of the first population to the second.
getUpperCIVariance() - Method in class com.imsl.stat.NormOneSample: Returns the upper confidence limits for the variance.
getUpperControlLimit() - Method in class com.imsl.chart.qc.ShewhartControlChart: Returns the upper control limit.
getUpperLimit() - Method in class com.imsl.math.SparseLP: Returns the upper limit of the constraints that have both a lower and an upper bound.
getUpperQuartile() - Method in class com.imsl.chart.BoxPlot.Statistics: Returns the upper quartile value.
getUpperRange(int) - Method in class com.imsl.io.MPSReader: Returns the upper range value for a constraint equation.
getURL(String) - Method in class com.imsl.io.AbstractFlatFile: Retrieves the value of the designated column in the current row of this ResultSet object as a java.net.URL object.
getURL(int) - Method in class com.imsl.io.AbstractFlatFile: Retrieves the value of the designated column in the current row of this ResultSet object as a java.net.URL object.
getUseBackPropagation() - Method in class com.imsl.datamining.neural.QuasiNewtonTrainer: Returns the use back propagation setting.
getV() - Method in class com.imsl.math.SVD: Returns the right singular vectors.
getValue() - Method in class com.imsl.chart.qc.ControlLimit: Returns the value of this control limit line.
getValue() - Method in class com.imsl.datamining.neural.InputNode: Returns the value of this node.
getValue() - Method in class com.imsl.datamining.neural.OutputPerceptron: Returns the value of the OutputPerceptron determined using the current network state and inputs.
getValue() - Method in class com.imsl.io.MPSReader.Element: Returns the value of the element.
getValues() - Method in class com.imsl.math.Eigen: Returns the eigenvalues of a matrix of type double.
getValues() - Method in class com.imsl.math.SymEigen: Returns the eigenvalues
getValues() - Method in class com.imsl.stat.FactorAnalysis: Returns the eigenvalues.
getValues() - Method in class com.imsl.stat.TableMultiWay.BalancedTable: Returns the values of the classification variables.
getVanDerWaerdenScores(double[]) - Method in class com.imsl.stat.Ranks: Gets the Van der Waerden version of normal scores for each observation.
getVarCov() - Method in class com.imsl.stat.distributions.MaximumLikelihoodEstimation: Returns the approximate variance-covariance matrix of the maximum likelihood estimates.
getVarCovAdjustedMeans() - Method in class com.imsl.stat.ANCOVA: Returns a matrix containing the estimated variances and covariances for the adjusted means assuming parallelism.
getVarCovarMatrix() - Method in class com.imsl.stat.GARCH: Returns the variance-covariance matrix.
getVarCovarMatrix() - Method in class com.imsl.stat.HoltWintersExponentialSmoothing: Returns the variance-covariance matrix of the smoothing parameters estimated by minimizing the mean squared forecast error.
getVarCovCoefficients() - Method in class com.imsl.stat.ANCOVA: Returns a matrix containing the estimated variances and covariances for the coefficients returned using getModelCoefficients.
getVariableType() - Method in class com.imsl.datamining.PredictiveModel: Returns an array containing the variable types in xy.
getVariance() - Method in class com.imsl.stat.ARMA: Returns the variance of the time series z.
getVariance() - Method in class com.imsl.stat.AutoCorrelation: Returns the variance of the time series x.
getVariance() - Method in class com.imsl.stat.Summary: Returns the population variance.
getVarianceCovarianceMatrix() - Method in class com.imsl.stat.ProportionalHazards: Returns the estimated asymptotic variance-covariance matrix of the parameters.
getVariances() - Method in class com.imsl.stat.FactorAnalysis: Gets the unique variances.
getVarianceX() - Method in class com.imsl.stat.CrossCorrelation: Returns the variance of time series x.
getVarianceX() - Method in class com.imsl.stat.MultiCrossCorrelation: Returns the variances of the channels of x.
getVarianceY() - Method in class com.imsl.stat.CrossCorrelation: Returns the variance of time series y.
getVarianceY() - Method in class com.imsl.stat.MultiCrossCorrelation: Returns the variances of the channels of y.
getVectorProducts() - Method in class com.imsl.math.GenMinRes: Returns the user-supplied functions for the inner product and, optionally, the norm used in the Gram-Schmidt implementations.
getVectors() - Method in class com.imsl.math.Eigen: Returns the eigenvectors.
getVectors() - Method in class com.imsl.math.SymEigen: Return the eigenvectors of a symmetric matrix of type double.
getVectors() - Method in class com.imsl.stat.FactorAnalysis: Returns the eigenvectors.
getViewPlatformTransformation(Transform3D) - Method in class com.imsl.chart3d.Chart3D: Sets the transformation for the view platform.
getViewport() - Method in class com.imsl.chart.ChartNode: Returns the value of the "Viewport" attribute.
getViewport() - Method in class com.imsl.chart3d.ChartNode3D: Returns the value of the "Viewport" attribute.
getViolation() - Method in class com.imsl.math.SparseLP: Returns the violation of the variable bounds.
getVirtualUniverse() - Method in class com.imsl.chart3d.ChartNode3D: Returns the value of the "Universe" attribute.
getVisibleFaces() - Method in class com.imsl.chart3d.AxisBox: Returns the flag indicating which faces of the box are to be drawn.
getWarning() - Static method in class com.imsl.Warning: Gets the WarningObject.
getWarnings() - Method in class com.imsl.io.AbstractFlatFile: Returns the first warning reported by calls on this ResultSet object.
getWbar() - Method in class com.imsl.chart.qc.XbarS: Returns the value of the "Wbar" attribute, the within sample variation for a series of samples.
getWeight() - Method in class com.imsl.datamining.neural.Link: Returns the weight for this Link.
getWeights(int, int) - Method in class com.imsl.datamining.KohonenSOM: Returns the weights of the node at (i, j) in the node grid.
getWeights() - Method in class com.imsl.datamining.KohonenSOM: Returns the weights of the nodes.
getWeights() - Method in class com.imsl.datamining.neural.FeedForwardNetwork: Returns the weights for the Links in this network.
getWeights() - Method in class com.imsl.datamining.neural.Network: Returns the weights.
getWeights() - Method in class com.imsl.datamining.PredictiveModel: Returns an array containing the case weights.
getWhiskers() - Method in class com.imsl.chart.BoxPlot: Returns the Whiskers node.
getWindow() - Method in class com.imsl.chart.Axis1D: Returns the window for an Axis1D.
getWindow() - Method in class com.imsl.chart.AxisR: Returns the Window attribute.
getWindow() - Method in class com.imsl.chart.AxisTheta: Returns the window for an AxisTheta.
getWindow() - Method in class com.imsl.chart3d.Axis3D: Returns the window for an Axis1D.
getWindow() - Method in class com.imsl.chart3d.ColormapLegend: Returns the window for a ColormapLegend.
getX() - Method in class com.imsl.chart.AbstractChartNode: Returns the value of the "X" attribute.
getX() - Method in class com.imsl.datamining.AssociationRule: The X components of the association rule.
getX() - Method in class com.imsl.stat.GARCH: Returns the estimated parameter array, x.
getXHi() - Method in class com.imsl.stat.EmpiricalQuantiles: Returns the smallest element of x greater than or equal to the desired quantile.
getXKnots() - Method in class com.imsl.math.Spline2D: Returns the knot sequences in the x-direction.
getXLo() - Method in class com.imsl.stat.EmpiricalQuantiles: Returns the largest element of x less than or equal to the desired quantile.
getXOrder() - Method in class com.imsl.math.Spline2DLeastSquares: Returns the order of the spline in the x-direction.
getXWeights() - Method in class com.imsl.math.Spline2DLeastSquares: Returns the weights for the least-squares fit in the x-direction.
getXY() - Method in class com.imsl.datamining.PredictiveModel: Returns a copy of the xy data.
getY() - Method in class com.imsl.chart.AbstractChartNode: Returns the value of the "Y" attribute.
getY() - Method in class com.imsl.datamining.AssociationRule: The Y components of the association rule.
getYearBasis() - Method in class com.imsl.finance.DayCountBasis: Returns the (days in year) portion of the Day Count Basis.
getYKnots() - Method in class com.imsl.math.Spline2D: Returns the knot sequences in the y-direction.
getYOrder() - Method in class com.imsl.math.Spline2DLeastSquares: Returns the order of the spline in the y-direction.
getYProb(int) - Method in class com.imsl.datamining.decisionTree.TreeNode: Returns a class probability at the current node, if the response variable is of categorical type.
getYProbs() - Method in class com.imsl.datamining.decisionTree.TreeNode: Returns class probabilities at the current node, if the response variable is of categorical type.
getYWeights() - Method in class com.imsl.math.Spline2DLeastSquares: Returns the weights for the least-squares fit in the y-direction.
getZ() - Method in class com.imsl.chart3d.ChartNode3D: Returns the value of the "Z" attribute.
getZ() - Method in class com.imsl.stat.KolmogorovOneSample: Returns the normalized D statistic without the continuity correction applied.
getZ() - Method in class com.imsl.stat.KolmogorovTwoSample: Returns the normalized D statistic without the continuity correction applied.
GINI_INDEX - Static variable in class com.imsl.datamining.decisionTree.DecisionTreeInfoGain.GainCriteria: A measure of statistical dispersion.
gradient(double[], double[]) - Method in class com.imsl.datamining.neural.QuasiNewtonTrainer.BlockGradObjective
gradient(double[], double[]) - Method in class com.imsl.datamining.neural.QuasiNewtonTrainer.GradObjective
gradient(double[], double[]) - Method in interface com.imsl.math.MinConGenLin.Gradient: Public interface for the user-supplied function to compute the gradient at point x.
gradient(double[], int, double[]) - Method in interface com.imsl.math.MinConNLP.Gradient: Computes the value of the gradient of the function at the given point.
gradient(double[], double[]) - Method in interface com.imsl.math.MinUnconMultiVar.Gradient: Public interface for the gradient of the multivariate function to be minimized.
gradient(double[]) - Method in class com.imsl.math.RadialBasis: Returns the gradient of the radial basis approximation at a point.
GradientBoosting - Class in com.imsl.datamining: Performs stochastic gradient boosting for a single response variable and multiple predictor variables.
GradientBoosting(double[][], int, PredictiveModel.VariableType[]) - Constructor for class com.imsl.datamining.GradientBoosting: Constructs a GradientBoosting object for a single response variable and multiple predictor variables.
GradientBoosting(PredictiveModel) - Constructor for class com.imsl.datamining.GradientBoosting: Constructs a gradient boosting object.
GradientBoosting.LossFunctionType - Class in com.imsl.datamining: The loss function type as specified by the error measure.
graphics - Variable in class com.imsl.chart.Draw
GREEN - Static variable in interface com.imsl.chart.Colormap: Linear green colormap.
GREEN_PINK - Static variable in interface com.imsl.chart.Colormap: Green/pink colormap.
GREEN_RED_BLUE_WHITE - Static variable in interface com.imsl.chart.Colormap: Green/red/blue/white colormap.
GREEN_WHITE_EXPONENTIAL - Static variable in interface com.imsl.chart.Colormap: Exponential green/white colormap.
GREEN_WHITE_LINEAR - Static variable in interface com.imsl.chart.Colormap: Linear green/white colormap.
Grid - Class in com.imsl.chart: Draws the grid lines perpendicular to an axis.
GRID_HEXAGONAL - Static variable in class com.imsl.datamining.KohonenSOM: Indicates a hexagonal grid.
GRID_RECTANGULAR - Static variable in class com.imsl.datamining.KohonenSOM: Indicates a rectangular grid.
GridPolar - Class in com.imsl.chart: Draws the grid lines for a polar plot.

H

hashCode() - Method in class com.imsl.math.Complex: Returns a hashcode for this Complex.
hasMoreTokens() - Method in class com.imsl.io.Tokenizer: Returns true if a call to nextToken will not generate an exception.
haveErrorBarProperties - Variable in class com.imsl.chart.Draw
haveFillProperties - Variable in class com.imsl.chart.Draw
haveImageProperties - Variable in class com.imsl.chart.Draw
haveLineProperties - Variable in class com.imsl.chart.Draw
haveMarkerProperties - Variable in class com.imsl.chart.Draw
haveTextProperties - Variable in class com.imsl.chart.Draw
Heatmap - Class in com.imsl.chart: Heatmap creates a chart from a two-dimensional array of double precision values or Color values.
Heatmap(AxisXY, double, double, double, double, Color[][]) - Constructor for class com.imsl.chart.Heatmap: Creates a Heatmap from an array of Color values.
Heatmap(AxisXY, double, double, double, double, double, double, double[][], Colormap) - Constructor for class com.imsl.chart.Heatmap: Creates a Heatmap from an array of double values and a Colormap.
Heatmap.Legend - Class in com.imsl.chart: A legend for use with a heatmap.
hessian(double[], double[][]) - Method in interface com.imsl.math.MinUnconMultiVar.Hessian: Public interface for the Hessian of the multivariate function to be minimized.
HiddenLayer - Class in com.imsl.datamining.neural: Hidden layer in a neural network.
HighLowClose - Class in com.imsl.chart: High-low-close plot of stock data.
HighLowClose(AxisXY, Date, double[], double[], double[]) - Constructor for class com.imsl.chart.HighLowClose: Constructs a high-low-close chart node beginning with specified start date.
HighLowClose(AxisXY, Date, double[], double[], double[], double[]) - Constructor for class com.imsl.chart.HighLowClose: Constructs a high-low-close-open chart node beginning with specified start date.
HighLowClose(AxisXY, double[], double[], double[], double[]) - Constructor for class com.imsl.chart.HighLowClose: Constructs a high-low-close chart node at specified axis points.
HighLowClose(AxisXY, double[], double[], double[], double[], double[]) - Constructor for class com.imsl.chart.HighLowClose: Constructs a high-low-close-open chart node at specified axis points.
HoltWintersExponentialSmoothing - Class in com.imsl.stat: Calculates parameters and forecasts using the Holt-Winters Multiplicative or Additive forecasting method for seasonal data.
HoltWintersExponentialSmoothing(int, double[]) - Constructor for class com.imsl.stat.HoltWintersExponentialSmoothing: Constructor for HoltWintersExponentialSmoothing.
horizontalStripe(int, int, Color, Color) - Static method in class com.imsl.chart.FillPaint: Returns a horizontally striped pattern.
HUBER_M - Static variable in class com.imsl.datamining.GradientBoosting.LossFunctionType: The loss criteria is the Huber-M weighted squared error and absolute deviation error with parameter .
Hyperbolic - Class in com.imsl.math: Pure Java implementation of the hyperbolic functions and their inverses.
hypergeometric(int, int, int, int) - Static method in class com.imsl.stat.Cdf: Evaluates the hypergeometric cumulative probability distribution function.
hypergeometric(int, int, int, int) - Static method in class com.imsl.stat.Pdf: Evaluates the hypergeometric probability density function.
HyperRectangleQuadrature - Class in com.imsl.math: HyperRectangleQuadrature integrates a function over a hypercube.
HyperRectangleQuadrature(int) - Constructor for class com.imsl.math.HyperRectangleQuadrature: Constructs a HyperRectangleQuadrature object.
HyperRectangleQuadrature(RandomSequence) - Constructor for class com.imsl.math.HyperRectangleQuadrature: Constructs a HyperRectangleQuadrature object.
HyperRectangleQuadrature.Function - Interface in com.imsl.math: Public interface function for the HyperRectangleQuadrature class.

I

I(double, int) - Static method in class com.imsl.math.Bessel: Evaluates a sequence of modified Bessel functions of the first kind with integer order and real argument.
I(double, double, int) - Static method in class com.imsl.math.Bessel: Evaluates a sequence of modified Bessel functions of the first kind with real order and real argument.
i - Static variable in class com.imsl.math.Complex: The imaginary unit.
IEEE - Class in com.imsl.math: Pure Java implementation of the IEEE 754 functions as specified in IEEE Standard for Binary Floating-Point Arithmetic, ANSI/IEEE Standard 754-1985 (IEEE, New York).
IEEEremainder(double, double) - Static method in class com.imsl.math.JMath: Returns the IEEE remainder from x divided by p.
IGNORE - Static variable in class com.imsl.datamining.PredictiveModel.VariableType: The associated variable will be ignored.
ignoreMissingValues(boolean) - Method in class com.imsl.datamining.NaiveBayesClassifier: Specifies whether or not missing values will be ignored during the training process.
ilogb(double) - Static method in class com.imsl.math.IEEE: Return the binary exponent of non-zero x.
imag() - Method in class com.imsl.math.Complex: Returns the imaginary part of a Complex object.
imag(Complex) - Static method in class com.imsl.math.Complex: Returns the imaginary part of a Complex object.
IMAGE - Static variable in class com.imsl.chart.Draw
image(ImageIcon) - Static method in class com.imsl.chart.FillPaint: Returns a tiling of an image.
IMAGE_FACTOR_ANALYSIS - Static variable in class com.imsl.stat.FactorAnalysis: Indicates image factor analysis.
imageObserver - Variable in class com.imsl.chart.Draw
IMSLException - Exception in com.imsl: Signals that a mathematical exception has occurred.
IMSLException() - Constructor for exception com.imsl.IMSLException: Constructs an IMSLException with no detail message.
IMSLException(String) - Constructor for exception com.imsl.IMSLException: Constructs an IMSLException with the specified detail message.
IMSLException(String, String, Object[]) - Constructor for exception com.imsl.IMSLException: Constructs an IMSLException with the specified detail message.
IMSLFormatter - Class in com.imsl: Simple formatter for classes that implement logging.
IMSLFormatter() - Constructor for class com.imsl.IMSLFormatter
IMSLRuntimeException - Exception in com.imsl: Signals that an error has occurred.
IMSLRuntimeException() - Constructor for exception com.imsl.IMSLRuntimeException: Constructs an IMSLRuntimeException with no detail message.
IMSLRuntimeException(String) - Constructor for exception com.imsl.IMSLRuntimeException: Constructs an IMSLRuntimeException with the specified detail message.
IMSLRuntimeException(String, String, Object[]) - Constructor for exception com.imsl.IMSLRuntimeException: Constructs an IMSLRuntimeException with the specified detail message.
IMSLUnexpectedErrorException - Exception in com.imsl: Signals that an unexpected error has occurred.
IMSLUnexpectedErrorException() - Constructor for exception com.imsl.IMSLUnexpectedErrorException: Constructs an IMSLUnexpectedErrorException.
incrementEpochCount() - Method in class com.imsl.datamining.neural.EpochTrainer: Increments the epoch counter.
index - Variable in class com.imsl.math.ComplexSparseMatrix.SparseArray: Jagged array containing column indices.
index - Variable in class com.imsl.math.SparseMatrix.SparseArray: Jagged array containing column indices.
INFINITY_NORM - Static variable in class com.imsl.stat.ClusterKNN: Indicates the distance is computed using the $L_{infty}$ norm method.
INFINITY_NORM - Static variable in class com.imsl.stat.Dissimilarities: Indicates the maximum difference ( norm) distance method.
infinityNorm(Complex[][]) - Static method in class com.imsl.math.ComplexMatrix: Return the infinity norm of a Complex matrix.
infinityNorm() - Method in class com.imsl.math.ComplexSparseMatrix: Returns the infinity norm of the matrix.
infinityNorm(double[][]) - Static method in class com.imsl.math.Matrix: Return the infinity norm of a matrix.
infinityNorm() - Method in class com.imsl.math.SparseMatrix: Returns the infinity norm of the matrix.
information(int[], int[], double[], double[], boolean) - Method in class com.imsl.datamining.decisionTree.DecisionTreeInfoGain: Returns the expected information of a variable y over a partition determined by the variable x.
init() - Method in class com.imsl.chart.ChartServlet
init(double[], double[], double, double[], double[], double[], double[]) - Method in interface com.imsl.math.FeynmanKac.InitialData: Method that allows for adjustment of initial data or as an opportunity for output during the integration steps.
innerproduct(double[], double[]) - Method in interface com.imsl.math.GenMinRes.VectorProducts: Used to compute the inner product of 2 vectors for the Gram-Schmidt implementation.
INNOVATIONAL - Static variable in class com.imsl.stat.ARMAOutlierIdentification: Indicates detection of an innovational outlier.
INNOVATIONAL - Static variable in class com.imsl.stat.AutoARIMA: Indicates detection of an innovational outlier.
InputLayer - Class in com.imsl.datamining.neural: Input layer in a neural network.
InputNode - Class in com.imsl.datamining.neural: A Node in the InputLayer.
insertRow() - Method in class com.imsl.io.AbstractFlatFile: Inserts the contents of the insert row into this ResultSet object and into the database.
INTEGER_VARIABLE - Static variable in class com.imsl.io.MPSReader: Variable must be an integer.
integral(double, double) - Method in class com.imsl.math.BSpline: Returns the value of an integral of the B-spline.
integral(double, double) - Method in class com.imsl.math.Spline: Returns the value of an integral of the spline.
integral(double, double, double, double) - Method in class com.imsl.math.Spline2D: Returns the value of an integral of a tensor-product spline on a rectangular domain.
INTERSECTION - Static variable in class com.imsl.stat.TimeSeriesOperations.MergeRule: The merge operation includes time points and values only at the matching time points and applies the CombineMethod to the values at the matching time points.
intrate(GregorianCalendar, GregorianCalendar, double, double, DayCountBasis) - Static method in class com.imsl.finance.Bond: Returns the interest rate of a fully invested security.
intValue() - Method in class com.imsl.math.Complex: Returns the value of the real part as an int.
intValue() - Method in class com.imsl.math.Physical: Returns the value of this dimensionless object.
InvCdf - Class in com.imsl.stat: Inverse cumulative probability distribution functions.
inverse() - Method in class com.imsl.math.Cholesky: Returns the inverse of this matrix
inverse() - Method in class com.imsl.math.ComplexLU: Returns the inverse of the matrix used to construct this instance.
inverse() - Method in class com.imsl.math.LU: Returns the inverse of the matrix used to construct this instance.
inverse() - Method in class com.imsl.math.SVD: Compute the Moore-Penrose generalized inverse of a real matrix.
InverseCdf - Class in com.imsl.stat: Inverse of user-supplied cumulative distribution function.
InverseCdf(CdfFunction) - Constructor for class com.imsl.stat.InverseCdf: Constructor for the inverse of a user-supplied cummulative distribution function.
InverseCdf.DidNotConvergeException - Exception in com.imsl.stat: The iteration did not converge
InverseCdf.DidNotConvergeException(String) - Constructor for exception com.imsl.stat.InverseCdf.DidNotConvergeException: Constructs a DidNotConvergeException object.
InverseCdf.DidNotConvergeException(String, Object[]) - Constructor for exception com.imsl.stat.InverseCdf.DidNotConvergeException: Constructs a DidNotConvergeException object.
inverseLowerTriangular(double[][]) - Static method in class com.imsl.math.Matrix: Returns the inverse of the lower triangular matrix a.
inverseUpperTriangular(double[][]) - Static method in class com.imsl.math.Matrix: Returns the inverse of the upper triangular matrix a.
ipmt(double, int, int, double, double, int) - Static method in class com.imsl.finance.Finance: Returns the interest payment for an investment for a given period.
ipvt - Variable in class com.imsl.math.ComplexLU: Vector of length n containing the pivot sequence for the factorization.
ipvt - Variable in class com.imsl.math.LU: Vector of length n containing the pivot sequence for the factorization.
irr(double[]) - Static method in class com.imsl.finance.Finance: Returns the internal rate of return for a schedule of cash flows.
irr(double[], double) - Static method in class com.imsl.finance.Finance: Returns the internal rate of return for a schedule of cash flows.
isA0Flag() - Method in class com.imsl.stat.VectorAutoregression: Returns the state of A0Flag.
isAfterLast() - Method in class com.imsl.io.AbstractFlatFile: Indicates whether the cursor is after the last row in this ResultSet object.
isAncestorOf(AbstractChartNode) - Method in class com.imsl.chart.AbstractChartNode: Returns true if this node is an ancestor of the argument node.
isAttributeSet(String) - Method in class com.imsl.chart.AbstractChartNode: Determines if an attribute is defined (may have been inherited).
isAttributeSetAtThisNode(String) - Method in class com.imsl.chart.AbstractChartNode: Determines if an attribute is defined in this node (not inherited).
isAutoPruningFlag() - Method in class com.imsl.datamining.decisionTree.DecisionTree: Returns the auto-pruning flag.
isBeforeFirst() - Method in class com.imsl.io.AbstractFlatFile: Indicates whether the cursor is before the first row in this ResultSet object.
isBitSet(int, int) - Static method in class com.imsl.chart.AbstractChartNode: Returns true if the bit set in flag is set in mask.
isBitSet(int, int) - Static method in class com.imsl.chart.ChartNode: Returns true if the bit set in flag is set in mask.
isClosed() - Method in class com.imsl.io.FlatFile: Retrieves whether this ResultSet object has been closed.
isDateIncrementInMillis() - Method in class com.imsl.stat.TimeSeries: Returns a boolean indicating whether or not the TimeSeries object has a date increment expressed in milliseconds.
isFirst() - Method in class com.imsl.io.AbstractFlatFile: Indicates whether the cursor is on the first row of this ResultSet object.
isHasDates() - Method in class com.imsl.stat.TimeSeries: Returns a boolean indicating whether or not the TimeSeries has a non-null dates attribute.
isInvertible(double[]) - Method in class com.imsl.stat.ARMAMaxLikelihood: Tests whether the coefficients in ma are invertible
isLast() - Method in class com.imsl.io.AbstractFlatFile: Indicates whether the cursor is on the last row of this ResultSet object.
isMustFitModelFlag() - Method in class com.imsl.datamining.PredictiveModel: Returns the current value of the mustFitModel flag.
isNaN(double) - Static method in class com.imsl.math.IEEE: NaN test on an argument of type double.
isNoMoreProgress() - Method in class com.imsl.math.QuadraticProgramming: Returns true if due to computer rounding error, a change in the variables fail to improve the objective function.
isProportionalWidth() - Method in class com.imsl.chart.BoxPlot: Returns the value of the attribute "ProportionalWidth".
isStationary(double[]) - Method in class com.imsl.stat.ARMAMaxLikelihood: Tests whether the coefficients in ar are stationary.
isTerminalNode(int) - Method in class com.imsl.datamining.decisionTree.Tree: Returns the terminal node indicator of the node at the given index.
isUserFixedNClasses() - Method in class com.imsl.datamining.PredictiveModel: Returns true if the number of classes was fixed by the user.
isWeekday(GregorianCalendar) - Method in class com.imsl.chart.TransformDate: Returns true if the specified date is a weekday.
isWrapAround() - Method in class com.imsl.datamining.KohonenSOM: Returns whether the opposite edges are connected or not.
isWrapperFor(Class) - Method in class com.imsl.io.FlatFile: Returns true if this either implements the interface argument or is directly or indirectly a wrapper for an object that does.
Itemsets - Class in com.imsl.datamining: Object containing a set of frequent items and the number of transactions examined to obtain the frequent item set.
iterator() - Method in class com.imsl.io.MPSReader.Row: Returns an iterator over the elements in this row.

J

J(double, int) - Static method in class com.imsl.math.Bessel: Evaluates a sequence of Bessel functions of the first kind with integer order and real argument.
J(double, double, int) - Static method in class com.imsl.math.Bessel: Evaluate a sequence of Bessel functions of the first kind with real order and real positive argument.
jacobian(double[], double[][]) - Method in interface com.imsl.math.NonlinLeastSquares.Jacobian: Public interface for the nonlinear least squares function.
jacobian(double[]) - Method in interface com.imsl.math.NumericalDerivatives.Jacobian: User-supplied function to compute the Jacobian.
jacobian(double, double[], double[]) - Method in interface com.imsl.math.OdeAdamsGear.Jacobian: Used to compute the Jacobian of the function at t.
jacobian(double[], double[][]) - Method in interface com.imsl.math.ZeroSystem.Jacobian: Returns the value of the Jacobian at the given point.
JFrameChart - Class in com.imsl.chart: JFrameChart is a JFrame that contains a chart.
JFrameChart() - Constructor for class com.imsl.chart.JFrameChart: Creates new JFrameChart to display a chart.
JFrameChart(Chart) - Constructor for class com.imsl.chart.JFrameChart: Creates new JFrameChart to display a given chart.
JFrameChart3D - Class in com.imsl.chart3d: JFrameChart3D is a JFrame that contains a chart.
JFrameChart3D() - Constructor for class com.imsl.chart3d.JFrameChart3D: Creates new JFrameChart3D to display a chart.
JFrameChart3D(Chart3D) - Constructor for class com.imsl.chart3d.JFrameChart3D: Creates new JFrameChart3D to display a given chart.
JMath - Class in com.imsl.math: Pure Java implementation of the standard java.lang.Math class.
JPanelChart - Class in com.imsl.chart: A Swing JPanel that contains a chart.
JPanelChart() - Constructor for class com.imsl.chart.JPanelChart: Creates new JPanelChart.
JPanelChart(Chart) - Constructor for class com.imsl.chart.JPanelChart: Creates new JPanelChart using a given Chart object.
JspBean - Class in com.imsl.chart: JspBean is used to refer to charts in a Java Server Page that are later rendered using the ChartServlet.
JspBean() - Constructor for class com.imsl.chart.JspBean: Creates a JspBean object.

K

K(double, int) - Static method in class com.imsl.math.Bessel: Evaluates a sequence of modified Bessel functions of the third kind with integer order and real argument.
K(double, double, int) - Static method in class com.imsl.math.Bessel: Evaluates a sequence of modified Bessel functions of the third kind with fractional order and real argument.
KalmanFilter - Class in com.imsl.stat: Performs Kalman filtering and evaluates the likelihood function for the state-space model.
KalmanFilter(double[], double[][], int, double, double) - Constructor for class com.imsl.stat.KalmanFilter: Constructor for KalmanFilter.
KaplanMeierECDF - Class in com.imsl.stat: Computes the Kaplan-Meier reliability function estimates or the CDF based on failure data that may be multi-censored.
KaplanMeierECDF(double[]) - Constructor for class com.imsl.stat.KaplanMeierECDF: Constructor for KaplanMeierECDF.
KaplanMeierEstimates - Class in com.imsl.stat: Computes Kaplan-Meier (or product-limit) estimates of survival probabilities for a sample of failure times that possibly contain right consoring.
KaplanMeierEstimates(double[][]) - Constructor for class com.imsl.stat.KaplanMeierEstimates: Constructor for KaplanMeierEstimates.
kappa(double, double) - Method in interface com.imsl.math.FeynmanKac.PdeCoefficients: Returns the value of the $kappa$ coefficient at the given point.
keySet() - Method in class com.imsl.chart.xml.ChartXML: Returns the Set view of all id's defined in the XML file.
knot - Variable in class com.imsl.math.BSpline: The knot array of length n + order, where n is the number of coefficients in the B-spline.
KohonenSOM - Class in com.imsl.datamining: A Kohonen self organizing map.
KohonenSOM(int, int, int) - Constructor for class com.imsl.datamining.KohonenSOM: Constructor for a KohonenSOM object.
KohonenSOMTrainer - Class in com.imsl.datamining: Trains a Kohonen network.
KohonenSOMTrainer() - Constructor for class com.imsl.datamining.KohonenSOMTrainer
KolmogorovOneSample - Class in com.imsl.stat: The class KolmogorovOneSample performs a Kolmogorov-Smirnov goodness-of-fit test in one sample.
KolmogorovOneSample(CdfFunction, double[]) - Constructor for class com.imsl.stat.KolmogorovOneSample: Constructs a one sample Kolmogorov-Smirnov goodness-of-fit test.
KolmogorovTwoSample - Class in com.imsl.stat: Performs a Kolmogorov-Smirnov two-sample test.
KolmogorovTwoSample(double[], double[]) - Constructor for class com.imsl.stat.KolmogorovTwoSample: Constructs a two sample Kolmogorov-Smirnov goodness-of-fit test.
kurtosis(double[]) - Static method in class com.imsl.stat.Summary: Returns the kurtosis of the given data set.
kurtosis(double[], double[]) - Static method in class com.imsl.stat.Summary: Returns the kurtosis of the given data set and associated weights.

L

L1_NORM - Static variable in class com.imsl.stat.ClusterKNN: Indicates the distance is computed using the norm method.
L1_NORM - Static variable in class com.imsl.stat.Dissimilarities: Indicates the sum of the absolute differences ( norm) distance method.
L2_NORM - Static variable in class com.imsl.stat.ClusterKNN: Indicates the distance is computed using the norm, or Euclidean distance measurement.
L2_NORM - Static variable in class com.imsl.stat.Dissimilarities: Indicates the Euclidean distance method ( norm).
LABEL_TYPE_NONE - Static variable in class com.imsl.chart.AbstractChartNode: Flag used to indicate the an element is not to be labeled.
LABEL_TYPE_PERCENT - Static variable in class com.imsl.chart.ChartNode: Flag used to indicate that a pie slice is to be labeled with a percentage value.
LABEL_TYPE_TITLE - Static variable in class com.imsl.chart.AbstractChartNode: Flag used to indicate that an element is to be labeled with the value of its title attribute.
LABEL_TYPE_X - Static variable in class com.imsl.chart.AbstractChartNode: Flag used to indicate that an element is to be labeled with the value of its x-coordinate.
LABEL_TYPE_Y - Static variable in class com.imsl.chart.AbstractChartNode: Flag used to indicate that an element is to be labeled with the value of its y-coordinate.
LABEL_TYPE_Z - Static variable in class com.imsl.chart.AbstractChartNode: Flag used to indicate that an element is to be labeled with the value of its y-coordinate.
LackOfFit - Class in com.imsl.stat: Performs lack-of-fit test for a univariate time series or transfer function given the appropriate correlation function.
LAST - Static variable in class com.imsl.chart.Draw: Flag for the last data marker.
last() - Method in class com.imsl.io.AbstractFlatFile: Moves the cursor to the last row in this ResultSet object.
Layer - Class in com.imsl.datamining.neural: The base class for Layers in a neural network.
Layer(FeedForwardNetwork) - Constructor for class com.imsl.datamining.neural.Layer: Constructs a Layer.
LEAST_ABSOLUTE_DEVIATION - Static variable in class com.imsl.datamining.GradientBoosting.LossFunctionType: The loss criteria is least absolute deviation error.
LEAST_SQUARES - Static variable in class com.imsl.datamining.GradientBoosting.LossFunctionType: The loss criteria is least squared error.
LEAST_SQUARES - Static variable in class com.imsl.stat.ARAutoUnivariate: Indicates that least-squares should be used for estimating the coefficients in the time series.
LEAST_SQUARES - Static variable in class com.imsl.stat.ARMA: Indicates autoregressive and moving average parameters are estimated by a least-squares procedure.
LEAST_SQUARES - Static variable in class com.imsl.stat.ARMAEstimateMissing: Estimate autoregressive coefficients using least squares.
LeastSquaresTrainer - Class in com.imsl.datamining.neural: Trains a FeedForwardNetwork using a Levenberg-Marquardt algorithm for minimizing a sum of squares error.
LeastSquaresTrainer() - Constructor for class com.imsl.datamining.neural.LeastSquaresTrainer: Creates a LeastSquaresTrainer.
LEAVE_OUT_LAST - Static variable in class com.imsl.stat.RegressorsForGLM: The dummies are the first n-1 indicator variables.
LEAVE_OUT_ONE - Static variable in class com.imsl.stat.DiscriminantAnalysis: Indicates leave-out-one classification method.
leftBoundaries(double, double[][]) - Method in interface com.imsl.math.FeynmanKac.Boundaries: Returns the coefficient values of the left boundary conditions.
Legend - Class in com.imsl.chart: The chart legend.
Legend(Chart) - Constructor for class com.imsl.chart.Legend
LENGTH - Static variable in class com.imsl.math.Physical
LEVEL_SHIFT - Static variable in class com.imsl.stat.ARMAOutlierIdentification: Indicates detection of a level shift outlier.
LEVEL_SHIFT - Static variable in class com.imsl.stat.AutoARIMA: Indicates detection of a level shift outlier.
LicenseManagerException - Exception in com.imsl: A LicenseManagerException exception is thrown if a license to use the product cannot be obtained.
LifeTables - Class in com.imsl.stat: Computes population (current) or cohort life tables based upon the observed population sizes at the middle (for population table) or the beginning (for cohort table) of some user specified age intervals.
LifeTables(int[], double[], double[]) - Constructor for class com.imsl.stat.LifeTables: Constructs a new LifeTables instance.
LillieforsTest() - Method in class com.imsl.stat.NormalityTest: Performs the Lilliefors test.
LINE - Static variable in class com.imsl.chart.Draw
LINEAR - Static variable in interface com.imsl.datamining.neural.Activation: The identity activation function, g(x) = x.
LINEAR - Static variable in class com.imsl.stat.DiscriminantAnalysis: Indicates a linear discrimination method.
LINEAR_AT_RESTART_AND_TERMINATION - Static variable in class com.imsl.math.GenMinRes: Indicates residual updating is to be done by linear combination upon restarting and at termination.
LINEAR_AT_RESTART_ONLY - Static variable in class com.imsl.math.GenMinRes: Indicates residual updating is to be done by linear combination upon restarting only.
LinearRegression - Class in com.imsl.stat: Fits a multiple linear regression model with or without an intercept.
LinearRegression(int, boolean) - Constructor for class com.imsl.stat.LinearRegression: Constructs a new linear regression object.
LinearRegression.CaseStatistics - Class in com.imsl.stat: Inner Class CaseStatistics allows for the computation of predicted values, confidence intervals, and diagnostics for detecting outliers and cases that greatly influence the fitted regression.
LinearRegression.CoefficientTTests - Class in com.imsl.stat: Contains statistics related to the regression coefficients.
lineColor - Variable in class com.imsl.chart.Draw
lineDashPattern - Variable in class com.imsl.chart.Draw
lineWidth - Variable in class com.imsl.chart.Draw
link(Node, Node) - Method in class com.imsl.datamining.neural.FeedForwardNetwork: Establishes a Link between two Nodes.
link(Node, Node, double) - Method in class com.imsl.datamining.neural.FeedForwardNetwork: Establishes a Link between two Nodes with a specified weight.
Link - Class in com.imsl.datamining.neural: A link in a neural network.
LINKAGE_AVG_BETWEEN_CLUSTERS - Static variable in class com.imsl.stat.ClusterHierarchical: Indicates the average distance between (average distance between objects in the two clusters) method.
LINKAGE_AVG_WITHIN_CLUSTERS - Static variable in class com.imsl.stat.ClusterHierarchical: Indicates the average distance within (average distance between objects within the merged cluster) method.
LINKAGE_COMPLETE - Static variable in class com.imsl.stat.ClusterHierarchical: Indicates the complete linkage (maximum distance) method.
LINKAGE_SINGLE - Static variable in class com.imsl.stat.ClusterHierarchical: Indicates the single linkage (minimum distance) method.
LINKAGE_WARDS - Static variable in class com.imsl.stat.ClusterHierarchical: Indicates the Ward's method.
linkAll(Layer, Layer) - Method in class com.imsl.datamining.neural.FeedForwardNetwork: Link all of the Nodes in one Layer to all of the Nodes in another Layer.
linkAll() - Method in class com.imsl.datamining.neural.FeedForwardNetwork: For each Layer in the Network, link each Node in the Layer to each Node in the next Layer.
log(Complex) - Static method in class com.imsl.math.Complex: Returns the logarithm of a Complex z, with a branch cut along the negative real axis.
log(double) - Static method in class com.imsl.math.JMath: Returns the natural logarithm of a double.
log10(double) - Static method in class com.imsl.math.Sfun: Returns the common (base 10) logarithm of a double.
log1p(double) - Static method in class com.imsl.math.Hyperbolic: Returns log(1+x), the logarithm of (x plus 1).
logBeta(double, double) - Static method in class com.imsl.math.Sfun: Returns the logarithm of the beta function.
logGamma(double) - Static method in class com.imsl.math.Sfun: Returns the logarithm of the absolute value of the Gamma function.
LOGISTIC - Static variable in interface com.imsl.datamining.neural.Activation: The logistic activation function, $g(x)=frac{1}{1+e^{-x}}$ .
logistic(double, double, double) - Static method in class com.imsl.stat.Cdf: Evaluates the logistic cumulative probability distribution function.
logistic(double, double, double) - Static method in class com.imsl.stat.InvCdf: Returns the inverse of the logistic cumulative probability distribution function.
logistic(double, double, double) - Static method in class com.imsl.stat.Pdf: Evaluates the logistic probability density function.
LOGISTIC_TABLE - Static variable in interface com.imsl.datamining.neural.Activation: The logistic activation function computed using a table.
logNormal(double, double, double) - Static method in class com.imsl.stat.Cdf: Evaluates the standard lognormal cumulative probability distribution function.
logNormal(double, double, double) - Static method in class com.imsl.stat.InvCdf: Returns the inverse of the standard lognormal cumulative probability distribution function.
logNormal(double, double, double) - Static method in class com.imsl.stat.Pdf: Evaluates the standard lognormal probability density function.
LogNormalDistribution - Class in com.imsl.stat: Evaluates a lognormal probability density for a given set of data.
LogNormalDistribution() - Constructor for class com.imsl.stat.LogNormalDistribution
longValue() - Method in class com.imsl.math.Complex: Returns the value of the real part as a long.
longValue() - Method in class com.imsl.math.Physical: Returns the value of this dimensionless object.
LOWER_TRIANGULAR - Static variable in class com.imsl.math.PrintMatrix: This flag as the argument to setMatrixType, indicates that only the lower triangular elements of the matrix are to be printed.
LU - Class in com.imsl.math: LU factorization of a matrix of type double.
LU(double[][]) - Constructor for class com.imsl.math.LU: Creates the LU factorization of a square matrix of type double.

M

MAHALANOBIS - Static variable in class com.imsl.stat.Dissimilarities: Indicates the Mahalanobis distance method.
main(String[]) - Static method in class com.imsl.chart.xml.ChartXML: Displays a chart created from an XML file.
main(String[]) - Static method in class com.imsl.Version: Print the version information about the environment and this library.
MajorTick - Class in com.imsl.chart: The major tick marks.
MajorTick - Class in com.imsl.chart3d: Major ticks marks.
MALLOWS_CP_CRITERION - Static variable in class com.imsl.stat.SelectionRegression: Indicates Mallow's criterion regression.
mapCubeToUser(double, double, double, double[]) - Method in class com.imsl.chart3d.AxisXYZ: Map the cube coordinates to user coordinates.
mapDeviceToUser(int, int, double[]) - Method in class com.imsl.chart.Axis: Maps the device coordinates to user coordinates.
mapDeviceToUser(int, int, double[]) - Method in class com.imsl.chart.AxisXY: Map the device coordinates to user coordinates.
mapDeviceToUser(int, int, double[]) - Method in class com.imsl.chart.Pie: Maps the device coordinates to user coordinates.
mapDeviceToUser(int, int, double[]) - Method in class com.imsl.chart.Polar: Map the device coordinates to polar coordinates.
mapUnitToUser(double) - Method in interface com.imsl.chart.Transform: Maps points in the interval [0,1] to user coordinates.
mapUnitToUser(double) - Method in class com.imsl.chart.TransformDate: Maps points in the interval [0,1] to user coordinates.
mapUserToCube(double, double, double, double[]) - Method in class com.imsl.chart3d.AxisXYZ: Map the user coordinates (userX,userY) to the cube coordinates cubeXYZ.
mapUserToDevice(double, double, int[]) - Method in class com.imsl.chart.Axis: Maps the user coordinates (userX,userY) to the device coordinates devXY.
mapUserToDevice(double, double, int[]) - Method in class com.imsl.chart.AxisXY: Map the user coordinates (userX,userY) to the device coordinates devXY.
mapUserToDevice(double, double, int[]) - Method in class com.imsl.chart.Pie: Maps the user coordinates (userX,userY) to the device coordinates devXY.
mapUserToDevice(double, double, int[]) - Method in class com.imsl.chart.Polar: Map the polar coordinates (userRadius,userAngle) to the device coordinates devXY.
mapUserToUnit(double) - Method in interface com.imsl.chart.Transform: Maps user coordinate to the interval [0,1].
mapUserToUnit(double) - Method in class com.imsl.chart.TransformDate: Maps user coordinate to the interval [0,1].
MARKER - Static variable in class com.imsl.chart.Draw
MARKER_SCALE - Static variable in class com.imsl.chart.Draw: Normal marker size in pixels is screen width times MARKER_SCALE.
MARKER_TYPE_ASTERISK - Static variable in class com.imsl.chart.ChartNode: Flag for a asterisk data marker.
MARKER_TYPE_CIRCLE_CIRCLE - Static variable in class com.imsl.chart.ChartNode: Flag for a circle in a circle data marker.
MARKER_TYPE_CIRCLE_PLUS - Static variable in class com.imsl.chart.ChartNode: Flag for a plus in a circle data marker.
MARKER_TYPE_CIRCLE_X - Static variable in class com.imsl.chart.ChartNode: Flag for an x in a circle data marker.
MARKER_TYPE_CUBE - Static variable in class com.imsl.chart3d.ChartNode3D: Flag for a cube data marker.
MARKER_TYPE_CUSTOM - Static variable in class com.imsl.chart3d.ChartNode3D: Flag for a custom marker
MARKER_TYPE_DIAMOND_PLUS - Static variable in class com.imsl.chart.ChartNode: Flag for a plus in a diamond data marker.
MARKER_TYPE_FILLED_CIRCLE - Static variable in class com.imsl.chart.ChartNode: Flag for a filled circle data marker.
MARKER_TYPE_FILLED_DIAMOND - Static variable in class com.imsl.chart.ChartNode: Flag for a filled diamond data marker.
MARKER_TYPE_FILLED_SQUARE - Static variable in class com.imsl.chart.ChartNode: Flag for a filled square data marker.
MARKER_TYPE_FILLED_TRIANGLE - Static variable in class com.imsl.chart.ChartNode: Flag for a filled triangle data marker.
MARKER_TYPE_HOLLOW_CIRCLE - Static variable in class com.imsl.chart.ChartNode: Flag for a hollow circle data marker.
MARKER_TYPE_HOLLOW_DIAMOND - Static variable in class com.imsl.chart.ChartNode: Flag for a hollow diamond data marker.
MARKER_TYPE_HOLLOW_SQUARE - Static variable in class com.imsl.chart.ChartNode: Flag for a hollow square data marker.
MARKER_TYPE_HOLLOW_TRIANGLE - Static variable in class com.imsl.chart.ChartNode: Flag for hollow triangle data marker.
MARKER_TYPE_OCTAGON_PLUS - Static variable in class com.imsl.chart.ChartNode: Flag for a plus in an octagon data marker.
MARKER_TYPE_OCTAGON_X - Static variable in class com.imsl.chart.ChartNode: Flag for a x in an octagon data marker.
MARKER_TYPE_PLUS - Static variable in class com.imsl.chart.ChartNode: Flag for a plus-shaped data marker.
MARKER_TYPE_PLUS - Static variable in class com.imsl.chart3d.ChartNode3D: Flag for a 3D plus sign data marker.
MARKER_TYPE_SIMPLE_CUBE - Static variable in class com.imsl.chart3d.ChartNode3D: Flag for a simple cube (no edge) data marker.
MARKER_TYPE_SIMPLE_PLUS - Static variable in class com.imsl.chart3d.ChartNode3D: Flag for a simple 2D plus sign (no edge) data marker.
MARKER_TYPE_SIMPLE_TETRAHEDRON - Static variable in class com.imsl.chart3d.ChartNode3D: Flag for a simple tetrahedron (no edge) data marker.
MARKER_TYPE_SPHERE - Static variable in class com.imsl.chart3d.ChartNode3D: Flag for a sphere data marker.
MARKER_TYPE_SQUARE_PLUS - Static variable in class com.imsl.chart.ChartNode: Flag for a plus in a square data marker.
MARKER_TYPE_SQUARE_X - Static variable in class com.imsl.chart.ChartNode: Flag for an x in a square data marker.
MARKER_TYPE_TETRAHEDRON - Static variable in class com.imsl.chart3d.ChartNode3D: Flag for a tetrahedron data marker.
MARKER_TYPE_X - Static variable in class com.imsl.chart.ChartNode: Flag for a x-shaped data marker.
markerColor - Variable in class com.imsl.chart.Draw
markerDashPattern - Variable in class com.imsl.chart.Draw
markerSize - Variable in class com.imsl.chart.Draw
markerThickness - Variable in class com.imsl.chart.Draw
markerType - Variable in class com.imsl.chart.Draw
MASS - Static variable in class com.imsl.math.Physical
Matrix - Class in com.imsl.math: Matrix manipulation functions.
max(int, int) - Static method in class com.imsl.math.JMath: Returns the larger of two ints.
max(long, long) - Static method in class com.imsl.math.JMath: Returns the larger of two longs.
max(float, float) - Static method in class com.imsl.math.JMath: Returns the larger of two floats.
max(double, double) - Static method in class com.imsl.math.JMath: Returns the larger of two doubles.
MAX - Static variable in class com.imsl.stat.TimeSeriesOperations.CombineMethod: Uses the maximum of the two values.
MAX_LIKELIHOOD - Static variable in class com.imsl.stat.ARAutoUnivariate: Indicates that maximum likelihood should be used for estimating the coefficients in the time series.
MAX_LIKELIHOOD - Static variable in class com.imsl.stat.ARMAEstimateMissing: Estimate autoregressive coefficients using maximum likelihood.
maximum(double[]) - Static method in class com.imsl.stat.Summary: Returns the maximum of the given data set.
maximum(int[]) - Static method in class com.imsl.stat.Summary: Returns the maximum of the given data set.
MAXIMUM_LIKELIHOOD - Static variable in class com.imsl.stat.FactorAnalysis: Indicates maximum likelihood method.
MAXIMUM_SUPERNODE_SIZE - Static variable in class com.imsl.math.ComplexSuperLU: A performance tuning parameter which can be adjusted via method setPerformanceTuningParameters.
MAXIMUM_SUPERNODE_SIZE - Static variable in class com.imsl.math.SuperLU: A performance tuning parameter which can be adjusted via method setPerformanceTuningParameters.
MaximumLikelihoodEstimation - Class in com.imsl.stat.distributions: Maximum likelihood parameter estimation
MaximumLikelihoodEstimation(double[], ProbabilityDistribution, double[]) - Constructor for class com.imsl.stat.distributions.MaximumLikelihoodEstimation: Constructor for maximum likelihood estimation
mduration(GregorianCalendar, GregorianCalendar, double, double, int, DayCountBasis) - Static method in class com.imsl.finance.Bond: Returns the modified Macaulay duration for a security with an assumed par value of $100.
mean(double[]) - Static method in class com.imsl.stat.Summary: Returns the mean of the given data set.
mean(double[], double[]) - Static method in class com.imsl.stat.Summary: Returns the mean of the given data set with associated weights.
MEDIAN - Static variable in class com.imsl.stat.ARMAEstimateMissing: Indicates that missing values should be estimated using the median of the values just before and after the missing value gap.
median(double[], double[]) - Static method in class com.imsl.stat.Summary: Returns the weighted median of the given data set and associated weights.
median(double[]) - Static method in class com.imsl.stat.Summary: Returns the median of the given data set.
merge(TimeSeries, TimeSeries) - Method in class com.imsl.stat.TimeSeriesOperations: Merges two time series objects.
MersenneTwister - Class in com.imsl.stat: A 32-bit Mersenne Twister generator.
MersenneTwister(int) - Constructor for class com.imsl.stat.MersenneTwister: Constructor for the MersenneTwister class with supplied seed.
MersenneTwister(int[]) - Constructor for class com.imsl.stat.MersenneTwister: Constructor for the MersenneTwister class with supplied array.
MersenneTwister64 - Class in com.imsl.stat: A 64-bit Mersenne Twister generator.
MersenneTwister64(long) - Constructor for class com.imsl.stat.MersenneTwister64: Constructor for the MersenneTwister64 class with supplied seed.
MersenneTwister64(long[]) - Constructor for class com.imsl.stat.MersenneTwister64: Constructor for the MersenneTwister64 class with supplied array.
Messages - Class in com.imsl: Retrieve and format message strings.
Messages() - Constructor for class com.imsl.Messages
METHOD_ADAMS - Static variable in class com.imsl.math.OdeAdamsGear: The Adams integration method
METHOD_BDF - Static variable in class com.imsl.math.OdeAdamsGear: The BDF integration method
METHOD_OF_MOMENTS - Static variable in class com.imsl.stat.ARAutoUnivariate: Indicates the method of moments should be used for estimating the coefficients in the time series.
METHOD_OF_MOMENTS - Static variable in class com.imsl.stat.ARMA: Indicates autoregressive and moving average parameters are estimated by a method of moments procedure.
METHOD_OF_MOMENTS - Static variable in class com.imsl.stat.ARMAEstimateMissing: Estimate autoregressive coefficients using method of moments.
METHOD_OF_PETZOLD - Static variable in class com.imsl.math.FeynmanKac: Used by method setStepControlMethod to indicate that the step control algorithm of the original Petzold code is used in the integration.
METHOD_OF_SOEDERLIND - Static variable in class com.imsl.math.FeynmanKac: Used by method setStepControlMethod to indicate that the step control method by Soederlind is used in the integration.
min(int, int) - Static method in class com.imsl.math.JMath: Returns the smaller of two ints.
min(long, long) - Static method in class com.imsl.math.JMath: Returns the smaller of two longs.
min(float, float) - Static method in class com.imsl.math.JMath: Returns the smaller of two floats.
min(double, double) - Static method in class com.imsl.math.JMath: Returns the smaller of two doubles.
MIN - Static variable in class com.imsl.stat.TimeSeriesOperations.CombineMethod: Uses the minimum of the two values.
MinConGenLin - Class in com.imsl.math: Minimizes a general objective function subject to linear equality/inequality constraints.
MinConGenLin(MinConGenLin.Function, int, int, int, double[], double[], double[], double[]) - Constructor for class com.imsl.math.MinConGenLin: Constructor for MinConGenLin.
MinConGenLin.ConstraintsInconsistentException - Exception in com.imsl.math: The equality constraints are inconsistent.
MinConGenLin.ConstraintsInconsistentException(String) - Constructor for exception com.imsl.math.MinConGenLin.ConstraintsInconsistentException: Constructs a ConstraintsInconsistentException object.
MinConGenLin.ConstraintsInconsistentException(String, Object[]) - Constructor for exception com.imsl.math.MinConGenLin.ConstraintsInconsistentException: Constructs a ConstraintsInconsistentException object.
MinConGenLin.ConstraintsNotSatisfiedException - Exception in com.imsl.math: No vector x satisfies all of the constraints.
MinConGenLin.ConstraintsNotSatisfiedException(String) - Constructor for exception com.imsl.math.MinConGenLin.ConstraintsNotSatisfiedException: Constructs a ConstraintsNotSatisfiedException object.
MinConGenLin.ConstraintsNotSatisfiedException(String, Object[]) - Constructor for exception com.imsl.math.MinConGenLin.ConstraintsNotSatisfiedException: Constructs a ConstraintsNotSatisfiedException object.
MinConGenLin.EqualityConstraintsException - Exception in com.imsl.math: the variables are determined by the equality constraints.
MinConGenLin.EqualityConstraintsException(String) - Constructor for exception com.imsl.math.MinConGenLin.EqualityConstraintsException: Constructs a EqualityConstraintsException object.
MinConGenLin.EqualityConstraintsException(String, Object[]) - Constructor for exception com.imsl.math.MinConGenLin.EqualityConstraintsException: Constructs a EqualityConstraintsException object.
MinConGenLin.Function - Interface in com.imsl.math: Public interface for the user-supplied function to evaluate the function to be minimized.
MinConGenLin.Gradient - Interface in com.imsl.math: Public interface for the user-supplied function to compute the gradient.
MinConGenLin.VarBoundsInconsistentException - Exception in com.imsl.math: The equality constraints and the bounds on the variables are found to be inconsistent.
MinConGenLin.VarBoundsInconsistentException(String) - Constructor for exception com.imsl.math.MinConGenLin.VarBoundsInconsistentException: Constructs a VarBoundsInconsistentException object.
MinConGenLin.VarBoundsInconsistentException(String, Object[]) - Constructor for exception com.imsl.math.MinConGenLin.VarBoundsInconsistentException: Constructs a VarBoundsInconsistentException object.
MinConNLP - Class in com.imsl.math: General nonlinear programming solver.
MinConNLP(int, int, int) - Constructor for class com.imsl.math.MinConNLP: Nonlinear programming solver constructor.
MinConNLP.BadInitialGuessException - Exception in com.imsl.math: Penalty function point infeasible for original problem.
MinConNLP.BadInitialGuessException(String) - Constructor for exception com.imsl.math.MinConNLP.BadInitialGuessException: Constructs a BadInitialGuessException object.
MinConNLP.BadInitialGuessException(String, Object[]) - Constructor for exception com.imsl.math.MinConNLP.BadInitialGuessException: Constructs a BadInitialGuessException object.
MinConNLP.ConstraintEvaluationException - Exception in com.imsl.math: Constraint evaluation returns an error with current point.
MinConNLP.ConstraintEvaluationException(String) - Constructor for exception com.imsl.math.MinConNLP.ConstraintEvaluationException: Constructs a ConstraintEvaluationException object.
MinConNLP.ConstraintEvaluationException(String, Object[]) - Constructor for exception com.imsl.math.MinConNLP.ConstraintEvaluationException: Constructs a ConstraintEvaluationException object.
MinConNLP.Function - Interface in com.imsl.math: Public interface for the user supplied function to the MinConNLP object.
MinConNLP.Gradient - Interface in com.imsl.math: Public interface for the user supplied function to compute the gradient for MinConNLP object.
MinConNLP.IllConditionedException - Exception in com.imsl.math: Problem is singular or ill-conditioned.
MinConNLP.IllConditionedException(String) - Constructor for exception com.imsl.math.MinConNLP.IllConditionedException: Constructs a IllConditionedException object.
MinConNLP.IllConditionedException(String, Object[]) - Constructor for exception com.imsl.math.MinConNLP.IllConditionedException: Constructs a IllConditionedException object.
MinConNLP.LimitingAccuracyException - Exception in com.imsl.math: Limiting accuracy reached for a singular problem.
MinConNLP.LimitingAccuracyException(String) - Constructor for exception com.imsl.math.MinConNLP.LimitingAccuracyException: Constructs a LimitingAccuracyException object.
MinConNLP.LimitingAccuracyException(String, Object[]) - Constructor for exception com.imsl.math.MinConNLP.LimitingAccuracyException: Constructs a LimitingAccuracyException object.
MinConNLP.LinearlyDependentGradientsException - Exception in com.imsl.math: Working set gradients are linearly dependent.
MinConNLP.LinearlyDependentGradientsException(String) - Constructor for exception com.imsl.math.MinConNLP.LinearlyDependentGradientsException: Constructs a LinearlyDependentGradientsException object.
MinConNLP.LinearlyDependentGradientsException(String, Object[]) - Constructor for exception com.imsl.math.MinConNLP.LinearlyDependentGradientsException: Constructs a LinearlyDependentGradientsException object.
MinConNLP.NoAcceptableStepsizeException - Exception in com.imsl.math: No acceptable stepsize in [SIGMA,SIGLA].
MinConNLP.NoAcceptableStepsizeException(String) - Constructor for exception com.imsl.math.MinConNLP.NoAcceptableStepsizeException: Constructs a NoAcceptableStepsizeException object.
MinConNLP.NoAcceptableStepsizeException(String, Object[]) - Constructor for exception com.imsl.math.MinConNLP.NoAcceptableStepsizeException: Constructs a NoAcceptableStepsizeException object.
MinConNLP.ObjectiveEvaluationException - Exception in com.imsl.math: Objective evaluation returns an error with current point.
MinConNLP.ObjectiveEvaluationException(String) - Constructor for exception com.imsl.math.MinConNLP.ObjectiveEvaluationException: Constructs a ObjectiveEvaluationException object.
MinConNLP.ObjectiveEvaluationException(String, Object[]) - Constructor for exception com.imsl.math.MinConNLP.ObjectiveEvaluationException: Constructs a ObjectiveEvaluationException object.
MinConNLP.PenaltyFunctionPointInfeasibleException - Exception in com.imsl.math: Penalty function point infeasible.
MinConNLP.PenaltyFunctionPointInfeasibleException(String) - Constructor for exception com.imsl.math.MinConNLP.PenaltyFunctionPointInfeasibleException: Constructs a PenaltyFunctionPointInfeasibleException object.
MinConNLP.PenaltyFunctionPointInfeasibleException(String, Object[]) - Constructor for exception com.imsl.math.MinConNLP.PenaltyFunctionPointInfeasibleException: Constructs a PenaltyFunctionPointInfeasibleException object.
MinConNLP.QPInfeasibleException - Exception in com.imsl.math: QP problem seemingly infeasible.
MinConNLP.QPInfeasibleException(String) - Constructor for exception com.imsl.math.MinConNLP.QPInfeasibleException: Constructs a QPInfeasibleException object.
MinConNLP.QPInfeasibleException(String, Object[]) - Constructor for exception com.imsl.math.MinConNLP.QPInfeasibleException: Constructs a QPInfeasibleException object.
MinConNLP.SingularException - Exception in com.imsl.math: Problem is singular.
MinConNLP.SingularException(String) - Constructor for exception com.imsl.math.MinConNLP.SingularException: Constructs a SingularException object.
MinConNLP.SingularException(String, Object[]) - Constructor for exception com.imsl.math.MinConNLP.SingularException: Constructs a SingularException object.
MinConNLP.TerminationCriteriaNotSatisfiedException - Exception in com.imsl.math: Termination criteria are not satisfied.
MinConNLP.TerminationCriteriaNotSatisfiedException(String) - Constructor for exception com.imsl.math.MinConNLP.TerminationCriteriaNotSatisfiedException: Constructs a TerminationCriteriaNotSatisfiedException object.
MinConNLP.TerminationCriteriaNotSatisfiedException(String, Object[]) - Constructor for exception com.imsl.math.MinConNLP.TerminationCriteriaNotSatisfiedException: Constructs a TerminationCriteriaNotSatisfiedException object.
MinConNLP.TooManyIterationsException - Exception in com.imsl.math: Maximum number of iterations exceeded.
MinConNLP.TooManyIterationsException(String) - Constructor for exception com.imsl.math.MinConNLP.TooManyIterationsException: Constructs a TooManyIterationsException object.
MinConNLP.TooManyIterationsException(String, Object[]) - Constructor for exception com.imsl.math.MinConNLP.TooManyIterationsException: Constructs a TooManyIterationsException object.
MinConNLP.TooMuchTimeException - Exception in com.imsl.math: Maximum time allowed for solve exceeded.
MinConNLP.TooMuchTimeException(long) - Constructor for exception com.imsl.math.MinConNLP.TooMuchTimeException: Constructs a TooMuchTimeException object.
MinConNLP.WorkingSetSingularException - Exception in com.imsl.math: Working set is singular in dual extended QP.
MinConNLP.WorkingSetSingularException(String) - Constructor for exception com.imsl.math.MinConNLP.WorkingSetSingularException: Constructs a WorkingSetSingularException object.
MinConNLP.WorkingSetSingularException(String, Object[]) - Constructor for exception com.imsl.math.MinConNLP.WorkingSetSingularException: Constructs a WorkingSetSingularException object.
minimum(double[]) - Static method in class com.imsl.stat.Summary: Returns the minimum of the given data set.
minimum(int[]) - Static method in class com.imsl.stat.Summary: Returns the minimum of the given data set.
MINIMUM_COLUMN_DIMENSION - Static variable in class com.imsl.math.ComplexSuperLU: A performance tuning parameter which can be adjusted via method setPerformanceTuningParameters.
MINIMUM_COLUMN_DIMENSION - Static variable in class com.imsl.math.SuperLU: A performance tuning parameter which can be adjusted via method setPerformanceTuningParameters.
MINIMUM_DEGREE_AT_A - Static variable in class com.imsl.math.ComplexSuperLU: For column ordering, use minimum degree ordering on the structure of $A^TA$ .
MINIMUM_DEGREE_AT_A - Static variable in class com.imsl.math.SuperLU: For column ordering, use minimum degree ordering on the structure of $A^TA$ .
MINIMUM_DEGREE_AT_PLUS_A - Static variable in class com.imsl.math.ComplexSuperLU: For column ordering, use minimum degree ordering on the structure of $A^T+A$ .
MINIMUM_DEGREE_AT_PLUS_A - Static variable in class com.imsl.math.SuperLU: For column ordering, use minimum degree ordering on the structure of $A^T+A$ .
MINIMUM_ROW_DIMENSION - Static variable in class com.imsl.math.ComplexSuperLU: A performance tuning parameter which can be adjusted via method setPerformanceTuningParameters.
MINIMUM_ROW_DIMENSION - Static variable in class com.imsl.math.SuperLU: A performance tuning parameter which can be adjusted via method setPerformanceTuningParameters.
MinorTick - Class in com.imsl.chart: The minor tick marks.
MinUncon - Class in com.imsl.math: Unconstrained minimization.
MinUncon() - Constructor for class com.imsl.math.MinUncon: Unconstrained minimum constructor for a smooth function of a single variable of type double.
MinUncon.Derivative - Interface in com.imsl.math: Public interface for the user supplied function to the MinUncon object.
MinUncon.Function - Interface in com.imsl.math: Public interface for the user supplied function to the MinUncon object.
MinUnconMultiVar - Class in com.imsl.math: Unconstrained multivariate minimization.
MinUnconMultiVar(int) - Constructor for class com.imsl.math.MinUnconMultiVar: Unconstrained minimum constructor for a function of n variables of type double.
MinUnconMultiVar.ApproximateMinimumException - Exception in com.imsl.math: Scaled step tolerance satisfied; the current point may be an approximate local solution, or the algorithm is making very slow progress and is not near a solution, or the scaled step tolerance is too big.
MinUnconMultiVar.ApproximateMinimumException(String) - Constructor for exception com.imsl.math.MinUnconMultiVar.ApproximateMinimumException: Constructs a ApproximateMinimumException object.
MinUnconMultiVar.ApproximateMinimumException(String, Object[]) - Constructor for exception com.imsl.math.MinUnconMultiVar.ApproximateMinimumException: Constructs a ApproximateMinimumException object.
MinUnconMultiVar.FalseConvergenceException - Exception in com.imsl.math: False convergence error; the iterates appear to be converging to a noncritical point.
MinUnconMultiVar.FalseConvergenceException(String) - Constructor for exception com.imsl.math.MinUnconMultiVar.FalseConvergenceException: Constructs a FalseConvergenceException object.
MinUnconMultiVar.FalseConvergenceException(String, Object[]) - Constructor for exception com.imsl.math.MinUnconMultiVar.FalseConvergenceException: Constructs a FalseConvergenceException object.
MinUnconMultiVar.Function - Interface in com.imsl.math: Public interface for the user supplied function to the MinUnconMultiVar object.
MinUnconMultiVar.Gradient - Interface in com.imsl.math: Public interface for the user supplied gradient to the MinUnconMultiVar object.
MinUnconMultiVar.Hessian - Interface in com.imsl.math: Public interface for the user supplied Hessian to the MinUnconMultiVar object.
MinUnconMultiVar.MaxIterationsException - Exception in com.imsl.math: Maximum number of iterations exceeded.
MinUnconMultiVar.MaxIterationsException(String) - Constructor for exception com.imsl.math.MinUnconMultiVar.MaxIterationsException: Constructs a MaxIterationsException object.
MinUnconMultiVar.MaxIterationsException(String, Object[]) - Constructor for exception com.imsl.math.MinUnconMultiVar.MaxIterationsException: Constructs a MaxIterationsException object.
MinUnconMultiVar.UnboundedBelowException - Exception in com.imsl.math: Five consecutive steps of the maximum allowable stepsize have been taken, either the function is unbounded below, or has a finite asymptote in some direction or the maximum allowable step size is too small.
MinUnconMultiVar.UnboundedBelowException(String) - Constructor for exception com.imsl.math.MinUnconMultiVar.UnboundedBelowException: Constructs a UnboundedBelowException object.
MinUnconMultiVar.UnboundedBelowException(String, Object[]) - Constructor for exception com.imsl.math.MinUnconMultiVar.UnboundedBelowException: Constructs a UnboundedBelowException object.
mirr(double[], double, double) - Static method in class com.imsl.finance.Finance: Returns the modified internal rate of return for a schedule of periodic cash flows.
mode(double[]) - Static method in class com.imsl.stat.Summary: Returns the mode of the given data set.
MODEL0 - Static variable in class com.imsl.stat.CategoricalGenLinModel: Indicates an exponential function is used to model the distribution parameter.
MODEL1 - Static variable in class com.imsl.stat.CategoricalGenLinModel: Indicates a logistic function is used to model the distribution parameter.
MODEL2 - Static variable in class com.imsl.stat.CategoricalGenLinModel: Indicates a logistic function is used to model the distribution parameter.
MODEL3 - Static variable in class com.imsl.stat.CategoricalGenLinModel: Indicates a logistic function is used to model the distribution parameter.
MODEL4 - Static variable in class com.imsl.stat.CategoricalGenLinModel: Indicates a probit function is used to model the distribution parameter.
MODEL5 - Static variable in class com.imsl.stat.CategoricalGenLinModel: Indicates a log-log function is used to model the distribution parameter.
MONTHLY - Static variable in class com.imsl.finance.Bond: Coupon payments are made monthly.
MORANS_FORMULA - Static variable in class com.imsl.stat.AutoCorrelation: Indicates standard error computation using Moran's formula.
mouseDragged(MouseEvent) - Method in class com.imsl.chart.ToolTip: Part of the MouseMotionListener interface.
mouseMoved(MouseEvent) - Method in class com.imsl.chart.ToolTip: Part of the MouseMotionListener interface.
moveToCurrentRow() - Method in class com.imsl.io.AbstractFlatFile: Moves the cursor to the remembered cursor position, usually the current row.
moveToInsertRow() - Method in class com.imsl.io.AbstractFlatFile: Moves the cursor to the insert row.
MPSReader - Class in com.imsl.io: Reads a linear programming problem from an MPS file.
MPSReader() - Constructor for class com.imsl.io.MPSReader
MPSReader.Element - Class in com.imsl.io: An element in the sparse contraint matrix.
MPSReader.InvalidMPSFileException - Exception in com.imsl.io: The MPS file is invalid.
MPSReader.InvalidMPSFileException(String) - Constructor for exception com.imsl.io.MPSReader.InvalidMPSFileException: Constructs a InvalidMPSFileException object.
MPSReader.InvalidMPSFileException(String, Object[]) - Constructor for exception com.imsl.io.MPSReader.InvalidMPSFileException: Constructs a InvalidMPSFileException object.
MPSReader.Row - Class in com.imsl.io: A row either in the constraint matrix or a free row.
mu(double, double) - Method in interface com.imsl.math.FeynmanKac.PdeCoefficients: Returns the value of the $mu$ coefficient at the given point.
MultiClassification - Class in com.imsl.datamining.neural: Classifies patterns into three or more classes.
MultiClassification(Network) - Constructor for class com.imsl.datamining.neural.MultiClassification: Creates a classifier.
MultiCrossCorrelation - Class in com.imsl.stat: Computes the multichannel cross-correlation function of two mutually stationary multichannel time series.
MultiCrossCorrelation(double[][], double[][], int) - Constructor for class com.imsl.stat.MultiCrossCorrelation: Constructor to compute the multichannel cross-correlation function of two mutually stationary multichannel time series.
MultiCrossCorrelation.NonPosVariancesException - Exception in com.imsl.stat: The problem is ill-conditioned.
MultiCrossCorrelation.NonPosVariancesException(String) - Constructor for exception com.imsl.stat.MultiCrossCorrelation.NonPosVariancesException: Constructs a NonPosVariancesException object.
MultiCrossCorrelation.NonPosVariancesException(String, Object[]) - Constructor for exception com.imsl.stat.MultiCrossCorrelation.NonPosVariancesException: Constructs a NonPosVariancesException object.
MULTIFRONTAL_METHOD - Static variable in class com.imsl.math.ComplexSparseCholesky: Indicates the multifrontal method will be used for numeric factorization.
MULTIFRONTAL_METHOD - Static variable in class com.imsl.math.SparseCholesky: Indicates the multifrontal method will be used for numeric factorization.
MULTINOMIAL_DEVIANCE - Static variable in class com.imsl.datamining.GradientBoosting.LossFunctionType: The loss criteria is the (K-class) multinomial negative log-likelihood, or multinomial deviance.
MultipleComparisons - Class in com.imsl.stat: Performs Student-Newman-Keuls multiple comparisons test.
MultipleComparisons(double[], int, double) - Constructor for class com.imsl.stat.MultipleComparisons: Constructor for MultipleComparisons.
MULTIPLICATION - Static variable in class com.imsl.stat.ClusterHierarchical: Indicates transformation by multiplication by -1.0.
multiply(Complex, Complex) - Static method in class com.imsl.math.Complex: Returns the product of two Complex objects, x * y.
multiply(Complex, double) - Static method in class com.imsl.math.Complex: Returns the product of a Complex object and a double, x * y.
multiply(double, Complex) - Static method in class com.imsl.math.Complex: Returns the product of a double and a Complex object, x * y.
multiply(Complex[], Complex[][]) - Static method in class com.imsl.math.ComplexMatrix: Return the product of the row vector x and the rectangular array a, both Complex.
multiply(Complex[][], Complex[]) - Static method in class com.imsl.math.ComplexMatrix: Multiply the rectangular array a and the column vector x, both Complex.
multiply(Complex[][], Complex[][]) - Static method in class com.imsl.math.ComplexMatrix: Multiply two Complex rectangular arrays, a * b.
multiply(Complex[][], Complex[][], int) - Static method in class com.imsl.math.ComplexMatrix: Multiply two Complex rectangular arrays, a * b, using multiple java.lang.Threads.
multiply(Complex[]) - Method in class com.imsl.math.ComplexSparseMatrix: Multiply the matrix by a vector.
multiply(ComplexSparseMatrix, Complex[]) - Static method in class com.imsl.math.ComplexSparseMatrix: Multiply sparse matrix A and column array x, $A x$ .
multiply(Complex[], ComplexSparseMatrix) - Static method in class com.imsl.math.ComplexSparseMatrix: Multiply row array x and sparse matrix A, $x^TA$ .
multiply(ComplexSparseMatrix, ComplexSparseMatrix) - Static method in class com.imsl.math.ComplexSparseMatrix: Multiply two sparse complex matrices A and B, $C leftarrow AB$ .
multiply(double[], double[][]) - Static method in class com.imsl.math.Matrix: Return the product of the row array x and the rectangular array a.
multiply(double[][], double[]) - Static method in class com.imsl.math.Matrix: Multiply the rectangular array a and the column array x.
multiply(double[][], double[][]) - Static method in class com.imsl.math.Matrix: Multiply two rectangular arrays, a * b.
multiply(double[][], double[][], int) - Static method in class com.imsl.math.Matrix: Multiply two rectangular arrays, a * b, using multiple java.lang.Threads.
multiply(Physical, Physical) - Static method in class com.imsl.math.Physical: Multiply two Physical objects.
multiply(Physical, double) - Static method in class com.imsl.math.Physical: Multiply a Physical object and a double
multiply(double, Physical) - Static method in class com.imsl.math.Physical: Multiply a double and a Physical object
multiply(double[]) - Method in class com.imsl.math.SparseMatrix: Multiply the matrix by a vector.
multiply(SparseMatrix, double[]) - Static method in class com.imsl.math.SparseMatrix: Multiply sparse matrix A and column array x, $A x$ .
multiply(double[], SparseMatrix) - Static method in class com.imsl.math.SparseMatrix: Multiply row array x and sparse matrix A, $x^TA$ .
multiply(SparseMatrix, SparseMatrix) - Static method in class com.imsl.math.SparseMatrix: Multiply two sparse matrices A and B, $C leftarrow AB$ .
multiplyHermitian(ComplexSparseMatrix, Complex[]) - Static method in class com.imsl.math.ComplexSparseMatrix: Multiply sparse Hermitian matrix A and column vector x.
multiplyImag(Complex, double) - Static method in class com.imsl.math.Complex: Returns the product of a Complex object and a pure imaginary double, x * iy.
multiplyImag(double, Complex) - Static method in class com.imsl.math.Complex: Returns the product of a pure imaginary double and a Complex object, ix * y.
multiplySymmetric(SparseMatrix, double[]) - Static method in class com.imsl.math.SparseMatrix: Multiply sparse symmetric matrix A and column vector x.

N

NaiveBayesClassifier - Class in com.imsl.datamining: Trains a Naive Bayes Classifier
NaiveBayesClassifier(int, int, int) - Constructor for class com.imsl.datamining.NaiveBayesClassifier: Constructs a NaiveBayesClassifier
NATURAL_ORDERING - Static variable in class com.imsl.math.ComplexSuperLU: For column ordering, use the natural ordering.
NATURAL_ORDERING - Static variable in class com.imsl.math.SuperLU: For column ordering, use the natural ordering.
nCoef - Variable in class com.imsl.math.BsLeastSquares: Number of B-spline coefficients.
negate(Complex) - Static method in class com.imsl.math.Complex: Returns the negative of a Complex object, -z.
negate(Physical) - Static method in class com.imsl.math.Physical: Negate a Physical object.
Network - Class in com.imsl.datamining.neural: Neural network base class.
Network() - Constructor for class com.imsl.datamining.neural.Network: Default constructor for Network.
next() - Method in class com.imsl.io.AbstractFlatFile: Moves the cursor down one row from its current position.
next(int) - Method in class com.imsl.stat.MersenneTwister: Generates the next pseudorandom number.
next(int) - Method in class com.imsl.stat.MersenneTwister64: Generates the next pseudorandom number.
next(int) - Method in interface com.imsl.stat.Random.BaseGenerator: Generates the next pseudorandom number.
next(int) - Method in class com.imsl.stat.Random: Generates the next pseudorandom number.
nextAfter(double, double) - Static method in class com.imsl.math.IEEE: Returns the next machine floating-point number next to x in the direction toward y.
nextBeta(double, double) - Method in class com.imsl.stat.Random: Generate a pseudorandom number from a beta distribution.
nextBinomial(int, double) - Method in class com.imsl.stat.Random: Generate a pseudorandom number from a binomial distribution.
nextCauchy() - Method in class com.imsl.stat.Random: Generates a pseudorandom number from a Cauchy distribution.
nextChiSquared(double) - Method in class com.imsl.stat.Random: Generates a pseudorandom number from a Chi-squared distribution.
nextDiscrete(int, double[]) - Method in class com.imsl.stat.Random: Generate a pseudorandom number from a general discrete distribution using an alias method.
nextDouble() - Method in class com.imsl.stat.FaureSequence: Returns the first value of the next point in the sequence.
nextDouble() - Method in class com.imsl.stat.MersenneTwister: Generates the next pseudorandom, uniformly distributed double value from this random number generator's sequence.
nextDouble() - Method in class com.imsl.stat.MersenneTwister64: Generates the next pseudorandom, uniformly distributed double value from this random number generator's sequence.
nextExponential() - Method in class com.imsl.stat.Random: Generates a pseudorandom number from a standard exponential distribution.
nextExponentialMix(double, double, double) - Method in class com.imsl.stat.Random: Generate a pseudorandom number from a mixture of two exponential distributions.
nextExtremeValue(double, double) - Method in class com.imsl.stat.Random: Generate a pseudorandom number from an extreme value distribution.
nextF(double, double) - Method in class com.imsl.stat.Random: Generate a pseudorandom number from the F distribution.
nextFloat() - Method in class com.imsl.stat.MersenneTwister: Generates the next pseudorandom, uniformly distributed float value from this random number generator's sequence.
nextFloat() - Method in class com.imsl.stat.MersenneTwister64: Generates the next pseudorandom, uniformly distributed float value from this random number generator's sequence.
nextGamma(double) - Method in class com.imsl.stat.Random: Generates a pseudorandom number from a standard gamma distribution.
nextGaussianCopula(Cholesky) - Method in class com.imsl.stat.Random: Generate pseudorandom numbers from a Gaussian Copula distribution.
nextGeometric(double) - Method in class com.imsl.stat.Random: Generate a pseudorandom number from a geometric distribution.
nextHypergeometric(int, int, int) - Method in class com.imsl.stat.Random: Generate a pseudorandom number from a hypergeometric distribution.
nextInt() - Method in class com.imsl.stat.MersenneTwister: Generates the next pseudorandom number.
nextLogarithmic(double) - Method in class com.imsl.stat.Random: Generate a pseudorandom number from a logarithmic distribution.
nextLogNormal(double, double) - Method in class com.imsl.stat.Random: Generate a pseudorandom number from a lognormal distribution.
nextLong() - Method in class com.imsl.stat.MersenneTwister64: Generates the next pseudorandom, uniformly distributed long value from this random number generator's sequence.
nextMultivariateNormal(Cholesky) - Method in class com.imsl.stat.Random: Generate pseudorandom numbers from a multivariate normal distribution.
nextNegativeBinomial(double, double) - Method in class com.imsl.stat.Random: Generate a pseudorandom number from a negative binomial distribution.
nextNormal() - Method in class com.imsl.stat.Random: Generate a pseudorandom number from a standard normal distribution using an inverse CDF method.
nextPoint() - Method in class com.imsl.stat.FaureSequence: Returns the next point in the sequence.
nextPoint() - Method in interface com.imsl.stat.RandomSequence: Returns the next multidimensional point in the sequence.
nextPoisson(double) - Method in class com.imsl.stat.Random: Generate a pseudorandom number from a Poisson distribution.
nextPrime(int) - Static method in class com.imsl.stat.FaureSequence: Returns the smallest prime greater than or equal to n.
nextRayleigh(double) - Method in class com.imsl.stat.Random: Generate a pseudorandom number from a Rayleigh distribution.
nextStudentsT(double) - Method in class com.imsl.stat.Random: Generate a pseudorandom number from a Student's t distribution.
nextStudentsTCopula(double, Cholesky) - Method in class com.imsl.stat.Random: Generate pseudorandom numbers from a Student's t Copula distribution.
nextToken() - Method in class com.imsl.io.Tokenizer: Returns the next token.
nextTriangular() - Method in class com.imsl.stat.Random: Generate a pseudorandom number from a triangular distribution on the interval (0,1).
nextUniformDiscrete(int) - Method in class com.imsl.stat.Random: Generate a pseudorandom number from a discrete uniform distribution.
nextVonMises(double) - Method in class com.imsl.stat.Random: Generate a pseudorandom number from a von Mises distribution.
nextWeibull(double) - Method in class com.imsl.stat.Random: Generate a pseudorandom number from a Weibull distribution.
nextZigguratNormalAR() - Method in class com.imsl.stat.Random: Generates pseudorandom numbers using the Ziggurat method.
nFunctionEvaluations - Variable in class com.imsl.datamining.neural.QuasiNewtonTrainer.GradObjective
nFunctionEvaluations - Variable in class com.imsl.datamining.neural.QuasiNewtonTrainer.Objective
NO_CENTER - Static variable in class com.imsl.stat.ARSeasonalFit: Indicates the transformed series should not be centered.
NO_SCALING - Static variable in class com.imsl.datamining.neural.ScaleFilter: Flag to indicate no scaling.
NO_SCALING - Static variable in class com.imsl.math.ComplexSuperLU: Indicates that input matrix A was not equilibrated before factorization.
NO_SCALING - Static variable in class com.imsl.math.SuperLU: Indicates that input matrix A was not equilibrated before factorization.
NO_SCALING - Static variable in class com.imsl.stat.Dissimilarities: Indicates no scaling.
nObs - Variable in class com.imsl.datamining.neural.QuasiNewtonTrainer.GradObjective
nObs - Variable in class com.imsl.datamining.neural.QuasiNewtonTrainer.Objective
node - Variable in class com.imsl.chart.Draw
Node - Class in com.imsl.datamining.neural: A Node in a neural network.
nominal(double, int) - Static method in class com.imsl.finance.Finance: Returns the nominal annual interest rate.
noncentralBeta(double, double, double, double) - Static method in class com.imsl.stat.Cdf: Evaluates the noncentral beta cumulative distribution function (CDF).
noncentralBeta(double, double, double, double) - Static method in class com.imsl.stat.InvCdf: Evaluates the inverse of the noncentral beta cumulative distribution function (CDF).
noncentralBeta(double, double, double, double) - Static method in class com.imsl.stat.Pdf: Evaluates the noncentral beta probability density function (PDF).
noncentralchi(double, double, double) - Static method in class com.imsl.stat.Cdf: Evaluates the noncentral chi-squared cumulative probability distribution function.
noncentralchi(double, double, double) - Static method in class com.imsl.stat.InvCdf: Evaluates the inverse of the noncentral chi-squared cumulative probability distribution function.
noncentralChi(double, double, double) - Static method in class com.imsl.stat.Pdf: Evaluates the noncentral chi-squared probability density function (PDF).
noncentralF(double, double, double, double) - Static method in class com.imsl.stat.Cdf: Evaluates the noncentral F cumulative distribution function.
noncentralF(double, double, double, double) - Static method in class com.imsl.stat.InvCdf: Evaluates the inverse of the noncentral F cumulative distribution function (CDF).
noncentralF(double, double, double, double) - Static method in class com.imsl.stat.Pdf: Evaluates the noncentral F probability density function (PDF).
noncentralstudentsT(double, int, double) - Static method in class com.imsl.stat.Cdf: Evaluates the noncentral Student's t cumulative probability distribution function.
noncentralstudentsT(double, int, double) - Static method in class com.imsl.stat.InvCdf: Evaluates the inverse of the noncentral Student's t cumulative probability distribution function.
noncentralStudentsT(double, double, double) - Static method in class com.imsl.stat.Pdf: Evaluates the noncentral Student's t probability density function.
NONE - Static variable in class com.imsl.chart.Draw
NONE - Static variable in class com.imsl.stat.ClusterHierarchical: Indicates no transformation.
NonlinearRegression - Class in com.imsl.stat: Fits a multivariate nonlinear regression model using least squares.
NonlinearRegression(int) - Constructor for class com.imsl.stat.NonlinearRegression: Constructs a new nonlinear regression object.
NonlinearRegression.Derivative - Interface in com.imsl.stat: Public interface for the user supplied function to compute the derivative for NonlinearRegression.
NonlinearRegression.Function - Interface in com.imsl.stat: Public interface for the user supplied function for NonlinearRegression.
NonlinearRegression.NegativeFreqException - Exception in com.imsl.stat: A negative frequency was encountered.
NonlinearRegression.NegativeFreqException(int, int, double) - Constructor for exception com.imsl.stat.NonlinearRegression.NegativeFreqException: Constructs a NegativeFreqException.
NonlinearRegression.NegativeWeightException - Exception in com.imsl.stat: A negative weight was encountered.
NonlinearRegression.NegativeWeightException(int, int, double) - Constructor for exception com.imsl.stat.NonlinearRegression.NegativeWeightException: Constructs a NegativeWeightException.
NonlinearRegression.TooManyIterationsException - Exception in com.imsl.stat: The number of iterations has exceeded the maximum allowed.
NonlinearRegression.TooManyIterationsException() - Constructor for exception com.imsl.stat.NonlinearRegression.TooManyIterationsException: Constructs a TooManyIterationsException.
NonlinLeastSquares - Class in com.imsl.math: Nonlinear least squares.
NonlinLeastSquares(int, int) - Constructor for class com.imsl.math.NonlinLeastSquares: Creates an object to solve a nonlinear least squares problem.
NonlinLeastSquares.Function - Interface in com.imsl.math: Public interface for the user supplied function to the NonlinLeastSquares object.
NonlinLeastSquares.Jacobian - Interface in com.imsl.math: Public interface for the user supplied function to the NonlinLeastSquares object.
NonlinLeastSquares.TooManyIterationsException - Exception in com.imsl.math: Too many iterations.
NonlinLeastSquares.TooManyIterationsException() - Constructor for exception com.imsl.math.NonlinLeastSquares.TooManyIterationsException: Constructs a TooManyIterationsException object.
NonlinLeastSquares.TooManyIterationsException(String, Object[]) - Constructor for exception com.imsl.math.NonlinLeastSquares.TooManyIterationsException: Constructs a TooManyIterationsException object.
NonlinLeastSquares.TooManyIterationsException(Object[]) - Constructor for exception com.imsl.math.NonlinLeastSquares.TooManyIterationsException: Constructs a TooManyIterationsException object.
NonNegativeLeastSquares - Class in com.imsl.math: Solves a linear least squares problem with nonnegativity constraints.
NonNegativeLeastSquares(double[][], double[]) - Constructor for class com.imsl.math.NonNegativeLeastSquares: Construct a new NonNegativeLeastSquares instance to solve Ax-b where x is a vector of n unknowns.
NonNegativeLeastSquares.TooManyIterException - Exception in com.imsl.math: Maximum number of iterations has been exceeded.
NonNegativeLeastSquares.TooManyIterException(String) - Constructor for exception com.imsl.math.NonNegativeLeastSquares.TooManyIterException: The maximum number of iterations has been exceeded.
NonNegativeLeastSquares.TooManyIterException(String, Object[]) - Constructor for exception com.imsl.math.NonNegativeLeastSquares.TooManyIterException: The maximum number of iterations has been exceeded.
NonNegativeLeastSquares.TooMuchTimeException - Exception in com.imsl.math: Maximum time allowed for solve is exceeded.
NonNegativeLeastSquares.TooMuchTimeException(String) - Constructor for exception com.imsl.math.NonNegativeLeastSquares.TooMuchTimeException: The maximum time allowed for solve is exceeded.
NonNegativeLeastSquares.TooMuchTimeException(String, Object[]) - Constructor for exception com.imsl.math.NonNegativeLeastSquares.TooMuchTimeException: The maximum time allowed for solve is exceeded.
norm(double[]) - Method in interface com.imsl.math.GenMinRes.Norm: Used to compute the norm $Vert X Vert$ in the Gram-Schmidt implementation.
normal(double) - Static method in class com.imsl.stat.Cdf: Evaluates the normal (Gaussian) cumulative probability distribution function.
normal(double) - Static method in class com.imsl.stat.InvCdf: Evaluates the inverse of the normal (Gaussian) cumulative probability distribution function.
normal(double, double, double) - Static method in class com.imsl.stat.Pdf: Evaluates the normal (Gaussian) probability density function.
NormalDistribution - Class in com.imsl.stat: Evaluates the normal (Gaussian) probability density for a given set of data.
NormalDistribution() - Constructor for class com.imsl.stat.NormalDistribution
NormalityTest - Class in com.imsl.stat: Performs a test for normality.
NormalityTest(double[]) - Constructor for class com.imsl.stat.NormalityTest: Constructor for NormalityTest.
NormalityTest.NoVariationInputException - Exception in com.imsl.stat: There is no variation in the input data.
NormalityTest.NoVariationInputException(String) - Constructor for exception com.imsl.stat.NormalityTest.NoVariationInputException: Constructs a NoVariationInputException object.
NormalityTest.NoVariationInputException(String, Object[]) - Constructor for exception com.imsl.stat.NormalityTest.NoVariationInputException: Constructs a NoVariationInputException object.
NormalPD - Class in com.imsl.stat.distributions: The normal (Gaussian) probability distribution
NormalPD() - Constructor for class com.imsl.stat.distributions.NormalPD: Constructor for the normal probability distribution
NormOneSample - Class in com.imsl.stat: Computes statistics for mean and variance inferences using a sample from a normal population.
NormOneSample(double[]) - Constructor for class com.imsl.stat.NormOneSample: Constructor to compute statistics for mean and variance inferences using a sample from a normal population.
NormTwoSample - Class in com.imsl.stat: Computes statistics for mean and variance inferences using samples from two normal populations.
NormTwoSample(double[], double[]) - Constructor for class com.imsl.stat.NormTwoSample: Constructor to compute statistics for mean and variance inferences using samples from two normal populations.
NOT_A_KNOT - Static variable in class com.imsl.math.CsInterpolate
NpChart - Class in com.imsl.chart.qc: NpChart is an np-chart for monitoring the number of defects when defects are not rare.
NpChart(AxisXY, int, int[]) - Constructor for class com.imsl.chart.qc.NpChart: Creates an np-Chart given the number of defects in a series of samples.
NpChart(AxisXY, int[], int[]) - Constructor for class com.imsl.chart.qc.NpChart: Creates a np-Chart given the number of defects in a series of samples, where the number of observations per sample is not constant.
nper(double, double, double, double, int) - Static method in class com.imsl.finance.Finance: Returns the number of periods for an investment for which periodic, and constant payments are made and the interest rate is constant.
npv(double, double[]) - Static method in class com.imsl.finance.Finance: Returns the net present value of a stream of equal periodic cash flows, which are subject to a given discount rate.
numberFormat - Variable in class com.imsl.math.PrintMatrixFormat: The NumberFormat to be used in formatting double and Complex entries.
numberOfColumns - Variable in class com.imsl.math.ComplexSparseMatrix.SparseArray: Number of columns in the matrix.
numberOfColumns - Variable in class com.imsl.math.SparseMatrix.SparseArray: Number of columns in the matrix.
numberOfNonZeros - Variable in class com.imsl.math.ComplexSparseMatrix.SparseArray: Number of nonzeros in the matrix.
numberOfNonZeros - Variable in class com.imsl.math.SparseMatrix.SparseArray: Number of nonzeros in the matrix.
numberOfObservations(double[]) - Static method in class com.imsl.stat.Summary: Returns the number of non-missing observations in the given data set.
numberOfRows - Variable in class com.imsl.math.ComplexSparseMatrix.SparseArray: Number of rows in the matrix.
numberOfRows - Variable in class com.imsl.math.SparseMatrix.SparseArray: Number of rows in the matrix.
NumericalDerivatives - Class in com.imsl.math: Compute the Jacobian matrix for a function $f(y)$ with m components in n independent variables.
NumericalDerivatives(NumericalDerivatives.Function) - Constructor for class com.imsl.math.NumericalDerivatives: Constructor for NumericalDerivatives.
NumericalDerivatives.Function - Interface in com.imsl.math: Public interface function.
NumericalDerivatives.Jacobian - Interface in com.imsl.math: Public interface for the user-supplied function to compute the Jacobian.
nY - Variable in class com.imsl.datamining.neural.QuasiNewtonTrainer.GradObjective
nY - Variable in class com.imsl.datamining.neural.QuasiNewtonTrainer.Objective

O

ODE - Class in com.imsl.math: ODE represents and solves an initial-value problem for ordinary differential equations.
ODE() - Constructor for class com.imsl.math.ODE
OdeAdamsGear - Class in com.imsl.math: Extension of the ODE class to solve a stiff initial-value problem for ordinary differential equations using the Adams-Gear methods.
OdeAdamsGear(OdeAdamsGear.Function) - Constructor for class com.imsl.math.OdeAdamsGear: Constructs an ODE solver to solve the initial value problem dy/dt = f(t,y)
OdeAdamsGear.DidNotConvergeException - Exception in com.imsl.math: The iteration did not converge within the maximum number of steps allowed (default 500).
OdeAdamsGear.DidNotConvergeException(String) - Constructor for exception com.imsl.math.OdeAdamsGear.DidNotConvergeException: Constructs a DidNotConvergeException with the specified detailed message.
OdeAdamsGear.DidNotConvergeException(String, Object[]) - Constructor for exception com.imsl.math.OdeAdamsGear.DidNotConvergeException: Constructs a DidNotConvergeException with the specified detailed message.
OdeAdamsGear.Function - Interface in com.imsl.math: Public interface for user supplied function to OdeAdamsGear object.
OdeAdamsGear.Jacobian - Interface in com.imsl.math: Public interface for the user supplied function to evaluate the Jacobian matrix.
OdeAdamsGear.MaxFcnEvalsExceededException - Exception in com.imsl.math: Maximum function evaluations exceeded.
OdeAdamsGear.MaxFcnEvalsExceededException(String) - Constructor for exception com.imsl.math.OdeAdamsGear.MaxFcnEvalsExceededException: Constructs a MaxFcnEvalsExceededException with the specified detailed message.
OdeAdamsGear.MaxFcnEvalsExceededException(String, Object[]) - Constructor for exception com.imsl.math.OdeAdamsGear.MaxFcnEvalsExceededException: Constructs a MaxFcnEvalsExceededException with the specified detailed message.
OdeAdamsGear.SingularMatrixException - Exception in com.imsl.math: The interpolation matrix is singular.
OdeAdamsGear.SingularMatrixException(String) - Constructor for exception com.imsl.math.OdeAdamsGear.SingularMatrixException: Constructs a SingularMatrixException with the specified detailed message.
OdeAdamsGear.SingularMatrixException(String, Object[]) - Constructor for exception com.imsl.math.OdeAdamsGear.SingularMatrixException: Constructs a SingularMatrixException with the specified detailed message.
OdeAdamsGear.ToleranceTooSmallException - Exception in com.imsl.math: Tolerance is too small or the problem is stiff.
OdeAdamsGear.ToleranceTooSmallException(String) - Constructor for exception com.imsl.math.OdeAdamsGear.ToleranceTooSmallException: Constructs a ToleranceTooSmallException with the specified detailed message.
OdeAdamsGear.ToleranceTooSmallException(String, Object[]) - Constructor for exception com.imsl.math.OdeAdamsGear.ToleranceTooSmallException: Constructs a ToleranceTooSmallException with the specified detailed message.
OdeRungeKutta - Class in com.imsl.math: Solves an initial-value problem for ordinary differential equations using the Runge-Kutta-Verner fifth-order and sixth-order method.
OdeRungeKutta(OdeRungeKutta.Function) - Constructor for class com.imsl.math.OdeRungeKutta: Constructs an ODE solver to solve the initial value problem dy/dt = f(t,y)
OdeRungeKutta.DidNotConvergeException - Exception in com.imsl.math: The iteration did not converge within the maximum number of steps allowed (default 500).
OdeRungeKutta.DidNotConvergeException(String) - Constructor for exception com.imsl.math.OdeRungeKutta.DidNotConvergeException: Constructs a DidNotConvergeException with the specified detailed message.
OdeRungeKutta.DidNotConvergeException(String, Object[]) - Constructor for exception com.imsl.math.OdeRungeKutta.DidNotConvergeException: Constructs a DidNotConvergeException with the specified detailed message.
OdeRungeKutta.Function - Interface in com.imsl.math: Public interface for user supplied function to OdeRungeKutta object.
OdeRungeKutta.ToleranceTooSmallException - Exception in com.imsl.math: Tolerance is too small or the problem is stiff.
OdeRungeKutta.ToleranceTooSmallException(String) - Constructor for exception com.imsl.math.OdeRungeKutta.ToleranceTooSmallException: Constructs a ToleranceTooSmallException with the specified detailed message.
OdeRungeKutta.ToleranceTooSmallException(String, Object[]) - Constructor for exception com.imsl.math.OdeRungeKutta.ToleranceTooSmallException: Constructs a ToleranceTooSmallException with the specified detailed message.
ONE_AT_A_TIME - Static variable in class com.imsl.stat.ANOVA: The One-at-a-Time (Fisher's LSD) method
ONE_SIDED - Static variable in class com.imsl.math.NumericalDerivatives: Indicates one sided differences.
oneNorm(Complex[][]) - Static method in class com.imsl.math.ComplexMatrix: Return the Complex matrix one norm.
oneNorm() - Method in class com.imsl.math.ComplexSparseMatrix: Returns the matrix one norm of the sparse matrix.
oneNorm(double[][]) - Static method in class com.imsl.math.Matrix: Return the matrix one norm.
oneNorm() - Method in class com.imsl.math.SparseMatrix: Returns the matrix one norm of the sparse matrix.
order - Variable in class com.imsl.math.BSpline: Order of the spline.
ORDERED_DISCRETE - Static variable in class com.imsl.datamining.PredictiveModel.VariableType: The associated variable can take on limitless but discrete values.
out - Variable in class com.imsl.WarningObject: The warning stream.
outline - Static variable in class com.imsl.chart.Draw: Markers defined on a [-1,1] x [-1,1] grid.
OutputLayer - Class in com.imsl.datamining.neural: Output layer in a neural network.
OutputPerceptron - Class in com.imsl.datamining.neural: A Perceptron in the OutputLayer.

P

paint(Draw) - Method in class com.imsl.chart.Annotation: Paints this node and all of its children.
paint(Draw) - Method in class com.imsl.chart.Axis: Paints this node and all of its children.
paint(Draw) - Method in class com.imsl.chart.Axis1D: Paints this node and all of its children.
paint(Draw) - Method in class com.imsl.chart.AxisLabel: Paints this node and all of its children.
paint(Draw) - Method in class com.imsl.chart.AxisLine: Paints this node and all of its children.
paint(Draw) - Method in class com.imsl.chart.AxisR: Paints this node and all of its children.
paint(Draw) - Method in class com.imsl.chart.AxisRLabel: Paints this node and all of its children.
paint(Draw) - Method in class com.imsl.chart.AxisRLine: Paints this node and all of its children.
paint(Draw) - Method in class com.imsl.chart.AxisRMajorTick: Paints this node and all of its children.
paint(Draw) - Method in class com.imsl.chart.AxisTheta: Paints this node and all of its children.
paint(Draw) - Method in class com.imsl.chart.AxisTitle: Paints this node and all of its children.
paint(Draw) - Method in class com.imsl.chart.AxisUnit: Paints this node and all of its children.
paint(Draw) - Method in class com.imsl.chart.AxisXY: Paints this node and all of its children.
paint(Draw) - Method in class com.imsl.chart.Background: Paint this node.
paint(Draw) - Method in class com.imsl.chart.Bar: Paints this node and all of its children.
paint(Draw) - Method in class com.imsl.chart.BarItem: Paints this node and all of its children.
paint(Draw) - Method in class com.imsl.chart.BarSet
paint(Draw) - Method in class com.imsl.chart.BoxPlot: Paints this node and all of its children.
paint(Draw) - Method in class com.imsl.chart.Candlestick: Paints this node and all of its children.
paint(Draw) - Method in class com.imsl.chart.CandlestickItem: Paints this node and all of its children.
paint(Draw) - Method in class com.imsl.chart.Chart: Paints this node and all of its children.
paint(Graphics) - Method in class com.imsl.chart.Chart: Paints this node and all of its children.
paint(Draw) - Method in class com.imsl.chart.ChartNode: Paints this node and all of its children.
paint(Draw) - Method in class com.imsl.chart.ChartTitle: Paints this node and all of its children.
paint(Draw) - Method in class com.imsl.chart.Contour.Legend: Paints this node and all of its children.
paint(Draw) - Method in class com.imsl.chart.Contour
paint(Draw) - Method in class com.imsl.chart.ContourLevel: Paints this node and all of its children.
paint(Draw) - Method in class com.imsl.chart.Data: Paints this node and all of its children.
paint(Draw) - Method in class com.imsl.chart.Dendrogram: Paints this node and all of its children.
paint(Draw) - Method in class com.imsl.chart.ErrorBar: Paints this node and all of its children.
paint(Draw) - Method in class com.imsl.chart.Grid: Paints this node and all of its children.
paint(Draw) - Method in class com.imsl.chart.GridPolar: Paints this node and all of its children.
paint(Draw) - Method in class com.imsl.chart.Heatmap.Legend: Paints this node and all of its children.
paint(Draw) - Method in class com.imsl.chart.Heatmap: Paints this node and all of its children.
paint(Draw) - Method in class com.imsl.chart.HighLowClose: Paints this node and all of its children.
paint(Draw) - Method in class com.imsl.chart.Legend: Paints this node and all of its children.
paint(Draw) - Method in class com.imsl.chart.MajorTick: Paints this node and all of its children.
paint(Draw) - Method in class com.imsl.chart.MinorTick: Paints this node and all of its children.
paint(Draw) - Method in class com.imsl.chart.PieSlice: Paints this node and all of its children.
paint(Draw) - Method in class com.imsl.chart.Polar: Paints this node and all of its children.
paint(Draw) - Method in class com.imsl.chart.qc.ControlLimit: Paints the horizontal control limit line as wide as the window.
paint(Draw) - Method in class com.imsl.chart.qc.ShewhartControlChart: Paints this node and all of its children.
paint(Draw) - Method in class com.imsl.chart.ToolTip: Paints this node and all of its children.
paint(Draw) - Method in class com.imsl.chart.Treemap.Legend: Paints this node and all of its children.
paint(Draw) - Method in class com.imsl.chart.Treemap: Paints this node and all of its children.
paint(Graphics) - Method in class com.imsl.chart3d.BufferedPaint: Paint the image onto the canvas.
paint(Graphics) - Method in class com.imsl.chart3d.Canvas3DChart: Paint method overriden to correct a problem in JDK 1.4.
paint(Graphics) - Method in interface com.imsl.chart3d.Canvas3DChart.Paint
paintChart(Graphics) - Method in class com.imsl.chart.Chart: Draw the chart using the given Graphics object.
paintComponent(Graphics) - Method in class com.imsl.chart.JPanelChart: Calls the UI delegate's paint method, if the UI delegate is non-null.
paintImage() - Method in class com.imsl.chart.Chart: Returns an Image of the chart.
PANEL_SIZE - Static variable in class com.imsl.math.ComplexSuperLU: A performance tuning parameter which can be adjusted via method setPerformanceTuningParameters.
PANEL_SIZE - Static variable in class com.imsl.math.SuperLU: A performance tuning parameter which can be adjusted via method setPerformanceTuningParameters.
Pareto(double, double, double) - Static method in class com.imsl.stat.Cdf: Evaluates the Pareto cumulative probability distribution function.
Pareto(double, double, double) - Static method in class com.imsl.stat.InvCdf: Returns the inverse of the Pareto cumulative probability density function.
Pareto(double, double, double) - Static method in class com.imsl.stat.Pdf: Evaluates the Pareto probability density function.
ParetoChart - Class in com.imsl.chart.qc: ParetoChart is a Pareto bar chart.
ParetoChart(AxisXY, String[], int[]) - Constructor for class com.imsl.chart.qc.ParetoChart: Constructs a Pareto chart.
ParetoChart(AxisXY, String[], int[], double, String) - Constructor for class com.imsl.chart.qc.ParetoChart: Constructs a Pareto chart showing only the most important bars.
ParetoChart(AxisXY, String[], int[], int, String) - Constructor for class com.imsl.chart.qc.ParetoChart: Constructs a Pareto chart showing only a limited number of bars.
parse(String) - Method in interface com.imsl.io.FlatFile.Parser: Parses a String into an Object.
parse(String) - Method in class com.imsl.io.Tokenizer: Sets the line to be tokenized.
PARSE_BYTE - Static variable in class com.imsl.io.FlatFile: Implements a Parser that converts a String to a Byte.
PARSE_DOUBLE - Static variable in class com.imsl.io.FlatFile: Implements a Parser that converts a String to a Double.
PARSE_FLOAT - Static variable in class com.imsl.io.FlatFile: Implements a Parser that converts a String to a Float.
PARSE_INTEGER - Static variable in class com.imsl.io.FlatFile: Implements a Parser that converts a String to an Integer.
PARSE_LONG - Static variable in class com.imsl.io.FlatFile: Implements a Parser that converts a String to a Long.
PARSE_SHORT - Static variable in class com.imsl.io.FlatFile: Implements a Parser that converts a String to a Short.
parseColor(String) - Static method in class com.imsl.chart.AbstractChartNode: Returns a color specified by name or a red-green-blue triple.
PartialCovariances - Class in com.imsl.stat: Class PartialCovariances computes the partial covariances or partial correlations from an input covariance or correlation matrix.
PartialCovariances(int, double[][], int) - Constructor for class com.imsl.stat.PartialCovariances: Creates a PartialCovariances object from a covariance or correleation matrix with a the independent variables in the initial columns and the dependent variables in the final columns.
PartialCovariances(int[], double[][], int) - Constructor for class com.imsl.stat.PartialCovariances: Creates a PartialCovariances object from a covariance or correleation matrix with a mix of dependent and independent variables.
PartialCovariances.InvalidMatrixException - Exception in com.imsl.stat: Exception thrown if a computed correlation is greater than one for some pair of variables.
PartialCovariances.InvalidMatrixException(int, int) - Constructor for exception com.imsl.stat.PartialCovariances.InvalidMatrixException: Creates an InvalidMatrixException thrown if a computed correlation is greater than one for some pair of variables.
PartialCovariances.InvalidPartialCorrelationException - Exception in com.imsl.stat: Exception thrown if a computed partial correlation is greater than one for some pair of variables.
PartialCovariances.InvalidPartialCorrelationException(int, int) - Constructor for exception com.imsl.stat.PartialCovariances.InvalidPartialCorrelationException: Creates an InvalidPartialCorrelationException thrown if a computed partial correlation is greater than one for some pair of variables.
path - Variable in class com.imsl.chart.Draw
PChart - Class in com.imsl.chart.qc: PChart is a p-chart for monitoring the defect rate when defects are not rare.
PChart(AxisXY, int, double[]) - Constructor for class com.imsl.chart.qc.PChart: Creates a p-Chart given the defect rates for a series of samples with equal sample sizes.
PChart(AxisXY, int[], double[]) - Constructor for class com.imsl.chart.qc.PChart: Creates a p-Chart given the defect rates for a series of samples with varying sample sizes.
PChart(AxisXY, int, int[]) - Constructor for class com.imsl.chart.qc.PChart: Creates a p-Chart given the number of defects for a series of samples with equal sample sizes.
PChart(AxisXY, int[], int[]) - Constructor for class com.imsl.chart.qc.PChart: Creates a p-Chart given the number of defects for a series of samples with varying sample sizes.
pdf(double, double[]) - Method in class com.imsl.stat.distributions.BetaPD: Returns the value of the beta probability density function.
pdf(double, double[]) - Method in class com.imsl.stat.distributions.GammaPD: Returns the value of the gamma probability density function.
pdf(double, double[]) - Method in class com.imsl.stat.distributions.NormalPD: Returns the value of the normal probability density function.
pdf(double, double[]) - Method in class com.imsl.stat.distributions.ProbabilityDistribution: Returns the value of the probability density function.
Pdf - Class in com.imsl.stat: Probability density functions.
Pdf.AltSeriesAccuracyLossException - Exception in com.imsl.stat: The magnitude of alternating series sum is too small relative to the sum of positive terms to permit a reliable accuracy.
Pdf.AltSeriesAccuracyLossException(String) - Constructor for exception com.imsl.stat.Pdf.AltSeriesAccuracyLossException: The magnitude of alternating series sum is too small relative to the sum of positive terms to permit a reliable accuracy.
Pdf.AltSeriesAccuracyLossException(String, Object[]) - Constructor for exception com.imsl.stat.Pdf.AltSeriesAccuracyLossException: The magnitude of alternating series sum is too small relative to the sum of positive terms to permit a reliable accuracy.
PDFGradientInterface - Interface in com.imsl.stat.distributions: A public interface for probability distributions that provide a method to calculate the gradient of the density function
PDFHessianInterface - Interface in com.imsl.stat.distributions: A public interface for probability distributions that provide methods to calculate the gradient and hessian of the density function
Perceptron - Class in com.imsl.datamining.neural: A Perceptron node in a neural network.
performanceIndex(double[][]) - Method in class com.imsl.math.Eigen: Returns the performance index of a real eigensystem.
performanceIndex(double[][]) - Method in class com.imsl.math.SymEigen: Returns the performance index of a real symmetric eigensystem.
Physical - Class in com.imsl.math: Return the value of various mathematical and physical constants.
Physical() - Constructor for class com.imsl.math.Physical: Constructs a new 0-valued, dimensionless object.
Physical(Physical) - Constructor for class com.imsl.math.Physical: Constructs a copy of a Physical object.
Physical(double, String) - Constructor for class com.imsl.math.Physical: Constructs a new Physical object and initializes this object to a double value.
Physical(double, int, int, int, int, int) - Constructor for class com.imsl.math.Physical: Constructs a new Physical object and initializes this object to a double value along with int values for length, mass, time, current, and temperature.
PI - Static variable in class com.imsl.math.JMath
pick(MouseEvent) - Method in class com.imsl.chart.Chart: Fire the PickListeners for the nodes hit by the event.
PickEvent - Class in com.imsl.chart: An event that indicates that a chart element has been selected.
PickEvent(MouseEvent) - Constructor for class com.imsl.chart.PickEvent: Construct a PickEvent object.
PickEvent(Component, int, long, int, int, int, int, boolean) - Constructor for class com.imsl.chart.PickEvent: Construct a PickEvent object at point (x,y).
PickListener - Interface in com.imsl.chart: The listener interface for receiving pick events.
pickNode() - Method in class com.imsl.chart.DrawPick: Register the currentNode as the "picked" node if the "PickListener" attribute is defined for the current node.
pickPerformed(PickEvent) - Method in interface com.imsl.chart.PickListener: Public interface for PickListener.
pickPerformed(PickEvent) - Method in class com.imsl.chart.ToolTip: Part of the PickListener interface.
Pie - Class in com.imsl.chart: A pie chart.
Pie(Chart) - Constructor for class com.imsl.chart.Pie: Constructs a Pie chart object.
Pie(Chart, double[]) - Constructor for class com.imsl.chart.Pie: Constructs a Pie chart object with a specified number of slices.
PieSlice - Class in com.imsl.chart: One wedge of a pie chart.
plusEquals(int, int, Complex) - Method in class com.imsl.math.ComplexSparseMatrix: Adds a value to an element in the matrix.
plusEquals(int, int, double) - Method in class com.imsl.math.SparseMatrix: Adds a value to an element in the matrix.
pmt(double, int, double, double, int) - Static method in class com.imsl.finance.Finance: Returns the periodic payment for an investment.
poch(double, double) - Static method in class com.imsl.math.Sfun: Returns a generalization of Pochhammer's symbol.
PointLight - Class in com.imsl.chart3d: A point light source.
PointLight(Chart3D) - Constructor for class com.imsl.chart3d.PointLight: Creates a point light source at the origin.
PointLight(Chart3D, double, double, double) - Constructor for class com.imsl.chart3d.PointLight: Creates a point light at a specified position.
pointToLine(int, int, int[], int[]) - Static method in class com.imsl.chart.PickEvent: Compute the distance from the point (Px,Py) to the line segment AB.
poisson(int, double) - Static method in class com.imsl.stat.Cdf: Evaluates the Poisson cumulative probability distribution function.
poisson(int, double) - Static method in class com.imsl.stat.Pdf: Evaluates the Poisson probability density function.
PoissonDistribution - Class in com.imsl.stat: Evaluates a Poisson probability density of a given set of data.
PoissonDistribution() - Constructor for class com.imsl.stat.PoissonDistribution
Polar - Class in com.imsl.chart: This Axis node is used for polar charts.
Polar(Chart) - Constructor for class com.imsl.chart.Polar: Create an AxisPolar.
poly(int[], int[]) - Method in class com.imsl.chart.DrawMap: Sets a polygon as the target.
POOL_INTERACTIONS - Static variable in class com.imsl.stat.ANOVAFactorial: Indicates factor nSubscripts is not error.
POOLED - Static variable in class com.imsl.stat.DiscriminantAnalysis: Indicates pooled covariances computation.
POOLED_GROUP - Static variable in class com.imsl.stat.DiscriminantAnalysis: Indicates pooled, group covariances computation.
postRender() - Method in class com.imsl.chart3d.Canvas3DChart: Calls the Paint objects added to the post-render list.
postSwap() - Method in class com.imsl.chart3d.Canvas3DChart: Writes the chart to a file as a bitmap image.
pow(Complex, double) - Static method in class com.imsl.math.Complex: Returns the Complex z raised to the x power, with a branch cut for the first parameter (z) along the negative real axis.
pow(Complex, Complex) - Static method in class com.imsl.math.Complex: Returns the Complex x raised to the Complex y power.
pow(double, double) - Static method in class com.imsl.math.JMath: Returns x to the power y.
ppmt(double, int, int, double, double, int) - Static method in class com.imsl.finance.Finance: Returns the payment on the principal for a specified period.
preconditioner(double[], double[]) - Method in interface com.imsl.math.ConjugateGradient.Preconditioner: Used to compute $z = M^{-1}r$ where M is the preconditioning matrix and r and z are arrays of length n, the order of matrix M.
preconditioner(double[], double[]) - Method in interface com.imsl.math.GenMinRes.Preconditioner: Used to compute $z = M^{-1}r$ where M is the preconditioning matrix and r and z are arrays of length n, the order of matrix M.
predict() - Method in class com.imsl.datamining.decisionTree.DecisionTree: Predicts the training examples (in-sample predictions) using the most recently grown tree.
predict(double[][]) - Method in class com.imsl.datamining.decisionTree.DecisionTree: Predicts new data using the most recently grown decision tree.
predict(double[][], double[]) - Method in class com.imsl.datamining.decisionTree.DecisionTree: Predicts new weighted data using the most recently grown decision tree.
predict() - Method in class com.imsl.datamining.GradientBoosting: Returns the predicted values on the training data.
predict(double[][]) - Method in class com.imsl.datamining.GradientBoosting: Returns the predicted values on the input test data.
predict(double[][], double[]) - Method in class com.imsl.datamining.GradientBoosting: Runs the gradient boosting on the training data and returns the predicted values on the weighted test data.
predict() - Method in class com.imsl.datamining.PredictiveModel: Predicts the response variable using the most recent fit.
predict(double[][]) - Method in class com.imsl.datamining.PredictiveModel: Predicts the response values using the most recent fit and the provided test data.
predict(double[][], double[]) - Method in class com.imsl.datamining.PredictiveModel: Predicts the response values using the most recent fit, the provided test data, and the test data case weights.
predictClass(double[], int[]) - Method in class com.imsl.datamining.NaiveBayesClassifier: Predicts the classification for the input pattern using the trained Naive Bayes classifier.
predictedClass(double[]) - Method in class com.imsl.datamining.neural.BinaryClassification: Calculates the classification probablities for the input pattern x, and returns either 0 or 1 identifying the class with the highest probability.
predictedClass(double[]) - Method in class com.imsl.datamining.neural.MultiClassification: Calculates the classification probablities for the input pattern x, and returns the class with the highest probability.
PredictiveModel - Class in com.imsl.datamining: Predictive model class.
PredictiveModel(PredictiveModel) - Constructor for class com.imsl.datamining.PredictiveModel: Constructs a PredictiveModel from an existing instance.
PredictiveModel(double[][], int, PredictiveModel.VariableType[]) - Constructor for class com.imsl.datamining.PredictiveModel: Constructs a PredictiveModel object for a single response variable and multiple predictor variables.
PredictiveModel.PredictiveModelException - Exception in com.imsl.datamining: An exception class intended to be the parent of all nested Exception classes where the enclosing class extends PredictiveModel.
PredictiveModel.PredictiveModelException(String) - Constructor for exception com.imsl.datamining.PredictiveModel.PredictiveModelException: Constructs a PredictiveModelException and issues the specified message.
PredictiveModel.PredictiveModelException(String, String, Object[]) - Constructor for exception com.imsl.datamining.PredictiveModel.PredictiveModelException
PredictiveModel.StateChangeException - Exception in com.imsl.datamining: Exception thrown when an input parameter has changed that might affect the model estimates or predictions.
PredictiveModel.StateChangeException(String) - Constructor for exception com.imsl.datamining.PredictiveModel.StateChangeException: Constructs a StateChangeException and issues the specified message.
PredictiveModel.StateChangeException(String, Object[]) - Constructor for exception com.imsl.datamining.PredictiveModel.StateChangeException: Constructs a StateChangeException with the specified detail message.
PredictiveModel.SumOfProbabilitiesNotOneException - Exception in com.imsl.datamining: Exception thrown when the sum of probabilities is not approximately one.
PredictiveModel.SumOfProbabilitiesNotOneException(String) - Constructor for exception com.imsl.datamining.PredictiveModel.SumOfProbabilitiesNotOneException: Constructs a SumOfProbabilitiesNotOneException and issues the specified message.
PredictiveModel.SumOfProbabilitiesNotOneException(String, Object[]) - Constructor for exception com.imsl.datamining.PredictiveModel.SumOfProbabilitiesNotOneException: Constructs a SumOfProbabilitiesNotOneException with the specified detail message.
PredictiveModel.VariableType - Class in com.imsl.datamining: An enumeration of data types/characteristics.
prePaint() - Method in class com.imsl.chart.ChartNode: The prePaint method is called in all nodes in a chart just before the chart is painted.
prePaint() - Method in class com.imsl.chart.qc.CChart: Setup chart with current settings.
prePaint() - Method in class com.imsl.chart.qc.CuSum
prePaint() - Method in class com.imsl.chart.qc.CuSumStatus: Setup chart with current settings.
prePaint() - Method in class com.imsl.chart.qc.EWMA: Setup chart with current settings.
prePaint() - Method in class com.imsl.chart.qc.NpChart: Setup chart with current settings.
prePaint() - Method in class com.imsl.chart.qc.PChart: Setup chart with current settings.
prePaint() - Method in class com.imsl.chart.qc.RChart: Setup chart with current settings.
prePaint() - Method in class com.imsl.chart.qc.SChart: Setup chart with current settings.
prePaint() - Method in class com.imsl.chart.qc.UChart: Setup chart with current settings.
prePaint() - Method in class com.imsl.chart.qc.XbarR: Setup chart with current settings.
prePaint() - Method in class com.imsl.chart.qc.XbarS: Setup chart with current settings.
prePaint() - Method in class com.imsl.chart.qc.XmR: Setup chart with current settings.
preRender() - Method in class com.imsl.chart3d.Canvas3DChart: Calls the Paint objects added to the pre-render list.
previous() - Method in class com.imsl.io.AbstractFlatFile: Moves the cursor to the previous row in this ResultSet object.
price(GregorianCalendar, GregorianCalendar, double, double, double, int, DayCountBasis) - Static method in class com.imsl.finance.Bond: Returns the price, per $100 face value, of a security that pays periodic interest.
price(GregorianCalendar, GregorianCalendar, GregorianCalendar, GregorianCalendar, double, double, double, int, DayCountBasis) - Static method in class com.imsl.finance.Bond: Returns the price of an odd first period, coupon bond, given its yield.
price(GregorianCalendar, GregorianCalendar, GregorianCalendar, double, double, double, int, DayCountBasis) - Static method in class com.imsl.finance.Bond: Returns the price of an odd last period coupon bond, given its yield.
pricedisc(GregorianCalendar, GregorianCalendar, double, double, DayCountBasis) - Static method in class com.imsl.finance.Bond: Returns the price of a discount bond given the discount rate.
pricemat(GregorianCalendar, GregorianCalendar, GregorianCalendar, double, double, DayCountBasis) - Static method in class com.imsl.finance.Bond: Returns the price, per $100 face value, of a discount bond.
priceyield(GregorianCalendar, GregorianCalendar, double, double, DayCountBasis) - Static method in class com.imsl.finance.Bond: Returns the price of a discount bond given the yield.
PRINCIPAL_COMPONENT_MODEL - Static variable in class com.imsl.stat.FactorAnalysis: Indicates principal component model.
PRINCIPAL_FACTOR_MODEL - Static variable in class com.imsl.stat.FactorAnalysis: Indicates principal factor model.
print(Graphics, PageFormat, int) - Method in class com.imsl.chart.Chart: This method implements the Printable interface.
print() - Method in class com.imsl.chart.JPanelChart: Print the chart, centered on a page.
print() - Method in class com.imsl.chart.qc.CuSumStatus: Prints the tabular CuSum results.
print() - Method in class com.imsl.datamining.AssociationRule: Print the member data in this object.
print(AssociationRule[]) - Static method in class com.imsl.datamining.AssociationRule: Print out the association rules in ar.
print() - Method in class com.imsl.datamining.Itemsets: Prints a standard representation of the members of this object.
print(String) - Method in class com.imsl.math.PrintMatrix: Print a string.
print(Object) - Method in class com.imsl.math.PrintMatrix: Prints an nRows by nColumns matrix with specified format.
print(PrintMatrixFormat, Object) - Method in class com.imsl.math.PrintMatrix: Prints an nRows by nColumns matrix with specified format.
print(Object, String, String, Object[]) - Static method in class com.imsl.Warning: Issue a warning message.
print(Object, String, String, Object[]) - Method in class com.imsl.WarningObject: Issue a warning message.
printDecisionTree(boolean) - Method in class com.imsl.datamining.decisionTree.DecisionTree: Prints the contents of the Decision Tree using distinct but general labels.
printDecisionTree(String, String[], String[], String[], boolean) - Method in class com.imsl.datamining.decisionTree.DecisionTree: Prints the contents of the Decision Tree.
printHTML(PrintMatrixFormat, Object, int, int) - Method in class com.imsl.math.PrintMatrix: Prints an nRows by nColumns matrix with specified format for HTML output.
println() - Method in class com.imsl.math.PrintMatrix: Print a newline.
PrintMatrix - Class in com.imsl.math: Matrix printing utilities.
PrintMatrix() - Constructor for class com.imsl.math.PrintMatrix: Creates an instance of the PrintMatrix class.
PrintMatrix(PrintStream) - Constructor for class com.imsl.math.PrintMatrix: Creates an instance of the PrintMatrix class with the specified PrintStream.
PrintMatrix(String) - Constructor for class com.imsl.math.PrintMatrix: Creates a PrintMatrix object and sets its title.
PrintMatrix(PrintStream, String) - Constructor for class com.imsl.math.PrintMatrix: Creates a PrintMatrix object with the specified PrintStream and sets its title.
PrintMatrixFormat - Class in com.imsl.math: This class can be used to customize the actions of PrintMatrix.
PrintMatrixFormat() - Constructor for class com.imsl.math.PrintMatrixFormat: Constructs a PrintMatrixFormat object.
PRIOR_EQUAL - Static variable in class com.imsl.stat.DiscriminantAnalysis: Indicates prior equal probabilities.
PRIOR_PROPORTIONAL - Static variable in class com.imsl.stat.DiscriminantAnalysis: Indicates prior proportional probabilities.
PRISM - Static variable in interface com.imsl.chart.Colormap: Prism colormap.
probabilities(double[], int[]) - Method in class com.imsl.datamining.NaiveBayesClassifier: Predicts the classification probabilities for the input pattern using the trained Naive Bayes classifier.
probabilities(double[]) - Method in class com.imsl.datamining.neural.BinaryClassification: Returns classification probabilities for the input pattern x.
probabilities(double[]) - Method in class com.imsl.datamining.neural.MultiClassification: Returns classification probabilities for the input pattern x.
ProbabilityDistribution - Class in com.imsl.stat.distributions: The ProbabilityDistribution abstract class defines members and methods common to univariate probability distributions and useful in parameter estimation.
ProbabilityDistribution(int) - Constructor for class com.imsl.stat.distributions.ProbabilityDistribution: Constructor for the probability distribution
ProbabilityDistribution - Interface in com.imsl.stat: Public interface for a user-supplied probability distribution.
processCommand(String, String) - Method in class com.imsl.io.MPSReader: Process a section of the MPS file.
ProportionalHazards - Class in com.imsl.stat: Analyzes survival and reliability data using Cox's proportional hazards model.
ProportionalHazards(double[][], int[], int[]) - Constructor for class com.imsl.stat.ProportionalHazards: Constructor for ProportionalHazards.
ProportionalHazards.ClassificationVariableLimitException - Exception in com.imsl.stat: The Classification Variable limit set by the user through setUpperBound has been exceeded.
ProportionalHazards.ClassificationVariableLimitException(String) - Constructor for exception com.imsl.stat.ProportionalHazards.ClassificationVariableLimitException: Constructs a ClassificationVariableLimitException.
ProportionalHazards.ClassificationVariableLimitException(String, Object[]) - Constructor for exception com.imsl.stat.ProportionalHazards.ClassificationVariableLimitException: The Classification Variable limit set by the user through setUpperBound has been exceeded.
pruneTree(double) - Method in class com.imsl.datamining.decisionTree.DecisionTree: Finds the minimum cost-complexity decision tree for the cost-complexity value, gamma.
psi(double) - Static method in class com.imsl.math.Sfun: Returns the derivative of the log gamma function, also called the digamma function.
psi1(double) - Static method in class com.imsl.math.Sfun: Returns the $psi _1$ function, also known as the trigamma function.
PURE_ERROR - Static variable in class com.imsl.stat.ANOVAFactorial: Indicates factor nSubscripts is error.
pv(double, int, double, double, int) - Static method in class com.imsl.finance.Finance: Returns the net present value of a stream of equal periodic cash flows, which are subject to a given discount rate.

Q

QR - Class in com.imsl.math: QR Decomposition of a matrix.
QR(double[][]) - Constructor for class com.imsl.math.QR: Constructs the QR decomposition of a matrix with elements of type double.
QUADRATIC - Static variable in class com.imsl.stat.DiscriminantAnalysis: Indicates a quadratic discrimination method.
QuadraticProgramming - Class in com.imsl.math: Solves the convex quadratic programming problem subject to equality or inequality constraints.
QuadraticProgramming(double[][], double[], double[][], double[], double[][], double[]) - Constructor for class com.imsl.math.QuadraticProgramming: Solve a quadratic programming problem.
QuadraticProgramming.InconsistentSystemException - Exception in com.imsl.math: The system of constraints is inconsistent.
QuadraticProgramming.InconsistentSystemException() - Constructor for exception com.imsl.math.QuadraticProgramming.InconsistentSystemException: The system of constraints is inconsistent.
QuadraticProgramming.InconsistentSystemException(String) - Constructor for exception com.imsl.math.QuadraticProgramming.InconsistentSystemException
QuadraticProgramming.NoLPSolutionException - Exception in com.imsl.math: No solution for the LP problem with h = 0 was found by DenseLP.
QuadraticProgramming.NoLPSolutionException() - Constructor for exception com.imsl.math.QuadraticProgramming.NoLPSolutionException: No solution for the LP problem with h = 0 was found by DenseLP.
QuadraticProgramming.NoLPSolutionException(String) - Constructor for exception com.imsl.math.QuadraticProgramming.NoLPSolutionException: No solution for the LP problem with h = 0 was found by DenseLP.
QuadraticProgramming.NoLPSolutionException(String, Object[]) - Constructor for exception com.imsl.math.QuadraticProgramming.NoLPSolutionException: No solution for the LP problem with h = 0 was found by DenseLP.
QuadraticProgramming.ProblemUnboundedException - Exception in com.imsl.math: The object value for the problem is unbounded.
QuadraticProgramming.ProblemUnboundedException() - Constructor for exception com.imsl.math.QuadraticProgramming.ProblemUnboundedException: The object value for the problem is unbounded.
QuadraticProgramming.ProblemUnboundedException(String) - Constructor for exception com.imsl.math.QuadraticProgramming.ProblemUnboundedException: The object value for the problem is unbounded.
QuadraticProgramming.SolutionNotFoundException - Exception in com.imsl.math: A solution was not found.
QuadraticProgramming.SolutionNotFoundException() - Constructor for exception com.imsl.math.QuadraticProgramming.SolutionNotFoundException: A solution was not found.
QuadraticProgramming.SolutionNotFoundException(String) - Constructor for exception com.imsl.math.QuadraticProgramming.SolutionNotFoundException: A solution was not found.
Quadrature - Class in com.imsl.math: Quadrature is a general-purpose integrator that uses a globally adaptive scheme in order to reduce the absolute error.
Quadrature() - Constructor for class com.imsl.math.Quadrature: Constructs a Quadrature object.
Quadrature.Function - Interface in com.imsl.math: Public interface function for the Quadrature class.
quantile(double[], double[], double) - Static method in class com.imsl.stat.Summary
QUANTITATIVE_CONTINUOUS - Static variable in class com.imsl.datamining.PredictiveModel.VariableType: The associated variable can take on limitless and continuous values.
QUARTERLY - Static variable in class com.imsl.finance.Bond: Coupon payments are made quarterly.
QuasiNewtonTrainer - Class in com.imsl.datamining.neural: Trains a network using the quasi-Newton method, MinUnconMultiVar.
QuasiNewtonTrainer() - Constructor for class com.imsl.datamining.neural.QuasiNewtonTrainer: Constructs a QuasiNewtonTrainer object.
QuasiNewtonTrainer.BlockGradObjective - Class in com.imsl.datamining.neural
QuasiNewtonTrainer.BlockGradObjective() - Constructor for class com.imsl.datamining.neural.QuasiNewtonTrainer.BlockGradObjective
QuasiNewtonTrainer.BlockObjective - Class in com.imsl.datamining.neural
QuasiNewtonTrainer.BlockObjective() - Constructor for class com.imsl.datamining.neural.QuasiNewtonTrainer.BlockObjective
QuasiNewtonTrainer.Error - Interface in com.imsl.datamining.neural: Error function to be minimized by trainer.
QuasiNewtonTrainer.GradObjective - Class in com.imsl.datamining.neural: The Objective class is passed to the optimizer.
QuasiNewtonTrainer.Objective - Class in com.imsl.datamining.neural: The Objective class is passed to the optimizer.
QUEST - Class in com.imsl.datamining.decisionTree: Generates a decision tree using the QUEST algorithm for a categorical response variable and categorical or quantitative predictor variables.
QUEST(double[][], int, PredictiveModel.VariableType[]) - Constructor for class com.imsl.datamining.decisionTree.QUEST: Instantiates a QUEST object for a single response variable and multiple predictor variables.

R

R_SQUARED_CRITERION - Static variable in class com.imsl.stat.SelectionRegression: Indicates criterion regression.
RadialBasis - Class in com.imsl.math: RadialBasis computes a least-squares fit to scattered data in ${bf R}^d$ , where d is the dimension.
RadialBasis(int, int) - Constructor for class com.imsl.math.RadialBasis: Creates a new instance of RadialBasis.
RadialBasis.Function - Interface in com.imsl.math: Public interface for the user supplied function to the RadialBasis object.
RadialBasis.Gaussian - Class in com.imsl.math: The Gaussian basis function, $e^{-ax^2}$ .
RadialBasis.Gaussian(double) - Constructor for class com.imsl.math.RadialBasis.Gaussian: Creates a Gaussian basis function $e^{-ax^2}$ .
RadialBasis.HardyMultiquadric - Class in com.imsl.math: The Hardy multiquadric basis function, $sqrt{r^2+delta^2}$ .
RadialBasis.HardyMultiquadric(double) - Constructor for class com.imsl.math.RadialBasis.HardyMultiquadric: Creates a Hardy multiquadric basis function $sqrt{r^2+ delta^2}$ .
RADIAN - Static variable in class com.imsl.chart.Draw
random() - Static method in class com.imsl.math.JMath: Returns a random number from a uniform distribution.
Random - Class in com.imsl.stat: Generate uniform and non-uniform random number distributions.
Random() - Constructor for class com.imsl.stat.Random: Constructor for the Random number generator class.
Random(long) - Constructor for class com.imsl.stat.Random: Constructor for the Random number generator class with supplied seed.
Random(Random.BaseGenerator) - Constructor for class com.imsl.stat.Random: Constructor for the Random number generator class with an alternate basic number generator.
Random.BaseGenerator - Interface in com.imsl.stat: Base pseudorandom number.
RandomSequence - Interface in com.imsl.stat: Interface implemented by generators of random or quasi-random multidimensional sequences.
RANGE - Static variable in class com.imsl.stat.Dissimilarities: Indicates scaling by the range.
rank(double) - Method in class com.imsl.math.QR: Returns the rank of the matrix given an input tolerance.
Ranks - Class in com.imsl.stat: Compute the ranks, normal scores, or exponential scores for a vector of observations.
Ranks() - Constructor for class com.imsl.stat.Ranks: Constructor for the Ranks class.
rate(int, double, double, double, int) - Static method in class com.imsl.finance.Finance: Returns the interest rate per period of an annuity.
rate(int, double, double, double, int, double) - Static method in class com.imsl.finance.Finance: Returns the interest rate per period of an annuity with an initial guess.
Rayleigh(double, double) - Static method in class com.imsl.stat.Cdf: Evaluates the Rayleigh cumulative probability distribution function.
Rayleigh(double, double) - Static method in class com.imsl.stat.InvCdf: Returns the inverse of the Rayleigh cumulative probability distribution function.
Rayleigh(double, double) - Static method in class com.imsl.stat.Pdf: Evaluates the Rayleigh probability density function.
RChart - Class in com.imsl.chart.qc: RChart is an R chart using sample ranges to monitor the variability of a process.
RChart(AxisXY, double[][]) - Constructor for class com.imsl.chart.qc.RChart: Creates an R chart given sample data.
RChart(AxisXY, int, double[]) - Constructor for class com.imsl.chart.qc.RChart: Creates an R chart given the ranges for a series of equally sized samples.
RChart(AxisXY, int[], double[]) - Constructor for class com.imsl.chart.qc.RChart: Creates an R chart given the means for a series of equally sized samples.
read(Reader) - Method in class com.imsl.io.MPSReader: Reads and parses the MPS file.
readLine() - Method in class com.imsl.io.FlatFile: Reads and returns a line from the input.
real() - Method in class com.imsl.math.Complex: Returns the real part of a Complex object.
real(Complex) - Static method in class com.imsl.math.Complex: Returns the real part of a Complex object.
received(GregorianCalendar, GregorianCalendar, double, double, DayCountBasis) - Static method in class com.imsl.finance.Bond: Returns the amount one receives when a fully invested security reaches the maturity date.
RECIPROCAL_ABS - Static variable in class com.imsl.stat.ClusterHierarchical: Indicates transformation by taking the reciprocal of the absolute value.
RECLASSIFICATION - Static variable in class com.imsl.stat.DiscriminantAnalysis: Indicates reclassification classification method.
rect(int, int, int, int) - Method in class com.imsl.chart.DrawMap: Sets a rectangle as the target.
RED - Static variable in interface com.imsl.chart.Colormap: Linear red colormap.
RED_PURPLE - Static variable in interface com.imsl.chart.Colormap: Red/purple colormap.
RED_TEMPERATURE - Static variable in interface com.imsl.chart.Colormap: Red temperature colormap.
refreshRow() - Method in class com.imsl.io.AbstractFlatFile: Refreshes the current row with its most recent value in the database.
registerChart(Chart, HttpServletRequest) - Method in class com.imsl.chart.JspBean: Saves the chart and sets the chart attribute "Size".
RegressionBasis - Interface in com.imsl.stat: Public interface for user supplied function to UserBasisRegression object.
RegressorsForGLM - Class in com.imsl.stat: Generates regressors for a general linear model.
RegressorsForGLM(double[][], int) - Constructor for class com.imsl.stat.RegressorsForGLM: Constructor where the class columns are the first columns.
RegressorsForGLM(double[][], int[]) - Constructor for class com.imsl.stat.RegressorsForGLM: Constructor with an explicit set of class column indicies.
relative(int) - Method in class com.imsl.io.AbstractFlatFile: Moves the cursor a relative number of rows, either positive or negative.
RELAXATION_PARAMETER - Static variable in class com.imsl.math.ComplexSuperLU: A performance tuning parameter which can be adjusted via method setPerformanceTuningParameters.
RELAXATION_PARAMETER - Static variable in class com.imsl.math.SuperLU: A performance tuning parameter which can be adjusted via method setPerformanceTuningParameters.
remove() - Method in class com.imsl.chart.AbstractChartNode: Removes the node from its parents list of children.
remove() - Method in class com.imsl.chart3d.ColormapLegend: Removes the node from its parents list of children.
remove(Link) - Method in class com.imsl.datamining.neural.FeedForwardNetwork: Removes a Link from the network.
removeControlLimit(ControlLimit) - Method in class com.imsl.chart.qc.ShewhartControlChart: Removes a control limit from the chart.
removePickListener(PickListener) - Method in class com.imsl.chart.ChartNode: Removes a PickListener from this node.
removePostRenderPaint(Canvas3DChart.Paint) - Method in class com.imsl.chart3d.Canvas3DChart: Removes a Paint object from the list of post-render Paint objects.
removePreRenderPaint(Canvas3DChart.Paint) - Method in class com.imsl.chart3d.Canvas3DChart: Removes a Paint object from the list of pre-render Paint objects.
render() - Method in class com.imsl.chart3d.Canvas3DChart: Creates a scene graph from the chart tree and starts rendering the scene graph into this canvas.
render() - Method in class com.imsl.chart3d.JFrameChart3D: Renders the 3D chart node tree into a Java 3D scene graph.
repaint() - Method in class com.imsl.chart.Chart: Prepares the chart to be repainted by deleting any double buffering image.
resetQ() - Method in class com.imsl.stat.KalmanFilter: Removes the Q matrix.
resetTransitionMatrix() - Method in class com.imsl.stat.KalmanFilter: Removes the transition matrix.
resetUpdate() - Method in class com.imsl.stat.KalmanFilter: Do not perform computation of the update equations.
resetViewPlatformTransformation() - Method in class com.imsl.chart3d.Chart3D: Resets the view platform transformation to its default value.
rightBoundaries(double, double[][]) - Method in interface com.imsl.math.FeynmanKac.Boundaries: Returns the coefficient values of the right boundary conditions.
rint(double) - Static method in class com.imsl.math.JMath: Returns the value of a double rounded toward the closest integral value.
round(float) - Static method in class com.imsl.math.JMath: Returns the integer closest to a given float.
round(double) - Static method in class com.imsl.math.JMath: Returns the long closest to a given double.
ROW_AND_COLUMN_SCALING - Static variable in class com.imsl.math.ComplexSuperLU: Indicates that input matrix A was row and column scaled before factorization.
ROW_AND_COLUMN_SCALING - Static variable in class com.imsl.math.SuperLU: Indicates that input matrix A was row and column scaled before factorization.
ROW_LABEL - Static variable in class com.imsl.math.PrintMatrixFormat: This flag as the type argument to format, indicates that the formatted string for a given row label is to be returned.
ROW_SCALING - Static variable in class com.imsl.math.ComplexSuperLU: Indicates that input matrix A was row scaled before factorization.
ROW_SCALING - Static variable in class com.imsl.math.SuperLU: Indicates that input matrix A was row scaled before factorization.
rowDeleted() - Method in class com.imsl.io.AbstractFlatFile: Indicates whether a row has been deleted.
ROWFIRST - Static variable in class com.imsl.chart.Treemap: Flag to set the treemap orientation drawing rows first.
rowInserted() - Method in class com.imsl.io.AbstractFlatFile: Indicates whether the current row has had an insertion.
rowUpdated() - Method in class com.imsl.io.AbstractFlatFile: Indicates whether the current row has been updated.

S

sampleStandardDeviation(double[]) - Static method in class com.imsl.stat.Summary: Returns the sample standard deviation of the given data set.
sampleStandardDeviation(double[], double[]) - Static method in class com.imsl.stat.Summary: Returns the sample standard deviation of the given data set and associated weights.
sampleVariance(double[]) - Static method in class com.imsl.stat.Summary: Returns the sample variance of the given data set.
sampleVariance(double[], double[]) - Static method in class com.imsl.stat.Summary: Returns the sample variance of the given data set and associated weights.
saveChart(Chart, HttpServletRequest) - Method in class com.imsl.chart.JspBean: Saves the chart so that a servlet can later render it.
scalbn(double, int) - Static method in class com.imsl.math.IEEE: Returns 2ⁿ computed by exponent manipulation rather than by actually performing an exponentiation or a multiplication.
scaledK(double, double, int) - Static method in class com.imsl.math.Bessel: Evaluate a sequence of exponentially scaled modified Bessel functions of the third kind with fractional order and real argument.
ScaleFilter - Class in com.imsl.datamining.neural: Scales or unscales continuous data prior to its use in neural network training, testing, or forecasting.
ScaleFilter(int) - Constructor for class com.imsl.datamining.neural.ScaleFilter: Constructor for ScaleFilter.
scaleFont - Variable in class com.imsl.chart.Draw
SChart - Class in com.imsl.chart.qc: SChart is an S chart using sample standard deviations to monitor the variability of a process.
SChart(AxisXY, double[][]) - Constructor for class com.imsl.chart.qc.SChart: Creates an S chart given sample data.
SChart(AxisXY, int, double[]) - Constructor for class com.imsl.chart.qc.SChart: Creates an S chart given the within sample standard deviations for a series of equally sized samples.
SChart(AxisXY, int[], double[]) - Constructor for class com.imsl.chart.qc.SChart: Creates an S chart given the within sample standard deviations for a series of unequally sized samples.
SCHEFFE - Static variable in class com.imsl.stat.ANOVA: The Scheffe method
SECOND_DERIVATIVE - Static variable in class com.imsl.math.CsInterpolate
SECOND_GRAM_SCHMIDT - Static variable in class com.imsl.math.GenMinRes: Indicates the second Gram-Schmidt implementation method is to be used.
SECOND_HOUSEHOLDER - Static variable in class com.imsl.math.GenMinRes: Indicates the second Householder implementation method is to be used.
SelectionRegression - Class in com.imsl.stat: Selects the best multiple linear regression models.
SelectionRegression(int) - Constructor for class com.imsl.stat.SelectionRegression: Constructs a new SelectionRegression object.
SelectionRegression.NoVariablesException - Exception in com.imsl.stat: No Variables can enter the model.
SelectionRegression.NoVariablesException() - Constructor for exception com.imsl.stat.SelectionRegression.NoVariablesException: Constructs a NoVariablesException.
SelectionRegression.Statistics - Class in com.imsl.stat: Statistics contains statistics related to the regression coefficients.
selectSplitVariable(double[][], double[], double[], double[], int[]) - Method in class com.imsl.datamining.decisionTree.ALACART: Selects the split variable for the present node using the CART^TM method.
selectSplitVariable(double[][], double[], double[], double[], int[]) - Method in class com.imsl.datamining.decisionTree.C45: Selects the split variable for the present node using the C45 method.
selectSplitVariable(double[][], double[], double[], double[], int[]) - Method in class com.imsl.datamining.decisionTree.CHAID: Selects the split variable for the current node using CHAID (Chi-square automatic interaction detection).
selectSplitVariable(double[][], double[], double[], double[], int[]) - Method in class com.imsl.datamining.decisionTree.DecisionTree: Abstract method for selecting the next split variable and split definition for the node.
selectSplitVariable(double[][], double[], double[], double[], int[]) - Method in class com.imsl.datamining.decisionTree.DecisionTreeInfoGain: Abstract method for selecting the next split variable and split definition for the node.
selectSplitVariable(double[][], double[], double[], double[], int[]) - Method in class com.imsl.datamining.decisionTree.QUEST: Selects the split variable for the present node using the QUEST method.
SEMIANNUAL - Static variable in class com.imsl.finance.Bond: Coupon payments are made semiannually (twice per year).
set(int, int, Complex) - Method in class com.imsl.math.ComplexSparseMatrix: Sets the value of an element in the matrix.
set(int, int, double) - Method in class com.imsl.math.SparseMatrix: Sets the value of an element in the matrix.
setA0Flag(boolean) - Method in class com.imsl.stat.VectorAutoregression: Sets the flag to include the leading autoregressive coefficient matrix in the model.
setAbsoluteError(double) - Method in class com.imsl.math.HyperRectangleQuadrature: Sets the absolute error tolerance.
setAbsoluteError(double) - Method in class com.imsl.math.Quadrature: Sets the absolute error tolerance.
setAbsoluteError(double) - Method in class com.imsl.math.ZerosFunction: Sets the second convergence criterion.
setAbsoluteErrorTolerances(double[]) - Method in class com.imsl.math.FeynmanKac: Sets the absolute error tolerances.
setAbsoluteErrorTolerances(double) - Method in class com.imsl.math.FeynmanKac: Sets the absolute error tolerances.
setAbsoluteFcnTol(double) - Method in class com.imsl.math.BoundedLeastSquares: Sets the absolute function tolerance.
setAbsoluteH(double) - Method in class com.imsl.chart.qc.CuSumStatus: Sets the value for h used for setting limits.
setAbsoluteTolerance(double) - Method in class com.imsl.math.NonlinLeastSquares: Set the absolute function tolerance.
setAbsoluteTolerance(double) - Method in class com.imsl.stat.NonlinearRegression: Sets the absolute function tolerance.
setAccuracy(double) - Method in class com.imsl.math.MinUncon: Set the required absolute accuracy in the final value returned by member function computeMin.
setAccuracyTolerance(double) - Method in class com.imsl.stat.ARMAOutlierIdentification: Sets the tolerance value controlling the accuracy of the parameter estimates.
setAccuracyTolerance(double) - Method in class com.imsl.stat.AutoARIMA: Sets the tolerance value controlling the accuracy of the parameter estimates.
setActivation(Activation) - Method in class com.imsl.datamining.neural.Perceptron: Sets the activation function.
setAdditive() - Method in class com.imsl.stat.HoltWintersExponentialSmoothing: Specifies the use of the Additive time series model.
setAlignment(int) - Method in class com.imsl.chart.Text: Sets the alignment for this Text object.
setAlpha(double) - Method in class com.imsl.stat.MultipleComparisons: Sets the significance level of the test
setALT(String) - Method in class com.imsl.chart.ChartNode: Sets the value of the "ALT" attribute.
setAngles(double, double) - Method in class com.imsl.chart.PieSlice: Sets the angles, in degrees, that determine the extent of this slice.
setAR(double[]) - Method in class com.imsl.stat.ARMAMaxLikelihood: Sets the initial values for the autoregressive terms to the p values in ar.
setARConstants(double[]) - Method in class com.imsl.stat.VectorAutoregression: Sets the constants for the autoregressive model.
setARLag(int) - Method in class com.imsl.stat.VectorAutoregression: Sets the autoregressive lag parameter.
setARLags(int[]) - Method in class com.imsl.stat.ARMA: Sets the order of the autoregressive parameters.
setArmaInfo(double, double[], double[], double) - Method in class com.imsl.stat.ARMA: Sets the ARMA_Info Object to previously determined values
setARModel(int[]) - Method in class com.imsl.stat.VectorAutoregression: Sets the form of the autoregressive terms of the model.
setAttribute(String, Object) - Method in class com.imsl.chart.AbstractChartNode: Sets an attribute.
setAutoPruningFlag(boolean) - Method in class com.imsl.datamining.decisionTree.DecisionTree: Sets the flag to automatically prune the tree during the fitting procedure.
setAutoscaleInput(int) - Method in class com.imsl.chart.AbstractChartNode: Sets the value of the "AutoscaleInput" attribute.
setAutoscaleMinimumTimeInterval(int) - Method in class com.imsl.chart.AbstractChartNode: Sets the value of the "AutoscaleMinimumTimeInterval" attribute.
setAutoscaleOutput(int) - Method in class com.imsl.chart.AbstractChartNode: Sets the value of the "AutoscaleOutput" attribute.
setAxisRange(double[]) - Method in class com.imsl.chart.Treemap: Set the axis range, range = {xmin,xmax,ymin,ymax}.
setAxisTitlePosition(int) - Method in class com.imsl.chart3d.ChartNode3D: Sets the value of the "AxisTitlePosition" attribute.
setBackcasting(int, double) - Method in class com.imsl.stat.ARMA: Sets backcasting option.
setBackwardOrigin(int) - Method in class com.imsl.stat.ARAutoUnivariate: Sets the maximum backward origin used in calculating the forecasts.
setBackwardOrigin(int) - Method in class com.imsl.stat.ARMA: Sets the maximum backward origin.
setBackwardOrigin(int) - Method in class com.imsl.stat.ARMAMaxLikelihood: Sets the maximum backward origin.
setBarData(double[][][]) - Method in class com.imsl.chart.Bar: Convenience routine to set the "BarData" attribute.
setBarGap(double) - Method in class com.imsl.chart.ChartNode: Sets the value of the "BarGap" attribute.
setBarType(int) - Method in class com.imsl.chart.ChartNode: Sets the value of the "BarType" attribute.
setBarWidth(double) - Method in class com.imsl.chart.ChartNode: Sets the value of the "BarWidth" attribute.
setBias(double) - Method in class com.imsl.datamining.neural.Perceptron: Sets the bias for this Perceptron.
setBias(double[]) - Method in class com.imsl.math.CsTCB: Sets the bias values at the data points.
setBindingThreshold(double) - Method in class com.imsl.math.MinConNLP: Set the binding threshold for constraints.
setBound(double) - Method in class com.imsl.math.MinUncon: Set the amount by which X may be changed from its initial value, xguess.
setBoundingSphere(BoundingSphere) - Method in class com.imsl.chart3d.ChartNode3D: Sets the spherical bounding region object BoundingSphere.
setBounds(double, double, double, double) - Method in class com.imsl.datamining.neural.ScaleFilter: Sets bounds to be used during bounded scaling and unscaling.
setBounds(double, double) - Method in class com.imsl.math.ZerosFunction: Sets the closed interval in which to search for the roots.
setBoundViolationBound(double) - Method in class com.imsl.math.MinConNLP: Set the amount by which bounds may be violated during numerical differentiation.
setBoxPlotType(int) - Method in class com.imsl.chart.BoxPlot: Sets the "BoxPlotType" attribute value.
setCanvas(Canvas3D) - Method in class com.imsl.chart3d.Chart3D
setCensor(int[]) - Method in class com.imsl.stat.KaplanMeierECDF: Set flags to note right-censoring
setCensorColumn(int) - Method in class com.imsl.stat.CategoricalGenLinModel: Sets the column number in x which contains the interval type for each observation.
setCensorColumn(int) - Method in class com.imsl.stat.KaplanMeierEstimates: Sets the column index of x containing the optional censoring code for each observation.
setCensorColumn(int) - Method in class com.imsl.stat.ProportionalHazards: Sets the column index of x containing the optional censoring code for each observation.
setCenter(double) - Method in class com.imsl.chart.qc.ShewhartControlChart: Sets the value of the attribute "Center".
setCenter(double) - Method in class com.imsl.datamining.neural.ScaleFilter: Set the measure of center to be used during z-score scaling.
setCenter(boolean) - Method in class com.imsl.stat.ARMA: Sets center option.
setCenter(int) - Method in class com.imsl.stat.ARSeasonalFit: Controls centering of the differenced series.
setCenter(boolean) - Method in class com.imsl.stat.VectorAutoregression: Sets the flag to center the data.
setChart(Chart) - Method in class com.imsl.chart.JFrameChart: Sets the chart to be handled.
setChart(Chart) - Method in class com.imsl.chart.JPanelChart: Sets the Chart to be handled by this container.
setChartServletName(String) - Method in class com.imsl.chart.JspBean: Sets the URL of the servlet used to render the chart.
setChartTitle(ChartTitle) - Method in class com.imsl.chart.ChartNode: Sets the value of the "ChartTitle" attribute.
setChiSquaredTestNull(double) - Method in class com.imsl.stat.NormOneSample: Sets the null hypothesis value for the chi-squared test.
setChiSquaredTestNull(double) - Method in class com.imsl.stat.NormTwoSample: Sets the null hypothesis value for the chi-squared test.
setClassCounts(double[]) - Method in class com.imsl.datamining.PredictiveModel: Sets the counts of each class of the response variable.
setClassificationMethod(int) - Method in class com.imsl.stat.DiscriminantAnalysis: Specifies the classification method to be either reclassification or leave-out-one.
setClassificationVariableColumn(int[]) - Method in class com.imsl.stat.CategoricalGenLinModel: Initializes an index vector to contain the column numbers in x that are classification variables.
setClassVarColumns(int[]) - Method in class com.imsl.stat.ProportionalHazards: Sets the column indices of x that are the classification variables.
setClip(Rectangle) - Method in class com.imsl.chart.Draw: Set the clipping rectangle.
setClipData(boolean) - Method in class com.imsl.chart.ChartNode: Sets the value of the "ClipData" attribute.
setClose(double[]) - Method in class com.imsl.chart.HighLowClose: Sets the attribute "Close".
setClosedForm(boolean) - Method in class com.imsl.stat.distributions.MaximumLikelihoodEstimation: Sets the flag indicating whether or not the closed form solution should be used.
setColorFunction(ColorFunction) - Method in class com.imsl.chart3d.ChartNode3D: Sets the value of the "ColorFunction" attribute.
setColormap(Colormap) - Method in class com.imsl.chart.Heatmap: Sets the value of the "Colormap" attribute.
setColormap(Colormap) - Method in class com.imsl.chart.Treemap: Sets the value of the "Colormap" attribute.
setColumnClass(int, Class) - Method in class com.imsl.io.AbstractFlatFile: Sets a column class.
setColumnClass(int, Class) - Method in class com.imsl.io.FlatFile: Sets a column class.
setColumnLabels(String[]) - Method in class com.imsl.math.PrintMatrixFormat: Turns on column labeling using the given labels.
setColumnName(int, String) - Method in class com.imsl.io.AbstractFlatFile: Sets a column name.
setColumnParser(int, FlatFile.Parser) - Method in class com.imsl.io.FlatFile: Sets the Parser for the specified column.
setColumnPermutationMethod(int) - Method in class com.imsl.math.ComplexSuperLU: Specifies how to permute the columns of the input matrix.
setColumnPermutationMethod(int) - Method in class com.imsl.math.SuperLU: Specifies how to permute the columns of the input matrix.
setColumnSpacing(int) - Method in class com.imsl.math.PrintMatrix: Sets the number of spaces between columns.
setCombineFunction(TimeSeriesOperations.Function) - Method in class com.imsl.stat.TimeSeriesOperations: Sets the combine function to a user supplied function.
setCombineMethod(TimeSeriesOperations.CombineMethod) - Method in class com.imsl.stat.TimeSeriesOperations: Sets the method for combining synchronous time series values.
setComponent(Component) - Method in class com.imsl.chart.Chart: Sets the Component for this chart.
setConfidence(double) - Method in class com.imsl.stat.ARAutoUnivariate: Sets the confidence level for calculating confidence limit deviations returned from getDeviations.
setConfidence(double) - Method in class com.imsl.stat.ARMA: Sets the confidence level for calculating confidence limit deviations returned from getDeviations.
setConfidence(double) - Method in class com.imsl.stat.ARMAMaxLikelihood: Sets the confidence level for calculating confidence limit deviations returned from getDeviations().
setConfidence(double) - Method in class com.imsl.stat.ARMAOutlierIdentification: Sets the confidence level for calculating confidence limit deviations returned from getDeviations.
setConfidence(double) - Method in class com.imsl.stat.AutoARIMA: Sets the confidence level for calculating confidence limit deviations returned by getDeviations.
setConfidence(double) - Method in class com.imsl.stat.HoltWintersExponentialSmoothing: Sets the confidence level to use in the calculation of the prediction intervals.
setConfidenceMean(double) - Method in class com.imsl.stat.NormOneSample: Sets the confidence level (in percent) for a two-sided interval estimate of the mean.
setConfidenceMean(double) - Method in class com.imsl.stat.NormTwoSample: Sets the confidence level (in percent) for a two-sided interval estimate of the mean of x - the mean of y, in percent.
setConfidenceVariance(double) - Method in class com.imsl.stat.NormOneSample: Sets the confidence level (in percent) for two-sided interval estimate of the variances.
setConfidenceVariance(double) - Method in class com.imsl.stat.NormTwoSample: Sets the confidence level (in percent) for two-sided interval estimate of the variances.
setConfiguration(PredictiveModel) - Method in class com.imsl.datamining.decisionTree.CHAID: Sets the configuration of PredictiveModel to that of the input model.
setConfiguration(PredictiveModel) - Method in class com.imsl.datamining.decisionTree.DecisionTree: Sets the configuration of PredictiveModel to that of the input model.
setConfiguration(PredictiveModel) - Method in class com.imsl.datamining.decisionTree.QUEST: Sets the configuration of PredictiveModel to that of the input model.
setConfiguration(PredictiveModel) - Method in class com.imsl.datamining.PredictiveModel: Sets the configuration of PredictiveModel to that of the input model.
setConLevelMean(double) - Method in class com.imsl.stat.LinearRegression.CaseStatistics: Sets the confidence level for two-sided confidence intervals of the population mean.
setConLevelPred(double) - Method in class com.imsl.stat.LinearRegression.CaseStatistics: Sets the confidence level for two-sided prediction intervals.
setConstant(double) - Method in class com.imsl.math.SparseLP: Sets the value of the constant term in the objective function.
setConstant(double) - Method in class com.imsl.stat.ARMAMaxLikelihood: Sets the initial value for the constant term in the ARMA model.
setConstantColumn(int) - Method in class com.imsl.stat.ProportionalHazards: Sets the column index of x containing the constant to be added to the linear response.
setConstraintType(int[]) - Method in class com.imsl.math.DenseLP: Sets the types of general constraints in the matrix a.
setConstraintType(int[]) - Method in class com.imsl.math.SparseLP: Sets the types of general constraints in the matrix A.
setContinuity(double[]) - Method in class com.imsl.math.CsTCB: Sets the continuity values at the data points.
setContinuousSmoothingValue(double) - Method in class com.imsl.datamining.NaiveBayesClassifier: Parameter for calculating smoothed estimates of conditional probabilities for continuous attributes.
setControlLimit(double) - Method in class com.imsl.chart.qc.ControlLimit: Sets the attribute "ControlLimit".
setConvergenceCriterion1(double) - Method in class com.imsl.stat.FactorAnalysis: Sets the convergence criterion used to terminate the iterations.
setConvergenceCriterion2(double) - Method in class com.imsl.stat.FactorAnalysis: Sets the convergence criterion used to switch to exact second derivatives.
setConvergenceTol(double) - Method in class com.imsl.stat.ProportionalHazards: Set the convergence tolerance.
setConvergenceTolerance(double) - Method in class com.imsl.stat.ARAutoUnivariate: Sets the tolerance level used to determine convergence of the nonlinear least-squares and maximum likelihood algorithms.
setConvergenceTolerance(double) - Method in class com.imsl.stat.ARMA: Sets the tolerance level used to determine convergence of the nonlinear least-squares algorithm.
setConvergenceTolerance(double) - Method in class com.imsl.stat.ARMAEstimateMissing: Sets the covergence tolerance used by the AR_1 and AR_P missing value estimation methods.
setConvergenceTolerance(double) - Method in class com.imsl.stat.CategoricalGenLinModel: Set the convergence criterion.
setCoordinates(double[][]) - Method in class com.imsl.chart.Dendrogram: Convenience routine to set the "Coordinates" attribute.
setCostComplexityValues(double[]) - Method in class com.imsl.datamining.decisionTree.DecisionTree: Sets the cost-complexity values.
setCostMatrix(double[][]) - Method in class com.imsl.datamining.PredictiveModel: Specifies the cost matrix for a categorical response variable.
setCovarianceComputation(int) - Method in class com.imsl.stat.DiscriminantAnalysis: Specifies the covariance matrix computation to be either pooled or pooled, group.
setCreateImageMap(boolean) - Method in class com.imsl.chart.JspBean: Sets a flag indicating if a client-size imagemap is to be generated.
setCriterionOption(int) - Method in class com.imsl.stat.SelectionRegression: Sets the Criterion to be used.
setCriticalValue(double) - Method in class com.imsl.stat.ARMAOutlierIdentification: Sets the critical value used as a threshold during outlier detection.
setCriticalValue(double) - Method in class com.imsl.stat.AutoARIMA: Sets the critical value used as a threshold during outlier detection.
setCross(double, double) - Method in class com.imsl.chart.AxisXY: Sets the value of the "Cross" attribute.
setCross(double[]) - Method in class com.imsl.chart.AxisXY: Sets the value of the "Cross" attribute.
setCustomMarker(Data.CustomMarkerFactory) - Method in class com.imsl.chart3d.Data: Sets a custom marker factory.
setCustomTransform(Transform) - Method in class com.imsl.chart.AbstractChartNode: Sets the value of the "CustomTransform" attribute.
setCustomTransform(Transform) - Method in class com.imsl.chart.ChartNode: Sets the value of the "CustomTransform" attribute.
setCutpoints(double[]) - Method in class com.imsl.stat.ChiSquaredTest: Sets the cutpoints.
setData(double[]) - Method in class com.imsl.chart.Pie: Changes the data in a Pie chart object.
setData(int[]) - Method in class com.imsl.chart.qc.ShewhartControlChart: Sets the integer data in the control chart.
setData(double[]) - Method in class com.imsl.chart.qc.ShewhartControlChart: Sets the data in the control chart.
setDataType(int) - Method in class com.imsl.chart.ChartNode: Sets the value of the "DataType" attribute.
setDataType(int) - Method in class com.imsl.chart3d.ChartNode3D: Sets the value of the "DataType" attribute.
setDateAxis(String) - Method in class com.imsl.chart.HighLowClose: Sets up the x-axis for high-low-close plot.
setDateColumnParser(int, String, Locale) - Method in class com.imsl.io.FlatFile: Creates for a pattern string and sets the Parser for the specified column.
setDateIncrement(int) - Method in class com.imsl.stat.TimeSeries: Sets the date increment in number of days.
setDateIncrementInMillis(long) - Method in class com.imsl.stat.TimeSeries: Sets the date increment in milliseconds.
setDates() - Method in class com.imsl.stat.TimeSeries: Sets the date array using the start date and date increment.
setDates(Date[]) - Method in class com.imsl.stat.TimeSeries: Sets the date array equal to user supplied dates.
setDefaultAlignment(int) - Method in class com.imsl.chart.Text: Sets the alignment to use, if it has not been set using setAlignment(int).
setDefaultOffset(double) - Method in class com.imsl.chart.Text: Sets the default value of the offset.
setDegreesOfFreedom(int) - Method in class com.imsl.stat.FactorAnalysis: Sets the number of degrees of freedom.
setDelta(double) - Method in class com.imsl.stat.ARMAOutlierIdentification: Sets the dampening effect parameter.
setDelta(double) - Method in class com.imsl.stat.AutoARIMA: Sets the dampening effect parameter.
setDensity(int) - Method in class com.imsl.chart.AbstractChartNode: Sets the value of the "Density" attribute.
setDerivtol(double) - Method in class com.imsl.math.MinUncon: Set the derivative tolerance used by member function computeMin to decide if the current point is a local minimum.
setDiagonalPivotThreshold(double) - Method in class com.imsl.math.ComplexSuperLU: Specifies the threshold used for a diagonal entry to be an acceptable pivot.
setDiagonalPivotThreshold(double) - Method in class com.imsl.math.SuperLU: Specifies the threshold used for a diagonal entry to be an acceptable pivot.
setDiagonalScalingMatrix(double[]) - Method in class com.imsl.math.BoundedLeastSquares: Sets the diagonal scaling matrix for the functions.
setDifferenceOrders(int[]) - Method in class com.imsl.stat.AutoARIMA: Defines the orders of the periodic differences used in the determination of the optimum model.
setDifferencingMethods(int[]) - Method in class com.imsl.math.NumericalDerivatives: Sets the methods used to compute the derivatives
setDifferentiationType(int) - Method in class com.imsl.math.MinConNLP: Set the type of numerical differentiation to be used.
setDigits(double) - Method in class com.imsl.math.MinUnconMultiVar: Set the number of good digits in the function.
setDigits(int) - Method in class com.imsl.math.NonlinLeastSquares: Set the number of good digits in the function.
setDigits(int) - Method in class com.imsl.stat.NonlinearRegression: Sets the number of good digits in the residuals.
setDInitial(int[][]) - Method in class com.imsl.stat.ARSeasonalFit: Sets the candidate values for selecting the optimum seasonal adjustment prior to calling the compute method.
setDirection(double, double, double) - Method in class com.imsl.chart3d.DirectionalLight: Sets the value of the "Direction" attribute to a light direction.
setDirection(Vector3f) - Method in class com.imsl.chart3d.DirectionalLight: Sets the value of the "Direction" attribute to a light direction.
setDiscreteSmoothingValue(double) - Method in class com.imsl.datamining.NaiveBayesClassifier: Parameter for calculating smoothed estimates of conditional probabilities for discrete (nominal) attributes.
setDiscriminationMethod(int) - Method in class com.imsl.stat.DiscriminantAnalysis: Specifies the discrimination method used to be either linear or quadratic discrimination.
setDistanceMethod(int) - Method in class com.imsl.stat.ClusterKNN: Sets the distance calculation method to be used.
setDistanceMethod(int) - Method in class com.imsl.stat.Dissimilarities: Sets the method to be used in computing the dissimilarities or similarities.
setDoubleBuffering(boolean) - Method in class com.imsl.chart.ChartNode: Sets the value of the "DoubleBuffering" attribute.
setDualInfeasibilityTolerance(double) - Method in class com.imsl.math.SparseLP: Sets the dual infeasibility tolerance.
setDualTolerance(double) - Method in class com.imsl.math.NonNegativeLeastSquares: Sets the dual tolerance.
setDummyMethod(int) - Method in class com.imsl.stat.RegressorsForGLM: Sets the dummy method.
setEffects(int[], int[]) - Method in class com.imsl.stat.CategoricalGenLinModel: Initializes an index vector to contain the column numbers in x associated with each effect.
setEffects(int[][]) - Method in class com.imsl.stat.RegressorsForGLM: Set the effects.
setEOM(boolean) - Method in class com.imsl.finance.DayCountBasis: Specifies whether to use the End-Of-Month rule.
setEpochNumber(int) - Method in class com.imsl.datamining.neural.LeastSquaresTrainer: Sets the epoch number for the trainer.
setEpochNumber(int) - Method in class com.imsl.datamining.neural.QuasiNewtonTrainer: Sets the epoch number for the trainer.
setEpochSize(int) - Method in class com.imsl.datamining.neural.EpochTrainer: Sets the number of randomly selected training patterns in stage 1 epoch.
setEqualColumnWidths(boolean) - Method in class com.imsl.math.PrintMatrix: Force all of the columns to have the same width.
setEqualWeights(double[][]) - Method in class com.imsl.datamining.neural.FeedForwardNetwork: Initializes network weights using equal weighting.
setEquilibrate(boolean) - Method in class com.imsl.math.ComplexSuperLU: Specifies if input matrix A should be equilibrated before factorization.
setEquilibrate(boolean) - Method in class com.imsl.math.SuperLU: Determines if input matrix A should be equilibrated before factorization.
setError(QuasiNewtonTrainer.Error) - Method in class com.imsl.datamining.neural.QuasiNewtonTrainer: Sets the function used to compute the network error.
setError(double) - Method in class com.imsl.math.ZerosFunction: Sets the first convergence criterion.
setErrorIncludeType(int) - Method in class com.imsl.stat.ANOVAFactorial: Sets error included type.
setEstimationMethod(int) - Method in class com.imsl.stat.ARAutoUnivariate: Sets the estimation method used for estimating the final estimates for the autoregressive coefficients.
setEstimationMethod(int) - Method in class com.imsl.stat.ARMAEstimateMissing: Sets the method used for estimating the autoregressive coefficients for missing value estimation methods AR_1 and AR_P.
setExact(boolean) - Method in class com.imsl.stat.distributions.MaximumLikelihoodEstimation: Sets the flag indicating whether or not the PDF supplies the exact gradient and Hessian.
setExclude(boolean) - Method in class com.imsl.stat.ARSeasonalFit: Controls whether to exclude or replace the inital values in the transformed series.
setExpectedMean(double) - Method in class com.imsl.chart.qc.CuSum: Sets the expected mean of all of the data from all of the samples.
setExpectedMean(double) - Method in class com.imsl.chart.qc.CuSumStatus: Sets the expected mean of all of the data from all of the samples.
setExplode(double) - Method in class com.imsl.chart.ChartNode: Sets the value of the "Explode" attribute.
setExtendedLikelihoodObservations(int[]) - Method in class com.imsl.stat.CategoricalGenLinModel: Initializes a vector indicating which observations are to be included in the extended likelihood.
setExtrapolation(boolean) - Method in class com.imsl.math.Quadrature: If true, the epsilon-algorithm for extrapolation is enabled.
setFactorLoadingEstimationMethod(int) - Method in class com.imsl.stat.FactorAnalysis: Sets the factor loading estimation method.
setFalseConvergenceTolerance(double) - Method in class com.imsl.datamining.neural.LeastSquaresTrainer: Set the false convergence tolerance.
setFalseConvergenceTolerance(double) - Method in class com.imsl.math.NonlinLeastSquares: Set the false convergence tolerance.
setFalseConvergenceTolerance(double) - Method in class com.imsl.stat.NonlinearRegression: Sets the false convergence tolerance.
setFetchDirection(int) - Method in class com.imsl.io.AbstractFlatFile: Gives a hint as to the direction in which the rows in this ResultSet object is processed.
setFetchSize(int) - Method in class com.imsl.io.AbstractFlatFile: Gives the JDBC driver a hint as to the number of rows that should be fetched from the database when more rows are needed for this ResultSet object.
setFillColor(Color) - Method in class com.imsl.chart.AbstractChartNode: Sets the value of the "FillColor" attribute.
setFillColor(String) - Method in class com.imsl.chart.AbstractChartNode: Sets the "FillColor" attribute to a color specified by name.
setFillOutlineColor(Color) - Method in class com.imsl.chart.ChartNode: Sets the value of the "FillOutlineColor" attribute.
setFillOutlineColor(String) - Method in class com.imsl.chart.ChartNode: Sets the value of the "FillOutlineColor" attribute to a color specified by name.
setFillOutlineType(int) - Method in class com.imsl.chart.ChartNode: Sets the value of the "FillOutlineType" attribute.
setFillPaint(Paint) - Method in class com.imsl.chart.ChartNode: Sets the value of the "FillPaint" attribute.
setFillPaint(ImageIcon) - Method in class com.imsl.chart.ChartNode: Sets the value of the "FillPaint" attribute.
setFillPaint(URL) - Method in class com.imsl.chart.ChartNode: Sets the value of the "FillPaint" attribute.
setFillType(int) - Method in class com.imsl.chart.ChartNode: Sets the value of the "FillType" attribute.
setFirstColumnNumber(int) - Method in class com.imsl.math.PrintMatrixFormat: Turns on column labeling with index numbers and sets the index for the label of the first column.
setFirstRowNumber(int) - Method in class com.imsl.math.PrintMatrixFormat: Turns on row labeling with index numbers and sets the index for the label of the first row.
setFirstTick(double) - Method in class com.imsl.chart.Axis1D: Convenience routine to set the "FirstTick" attribute.
setFirstTick(double) - Method in class com.imsl.chart3d.Axis3D: Convenience routine to set the "FirstTick" attribute.
setFitModelFlag(boolean) - Method in class com.imsl.datamining.PredictiveModel: Sets the flag of whether or not the model needs to be fit or re-estimated because of a change in the data or configuration.
setFixedParameterColumn(int) - Method in class com.imsl.stat.CategoricalGenLinModel: Sets the column number in x that contains a fixed parameter for each observation that is added to the linear response prior to computing the model parameter.
setFloor(double) - Method in class com.imsl.math.ODE: Sets the value used in the norm computation.
setFont(Font) - Method in class com.imsl.chart.AbstractChartNode: Sets the value of the font attributes.
setFontName(String) - Method in class com.imsl.chart.AbstractChartNode: Sets the value of the "FontName" attribute.
setFontSize(int) - Method in class com.imsl.chart.AbstractChartNode: Sets the value of the "FontSize" attribute.
setFontStyle(int) - Method in class com.imsl.chart.AbstractChartNode: Sets the value of the "FontStyle" attribute.
setForce(int) - Method in class com.imsl.stat.StepwiseRegression: Forces independent variables into the model based on their level assigned from setlevels.
setForcingTerm(FeynmanKac.ForcingTerm) - Method in class com.imsl.math.FeynmanKac: Sets the user-supplied method that computes approximations to the forcing term $phi(x)$ and its derivative $partial phi/partial y$ used in the FeynmanKac PDE.
setFrequencies(double[]) - Method in class com.imsl.stat.ClusterKMeans: Sets the frequency for each observation.
setFrequencies(double[]) - Method in class com.imsl.stat.Covariances: Sets the frequency for each observation.
setFrequencies(double[]) - Method in class com.imsl.stat.TableMultiWay: Sets the frequencies for each observation in x.
setFrequency(int[]) - Method in class com.imsl.stat.KaplanMeierECDF: Sets the frequency for each entry in t
setFrequencyColumn(int) - Method in class com.imsl.stat.CategoricalGenLinModel: Sets the column number in x that contains the frequency of response for each observation.
setFrequencyColumn(int) - Method in class com.imsl.stat.KaplanMeierEstimates: Sets the column index of x containing the frequency of response for each observation.
setFrequencyColumn(int) - Method in class com.imsl.stat.ProportionalHazards: Sets the column index of x containing the frequency of response for each observation.
setFscale(double) - Method in class com.imsl.math.MinUnconMultiVar: Set the function scaling value for scaling the gradient.
setFscale(double[]) - Method in class com.imsl.math.NonlinLeastSquares: Set the diagonal scaling matrix for the functions.
setFunctionPrecision(double) - Method in class com.imsl.math.MinConNLP: Set the relative precision of the function evaluation routine.
setFuzz(double) - Method in class com.imsl.stat.Ranks: Sets the fuzz factor used in determining ties.
setFuzz(double) - Method in class com.imsl.stat.WilcoxonRankSum: Sets the nonnegative constant used to determine ties in computing ranks in the combined samples.
setGainCriteria(DecisionTreeInfoGain.GainCriteria) - Method in class com.imsl.datamining.decisionTree.DecisionTreeInfoGain: Specifies which criteria to use in gain calculations in order to determine the best split at each node.
setGaussLegendreDegree(int) - Method in class com.imsl.math.FeynmanKac: Sets the number of quadrature points used in the Gauss-Legendre quadrature formula.
setGoodDigit(int) - Method in class com.imsl.math.BoundedLeastSquares: Sets the number of good digits in the function.
setGradient(Color, Color, Color, Color) - Method in class com.imsl.chart.ChartNode: Sets the value of the "Gradient" attribute.
setGradient(String, String, String, String) - Method in class com.imsl.chart.ChartNode: Sets the value of the "Gradient" attribute using named colors.
setGradient(Color[]) - Method in class com.imsl.chart.ChartNode: Sets the value of the "Gradient" attribute.
setGradientPrecision(double) - Method in class com.imsl.math.MinConNLP: Set the relative precision in gradients.
setGradientTol(double) - Method in class com.imsl.math.BoundedLeastSquares: Sets the scaled gradient tolerance.
setGradientTolerance(double) - Method in class com.imsl.datamining.neural.LeastSquaresTrainer: Set the gradient tolerance.
setGradientTolerance(double) - Method in class com.imsl.datamining.neural.QuasiNewtonTrainer: Set the gradient tolerance.
setGradientTolerance(double) - Method in class com.imsl.math.MinUnconMultiVar: Sets the gradient tolerance.
setGradientTolerance(double) - Method in class com.imsl.math.NonlinLeastSquares: Set the scaled gradient tolerance stopping critierion.
setGradientTolerance(double) - Method in class com.imsl.stat.ARMAMaxLikelihood: Sets the tolerance for the convergence algorithm.
setGradientTolerance(double) - Method in class com.imsl.stat.NonlinearRegression: Sets the gradient tolerance used to compute the gradient.
setGridType(int) - Method in class com.imsl.datamining.KohonenSOM: Sets the grid type.
setGuess(double[]) - Method in class com.imsl.math.BoundedLeastSquares: Sets the initial guess of the solution.
setGuess(double[]) - Method in class com.imsl.math.GenMinRes: Set the initial guess of the solution.
setGuess(double[]) - Method in class com.imsl.math.MinConGenLin: Sets an initial guess of the solution.
setGuess(double[]) - Method in class com.imsl.math.MinConNLP: Set the initial guess of the minimum point of the input function.
setGuess(double) - Method in class com.imsl.math.MinUncon: Set the initial guess of the minimum point of the input function.
setGuess(double[]) - Method in class com.imsl.math.MinUnconMultiVar: Set the initial guess of the minimum point of the input function.
setGuess(double[]) - Method in class com.imsl.math.NonlinLeastSquares: Set the initial guess of the minimum point of the input function.
setGuess(double[]) - Method in class com.imsl.math.NonNegativeLeastSquares: Sets the initial guess.
setGuess(double[]) - Method in class com.imsl.math.ZerosFunction: Sets the initial guess for the zeros.
setGuess(double[]) - Method in class com.imsl.math.ZeroSystem: Sets the initial estimate of the root.
setGuess(double[]) - Method in class com.imsl.stat.distributions.MaximumLikelihoodEstimation: Sets the guess or starting values of the parameters.
setGuess(double[]) - Method in class com.imsl.stat.NonlinearRegression: Sets the initial guess of the parameter values
setHeatmapLabels(String[][]) - Method in class com.imsl.chart.Heatmap: Sets the value of the "HeatmapLabels" attribute.
setHeatmapLabels(Text[][]) - Method in class com.imsl.chart.Heatmap: Sets the value of the "HeatmapLabels" attribute.
setHessianOption(boolean) - Method in class com.imsl.stat.ProportionalHazards: Set the option to have the Hessian and gradient be computed at the initial estimates.
setHigh(double[]) - Method in class com.imsl.chart.ErrorBar: Convenience routine to set the "High" attribute.
setHigh(double[]) - Method in class com.imsl.chart.HighLowClose: Convenience routine to set the "High" attribute.
setHREF(String) - Method in class com.imsl.chart.ChartNode: Sets the value of the "HREF" attribute.
setIhess(int) - Method in class com.imsl.math.MinUnconMultiVar: Set the Hessian initialization parameter.
setImage(ImageIcon) - Method in class com.imsl.chart.AbstractChartNode: Sets the value of the "Image" attribute.
setImage(Image) - Method in class com.imsl.chart.ChartNode: Sets the value of the "Image" attribute.
setIndex(int[]) - Method in class com.imsl.stat.Dissimilarities: Sets the indices of the rows (columns).
setInfiniteEstimateMethod(int) - Method in class com.imsl.stat.CategoricalGenLinModel: Sets the method to be used for handling infinite estimates.
setInitialCMinus(double) - Method in class com.imsl.chart.qc.CuSumStatus: Sets the initial value of $C^{-}$ .
setInitialCPlus(double) - Method in class com.imsl.chart.qc.CuSumStatus: Sets the initial value of $C^{+}$ .
setInitialData(FeynmanKac.InitialData) - Method in class com.imsl.math.FeynmanKac: Sets the user-supplied method for adjustment of initial data or as an opportunity for output during the integration steps.
setInitialEstimates(double[], double[]) - Method in class com.imsl.stat.ARMA: Sets preliminary estimates for the LEAST_SQUARES estimation method.
setInitialEstimates(int, double[]) - Method in class com.imsl.stat.CategoricalGenLinModel: Sets the initial parameter estimates option.
setInitialEstimates(double[]) - Method in class com.imsl.stat.ProportionalHazards: Sets the initial parameter estimates.
setInitialF(double[]) - Method in class com.imsl.math.NumericalDerivatives: Set the initial function values.
setInitialStepsize(double) - Method in class com.imsl.math.FeynmanKac: Sets the starting stepsize for the integration.
setInitialStepsize(double) - Method in class com.imsl.math.ODE: Sets the initial internal step size.
setInitialTrustRegion(double) - Method in class com.imsl.datamining.neural.LeastSquaresTrainer: Sets the intial trust region.
setInitialTrustRegion(double) - Method in class com.imsl.math.NonlinLeastSquares: Set the initial trust region radius.
setInitialTrustRegion(double) - Method in class com.imsl.stat.NonlinearRegression: Sets the initial trust region radius.
setInitialValues(double[][]) - Method in class com.imsl.stat.HoltWintersExponentialSmoothing: Sets the initial values for the level, trend, and seasonal component sequences.
setIntegrationMethod(int) - Method in class com.imsl.math.OdeAdamsGear: Indicates which integration method is to be used.
setInternalScale() - Method in class com.imsl.math.BoundedLeastSquares: Sets the internal variable scaling option.
setIterations(int) - Method in class com.imsl.datamining.KohonenSOMTrainer: Sets the number of iterations to be used for training.
setIterationsArray(int[]) - Method in class com.imsl.datamining.GradientBoosting: Sets the array of different numbers of iterations.
setIterativeRefinement(boolean) - Method in class com.imsl.math.ComplexSuperLU: Specifies whether to perform iterative refinement.
setIterativeRefinement(boolean) - Method in class com.imsl.math.SuperLU: Specifies whether to perform iterative refinement.
setJacobi(double[]) - Method in class com.imsl.math.ConjugateGradient: Defines a Jacobi preconditioner as the preconditioning matrix, that is, M is the diagonal of A.
setJacobian(BoundedLeastSquares.Jacobian) - Method in class com.imsl.math.BoundedLeastSquares: Sets the Jacobian.
setKeyboard(boolean) - Method in class com.imsl.chart3d.Chart3D: Sets the value of the "Keyboard" attribute.
setLabels(String[]) - Method in class com.imsl.chart.AxisLabel: Sets the axis label values for this node to be used instead of the default numbers.
setLabels(String[]) - Method in class com.imsl.chart.AxisRLabel: Sets the axis label values for this node to be used instead of the default numbers.
setLabels(String[], int) - Method in class com.imsl.chart.Bar: Sets up an axis with bar labels.
setLabels(String[]) - Method in class com.imsl.chart.Bar: Sets up an axis with bar labels.
setLabels(String[], int) - Method in class com.imsl.chart.BoxPlot: Sets up an axis with labels.
setLabels(String[]) - Method in class com.imsl.chart.BoxPlot: Sets up an axis with labels.
setLabels(String[]) - Method in class com.imsl.chart.Dendrogram: Sets up the axis labels for dendrogram plot.
setLabels(String[]) - Method in class com.imsl.chart.Treemap: Sets the value of the "TreemapLabels" attribute.
setLabels(Text[]) - Method in class com.imsl.chart.Treemap: Sets the value of the "TreemapLabels" attribute.
setLabels(String[]) - Method in class com.imsl.chart3d.AxisLabel: Sets the axis label values for this node to be used instead of the default numbers.
setLabelType(int) - Method in class com.imsl.chart.AbstractChartNode: Sets the value of the "LabelType" attribute.
setLambda(double) - Method in class com.imsl.chart.qc.EWMA: Sets the value of the attribute "Lambda".
setLeftEndTangent(double) - Method in class com.imsl.math.CsTCB: Sets the value of the tangent at the left endpoint.
setLeftSons(int[]) - Method in class com.imsl.chart.Dendrogram: Convenience routine to set the "LeftSons" attribute.
setLevels(double[]) - Method in class com.imsl.chart.Dendrogram: Convenience routine to set the "Levels" attribute.
setLevels(int[]) - Method in class com.imsl.stat.StepwiseRegression: Sets the levels of priority for variables entering and leaving the regression.
setLightColor(Color) - Method in class com.imsl.chart.AbstractChartNode: Sets the value of the "LightColor" attribute.
setLightColor(String) - Method in class com.imsl.chart.AbstractChartNode: Sets the value of the "LightColor" attribute to a color specified by name.
setLightingEnabled(boolean) - Method in class com.imsl.chart3d.ChartNode3D: Sets the value of the "LightingEnabled" attribute.
setLineColor(Color) - Method in class com.imsl.chart.AbstractChartNode: Sets the value of the "LineColor" attribute.
setLineColor(String) - Method in class com.imsl.chart.AbstractChartNode: Sets the value of the "LineColor" attribute.
setLineColor(String[]) - Method in class com.imsl.chart.Dendrogram: Define colors for individual clusters.
setLineColor(Color[]) - Method in class com.imsl.chart.Dendrogram: Define colors for individual clusters.
setLineDashPattern(double[]) - Method in class com.imsl.chart.ChartNode: Sets the value of the "LineDashPattern" attribute.
setLineWidth(double) - Method in class com.imsl.chart.AbstractChartNode: Sets the value of the "LineWidth" attribute.
setLocale(Locale) - Method in class com.imsl.chart.AbstractChartNode: Sets the value of the "Locale" attribute.
setLocation(double, double) - Method in class com.imsl.chart.Annotation: Update the location of this Annotation instance.
setLossFunctionType(GradientBoosting.LossFunctionType) - Method in class com.imsl.datamining.GradientBoosting: Sets the loss function type for the gradient boosting algorithm.
setLow(double[]) - Method in class com.imsl.chart.ErrorBar: Convenience routine to set the "Low" attribute.
setLow(double[]) - Method in class com.imsl.chart.HighLowClose: Convenience routine to set the "Low" attribute.
setLowerBound(double[]) - Method in class com.imsl.math.DenseLP: Sets the lower bound, $x_l$ , on the variables.
setLowerBound(double[]) - Method in class com.imsl.math.SparseLP: Sets the lower bound on the variables.
setLowerBounds(double[]) - Method in class com.imsl.stat.HoltWintersExponentialSmoothing: Sets the lower bounds for each of the smoothing parameters, (, , ).
setLowerEndpointColumn(int) - Method in class com.imsl.stat.CategoricalGenLinModel: Sets the column number in x that contains the lower endpoint of the observation interval for full interval and right interval observations.
setMA(double[]) - Method in class com.imsl.stat.ARMAMaxLikelihood: Sets the initial values for the moving average terms to the q values in ma.
setMALags(int[]) - Method in class com.imsl.stat.ARMA: Sets the order of the moving average parameters.
setMarkerColor(Color) - Method in class com.imsl.chart.AbstractChartNode: Sets the value of the "MarkerColor" attribute.
setMarkerColor(String) - Method in class com.imsl.chart.AbstractChartNode: Sets the value of the "MarkerColor" attribute to a color specified by name.
setMarkerDashPattern(double[]) - Method in class com.imsl.chart.ChartNode: Sets the value of the "MarkerDashPattern" attribute.
setMarkerPulsingCycle(double) - Method in class com.imsl.chart3d.ChartNode3D: Sets the value of the "MarkerPulsingCycle" attribute.
setMarkerPulsingCycleOffset(double) - Method in class com.imsl.chart3d.ChartNode3D: Sets the value of the "MarkerPulsingCycleOffset" attribute.
setMarkerPulsingMaximumScale(double) - Method in class com.imsl.chart3d.ChartNode3D: Sets the value of the "MarkerPulsingMaximumScale" attribute.
setMarkerPulsingMinimumScale(double) - Method in class com.imsl.chart3d.ChartNode3D: Sets the value of the "MarkerPulsingMinimumScale" attribute.
setMarkerRotatingAxis(double, double, double) - Method in class com.imsl.chart3d.ChartNode3D: Sets the value of the "MarkerRotatingAxis" attribute.
setMarkerRotatingCycle(double) - Method in class com.imsl.chart3d.ChartNode3D: Sets the value of the "MarkerRotatingCycle" attribute.
setMarkerRotatingCycleOffset(double) - Method in class com.imsl.chart3d.ChartNode3D: Sets the value of the "MarkerRotatingCycleOffset" attribute.
setMarkerSize(double) - Method in class com.imsl.chart.AbstractChartNode: Sets the value of the "MarkerSize" attribute.
setMarkerThickness(double) - Method in class com.imsl.chart.ChartNode: Sets the value of the "MarkerThickness" attribute.
setMarkerType(int) - Method in class com.imsl.chart.ChartNode: Sets the value of the "MarkerType" attribute.
setMarkerType(int) - Method in class com.imsl.chart3d.ChartNode3D: Sets the value of the "MarkerType" attribute.
setMaterial(Material) - Method in class com.imsl.chart3d.ChartNode3D: Sets the value of the "Material" attribute.
setMatrixType(int) - Method in class com.imsl.math.PrintMatrix: Set matrix type.
setMaxClass(int) - Method in class com.imsl.stat.ProportionalHazards: Sets an upper bound on the sum of the number of distinct values found among the classification variables in x.
setMaxDepth(int) - Method in class com.imsl.datamining.decisionTree.DecisionTree: Specifies the maximum tree depth allowed.
setMaxEvaluations(int) - Method in class com.imsl.math.ZerosFunction: Sets the maximum number of function evaluations allowed.
setMaximumARLag(int) - Method in class com.imsl.stat.AutoARIMA: Defines the maximum AR lag used in the determination of the optimum (s,d) combination of method compute(int[] arOrders, int[] maOrders).
setMaximumBDFOrder(int) - Method in class com.imsl.math.FeynmanKac: Sets the maximum order of the BDF formulas.
setMaximumBestFound(int) - Method in class com.imsl.stat.SelectionRegression: Sets the maximum number of best regressions to be found.
setMaximumFunctionEvals(int) - Method in class com.imsl.math.BoundedLeastSquares: Sets the maximum number of function evaluations.
setMaximumFunctionEvaluations(int) - Method in class com.imsl.math.OdeAdamsGear: Sets the maximum number of function evaluations of $y'$ allowed.
setMaximumGoodSaved(int) - Method in class com.imsl.stat.SelectionRegression: Sets the maximum number of good regressions for each subset size saved.
setMaximumIteration(int) - Method in class com.imsl.math.BoundedLeastSquares: Sets the maximum number of iterations.
setMaximumJacobianEvals(int) - Method in class com.imsl.math.BoundedLeastSquares: Sets the maximum number of Jacobian evaluations.
setMaximumStepsize(double) - Method in class com.imsl.datamining.neural.LeastSquaresTrainer: Sets the maximum step size.
setMaximumStepsize(double) - Method in class com.imsl.datamining.neural.QuasiNewtonTrainer: Sets the maximum step size.
setMaximumStepSize(double) - Method in class com.imsl.math.BoundedLeastSquares: Sets the maximum allowable step size.
setMaximumStepsize(double) - Method in class com.imsl.math.FeynmanKac: Sets the maximum internal step size used by the integrator.
setMaximumStepsize(double) - Method in class com.imsl.math.MinUnconMultiVar: Set the maximum allowable stepsize to use.
setMaximumStepsize(double) - Method in class com.imsl.math.NonlinLeastSquares: Set the maximum allowable stepsize to use.
setMaximumStepsize(double) - Method in class com.imsl.math.ODE: Sets the maximum internal step size.
setMaximumStepsize(double) - Method in class com.imsl.math.OdeAdamsGear: Sets the maximum internal step size.
setMaximumStepsize(double) - Method in class com.imsl.math.OdeRungeKutta: Sets the maximum internal step size.
setMaximumSubsetSize(int) - Method in class com.imsl.stat.SelectionRegression: Sets the maximum subset size if criterion is used.
setMaximumTime(long) - Method in class com.imsl.math.MinConNLP: Sets the maximum time allowed for the solve step.
setMaximumTime(long) - Method in class com.imsl.math.NonNegativeLeastSquares: Sets the maximum time allowed for the solve step.
setMaximumTrainingIterations(int) - Method in class com.imsl.datamining.neural.LeastSquaresTrainer: Sets the maximum number of iterations used by the nonlinear least squares solver.
setMaximumTrainingIterations(int) - Method in class com.imsl.datamining.neural.QuasiNewtonTrainer: Sets the maximum number of iterations to use in a training.
setMaximumValue(double) - Method in class com.imsl.chart.qc.ControlLimit: Set the maximum value of this control limit line.
setMaxIterations(int) - Method in class com.imsl.math.BoundedVariableLeastSquares: Sets the maximum number of iterations.
setMaxIterations(int) - Method in class com.imsl.math.ConjugateGradient: Sets the maximum number of iterations allowed.
setMaxIterations(int) - Method in class com.imsl.math.Eigen: Set the maximum number of iterations allowed.
setMaxIterations(int) - Method in class com.imsl.math.GenMinRes: Set the maximum number of iterations allowed.
setMaxIterations(int) - Method in class com.imsl.math.MinConNLP: Set the maximum number of iterations allowed.
setMaxIterations(int) - Method in class com.imsl.math.MinUnconMultiVar: Set the maximum number of iterations allowed.
setMaxIterations(int) - Method in class com.imsl.math.NonlinLeastSquares: Set the maximum number of iterations allowed.
setMaxIterations(int) - Method in class com.imsl.math.NonNegativeLeastSquares: Sets the maximum number of iterations.
setMaxIterations(int) - Method in class com.imsl.math.SparseLP: Sets the maximum number of iterations allowed for the primal-dual solver.
setMaxIterations(int) - Method in class com.imsl.math.ZeroPolynomial: Sets the maximum number of iterations allowed.
setMaxIterations(int) - Method in class com.imsl.math.ZeroSystem: Sets the maximum number of iterations allowed.
setMaxIterations(int) - Method in class com.imsl.stat.ARAutoUnivariate: Sets the maximum number of iterations used for estimating the autoregressive coefficients.
setMaxIterations(int) - Method in class com.imsl.stat.ARMA: Sets the maximum number of iterations.
setMaxIterations(int) - Method in class com.imsl.stat.ARMAEstimateMissing: Sets the maximum number of estimation iterations for missing value estimation methods AR_1 and AR_P.
setMaxIterations(int) - Method in class com.imsl.stat.ARMAMaxLikelihood: Sets the maximum number of iterations.
setMaxIterations(int) - Method in class com.imsl.stat.CategoricalGenLinModel: Set the maximum number of iterations allowed.
setMaxIterations(int) - Method in class com.imsl.stat.ClusterKMeans: Sets the maximum number of iterations.
setMaxIterations(int) - Method in class com.imsl.stat.FactorAnalysis: Sets the maximum number of iterations in the iterative procedure.
setMaxIterations(int) - Method in class com.imsl.stat.NonlinearRegression: Sets the maximum number of iterations allowed during optimization
setMaxIterations(int) - Method in class com.imsl.stat.ProportionalHazards: Set the maximum number of iterations allowed.
setMaxKrylovDim(int) - Method in class com.imsl.math.GenMinRes: Set the maximum Krylov subspace dimension, i.e., the maximum allowable number of GMRES iterations allowed before restarting.
setMaxlag(int) - Method in class com.imsl.stat.ARMAEstimateMissing: Sets the maximum number of autoregressive lags when method AR_P is selected as the missing value estimation method.
setMaxLag(int) - Method in class com.imsl.stat.VectorAutoregression: Sets the maximum lag.
setMaxNodes(int) - Method in class com.imsl.datamining.decisionTree.DecisionTree: Sets the maximum number of nodes allowed in a tree.
setMaxNumberOfCategories(int) - Method in class com.imsl.datamining.PredictiveModel: Sets the maximum number of categories allowed within categorical predictor variables.
setMaxOrder(int) - Method in class com.imsl.math.OdeAdamsGear: Sets the highest order formula to use of implicit METHOD_ADAMS type or METHOD_BDF type.
setMaxSigma(double) - Method in class com.imsl.stat.GARCH: Sets the value of the upperbound on the first element (sigma) of the array of returned estimated coefficients.
setMaxStep(int) - Method in class com.imsl.stat.FactorAnalysis: Sets the maximum number of step halvings allowed during an iteration.
setMaxSteps(int) - Method in class com.imsl.math.FeynmanKac: Sets the maximum number of internal steps allowed.
setMaxSteps(int) - Method in class com.imsl.math.ODE: Sets the maximum number of internal steps allowed.
setMaxStepsize(double) - Method in class com.imsl.stat.NonlinearRegression: Sets the maximum allowable stepsize.
setMaxSubintervals(int) - Method in class com.imsl.math.Quadrature: Sets the maximum number of subintervals allowed.
setMean(double) - Method in class com.imsl.stat.ARAutoUnivariate: Sets the estimate of the mean used for centering the time series z.
setMean(double) - Method in class com.imsl.stat.ARMA: Sets an initial estimate of the mean of the time series z.
setMean(double) - Method in class com.imsl.stat.ARMAEstimateMissing: Sets the mean value used to center the series.
setMean(double) - Method in class com.imsl.stat.ARMAMaxLikelihood: Sets the mean used for centering the series.
setMean(double) - Method in class com.imsl.stat.AutoCorrelation: Estimate mean of the time series x.
setMeans(double[]) - Method in class com.imsl.stat.StepwiseRegression: Sets the means of the variables.
setMeanX(double) - Method in class com.imsl.stat.CrossCorrelation: Estimate of the mean of time series x.
setMeanX(double[]) - Method in class com.imsl.stat.MultiCrossCorrelation: Estimate of the mean of each channel of x.
setMeanY(double) - Method in class com.imsl.stat.CrossCorrelation: Estimate of the mean of time series y.
setMeanY(double[]) - Method in class com.imsl.stat.MultiCrossCorrelation: Estimate of the mean of each channel of y.
setMergeCategoriesSignificanceLevel(double) - Method in class com.imsl.datamining.decisionTree.CHAID: Sets the significance level for merging categories.
setMergeRule(TimeSeriesOperations.MergeRule) - Method in class com.imsl.stat.TimeSeriesOperations: Sets the rule that defines how two time series are merged.
setMethod(int) - Method in class com.imsl.math.GenMinRes: Set the implementation method to be used.
setMethod(int) - Method in class com.imsl.stat.ARMA: Sets the estimation method used for estimating the ARMA parameters.
setMethod(int) - Method in class com.imsl.stat.ClusterHierarchical: Sets the clustering method to be used.
setMethod(int) - Method in class com.imsl.stat.StepwiseRegression: Specifies the stepwise selection method, forward, backward, or stepwise Regression.
setMinCostComplexityValue(double) - Method in class com.imsl.datamining.decisionTree.DecisionTree: Sets the value of the minimum cost-complexity value.
setMinimumSeparation(double) - Method in class com.imsl.math.ZerosFunction: Sets the minimum separation between accepted roots.
setMinimumStepsize(double) - Method in class com.imsl.math.ODE: Sets the minimum internal step size.
setMinimumValue(double) - Method in class com.imsl.chart.qc.ControlLimit: Set the minimum value of this control limit line.
setMinObsPerChildNode(int) - Method in class com.imsl.datamining.decisionTree.DecisionTree: Specifies the minimum number of observations that a child node must have in order to split, one of several tree size and splitting control parameters.
setMinObsPerNode(int) - Method in class com.imsl.datamining.decisionTree.DecisionTree: Specifies the minimum number of observations a node must have to allow a split, one of several tree size and splitting control parameters.
setMissingTestYFlag(boolean) - Method in class com.imsl.datamining.GradientBoosting: Sets the flag determining whether the test data is missing the response variable data.
setMissingValueMethod(int) - Method in class com.imsl.stat.ARMAEstimateMissing: Sets the current missing value estimation method to MEDIAN, CUBIC_SPLINE, AR_1, or AR_P.
setMissingValueMethod(int) - Method in class com.imsl.stat.Covariances: Sets the method used to exclude missing values in x from the computations, where Double.NaN is interpreted as the missing value code.
setModelIntercept(int) - Method in class com.imsl.stat.CategoricalGenLinModel: Sets the intercept option.
setModelOrder(int) - Method in class com.imsl.stat.ANOVAFactorial: Sets the number of factors to be included in the highest-way interaction in the model.
setModelOrder(int) - Method in class com.imsl.stat.RegressorsForGLM: Sets the order of the model.
setModelSelectionCriterion(int) - Method in class com.imsl.stat.AutoARIMA: Sets the model selection criterion.
setMRBar(double) - Method in class com.imsl.chart.qc.XmR: Sets the expected mean of all of the moving ranges of two observations.
setMullerTolerance(double) - Method in class com.imsl.math.ZerosFunction: Sets the tolerance used during refinement to determine if Müllers method is started.
setMultiplier(int) - Method in class com.imsl.stat.Random: Sets the multiplier for a linear congruential random number generator.
setMultiplierError(double) - Method in class com.imsl.math.MinConNLP: Set the error allowed in the multipliers.
setName(String) - Method in class com.imsl.chart.AbstractChartNode: Sets the value of the "Name" attribute.
setNameBounds(String) - Method in class com.imsl.io.MPSReader: Sets the name of the BOUNDS set to be used.
setNameObjective(String) - Method in class com.imsl.io.MPSReader: Sets the name of the free row containing the objective.
setNameRanges(String) - Method in class com.imsl.io.MPSReader: Sets the name of the RANGES set to be used.
setNameRHS(String) - Method in class com.imsl.io.MPSReader: Sets the name of the RHS set to be used.
setNeighborhoodType(int) - Method in class com.imsl.datamining.KohonenSOM: Sets the neighborhood type.
setNoColumnLabels() - Method in class com.imsl.math.PrintMatrixFormat: Turns off column labels.
setNode(ChartNode) - Method in class com.imsl.chart.Draw: Set the current ChartNode.
setNode(ChartNode) - Method in class com.imsl.chart.DrawMap: Set the current ChartNode.
setNode(ChartNode) - Method in class com.imsl.chart.DrawPick: Set the current ChartNode.
setNode(ChartNode) - Method in class com.imsl.chart.PickEvent: Sets the ChartNode.
setNode(int, TreeNode) - Method in class com.imsl.datamining.decisionTree.Tree: Inserts a TreeNode into the specified location of the decision tree.
setNodes(TreeNode[]) - Method in class com.imsl.datamining.decisionTree.Tree: Sets the nodes within a decision tree.
setNonseasonalConstant() - Method in class com.imsl.stat.HoltWintersExponentialSmoothing: Remove the trend and the seasonal components and fit only the level component.
setNonseasonalTrend() - Method in class com.imsl.stat.HoltWintersExponentialSmoothing: Remove the seasonal component and fit only the level and trend components.
setNorm(int) - Method in class com.imsl.math.ODE: Sets the switch for determining the error norm.
setNormTolerance(double) - Method in class com.imsl.math.NonNegativeLeastSquares: Sets the residual norm tolerance.
setNoRowLabels() - Method in class com.imsl.math.PrintMatrixFormat: Turns off row labels.
setNotch(boolean) - Method in class com.imsl.chart.BoxPlot: Sets the attribute "Notch".
setNumber(int) - Method in class com.imsl.chart.AbstractChartNode: Sets the value of the "Number" attribute.
setNumberBootstrapSamples(int) - Method in class com.imsl.datamining.BootstrapAggregation: Sets the number of bootstrap samples.
setNumberEval(int) - Method in class com.imsl.stat.HoltWintersExponentialSmoothing: Sets the number of evaluations of the residual norm that are sampled to obtain starting values for the smoothing parameters, (, , ).
setNumberForecasts(int) - Method in class com.imsl.stat.HoltWintersExponentialSmoothing: Sets the number of forecasts desired past the series data.
setNumberFormat(NumberFormat) - Method in class com.imsl.math.PrintMatrixFormat: Sets the NumberFormat to be used in formatting double and Complex entries.
setNumberGridPointsX(int) - Method in class com.imsl.chart3d.Surface: Sets the value of the "NumberGridPointsX" attribute.
setNumberGridPointsY(int) - Method in class com.imsl.chart3d.Surface: Sets the value of the "NumberGridPointsY" attribute.
setNumberOfChildren(int) - Method in class com.imsl.datamining.decisionTree.TreeNode: Sets the number of child nodes associated with the current node.
setNumberOfClasses(int) - Method in class com.imsl.datamining.PredictiveModel: Sets the number of distinct classes of the response variable.
setNumberOfEpochs(int) - Method in class com.imsl.datamining.neural.EpochTrainer: Sets the number of epochs.
setNumberOfIterations(int) - Method in class com.imsl.datamining.GradientBoosting: Sets the number of iterations.
setNumberOfLevels(int) - Method in class com.imsl.datamining.decisionTree.Tree: Sets the number of levels determined for a tree (depth).
setNumberOfNodes(int) - Method in class com.imsl.datamining.decisionTree.Tree: Sets the number of nodes (size of a tree).
setNumberOfObservations(int) - Method in class com.imsl.stat.HoltWintersExponentialSmoothing: Sets the number of equally spaced series values.
setNumberOfRoots(int) - Method in class com.imsl.math.ZerosFunction: Sets the number of roots to be found.
setNumberOfSampleFolds(int) - Method in class com.imsl.datamining.CrossValidation: Sets the number of folds to use in cross validation selection.
setNumberOfSurrogateSplits(int) - Method in class com.imsl.datamining.decisionTree.ALACART: Sets the number of surrogate splits.
setNumberOfSurrogateSplits(int) - Method in interface com.imsl.datamining.decisionTree.DecisionTreeSurrogateMethod: Indicates the number of surrogate splits.
setNumberOfSurrogateSplits(int) - Method in class com.imsl.datamining.decisionTree.Tree: Sets the number of surrogate splits to search for at each tree node.
setNumberOfThreads(int) - Method in class com.imsl.datamining.BootstrapAggregation: Sets the maximum number of java.lang.Thread instances that may be used for parallel processing.
setNumberOfThreads(int) - Method in class com.imsl.datamining.CrossValidation: Sets the maximum number of java.lang.Thread instances that may be used for parallel processing.
setNumberOfThreads(int) - Method in class com.imsl.datamining.KohonenSOMTrainer: Sets the number of java.lang.Thread instances to be used for parallel processing.
setNumberOfThreads(int) - Method in class com.imsl.datamining.neural.EpochTrainer: Sets the number of java.lang.Thread instances to be used for parallel processing.
setNumberOfThreads(int) - Method in class com.imsl.math.BoundedLeastSquares: Sets the number of java.lang.Thread instances to be used for parallel processing.
setNumberOfThreads(int) - Method in class com.imsl.math.MinConGenLin: Sets the number of java.lang.Thread instances to be used for parallel processing.
setNumberOfThreads(int) - Method in class com.imsl.math.MinConNLP: Sets the number of java.lang.Thread instances to be used for parallel processing.
setNumberOfThreads(int) - Method in class com.imsl.math.MinUnconMultiVar: Sets the number of java.lang.Thread instances to be used for parallel processing.
setNumberOfThreads(int) - Method in class com.imsl.math.NonlinLeastSquares: Sets the number of java.lang.Thread instances to be used for parallel processing.
setNumberOfThreads(int) - Method in class com.imsl.stat.AutoCorrelation: Sets the number of java.lang.Thread instances to be used for parallel processing.
setNumericFactor(ComplexSparseCholesky.NumericFactor) - Method in class com.imsl.math.ComplexSparseCholesky: Sets the numeric Cholesky factor to use in solving a sparse complex Hermitian positive definite system of linear equations $Ax=b$ .
setNumericFactor(SparseCholesky.NumericFactor) - Method in class com.imsl.math.SparseCholesky: Sets the numeric Cholesky factor to use in solving of a sparse positive definite system of linear equations $Ax=b$ .
setNumericFactorizationMethod(int) - Method in class com.imsl.math.ComplexSparseCholesky: Defines the method used in the numerical factorization of the permuted input matrix.
setNumericFactorizationMethod(int) - Method in class com.imsl.math.SparseCholesky: Defines the method used in the numerical factorization of the permuted input matrix.
setObservationMax(int) - Method in class com.imsl.stat.CategoricalGenLinModel: Sets the maximum number of observations that can be handled in the linear programming.
setOffset(double) - Method in class com.imsl.chart.Text: Sets the offset.
setOpen(double[]) - Method in class com.imsl.chart.HighLowClose: Sets the attribute "Open".
setOptionalDistributionParameterColumn(int) - Method in class com.imsl.stat.CategoricalGenLinModel: Sets the column number in x that contains an optional distribution parameter for each observation.
setOrbit(boolean) - Method in class com.imsl.chart3d.Chart3D: Sets the value of the "Orbit" attribute.
setOrder(int[]) - Method in class com.imsl.chart.Dendrogram: Convenience routine to set the "Order" attribute.
setOrders(int[]) - Method in class com.imsl.stat.Difference: Sets the orders for the Difference object
setOrientation(int) - Method in class com.imsl.chart.Treemap: Sets the value of the "Orientation" attribute.
setOut(PrintStream) - Static method in class com.imsl.Warning: Reassigns the output stream.
setOut(PrintStream) - Method in class com.imsl.WarningObject: Reassigns the output stream.
setPageWidth(int) - Method in class com.imsl.math.PrintMatrix: Sets the page width.
setPaint(boolean) - Method in class com.imsl.chart.AbstractChartNode: Sets the value of the "Paint" attribute.
setParallelMode(ArrayList[]) - Method in class com.imsl.datamining.neural.LeastSquaresTrainer: Sets the trainer to be used in multi-threaded EpochTainer.
setParallelMode(ArrayList[]) - Method in class com.imsl.datamining.neural.QuasiNewtonTrainer: Sets the trainer to be used in multi-threaded EpochTainer.
setParameters(double[]) - Method in class com.imsl.stat.HoltWintersExponentialSmoothing: Sets the values of the smoothing parameters for the level (), the trend (), and the seasonal () component sequences.
setPenaltyBound(double) - Method in class com.imsl.math.MinConNLP: Set the universal bound for describing how much the unscaled penalty-term may deviate from zero.
setPercentage(double) - Method in class com.imsl.stat.SignTest: Sets the percentage percentile of the population.
setPercentageFactor(double[]) - Method in class com.imsl.math.NumericalDerivatives: Sets the percentage factor for differencing
setPercentages(double[]) - Method in class com.imsl.datamining.neural.UnsupervisedOrdinalFilter: Set the untransformed cumulative percentages used during encoding and decoding.
setPercentile(double) - Method in class com.imsl.stat.SignTest: Sets the hypothesized percentile of the population.
setPerformanceTuningParameters(int, int) - Method in class com.imsl.math.ComplexSuperLU: Sets performance tuning parameters.
setPerformanceTuningParameters(int, int) - Method in class com.imsl.math.SuperLU: Sets performance tuning parameters.
setPeriods(int[]) - Method in class com.imsl.stat.AutoARIMA: Defines the periods used in the determination of the optimum model.
setPivotGrowth(boolean) - Method in class com.imsl.math.ComplexSuperLU: Specifies whether to compute the reciprocal pivot growth factor.
setPivotGrowth(boolean) - Method in class com.imsl.math.SuperLU: Specifies whether to compute the reciprocal pivot growth factor.
setPixels(int, int, int, int, ColorModel, byte[], int, int) - Method in class com.imsl.chart.WebSafeImageFilter
setPixels(int, int, int, int, ColorModel, int[], int, int) - Method in class com.imsl.chart.WebSafeImageFilter
setPopulationSize(int) - Method in class com.imsl.stat.LifeTables: Sets the population size at the beginning of the first age interval in requesting a population table.
setPosition(int, int) - Method in class com.imsl.chart3d.ColormapLegend: Sets the position of the legend.
setPosition(double, double, double) - Method in class com.imsl.chart3d.PointLight: Sets the value of the "Point" attribute to a light point.
setPosition(Point3f) - Method in class com.imsl.chart3d.PointLight: Sets the value of the "Point" attribute to a light point.
setPredictorIndex(int[]) - Method in class com.imsl.datamining.PredictiveModel: Sets the array of indices into xy where the predictor variables reside.
setPredictorTypes(PredictiveModel.VariableType[]) - Method in class com.imsl.datamining.PredictiveModel: Sets the VariableType objects that correspond to the predictor data types in xy.
setPreordering(int) - Method in class com.imsl.math.SparseLP: Sets the variant of the Minimum Degree Ordering (MDO) algorithm used in the preordering of the normal equations or augmented system matrix.
setPresolve(int) - Method in class com.imsl.math.SparseLP: Sets the presolve option.
setPrimalInfeasibilityTolerance(double) - Method in class com.imsl.math.SparseLP: Sets the primal infeasibility tolerance.
setPrintLevel(int) - Method in class com.imsl.datamining.BootstrapAggregation: Sets the print level for the predictive model.
setPrintLevel(int) - Method in class com.imsl.datamining.PredictiveModel: Sets a print level that determines the information printed for a PredictiveModel.
setPrintLevel(int) - Method in class com.imsl.math.SparseLP: Sets the print level.
setPrior(int) - Method in class com.imsl.stat.DiscriminantAnalysis: Specifies the prior probabilities to be calculated as either equal or proportional priors.
setPrior(double[]) - Method in class com.imsl.stat.DiscriminantAnalysis: Specifies user supplied prior probabilities.
setPriorProbabilities(double[]) - Method in class com.imsl.datamining.PredictiveModel: Set the prior probabilities for class membership.
setProportionalWidth(boolean) - Method in class com.imsl.chart.BoxPlot: Sets the value of the attribute "ProportionalWidth".
setPValueIn(double) - Method in class com.imsl.stat.StepwiseRegression: Defines the largest p-value for variables entering the model.
setPValueOut(double) - Method in class com.imsl.stat.StepwiseRegression: Defines the smallest p-value for removing variables.
setQ(double[][]) - Method in class com.imsl.stat.KalmanFilter: Sets the Q matrix.
setRadialFunction(RadialBasis.Function) - Method in class com.imsl.math.RadialBasis: Sets the radial function.
setRandom(Random) - Method in class com.imsl.datamining.neural.EpochTrainer: Sets the random number generator used to perturb the initial stage 1 guesses.
setRandom(Random) - Method in class com.imsl.stat.Ranks: Sets the Random object.
setRandomObject(Random) - Method in class com.imsl.datamining.BootstrapAggregation: Sets a random object for the bootstrap random sampling scheme.
setRandomObject(Random) - Method in class com.imsl.datamining.CrossValidation: Sets the random object to be used in the permutation of observation data.
setRandomObject(Random) - Method in class com.imsl.datamining.PredictiveModel: Sets the random object to be used in the permutation of observation data.
setRandomSamples(Random, Random) - Method in class com.imsl.datamining.neural.EpochTrainer: Sets the random number generators used to select random training patterns in stage 1.
setRandomWeights(double[][], Random) - Method in class com.imsl.datamining.neural.FeedForwardNetwork: Initializes network weights using random weights.
setRange(double, double) - Method in class com.imsl.stat.ChiSquaredTest: Sets endpoints of the range of the distribution.
setRangeOfX(double[]) - Method in class com.imsl.stat.distributions.ProbabilityDistribution: Sets the proper range of the random variable having the current probability distribution.
setRankTolerance(double) - Method in class com.imsl.math.NonNegativeLeastSquares: Sets the tolerance used for the incoming column rank deficient check.
setRbar(double) - Method in class com.imsl.chart.qc.XbarR: Sets the value of the "Rbar" attribute, the mean of the ranges for a series of samples.
setReference(double) - Method in class com.imsl.chart.ChartNode: Sets the value of the "Reference" attribute.
setRefinementType(int) - Method in class com.imsl.math.DenseLP: Set the type of refinement used.
setRelativeError(double) - Method in class com.imsl.math.ConjugateGradient: Sets the relative error used for stopping the algorithm.
setRelativeError(double) - Method in class com.imsl.math.GenMinRes: Set the stopping tolerance.
setRelativeError(double) - Method in class com.imsl.math.HyperRectangleQuadrature: Sets the relative error tolerance.
setRelativeError(double) - Method in class com.imsl.math.Quadrature: Sets the relative error tolerance.
setRelativeError(double) - Method in class com.imsl.math.ZeroSystem: Sets the relative error tolerance.
setRelativeError(double) - Method in class com.imsl.stat.ARMA: Sets the stopping criterion for use in the nonlinear equation solver.
setRelativeError(double) - Method in class com.imsl.stat.ARMAEstimateMissing: Sets the relative error used for the METHOD_OF_MOMENTS and LEAST_SQUARES estimation methods.
setRelativeError(double) - Method in class com.imsl.stat.ARMAOutlierIdentification: Sets the stopping criterion for use in the nonlinear equation solver.
setRelativeError(double) - Method in class com.imsl.stat.AutoARIMA: Sets the stopping criterion for use in the nonlinear equation solver.
setRelativeErrorTolerances(double[]) - Method in class com.imsl.math.FeynmanKac: Sets the relative error tolerances.
setRelativeErrorTolerances(double) - Method in class com.imsl.math.FeynmanKac: Sets the relative error tolerances.
setRelativeFcnTol(double) - Method in class com.imsl.math.BoundedLeastSquares: Sets the relative function tolerance.
setRelativeH(double) - Method in class com.imsl.chart.qc.CuSumStatus: Sets the value for relative h.
setRelativeOptimalityTolerance(double) - Method in class com.imsl.math.SparseLP: Sets the relative optimality tolerance.
setRelativeTolerance(double) - Method in class com.imsl.datamining.neural.LeastSquaresTrainer: Sets the relative tolerance.
setRelativeTolerance(double) - Method in class com.imsl.math.NonlinLeastSquares: Set the relative function tolerance.
setRelativeTolerance(double) - Method in class com.imsl.stat.NonlinearRegression: Sets the relative function tolerance
setResidualUpdating(int) - Method in class com.imsl.math.GenMinRes: Set the residual updating method to be used.
setResponseColumn(int) - Method in class com.imsl.stat.KaplanMeierEstimates: Sets the column index of x containing the response time for each observation.
setResponseColumn(int) - Method in class com.imsl.stat.ProportionalHazards: Sets the column index of x containing the response variable.
setRightEndTangent(double) - Method in class com.imsl.math.CsTCB: Sets the value of the tangent at the right endpoint.
setRightSons(int[]) - Method in class com.imsl.chart.Dendrogram: Convenience routine to set the "RightSons" attribute.
setRow(boolean) - Method in class com.imsl.stat.Dissimilarities: Identifies whether distances are computed between rows or columns of x.
setRule(int) - Method in class com.imsl.math.Quadrature: Set the Gauss-Kronrod rule.
setSample(double[]) - Method in class com.imsl.stat.distributions.MaximumLikelihoodEstimation: Sets the sample data to use in the estimation procedure.
setSampleSize(int) - Method in class com.imsl.chart.qc.ShewhartControlChart: Sets the value of the attribute "SampleSize".
setSampleSize(int[]) - Method in class com.imsl.chart.qc.ShewhartControlChart: Sets the value of the attribute "SampleSize".
setSampleSizeProportion(double) - Method in class com.imsl.datamining.GradientBoosting: Sets the sample size proportion.
setScale(double) - Method in class com.imsl.math.ODE: Sets the scaling factor.
setScale(double[]) - Method in class com.imsl.stat.NonlinearRegression: Sets the scaling array for theta.
setScale(boolean) - Method in class com.imsl.stat.VectorAutoregression: Sets the flag to scale the data.
setScaledStepTol(double) - Method in class com.imsl.math.BoundedLeastSquares: Sets the scaled step tolerance.
setScaleFont(double) - Method in class com.imsl.chart.Draw: Set a factor by which fonts are to be scaled.
setScalingBound(double) - Method in class com.imsl.math.MinConNLP: Set the scaling bound for the internal automatic scaling of the objective function.
setScalingFactors(double[]) - Method in class com.imsl.math.NumericalDerivatives: Sets the scaling factors for the y values.
setScalingOption(int) - Method in class com.imsl.stat.Dissimilarities: Sets the scaling option used if the L2_NORM, L1_NORM, or INFINITY_NORM distance methods are specified.
setScalingVector(double[]) - Method in class com.imsl.math.BoundedLeastSquares: Sets the scaling vector for the variables.
setScreenSize(Dimension) - Method in class com.imsl.chart.ChartNode: Sets the value of the "ScreenSize" attribute.
setSeed(long) - Method in class com.imsl.stat.Random: Sets the seed.
setSeriesIncrement(int) - Method in class com.imsl.stat.HoltWintersExponentialSmoothing: Sets the constant stride through the series data y.
setSeriesValues(double[]) - Method in class com.imsl.stat.TimeSeries: Sets the values of a univariate time series and initializes the time index.
setSeriesValues(double[], int) - Method in class com.imsl.stat.TimeSeries: Sets the values of a multivariate time series and initializes the time index array.
setSeriesValues(double[][]) - Method in class com.imsl.stat.TimeSeries: Sets the values of the TimeSeries.
setShrinkageParameter(double) - Method in class com.imsl.datamining.GradientBoosting: Sets the value of the shrinkage parameter.
setSigma(double) - Method in class com.imsl.chart.qc.CuSumStatus: Sets the standard deviation of the data.
setSize(Dimension) - Method in class com.imsl.chart.ChartNode: Sets the value of the "Size" attribute.
setSize(Dimension) - Method in class com.imsl.chart.JspBean: Sets the size of the generated image.
setSize(int, int) - Method in class com.imsl.chart.JspBean: Sets the size of the generated image.
setSkipWeekends(boolean) - Method in class com.imsl.chart.ChartNode: Sets the value of the "SkipWeekends" attribute.
setSolveMethod(int) - Method in class com.imsl.math.OdeAdamsGear: Indicates which method to use for solving the formula equations.
setSorted(boolean) - Method in class com.imsl.stat.KaplanMeierEstimates: Sets the boolean to indicate that the column of response times in x are already sorted.
setSplitMergedCategoriesSigLevel(double) - Method in class com.imsl.datamining.decisionTree.CHAID: Sets the significance level for splitting previously merged categories.
setSplitVariableSelectionCriterion(double) - Method in class com.imsl.datamining.decisionTree.QUEST: Sets the significance level for split variable selection.
setSplitVariableSignificanceLevel(double) - Method in class com.imsl.datamining.decisionTree.CHAID: Sets the significance level for split variable selection.
setSpread(double) - Method in class com.imsl.datamining.neural.ScaleFilter: Set the measure of spread to be used during z-score scaling.
setStartDate(Date) - Method in class com.imsl.stat.TimeSeries: Sets the start date of the series.
setStep(double) - Method in class com.imsl.math.MinUncon: Set the stepsize to use when changing x.
setStepControlMethod(int) - Method in class com.imsl.math.FeynmanKac: Sets the step control method used in the integration of the Feynman-Kac PDE.
setStepTolerance(double) - Method in class com.imsl.datamining.neural.LeastSquaresTrainer: Set the step tolerance used to step between weights.
setStepTolerance(double) - Method in class com.imsl.datamining.neural.QuasiNewtonTrainer: Sets the scaled step tolerance.
setStepTolerance(double) - Method in class com.imsl.math.MinUnconMultiVar: Set the scaled step tolerance to use when changing x.
setStepTolerance(double) - Method in class com.imsl.math.NonlinLeastSquares: Set the scaled step tolerance.
setStepTolerance(double) - Method in class com.imsl.stat.NonlinearRegression: Sets the step tolerance used to step between two points.
setStratumColumn(int) - Method in class com.imsl.stat.KaplanMeierEstimates: Sets the column index of x containing the stratum number for each observation.
setStratumColumn(int) - Method in class com.imsl.stat.ProportionalHazards: Sets the column index of x containing the stratification variable.
setStratumRatio(double) - Method in class com.imsl.stat.ProportionalHazards: Set the ratio at which a stratum is split into two strata.
setString(String) - Method in class com.imsl.chart.Annotation: Sets the String for the Text object to render.
setString(String) - Method in class com.imsl.chart.Text: Sets the string for this Text object.
setSurfaceType(int) - Method in class com.imsl.chart3d.Surface: Sets the attribute "SurfaceType".
setSurrogateInfo(double[]) - Method in class com.imsl.datamining.decisionTree.TreeNode: Sets the surrogate split information.
setSymbolicFactor(ComplexSparseCholesky.SymbolicFactor) - Method in class com.imsl.math.ComplexSparseCholesky: Sets the symbolic Cholesky factor to use in solving a sparse complex Hermitian positive definite system of linear equations $Ax=b$ .
setSymbolicFactor(SparseCholesky.SymbolicFactor) - Method in class com.imsl.math.SparseCholesky: Sets the symbolic Cholesky factor to use in solving a sparse positive definite system of linear equations $Ax=b$ .
setSymmetricMode(boolean) - Method in class com.imsl.math.ComplexSuperLU: Specifies whether to use the symmetric mode.
setSymmetricMode(boolean) - Method in class com.imsl.math.SuperLU: Specifies whether to use the symmetric mode.
setTension(double[]) - Method in class com.imsl.math.CsTCB: Sets the tension values at the data points.
setTerminalNode(int, boolean) - Method in class com.imsl.datamining.decisionTree.Tree: Sets the terminal node indicator of the node at the given index.
setTerminalNodeIndicators(boolean[]) - Method in class com.imsl.datamining.decisionTree.Tree: Sets the terminal node indicator array.
setTestData(double[][], double[]) - Method in class com.imsl.datamining.BootstrapAggregation: Sets the test data to be predicted along with weights for each row in the test data.
setTestData(double[][]) - Method in class com.imsl.datamining.BootstrapAggregation: Sets the test data to be predicted.
setText(Text) - Method in class com.imsl.chart.Annotation: Sets the Text object to render.
setTextAngle(int) - Method in class com.imsl.chart.ChartNode: Sets the value of the "TextAngle" attribute.
setTextColor(Color) - Method in class com.imsl.chart.AbstractChartNode: Sets the value of the "TextColor" attribute.
setTextColor(String) - Method in class com.imsl.chart.AbstractChartNode: Sets the value of the "TextColor" attribute to a color specified by name.
setTextColor(Color) - Method in class com.imsl.chart.ChartNode: Sets the value of the "TextColor" attribute.
setTextColor(String) - Method in class com.imsl.chart.ChartNode: Sets the value of the "TextColor" attribute to a color specified by name.
setTextFormat(Format) - Method in class com.imsl.chart.AbstractChartNode: Sets the value of the "TextFormat" attribute.
setTextFormat(String) - Method in class com.imsl.chart.AbstractChartNode: Sets the value of the "TextFormat" attribute.
setTickInterval(double) - Method in class com.imsl.chart.Axis1D: Sets the tick interval.
setTickInterval(double) - Method in class com.imsl.chart.AxisR: Sets the tick interval.
setTickInterval(double) - Method in class com.imsl.chart3d.Axis3D: Sets the tick interval.
setTickLength(double) - Method in class com.imsl.chart.AbstractChartNode: Sets the value of the "TickLength" attribute.
setTicks(double[]) - Method in class com.imsl.chart.Axis1D: Sets the value of the "Ticks" attribute.
setTicks(double[]) - Method in class com.imsl.chart3d.Axis3D: Sets the value of the "Ticks" attribute.
setTicks(double[]) - Method in class com.imsl.chart3d.ColormapLegend: Sets the value of the "Ticks" attribute.
setTieBreaker(int) - Method in class com.imsl.stat.Ranks: Sets the tie breaker for Ranks.
setTiesOption(int) - Method in class com.imsl.stat.ProportionalHazards: Sets the method for handling ties.
setTimeBarrier(double) - Method in class com.imsl.math.FeynmanKac: Sets a barrier for the integration in the time direction.
setTimeDependence(boolean[]) - Method in class com.imsl.math.FeynmanKac: Sets the time dependence of the coefficients, boundary conditions and function $phi$ in the Feynman Kac equation.
setTimeZone(TimeZone) - Method in class com.imsl.stat.TimeSeries: Sets the time zone for the time series to the given TimeZone.
setTimeZone(int) - Method in class com.imsl.stat.TimeSeries: Sets the time zone for the time series to the time zone associated with the given offset from GMT.
setTimeZone(int, String) - Method in class com.imsl.stat.TimeSeries: Sets the time zone for the time series using the offset and String id.
setTitle(String) - Method in class com.imsl.chart.ChartNode: Sets the value of the "Title" attribute.
setTitle(Text) - Method in class com.imsl.chart.ChartNode: Sets the value of the "Title" attribute.
setTitle(String) - Method in class com.imsl.chart3d.ChartNode3D: Sets the value of the "Title" attribute.
setTitle(String) - Method in class com.imsl.math.PrintMatrix: Sets matrix title
setTolerance(int) - Method in class com.imsl.chart.DrawMap: Set the minimum distance that an event can be from a point or a line and still be considered a hit.
setTolerance(int) - Method in class com.imsl.chart.DrawPick: Set the minimum distance that an event can be from a point or a line and still be considered a hit.
setTolerance(double) - Method in class com.imsl.math.BoundedVariableLeastSquares: Sets the internal tolerance used to determine the relative linear dependence of a column vector for a variable moved from its initial value.
setTolerance(double) - Method in class com.imsl.math.MinConGenLin: Sets the nonnegative tolerance on the first order conditions at the calculated solution.
setTolerance(double) - Method in class com.imsl.math.MinConNLP: Set the desired precision of the solution.
setTolerance(double) - Method in class com.imsl.math.ODE: Sets the error tolerance.
setTolerance(double) - Method in class com.imsl.stat.ARMAMaxLikelihood: Sets the tolerance for the convergence algorithm.
setTolerance(double) - Method in class com.imsl.stat.CategoricalGenLinModel: Initializes the tolerance used in determining linear dependence.
setTolerance(double) - Method in class com.imsl.stat.InverseCdf: Sets the tolerance to be used as the convergence criterion.
setTolerance(double) - Method in class com.imsl.stat.KalmanFilter: Sets the tolerance used in determining linear dependence.
setTolerance(double) - Method in class com.imsl.stat.StepwiseRegression: The tolerance used to detect linear dependence among the independent variables.
setToolTip(String) - Method in class com.imsl.chart.ChartNode: Sets the value of the "ToolTip" attribute.
setTransform(int) - Method in class com.imsl.chart.AbstractChartNode: Sets the value of the "Transform" attribute.
setTransformType(int) - Method in class com.imsl.stat.ClusterHierarchical: Sets the type of transformation.
setTransitionMatrix(double[][]) - Method in class com.imsl.stat.KalmanFilter: Sets the transition matrix.
setTrend(boolean) - Method in class com.imsl.stat.VectorAutoregression: Sets the flag to fit a trend parameter in the model.
setTrustRegion(double) - Method in class com.imsl.math.BoundedLeastSquares: Sets the size of initial trust region radius.
setTTestNull(double) - Method in class com.imsl.stat.NormOneSample: Sets the Null hypothesis value for t test for the mean.
setTTestNull(double) - Method in class com.imsl.stat.NormTwoSample: Sets the Null hypothesis value for t-test for the mean.
setType(int) - Method in class com.imsl.chart.Axis1D: Sets the type of this node.
setUnequalVariances(boolean) - Method in class com.imsl.stat.NormTwoSample: Specifies whether to return statistics based on equal or unequal variances.
setupMapping() - Method in class com.imsl.chart.Axis: Initializes the mappings between user and coordinate space.
setupMapping() - Method in class com.imsl.chart.AxisXY: Initializes the mappings between user and coordinate space.
setupMapping() - Method in class com.imsl.chart.Pie: Initializes the mappings between user and coordinate space.
setupMapping() - Method in class com.imsl.chart.Polar: Initializes the mappings between user and coordinate space.
setupMapping(Axis1D) - Method in interface com.imsl.chart.Transform: Initializes the mappings between user and coordinate space.
setupMapping(Axis1D) - Method in class com.imsl.chart.TransformDate: Initializes the mappings between user and coordinate space.
setUpperBound(double[]) - Method in class com.imsl.math.DenseLP: Sets the upper bound, $x_u$ , on the variables.
setUpperBound(double[]) - Method in class com.imsl.math.SparseLP: Sets the upper bound on the variables.
setUpperBound(int) - Method in class com.imsl.stat.CategoricalGenLinModel: Sets the upper bound on the sum of the number of distinct values taken on by each classification variable.
setUpperBounds(double[]) - Method in class com.imsl.stat.HoltWintersExponentialSmoothing: Sets the upper bounds for each of the smoothing parameters, (, , ).
setUpperEndpointColumn(int) - Method in class com.imsl.stat.CategoricalGenLinModel: Sets the column number in x that contains the upper endpoint of the observation interval for full interval and left interval observations.
setUpperLimit(double[]) - Method in class com.imsl.math.DenseLP: Sets the upper limit of the constraints.
setUpperLimit(double[]) - Method in class com.imsl.math.SparseLP: Sets the upper limit of the constraints that have both a lower and an upper bound.
setUseBackPropagation(boolean) - Method in class com.imsl.datamining.neural.QuasiNewtonTrainer: Sets whether or not to use the back propagation algorithm for gradient calculations during network training.
setUseRatio(boolean) - Method in class com.imsl.datamining.decisionTree.DecisionTreeInfoGain: Sets the flag to use or not use the gain ratio instead of the gain to determine the best split.
setValue(double) - Method in class com.imsl.chart.qc.ControlLimit: Sets the value of this control limit line.
setValue(double[]) - Method in class com.imsl.chart.qc.ControlLimit: Sets the value of this control limit line to an array of values.
setValue(double) - Method in class com.imsl.datamining.neural.InputNode: Sets the value of this Node.
setVarianceEstimationMethod(int) - Method in class com.imsl.stat.FactorAnalysis: Sets the variance estimation method.
setVariances(double[]) - Method in class com.imsl.stat.FactorAnalysis: Sets the variances.
setVectorProducts(GenMinRes.VectorProducts) - Method in class com.imsl.math.GenMinRes: Sets the user-supplied functions for the inner product and, optionally, the norm to be used in the Gram-Schmidt implementations.
setViewPlatformTransformation(Transform3D) - Method in class com.imsl.chart3d.Chart3D: Sets the transformation for the view platform.
setViewport(double[]) - Method in class com.imsl.chart.ChartNode: Sets the value of the "Viewport" attribute.
setViewport(double, double, double, double) - Method in class com.imsl.chart.ChartNode: Sets the value of the "Viewport" attribute.
setViewport(double[]) - Method in class com.imsl.chart3d.ChartNode3D: Sets the value of the "Viewport" attribute.
setViewport(double, double, double, double, double, double) - Method in class com.imsl.chart3d.ChartNode3D: Sets the value of the "Viewport" attribute.
setViolationBound(double) - Method in class com.imsl.math.MinConNLP: Set the scalar which defines allowable constraint violations of the final accepted result.
setVisibleFaces(int) - Method in class com.imsl.chart3d.AxisBox: Sets the "VisibleFaces" attribute indicating which faces of the box are to be drawn.
setWarning(SQLWarning) - Method in class com.imsl.io.AbstractFlatFile: Sets a SQLWarning.
setWarning(WarningObject) - Static method in class com.imsl.Warning: Sets a new WarningObject.
setWbar(double) - Method in class com.imsl.chart.qc.XbarS: Sets the value of the "Wbar" attribute, the within sample variation for a series of samples.
setWeight(double) - Method in class com.imsl.datamining.neural.Link: Sets the weight for this Link.
setWeights() - Method in class com.imsl.datamining.KohonenSOM: Sets the weights of the nodes using random numbers.
setWeights(Random) - Method in class com.imsl.datamining.KohonenSOM: Sets the weights of the nodes using a Random object.
setWeights(double[][][]) - Method in class com.imsl.datamining.KohonenSOM: Sets the weights of the nodes.
setWeights(int, int, double[]) - Method in class com.imsl.datamining.KohonenSOM: Sets the weights of the node at (i, j) in the node grid.
setWeights(double[]) - Method in class com.imsl.datamining.neural.FeedForwardNetwork: Sets the weights for the Links in this Network.
setWeights(double[]) - Method in class com.imsl.datamining.neural.Network: Sets the weights.
setWeights(double[]) - Method in class com.imsl.datamining.PredictiveModel: Specifies the case weights.
setWeights(double[]) - Method in class com.imsl.stat.ClusterKMeans: Sets the weight for each observation.
setWeights(double[]) - Method in class com.imsl.stat.Covariances: Sets the weight for each observation.
setWindow(double, double) - Method in class com.imsl.chart.Axis1D: Sets the window for an Axis1D.
setWindow(double[]) - Method in class com.imsl.chart.Axis1D: Sets the window for an Axis1D.
setWindow(double) - Method in class com.imsl.chart.AxisR: Sets the Window attribute.
setWindow(double, double) - Method in class com.imsl.chart.AxisTheta: Sets the window for an AxisTheta.
setWindow(double[]) - Method in class com.imsl.chart.AxisTheta: Sets the window for an AxisTheta.
setWindow(double[]) - Method in class com.imsl.chart.AxisXY: Sets the window in user coordinates along an axis.
setWindow(double, double) - Method in class com.imsl.chart3d.Axis3D: Sets the window for an Axis1D.
setWindow(double[]) - Method in class com.imsl.chart3d.Axis3D: Sets the window for an Axis1D.
setWindow(double, double) - Method in class com.imsl.chart3d.ColormapLegend: Sets the window for a ColormapLegend.
setWindow(double[]) - Method in class com.imsl.chart3d.ColormapLegend: Sets the window for a ColormapLegend.
setX(Object) - Method in class com.imsl.chart.AbstractChartNode: Sets the value of the "X" attribute.
setX(double[]) - Method in class com.imsl.chart.qc.CuSumStatus: Sets the x-coordinates of the bars.
setX(double[]) - Method in class com.imsl.chart.qc.ShewhartControlChart: Sets the x-coordinates of the data.
setXKnots(double[]) - Method in class com.imsl.math.Spline2DLeastSquares: Sets the knot sequences of the spline in the x-direction.
setXlowerBound(double[]) - Method in class com.imsl.math.MinConNLP: Set the lower bounds on the variables.
setXOrder(int) - Method in class com.imsl.math.Spline2DLeastSquares: Sets the order of the spline in the x-direction.
setXscale(double[]) - Method in class com.imsl.math.MinConNLP: Set the internal scaling of the variables.
setXscale(double[]) - Method in class com.imsl.math.MinUnconMultiVar: Set the diagonal scaling matrix for the variables.
setXscale(double[]) - Method in class com.imsl.math.NonlinLeastSquares: Set the diagonal scaling matrix for the variables.
setXScale(double) - Method in class com.imsl.math.ZerosFunction: Sets the the scaling in the x-coordinate.
setXupperBound(double[]) - Method in class com.imsl.math.MinConNLP: Set the upper bounds on the variables.
setXWeights(double[]) - Method in class com.imsl.math.Spline2DLeastSquares: Sets the weights for the least-squares fit in the x-direction.
setY(Object) - Method in class com.imsl.chart.AbstractChartNode: Sets the value of the "Y" attribute.
setYKnots(double[]) - Method in class com.imsl.math.Spline2DLeastSquares: Sets the knot sequences of the spline in the y-direction.
setYOrder(int) - Method in class com.imsl.math.Spline2DLeastSquares: Sets the order of the spline in the y-direction.
setYProb(int, double) - Method in class com.imsl.datamining.decisionTree.TreeNode: Sets a class probability at the current node, if the response variable is of categorical type.
setYWeights(double[]) - Method in class com.imsl.math.Spline2DLeastSquares: Sets the weights for the least-squares fit in the y-direction.
setZ(Object) - Method in class com.imsl.chart3d.ChartNode3D: Sets the value of the "Z" attribute.
setZeroCorrection(double) - Method in class com.imsl.datamining.NaiveBayesClassifier: Specifies the replacement value to be used for conditional probabilities equal to zero.
setZRange(double[]) - Method in class com.imsl.chart.Treemap: Set the Z data (shading) range, range = {zmin,zmax}.
Sfun - Class in com.imsl.math: Collection of special functions.
SHANNON_ENTROPY - Static variable in class com.imsl.datamining.decisionTree.DecisionTreeInfoGain.GainCriteria: A measure of randomness or uncertainty.
ShapiroWilkWTest() - Method in class com.imsl.stat.NormalityTest: Performs the Shapiro-Wilk W test.
ShewhartControlChart - Class in com.imsl.chart.qc: ShewhartControlChart is the base class for the Shewhart control charts.
ShewhartControlChart(AxisXY) - Constructor for class com.imsl.chart.qc.ShewhartControlChart: Constructs a Shewhart control chart.
shortValue() - Method in class com.imsl.math.Complex: Returns the value of the real part as a short.
sigma(double, double) - Method in interface com.imsl.math.FeynmanKac.PdeCoefficients: Returns the value of the $sigma$ coefficient at the given point.
sigmaPrime(double, double) - Method in interface com.imsl.math.FeynmanKac.PdeCoefficients: Returns the value of $sigma^prime=frac{partial sigma(x,t)}{partial x}$ at the given point.
sign(double, double) - Static method in class com.imsl.math.Sfun: Returns the value of x with the sign of y.
SignTest - Class in com.imsl.stat: Performs a sign test.
SignTest(double[]) - Constructor for class com.imsl.stat.SignTest: Constructor for SignTest.
sin(Complex) - Static method in class com.imsl.math.Complex: Returns the sine of a Complex.
sin(double) - Static method in class com.imsl.math.JMath: Returns the sine of a double.
SingularMatrixException - Exception in com.imsl.math: The matrix is singular.
SingularMatrixException() - Constructor for exception com.imsl.math.SingularMatrixException
sinh(Complex) - Static method in class com.imsl.math.Complex: Returns the hyperbolic sine of a Complex.
sinh(double) - Static method in class com.imsl.math.Hyperbolic: Returns the hyperbolic sine of its argument.
skewness(double[]) - Static method in class com.imsl.stat.Summary: Returns the skewness of the given data set.
skewness(double[], double[]) - Static method in class com.imsl.stat.Summary: Returns the skewness of the given data set and associated weights.
SKIP - Static variable in class com.imsl.math.NumericalDerivatives: Indicates a variable to be skipped.
skip(int) - Method in class com.imsl.stat.Random: Resets the seed to skip ahead in the base linear congruential generator.
sln(double, double, int) - Static method in class com.imsl.finance.Finance: Returns the depreciation of an asset using the straight line method.
SOFTMAX - Static variable in interface com.imsl.datamining.neural.Activation: The softmax activation function.
solve() - Method in class com.imsl.math.BoundedLeastSquares: Solves a nonlinear least-squares problem subject to bounds on the variables using a modified Levenberg-Marquardt algorithm.
solve() - Method in class com.imsl.math.BoundedVariableLeastSquares: Find the solution x to the problem for the current constraints.
solve(double[]) - Method in class com.imsl.math.Cholesky: Solve Ax = b where A is a positive definite matrix with elements of type double.
solve(Complex[]) - Method in class com.imsl.math.ComplexLU: Return the solution x of the linear system Ax = b using the LU factorization of A.
solve(Complex[][], Complex[]) - Static method in class com.imsl.math.ComplexLU: Solve Ax = b for x using the LU factorization of A.
solve(Complex[]) - Method in class com.imsl.math.ComplexSparseCholesky: Computes the solution of a sparse Hermitian positive definite system of linear equations $Ax=b$ .
solve(Complex[]) - Method in class com.imsl.math.ComplexSuperLU: Computation of the solution vector for the system $Ax = b$ .
solve(double[]) - Method in class com.imsl.math.ConjugateGradient: Solves a real symmetric positive or negative definite system $Ax=b$ using a conjugate gradient method with or without preconditioning.
solve() - Method in class com.imsl.math.DenseLP: Solves the problem using an active set method.
solve(double[][], boolean) - Method in class com.imsl.math.Eigen: Solves for the eigenvalues and (optionally) the eigenvectors of a real square matrix.
solve(double[]) - Method in class com.imsl.math.GenMinRes: Generate an approximate solution to $Ax=b$ using the Generalized Residual Method.
solve(double[]) - Method in class com.imsl.math.LU: Return the solution x of the linear system Ax = b using the LU factorization of A.
solve(double[][], double[]) - Static method in class com.imsl.math.LU: Solve Ax = b for x using the LU factorization of A.
solve() - Method in class com.imsl.math.MinConGenLin: Minimizes a general objective function subject to linear equality/inequality constraints.
solve(MinConNLP.Function) - Method in class com.imsl.math.MinConNLP: Solve a general nonlinear programming problem using the successive quadratic programming algorithm with a finite-difference gradient or with a user-supplied gradient.
solve(NonlinLeastSquares.Function) - Method in class com.imsl.math.NonlinLeastSquares: Solve a nonlinear least-squares problem using a modified Levenberg-Marquardt algorithm and a Jacobian.
solve() - Method in class com.imsl.math.NonNegativeLeastSquares: Finds the solution to the problem for the current constraints.
solve(double, double, double[]) - Method in class com.imsl.math.OdeAdamsGear: Integrates the ODE system from t to tEnd.
solve(double, double, double[]) - Method in class com.imsl.math.OdeRungeKutta: Integrates the ODE system from t to tEnd.
solve(double[]) - Method in class com.imsl.math.QR: Returns the solution to the least-squares problem Ax = b.
solve(double[], double) - Method in class com.imsl.math.QR: Returns the solution to the least-squares problem Ax = b using an input tolerance.
solve(double[]) - Method in class com.imsl.math.SparseCholesky: Computes the solution of a sparse real symmetric positive definite system of linear equations $Ax=b$ .
solve() - Method in class com.imsl.math.SparseLP: Solves the sparse linear programming problem by an infeasible primal-dual interior-point method.
solve(double[]) - Method in class com.imsl.math.SuperLU: Computation of the solution vector for the system $Ax = b$ .
solve(ZeroSystem.Function) - Method in class com.imsl.math.ZeroSystem: Solve a system of nonlinear equations using the modified Powell hybrid algorithm
solve() - Method in class com.imsl.stat.CategoricalGenLinModel: Returns the parameter estimates and associated statistics for a CategoricalGenLinModel object.
solve(NonlinearRegression.Function) - Method in class com.imsl.stat.NonlinearRegression: Solves the least squares problem and returns the regression coefficients.
SOLVE_CHORD_COMPUTED_DIAGONAL - Static variable in class com.imsl.math.OdeAdamsGear: A chord method and a diagonal matrix based on a directional directive
SOLVE_CHORD_COMPUTED_JACOBIAN - Static variable in class com.imsl.math.OdeAdamsGear: A chord or modified Newton method and a divided differences Jacobian
SOLVE_CHORD_USER_JACOBIAN - Static variable in class com.imsl.math.OdeAdamsGear: A chord or modified Newton method and a user-supplied Jacobian
SOLVE_FUNCTION_ITERATION - Static variable in class com.imsl.math.OdeAdamsGear: A function iteration or successive substitution method
solveConjugateTranspose(Complex[]) - Method in class com.imsl.math.ComplexSuperLU: Computation of the solution vector for the system $A^Hx = b$ .
solveTranspose(Complex[]) - Method in class com.imsl.math.ComplexLU: Return the solution x of the linear system $A^T x = b$ .
solveTranspose(Complex[]) - Method in class com.imsl.math.ComplexSuperLU: Computation of the solution vector for the system $A^Tx = b$ .
solveTranspose(double[]) - Method in class com.imsl.math.LU: Return the solution x of the linear system $A^T = b$ .
solveTranspose(double[]) - Method in class com.imsl.math.SuperLU: Computation of the solution vector for the system $A^Tx = b$ .
Sort - Class in com.imsl.stat: A collection of sorting functions.
SORTED_AS_PER_OBSERVATIONS - Static variable in class com.imsl.stat.ProportionalHazards: Failures are assumed to occur in the same order as the observations input in x.
SparseCholesky - Class in com.imsl.math: Sparse Cholesky factorization of a matrix of type SparseMatrix.
SparseCholesky(SparseMatrix) - Constructor for class com.imsl.math.SparseCholesky: Constructs the matrix structure for the Cholesky factorization of a sparse symmetric positive definite matrix of type SparseMatrix.
SparseCholesky.NotSPDException - Exception in com.imsl.math: The matrix is not symmetric, positive definite.
SparseCholesky.NotSPDException() - Constructor for exception com.imsl.math.SparseCholesky.NotSPDException: Constructs a NotSPDException object.
SparseCholesky.NumericFactor - Class in com.imsl.math: The numeric Cholesky factorization of a matrix.
SparseCholesky.SymbolicFactor - Class in com.imsl.math: The symbolic Cholesky factorization of a matrix.
SparseLP - Class in com.imsl.math: Solves a sparse linear programming problem by an infeasible primal-dual interior-point method.
SparseLP(SparseMatrix, double[], double[]) - Constructor for class com.imsl.math.SparseLP: Constructs a SparseLP object.
SparseLP(MPSReader) - Constructor for class com.imsl.math.SparseLP: Constructs a SparseLP object using an MPSReader object.
SparseLP(int[], int[], double[], double[], double[]) - Constructor for class com.imsl.math.SparseLP: Constructs a SparseLP object using Compressed Sparse Column (CSC), or Harwell-Boeing format.
SparseLP.CholeskyFactorizationAccuracyException - Exception in com.imsl.math: The Cholesky factorization failed because of accuracy problems.
SparseLP.CholeskyFactorizationAccuracyException(String) - Constructor for exception com.imsl.math.SparseLP.CholeskyFactorizationAccuracyException: The Cholesky factorization failed because of accuracy problems.
SparseLP.CholeskyFactorizationAccuracyException(String, Object[]) - Constructor for exception com.imsl.math.SparseLP.CholeskyFactorizationAccuracyException: The Cholesky factorization failed because of accuracy problems.
SparseLP.DiagonalWeightMatrixException - Exception in com.imsl.math: A diagonal element of the diagonal weight matrix is too small.
SparseLP.DiagonalWeightMatrixException(String) - Constructor for exception com.imsl.math.SparseLP.DiagonalWeightMatrixException: A diagonal element of the diagonal weight matrix is too small.
SparseLP.DiagonalWeightMatrixException(String, Object[]) - Constructor for exception com.imsl.math.SparseLP.DiagonalWeightMatrixException: A diagonal element of the diagonal weight matrix is too small.
SparseLP.DualInfeasibleException - Exception in com.imsl.math: The dual problem is infeasible.
SparseLP.DualInfeasibleException(String) - Constructor for exception com.imsl.math.SparseLP.DualInfeasibleException: The dual problem is infeasible.
SparseLP.DualInfeasibleException(String, Object[]) - Constructor for exception com.imsl.math.SparseLP.DualInfeasibleException: The dual problem is infeasible.
SparseLP.IllegalBoundsException - Exception in com.imsl.math: The lower bound is greater than the upper bound.
SparseLP.IllegalBoundsException(String) - Constructor for exception com.imsl.math.SparseLP.IllegalBoundsException: The lower bound is greater than the upper bound.
SparseLP.IllegalBoundsException(String, Object[]) - Constructor for exception com.imsl.math.SparseLP.IllegalBoundsException: The lower bound is greater than the upper bound.
SparseLP.IncorrectlyActiveException - Exception in com.imsl.math: One or more LP variables are falsely characterized by the internal presolver.
SparseLP.IncorrectlyActiveException(String) - Constructor for exception com.imsl.math.SparseLP.IncorrectlyActiveException: One or more LP variables are falsely characterized by the internal presolver.
SparseLP.IncorrectlyActiveException(String, Object[]) - Constructor for exception com.imsl.math.SparseLP.IncorrectlyActiveException: One or more LP variables are falsely characterized by the internal presolver.
SparseLP.IncorrectlyEliminatedException - Exception in com.imsl.math: One or more LP variables are falsely characterized by the internal presolver.
SparseLP.IncorrectlyEliminatedException(String) - Constructor for exception com.imsl.math.SparseLP.IncorrectlyEliminatedException: One or more LP variables are falsely characterized by the internal presolver.
SparseLP.IncorrectlyEliminatedException(String, Object[]) - Constructor for exception com.imsl.math.SparseLP.IncorrectlyEliminatedException: One or more LP variables are falsely characterized by the internal presolver.
SparseLP.InitialSolutionInfeasibleException - Exception in com.imsl.math: The initial solution for the one-row linear program is infeasible.
SparseLP.InitialSolutionInfeasibleException(String) - Constructor for exception com.imsl.math.SparseLP.InitialSolutionInfeasibleException: The initial solution for the one-row linear program is infeasible.
SparseLP.InitialSolutionInfeasibleException(String, Object[]) - Constructor for exception com.imsl.math.SparseLP.InitialSolutionInfeasibleException: The initial solution for the one-row linear program is infeasible.
SparseLP.PrimalInfeasibleException - Exception in com.imsl.math: The primal problem is infeasible.
SparseLP.PrimalInfeasibleException(String) - Constructor for exception com.imsl.math.SparseLP.PrimalInfeasibleException: The primal problem is infeasible.
SparseLP.PrimalInfeasibleException(String, Object[]) - Constructor for exception com.imsl.math.SparseLP.PrimalInfeasibleException: The primal problem is infeasible.
SparseLP.PrimalUnboundedException - Exception in com.imsl.math: The primal problem is unbounded.
SparseLP.PrimalUnboundedException(String) - Constructor for exception com.imsl.math.SparseLP.PrimalUnboundedException: The primal problem is unbounded.
SparseLP.PrimalUnboundedException(String, Object[]) - Constructor for exception com.imsl.math.SparseLP.PrimalUnboundedException: The primal problem is unbounded.
SparseLP.ProblemUnboundedException - Exception in com.imsl.math: The problem is unbounded.
SparseLP.ProblemUnboundedException(String) - Constructor for exception com.imsl.math.SparseLP.ProblemUnboundedException: The problem is unbounded.
SparseLP.ProblemUnboundedException(String, Object[]) - Constructor for exception com.imsl.math.SparseLP.ProblemUnboundedException: The problem is unbounded.
SparseLP.TooManyIterationsException - Exception in com.imsl.math: The maximum number of iterations has been exceeded.
SparseLP.TooManyIterationsException(String) - Constructor for exception com.imsl.math.SparseLP.TooManyIterationsException: The maximum number of iterations has been exceeded.
SparseLP.TooManyIterationsException(String, Object[]) - Constructor for exception com.imsl.math.SparseLP.TooManyIterationsException: The maximum number of iterations has been exceeded.
SparseLP.ZeroColumnException - Exception in com.imsl.math: A column of the constraint matrix has no entries.
SparseLP.ZeroColumnException(String) - Constructor for exception com.imsl.math.SparseLP.ZeroColumnException: A column of the constraint matrix has no entries.
SparseLP.ZeroColumnException(String, Object[]) - Constructor for exception com.imsl.math.SparseLP.ZeroColumnException: A column of the constraint matrix has no entries.
SparseLP.ZeroRowException - Exception in com.imsl.math: A row of the constraint matrix has no entries.
SparseLP.ZeroRowException(String) - Constructor for exception com.imsl.math.SparseLP.ZeroRowException: A row of the constraint matrix has no entries.
SparseLP.ZeroRowException(String, Object[]) - Constructor for exception com.imsl.math.SparseLP.ZeroRowException: A row of the constraint matrix has no entries.
SparseMatrix - Class in com.imsl.math: Sparse matrix of type double.
SparseMatrix(int, int) - Constructor for class com.imsl.math.SparseMatrix: Creates a new instance of SparseMatrix.
SparseMatrix(SparseMatrix) - Constructor for class com.imsl.math.SparseMatrix: Creates a new instance of SparseMatrix which is a copy of another SparseMatrix.
SparseMatrix(SparseMatrix.SparseArray) - Constructor for class com.imsl.math.SparseMatrix: Constructs a sparse matrix from a SparseArray object.
SparseMatrix(int, int, int[][], double[][]) - Constructor for class com.imsl.math.SparseMatrix: Constructs a sparse matrix from SparseArray (Java Sparse Array) data.
SparseMatrix.SparseArray - Class in com.imsl.math: The SparseArray class uses public fields to hold the data for a sparse matrix in the Java Sparse Array format.
SparseMatrix.SparseArray() - Constructor for class com.imsl.math.SparseMatrix.SparseArray
SPECTRAL - Static variable in interface com.imsl.chart.Colormap: Spectral colormap.
Spline - Class in com.imsl.math: Spline represents and evaluates univariate piecewise polynomial splines.
Spline() - Constructor for class com.imsl.math.Spline
Spline2D - Class in com.imsl.math: Represents and evaluates tensor-product splines.
Spline2D() - Constructor for class com.imsl.math.Spline2D
Spline2DInterpolate - Class in com.imsl.math: Computes a two-dimensional, tensor-product spline interpolant from two-dimensional, tensor-product data.
Spline2DInterpolate(double[], double[], double[][]) - Constructor for class com.imsl.math.Spline2DInterpolate: Constructor for Spline2DInterpolate.
Spline2DInterpolate(double[], double[], double[][], int, int) - Constructor for class com.imsl.math.Spline2DInterpolate: Constructor for Spline2DInterpolate.
Spline2DInterpolate(double[], double[], double[][], int, int, double[], double[]) - Constructor for class com.imsl.math.Spline2DInterpolate: Constructor for Spline2DInterpolate.
Spline2DLeastSquares - Class in com.imsl.math: Computes a two-dimensional, tensor-product spline approximant using least squares.
Spline2DLeastSquares(double[], double[], double[][], int, int) - Constructor for class com.imsl.math.Spline2DLeastSquares: Constructor for Spline2DLeastSquares.
SplineData - Class in com.imsl.chart: A data set created from a Spline.
SplineData(ChartNode, Spline) - Constructor for class com.imsl.chart.SplineData: Creates a data node from Spline values.
sqrt(Complex) - Static method in class com.imsl.math.Complex: Returns the square root of a Complex, with a branch cut along the negative real axis.
sqrt(double) - Static method in class com.imsl.math.JMath: Returns the square root of a double.
SQUASH - Static variable in interface com.imsl.datamining.neural.Activation: The squash activation function, $g(x) = frac{x}{1+|x|}$
stack(TimeSeries) - Method in class com.imsl.stat.TimeSeriesOperations: Stacks or vectorizes the values of a multivariate TimeSeries.
STANDARD_GAMMA - Static variable in interface com.imsl.chart.Colormap: Standard gamma colormap.
STANDARD_METHOD - Static variable in class com.imsl.math.ComplexSparseCholesky: Indicates that the method of George/Liu (1981) is used for numeric factorization.
STANDARD_METHOD - Static variable in class com.imsl.math.SparseCholesky: Indicates the method of George/Liu (1981) will be used for numeric factorization.
standardDeviation(double[]) - Static method in class com.imsl.stat.Summary: Returns the population standard deviation of the given data set.
standardDeviation(double[], double[]) - Static method in class com.imsl.stat.Summary: Returns the population standard deviation of the given data set and associated weights.
start(Chart) - Method in class com.imsl.chart.Draw: Called just before a chart is drawn.
startErrorBar() - Method in class com.imsl.chart.Draw: Start drawing an error bar.
startErrorBar() - Method in class com.imsl.chart.DrawMap
startErrorBar() - Method in class com.imsl.chart.DrawPick: Start ErrorBar
startFill() - Method in class com.imsl.chart.Draw: Start drawing a filled region.
startFill() - Method in class com.imsl.chart.DrawMap
startFill() - Method in class com.imsl.chart.DrawPick: Fill
startImage() - Method in class com.imsl.chart.Draw: Start drawing an image.
startImage() - Method in class com.imsl.chart.DrawMap
startImage() - Method in class com.imsl.chart.DrawPick: Start Image
startLine() - Method in class com.imsl.chart.Draw: Start drawing lines.
startLine() - Method in class com.imsl.chart.DrawMap: Start drawing lines.
startLine() - Method in class com.imsl.chart.DrawPick: Start drawing lines.
startMarker() - Method in class com.imsl.chart.Draw: Start drawing markers.
startMarker() - Method in class com.imsl.chart.DrawMap: Start drawing markers.
startMarker() - Method in class com.imsl.chart.DrawPick: Start drawing markers.
startText() - Method in class com.imsl.chart.Draw: Start drawing text.
startText() - Method in class com.imsl.chart.DrawMap
startText() - Method in class com.imsl.chart.DrawPick: Start drawing text
STD_DEV - Static variable in class com.imsl.stat.Dissimilarities: Indicates scaling by the standard deviation.
STDEV_CORRELATION_MATRIX - Static variable in class com.imsl.stat.Covariances: Indicates correlation matrix except for the diagonal elements which are the standard deviations
STEPWISE_REGRESSION - Static variable in class com.imsl.stat.StepwiseRegression: Indicates stepwise regression.
StepwiseRegression - Class in com.imsl.stat: Builds multiple linear regression models using forward selection, backward selection, or stepwise selection.
StepwiseRegression(double[][], double[]) - Constructor for class com.imsl.stat.StepwiseRegression: Creates a new instance of StepwiseRegression.
StepwiseRegression(double[][], double[], double[]) - Constructor for class com.imsl.stat.StepwiseRegression: Creates a new instance of weighted StepwiseRegression.
StepwiseRegression(double[][], double[], double[], double[]) - Constructor for class com.imsl.stat.StepwiseRegression: Creates a new instance of weighted StepwiseRegression using observation frequencies.
StepwiseRegression(double[][], int) - Constructor for class com.imsl.stat.StepwiseRegression: Creates a new instance of StepwiseRegression from a user-supplied variance-covariance matrix.
StepwiseRegression.CoefficientTTests - Class in com.imsl.stat: CoefficientTTests contains statistics related to the student-t test, for each regression coefficient.
StepwiseRegression.CyclingIsOccurringException - Exception in com.imsl.stat: Cycling is occurring.
StepwiseRegression.CyclingIsOccurringException(int) - Constructor for exception com.imsl.stat.StepwiseRegression.CyclingIsOccurringException: Constructs a CyclingIsOccurringException.
StepwiseRegression.NoVariablesEnteredException - Exception in com.imsl.stat: No Variables can enter the model.
StepwiseRegression.NoVariablesEnteredException() - Constructor for exception com.imsl.stat.StepwiseRegression.NoVariablesEnteredException: Constructs a NoVariablesEnteredException.
stop() - Method in class com.imsl.chart.Draw: Called when a chart is finished being drawn.
STRICT_LOWER_TRIANGULAR - Static variable in class com.imsl.math.PrintMatrix: This flag as the argument to setMatrixType, indicates that only the strict lower triangular elements of the matrix are to be printed.
STRICT_UPPER_TRIANGULAR - Static variable in class com.imsl.math.PrintMatrix: This flag as the argument to setMatrixType, indicates that only the strict upper triangular elements of the matrix are to be printed.
stringToObject(Class, String) - Method in class com.imsl.chart.xml.ChartXML: Converts a String into an Object of the given class.
studentsT(double, double) - Static method in class com.imsl.stat.Cdf: Evaluates the Student's t cumulative probability distribution function.
studentsT(double, double) - Static method in class com.imsl.stat.InvCdf: Returns the inverse of the Student's t cumulative probability distribution function.
subtract(Complex, Complex) - Static method in class com.imsl.math.Complex: Returns the difference of two Complex objects, x-y.
subtract(Complex, double) - Static method in class com.imsl.math.Complex: Returns the difference of a Complex object and a double, x-y.
subtract(double, Complex) - Static method in class com.imsl.math.Complex: Returns the difference of a double and a Complex object, x-y.
subtract(Complex[][], Complex[][]) - Static method in class com.imsl.math.ComplexMatrix: Subtract two Complex rectangular arrays, a - b.
subtract(double[][], double[][]) - Static method in class com.imsl.math.Matrix: Subtract two rectangular arrays, a - b.
subtract(Physical, Physical) - Static method in class com.imsl.math.Physical: Subtract two compatible Physical objects.
suffix - Static variable in class com.imsl.math.Complex: String used in converting Complex to String.
sum(int[], int[]) - Static method in class com.imsl.datamining.Apriori: Sums up the itemset frequencies.
SUM - Static variable in class com.imsl.stat.TimeSeriesOperations.CombineMethod: Takes the sum of the two values.
SUM_OF_SQUARES - Static variable in class com.imsl.datamining.neural.QuasiNewtonTrainer: Compute the sum of squares error.
SUM_TO_ZERO - Static variable in class com.imsl.stat.RegressorsForGLM: The dummies are defined in terms of the indicator variables so that for balanced data, the usual summation restrictions are imposed on the regression coefficients.
Summary - Class in com.imsl.stat: Computes basic univariate statistics.
Summary() - Constructor for class com.imsl.stat.Summary: Constructs a new summary statistics object.
SuperLU - Class in com.imsl.math: Computes the LU factorization of a general sparse matrix of type SparseMatrix by a column method and solves the real sparse linear system of equations $Ax=b$ .
SuperLU(SparseMatrix) - Constructor for class com.imsl.math.SuperLU: Constructor for SuperLU.
Surface - Class in com.imsl.chart3d: Surface from a function or from a set of scattered data points.
Surface(AxisXYZ, Surface.ZFunction, double, double, double, double) - Constructor for class com.imsl.chart3d.Surface: Creates a surface from a function.
Surface(AxisXYZ, double[], double[], double[][]) - Constructor for class com.imsl.chart3d.Surface: Creates a surface from a gridded data set.
Surface(AxisXYZ, double[], double[], double[][], Color[][]) - Constructor for class com.imsl.chart3d.Surface: Creates a colored surface from a gridded data set.
Surface(AxisXYZ, double[], double[], double[]) - Constructor for class com.imsl.chart3d.Surface: Creates a surface from a scattered set of 3D points.
Surface(AxisXYZ, double[], double[], double[], Color[]) - Constructor for class com.imsl.chart3d.Surface: Creates a surface from a scattered set of 3D points with a color given at each point.
Surface.ZFunction - Interface in com.imsl.chart3d: Functional representation of a surface.
SURFACE_TYPE_FLAT - Static variable in class com.imsl.chart3d.Surface: Draws the surface using flat shading.
SURFACE_TYPE_GOURAUD - Static variable in class com.imsl.chart3d.Surface: Draws the surface using Gouraud shading.
SURFACE_TYPE_MESH - Static variable in class com.imsl.chart3d.Surface: Draws the surface as a mesh.
SURFACE_TYPE_NICEST - Static variable in class com.imsl.chart3d.Surface: Draws the surface using the best shading available.
SVD - Class in com.imsl.math: Singular Value Decomposition (SVD) of a rectangular matrix of type double.
SVD(double[][], double) - Constructor for class com.imsl.math.SVD: Construct the singular value decomposition of a rectangular matrix with a given tolerance.
SVD(double[][]) - Constructor for class com.imsl.math.SVD: Construct the singular value decomposition of a rectangular matrix with default tolerance.
SVD.DidNotConvergeException - Exception in com.imsl.math: The iteration did not converge
SVD.DidNotConvergeException(String) - Constructor for exception com.imsl.math.SVD.DidNotConvergeException: Constructs a DidNotConvergeException object.
SVD.DidNotConvergeException(String, Object[]) - Constructor for exception com.imsl.math.SVD.DidNotConvergeException: Constructs a DidNotConvergeException object.
syd(double, double, int, int) - Static method in class com.imsl.finance.Finance: Returns the depreciation of an asset using the sum-of-years digits method.
SymEigen - Class in com.imsl.math: Computes the eigenvalues and eigenvectors of a real symmetric matrix.
SymEigen(double[][]) - Constructor for class com.imsl.math.SymEigen: Constructs the eigenvalues and the eigenvectors for a real symmetric matrix.
SymEigen(double[][], boolean) - Constructor for class com.imsl.math.SymEigen: Constructs the eigenvalues and (optionally) the eigenvectors for a real symmetric matrix.

T

TableMultiWay - Class in com.imsl.stat: Tallies observations into a multi-way frequency table.
TableMultiWay(double[][], int) - Constructor for class com.imsl.stat.TableMultiWay: Constructor for TableMultiWay.
TableMultiWay(double[][], int[]) - Constructor for class com.imsl.stat.TableMultiWay: Constructor for TableMultiWay.
TableMultiWay.BalancedTable - Class in com.imsl.stat: Tallies the number of unique values of each variable.
TableMultiWay.UnbalancedTable - Class in com.imsl.stat: Tallies the frequency of each cell in x.
TableOneWay - Class in com.imsl.stat: Class TableOneWay calculates a frequency table for a data array.
TableOneWay(double[], int) - Constructor for class com.imsl.stat.TableOneWay: Constructor for TableOneWay.
TableTwoWay - Class in com.imsl.stat: Class TableTwoWay calculates a two-dimensional frequency table for a data array based upon two variables.
TableTwoWay(double[], int, double[], int) - Constructor for class com.imsl.stat.TableTwoWay: Constructor for TableTwoWay.
tan(Complex) - Static method in class com.imsl.math.Complex: Returns the tangent of a Complex.
tan(double) - Static method in class com.imsl.math.JMath: Returns the tangent of a double.
TANH - Static variable in interface com.imsl.datamining.neural.Activation: The hyperbolic tangent activation function, $g(x)=tanh{x}= frac{e^x-e^{-x}}{e^x+e^{-x}}$ .
tanh(Complex) - Static method in class com.imsl.math.Complex: Returns the hyperbolic tanh of a Complex.
tanh(double) - Static method in class com.imsl.math.Hyperbolic: Returns the hyperbolic tangent of its argument.
tbilleq(GregorianCalendar, GregorianCalendar, double) - Static method in class com.imsl.finance.Bond: Returns the bond-equivalent yield of a Treasury bill.
tbillprice(GregorianCalendar, GregorianCalendar, double) - Static method in class com.imsl.finance.Bond: Returns the price, per $100 face value, of a Treasury bill.
tbillyield(GregorianCalendar, GregorianCalendar, double) - Static method in class com.imsl.finance.Bond: Returns the yield of a Treasury bill.
TEMPERATURE - Static variable in class com.imsl.math.Physical
TEMPORARY_CHANGE - Static variable in class com.imsl.stat.ARMAOutlierIdentification: Indicates detection of a temporary change outlier.
TEMPORARY_CHANGE - Static variable in class com.imsl.stat.AutoARIMA: Indicates detection of a temporary change outlier.
terminal(double) - Method in interface com.imsl.math.FeynmanKac.Boundaries: Returns the terminal condition value.
TEXT - Static variable in class com.imsl.chart.Draw
Text - Class in com.imsl.chart: The value of the attribute "Title".
Text(String) - Constructor for class com.imsl.chart.Text: Construct a Text object.
Text(String, int) - Constructor for class com.imsl.chart.Text: Construct a Text object with specified alignment.
Text(Format, double) - Constructor for class com.imsl.chart.Text: Creates a text object by applying a java.text.Format to a double.
TEXT_X_CENTER - Static variable in class com.imsl.chart.ChartNode: Value for attribute "TextAlignment" indicating that the text should be centered.
TEXT_X_LEFT - Static variable in class com.imsl.chart.ChartNode: Value for attribute "TextAlignment" indicating that the text should be left adjusted.
TEXT_X_RIGHT - Static variable in class com.imsl.chart.ChartNode: Value for attribute "TextAlignment" indicating that the text should be right adjusted.
TEXT_Y_BOTTOM - Static variable in class com.imsl.chart.ChartNode: Value for attribute "TextAlignment" indicating that the text should be drawn on the baseline.
TEXT_Y_CENTER - Static variable in class com.imsl.chart.ChartNode: Value for attribute "TextAlignment" indicating that the text should be vertically centered.
TEXT_Y_TOP - Static variable in class com.imsl.chart.ChartNode: Value for attribute "TextAlignment" indicating that the text should be drawn with the top of the letters touching the top of the drawing region.
textAngle - Variable in class com.imsl.chart.Draw
textColor - Variable in class com.imsl.chart.Draw
textFont - Variable in class com.imsl.chart.Draw
throwIllegalArgumentException(String, String, Object[]) - Static method in class com.imsl.Messages: Throws an IllegalArgumentException with a formatted String argument.
throwIllegalStateException(String, String, Object[]) - Static method in class com.imsl.Messages: Throws an IllegalStateException with a formatted String argument.
TIE_AVERAGE - Static variable in class com.imsl.stat.Ranks: In case of ties, use the average of the scores of the tied observations.
TIE_HIGHEST - Static variable in class com.imsl.stat.Ranks: In case of ties, use the highest score in the group of ties.
TIE_LOWEST - Static variable in class com.imsl.stat.Ranks: In case of ties, use the lowest score in the group of ties.
TIE_RANDOM - Static variable in class com.imsl.stat.Ranks: In case of ties, use one of the group of ties chosen at random.
TIME - Static variable in class com.imsl.math.Physical
TimeSeries - Class in com.imsl.stat: A specialized class for time series data and analysis.
TimeSeries() - Constructor for class com.imsl.stat.TimeSeries: Constructor for TimeSeries.
TimeSeriesClassFilter - Class in com.imsl.datamining.neural: Converts time series data contained within nominal categories to a lagged format for processing by a neural network.
TimeSeriesClassFilter(int) - Constructor for class com.imsl.datamining.neural.TimeSeriesClassFilter: Constructor for TimeSeriesClassFilter.
TimeSeriesFilter - Class in com.imsl.datamining.neural: Converts time series data to a lagged format used as input to a neural network.
TimeSeriesFilter() - Constructor for class com.imsl.datamining.neural.TimeSeriesFilter: Constructor for TimeSeriesClassFilter.
TimeSeriesOperations - Class in com.imsl.stat: A class of operations and methods for objects of class TimeSeries.
TimeSeriesOperations() - Constructor for class com.imsl.stat.TimeSeriesOperations: Constructor for TimeSeriesOperations.
TimeSeriesOperations.CombineMethod - Class in com.imsl.stat: Public enum of methods for combining synchronous time series values.
TimeSeriesOperations.Function - Interface in com.imsl.stat: Public interface for the user-supplied function that defines how to combine two synchronous time series values.
TimeSeriesOperations.MergeRule - Class in com.imsl.stat: Public enum of merge rules that defines how two time series should be merged.
toDenseMatrix() - Method in class com.imsl.math.ComplexSparseMatrix: Returns the sparse matrix as a dense matrix.
toDenseMatrix() - Method in class com.imsl.math.SparseMatrix: Returns the sparse matrix as a dense matrix.
Tokenizer - Class in com.imsl.io: Breaks a line into tokens.
Tokenizer(String, char, boolean) - Constructor for class com.imsl.io.Tokenizer: Creates a Tokenizer.
ToolTip - Class in com.imsl.chart: A ToolTip for a chart element.
ToolTip(ChartNode) - Constructor for class com.imsl.chart.ToolTip: Creates a ToolTip node that enables ToolTips on charts.
toSparseArray() - Method in class com.imsl.math.ComplexSparseMatrix: Returns the sparse matrix in the SparseArray form.
toSparseArray() - Method in class com.imsl.math.SparseMatrix: Returns the sparse matrix in the SparseArray form.
toString() - Method in class com.imsl.chart.AbstractChartNode: Returns the name of this ChartNode
toString() - Method in class com.imsl.math.Complex: Returns a String representation for the specified Complex.
toString() - Method in class com.imsl.math.Physical: Returns a String containing the value and units, if any.
train(KohonenSOM, double[][]) - Method in class com.imsl.datamining.KohonenSOMTrainer: Trains a Kohonen network.
train(double[][], int[]) - Method in class com.imsl.datamining.NaiveBayesClassifier: Trains a Naive Bayes classifier for classifying data into one of nClasses target classifications.
train(int[][], int[]) - Method in class com.imsl.datamining.NaiveBayesClassifier: Trains a Naive Bayes classifier for classifying data into one of nClasses target classifications.
train(double[][], int[][], int[]) - Method in class com.imsl.datamining.NaiveBayesClassifier: Trains a Naive Bayes classifier for classifying data into one of nClasses target classifications.
train(Trainer, double[][], int[]) - Method in class com.imsl.datamining.neural.BinaryClassification: Trains the classification neural network using supplied trainer and patterns.
train(Network, double[][], double[][]) - Method in class com.imsl.datamining.neural.EpochTrainer: Trains the neural network using supplied training patterns.
train(Network, double[][], double[][]) - Method in class com.imsl.datamining.neural.LeastSquaresTrainer: Trains the neural network using supplied training patterns.
train(Trainer, double[][], int[]) - Method in class com.imsl.datamining.neural.MultiClassification: Trains the classification neural network using supplied training patterns.
train(Network, double[][], double[][]) - Method in class com.imsl.datamining.neural.QuasiNewtonTrainer: Trains the neural network using supplied training patterns.
train(Network, double[][], double[][]) - Method in interface com.imsl.datamining.neural.Trainer: Trains the neural network using supplied training patterns.
Trainer - Interface in com.imsl.datamining.neural: Interface implemented by classes used to train a network.
Transform - Interface in com.imsl.chart: Defines a custom transformation along an axis.
TRANSFORM_ASIN_SQRT - Static variable in class com.imsl.datamining.neural.UnsupervisedOrdinalFilter: Flag to indicate the arcsine square root transform will be applied to the percentages.
TRANSFORM_CUSTOM - Static variable in class com.imsl.chart.AbstractChartNode: Flag used to indicate that the axis using a custom transformation.
TRANSFORM_LINEAR - Static variable in class com.imsl.chart.AbstractChartNode: Flag used to indicate that the axis uses linear scaling.
TRANSFORM_LOG - Static variable in class com.imsl.chart.AbstractChartNode: Flag used to indicate that the axis uses logarithmic scaling.
TRANSFORM_NONE - Static variable in class com.imsl.datamining.neural.UnsupervisedOrdinalFilter: Flag to indicate no transformation of percentages.
TRANSFORM_SQRT - Static variable in class com.imsl.datamining.neural.UnsupervisedOrdinalFilter: Flag to indicate the square root transform will be applied to the percentages.
TransformDate - Class in com.imsl.chart: Defines a transformation along an axis that skips weekend dates.
TransformDate() - Constructor for class com.imsl.chart.TransformDate
translate(int, int) - Method in class com.imsl.chart.Draw: Translates the origin to the point (x,y)
translate(int, int) - Method in class com.imsl.chart.DrawMap: Translates the origin to the point (x,y)
translate(int, int) - Method in class com.imsl.chart.DrawPick: Translates the origin to the point (x,y)
transpose(Complex[][]) - Static method in class com.imsl.math.ComplexMatrix: Return the transpose of a Complex matrix.
transpose(double[][]) - Static method in class com.imsl.math.Matrix: Return the transpose of a matrix.
transpose() - Method in class com.imsl.math.SparseMatrix: Returns the transpose of the matrix.
Tree - Class in com.imsl.datamining.decisionTree: Serves as the root node of a decision tree and contains information about the relationship of child nodes.
Tree(int, int, int, int, int, int, PredictiveModel.VariableType, int[], PredictiveModel.VariableType[], int) - Constructor for class com.imsl.datamining.decisionTree.Tree: Creates the root node of a decision tree and contains information about the relationship of child nodes.
Treemap - Class in com.imsl.chart: Treemap creates a chart from two arrays of double precision values or one data array and one array of Color values.
Treemap(AxisXY, double[], Color[]) - Constructor for class com.imsl.chart.Treemap: Constructs a treemap using supplied data and colors array.
Treemap(AxisXY, double[], double[], Colormap) - Constructor for class com.imsl.chart.Treemap: Constructs a treemap using supplied data and a colormap.
Treemap.Legend - Class in com.imsl.chart: A legend for use with a treemap.
TreeNode - Class in com.imsl.datamining.decisionTree: A DecisionTree node which may be used as a child of Tree.
TreeNode() - Constructor for class com.imsl.datamining.decisionTree.TreeNode: Constructs a DecisionTreeNode object.
trim() - Method in class com.imsl.chart3d.BufferedPaint: Returns a subimage with the white space trimmed off.
TUKEY - Static variable in class com.imsl.stat.ANOVA: The Tukey method
TUKEY_KRAMER - Static variable in class com.imsl.stat.ANOVA: The Tukey-Kramer method
TYPE_MOORE - Static variable in class com.imsl.datamining.KohonenSOM: Indicates a Moore neighborhood type.
TYPE_VON_NEUMANN - Static variable in class com.imsl.datamining.KohonenSOM: Indicates a Von Neumann neighborhood type.

U

UChart - Class in com.imsl.chart.qc: UChart is a u-chart for monitoring the defect rate when defects are rare.
UChart(AxisXY, double, int[]) - Constructor for class com.imsl.chart.qc.UChart: Creates a u-Chart given the number of defects for a series of samples with equal sample sizes.
UChart(AxisXY, double[], int[]) - Constructor for class com.imsl.chart.qc.UChart: Creates a u-Chart given the number of defects rates for a series of samples with varying sample sizes.
UNABLE_TO_IDENTIFY - Static variable in class com.imsl.stat.ARMAOutlierIdentification: Indicates detection of an outlier that cannnot be categorized.
UNABLE_TO_IDENTIFY - Static variable in class com.imsl.stat.AutoARIMA: Indicates detection of an outlier that cannnot be categorized.
UNBOUNDED_Z_SCORE_SCALING_MEAN_STDEV - Static variable in class com.imsl.datamining.neural.ScaleFilter: Flag to indicate unbounded z-score scaling using the mean and standard deviation.
UNBOUNDED_Z_SCORE_SCALING_MEDIAN_MAD - Static variable in class com.imsl.datamining.neural.ScaleFilter: Flag to indicate unbounded z-score scaling using the median and mean absolute difference.
uniform(double, double, double) - Static method in class com.imsl.stat.Cdf: Evaluates the uniform cumulative probability distribution function.
uniform(double, double, double) - Static method in class com.imsl.stat.InvCdf: Returns the inverse of the uniform cumulative probability distribution function.
UNION - Static variable in class com.imsl.stat.TimeSeriesOperations.MergeRule: The merge operation includes all time points and values from each time series and applies the CombineMethod to the values at the matching time points.
UNION_MISSING - Static variable in class com.imsl.stat.TimeSeriesOperations.MergeRule: The merge operation includes all time points but applies the CombineMethod only to values at matching time points, indicating a missing value Double.NaN for time points that are not in the intersection.
unitsString() - Method in class com.imsl.math.Physical: Returns a String containing the units only.
unordered(double, double) - Static method in class com.imsl.math.IEEE: Unordered test on a pair of doubles.
UnsupervisedNominalFilter - Class in com.imsl.datamining.neural: Converts nominal data into a series of binary encoded columns for input to a neural network.
UnsupervisedNominalFilter(int) - Constructor for class com.imsl.datamining.neural.UnsupervisedNominalFilter: Constructor for UnsupervisedNominalFilter.
UnsupervisedOrdinalFilter - Class in com.imsl.datamining.neural: Encodes ordinal data into percentages for input to a neural network.
UnsupervisedOrdinalFilter(int, int) - Constructor for class com.imsl.datamining.neural.UnsupervisedOrdinalFilter: Constructor for UnsupervisedOrdinalFilter.
UNWEIGHTED_LEAST_SQUARES - Static variable in class com.imsl.stat.FactorAnalysis: Indicates unweighted least squares method.
unwrap(Class) - Method in class com.imsl.io.FlatFile: Returns an object that implements the given interface to allow access to non-standard methods, or standard methods not exposed by the proxy.
update(Graphics) - Method in class com.imsl.chart.Chart
update() - Method in class com.imsl.chart3d.Data: Update the surface by reevaluation of the z-function and the color function.
update(double[]) - Method in class com.imsl.math.Cholesky: Updates the factorization by adding a rank-1 matrix.
update(double[], double) - Method in class com.imsl.math.RadialBasis: Adds a data point with weight = 1.
update(double[], double, double) - Method in class com.imsl.math.RadialBasis: Adds a data point with a specified weight.
update(double[][], double[]) - Method in class com.imsl.math.RadialBasis: Adds a set of data points, all with weight = 1.
update(double[][], double[], double[]) - Method in class com.imsl.math.RadialBasis: Adds a set of data points with user-specified weights.
update(double[]) - Method in class com.imsl.stat.ChiSquaredTest: Adds new observations to the test.
update(double) - Method in class com.imsl.stat.ChiSquaredTest: Adds a new observation to the test.
update(double[], double[]) - Method in class com.imsl.stat.ChiSquaredTest: Adds new observations to the test.
update(double, double) - Method in class com.imsl.stat.ChiSquaredTest: Adds a new observation to the test.
update(double[][], int[]) - Method in class com.imsl.stat.DiscriminantAnalysis: Trains a set of observations and associated frequencies and weights by performing a linear or quadratic discriminant function analysis among several known groups.
update(double[][], int[], int[]) - Method in class com.imsl.stat.DiscriminantAnalysis: Trains a set of observations and associated frequencies and weights by performing a linear or quadratic discriminant function analysis among several known groups.
update(double[][], int[], int[], double[]) - Method in class com.imsl.stat.DiscriminantAnalysis: Trains a set of observations and associated frequencies and weights by performing a linear or quadratic discriminant function analysis among several known groups.
update(double[][], int[], int[], int[], double[]) - Method in class com.imsl.stat.DiscriminantAnalysis: Trains a set of observations and associated frequencies and weights by performing a linear or quadratic discriminant function analysis among several known groups.
update(double[], double[][], double[][]) - Method in class com.imsl.stat.KalmanFilter: Performs computation of the update equations.
update(double[], double) - Method in class com.imsl.stat.LinearRegression: Updates the regression object with a new observation.
update(double[], double, double) - Method in class com.imsl.stat.LinearRegression: Updates the regression object with a new observation and weight.
update(double[][], double[]) - Method in class com.imsl.stat.LinearRegression: Updates the regression object with a new set of observations.
update(double[][], double[], double[]) - Method in class com.imsl.stat.LinearRegression: Updates the regression object with a new set of observations and weights.
update(double[], double[]) - Method in class com.imsl.stat.NormTwoSample: Concatenates samples x and y to the samples provided in the constructor.
update(double) - Method in class com.imsl.stat.Summary: Adds an observation to the Summary object.
update(double, double) - Method in class com.imsl.stat.Summary: Adds an observation and associated weight to the Summary object.
update(double[]) - Method in class com.imsl.stat.Summary: Adds a set of observations to the Summary object.
update(double[], double[]) - Method in class com.imsl.stat.Summary: Adds a set of observations and associated weights to the Summary object.
update(double, double, double) - Method in class com.imsl.stat.UserBasisRegression: Adds a new observation and associated weight to the RegressionBasis object.
updateArray(String, Array) - Method in class com.imsl.io.AbstractFlatFile: Updates the designated column with an Array value.
updateArray(int, Array) - Method in class com.imsl.io.AbstractFlatFile: Updates the designated column with an Array value.
updateAsciiStream(int, InputStream, int) - Method in class com.imsl.io.AbstractFlatFile: Updates the designated column with an ASCII stream value.
updateAsciiStream(String, InputStream, int) - Method in class com.imsl.io.AbstractFlatFile: Updates the designated column with an ASCII stream value.
updateAsciiStream(int, InputStream) - Method in class com.imsl.io.FlatFile: Updates the designated column with an ASCII stream value.
updateAsciiStream(int, InputStream, int) - Method in class com.imsl.io.FlatFile: Updates the designated column with an ASCII stream value, which is the specified number of bytes.
updateAsciiStream(int, InputStream, long) - Method in class com.imsl.io.FlatFile: Updates the designated column with an ASCII stream value, which is the specified number of bytes.
updateAsciiStream(String, InputStream) - Method in class com.imsl.io.FlatFile: Updates the designated column with an ASCII stream value.
updateAsciiStream(String, InputStream, int) - Method in class com.imsl.io.FlatFile: Updates the designated column with an ASCII stream value, which is the specified number of bytes.
updateAsciiStream(String, InputStream, long) - Method in class com.imsl.io.FlatFile: Updates the designated column with an ASCII stream value, which is the specified number of bytes.
updateBigDecimal(int, BigDecimal) - Method in class com.imsl.io.AbstractFlatFile: Updates the designated column with a java.math.BigDecimal value.
updateBigDecimal(String, BigDecimal) - Method in class com.imsl.io.AbstractFlatFile: Updates the designated column with a java.sql.BigDecimal value.
updateBinaryStream(int, InputStream, int) - Method in class com.imsl.io.AbstractFlatFile: Updates the designated column with a binary stream value.
updateBinaryStream(String, InputStream, int) - Method in class com.imsl.io.AbstractFlatFile: Updates the designated column with a binary stream value.
updateBinaryStream(int, InputStream) - Method in class com.imsl.io.FlatFile: Updates the designated column with a binary stream value.
updateBinaryStream(int, InputStream, int) - Method in class com.imsl.io.FlatFile: Updates the designated column with a binary stream value, which is the specified number of bytes.
updateBinaryStream(int, InputStream, long) - Method in class com.imsl.io.FlatFile: Updates the designated column with a binary stream value, which is the specified number of bytes.
updateBinaryStream(String, InputStream) - Method in class com.imsl.io.FlatFile: Updates the designated column with a binary stream value.
updateBinaryStream(String, InputStream, int) - Method in class com.imsl.io.FlatFile: Updates the designated column with a binary stream value.
updateBinaryStream(String, InputStream, long) - Method in class com.imsl.io.FlatFile: Updates the designated column with a binary stream value.
updateBlob(int, Blob) - Method in class com.imsl.io.AbstractFlatFile: Updates the designated column with an java.sql.Blob value.
updateBlob(String, Blob) - Method in class com.imsl.io.AbstractFlatFile: Updates the designated column with an java.sql.Blob value.
updateBlob(int, Blob) - Method in class com.imsl.io.FlatFile: Updates the designated column with a java.sql.Blob value.
updateBlob(int, InputStream) - Method in class com.imsl.io.FlatFile: Updates the designated column using the given input stream.
updateBlob(int, InputStream, long) - Method in class com.imsl.io.FlatFile: Updates the designated column using the given input stream, which is the specified number of bytes.
updateBlob(String, Blob) - Method in class com.imsl.io.FlatFile: Updates the designated column with a java.sql.Blob value.
updateBlob(String, InputStream) - Method in class com.imsl.io.FlatFile: Updates the designated column using the given input stream.
updateBlob(String, InputStream, long) - Method in class com.imsl.io.FlatFile: Updates the designated column using the given input stream, which is the specified number of bytes.
updateBoolean(int, boolean) - Method in class com.imsl.io.AbstractFlatFile: Updates the designated column with a boolean value.
updateBoolean(String, boolean) - Method in class com.imsl.io.AbstractFlatFile: Updates the designated column with a boolean value.
updateByte(int, byte) - Method in class com.imsl.io.AbstractFlatFile: Updates the designated column with a byte value.
updateByte(String, byte) - Method in class com.imsl.io.AbstractFlatFile: Updates the designated column with a byte value.
updateBytes(int, byte[]) - Method in class com.imsl.io.AbstractFlatFile: Updates the designated column with a byte array value.
updateBytes(String, byte[]) - Method in class com.imsl.io.AbstractFlatFile: Updates the designated column with a byte value.
updateCharacterStream(int, Reader, int) - Method in class com.imsl.io.AbstractFlatFile: Updates the designated column with a character stream value.
updateCharacterStream(String, Reader, int) - Method in class com.imsl.io.AbstractFlatFile: Updates the designated column with a character stream value.
updateCharacterStream(int, Reader) - Method in class com.imsl.io.FlatFile: Updates the designated column with a character stream value.
updateCharacterStream(int, Reader, int) - Method in class com.imsl.io.FlatFile: Updates the designated column with a character stream value, which is the specified number of bytes.
updateCharacterStream(int, Reader, long) - Method in class com.imsl.io.FlatFile: Updates the designated column with a character stream value, which is the specified number of bytes.
updateCharacterStream(String, Reader) - Method in class com.imsl.io.FlatFile: Updates the designated column with a character stream value.
updateCharacterStream(String, Reader, int) - Method in class com.imsl.io.FlatFile: Updates the designated column with a character stream value, which is the specified number of bytes.
updateCharacterStream(String, Reader, long) - Method in class com.imsl.io.FlatFile: Updates the designated column with a character stream value, which is the specified number of bytes.
updateClob(String, Clob) - Method in class com.imsl.io.AbstractFlatFile: Updates the designated column with an java.sql.Clob value.
updateClob(int, Clob) - Method in class com.imsl.io.AbstractFlatFile: Updates the designated column with an java.sql.Clob value.
updateClob(int, Clob) - Method in class com.imsl.io.FlatFile: Updates the designated column with a java.sql.Clob value.
updateClob(int, Reader) - Method in class com.imsl.io.FlatFile: Updates the designated column using the given Reader object.
updateClob(int, Reader, long) - Method in class com.imsl.io.FlatFile: Updates the designated column using the given Reader object, which is the given number of characters long.
updateClob(String, Clob) - Method in class com.imsl.io.FlatFile: Updates the designated column with a java.sql.Clob value.
updateClob(String, Reader) - Method in class com.imsl.io.FlatFile: Updates the designated column using the given Reader object.
updateClob(String, Reader, long) - Method in class com.imsl.io.FlatFile: Updates the designated column using the given Reader object, which is the given number of characters long.
updateDate(int, Date) - Method in class com.imsl.io.AbstractFlatFile: Updates the designated column with a java.sql.Date value.
updateDate(String, Date) - Method in class com.imsl.io.AbstractFlatFile: Updates the designated column with a java.sql.Date value.
updateDouble(int, double) - Method in class com.imsl.io.AbstractFlatFile: Updates the designated column with a double value.
updateDouble(String, double) - Method in class com.imsl.io.AbstractFlatFile: Updates the designated column with a double value.
updateFloat(int, float) - Method in class com.imsl.io.AbstractFlatFile: Updates the designated column with a float value.
updateFloat(String, float) - Method in class com.imsl.io.AbstractFlatFile: Updates the designated column with a float value.
updateFrequentItemsets(Itemsets, int[]) - Static method in class com.imsl.datamining.Apriori: Updates the set of frequent items in candItemsets.
updateInt(int, int) - Method in class com.imsl.io.AbstractFlatFile: Updates the designated column with an int value.
updateInt(String, int) - Method in class com.imsl.io.AbstractFlatFile: Updates the designated column with an int value.
updateLong(int, long) - Method in class com.imsl.io.AbstractFlatFile: Updates the designated column with a long value.
updateLong(String, long) - Method in class com.imsl.io.AbstractFlatFile: Updates the designated column with a long value.
updateNCharacterStream(int, Reader) - Method in class com.imsl.io.FlatFile: Updates the designated column with a character stream value.
updateNCharacterStream(int, Reader, long) - Method in class com.imsl.io.FlatFile: Updates the designated column with a character stream value, which is the specified number of bytes.
updateNCharacterStream(String, Reader) - Method in class com.imsl.io.FlatFile: Updates the designated column with a character stream value.
updateNCharacterStream(String, Reader, long) - Method in class com.imsl.io.FlatFile: Updates the designated column with a character stream value, which is the specified number of bytes.
updateNClob(int, NClob) - Method in class com.imsl.io.FlatFile: Updates the designated column with a java.sql.NClob value.
updateNClob(int, Reader) - Method in class com.imsl.io.FlatFile: Updates the designated column using the given Reader.
updateNClob(int, Reader, long) - Method in class com.imsl.io.FlatFile: Updates the designated column using the given Reader object, which is the given number of characters long.
updateNClob(String, NClob) - Method in class com.imsl.io.FlatFile: Updates the designated column with a java.sql.NClob value.
updateNClob(String, Reader) - Method in class com.imsl.io.FlatFile: Updates the designated column using the given Reader object.
updateNClob(String, Reader, long) - Method in class com.imsl.io.FlatFile: Updates the designated column using the given Reader object, which is the given number of characters long.
updateNString(int, String) - Method in class com.imsl.io.FlatFile: Updates the designated column with a String value.
updateNString(String, String) - Method in class com.imsl.io.FlatFile: Updates the designated column with a String value.
updateNull(int) - Method in class com.imsl.io.AbstractFlatFile: Gives a nullable column a null value.
updateNull(String) - Method in class com.imsl.io.AbstractFlatFile: Updates the designated column with a null value.
updateObject(int, Object, int) - Method in class com.imsl.io.AbstractFlatFile: Updates the designated column with an Object value.
updateObject(int, Object) - Method in class com.imsl.io.AbstractFlatFile: Updates the designated column with an Object value.
updateObject(String, Object, int) - Method in class com.imsl.io.AbstractFlatFile: Updates the designated column with an Object value.
updateObject(String, Object) - Method in class com.imsl.io.AbstractFlatFile: Updates the designated column with an Object value.
updateRef(String, Ref) - Method in class com.imsl.io.AbstractFlatFile: Updates the designated column with an java.sql.Ref value.
updateRef(int, Ref) - Method in class com.imsl.io.AbstractFlatFile: Updates the designated column with an java.sql.Ref value.
updateRow() - Method in class com.imsl.io.AbstractFlatFile: Updates the underlying database with the new contents of the current row of this ResultSet object.
updateRowId(int, RowId) - Method in class com.imsl.io.FlatFile: Updates the designated column with a RowId value.
updateRowId(String, RowId) - Method in class com.imsl.io.FlatFile: Updates the designated column with a RowId value.
updateShort(int, short) - Method in class com.imsl.io.AbstractFlatFile: Updates the designated column with a short value.
updateShort(String, short) - Method in class com.imsl.io.AbstractFlatFile: Updates the designated column with a short value.
updateSQLXML(int, SQLXML) - Method in class com.imsl.io.FlatFile: Updates the designated column with a java.sql.SQLXML value.
updateSQLXML(String, SQLXML) - Method in class com.imsl.io.FlatFile: Updates the designated column with a java.sql.SQLXML value.
updateString(int, String) - Method in class com.imsl.io.AbstractFlatFile: Updates the designated column with a String value.
updateString(String, String) - Method in class com.imsl.io.AbstractFlatFile: Updates the designated column with a String value.
updateTime(int, Time) - Method in class com.imsl.io.AbstractFlatFile: Updates the designated column with a java.sql.Time value.
updateTime(String, Time) - Method in class com.imsl.io.AbstractFlatFile: Updates the designated column with a java.sql.Time value.
updateTimestamp(int, Timestamp) - Method in class com.imsl.io.AbstractFlatFile: Updates the designated column with a java.sql.Timestamp value.
updateTimestamp(String, Timestamp) - Method in class com.imsl.io.AbstractFlatFile: Updates the designated column with a java.sql.Timestamp value.
updateX(double[]) - Method in class com.imsl.stat.NormTwoSample: Concatenates the values in x to the first sample provided in the constructor.
updateY(double[]) - Method in class com.imsl.stat.NormTwoSample: Concatenates the values in y to the second sample provided in the constructor.
UPPER_TRIANGULAR - Static variable in class com.imsl.math.PrintMatrix: This flag as the argument to setMatrixType, indicates that only the upper triangular elements of the matrix are to be printed.
useGainRatio() - Method in class com.imsl.datamining.decisionTree.DecisionTreeInfoGain: Returns whether or not the gain ratio is to be used instead of the gain to determine the best split.
UserBasisRegression - Class in com.imsl.stat: Fits a linear function of the form , where are the user basis functions evaluated at index values is the intercept, are the coefficients associated with the basis functions, and is the random error associated with y.
UserBasisRegression(RegressionBasis, int, boolean) - Constructor for class com.imsl.stat.UserBasisRegression: Constructs a UserBasisRegression object

V

validateLink(Node, Node) - Method in class com.imsl.datamining.neural.FeedForwardNetwork: Checks that a Link between two Nodes is valid.
value(double) - Method in class com.imsl.math.BSpline: Returns the value of the B-spline at a point.
value(double[]) - Method in class com.imsl.math.BSpline: Returns the value of the B-spline at each point of an array.
value - Variable in class com.imsl.math.Physical
value(double[]) - Method in class com.imsl.math.RadialBasis: Returns the value of the radial basis approximation at a point.
value(double[][]) - Method in class com.imsl.math.RadialBasis: Returns the value of the radial basis at a point.
value(double) - Method in class com.imsl.math.Spline: Returns the value of the spline at a point.
value(double[]) - Method in class com.imsl.math.Spline: Returns the value of the spline at each point of an array.
value(double, double) - Method in class com.imsl.math.Spline2D: Returns the value of the tensor-product spline at the point (x, y).
value(double[], double[]) - Method in class com.imsl.math.Spline2D: Returns the values of the tensor-product spline of an array of points.
valueOf(String) - Static method in class com.imsl.datamining.decisionTree.DecisionTreeInfoGain.GainCriteria
valueOf(String) - Static method in class com.imsl.datamining.GradientBoosting.LossFunctionType
valueOf(String) - Static method in class com.imsl.datamining.PredictiveModel.VariableType
valueOf(String) - Static method in class com.imsl.math.Complex: Parses a String into a Complex.
valueOf(String) - Static method in class com.imsl.stat.TimeSeriesOperations.CombineMethod
valueOf(String) - Static method in class com.imsl.stat.TimeSeriesOperations.MergeRule
values() - Static method in class com.imsl.datamining.decisionTree.DecisionTreeInfoGain.GainCriteria
values() - Static method in class com.imsl.datamining.GradientBoosting.LossFunctionType
values() - Static method in class com.imsl.datamining.PredictiveModel.VariableType
values - Variable in class com.imsl.math.ComplexSparseMatrix.SparseArray: Jagged array containing sparse array values.
values - Variable in class com.imsl.math.SparseMatrix.SparseArray: Jagged array containing sparse array values.
values() - Static method in class com.imsl.stat.TimeSeriesOperations.CombineMethod
values() - Static method in class com.imsl.stat.TimeSeriesOperations.MergeRule
variance(double[]) - Static method in class com.imsl.stat.Summary: Returns the population variance of the given data set.
variance(double[], double[]) - Static method in class com.imsl.stat.Summary: Returns the population variance of the given data set and associated weights.
VARIANCE_COVARIANCE_MATRIX - Static variable in class com.imsl.stat.Covariances: Indicates variance-covariance matrix.
VARIANCE_COVARIANCE_MATRIX - Static variable in class com.imsl.stat.FactorAnalysis: Indicates variance-covariance matrix.
vdb(double, double, int, int, int, double, boolean) - Static method in class com.imsl.finance.Finance: Returns the depreciation of an asset for any given period using the variable-declining balance method.
VectorAutoregression - Class in com.imsl.stat: Performs vector autoregression for a multivariate time series.
VectorAutoregression(TimeSeries) - Constructor for class com.imsl.stat.VectorAutoregression: Constructor for the class.
Version - Class in com.imsl: Print the version information.
Version() - Constructor for class com.imsl.Version
verticalStripe(int, int, Color, Color) - Static method in class com.imsl.chart.FillPaint: Returns a vertically striped pattern.
vnorm(double[], double[], double[]) - Method in class com.imsl.math.ODE: Returns the norm of a vector.

W

warning(SAXParseException) - Method in class com.imsl.chart.xml.ChartXML: Receive notification of a warning.
Warning - Class in com.imsl: Handle warning messages.
Warning() - Constructor for class com.imsl.Warning
WarningObject - Class in com.imsl: Handle warning messages.
WarningObject() - Constructor for class com.imsl.WarningObject
wasNull() - Method in class com.imsl.io.AbstractFlatFile: Reports whether the last column read had a value of SQL NULL.
WB_LINEAR - Static variable in interface com.imsl.chart.Colormap: Black and white (grayscale) colormap, the reverse of BW_LINEAR.
WebSafeImageFilter - Class in com.imsl.chart: Maps colors in an image to the nearest Web-safe color.
WebSafeImageFilter() - Constructor for class com.imsl.chart.WebSafeImageFilter
Weibull(double, double, double) - Static method in class com.imsl.stat.Cdf: Evaluates the Weibull cumulative probability distribution function.
Weibull(double, double, double) - Static method in class com.imsl.stat.InvCdf: Returns the inverse of the Weibull cumulative probability distribution function.
Weibull(double, double, double) - Static method in class com.imsl.stat.Pdf: Evaluates the Weibull probability density function.
weight - Variable in class com.imsl.math.BsLeastSquares: The weight array of length n, where n is the number of data points fit.
WilcoxonRankSum - Class in com.imsl.stat: Performs a Wilcoxon rank sum test.
WilcoxonRankSum(double[], double[]) - Constructor for class com.imsl.stat.WilcoxonRankSum: Constructor for WilcoxonRankSum.
wrapAround() - Method in class com.imsl.datamining.KohonenSOM: Sets a flag to indicate the map should wrap around or connect opposite edges.
write(String, String) - Method in class com.imsl.chart3d.Canvas3DChart: Write the canvas as an image file after it is next redrawn.
writePNG(OutputStream, int, int) - Method in class com.imsl.chart.Chart: Writes the chart as an PNG file.
writeSVG(Writer, boolean) - Method in class com.imsl.chart.Chart: Writes the chart as an SVG file.

X

XbarR - Class in com.imsl.chart.qc: XbarR is an X-bar chart for monitoring a process using sample ranges.
XbarR(AxisXY, double[][]) - Constructor for class com.imsl.chart.qc.XbarR: Creates an X-bar chart from sample data using sample ranges.
XbarR(AxisXY, int, double[], double[]) - Constructor for class com.imsl.chart.qc.XbarR: Creates an X-bar control chart given the means and ranges for a series of equally sized samples.
XbarR(AxisXY, int[], double[], double[]) - Constructor for class com.imsl.chart.qc.XbarR: Creates an X-bar control chart given the means and ranges for a series of unequally sized samples.
XbarS - Class in com.imsl.chart.qc: XbarS is an X-bar chart for monitoring a process using sample standard deviations.
XbarS(AxisXY, double[][]) - Constructor for class com.imsl.chart.qc.XbarS: Creates an XbarS chart from sample data using within sample standard deviations.
XbarS(AxisXY, int, double[], double[]) - Constructor for class com.imsl.chart.qc.XbarS: Creates an XbarS chart given the means and standard deviations for a series of equally sized samples.
XbarS(AxisXY, int[], double[], double[]) - Constructor for class com.imsl.chart.qc.XbarS: Creates an XbarS chart given the means and standard deviations for a series of unequally sized samples.
xirr(double[], Date[]) - Static method in class com.imsl.finance.Finance: Returns the internal rate of return for a schedule of cash flows.
xirr(double[], Date[], double) - Static method in class com.imsl.finance.Finance: Returns the internal rate of return for a schedule of cash flows with a user supplied initial guess.
XmR - Class in com.imsl.chart.qc: XmR is an XmR chart for monitoring a process using moving ranges.
XmR(AxisXY, double[]) - Constructor for class com.imsl.chart.qc.XmR: Creates an XmR chart given sample data.
xnpv(double, double[], Date[]) - Static method in class com.imsl.finance.Finance: Returns the present value for a schedule of cash flows.

Y

Y(double, double, int) - Static method in class com.imsl.math.Bessel: Evaluate a sequence of Bessel functions of the second kind with real nonnegative order and real positive argument.
yearfrac(GregorianCalendar, GregorianCalendar, DayCountBasis) - Static method in class com.imsl.finance.Bond: Returns the fraction of a year represented by the number of whole days between two dates.
yield(GregorianCalendar, GregorianCalendar, double, double, double, int, DayCountBasis) - Static method in class com.imsl.finance.Bond: Returns the yield of a security that pays periodic interest.
yield(GregorianCalendar, GregorianCalendar, GregorianCalendar, GregorianCalendar, double, double, double, int, DayCountBasis) - Static method in class com.imsl.finance.Bond: Returns the yield of a security with an odd first coupon period that pays periodic interest.
yield(GregorianCalendar, GregorianCalendar, GregorianCalendar, double, double, double, int, DayCountBasis) - Static method in class com.imsl.finance.Bond: Returns the yield of a security with an odd last coupon period that pays periodic interest.
yielddisc(GregorianCalendar, GregorianCalendar, double, double, DayCountBasis) - Static method in class com.imsl.finance.Bond: Returns the annual yield of a discount bond.
yieldmat(GregorianCalendar, GregorianCalendar, GregorianCalendar, double, double, DayCountBasis) - Static method in class com.imsl.finance.Bond: Returns the annual yield of a security that pays interest at maturity.

Z

ZeroPolynomial - Class in com.imsl.math: The ZeroPolynomial class computes the zeros of a polynomial with complex coefficients, Aberth's method.
ZeroPolynomial() - Constructor for class com.imsl.math.ZeroPolynomial: Creates an instance of the solver.
ZeroPolynomial.DidNotConvergeException - Exception in com.imsl.math: The iteration did not converge
ZeroPolynomial.DidNotConvergeException(String) - Constructor for exception com.imsl.math.ZeroPolynomial.DidNotConvergeException
ZeroPolynomial.DidNotConvergeException(String, Object[]) - Constructor for exception com.imsl.math.ZeroPolynomial.DidNotConvergeException
ZerosFunction - Class in com.imsl.math: Finds the real zeros of a real, continuous, univariate function, f(x).
ZerosFunction() - Constructor for class com.imsl.math.ZerosFunction: Creates an instance of the solver.
ZerosFunction.Function - Interface in com.imsl.math: Public interface for the user supplied function to ZerosFunction.
ZeroSystem - Class in com.imsl.math: Solves a system of n nonlinear equations f(x) = 0 using a modified Powell hybrid algorithm.
ZeroSystem(int) - Constructor for class com.imsl.math.ZeroSystem: Creates an object to find the zeros of a system of n equations.
ZeroSystem.DidNotConvergeException - Exception in com.imsl.math: The iteration did not converge.
ZeroSystem.DidNotConvergeException(String) - Constructor for exception com.imsl.math.ZeroSystem.DidNotConvergeException
ZeroSystem.DidNotConvergeException(String, Object[]) - Constructor for exception com.imsl.math.ZeroSystem.DidNotConvergeException
ZeroSystem.Function - Interface in com.imsl.math: Public interface for user supplied function to ZeroSystem object.
ZeroSystem.Jacobian - Interface in com.imsl.math: Public interface for user supplied function to ZeroSystem object.
ZeroSystem.ToleranceTooSmallException - Exception in com.imsl.math: Tolerance too small
ZeroSystem.ToleranceTooSmallException(String, Object[]) - Constructor for exception com.imsl.math.ZeroSystem.ToleranceTooSmallException
ZeroSystem.TooManyIterationsException - Exception in com.imsl.math: Too many iterations.
ZeroSystem.TooManyIterationsException() - Constructor for exception com.imsl.math.ZeroSystem.TooManyIterationsException
ZeroSystem.TooManyIterationsException(String, Object[]) - Constructor for exception com.imsl.math.ZeroSystem.TooManyIterationsException
ZeroSystem.TooManyIterationsException(Object[]) - Constructor for exception com.imsl.math.ZeroSystem.TooManyIterationsException

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