Index (JMSL Numerical Library (jmsl) 2025.1.0 API)

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
All Classes and Interfaces|All Packages|Constant Field Values|Serialized Form

A

abs(double) - Static method in class com.imsl.math.JMath: Returns the absolute value of a double.
abs(float) - Static method in class com.imsl.math.JMath: Returns the absolute value of a float.
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(Complex) - Static method in class com.imsl.math.Complex: Returns the absolute value (modulus) of a Complex, |z|.
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 - Enum constant in enum 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.
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(double) - Static method in class com.imsl.math.JMath: Returns the inverse (arc) cosine of a double.
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.
acosh(double) - Static method in class com.imsl.math.Hyperbolic: Returns the inverse hyperbolic cosine of its argument.
acosh(Complex) - Static method in class com.imsl.math.Complex: $ \DeclareMathOperator{\arccosh}{arccosh} $ Returns the inverse hyperbolic cosine (arc cosh) of a Complex, with a branch cut at values less than one along the real axis.
Activation - Interface in com.imsl.datamining.neural: Interface implemented by perceptron activation functions.
ADABOOST - Enum constant in enum class com.imsl.datamining.GradientBoosting.LossFunctionType: The loss criteria is the AdaBoost.M1 criterion.
add(double[][], double[][]) - Static method in class com.imsl.math.Matrix: Add two rectangular arrays, a + b.
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, $C \leftarrow \alpha A + \beta B.$
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, double) - Static method in class com.imsl.math.Complex: Returns the sum of a Complex and a double, x+y.
add(Complex, Complex) - Static method in class com.imsl.math.Complex: Returns the sum of two Complex objects, x+y.
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(Physical, Physical) - Static method in class com.imsl.math.Physical: Add two compatible Physical objects.
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.
addNode(Node) - Method in class com.imsl.datamining.neural.Layer: Associates a Perceptron with this Layer.
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.
ADJUSTED_R_SQUARED_CRITERION - Static variable in class com.imsl.stat.SelectionRegression: Indicates $R^2_a$ (adjusted $R^2$) 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.
aggregateModels(LogisticRegressionModelObject) - Method in class com.imsl.datamining.LogisticRegressionModelObject: Aggregates or combines a different model object to this logistic regression model object.
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.
ALACART(ALACART) - Constructor for class com.imsl.datamining.decisionTree.ALACART: Constructs a copy of the input ALACART decision tree.
ALL - Static variable in class com.imsl.stat.RegressorsForGLM: The n indicator variables are the dummy variables.
AllConstraintsNotSatisfiedException() - Constructor for exception com.imsl.math.DenseLP.AllConstraintsNotSatisfiedException: All constraints are not satisfied.
AllConstraintsNotSatisfiedException(String) - Constructor for exception com.imsl.math.DenseLP.AllConstraintsNotSatisfiedException: All constraints are not satisfied.
AllConstraintsNotSatisfiedException(String, Object[]) - Constructor for exception com.imsl.math.DenseLP.AllConstraintsNotSatisfiedException: All constraints are not satisfied.
allConverged() - Method in class com.imsl.math.ZeroFunction: Deprecated.

Returns true if the iterations for all of the roots have converged.
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.
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.
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.
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.
amultp(double[], double[]) - Method in class com.imsl.test.example.math.ConjugateGradientEx1
amultp(double[], double[]) - Method in class com.imsl.test.example.math.ConjugateGradientEx2
amultp(double[], double[]) - Method in class com.imsl.test.example.math.GenMinResEx1: Obtains the multiplication of the matrix a and the input p.
amultp(double[], double[]) - Method in class com.imsl.test.example.math.GenMinResEx2: Multiplies the matrix a and the input vector p.
amultp(double[], double[]) - Method in class com.imsl.test.example.math.GenMinResEx3: Obtains the multiplication of the matrix a and the input p.
amultp(double[], double[]) - Method in class com.imsl.test.example.math.GenMinResEx4: Obtains the multiplication of the matrix a and the input p.
amultp(double[], double[]) - Method in class com.imsl.test.example.math.GenMinResEx5: Obtains the multiplication of the matrix a and the input p.
amultp(double[], double[]) - Method in class com.imsl.test.example.math.GenMinResEx6: Obtains the multiplication of the matrix a and the input p.
amultp(double[], double[]) - Method in class com.imsl.test.example.math.GenMinResEx7: Obtains the multiplication of the matrix a and the input p.
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.
ANCOVAEx1 - Class in com.imsl.test.example.stat: Performs a one-way analysis of covariance.
ANCOVAEx1() - Constructor for class com.imsl.test.example.stat.ANCOVAEx1
ANCOVAEx2 - Class in com.imsl.test.example.stat: Performs one-way analysis of covariance and tests for parallelism.
ANCOVAEx2() - Constructor for class com.imsl.test.example.stat.ANCOVAEx2
ANDERSON_RUBIN - Enum constant in enum class com.imsl.stat.FactorAnalysis.ScoreMethod: Use the method of Anderson and Rubin.
ANGLE_IN_RADIANS - Static variable in class com.imsl.stat.Dissimilarities: Indicates the angle in radians (0, $\pi$) between the lines through the origin defined by the vectors distance method.
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.
ANOVAEx1 - Class in com.imsl.test.example.stat: Performs a one-way analysis of variance.
ANOVAEx1() - Constructor for class com.imsl.test.example.stat.ANOVAEx1
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.
ANOVAFactorialEx1 - Class in com.imsl.test.example.stat: Performs a two-way factorial analysis of variance.
ANOVAFactorialEx1() - Constructor for class com.imsl.test.example.stat.ANOVAFactorialEx1
ANOVAFactorialEx2 - Class in com.imsl.test.example.stat: Performs a two-way factorial analysis of variance with additional printed output.
ANOVAFactorialEx2() - Constructor for class com.imsl.test.example.stat.ANOVAFactorialEx2
ANOVAFactorialEx3 - Class in com.imsl.test.example.stat: Performs a three-way factorial analysis of variance.
ANOVAFactorialEx3() - Constructor for class com.imsl.test.example.stat.ANOVAFactorialEx3
ApproximateMinimumException(String) - Constructor for exception com.imsl.math.MinUnconMultiVar.ApproximateMinimumException: Constructs a ApproximateMinimumException object.
ApproximateMinimumException(String, Object[]) - Constructor for exception com.imsl.math.MinUnconMultiVar.ApproximateMinimumException: Constructs a ApproximateMinimumException object.
Apriori - Class in com.imsl.datamining: Performs the Apriori algorithm for association rule discovery.
AprioriEx1 - Class in com.imsl.test.example.datamining: Finds frequent itemsets and strong association rules for a small set of transactions.
AprioriEx1() - Constructor for class com.imsl.test.example.datamining.AprioriEx1
AprioriEx2 - Class in com.imsl.test.example.datamining: Applies the Apriori algorithm to separate sets of transactions.
AprioriEx2() - Constructor for class com.imsl.test.example.datamining.AprioriEx2
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.Formatter - Class in com.imsl.stat: Deprecated.
Use IMSLFormatter instead.
ARAutoUnivariate.TriangularMatrixSingularException - Exception in com.imsl.stat: The input triangular matrix is singular.
ARAutoUnivariateEx1 - Class in com.imsl.test.example.stat: Finds the minimum AIC autoregressive model for the Wolfer sunspot data.
ARAutoUnivariateEx1() - Constructor for class com.imsl.test.example.stat.ARAutoUnivariateEx1
ARAutoUnivariateEx2 - Class in com.imsl.test.example.stat: Finds the minimum AIC autoregressive model for the Canadian lynx data.
ARAutoUnivariateEx2() - Constructor for class com.imsl.test.example.stat.ARAutoUnivariateEx2
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.IncreaseErrRelException - Exception in com.imsl.stat: The bound for the relative error is too small.
ARMA.MatrixSingularException - Exception in com.imsl.stat: The input matrix is singular.
ARMA.NewInitialGuessException - Exception in com.imsl.stat: The iteration has not made good progress.
ARMA.ResidualsTooLargeException - Exception in com.imsl.stat: The residuals have become too large in one step of the Least Squares estimation of the ARMA coefficients.
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.TooManyFcnEvalException - Exception in com.imsl.stat: Maximum number of function evaluations exceeded.
ARMA.TooManyITNException - Exception in com.imsl.stat: Maximum number of iterations exceeded.
ARMA.TooManyJacobianEvalException - Exception in com.imsl.stat: Maximum number of Jacobian evaluations exceeded.
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.
ARMAEstimateMissingEx1 - Class in com.imsl.test.example.stat: Estimates missing values for a generated $ \text{AR}(1) $ series.
ARMAEstimateMissingEx1() - Constructor for class com.imsl.test.example.stat.ARMAEstimateMissingEx1
ARMAEx1 - Class in com.imsl.test.example.stat: Fits an $\text{ARMA}(2,1)$ to the Wolfer sunspot data using the method of moments.
ARMAEx1() - Constructor for class com.imsl.test.example.stat.ARMAEx1
ARMAEx2 - Class in com.imsl.test.example.stat: Fits an $\text{ARMA}(2,1)$ to the Wolfer sunspot data using the method of least squares.
ARMAEx2() - Constructor for class com.imsl.test.example.stat.ARMAEx2
ARMAEx3 - Class in com.imsl.test.example.stat: Fits an $\text{ARMA}(2,1)$ to the Wolfer sunspot data and produces a forecast table.
ARMAEx3() - Constructor for class com.imsl.test.example.stat.ARMAEx3
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.NonInvertibleException - Exception in com.imsl.stat: The solution is noninvertible.
ARMAMaxLikelihood.NonStationaryException - Exception in com.imsl.stat: The solution is nonstationary.
ARMAMaxLikelihoodEx1 - Class in com.imsl.test.example.stat: Fits an $\text{ARMA}(2,1)$ to the Wolfer sunspot data using the method of maximum likelihood.
ARMAMaxLikelihoodEx1() - Constructor for class com.imsl.test.example.stat.ARMAMaxLikelihoodEx1
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.
ARMAOutlierIdentificationEx1 - Class in com.imsl.test.example.stat: Performs outlier identification and simultaneously fits an $\text{ARIMA}(2,2,0)$ to the Canadian Lynx dataset.
ARMAOutlierIdentificationEx1() - Constructor for class com.imsl.test.example.stat.ARMAOutlierIdentificationEx1
ARMAOutlierIdentificationEx2 - Class in com.imsl.test.example.stat: Performs outlier identification on an $ \text{ARMA}(1,1)$ process contaminated by outliers.
ARMAOutlierIdentificationEx2() - Constructor for class com.imsl.test.example.stat.ARMAOutlierIdentificationEx2
ARMAOutlierIdentificationEx3 - Class in com.imsl.test.example.stat: Forecasts an $\text{ARMA}(2,1)$ time series contaminated by outliers.
ARMAOutlierIdentificationEx3() - Constructor for class com.imsl.test.example.stat.ARMAOutlierIdentificationEx3
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.
ARSeasonalFitEx1 - Class in com.imsl.test.example.stat: Searches for the best fit seasonality for the Airline data.
ARSeasonalFitEx1() - Constructor for class com.imsl.test.example.stat.ARSeasonalFitEx1
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 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 by specified keys and returns the permutation vector.
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(double[], int[]) - Static method in class com.imsl.stat.Sort: Sorts an array into ascending order and returns the permutation vector.
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 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 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 according to the first nKeys keys and returns the permutation vector.
ascending(int[], int[]) - Static method in class com.imsl.stat.Sort: Sorts an integer array into ascending order and returns the permutation vector.
asin(double) - Static method in class com.imsl.math.JMath: Returns the inverse (arc) sine of a double.
asin(Complex) - Static method in class com.imsl.math.Complex: $ \DeclareMathOperator{\arcsinh}{arcsinh} $ Returns the inverse sine (arc sine) of a Complex, with branch cuts outside the interval [-1,1] along the real axis.
asinh(double) - Static method in class com.imsl.math.Hyperbolic: Returns the inverse hyperbolic sine of its argument.
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].
AssociationRule - Class in com.imsl.datamining: Contains association rules discovered by the Apriori algorithm.
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(double) - Static method in class com.imsl.math.JMath: Returns the inverse (arc) tangent of a double.
atan(Complex) - Static method in class com.imsl.math.Complex: $ \DeclareMathOperator{\arctanh}{arctanh} $ Returns the inverse tangent (arc tangent) of a Complex, with branch cuts outside the interval [-i,i] along the imaginary axis.
atan2(double, double) - Static method in class com.imsl.math.JMath: Returns the angle corresponding to a Cartesian point.
atanh(double) - Static method in class com.imsl.math.Hyperbolic: Returns the inverse hyperbolic tangent of its argument.
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.
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.
AutoARIMAEx1 - Class in com.imsl.test.example.stat: Searches for the best fitting non-seasonal $ \text{ARIMA} $.
AutoARIMAEx1() - Constructor for class com.imsl.test.example.stat.AutoARIMAEx1
AutoARIMAEx2 - Class in com.imsl.test.example.stat: Searches for the best fitting $\text{ARIMA}(p,d,q)$ model.
AutoARIMAEx2() - Constructor for class com.imsl.test.example.stat.AutoARIMAEx2
AutoARIMAEx3 - Class in com.imsl.test.example.stat: Fits an $\text{ARIMA}(p,d,q)$ model with fixed parameter values.
AutoARIMAEx3() - Constructor for class com.imsl.test.example.stat.AutoARIMAEx3
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.
AutoCorrelationEx1 - Class in com.imsl.test.example.stat: Computes autocorrelations of the Wolfer sunspot data.
AutoCorrelationEx1() - Constructor for class com.imsl.test.example.stat.AutoCorrelationEx1
AVERAGE - Enum constant in enum class com.imsl.stat.TimeSeriesOperations.CombineMethod: Takes the average of the two values.

B

backshift(TimeSeries, int) - Method in class com.imsl.stat.TimeSeriesOperations: Returns the backshifted version of the time series.
backward(double[]) - Method in class com.imsl.math.FFT: Compute the real periodic sequence from its Fourier coefficients.
backward(Complex[]) - Method in class com.imsl.math.ComplexFFT: Compute the complex periodic sequence from its Fourier coefficients.
BACKWARD_REGRESSION - Static variable in class com.imsl.stat.StepwiseRegression: Indicates backward regression.
BadInitialGuessException(String) - Constructor for exception com.imsl.math.MinConNLP.BadInitialGuessException: Constructs a BadInitialGuessException object.
BadInitialGuessException(String, Object[]) - Constructor for exception com.imsl.math.MinConNLP.BadInitialGuessException: Constructs a BadInitialGuessException object.
BadVarianceException(String) - Constructor for exception com.imsl.stat.FactorAnalysis.BadVarianceException: Constructs a BadVarianceException object.
BadVarianceException(String, Object[]) - Constructor for exception com.imsl.stat.FactorAnalysis.BadVarianceException: Constructs a BadVarianceException object.
BARTLETT - Enum constant in enum class com.imsl.stat.FactorAnalysis.ScoreMethod: Use Bartlett's weighted least squares method.
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 - Enum constant in enum 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.
BesselEx1 - Class in com.imsl.test.example.math: Evaluates the Bessel functions I, J, and K.
BesselEx1() - Constructor for class com.imsl.test.example.math.BesselEx1
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: Returns 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.
BetaPDEx1 - Class in com.imsl.test.example.stat.distributions: Evaluates the beta probability distribution.
BetaPDEx1() - Constructor for class com.imsl.test.example.stat.distributions.BetaPDEx1
betaProb(double, double, double) - Static method in class com.imsl.stat.Cdf: Deprecated.
Use Pdf.beta(double, double, double) instead.
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.
BinaryClassificationEx1 - Class in com.imsl.test.example.datamining.neural: Trains a 3-layer network with a binary output variable and 4 categorical input attributes.
BinaryClassificationEx1() - Constructor for class com.imsl.test.example.datamining.neural.BinaryClassificationEx1
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.
BinomialPD - Class in com.imsl.stat.distributions: The binomial probability distribution.
BinomialPD() - Constructor for class com.imsl.stat.distributions.BinomialPD: Constructor for the binomial probability distribution.
BinomialPDEx1 - Class in com.imsl.test.example.stat.distributions: Evaluates the binomial probability distribution.
BinomialPDEx1() - Constructor for class com.imsl.test.example.stat.distributions.BinomialPDEx1
binomialProb(int, int, double) - Static method in class com.imsl.stat.Cdf: Deprecated.
Use Pdf.binomial(int, int, double) instead.
bivariateNormal(double, double, double) - Static method in class com.imsl.stat.Cdf: Evaluates the bivariate normal cumulative probability distribution function.
BlockGradObjective() - Constructor for class com.imsl.datamining.neural.QuasiNewtonTrainer.BlockGradObjective
BlockObjective() - Constructor for class com.imsl.datamining.neural.QuasiNewtonTrainer.BlockObjective
Bond - Class in com.imsl.finance: Collection of bond functions.
BondAccruedInterestEx1 - Class in com.imsl.test.example.finance: Computes the accrued interest on a bond paying semiannually.
BondAccruedInterestEx1() - Constructor for class com.imsl.test.example.finance.BondAccruedInterestEx1
BondAccruedInterestEx2 - Class in com.imsl.test.example.finance: Computes the accrued interest on a bond paying at maturity.
BondAccruedInterestEx2() - Constructor for class com.imsl.test.example.finance.BondAccruedInterestEx2
BondConvexityEx1 - Class in com.imsl.test.example.finance: Computes the convexity of a 10 year bond.
BondConvexityEx1() - Constructor for class com.imsl.test.example.finance.BondConvexityEx1
BondCoupdaysbsEx1 - Class in com.imsl.test.example.finance: Computes the number of days from the beginning of the period to the settlement date.
BondCoupdaysbsEx1() - Constructor for class com.imsl.test.example.finance.BondCoupdaysbsEx1
BondCoupdaysEx1 - Class in com.imsl.test.example.finance: Computes the number of days in a coupon period.
BondCoupdaysEx1() - Constructor for class com.imsl.test.example.finance.BondCoupdaysEx1
BondCoupdaysncEx1 - Class in com.imsl.test.example.finance: Computes the number of days between the settlement date and the next coupon date.
BondCoupdaysncEx1() - Constructor for class com.imsl.test.example.finance.BondCoupdaysncEx1
BondCoupncdEx1 - Class in com.imsl.test.example.finance: Computes the next coupon date after the settlement date.
BondCoupncdEx1() - Constructor for class com.imsl.test.example.finance.BondCoupncdEx1
BondCoupnumEx1 - Class in com.imsl.test.example.finance: Computes the number of payable coupons between the settlement date and the maturity date.
BondCoupnumEx1() - Constructor for class com.imsl.test.example.finance.BondCoupnumEx1
BondCouppcdEx1 - Class in com.imsl.test.example.finance: Computes the previous coupon date before the settlement date.
BondCouppcdEx1() - Constructor for class com.imsl.test.example.finance.BondCouppcdEx1
BondDepreciationEx1 - Class in com.imsl.test.example.finance: Computes the depreciation for the second accounting period.
BondDepreciationEx1() - Constructor for class com.imsl.test.example.finance.BondDepreciationEx1
BondDepreciationEx2 - Class in com.imsl.test.example.finance: Computes the depreciation for the second accounting period.
BondDepreciationEx2() - Constructor for class com.imsl.test.example.finance.BondDepreciationEx2
BondDiscEx1 - Class in com.imsl.test.example.finance: Computes the discount rate for a security.
BondDiscEx1() - Constructor for class com.imsl.test.example.finance.BondDiscEx1
BondDurationEx1 - Class in com.imsl.test.example.finance: Computes the annual duration of a 10 year bond.
BondDurationEx1() - Constructor for class com.imsl.test.example.finance.BondDurationEx1
BondIntrateEx1 - Class in com.imsl.test.example.finance: Computes the discount rate of a 10 year bond.
BondIntrateEx1() - Constructor for class com.imsl.test.example.finance.BondIntrateEx1
BondMdurationEx1 - Class in com.imsl.test.example.finance: Computes the modified Macauley duration of a 10 year bond.
BondMdurationEx1() - Constructor for class com.imsl.test.example.finance.BondMdurationEx1
BondPricediscEx1 - Class in com.imsl.test.example.finance: Computes the price of a discounted bond.
BondPricediscEx1() - Constructor for class com.imsl.test.example.finance.BondPricediscEx1
BondPriceEx1 - Class in com.imsl.test.example.finance: Computes the price of a 10 year bond paying semiannual interest.
BondPriceEx1() - Constructor for class com.imsl.test.example.finance.BondPriceEx1
BondPriceEx2 - Class in com.imsl.test.example.finance: Computes the price of a bond with an odd long first coupon.
BondPriceEx2() - Constructor for class com.imsl.test.example.finance.BondPriceEx2
BondPriceEx3 - Class in com.imsl.test.example.finance: Computes the price of a bond with an odd long last coupon and multiple coupon periods.
BondPriceEx3() - Constructor for class com.imsl.test.example.finance.BondPriceEx3
BondPriceEx4 - Class in com.imsl.test.example.finance: Computes price of a bond with an odd long last coupon and one coupon period.
BondPriceEx4() - Constructor for class com.imsl.test.example.finance.BondPriceEx4
BondPriceEx5 - Class in com.imsl.test.example.finance: Computes the price of a bond with an odd short first coupon.
BondPriceEx5() - Constructor for class com.imsl.test.example.finance.BondPriceEx5
BondPriceEx6 - Class in com.imsl.test.example.finance: Computes the price of a bond with an odd short last coupon and multiple coupon periods.
BondPriceEx6() - Constructor for class com.imsl.test.example.finance.BondPriceEx6
BondPriceEx7 - Class in com.imsl.test.example.finance: Computes the price of a bond with an odd short last coupon and one or less coupon periods to redemption.
BondPriceEx7() - Constructor for class com.imsl.test.example.finance.BondPriceEx7
BondPricematEx1 - Class in com.imsl.test.example.finance: Computes the price of a bond paying interest at maturity.
BondPricematEx1() - Constructor for class com.imsl.test.example.finance.BondPricematEx1
BondPriceyieldEx1 - Class in com.imsl.test.example.finance: Computes the price of a discounted 1 year bond.
BondPriceyieldEx1() - Constructor for class com.imsl.test.example.finance.BondPriceyieldEx1
BondReceivedEx1 - Class in com.imsl.test.example.finance: Computes the amount to be received at maturity for a 10 year bond.
BondReceivedEx1() - Constructor for class com.imsl.test.example.finance.BondReceivedEx1
BondTbilleqEx1 - Class in com.imsl.test.example.finance: Computes the bond-equivalent yield for a treasury bill.
BondTbilleqEx1() - Constructor for class com.imsl.test.example.finance.BondTbilleqEx1
BondTbillpriceEx1 - Class in com.imsl.test.example.finance: Computes the price of a 1 year treasury bill.
BondTbillpriceEx1() - Constructor for class com.imsl.test.example.finance.BondTbillpriceEx1
BondTbillyieldEx1 - Class in com.imsl.test.example.finance: Computes the yield for a 1 year treasury bill.
BondTbillyieldEx1() - Constructor for class com.imsl.test.example.finance.BondTbillyieldEx1
BondYearfracEx1 - Class in com.imsl.test.example.finance: Computes a specified year fraction.
BondYearfracEx1() - Constructor for class com.imsl.test.example.finance.BondYearfracEx1
BondYielddiscEx1 - Class in com.imsl.test.example.finance: Computes the yield on a discounted 10 year bond.
BondYielddiscEx1() - Constructor for class com.imsl.test.example.finance.BondYielddiscEx1
BondYieldEx1 - Class in com.imsl.test.example.finance: Computes the yield on a 10 year bond paying semiannually.
BondYieldEx1() - Constructor for class com.imsl.test.example.finance.BondYieldEx1
BondYieldEx2 - Class in com.imsl.test.example.finance: Computes the yield of a bond with an odd long first coupon.
BondYieldEx2() - Constructor for class com.imsl.test.example.finance.BondYieldEx2
BondYieldEx3 - Class in com.imsl.test.example.finance: Computes the yield of a bond with an odd long last coupon and multiple periods.
BondYieldEx3() - Constructor for class com.imsl.test.example.finance.BondYieldEx3
BondYieldEx4 - Class in com.imsl.test.example.finance: Computes the yield of a bond with an odd long last coupon and one coupon period.
BondYieldEx4() - Constructor for class com.imsl.test.example.finance.BondYieldEx4
BondYieldEx5 - Class in com.imsl.test.example.finance: Computes the yield of a bond with an odd short first coupon.
BondYieldEx5() - Constructor for class com.imsl.test.example.finance.BondYieldEx5
BondYieldEx6 - Class in com.imsl.test.example.finance: Computes the yield of a bond with an odd short last coupon and multiple coupon periods.
BondYieldEx6() - Constructor for class com.imsl.test.example.finance.BondYieldEx6
BondYieldEx7 - Class in com.imsl.test.example.finance: Computes the yield of a bond with an odd short last coupon and one or fewer coupon periods.
BondYieldEx7() - Constructor for class com.imsl.test.example.finance.BondYieldEx7
BondYieldmatEx1 - Class in com.imsl.test.example.finance: Computes the yield on a bond paying at maturity.
BondYieldmatEx1() - Constructor for class com.imsl.test.example.finance.BondYieldmatEx1
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.
BootstrapAggregationEx1 - Class in com.imsl.test.example.datamining: Performs bootstrap aggregation on a decision tree.
BootstrapAggregationEx1() - Constructor for class com.imsl.test.example.datamining.BootstrapAggregationEx1
BootstrapAggregationEx2 - Class in com.imsl.test.example.datamining: Performs bootstrap aggregation on a logistic regression model.
BootstrapAggregationEx2() - Constructor for class com.imsl.test.example.datamining.BootstrapAggregationEx2
BoundaryInconsistentException(String, Object[]) - Constructor for exception com.imsl.math.FeynmanKac.BoundaryInconsistentException: The boundary conditions are inconsistent.
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.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.
BoundedLeastSquaresEx1 - Class in com.imsl.test.example.math: Solves a nonlinear least squares problem subject to bounds.
BoundedLeastSquaresEx1() - Constructor for class com.imsl.test.example.math.BoundedLeastSquaresEx1
BoundedLeastSquaresEx2 - Class in com.imsl.test.example.math: Solves a nonlinear least squares problem subject to bounds with a supplied Jacobian and initial guess.
BoundedLeastSquaresEx2() - Constructor for class com.imsl.test.example.math.BoundedLeastSquaresEx2
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.
BoundedVariableLeastSquaresEx1 - Class in com.imsl.test.example.math: Solves a linear least squares problem with bounds on the variables.
BoundedVariableLeastSquaresEx1() - Constructor for class com.imsl.test.example.math.BoundedVariableLeastSquaresEx1
BoundsInconsistentException(String) - Constructor for exception com.imsl.math.DenseLP.BoundsInconsistentException: The bounds given are inconsistent.
BoundsInconsistentException(String) - Constructor for exception com.imsl.math.LinearProgramming.BoundsInconsistentException: Deprecated.
BoundsInconsistentException(String, Object[]) - Constructor for exception com.imsl.math.DenseLP.BoundsInconsistentException: The bounds given are inconsistent.
BoundsInconsistentException(String, Object[]) - Constructor for exception com.imsl.math.LinearProgramming.BoundsInconsistentException: Deprecated.
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.
BsInterpolateEx1 - Class in com.imsl.test.example.math: Fits a B-spline to data.
BsInterpolateEx1() - Constructor for class com.imsl.test.example.math.BsInterpolateEx1
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.
BsLeastSquaresEx1 - Class in com.imsl.test.example.math: Fits a least squares B-spline to data.
BsLeastSquaresEx1() - Constructor for class com.imsl.test.example.math.BsLeastSquaresEx1
BSpline - Class in com.imsl.math: BSpline represents and evaluates univariate B-splines.
BSpline() - Constructor for class com.imsl.math.BSpline
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.
C45(C45) - Constructor for class com.imsl.datamining.decisionTree.C45: Constructs a copy of the input C45 decision tree.
cancelRowUpdates() - Method in class com.imsl.io.AbstractFlatFile: Cancels the updates made to the current row in this ResultSet object.
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.
canonicalCorrelationSTC(double, double[][], double[][]) - Static method in class com.imsl.stat.Random: Deprecated.
CaseStatistics(double[], double) - Constructor for class com.imsl.stat.LinearRegression.CaseStatistics: Deprecated.
The CaseStatistics constructors have been deprecated in favor of getter methods in LinearRegression.
CaseStatistics(double[], double, double) - Constructor for class com.imsl.stat.LinearRegression.CaseStatistics: Deprecated.
The CaseStatistics constructors have been deprecated in favor of getter methods in LinearRegression.
CaseStatistics(double[], double, double, int) - Constructor for class com.imsl.stat.LinearRegression.CaseStatistics: Deprecated.
The CaseStatistics constructors have been deprecated in favor of getter methods in LinearRegression.
CaseStatistics(double[], double, int) - Constructor for class com.imsl.stat.LinearRegression.CaseStatistics: Deprecated.
The CaseStatistics constructors have been deprecated in favor of getter methods in LinearRegression.
CATEGORICAL - Enum constant in enum class com.imsl.datamining.PredictiveModel.VariableType: The associated variable can assume one of a limited number of values (categories).
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.ClassificationVariableLimitException - Exception in com.imsl.stat: The Classification Variable limit set by the user through setUpperBound has been exceeded.
CategoricalGenLinModel.ClassificationVariableValueException - Exception in com.imsl.stat: The number of distinct values for each Classification Variable must be greater than 1.
CategoricalGenLinModel.DeleteObservationsException - Exception in com.imsl.stat: The number of observations to be deleted (set by setObservationMax) has grown too large.
CategoricalGenLinModel.RankDeficientException - Exception in com.imsl.stat: The model has been determined to be rank deficient.
CategoricalGenLinModelEx1 - Class in com.imsl.test.example.stat: Fits a logit and probit categorical model to beetle mortality data.
CategoricalGenLinModelEx1() - Constructor for class com.imsl.test.example.stat.CategoricalGenLinModelEx1
CategoricalGenLinModelEx2 - Class in com.imsl.test.example.stat: Analyzes interval type data with the Poisson model.
CategoricalGenLinModelEx2() - Constructor for class com.imsl.test.example.stat.CategoricalGenLinModelEx2
cdf(double) - Method in interface com.imsl.stat.CdfFunction: Public interface for the user-supplied cumulative distribution function to be used by InverseCdf.
cdf(double) - Method in class com.imsl.test.example.stat.InverseCdfEx1
cdf(double) - Method in class com.imsl.test.example.stat.RandomEx1
Cdf - Class in com.imsl.stat: Cumulative probability distribution functions.
CdfEx1 - Class in com.imsl.test.example.stat: Evaluates various cumulative distribution functions.
CdfEx1() - Constructor for class com.imsl.test.example.stat.CdfEx1
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.
CHAID(CHAID) - Constructor for class com.imsl.datamining.decisionTree.CHAID: Constructs a copy of the input CHAID decision tree.
checkCompatibility(Physical, Physical) - Static method in class com.imsl.math.Physical: Checks the compatibility of two Physical objects.
checkMatrix(double[][]) - Static method in class com.imsl.math.Matrix: Check that all of the rows in the matrix have the same length.
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: Deprecated.
Use Matrix.checkMatrix(double[][]) instead.
CheckMatrix(Complex[][]) - Static method in class com.imsl.math.ComplexMatrix: Deprecated.
Use ComplexMatrix.checkMatrix(Complex[][]) instead.
checkSquareMatrix() - Method in class com.imsl.math.ComplexSparseMatrix: Check that the matrix is square.
checkSquareMatrix() - Method in class com.imsl.math.SparseMatrix: Check that the matrix is square.
checkSquareMatrix(double[][]) - Static method in class com.imsl.math.Matrix: Check that the matrix is square.
checkSquareMatrix(Complex[][]) - Static method in class com.imsl.math.ComplexMatrix: Check that the Complex matrix is square.
CheckSquareMatrix(double[][]) - Static method in class com.imsl.math.Matrix: Deprecated.
Use Matrix.checkSquareMatrix(double[][]) instead.
CheckSquareMatrix(Complex[][]) - Static method in class com.imsl.math.ComplexMatrix: Deprecated.
Use ComplexMatrix.checkSquareMatrix(Complex[][]) instead.
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: Returns 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
chiProb(double, double) - Static method in class com.imsl.stat.Cdf: Deprecated.
Use Pdf.chi(double, double) instead.
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.NoObservationsException - Exception in com.imsl.stat: There are no observations.
ChiSquaredTest.NotCDFException - Exception in com.imsl.stat: The function is not a Cumulative Distribution Function (CDF).
ChiSquaredTestEx1 - Class in com.imsl.test.example.stat: Performs a chi-squared test on simulated data.
ChiSquaredTestEx1() - Constructor for class com.imsl.test.example.stat.ChiSquaredTestEx1
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.
CholeskyEx1 - Class in com.imsl.test.example.math: Solves a system using Cholesky factorization.
CholeskyEx1() - Constructor for class com.imsl.test.example.math.CholeskyEx1
CholeskyFactorizationAccuracyException(String) - Constructor for exception com.imsl.math.SparseLP.CholeskyFactorizationAccuracyException: The Cholesky factorization failed because of accuracy problems.
CholeskyFactorizationAccuracyException(String, Object[]) - Constructor for exception com.imsl.math.SparseLP.CholeskyFactorizationAccuracyException: The Cholesky factorization failed because of accuracy problems.
classError(double[], int[], int) - Method in class com.imsl.datamining.NaiveBayesClassifier: Returns the classification probability error for the input pattern and known target classification.
ClassificationVariableException() - Constructor for exception com.imsl.stat.CategoricalGenLinModel.ClassificationVariableException: Constructs a ClassificationVariableException.
ClassificationVariableLimitException(int) - Constructor for exception com.imsl.stat.CategoricalGenLinModel.ClassificationVariableLimitException: Constructs a ClassificationVariableLimitException.
ClassificationVariableLimitException(String) - Constructor for exception com.imsl.stat.ProportionalHazards.ClassificationVariableLimitException: Constructs a ClassificationVariableLimitException.
ClassificationVariableLimitException(String, Object[]) - Constructor for exception com.imsl.stat.ProportionalHazards.ClassificationVariableLimitException: The Classification Variable limit set by the user through setUpperBound has been exceeded.
ClassificationVariableValueException(int, int) - Constructor for exception com.imsl.stat.CategoricalGenLinModel.ClassificationVariableValueException: Constructs a ClassificationVariableValueException.
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.ClusterKNN: Classify a set of observations using k nearest neighbors.
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[]) - 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[], 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[], 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.
classify(double[], int) - Method in class com.imsl.stat.ClusterKNN: Classify an observation using k nearest neighbors.
clearWarnings() - Method in class com.imsl.io.AbstractFlatFile: Clears all warnings reported on this ResultSet object.
clone() - Method in class com.imsl.datamining.decisionTree.ALACART: Clones an ALACART decision tree.
clone() - Method in class com.imsl.datamining.decisionTree.C45: Clones a C45 decision tree.
clone() - Method in class com.imsl.datamining.decisionTree.CHAID: Clones a CHAID decision tree.
clone() - Method in class com.imsl.datamining.decisionTree.QUEST: Clones a QUEST decision tree.
clone() - Method in class com.imsl.datamining.decisionTree.RandomTrees: Clones a RandomTrees predictive model.
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.GradientBoosting: Clones a GradientBoosting predictive model.
clone() - Method in class com.imsl.datamining.LogisticRegression: Clones a LogisticRegression predictive model.
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.datamining.PredictiveModel: Abstract clone method.
clone() - Method in class com.imsl.datamining.supportvectormachine.Kernel: Returns a clone of this object.
clone() - Method in class com.imsl.datamining.supportvectormachine.LinearKernel: Clones a LinearKernel kernel.
clone() - Method in class com.imsl.datamining.supportvectormachine.PolynomialKernel: Clones a PolynomialKernel kernel.
clone() - Method in class com.imsl.datamining.supportvectormachine.RadialBasisKernel: Clones a RadialBasisKernel kernel.
clone() - Method in class com.imsl.datamining.supportvectormachine.SigmoidKernel: Clones a SigmoidKernel kernel.
clone() - Method in class com.imsl.datamining.supportvectormachine.SVClassification: Clones an SVClassification predictive model.
clone() - Method in class com.imsl.datamining.supportvectormachine.SVOneClass: Clones an SVOneClass predictive model.
clone() - Method in class com.imsl.datamining.supportvectormachine.SVRegression: Clones an SVRegression predictive model.
clone() - Method in class com.imsl.math.DenseLP: Creates and returns a copy of this object.
clone() - Method in class com.imsl.math.LinearProgramming: Deprecated.

Creates and returns a copy of this object.
clone() - Method in class com.imsl.stat.DBSCAN: Clones a DBSCAN cluster object.
clone() - Method in class com.imsl.stat.DBSCAN.DBSCANParams: Clones a DBSCANParams 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.
CloneNotSupportedException(String) - Constructor for exception com.imsl.datamining.PredictiveModel.CloneNotSupportedException: Constructs a CloneNotSupportedException and issues the specified message.
CloneNotSupportedException(String, Object[]) - Constructor for exception com.imsl.datamining.PredictiveModel.CloneNotSupportedException: Constructs a CloneNotSupportedException with the specified detail message.
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.
ClusterHierarchical(double[][], int, int) - Constructor for class com.imsl.stat.ClusterHierarchical: Deprecated.
Use ClusterHierarchical(double[][]) instead.
ClusterHierarchicalEx1 - Class in com.imsl.test.example.stat: Performs hierarchical clustering on Fisher's iris data.
ClusterHierarchicalEx1() - Constructor for class com.imsl.test.example.stat.ClusterHierarchicalEx1
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(double[][], int) - Constructor for class com.imsl.stat.ClusterKMeans: Constructor for ClusterKMeans using the K-means++ algorithm to select the initial seeds.
ClusterKMeans(double[][], int, Random) - Constructor for class com.imsl.stat.ClusterKMeans: Constructor for ClusterKMeans using the K-means++ algorithm to set the initial seeds.
ClusterKMeans.ClusterNoPointsException - Exception in com.imsl.stat: There is a cluster with no points
ClusterKMeans.NoConvergenceException - Exception in com.imsl.stat: Convergence did not occur within the maximum number of iterations.
ClusterKMeans.NonnegativeFreqException - Exception in com.imsl.stat: Deprecated.
No longer used, replaced with an IllegalArgumentException.
ClusterKMeans.NonnegativeWeightException - Exception in com.imsl.stat: Deprecated.
No longer used, replaced with an IllegalArgumentException.
ClusterKMeansEx1 - Class in com.imsl.test.example.stat: Performs K-Means clustering on Fisher's iris data.
ClusterKMeansEx1() - Constructor for class com.imsl.test.example.stat.ClusterKMeansEx1
ClusterKMeansEx2 - Class in com.imsl.test.example.stat: Performs K-Means++ clustering on Fisher's iris data.
ClusterKMeansEx2() - Constructor for class com.imsl.test.example.stat.ClusterKMeansEx2
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.
ClusterKNNEx1 - Class in com.imsl.test.example.stat: Performs K-Nearest Neighbor clustering on Fisher's iris data.
ClusterKNNEx1() - Constructor for class com.imsl.test.example.stat.ClusterKNNEx1
ClusterNoPointsException(String) - Constructor for exception com.imsl.stat.ClusterKMeans.ClusterNoPointsException: Constructs a ClusterNoPointsException object.
ClusterNoPointsException(String, Object[]) - Constructor for exception com.imsl.stat.ClusterKMeans.ClusterNoPointsException: Constructs a ClusterNoPointsException object.
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.
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.
com.imsl - package com.imsl: Utilities used by the library.
com.imsl.datamining - package com.imsl.datamining: Data mining and machine learning.
com.imsl.datamining.decisionTree - package com.imsl.datamining.decisionTree: Decision trees.
com.imsl.datamining.neural - package com.imsl.datamining.neural: Neural networks.
com.imsl.datamining.supportvectormachine - package com.imsl.datamining.supportvectormachine: Support vector machines.
com.imsl.finance - package com.imsl.finance: Financial computations.
com.imsl.io - package com.imsl.io: Methods for reading files.
com.imsl.math - package com.imsl.math: Mathematical functions and algorithms.
com.imsl.stat - package com.imsl.stat: Statistical methods.
com.imsl.stat.distributions - package com.imsl.stat.distributions: Probability distributions and parameter estimation.
com.imsl.test.example - package com.imsl.test.example: Warning message example.
com.imsl.test.example.datamining - package com.imsl.test.example.datamining: Data mining examples.
com.imsl.test.example.datamining.decisionTree - package com.imsl.test.example.datamining.decisionTree: Decision tree examples.
com.imsl.test.example.datamining.neural - package com.imsl.test.example.datamining.neural: Neural network examples.
com.imsl.test.example.datamining.supportvectormachine - package com.imsl.test.example.datamining.supportvectormachine: Support vector machine examples.
com.imsl.test.example.finance - package com.imsl.test.example.finance: Financial computations examples.
com.imsl.test.example.io - package com.imsl.test.example.io: Input/output examples.
com.imsl.test.example.math - package com.imsl.test.example.math: Math examples.
com.imsl.test.example.stat - package com.imsl.test.example.stat: Statistics examples.
com.imsl.test.example.stat.distributions - package com.imsl.test.example.stat.distributions: Statistical distributions examples.
compareTo(Complex) - Method in class com.imsl.math.Complex: Compares two Complex objects.
compareTo(Object) - Method in class com.imsl.math.Complex: Compares this Complex to another Object.
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() - Constructor for class com.imsl.math.Complex: Constructs a Complex equal to zero.
Complex(double) - Constructor for class com.imsl.math.Complex: Constructs a Complex with a zero imaginary part.
Complex(double, double) - Constructor for class com.imsl.math.Complex: Constructs a Complex with real and imaginary parts given by the input arguments.
Complex(Complex) - Constructor for class com.imsl.math.Complex: Constructs a Complex equal to the argument.
ComplexEigen - Class in com.imsl.math: Collection of complex Eigen System functions.
ComplexEigen(Complex[][]) - Constructor for class com.imsl.math.ComplexEigen: Constructor for the computation of the eigenvalues and eigenvectors of a complex square matrix.
ComplexEigen(ComplexEigen) - Constructor for class com.imsl.math.ComplexEigen: Copy constructor for the computation of the eigenvalues and eigenvectors of a complex square matrix.
ComplexEigen.DidNotConvergeException - Exception in com.imsl.math: The iteration did not converge.
ComplexEigenEx1 - Class in com.imsl.test.example.math: Computes the eigenvalues and eigenvectors of a complex matrix.
ComplexEigenEx1() - Constructor for class com.imsl.test.example.math.ComplexEigenEx1
ComplexEx1 - Class in com.imsl.test.example.math: Converts a real matrix to a complex matrix.
ComplexEx1() - Constructor for class com.imsl.test.example.math.ComplexEx1
ComplexFFT - Class in com.imsl.math: Complex FFT.
ComplexFFT(int) - Constructor for class com.imsl.math.ComplexFFT: Constructs a complex FFT object.
ComplexFFTEx1 - Class in com.imsl.test.example.math: Finds the Fourier coefficients of a complex sequence.
ComplexFFTEx1() - Constructor for class com.imsl.test.example.math.ComplexFFTEx1
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.
ComplexLUEx1 - Class in com.imsl.test.example.math: Computes the LU factorization of a complex matrix.
ComplexLUEx1() - Constructor for class com.imsl.test.example.math.ComplexLUEx1
ComplexMatrix - Class in com.imsl.math: Complex matrix manipulation functions.
ComplexMatrix.MatrixType - Enum Class in com.imsl.math: Indicates which matrix type is used.
ComplexMatrixEx1 - Class in com.imsl.test.example.math: Initializes and prints a complex matrix.
ComplexMatrixEx1() - Constructor for class com.imsl.test.example.math.ComplexMatrixEx1
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.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.
ComplexSparseCholeskyEx1 - Class in com.imsl.test.example.math: Uses the Cholesky factorization of a complex sparse matrix to solve a linear system.
ComplexSparseCholeskyEx1() - Constructor for class com.imsl.test.example.math.ComplexSparseCholeskyEx1
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(int, int, int[][], Complex[][]) - Constructor for class com.imsl.math.ComplexSparseMatrix: Constructs a sparse matrix from SparseArray (Java Sparse Array) data.
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.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.
ComplexSparseMatrixEx1 - Class in com.imsl.test.example.math: Performs operations on a sparse complex matrix.
ComplexSparseMatrixEx1() - Constructor for class com.imsl.test.example.math.ComplexSparseMatrixEx1
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.
ComplexSuperLUEx1 - Class in com.imsl.test.example.math: Computes the LU factorization of a sparse complex matrix.
ComplexSuperLUEx1() - Constructor for class com.imsl.test.example.math.ComplexSuperLUEx1
ComplexSVD - Class in com.imsl.math: Singular Value Decomposition (SVD) of a rectangular matrix of type Complex.
ComplexSVD(Complex[][]) - Constructor for class com.imsl.math.ComplexSVD: Construct the singular value decomposition of a rectangular matrix with default tolerance.
ComplexSVD(Complex[][], double) - Constructor for class com.imsl.math.ComplexSVD: Construct the singular value decomposition of a rectangular matrix with a given tolerance.
ComplexSVD(ComplexSVD) - Constructor for class com.imsl.math.ComplexSVD: Copy constructor for the computation of the singular value decomposition of a complex matrix.
ComplexSVD.DidNotConvergeException - Exception in com.imsl.math: The iteration did not converge
ComplexSVDEx1 - Class in com.imsl.test.example.math: Computes the SVD factorization of a complex matrix.
ComplexSVDEx1() - Constructor for class com.imsl.test.example.math.ComplexSVDEx1
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() - 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, $W_t(s,d)$.
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() - 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() - Method in class com.imsl.stat.MultidimensionalScaling: Performs the multidimensional scaling.
compute() - Method in class com.imsl.stat.MultipleComparisons: Performs Student-Newman-Keuls multiple comparisons test.
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() - Method in class com.imsl.stat.WilcoxonRankSum: Performs a Wilcoxon rank sum test using an approximate p-value calculation.
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(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(double[], int[]) - Method in class com.imsl.stat.Difference: Computes a Difference series.
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(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) - Method in class com.imsl.stat.Covariances: Computes the matrix.
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(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, double) - Method in class com.imsl.stat.DBSCAN: Clusters the observations using the DBSCAN algorithm with user-defined epsilon radius and minimum number of points per core point.
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(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(MaximumLikelihoodEstimation.OptimizationMethod) - Method in class com.imsl.stat.distributions.MaximumLikelihoodEstimation: Computes the maximum likelihood estimates with the specified optimization method.
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.
computeDBSCANParams(double) - Method in class com.imsl.stat.DBSCAN: Computes minimum number of points in a core point and epsilon radius for preponderant homogeneous data.
computeDistance(double[], double[]) - Method in interface com.imsl.stat.DBSCAN.Function: Public interface for the user-supplied function to compute the distances between points.
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 $\psi$ 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[][], double[][]) - Method in class com.imsl.datamining.neural.Network: Computes error statistics.
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.
computeZeros(ZeroFunction.Function, double[]) - Method in class com.imsl.math.ZeroFunction: Deprecated.

Returns the zeros of a univariate function.
computeZeros(ZerosFunction.Function) - Method in class com.imsl.math.ZerosFunction: Returns the zeros of a univariate function.
condition(double[][]) - Method in class com.imsl.math.LU: Return an estimate of the reciprocal of the $L_1$ condition number of a matrix.
condition(Complex[][]) - Method in class com.imsl.math.ComplexLU: Return an estimate of the reciprocal of the $L_1$ condition number.
conditionalVarianceFunction(double) - Method in class com.imsl.stat.EGARCH: Implements the function $h =\log(\sigma^2)$ for the EGARCH model.
conditionalVarianceFunction(double) - Method in class com.imsl.stat.ExtendedGARCH: Abstract specification for the conditional variance $\sigma^2_t$ function.
conditionalVarianceInverseFunction(double) - Method in class com.imsl.stat.EGARCH: Implements the inverse function $h^{-1}(f)= \exp(h) = \sigma^2$ for the EGARCH model.
conditionalVarianceInverseFunction(double) - Method in class com.imsl.stat.ExtendedGARCH: Abstract specification for the conditional variance $\sigma^2_t$ inverse function.
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.NotDefiniteAMatrixException - Exception in com.imsl.math: The input matrix A is indefinite, that is the matrix is not positive or negative definite.
ConjugateGradient.NotDefiniteJacobiPreconditionerException - Exception in com.imsl.math: The Jacobi preconditioner is not strictly positive or negative definite.
ConjugateGradient.NotDefinitePreconditionMatrixException - Exception in com.imsl.math: The Precondition matrix is indefinite.
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.
ConjugateGradientEx1 - Class in com.imsl.test.example.math: Solves a positive definite linear system using the conjugate gradient method.
ConjugateGradientEx1() - Constructor for class com.imsl.test.example.math.ConjugateGradientEx1
ConjugateGradientEx2 - Class in com.imsl.test.example.math: Solves a sparse linear system using the conjugate gradient method with preconditioning.
ConjugateGradientEx2(int, int) - Constructor for class com.imsl.test.example.math.ConjugateGradientEx2
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.
ConstraintEvaluationException(String) - Constructor for exception com.imsl.math.MinConNLP.ConstraintEvaluationException: Constructs a ConstraintEvaluationException object.
ConstraintEvaluationException(String, Object[]) - Constructor for exception com.imsl.math.MinConNLP.ConstraintEvaluationException: Constructs a ConstraintEvaluationException object.
ConstraintsInconsistentException(String) - Constructor for exception com.imsl.math.MinConGenLin.ConstraintsInconsistentException: Constructs a ConstraintsInconsistentException object.
ConstraintsInconsistentException(String, Object[]) - Constructor for exception com.imsl.math.FeynmanKac.ConstraintsInconsistentException: The constraints are inconsistent.
ConstraintsInconsistentException(String, Object[]) - Constructor for exception com.imsl.math.MinConGenLin.ConstraintsInconsistentException: Constructs a ConstraintsInconsistentException object.
ConstraintsNotSatisfiedException(String) - Constructor for exception com.imsl.math.MinConGenLin.ConstraintsNotSatisfiedException: Constructs a ConstraintsNotSatisfiedException object.
ConstraintsNotSatisfiedException(String, Object[]) - Constructor for exception com.imsl.math.MinConGenLin.ConstraintsNotSatisfiedException: Constructs a ConstraintsNotSatisfiedException object.
ConstrInconsistentException(String) - Constructor for exception com.imsl.stat.GARCH.ConstrInconsistentException: Constructs a ConstrInconsistentException object.
ConstrInconsistentException(String, Object[]) - Constructor for exception com.imsl.stat.GARCH.ConstrInconsistentException: Constructs a ConstrInconsistentException object.
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.
ContingencyTableEx1 - Class in com.imsl.test.example.stat: Performs a chi-squared test for independence.
ContingencyTableEx1() - Constructor for class com.imsl.test.example.stat.ContingencyTableEx1
ContingencyTableEx2 - Class in com.imsl.test.example.stat: Calculates a number of statistics associated with a contingency table.
ContingencyTableEx2() - Constructor for class com.imsl.test.example.stat.ContingencyTableEx2
CONTINUOUS_VARIABLE - Static variable in class com.imsl.io.MPSReader: Variable is a real number.
continuousUniform(double, double, double) - Static method in class com.imsl.stat.Cdf: Evaluates the continuous uniform cumulative distribution function.
continuousUniform(double, double, double) - Static method in class com.imsl.stat.InvCdf: Returns the inverse of the continuous uniform cumulative distribution function.
continuousUniform(double, double, double) - Static method in class com.imsl.stat.Pdf: Evaluates the continuous uniform probability density function.
ContinuousUniformPD - Class in com.imsl.stat.distributions: The continuous uniform probability distribution.
ContinuousUniformPD() - Constructor for class com.imsl.stat.distributions.ContinuousUniformPD: Constructs a continuous uniform probability distribution.
ContinuousUniformPDEx1 - Class in com.imsl.test.example.stat.distributions: Evaluates the continuous uniform probability distribution.
ContinuousUniformPDEx1() - Constructor for class com.imsl.test.example.stat.distributions.ContinuousUniformPDEx1
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.
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.
CorrectorConvergenceException(String, Object[]) - Constructor for exception com.imsl.math.FeynmanKac.CorrectorConvergenceException: Corrector failed to converge.
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(double) - Static method in class com.imsl.math.JMath: Returns the cosine of a double.
cos(Complex) - Static method in class com.imsl.math.Complex: Returns the cosine of a Complex.
cosh(double) - Static method in class com.imsl.math.Hyperbolic: Returns the hyperbolic cosine of its argument.
cosh(Complex) - Static method in class com.imsl.math.Complex: Returns the hyperbolic cosh of a Complex.
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.
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.
coupdaysbs(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.
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.DiffObsDeletedException - Exception in com.imsl.stat: Deprecated.
Covariances.MoreObsDelThanEnteredException - Exception in com.imsl.stat: Deprecated.
Covariances.NonnegativeFreqException - Exception in com.imsl.stat: Frequencies must be nonnegative.
Covariances.NonnegativeWeightException - Exception in com.imsl.stat: Weights must be nonnegative.
Covariances.TooManyObsDeletedException - Exception in com.imsl.stat: Deprecated.
CovariancesEx1 - Class in com.imsl.test.example.stat: Calculates a variance-covariance matrix.
CovariancesEx1() - Constructor for class com.imsl.test.example.stat.CovariancesEx1
CovarianceSingularException(String) - Constructor for exception com.imsl.stat.DiscriminantAnalysis.CovarianceSingularException: The variance-covariance matrix is singular.
CovarianceSingularException(String, Object[]) - Constructor for exception com.imsl.stat.DiscriminantAnalysis.CovarianceSingularException: The variance-covariance matrix is singular.
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.
createFromTransactions(double[][], int[]) - Method in class com.imsl.datamining.SequenceDatabase: Creates a sequence database from a transaction database.
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() - Method in class com.imsl.datamining.neural.HiddenLayer: Creates a Perceptron in this Layer of the neural network.
createPerceptron() - Method in class com.imsl.datamining.neural.OutputLayer: Creates a Perceptron in this Layer of the neural network.
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(Activation, double) - Method in class com.imsl.datamining.neural.OutputLayer: Creates a Perceptron in this Layer with a specified Activation and bias.
createPerceptrons(int) - Method in class com.imsl.datamining.neural.HiddenLayer: Creates a number of Perceptrons in this Layer of the neural network.
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.HiddenLayer: Creates a number of Perceptrons in this Layer with the specified bias.
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.
CrossCorrelationEx1 - Class in com.imsl.test.example.stat: Computes the cross-covariances and cross-correlations for the gas furnace data.
CrossCorrelationEx1() - Constructor for class com.imsl.test.example.stat.CrossCorrelationEx1
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.
CrossValidationEx1 - Class in com.imsl.test.example.datamining: Uses cross-validation to determine the optimally pruned decision tree.
CrossValidationEx1() - Constructor for class com.imsl.test.example.datamining.CrossValidationEx1
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.
CsAkimaEx1 - Class in com.imsl.test.example.math: Computes the Akima cubic spline.
CsAkimaEx1() - Constructor for class com.imsl.test.example.math.CsAkimaEx1
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.
CsInterpolateEx1 - Class in com.imsl.test.example.math: Computes a cubic spline.
CsInterpolateEx1() - Constructor for class com.imsl.test.example.math.CsInterpolateEx1
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.
CsPeriodicEx1 - Class in com.imsl.test.example.math: Computes a cubic spline interpolant with periodic boundary conditions.
CsPeriodicEx1() - Constructor for class com.imsl.test.example.math.CsPeriodicEx1
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.
CsShapeEx1 - Class in com.imsl.test.example.math: Computes a shape preserving cubic spline.
CsShapeEx1() - Constructor for class com.imsl.test.example.math.CsShapeEx1
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.
CsSmoothC2Ex1 - Class in com.imsl.test.example.math: Computes a smooth cubic spline on noisy data.
CsSmoothC2Ex1() - Constructor for class com.imsl.test.example.math.CsSmoothC2Ex1
CsSmoothEx1 - Class in com.imsl.test.example.math: Computes a smooth cubic spline on noisy data using an "optimized" smoothing parameter value.
CsSmoothEx1() - Constructor for class com.imsl.test.example.math.CsSmoothEx1
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.
CsTCBEx1 - Class in com.imsl.test.example.math: Computes the Kochanek-Bartels cubic spline.
CsTCBEx1() - Constructor for class com.imsl.test.example.math.CsTCBEx1
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
CUSTOM - Enum constant in enum class com.imsl.stat.TimeSeriesOperations.CombineMethod: Uses a custom combine function that is provided by the user.
CyclingIsOccurringException(int) - Constructor for exception com.imsl.stat.StepwiseRegression.CyclingIsOccurringException: Constructs a CyclingIsOccurringException.
CyclingOccurringException() - Constructor for exception com.imsl.math.DenseLP.CyclingOccurringException: The algorithm appears to be cycling.
CyclingOccurringException(String) - Constructor for exception com.imsl.math.DenseLP.CyclingOccurringException: The algorithm appears to be cycling.
CyclingOccurringException(String, Object[]) - Constructor for exception com.imsl.math.DenseLP.CyclingOccurringException: The algorithm appears to be cycling.

D

DataNode - Class in com.imsl.datamining.supportvectormachine: Specifies a data node for a support vector machine.
DataNode() - Constructor for class com.imsl.datamining.supportvectormachine.DataNode
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.
DBSCAN - Class in com.imsl.stat: Perform a DBSCAN cluster analysis.
DBSCAN(DBSCAN.Function, double[][]) - Constructor for class com.imsl.stat.DBSCAN: Constructor for class DBSCAN.
DBSCAN.DBSCANParams - Class in com.imsl.stat: Class that holds the minimum number of points and epsilon parameters of the DBSCAN algorithm.
DBSCAN.Function - Interface in com.imsl.stat: Public interface for the user-supplied function to compute the distances between points.
DBSCANEx1 - Class in com.imsl.test.example.stat: Performs DBSCAN clustering on Fisher's iris data.
DBSCANEx1() - Constructor for class com.imsl.test.example.stat.DBSCANEx1
DBSCANEx2 - Class in com.imsl.test.example.stat: Performs DBSCAN clustering on an artificial data set.
DBSCANEx2() - Constructor for class com.imsl.test.example.stat.DBSCANEx2
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.PruningFailedToConvergeException - Exception in com.imsl.datamining.decisionTree: Exception thrown when pruning fails to converge.
DecisionTree.PureNodeException - Exception in com.imsl.datamining.decisionTree: Exception thrown when attempting to split a node that is already pure (response variable is constant).
DecisionTreeEx1 - Class in com.imsl.test.example.datamining.decisionTree: Fits a decision tree to the golf data using C45 and ALACART.
DecisionTreeEx1() - Constructor for class com.imsl.test.example.datamining.decisionTree.DecisionTreeEx1
DecisionTreeEx2 - Class in com.imsl.test.example.datamining.decisionTree: Fits a decision tree to categorical data using CHAID and prints the decision tree.
DecisionTreeEx2() - Constructor for class com.imsl.test.example.datamining.decisionTree.DecisionTreeEx2
DecisionTreeEx3 - Class in com.imsl.test.example.datamining.decisionTree: Fits a decision tree to categorical data using C45 and prints the decision tree.
DecisionTreeEx3() - Constructor for class com.imsl.test.example.datamining.decisionTree.DecisionTreeEx3
DecisionTreeEx4 - Class in com.imsl.test.example.datamining.decisionTree: Fits a decision tree to the Kyphosis data using QUEST.
DecisionTreeEx4() - Constructor for class com.imsl.test.example.datamining.decisionTree.DecisionTreeEx4
DecisionTreeEx5 - Class in com.imsl.test.example.datamining.decisionTree: Fits a decision tree to mixed-type data using QUEST and prunes the decision tree.
DecisionTreeEx5() - Constructor for class com.imsl.test.example.datamining.decisionTree.DecisionTreeEx5
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 - Enum 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.UnsupervisedOrdinalFilter: Decodes an encoded ordinal variable.
decode(double[]) - Method in class com.imsl.datamining.neural.ScaleFilter: Unscales an array of values.
decode(double[]) - Method in class com.imsl.datamining.neural.UnsupervisedOrdinalFilter: Decodes an array of encoded ordinal 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(int, double[][]) - Method in class com.imsl.datamining.neural.ScaleFilter: Unscales a single column of a two dimensional array of 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.
DeleteObservationsException(int) - Constructor for exception com.imsl.stat.CategoricalGenLinModel.DeleteObservationsException: Constructs a DeleteObservationsException.
deleteRow() - Method in class com.imsl.io.AbstractFlatFile: Deletes the current row from this ResultSet object and from the underlying database.
DenseLP - Class in com.imsl.math: Solves a linear programming problem using an active set strategy.
DenseLP(double[][], double[], double[]) - Constructor for class com.imsl.math.DenseLP: Constructor variables of type double.
DenseLP(MPSReader) - Constructor for class com.imsl.math.DenseLP: Constructor using an MPSReader object.
DenseLP.AllConstraintsNotSatisfiedException - Exception in com.imsl.math: All constraints are not satisfied.
DenseLP.BoundsInconsistentException - Exception in com.imsl.math: The bounds given are inconsistent.
DenseLP.CyclingOccurringException - Exception in com.imsl.math: The algorithm appears to be cycling.
DenseLP.MultipleSolutionsException - Exception in com.imsl.math: The problem has multiple solutions giving essentially the same minimum.
DenseLP.NoAcceptablePivotException - Exception in com.imsl.math: No acceptable pivot could be found.
DenseLP.NoConstraintsAvailableException - Exception in com.imsl.math: The LP problem has no constraints.
DenseLP.ProblemUnboundedException - Exception in com.imsl.math: The problem is unbounded.
DenseLP.ProblemVacuousException - Exception in com.imsl.math: The problem is vacuous.
DenseLP.SomeConstraintsDiscardedException - Exception in com.imsl.math: Some constraints were discarded because they were too linearly dependent on other active constraints.
DenseLP.WrongConstraintTypeException - Exception in com.imsl.math: Deprecated.
No longer used, replaced with an IllegalArgumentException.
DenseLPEx1 - Class in com.imsl.test.example.math: Solves a linear programming problem.
DenseLPEx1() - Constructor for class com.imsl.test.example.math.DenseLPEx1
DenseLPEx2 - Class in com.imsl.test.example.math: Solves a linear programming problem.
DenseLPEx2() - Constructor for class com.imsl.test.example.math.DenseLPEx2
DenseLPEx3 - Class in com.imsl.test.example.math: Solves a linear programming problem in $5$ variables.
DenseLPEx3() - Constructor for class com.imsl.test.example.math.DenseLPEx3
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) - Method in class com.imsl.math.Spline: Returns the value of the first derivative of the spline at a point.
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) - 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[], 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[], 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.
derivative(double, double) - Method in interface com.imsl.datamining.neural.Activation: Returns the value of the derivative of the activation function.
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, 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.Spline: Returns the value of the derivative of the spline at a point.
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 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 specified keys and return the permutation vector.
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[]) - Static method in class com.imsl.stat.Sort: Sorts an array into descending order and returns the permutation vector.
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.
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 - Enum constant in enum class com.imsl.datamining.decisionTree.DecisionTreeInfoGain.GainCriteria: A measure of the quality of fit.
DiagonalWeightMatrixException(String) - Constructor for exception com.imsl.math.SparseLP.DiagonalWeightMatrixException: A diagonal element of the diagonal weight matrix is too small.
DiagonalWeightMatrixException(String, Object[]) - Constructor for exception com.imsl.math.SparseLP.DiagonalWeightMatrixException: A diagonal element of the diagonal weight matrix is too small.
DidNotConvergeException(String) - Constructor for exception com.imsl.math.ComplexEigen.DidNotConvergeException: Constructs a DidNotConvergeException object.
DidNotConvergeException(String) - Constructor for exception com.imsl.math.ComplexSVD.DidNotConvergeException: Constructs a DidNotConvergeException object.
DidNotConvergeException(String) - Constructor for exception com.imsl.math.Eigen.DidNotConvergeException: Constructs a DidNotConvergeException object.
DidNotConvergeException(String) - Constructor for exception com.imsl.math.OdeAdamsGear.DidNotConvergeException: Constructs a DidNotConvergeException with the specified detailed message.
DidNotConvergeException(String) - Constructor for exception com.imsl.math.OdeRungeKutta.DidNotConvergeException: Constructs a DidNotConvergeException with the specified detailed message.
DidNotConvergeException(String) - Constructor for exception com.imsl.math.SVD.DidNotConvergeException: Constructs a DidNotConvergeException object.
DidNotConvergeException(String) - Constructor for exception com.imsl.math.ZeroPolynomial.DidNotConvergeException
DidNotConvergeException(String) - Constructor for exception com.imsl.math.ZeroSystem.DidNotConvergeException
DidNotConvergeException(String) - Constructor for exception com.imsl.stat.ChiSquaredTest.DidNotConvergeException: Constructs a DidNotConvergeException object.
DidNotConvergeException(String) - Constructor for exception com.imsl.stat.InverseCdf.DidNotConvergeException: Constructs a DidNotConvergeException object.
DidNotConvergeException(String, Object[]) - Constructor for exception com.imsl.math.ComplexEigen.DidNotConvergeException: Constructs a DidNotConvergeException object.
DidNotConvergeException(String, Object[]) - Constructor for exception com.imsl.math.ComplexSVD.DidNotConvergeException: Constructs a DidNotConvergeException object.
DidNotConvergeException(String, Object[]) - Constructor for exception com.imsl.math.Eigen.DidNotConvergeException: Constructs a DidNotConvergeException object.
DidNotConvergeException(String, Object[]) - Constructor for exception com.imsl.math.OdeAdamsGear.DidNotConvergeException: Constructs a DidNotConvergeException with the specified detailed message.
DidNotConvergeException(String, Object[]) - Constructor for exception com.imsl.math.OdeRungeKutta.DidNotConvergeException: Constructs a DidNotConvergeException with the specified detailed message.
DidNotConvergeException(String, Object[]) - Constructor for exception com.imsl.math.SVD.DidNotConvergeException: Constructs a DidNotConvergeException object.
DidNotConvergeException(String, Object[]) - Constructor for exception com.imsl.math.ZeroPolynomial.DidNotConvergeException
DidNotConvergeException(String, Object[]) - Constructor for exception com.imsl.math.ZeroSystem.DidNotConvergeException
DidNotConvergeException(String, Object[]) - Constructor for exception com.imsl.stat.ChiSquaredTest.DidNotConvergeException: Constructs a DidNotConvergeException object.
DidNotConvergeException(String, Object[]) - Constructor for exception com.imsl.stat.InverseCdf.DidNotConvergeException: Constructs a DidNotConvergeException object.
DIFF - Enum constant in enum 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.
DifferenceEx1 - Class in com.imsl.test.example.stat: Computes a lagged difference formula for the airline data.
DifferenceEx1() - Constructor for class com.imsl.test.example.stat.DifferenceEx1
DifferenceEx2 - Class in com.imsl.test.example.stat: Computes a lagged difference formula for the airline data excluding the lost observations.
DifferenceEx2() - Constructor for class com.imsl.test.example.stat.DifferenceEx2
DiffObsDeletedException(String) - Constructor for exception com.imsl.stat.Covariances.DiffObsDeletedException: Deprecated.

Constructs a DiffObsDeletedException object.
DiffObsDeletedException(String, Object[]) - Constructor for exception com.imsl.stat.Covariances.DiffObsDeletedException: Deprecated.

Constructs a DiffObsDeletedException object.
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.
disc(GregorianCalendar, GregorianCalendar, double, double, DayCountBasis) - Static method in class com.imsl.finance.Bond: Returns the implied interest rate of a discount bond.
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.Cdf: Evaluates 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.
DiscreteUniformPD - Class in com.imsl.stat.distributions: The discrete uniform probability distribution.
DiscreteUniformPD() - Constructor for class com.imsl.stat.distributions.DiscreteUniformPD: Constructor for the discrete uniform probability distribution.
DiscreteUniformPDEx1 - Class in com.imsl.test.example.stat.distributions: Evaluates the discrete uniform probability distribution.
DiscreteUniformPDEx1() - Constructor for class com.imsl.test.example.stat.distributions.DiscreteUniformPDEx1
discreteUniformProb(int, int) - Static method in class com.imsl.stat.Cdf: Deprecated.
Use Pdf.discreteUniform(int, int) instead.
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.EmptyGroupException - Exception in com.imsl.stat: There are no observations in a group.
DiscriminantAnalysis.SumOfWeightsNegException - Exception in com.imsl.stat: The sum of the weights have become negative.
DiscriminantAnalysisEx1 - Class in com.imsl.test.example.stat: Performs a discriminant analysis on Fisher's iris data.
DiscriminantAnalysisEx1() - Constructor for class com.imsl.test.example.stat.DiscriminantAnalysisEx1
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(double[][], int, int, int) - Constructor for class com.imsl.stat.Dissimilarities: Deprecated.
Use Dissimilarities(double[][]) instead.
Dissimilarities(double[][], int, int, int, int[]) - Constructor for class com.imsl.stat.Dissimilarities: Deprecated.
Use Dissimilarities(double[][]) instead.
Dissimilarities.NoPositiveVarianceException - Exception in com.imsl.stat: No variable has positive variance.
Dissimilarities.ScaleFactorZeroException - Exception in com.imsl.stat: The computations cannot continue because a scale factor is zero.
Dissimilarities.ZeroNormException - Exception in com.imsl.stat: The computations cannot continue because the Euclidean norm of the column is equal to zero.
DissimilaritiesEx1 - Class in com.imsl.test.example.stat: Computes a dissimilarity matrix using the Euclidean distance.
DissimilaritiesEx1() - Constructor for class com.imsl.test.example.stat.DissimilaritiesEx1
Distribution - Interface in com.imsl.stat: Public interface for the user-supplied distribution function.
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(double, Physical) - Static method in class com.imsl.math.Physical: Divide a double by a Physical object.
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(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(Physical, double) - Static method in class com.imsl.math.Physical: Divide a Physical object by a double.
divide(Physical, Physical) - Static method in class com.imsl.math.Physical: Divide two Physical objects.
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(DataNode[], DataNode[]) - Method in class com.imsl.datamining.supportvectormachine.Kernel: Calculates the dot product between two DataNode arrays.
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.
downdateX(double[]) - Method in class com.imsl.stat.WelchsTTest: 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.
downdateY(double[]) - Method in class com.imsl.stat.WelchsTTest: Removes the observations in y from the second sample.
DualInfeasibleException(String) - Constructor for exception com.imsl.math.SparseLP.DualInfeasibleException: The dual problem is infeasible.
DualInfeasibleException(String, Object[]) - Constructor for exception com.imsl.math.SparseLP.DualInfeasibleException: The dual problem is infeasible.
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.
EGARCH - Class in com.imsl.stat: Implements the exponential GARCH (EGARCH) model.
EGARCH(TimeSeries) - Constructor for class com.imsl.stat.EGARCH: Constructs an EGARCH(1,1) model
EGARCH(TimeSeries, int, int, int[]) - Constructor for class com.imsl.stat.EGARCH: Constructs an EGARCH(p, q) model with a possible ARMA configuration for the mean.
EGARCHEx1 - Class in com.imsl.test.example.stat: Fits an EGARCH(1, 1) to a segment of S&P 500 returns.
EGARCHEx1() - Constructor for class com.imsl.test.example.stat.EGARCHEx1
EGARCHEx2 - Class in com.imsl.test.example.stat: Fits an EGARCH(1, 1) with a user defined distribution on $z_t$.
EGARCHEx2() - Constructor for class com.imsl.test.example.stat.EGARCHEx2
EGARCHEx3 - Class in com.imsl.test.example.stat: Fits an EGARCH(1, 1) with an ARMA(1,1) on the mean.
EGARCHEx3() - Constructor for class com.imsl.test.example.stat.EGARCHEx3
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(double[][]) - Constructor for class com.imsl.math.Eigen: Deprecated.
Use Eigen() instead.
Eigen(double[][], boolean) - Constructor for class com.imsl.math.Eigen: Deprecated.
Use Eigen() instead.
Eigen.DidNotConvergeException - Exception in com.imsl.math: The iteration did not converge
EigenEx1 - Class in com.imsl.test.example.math: Computes the eigenvalues and eigenvectors of a matrix.
EigenEx1() - Constructor for class com.imsl.test.example.math.EigenEx1
EigenvalueException(String) - Constructor for exception com.imsl.stat.FactorAnalysis.EigenvalueException: Constructs a EigenvalueException object.
EigenvalueException(String, Object[]) - Constructor for exception com.imsl.stat.FactorAnalysis.EigenvalueException: Constructs a EigenvalueException 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.
EmpiricalQuantilesEx1 - Class in com.imsl.test.example.stat: Computes empirical quantiles for rainfall data.
EmpiricalQuantilesEx1() - Constructor for class com.imsl.test.example.stat.EmpiricalQuantilesEx1
EmptyGroupException(String) - Constructor for exception com.imsl.stat.DiscriminantAnalysis.EmptyGroupException: There are no observations in a group.
EmptyGroupException(String, Object[]) - Constructor for exception com.imsl.stat.DiscriminantAnalysis.EmptyGroupException: There are no observations in a group.
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) - 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 ordinal category.
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.UnsupervisedOrdinalFilter: Encodes an array of ordinal categories into an array of transformed percentages.
encode(int, double[][]) - Method in class com.imsl.datamining.neural.ScaleFilter: Scales a single column of a two dimensional array of values.
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.
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: Performs 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 a two-stage EpochTrainer.
EpochTrainerEx1 - Class in com.imsl.test.example.datamining.neural: Trains a 2-layer network using the 2-stage Epoch trainer.
EpochTrainerEx1() - Constructor for class com.imsl.test.example.datamining.neural.EpochTrainerEx1
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.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.
EpsilonAlgorithmEx1 - Class in com.imsl.test.example.math: Accelerates a series of partial sums using the Epsilon algorithm.
EpsilonAlgorithmEx1() - Constructor for class com.imsl.test.example.math.EpsilonAlgorithmEx1
EqConstrInconsistentException(String) - Constructor for exception com.imsl.stat.GARCH.EqConstrInconsistentException: Constructs a EqConstrInconsistentException object.
EqConstrInconsistentException(String, Object[]) - Constructor for exception com.imsl.stat.GARCH.EqConstrInconsistentException: Constructs a EqConstrInconsistentException object.
EqualityConstraintsException(String) - Constructor for exception com.imsl.math.MinConGenLin.EqualityConstraintsException: Constructs a EqualityConstraintsException object.
EqualityConstraintsException(String, Object[]) - Constructor for exception com.imsl.math.MinConGenLin.EqualityConstraintsException: Constructs a EqualityConstraintsException object.
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(double[], double[]) - Method in interface com.imsl.datamining.neural.QuasiNewtonTrainer.Error: Returns the contribution to the error from a single training output target.
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 $${s = \sqrt {\sum_{i=1}^{neq}{\frac{{e_i}^2}{{w_i}^2}}}}$$ where $w_i = e_i/max(|y_i(t)|,1.0)$ and $neq$ is the number of equations
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 $e_i/max(|y_i(t)|, 1)$
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.
ErrorTestException(String, Object[]) - Constructor for exception com.imsl.math.FeynmanKac.ErrorTestException: Error test failure detected.
estimate() - Method in class com.imsl.stat.ExtendedGARCH: Performs the estimation for the specified model.
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[]) - Method in class com.imsl.stat.LogNormalDistribution: Fits a lognormal probability distribution to xData and returns the probability density at each value.
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[]) - Method in class com.imsl.stat.PoissonDistribution: Fits a Poisson probability distribution to xData and returns the probability density at each value.
eval(double[]) - Method in class com.imsl.test.example.datamining.NaiveBayesClassifierEx3.TestGaussFcn1
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.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.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.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 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 class com.imsl.test.example.datamining.NaiveBayesClassifierEx3.TestGaussFcn1
eval(double, double) - Method in class com.imsl.stat.InverseCdf: Evaluates the inverse CDF function.
eval(double, Object[]) - Method in class com.imsl.stat.GammaDistribution: Evaluates a gamma probability density at a given point xData.
eval(double, Object[]) - Method in class com.imsl.stat.LogNormalDistribution: Evaluates a lognormal probability density function at a given point xData.
eval(double, Object[]) - Method in class com.imsl.stat.NormalDistribution: Evaluates a normal (Gaussian) probability density at a given point xData.
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: Evaluation method for the user-supplied distribution function and parameters.
eval(double, Object[]) - Method in class com.imsl.test.example.datamining.NaiveBayesClassifierEx3.TestGaussFcn1
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.
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.
evaluateF(int, double[]) - Method in class com.imsl.test.example.math.NumericalDerivativesEx4
evaluateF(int, double[]) - Method in class com.imsl.test.example.math.NumericalDerivativesEx5
evaluateJ(double[]) - Method in class com.imsl.math.NumericalDerivatives: Evaluates the Jacobian for a system of (m) equations in (n) variables.
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(double) - Static method in class com.imsl.math.JMath: Returns the exponential of a double.
exp(Complex) - Static method in class com.imsl.math.Complex: Returns the exponential of a Complex z, exp(z).
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: Returns 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
ExponentialPD - Class in com.imsl.stat.distributions: The exponential probability distribution.
ExponentialPD() - Constructor for class com.imsl.stat.distributions.ExponentialPD: Constructs an exponential probability distribution.
ExponentialPDEx1 - Class in com.imsl.test.example.stat.distributions: Evaluates the exponential probability distribution.
ExponentialPDEx1() - Constructor for class com.imsl.test.example.stat.distributions.ExponentialPDEx1
exponentialProb(double, double) - Static method in class com.imsl.stat.Cdf: Deprecated.
Use Pdf.exponential(double, double) instead.
ExtendedGARCH - Class in com.imsl.stat: Abstract class for extended GARCH models.
ExtendedGARCH(TimeSeries, int, int, int[]) - Constructor for class com.imsl.stat.ExtendedGARCH: Constructor for the extended GARCH class.
ExtendedGARCH.Solver - Enum Class in com.imsl.stat: An enumeration of the types of solvers available to the estimation procedure.
ExtendedGARCH.zDistribution - Interface in com.imsl.stat: Public interface for specifying the distribution of $z_t$.
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.
ExtremeValuePD - Class in com.imsl.stat.distributions: The extreme value/Gumbel probability distribution.
ExtremeValuePD() - Constructor for class com.imsl.stat.distributions.ExtremeValuePD: Constructor for the extreme value/Gumbel probability distribution.
ExtremeValuePDEx1 - Class in com.imsl.test.example.stat.distributions: Evaluates the extreme value probability distribution.
ExtremeValuePDEx1() - Constructor for class com.imsl.test.example.stat.distributions.ExtremeValuePDEx1
extremeValueProb(double, double, double) - Static method in class com.imsl.stat.Cdf: Deprecated.
Use Pdf.extremeValue(double, double, double) instead.

F

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.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.ZeroFunction.Function: Deprecated.

Returns the value of the function at the given point.
f(double) - Method in interface com.imsl.math.ZerosFunction.Function: Returns the value of the function at the given point.
f(double) - Method in class com.imsl.test.example.math.MinUnconEx2
f(double) - Method in class com.imsl.test.example.math.RadialBasisEx2.PolyHarmonicSpline
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[]) - Method in interface com.imsl.math.MinUnconMultiVar.Function: Public interface for the multivariate function to be minimized.
f(double[]) - Method in interface com.imsl.math.NelderMead.Function: Public interface for the user-supplied function to evaluate the objective function of the minimization problem.
f(double[]) - Method in class com.imsl.test.example.stat.KalmanFilterEx2
f(double[], double[]) - Method in interface com.imsl.math.NonlinLeastSquares.Function: Public interface for the nonlinear least-squares function.
f(double[], double[]) - Method in interface com.imsl.math.ZeroSystem.Function: Returns the value of the function at the given point.
f(double[], int, boolean[]) - Method in interface com.imsl.math.MinConNLP.Function: Compute the value of the function at the given point.
f(double[], int, boolean[]) - Method in class com.imsl.test.example.math.MinConNLPEx1: Defines the objective function and constraints.
f(double[], int, boolean[]) - Method in class com.imsl.test.example.math.MinConNLPEx2: Defines the objective function and constraints.
f(double[], int, boolean[]) - Method in class com.imsl.test.example.math.MinConNLPEx3: Defines the objective function and constraints.
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[]) - 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(int, double[]) - Method in interface com.imsl.math.NumericalDerivatives.Function: Returns the equations evaluated at the point y.
f(int, int, double[], boolean[], double[]) - Method in interface com.imsl.math.MinConNonlin.Function: Deprecated.

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, double, double) - Static method in class com.imsl.stat.Pdf: Evaluates the F probability density function.
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.EigenvalueException - Exception in com.imsl.stat: Eigenvalue error.
FactorAnalysis.NonPositiveEigenvalueException - Exception in com.imsl.stat: Non positive eigenvalue error.
FactorAnalysis.NotPositiveDefiniteException - Exception in com.imsl.stat: Matrix not positive definite.
FactorAnalysis.NotPositiveSemiDefiniteException - Exception in com.imsl.stat: Covariance matrix not positive semi-definite.
FactorAnalysis.NotSemiDefiniteException - Exception in com.imsl.stat: Hessian matrix not semi-definite.
FactorAnalysis.RankException - Exception in com.imsl.stat: Rank of covariance matrix error.
FactorAnalysis.ScoreMethod - Enum Class in com.imsl.stat: Indicates which method is used for computing the factor score coefficients.
FactorAnalysis.SingularException - Exception in com.imsl.stat: Covariance matrix singular error.
FactorAnalysisEx1 - Class in com.imsl.test.example.stat: Computes the principal components on a 9 variable correlation matrix.
FactorAnalysisEx1() - Constructor for class com.imsl.test.example.stat.FactorAnalysisEx1
FactorAnalysisEx2 - Class in com.imsl.test.example.stat: Compute the factors on 9 variables by the method of maximum likelihood.
FactorAnalysisEx2() - Constructor for class com.imsl.test.example.stat.FactorAnalysisEx2
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.
FalseConvergenceException(String) - Constructor for exception com.imsl.math.BoundedLeastSquares.FalseConvergenceException: Constructs an FalseConvergenceException with the specified detail message.
FalseConvergenceException(String) - Constructor for exception com.imsl.math.MinUnconMultiVar.FalseConvergenceException: Constructs a FalseConvergenceException object.
FalseConvergenceException(String, Object[]) - Constructor for exception com.imsl.math.BoundedLeastSquares.FalseConvergenceException: Constructs an FalseConvergenceException with the specified detail message.
FalseConvergenceException(String, Object[]) - Constructor for exception com.imsl.math.MinUnconMultiVar.FalseConvergenceException: Constructs a FalseConvergenceException object.
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.
FaureSequenceEx1 - Class in com.imsl.test.example.stat: Generates points of the Faure sequence.
FaureSequenceEx1() - Constructor for class com.imsl.test.example.stat.FaureSequenceEx1
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.ConstraintsInconsistentException - Exception in com.imsl.math: The constraints are inconsistent.
FeynmanKac.CorrectorConvergenceException - Exception in com.imsl.math: Corrector failed to converge.
FeynmanKac.ErrorTestException - Exception in com.imsl.math: 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.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.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.TEqualsToutException - Exception in com.imsl.math: 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.ToleranceTooSmallException - Exception in com.imsl.math: Tolerance is too small.
FeynmanKac.TooManyIterationsException - Exception in com.imsl.math: Too many iterations required by the DAE solver.
FeynmanKacEx1 - Class in com.imsl.test.example.math: Compares American vs European options on a vanilla put.
FeynmanKacEx1() - Constructor for class com.imsl.test.example.math.FeynmanKacEx1
FeynmanKacEx2 - Class in com.imsl.test.example.math: Applies a diffusion model for options pricing.
FeynmanKacEx2() - Constructor for class com.imsl.test.example.math.FeynmanKacEx2
FeynmanKacEx3 - Class in com.imsl.test.example.math: Evaluates the price of a European option with two payoff strategies.
FeynmanKacEx3() - Constructor for class com.imsl.test.example.math.FeynmanKacEx3
FeynmanKacEx4 - Class in com.imsl.test.example.math: Evaluates the price of a convertible bond.
FeynmanKacEx4() - Constructor for class com.imsl.test.example.math.FeynmanKacEx4
FeynmanKacEx5 - Class in com.imsl.test.example.math: Solves for the "Greeks" of mathematical finance.
FeynmanKacEx5() - Constructor for class com.imsl.test.example.math.FeynmanKacEx5
FFT - Class in com.imsl.math: FFT functions.
FFT(int) - Constructor for class com.imsl.math.FFT: Constructs an FFT object.
FFTEx1 - Class in com.imsl.test.example.math: Computes the Fourier coefficients of a periodic sequence.
FFTEx1() - Constructor for class com.imsl.test.example.math.FFTEx1
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.
filter() - Method in class com.imsl.stat.KalmanFilter: Performs Kalman filtering and evaluates the likelihood function for the state-space model.
filter(double[]) - Method in class com.imsl.stat.ExtendedGARCH: Performs filtering as specified by the conditional variance and the mean model configuration using a set of fixed parameters.
Finance - Class in com.imsl.finance: Collection of finance functions.
FinanceCumipmtEx1 - Class in com.imsl.test.example.finance: Computes the amount of interest paid in the first year of a 30 year fixed mortgage.
FinanceCumipmtEx1() - Constructor for class com.imsl.test.example.finance.FinanceCumipmtEx1
FinanceCumprincEx1 - Class in com.imsl.test.example.finance: Computes the amount of principal paid in the first year of a 30 year fixed rate mortgage.
FinanceCumprincEx1() - Constructor for class com.imsl.test.example.finance.FinanceCumprincEx1
FinanceDbEx1 - Class in com.imsl.test.example.finance: Computes the depreciation of an asset.
FinanceDbEx1() - Constructor for class com.imsl.test.example.finance.FinanceDbEx1
FinanceDdbEx1 - Class in com.imsl.test.example.finance: Computes the depreciation of an asset using the double-declining balance method.
FinanceDdbEx1() - Constructor for class com.imsl.test.example.finance.FinanceDdbEx1
FinanceDollardeEx1 - Class in com.imsl.test.example.finance: Converts a fractional dollar price to a decimal price.
FinanceDollardeEx1() - Constructor for class com.imsl.test.example.finance.FinanceDollardeEx1
FinanceDollarfrEx1 - Class in com.imsl.test.example.finance: Converts a decimal dollar price to a fractional dollar price.
FinanceDollarfrEx1() - Constructor for class com.imsl.test.example.finance.FinanceDollarfrEx1
FinanceEffectEx1 - Class in com.imsl.test.example.finance: Computes the effective rate from a nominal rate compounded quarterly.
FinanceEffectEx1() - Constructor for class com.imsl.test.example.finance.FinanceEffectEx1
FinanceFvEx1 - Class in com.imsl.test.example.finance: Computes the future value of an investment.
FinanceFvEx1() - Constructor for class com.imsl.test.example.finance.FinanceFvEx1
FinanceFvscheduleEx1 - Class in com.imsl.test.example.finance: Computes the future value of an investment with scheduled rate of growth.
FinanceFvscheduleEx1() - Constructor for class com.imsl.test.example.finance.FinanceFvscheduleEx1
FinanceIpmtEx1 - Class in com.imsl.test.example.finance: Computes the interest due the second year of a loan.
FinanceIpmtEx1() - Constructor for class com.imsl.test.example.finance.FinanceIpmtEx1
FinanceIrrEx1 - Class in com.imsl.test.example.finance: Computes the internal rate of return on an investment.
FinanceIrrEx1() - Constructor for class com.imsl.test.example.finance.FinanceIrrEx1
FinanceMirrEx1 - Class in com.imsl.test.example.finance: Computes the modified internal rate of return on an investment.
FinanceMirrEx1() - Constructor for class com.imsl.test.example.finance.FinanceMirrEx1
FinanceNominalEx1 - Class in com.imsl.test.example.finance: Computes the nominal interest rate.
FinanceNominalEx1() - Constructor for class com.imsl.test.example.finance.FinanceNominalEx1
FinanceNperEx1 - Class in com.imsl.test.example.finance: Computes the number of payment periods for a loan.
FinanceNperEx1() - Constructor for class com.imsl.test.example.finance.FinanceNperEx1
FinanceNpvEx1 - Class in com.imsl.test.example.finance: Computes the net present value of a lottery prize using the stream of payments as input.
FinanceNpvEx1() - Constructor for class com.imsl.test.example.finance.FinanceNpvEx1
FinancePmtEx1 - Class in com.imsl.test.example.finance: Computes the payment due each year on a loan.
FinancePmtEx1() - Constructor for class com.imsl.test.example.finance.FinancePmtEx1
FinancePpmtEx1 - Class in com.imsl.test.example.finance: Computes the payment on principal the first year of a loan.
FinancePpmtEx1() - Constructor for class com.imsl.test.example.finance.FinancePpmtEx1
FinancePvEx1 - Class in com.imsl.test.example.finance: Computes the net present value of a lottery prize.
FinancePvEx1() - Constructor for class com.imsl.test.example.finance.FinancePvEx1
FinanceRateEx1 - Class in com.imsl.test.example.finance: Computes the interest rate on a loan.
FinanceRateEx1() - Constructor for class com.imsl.test.example.finance.FinanceRateEx1
FinanceSlnEx1 - Class in com.imsl.test.example.finance: Computes the straight line depreciation of an asset.
FinanceSlnEx1() - Constructor for class com.imsl.test.example.finance.FinanceSlnEx1
FinanceSydEx1 - Class in com.imsl.test.example.finance: Computes sum-of-year's depreciation.
FinanceSydEx1() - Constructor for class com.imsl.test.example.finance.FinanceSydEx1
FinanceVdbEx1 - Class in com.imsl.test.example.finance: Computes the depreciation of an asset using the variable-declining balance method.
FinanceVdbEx1() - Constructor for class com.imsl.test.example.finance.FinanceVdbEx1
FinanceXirrEx1 - Class in com.imsl.test.example.finance: Computes the internal rate of return of an investment with variable schedule.
FinanceXirrEx1() - Constructor for class com.imsl.test.example.finance.FinanceXirrEx1
FinanceXnpvEx1 - Class in com.imsl.test.example.finance: Computes the net present value for a schedule of payments.
FinanceXnpvEx1() - Constructor for class com.imsl.test.example.finance.FinanceXnpvEx1
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.
first() - Method in class com.imsl.io.AbstractFlatFile: Moves the cursor to the first row in this ResultSet object.
FIRST - Enum constant in enum 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.decisionTree.RandomTrees: Fits the random forest to the training data.
fitModel() - Method in class com.imsl.datamining.GradientBoosting: Performs the gradient boosting on the training data.
fitModel() - Method in class com.imsl.datamining.LogisticRegression: Fits the logistic regression predictive model.
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).
fitModel() - Method in class com.imsl.datamining.supportvectormachine.SupportVectorMachine: Fits the model to the training data, i.e, trains the support vector machine.
FlatFile - Class in com.imsl.io: Reads a text file as a ResultSet.
FlatFile(BufferedReader) - Constructor for class com.imsl.io.FlatFile: Creates a FlatFile with the CSV tokenizer.
FlatFile(BufferedReader, Tokenizer) - Constructor for class com.imsl.io.FlatFile: Creates a FlatFile from a BufferedReader.
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.
FlatFileEx1 - Class in com.imsl.test.example.io: Reads Fisher's Iris data set from a CSV file.
FlatFileEx1(InputStream) - Constructor for class com.imsl.test.example.io.FlatFileEx1
FlatFileEx2 - Class in com.imsl.test.example.io: Reads in a data set in a space separated form.
FlatFileEx2(BufferedReader, Tokenizer) - Constructor for class com.imsl.test.example.io.FlatFileEx2
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.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(double[][]) - Method in class com.imsl.datamining.KohonenSOM: Returns forecasts computed using the KohonenSOM object.
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 $l=1,2,\ldots,\rm{nForecast}$ at origins z.length-backwardOrigin-1+j where $j=1,\ldots,\rm{backwardOrigin}+1$.
forecast(int) - Method in class com.imsl.stat.AutoARIMA: Computes forecasts, associated probability limits and $\psi$ weights for the given outlier contaminated time series.
forecast(int, int) - Method in class com.imsl.stat.ExtendedGARCH: Forecasts the conditional variance and the residual series for the fitted extended GARCH model.
format(int, Object, int, int, ParsePosition) - Method in class com.imsl.math.PrintMatrixFormat: Returns a formatted string.
format(int, Object, int, int, ParsePosition) - Method in class com.imsl.test.example.math.PrintMatrixFormatEx2
format(LogRecord) - Method in class com.imsl.IMSLFormatter: Format the given log record and return the formatted string.
format(LogRecord) - Method in class com.imsl.math.GenMinRes.Formatter: Deprecated.
format(LogRecord) - Method in class com.imsl.math.MinConNLP.Formatter: Deprecated.
format(LogRecord) - Method in class com.imsl.stat.ARAutoUnivariate.Formatter: Deprecated.
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.
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.
Formatter() - Constructor for class com.imsl.math.GenMinRes.Formatter: Deprecated.
Formatter() - Constructor for class com.imsl.math.MinConNLP.Formatter: Deprecated.
Formatter() - Constructor for class com.imsl.stat.ARAutoUnivariate.Formatter: Deprecated.
forward(double[]) - Method in class com.imsl.math.FFT: Compute the Fourier coefficients of a real periodic sequence.
forward(Complex[]) - Method in class com.imsl.math.ComplexFFT: Compute the Fourier coefficients of a complex periodic sequence.
FORWARD_REGRESSION - Static variable in class com.imsl.stat.StepwiseRegression: Indicates forward regression.
FProb(double, double, double) - Static method in class com.imsl.stat.Cdf: Deprecated.
Use Pdf.F(double, double, double) instead.
frobeniusNorm() - Method in class com.imsl.math.ComplexSparseMatrix: Returns the Frobenius norm of the matrix.
frobeniusNorm() - Method in class com.imsl.math.SparseMatrix: 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(Complex[][]) - Static method in class com.imsl.math.ComplexMatrix: Return the Frobenius norm of a Complex 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.
g(double) - Method in class com.imsl.test.example.math.MinUnconEx2
g(double) - Method in class com.imsl.test.example.math.RadialBasisEx2.PolyHarmonicSpline
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: Returns 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.
GammaPDEx1 - Class in com.imsl.test.example.stat.distributions: Evaluates the gamma probability distribution.
GammaPDEx1() - Constructor for class com.imsl.test.example.stat.distributions.GammaPDEx1
gammaProb(double, double, double) - Static method in class com.imsl.stat.Cdf: Deprecated.
Use Pdf.gamma(double, double, double) instead.
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.EqConstrInconsistentException - Exception in com.imsl.stat: The equality constraints and the bounds on the variables are found to be inconsistent.
GARCH.NoVectorXException - Exception in com.imsl.stat: No vector X satisfies all of the constraints.
GARCH.TooManyIterationsException - Exception in com.imsl.stat: Number of function evaluations exceeded 1000.
GARCH.VarsDeterminedException - Exception in com.imsl.stat: The variables are determined by the equality constraints.
GARCHEx1 - Class in com.imsl.test.example.stat: Estimates a $\text{GARCH}(p,q)$ model from simulated data.
GARCHEx1() - Constructor for class com.imsl.test.example.stat.GARCHEx1
Gaussian(double) - Constructor for class com.imsl.math.RadialBasis.Gaussian: Creates a Gaussian basis function $e^{-ax^2}$.
GENERAL - Enum constant in enum class com.imsl.math.ComplexMatrix.MatrixType: Matrix is general rectangular.
GENERAL - Enum constant in enum class com.imsl.math.Matrix.MatrixType: Matrix is general rectangular.
GENERALIZED_LEAST_SQUARES - Static variable in class com.imsl.stat.FactorAnalysis: Indicates generalized least squares method.
generalizedExtremeValue(double, double, double, double) - Static method in class com.imsl.stat.Cdf: Evaluates the generalized extreme value cumulative distribution function.
generalizedExtremeValue(double, double, double, double) - Static method in class com.imsl.stat.InvCdf: Returns the inverse of the generalized extreme value cumulative distribution function.
generalizedExtremeValue(double, double, double, double) - Static method in class com.imsl.stat.Pdf: Evaluates the generalized extreme value probability density function.
GeneralizedExtremeValuePDEx1 - Class in com.imsl.test.example.stat.distributions
GeneralizedExtremeValuePDEx1() - Constructor for class com.imsl.test.example.stat.distributions.GeneralizedExtremeValuePDEx1
generalizedGaussian(double, double, double, double) - Static method in class com.imsl.stat.Cdf: Evaluates the generalized Gaussian (normal) cumulative distribution function.
generalizedGaussian(double, double, double, double) - Static method in class com.imsl.stat.InvCdf: Returns the inverse of the generalized Gaussian (normal) cumulative distribution function.
generalizedGaussian(double, double, double, double) - Static method in class com.imsl.stat.Pdf: Evaluates the generalized Gaussian (normal) probability density function.
GeneralizedGaussianPD - Class in com.imsl.stat.distributions: The generalized Gaussian probability distribution.
GeneralizedGaussianPD() - Constructor for class com.imsl.stat.distributions.GeneralizedGaussianPD: Constructs a generalized Gaussian probability distribution.
generalizedPareto(double, double, double, double) - Static method in class com.imsl.stat.Cdf: Evaluates the generalized Pareto cumulative distribution function.
generalizedPareto(double, double, double, double) - Static method in class com.imsl.stat.InvCdf: Returns the inverse of the generalized Pareto cumulative distribution function.
generalizedPareto(double, double, double, double) - Static method in class com.imsl.stat.Pdf: Evaluates the generalized Pareto probability density function.
GeneralizedParetoPDEx1 - Class in com.imsl.test.example.stat.distributions
GeneralizedParetoPDEx1() - Constructor for class com.imsl.test.example.stat.distributions.GeneralizedParetoPDEx1
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.Formatter - Class in com.imsl.math: Deprecated.
Use IMSLFormatter instead.
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.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.
GenMinResEx1 - Class in com.imsl.test.example.math: Solves a small linear system with the Generalized Minimum Residual (GMRES) method.
GenMinResEx1() - Constructor for class com.imsl.test.example.math.GenMinResEx1
GenMinResEx2 - Class in com.imsl.test.example.math: Solves a small linear system with user supplied inner product.
GenMinResEx2() - Constructor for class com.imsl.test.example.math.GenMinResEx2
GenMinResEx3 - Class in com.imsl.test.example.math: Solves a small linear system stored in sparse form.
GenMinResEx3() - Constructor for class com.imsl.test.example.math.GenMinResEx3
GenMinResEx4 - Class in com.imsl.test.example.math: Solves a small linear system stored in sparse form with preconditioning.
GenMinResEx4() - Constructor for class com.imsl.test.example.math.GenMinResEx4
GenMinResEx5 - Class in com.imsl.test.example.math: Solves the Poisson equation using the second Householder implementation.
GenMinResEx5() - Constructor for class com.imsl.test.example.math.GenMinResEx5
GenMinResEx6 - Class in com.imsl.test.example.math: Solves the Poisson equation using the second Householder implementation and preconditioning.
GenMinResEx6(int) - Constructor for class com.imsl.test.example.math.GenMinResEx6
GenMinResEx7 - Class in com.imsl.test.example.math: Solves a small linear system with logging.
GenMinResEx7() - Constructor for class com.imsl.test.example.math.GenMinResEx7
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.Cdf: Evaluates the discrete geometric cumulative probability distribution function.
geometric(int, double) - Static method in class com.imsl.stat.Pdf: Evaluates the geometric probability density (or mass) function.
GeometricPD - Class in com.imsl.stat.distributions: The geometric probability distribution.
GeometricPD() - Constructor for class com.imsl.stat.distributions.GeometricPD: Constructor for the geometric probability distribution.
GeometricPDEx1 - Class in com.imsl.test.example.stat.distributions: Evaluates the geometric probability distribution.
GeometricPDEx1() - Constructor for class com.imsl.test.example.stat.distributions.GeometricPDEx1
geometricProb(int, double) - Static method in class com.imsl.stat.Cdf: Deprecated.
Use Pdf.geometric(int, double) instead.
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.
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.
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: Gets 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 $W_t$.
getAR() - Method in class com.imsl.stat.AutoARIMA: Returns the final autoregressive parameter estimates of the optimum model.
getAR() - Method in class com.imsl.stat.GARCH: Deprecated.
Use GARCH.getGARCH() instead.
getARCH() - Method in class com.imsl.stat.ExtendedGARCH: Returns the value of the ARCH lag parameter for the given instance.
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() - Method in class com.imsl.stat.ANOVA: Returns the ANOVA values as an array.
getArray(int) - Method in class com.imsl.io.AbstractFlatFile: Deprecated.
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.
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.
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.
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.
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(int, int) - Method in class com.imsl.io.AbstractFlatFile: Deprecated.
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.
getBigDecimal(String, int) - Method in class com.imsl.io.AbstractFlatFile: Deprecated.
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.
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.
getBounds() - Method in class com.imsl.datamining.neural.ScaleFilter: Retrieves bounds used during bounded scaling.
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.
getCaseAnalysis() - Method in class com.imsl.stat.CategoricalGenLinModel: Returns the case analysis.
getCaseStatistics() - Method in class com.imsl.stat.LinearRegression.CaseStatistics: Deprecated.
Recommend using specific getter methods.
getCaseStatistics() - Method in class com.imsl.stat.ProportionalHazards: Returns the case statistics for each observation.
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, 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(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.
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.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.
getCentroidDistance() - Method in class com.imsl.math.NelderMead: Returns the average distance from the final complex vertices to their centroid.
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.
getChildId(int) - Method in class com.imsl.datamining.decisionTree.TreeNode: Returns the id of a child 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 the (assumed) common variance.
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 variance.
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 value than the chi-squared statistic associated with the test for the common variance, assuming the null hypothesis is true (i.e., the p-value for the test).
getClassCounts() - Method in class com.imsl.datamining.PredictiveModel: Returns the counts of each class (level) of the categorical response variable.
getClassCounts(int[]) - Method in class com.imsl.datamining.NaiveBayesClassifier: Returns the number of patterns for each target classification.
getClassErrors(double[], double[]) - Method in class com.imsl.datamining.PredictiveModel: Returns classification error information.
getClassErrors(int[], int[]) - Method in class com.imsl.datamining.PredictiveModel: Returns classification error information.
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.
getClassLabels() - Method in class com.imsl.datamining.PredictiveModel: Returns the current class labels for a categorical response variable.
getClassLabels() - Method in class com.imsl.datamining.supportvectormachine.SVModel: Returns the class labels.
getClassMeans() - Method in class com.imsl.datamining.LogisticRegression: Returns the class means.
getClassMembership() - Method in class com.imsl.stat.DiscriminantAnalysis: Returns the group number to which the observation was classified.
getClassPenaltyWeights() - Method in class com.imsl.datamining.supportvectormachine.SVClassification: Returns the class weights.
getClassPredictorValues() - Method in class com.imsl.datamining.GradientBoostingModelObject: Returns the predictor function values $\{f(x_i)\}$ on the test data for a categorical (binomial or multinomial) response variable.
getClassProbabilities() - Method in class com.imsl.datamining.GradientBoosting: Returns the predicted probabilities on the training data for a categorical response variable.
getClassProbabilities() - Method in class com.imsl.datamining.GradientBoostingModelObject: Returns the predicted probabilities on the test data for a categorical response variable.
getClassProbabilities() - Method in class com.imsl.datamining.PredictiveModel: Returns a matrix containing the predicted class probabilities for each observation in the training data
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.
getClassWeightLabels() - Method in class com.imsl.datamining.supportvectormachine.SVClassification: Returns the weight labels array.
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.
getClosedFormMLE(double[]) - Method in class com.imsl.stat.distributions.BinomialPD: Returns the closed form maximum likelihood estimate.
getClosedFormMLE(double[]) - Method in interface com.imsl.stat.distributions.ClosedFormMaximumLikelihoodInterface: Returns the maximum likelihood estimates (MLEs).
getClosedFormMLE(double[]) - Method in class com.imsl.stat.distributions.ContinuousUniformPD: Returns the closed form maximum likelihood estimates.
getClosedFormMLE(double[]) - Method in class com.imsl.stat.distributions.DiscreteUniformPD: Returns the maximum likelihood estimate for the parameter.
getClosedFormMLE(double[]) - Method in class com.imsl.stat.distributions.ExponentialPD: Returns the closed form maximum likelihood estimates.
getClosedFormMLE(double[]) - Method in class com.imsl.stat.distributions.GeometricPD: Returns the maximum likelihood estimate for the parameter.
getClosedFormMLE(double[]) - Method in class com.imsl.stat.distributions.InverseGaussianPD: Returns the closed form maximum likelihood estimates.
getClosedFormMLE(double[]) - Method in class com.imsl.stat.distributions.LogNormalPD: Returns the closed form maximum likelihood estimates.
getClosedFormMLE(double[]) - Method in class com.imsl.stat.distributions.NegativeBinomialPD: Returns the closed form maximum likelihood estimate.
getClosedFormMLE(double[]) - Method in class com.imsl.stat.distributions.NormalPD: Returns the closed form maximum likelihood estimates.
getClosedFormMLE(double[]) - Method in class com.imsl.stat.distributions.PoissonPD: Returns the closed form maximum likelihood estimate.
getClosedFormMLE(double[]) - Method in class com.imsl.stat.distributions.RayleighPD: Returns the closed form maximum likelihood estimate.
getClosedFormMlStandardError(double[]) - Method in class com.imsl.stat.distributions.BinomialPD: Returns the standard error based on the closed form maximum likelihood estimate.
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.
getClosedFormMlStandardError(double[]) - Method in class com.imsl.stat.distributions.ContinuousUniformPD: Returns the standard error based on the closed form maximum likelihood estimates.
getClosedFormMlStandardError(double[]) - Method in class com.imsl.stat.distributions.DiscreteUniformPD: Returns the standard error of the maximum likelihood estimate.
getClosedFormMlStandardError(double[]) - Method in class com.imsl.stat.distributions.ExponentialPD: Returns the standard error based on the closed form maximum likelihood estimates.
getClosedFormMlStandardError(double[]) - Method in class com.imsl.stat.distributions.GeometricPD: Returns the standard error of the maximum likelihood estimate.
getClosedFormMlStandardError(double[]) - Method in class com.imsl.stat.distributions.InverseGaussianPD: Returns the standard errors of the closed form maximum likelihood estimates.
getClosedFormMlStandardError(double[]) - Method in class com.imsl.stat.distributions.LogNormalPD: Returns the standard errors of the closed form maximum likelihood estimates.
getClosedFormMlStandardError(double[]) - Method in class com.imsl.stat.distributions.NegativeBinomialPD: Returns the standard error based on the closed form maximum likelihood estimate.
getClosedFormMlStandardError(double[]) - Method in class com.imsl.stat.distributions.NormalPD: Returns the standard errors of the closed form maximum likelihood estimates.
getClosedFormMlStandardError(double[]) - Method in class com.imsl.stat.distributions.PoissonPD: Returns the standard error based on the closed form maximum likelihood estimate.
getClosedFormMlStandardError(double[]) - Method in class com.imsl.stat.distributions.RayleighPD: Returns the standard error based on the closed form maximum likelihood estimate.
getClusterCounts() - Method in class com.imsl.stat.ClusterKMeans: Returns the number of observations in each cluster.
getClusterCounts() - Method in class com.imsl.stat.DBSCAN: 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() - Method in class com.imsl.stat.ClusterKMeans: Returns the cluster membership for each observation.
getClusterMembership() - Method in class com.imsl.stat.DBSCAN: Returns the cluster membership for each observation.
getClusterMembership(int) - Method in class com.imsl.stat.ClusterHierarchical: Returns the cluster membership of each observation.
getClusterRightSons() - Method in class com.imsl.stat.ClusterHierarchical: Returns the right sons of each merged cluster.
getClusters() - Method in class com.imsl.stat.DBSCAN: Returns the clusters and outliers computed by DBSCAN.
getClusterSSQ() - Method in class com.imsl.stat.ClusterKMeans: Returns the within sum of squares for each cluster.
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.datamining.LogisticRegression: Returns the current values of the coefficient estimates.
getCoefficients() - Method in class com.imsl.datamining.LogisticRegressionModelObject: Returns the coefficients.
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.
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.
getConcurrency() - Method in class com.imsl.io.AbstractFlatFile: Returns the concurrency mode of this ResultSet object.
getConditionalVariance() - Method in class com.imsl.stat.ExtendedGARCH: Returns the current conditional variance array.
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() - Method in class com.imsl.stat.LinearRegression.CaseStatistics: Returns the Confidence Interval of the population mean for an observation.
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.
getConfidenceIntervals() - Method in class com.imsl.datamining.LogisticRegression: Returns the confidence intervals for the predictions.
getConfidenceLevel() - Method in class com.imsl.datamining.LogisticRegression: Returns the confidence level for calculating the confidence limits for the predictions.
getConfiguration() - Method in class com.imsl.stat.MultidimensionalScaling: Returns the configuration matrix.
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.
getContinuousPredictorValues() - Method in class com.imsl.datamining.GradientBoostingModelObject: Returns the predictor function values $\{f(x_i)\}$ for a continuous response variable on the test data.
getContributions() - Method in class com.imsl.stat.ContingencyTable: Returns the contributions to chi-squared for each cell in the table.
getConvergenceTol() - Method in class com.imsl.stat.ProportionalHazards: Returns the convergence tolerance used.
getConvergenceTolerance() - Method in class com.imsl.datamining.supportvectormachine.SupportVectorMachine: Returns the convergence tolerance.
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.
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
getCountXY(double[][], int, int, int, int, int[], int[], double[]) - Method in class com.imsl.datamining.decisionTree.DecisionTreeInfoGain: Calculates a two-way frequency table with input frequencies.
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.
getCramersV() - Method in class com.imsl.stat.ContingencyTable: Returns Cramer's V.
getCriteriaValueCategorical(double[][], double[], int, int) - Method in class com.imsl.datamining.decisionTree.DecisionTreeInfoGain: Calculates and returns the value of the criterion on the node represented by the data set S = xy.
getCriterionFunctionWeights() - Method in class com.imsl.stat.MultidimensionalScaling: Returns the criterion function weight for each subject.
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 best criterion values for variable subsets of a given size.
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.
getCursorName() - Method in class com.imsl.io.AbstractFlatFile: Gets the name of the SQL cursor used by this ResultSet object.
getCutpoints() - Method in class com.imsl.stat.ChiSquaredTest: Returns the cutpoints.
getDataLength() - Method in class com.imsl.datamining.SequenceDatabase: Returns the length of the sequence data.
getDataSeries() - Method in class com.imsl.stat.ExtendedGARCH: Returns the data series, usually the time series of asset returns.
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(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) - 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, 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.
getDaysInYear(GregorianCalendar, GregorianCalendar) - Method in interface com.imsl.finance.BasisPart: Deprecated.
Use BasisPart.getDaysInYear(GregorianCalendar) instead.
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.
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.
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 sample means.
getDiffMean() - Method in class com.imsl.stat.WelchsTTest: Returns the difference in sample means.
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.
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.
getDistances() - Method in class com.imsl.stat.MultidimensionalScaling: Returns the predicted distances.
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.
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.LinearProgramming: Deprecated.

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.
getDualSolution() - Method in class com.imsl.math.Transport: Returns the dual solution of the transportation problem.
getDummyMethod() - Method in class com.imsl.stat.RegressorsForGLM: Returns the dummy method.
getDunnSidak(int, int) - Method in class com.imsl.stat.ANOVA: Deprecated.
Use ANOVA.getConfidenceInterval(double, int, int, int) instead.
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.
getEpochSize() - Method in class com.imsl.datamining.neural.EpochTrainer: Returns the number of sample training patterns in each stage 1 epoch.
getEpsilon() - Method in class com.imsl.stat.DBSCAN.DBSCANParams: Returns the epsilon radius.
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.LicenseException: 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.
getExpectedValues() - Method in class com.imsl.stat.ContingencyTable: Returns the expected values of each cell in the table.
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.
getFactorScoreCoefficients(FactorAnalysis.ScoreMethod) - Method in class com.imsl.stat.FactorAnalysis: Computes the matrix of factor score coefficients.
getFactorScores(double[][], double[][]) - Method in class com.imsl.stat.FactorAnalysis: Computes factor scores.
getFeature() - Method in exception com.imsl.LicenseException: 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.
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.
getFinalLogLikelihood() - Method in class com.imsl.stat.ExtendedGARCH: Returns the value of the log-likelihood at the final estimates of the parameters.
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.
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.
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
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.QuasiNewtonTrainer: Returns the logging formatter object.
getFormatter() - Static method in class com.imsl.datamining.neural.Trace: Returns the formatter.
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() - Method in class com.imsl.stat.TableTwoWay: Returns the two-way frequency table.
getFrequencyTable(double, double) - Method in class com.imsl.stat.TableOneWay: Returns a one-way frequency table using known bounds.
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.
getFrequentSequences(double) - Method in class com.imsl.datamining.PrefixSpan: Returns the frequent subsequences (sequential patterns) determined by the given minimum support percentage.
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 statistic value calculated in an 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 in the $F$-test for equality of variances.
getFTestP() - Method in class com.imsl.stat.NormTwoSample: Returns the probability of a larger (in absolute value) F statistic value, assuming equal variances (i.e., the p-value for the test).
getGammaList() - Method in class com.imsl.datamining.GradientBoosting: Returns the gradient descent minimizing values calculated at each iteration for continuous response variables.
getGammaListMNL() - Method in class com.imsl.datamining.GradientBoosting: Returns the gradient descent minimizing values calculated at each iteration for categorical response variables.
getGARCH() - Method in class com.imsl.stat.ExtendedGARCH: Returns the value of the GARCH lag parameter for the given instance.
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.datamining.LogisticRegression: Returns the gradient.
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.
getGridType() - Method in class com.imsl.datamining.KohonenSOM: Returns the grid type.
getGroupCounts() - Method in class com.imsl.stat.DiscriminantAnalysis: Returns the group counts.
getGroupCounts() - Method in class com.imsl.stat.PooledCovariances: Returns the number of observations in each group.
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.
getHessian() - Method in class com.imsl.datamining.LogisticRegression: Returns the Hessian matrix.
getHessian() - Method in class com.imsl.datamining.LogisticRegressionModelObject: Returns the Hessian matrix.
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.
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.
getHuberDeltas() - Method in class com.imsl.datamining.GradientBoosting: Returns the values of the Huber parameter, $\delta_m$, calculated at each iteration during training with the HUBER_M loss function.
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 variable subsets of a given size 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.datamining.supportvectormachine.DataNode: Returns the index of the node.
getIndex() - Method in class com.imsl.stat.Dissimilarities: Returns the indices of the rows (columns) used in computing the distance measure.
getIndices() - Method in class com.imsl.stat.RandomSamples: Returns the indices computed from a call to getSamples().
getInfo() - Method in class com.imsl.math.ComplexSVD: Returns convergence information about the singular values.
getInfo() - Method in class com.imsl.math.SVD: Returns convergence information about S, U, and V.
getInitialCenters() - Method in class com.imsl.stat.ClusterKMeans: Returns the initial cluster centers.
getInitialComplex() - Method in class com.imsl.math.NelderMead: Returns the initial complex.
getInitialEstimates() - Method in class com.imsl.stat.ProportionalHazards: Gets the initial parameter estimates.
getInitialLoglikelihood() - Method in class com.imsl.datamining.LogisticRegression: Returns the log-likelihood of the model evaluated at the starting coefficients.
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.
getInitialValue() - Method in class com.imsl.datamining.GradientBoosting: Returns the initial value of the predictor function $f_0$.
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.
getInsensitivityBand() - Method in class com.imsl.datamining.supportvectormachine.SVRegression: Returns the insensitivity band parameter, $\epsilon$, in the standard formulation of the SVM regression problem.
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.
getIntegrationMethod() - Method in class com.imsl.math.OdeAdamsGear: Returns the integration method used.
getIntercept() - Method in class com.imsl.stat.StepwiseRegression: Returns the intercept.
getIntercepts() - Method in class com.imsl.stat.MultidimensionalScaling: Returns the intercept for each subject.
getInverse() - Method in class com.imsl.math.ComplexSVD: Compute the Moore-Penrose generalized inverse.
getInverse() - Method in class com.imsl.math.SVD: Compute the Moore-Penrose generalized inverse.
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.
getIterations(int) - Method in class com.imsl.math.ZeroFunction: Deprecated.

Returns the number of iterations used to compute a root.
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.
getKernel() - Method in class com.imsl.datamining.supportvectormachine.SupportVectorMachine: Returns the kernel object being used in the optimization.
getKernelParameters() - Method in class com.imsl.datamining.supportvectormachine.SupportVectorMachine: Returns the kernel parameters.
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.
getLagrangeMultiplerEst() - Method in class com.imsl.math.MinConGenLin: Deprecated.
Method name misspelled. Use MinConGenLin.getLagrangeMultiplierEst() instead.
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.
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.
getLearningCoefficient(int) - Method in class com.imsl.test.example.datamining.KohonenSOMEx1
getLeftEndTangent() - Method in class com.imsl.math.CsTCB: Returns the value of the tangent at the leftmost endpoint.
getLength() - Method in class com.imsl.stat.TimeSeries: Returns the length of the time series.
getLeverage() - Method in class com.imsl.stat.LinearRegression.CaseStatistics: Returns the Leverage for an observation.
getLicensePath() - Method in exception com.imsl.LicenseException: 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.
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 $-2\ln(L)$, where $L$ is equal to the likelihood function evaluated using the final parameter estimates.
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.
getLocalizedMessage() - Method in exception com.imsl.LicenseException: 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 * \sigma^2 $ is the variance-covariance matrix of the observations.
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.
getLogger(String) - Static method in class com.imsl.datamining.neural.Trace: Returns a logger.
getLoglikelihood() - Method in class com.imsl.datamining.LogisticRegression: Returns the log-likelihood evaluated at the estimated coefficients.
getLoglikelihood() - Method in class com.imsl.datamining.LogisticRegressionModelObject: Returns the log-likeliood.
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.
getLogLikelihood(double[], double...) - Method in class com.imsl.stat.distributions.MaximumLikelihoodEstimation: Returns the log-likelihood.
getLogLikelihoodRatio() - Method in class com.imsl.datamining.LogisticRegression: Returns the log-likelihood ratio statistic.
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.
getLowerBound() - Method in class com.imsl.math.SparseLP: Returns the lower bound on the variables.
getLowerBound(int) - Method in class com.imsl.io.MPSReader: Returns the lower bound for a variable.
getLowerCICommonVariance() - Method in class com.imsl.stat.NormTwoSample: Returns the lower confidenceVariance $*100%$ confidence limit for the common variance.
getLowerCIDiff() - Method in class com.imsl.stat.NormTwoSample: Returns the lower confidence limit for the difference, $\mu_x - \mu_y$ for equal or unequal variances depending on the value set by setUnequalVariances.
getLowerCIDiff() - Method in class com.imsl.stat.WelchsTTest: Returns the (approximate) lower confidenceMean*100% confidence limit for the difference in population means, $\mu_x - \mu_y$.
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 in an interval estimate for the ratio of variances, $\sigma_1^2/\sigma_2^2$.
getLowerCIVariance() - Method in class com.imsl.stat.NormOneSample: Returns the lower confidence limits for the variance.
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.
getMA() - Method in class com.imsl.stat.GARCH: Deprecated.
Use GARCH.getARCH() instead.
getMahalanobis() - Method in class com.imsl.stat.DiscriminantAnalysis: Returns the Mahalanobis distances between the group means.
getMannWhitney() - Method in class com.imsl.stat.WilcoxonRankSum: Returns the Mann-Whitney test statistic.
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.
getMaxFunctionEvaluations() - Method in class com.imsl.stat.ARMA: Returns the maximum number of function evaluations.
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.
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.ComplexEigen: Returns the maximum number of iterations.
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.math.Transport: Returns the maximum iteration number allowed in the solution of the transportation problem.
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.ARMA: Returns the maximum number of iterations.
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 categories allowed.
getMaxNumberOfIterations() - Method in class com.imsl.datamining.PredictiveModel: Returns the maximum number of iterations allowed for the fitting procedure or training algorithm.
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.
getMeanCenteredSeries(TimeSeries) - Method in class com.imsl.stat.TimeSeriesOperations: Returns the mean-centered values of all the series in a TimeSeries object.
getMeanCenteredSeries(TimeSeries, int) - Method in class com.imsl.stat.TimeSeriesOperations: Returns the mean-centered values of the k-th series in a TimeSeries object.
getMeanEstimate() - Method in class com.imsl.stat.ARMA: Deprecated.
Use ARMA.getMean() instead.
getMeanModel() - Method in class com.imsl.stat.ExtendedGARCH: Returns the mean ARMA model specification as an integer array, $\{p,d,q\}$.
getMeanModelParameters() - Method in class com.imsl.stat.ExtendedGARCH: Returns the current estimates of the mean model parameters.
getMeanOfY() - Method in class com.imsl.stat.ANOVA: Returns the mean of the response (dependent variable).
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.PooledCovariances: Returns the means of each group.
getMeans() - Method in class com.imsl.stat.ProportionalHazards: Returns the means of the design variables.
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.
getMeanSquaredPredictionError() - Method in class com.imsl.datamining.BootstrapAggregation: Deprecated.
Renamed to BootstrapAggregation.getPredictionError().
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.
getMeanX() - Method in class com.imsl.stat.WelchsTTest: Returns the mean of the first sample.
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.
getMeanY() - Method in class com.imsl.stat.WelchsTTest: Returns the mean of the second sample.
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.
getMethodOfMomentsEstimates(double[]) - Method in class com.imsl.stat.distributions.BinomialPD: Returns the Method of Moments estimate given the sample data.
getMethodOfMomentsEstimates(double[]) - Method in class com.imsl.stat.distributions.GeometricPD: Returns the method-of-moments estimates given the sample data.
getMethodOfMomentsEstimates(double[]) - Method in class com.imsl.stat.distributions.LogisticPD: Returns the method-of-moments estimates given the sample data.
getMethodOfMomentsEstimates(double[]) - Method in class com.imsl.stat.distributions.LogLogisticPD: Returns the method-of-moments estimates given the sample data.
getMethodOfMomentsEstimates(double[]) - Method in class com.imsl.stat.distributions.LogNormalPD: Returns the method-of-moments estimates given the sample data.
getMethodOfMomentsEstimates(double[]) - Method in class com.imsl.stat.distributions.ParetoPD: Returns the method-of-moments estimates given the sample data.
getMethodOfMomentsEstimates(double[]) - Method in class com.imsl.stat.distributions.PoissonPD: Returns the method-of-moments estimate given the sample data.
getMethodOfMomentsEstimates(double[]) - Method in class com.imsl.stat.distributions.RayleighPD: Returns the method-of-moments estimate given the sample data.
getMethodOfMomentsEstimates(double[]) - Method in class com.imsl.stat.distributions.WeibullPD: Returns the method-of-moments estimates given the sample data.
getMinCostComplexityValue() - Method in class com.imsl.datamining.decisionTree.DecisionTree
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.
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.
getMinPoints() - Method in class com.imsl.stat.DBSCAN.DBSCANParams: Returns the minimum number of points in the epsilon neighborhood of a core point.
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 indicating 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: Deprecated.
Use NormalPD.getClosedFormMLE(double[]) instead.
getModel() - Method in class com.imsl.datamining.supportvectormachine.SupportVectorMachine: Returns the model object.
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.
getMultinomialResponse() - Method in class com.imsl.datamining.GradientBoosting: Returns the multinomial representation of the response variable.
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.
getNeighborhoodValue(int, double) - Method in class com.imsl.test.example.datamining.KohonenSOMEx1
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.
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.
getNodeAssigments(double[][]) - Method in class com.imsl.datamining.decisionTree.Tree: Returns the node assignments for testData using the tree.
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.
getNodeSplitCriteriaValue() - Method in class com.imsl.datamining.decisionTree.TreeNode: Returns the value of the split criteria at the node.
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 categorical
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).
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.
getNRowsMissing() - Method in class com.imsl.stat.DiscriminantAnalysis: Deprecated.
Use DiscriminantAnalysis.getNumberOfRowsMissing() instead.
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.
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.
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.
getNumberOfBackcasts() - Method in class com.imsl.stat.ARMA: Returns the number of backcasts used to calculate the AR coefficients for the time series z.
getNumberOfBestRegressions() - Method in class com.imsl.stat.SelectionRegression: Returns the number of best regression models computed.
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.LogisticRegressionModelObject: Returns the number of classes.
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 distinct classes found (or set) in the categorical response data.
getNumberOfCoefficients() - Method in class com.imsl.datamining.LogisticRegression: Returns the number of coefficients (per class).
getNumberOfCoefficients() - Method in class com.imsl.datamining.LogisticRegressionModelObject: Returns the number of coefficients (per class).
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 the training data 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: Returns the number of cost complexity values determined by the pruning algorithm.
getNumberOfCustomers() - Method in class com.imsl.datamining.SequenceDatabase: Returns the number of customers.
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.
getNumberOfFunctionEvaluations() - Method in class com.imsl.math.NelderMead: The number of function evaluations needed.
getNumberOfGroups() - Method in class com.imsl.stat.PooledCovariances: Returns the number of groups used in the analysis.
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.
getNumberOfItems() - Method in class com.imsl.datamining.SequenceDatabase: Returns the number of items.
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.PooledCovariances: Returns the total number of observations that contain missing values (Double.NaN or group[i] == 0).
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.math.ComplexSparseCholesky: Returns the number of nonzeros in the Cholesky factor.
getNumberOfNonzeros() - Method in class com.imsl.math.SparseCholesky: Returns the number of nonzeros in the Cholesky factor.
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.ComplexSparseMatrix: Returns the number of nonzeros in the matrix.
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.
getNumberOfOriginalSequences() - Method in class com.imsl.datamining.SequenceDatabase: Returns the number of original sequences for this SequenceDatabase.
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.
getNumberOfParameters() - Method in class com.imsl.stat.ExtendedGARCH: Returns the number of parameters in the Extended GARCH model.
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.
getNumberOfRandomFeatures() - Method in class com.imsl.datamining.decisionTree.DecisionTree: Returns the number of random features used in the splitting rules when randomFeatureSelection=true.
getNumberOfRandomFeatures() - Method in class com.imsl.datamining.decisionTree.RandomTrees: Returns the number of random features used in the splitting rules.
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 (observations) in the training data.
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.
getNumberOfSamples() - Method in class com.imsl.datamining.BootstrapAggregation: Returns the number of bootstrap samples.
getNumberOfSequences() - Method in class com.imsl.datamining.SequenceDatabase: Returns the number of sequences contained in the sequence data.
getNumberOfSeries() - Method in class com.imsl.stat.TimeSeries: Returns the number of series stored in this TimeSeries object.
getNumberOfSteps() - Method in class com.imsl.math.OdeAdamsGear: Returns the number of internal steps taken.
getNumberOfSuccesses() - Method in class com.imsl.stat.distributions.NegativeBinomialPD: Returns the number of successes.
getNumberOfSupportVectors() - Method in class com.imsl.datamining.supportvectormachine.SVModel: Returns the number of support vectors.
getNumberOfSupportVectorsPerClass() - Method in class com.imsl.datamining.supportvectormachine.SVModel: Returns the number of support vectors per class.
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.NelderMead: 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.math.Transport: 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.
getNumberOfThreads() - Method in class com.imsl.stat.DBSCAN: 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.
getNumberOfTrees() - Method in class com.imsl.datamining.decisionTree.RandomTrees: Returns the number of trees.
getNumberOfTrials() - Method in class com.imsl.stat.distributions.BinomialPD: Returns the number of independent Bernoulli trials.
getNumberOfUniquePredictorValues() - Method in class com.imsl.datamining.PredictiveModel: Returns an array containing the number of distinct values of each predictor found in the input data.
getNumberOfVariables() - Method in class com.imsl.stat.PooledCovariances: Returns the number of variables used in the analysis.
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.
getNuParameter() - Method in class com.imsl.datamining.supportvectormachine.SupportVectorMachine: Returns the value of the $\nu$ parameter.
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(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.
getObject(int, Class<T>) - 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, 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) - 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, Class<T>) - 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.
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.
getObjectiveValue() - Method in class com.imsl.math.NelderMead: Returns the value of the objective function at the computed solution.
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.
getOmegaWeights() - Method in class com.imsl.stat.ARMAOutlierIdentification: Returns the $\omega$ 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.
getOptimalRouting() - Method in class com.imsl.math.Transport: Returns the optimal routing.
getOptimalValue() - Method in class com.imsl.math.DenseLP: Returns the optimal value of the objective function.
getOptimalValue() - Method in class com.imsl.math.LinearProgramming: Deprecated.

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 $(p,0,q)\times(0,d,0)_s$ of the optimum model.
getOptimumS() - Method in class com.imsl.stat.ARSeasonalFit: Returns the optimum values for s selected among the candidates in sInitial.
getOrder() - Method in class com.imsl.stat.ARAutoUnivariate: Returns the order of the AR model selected with the minimum AIC.
getOrder() - Method in class com.imsl.test.example.math.RadialBasisEx2.PolyHarmonicSpline
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.
getOutOfBagPredictionError() - Method in class com.imsl.datamining.BootstrapAggregation: Returns the out-of-bag mean squared prediction error for regression problems, or the out-of-bag classification percentage error for classification problems.
getOutOfBagPredictionError() - Method in class com.imsl.datamining.decisionTree.RandomTrees: Returns the out-of-bag prediction error.
getOutOfBagPredictions() - Method in class com.imsl.datamining.BootstrapAggregation: Returns the out-of-bag predicted values.
getOutOfBagPredictions() - Method in class com.imsl.datamining.decisionTree.RandomTrees: Returns the out-of-bag predicted values for the examples in the training data.
getOutputLayer() - Method in class com.imsl.datamining.neural.FeedForwardNetwork: Returns the OutputLayer.
getOutputLayer() - Method in class com.imsl.datamining.neural.Network: Returns the OutputLayer.
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.
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 of the parameters.
getParameterLowerBounds() - Method in class com.imsl.stat.distributions.BinomialPD: Returns the lower bound of the parameter.
getParameterLowerBounds() - Method in class com.imsl.stat.distributions.ContinuousUniformPD: Returns the lower bounds of the parameters.
getParameterLowerBounds() - Method in class com.imsl.stat.distributions.DiscreteUniformPD: Returns the lower bound of the parameter.
getParameterLowerBounds() - Method in class com.imsl.stat.distributions.ExponentialPD: Returns the lower bound for the parameter.
getParameterLowerBounds() - Method in class com.imsl.stat.distributions.ExtremeValuePD: Returns the lower bounds of the parameters.
getParameterLowerBounds() - Method in class com.imsl.stat.distributions.GammaPD: Returns the lower bounds of the parameters.
getParameterLowerBounds() - Method in class com.imsl.stat.distributions.GeneralizedGaussianPD: Returns the lower bounds of the parameters.
getParameterLowerBounds() - Method in class com.imsl.stat.distributions.GeometricPD: Returns the lower bound of the parameter.
getParameterLowerBounds() - Method in class com.imsl.stat.distributions.InverseGaussianPD: Returns the lower bounds of the parameters.
getParameterLowerBounds() - Method in class com.imsl.stat.distributions.LogisticPD: Returns the lower bounds of the parameters.
getParameterLowerBounds() - Method in class com.imsl.stat.distributions.LogLogisticPD: Returns the lower bounds of the parameters.
getParameterLowerBounds() - Method in class com.imsl.stat.distributions.LogNormalPD: Returns the lower bounds of the parameters.
getParameterLowerBounds() - Method in class com.imsl.stat.distributions.NegativeBinomialPD: Returns the lower bound of the parameter.
getParameterLowerBounds() - Method in class com.imsl.stat.distributions.NormalPD: Returns the lower bounds of the parameters.
getParameterLowerBounds() - Method in class com.imsl.stat.distributions.ParetoPD: Returns the lower bounds of the parameters.
getParameterLowerBounds() - Method in class com.imsl.stat.distributions.PoissonPD: Returns the lower bound of the parameter.
getParameterLowerBounds() - Method in class com.imsl.stat.distributions.ProbabilityDistribution: Returns the lower bounds of the parameters.
getParameterLowerBounds() - Method in class com.imsl.stat.distributions.RayleighPD: Returns the lower bound for the parameter.
getParameterLowerBounds() - Method in class com.imsl.stat.distributions.WeibullPD: Returns the lower bounds of the parameters.
getParameterLowerBounds() - Method in class com.imsl.stat.ExtendedGARCH: Returns the parameter lower bounds.
getParameters() - Method in class com.imsl.datamining.supportvectormachine.Kernel: Returns the kernel parameters.
getParameters() - Method in class com.imsl.stat.CategoricalGenLinModel: Returns the parameter estimates and associated statistics.
getParameters() - Method in class com.imsl.stat.ExtendedGARCH: Returns the (current) parameter estimates of the extended GARCH object.
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.
getParameters() - Method in class com.imsl.test.example.datamining.NaiveBayesClassifierEx3.TestGaussFcn1
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 of the parameters.
getParameterUpperBounds() - Method in class com.imsl.stat.distributions.BinomialPD: Returns the upper bound of the parameter.
getParameterUpperBounds() - Method in class com.imsl.stat.distributions.ContinuousUniformPD: Returns the upper bounds of the parameters.
getParameterUpperBounds() - Method in class com.imsl.stat.distributions.DiscreteUniformPD: Returns the upper bound of the parameter.
getParameterUpperBounds() - Method in class com.imsl.stat.distributions.ExponentialPD: Returns the upper bound for the parameter.
getParameterUpperBounds() - Method in class com.imsl.stat.distributions.ExtremeValuePD: Returns the upper bounds of the parameters.
getParameterUpperBounds() - Method in class com.imsl.stat.distributions.GammaPD: Returns the upper bounds of the parameters.
getParameterUpperBounds() - Method in class com.imsl.stat.distributions.GeneralizedGaussianPD: Returns the upper bounds of the parameters.
getParameterUpperBounds() - Method in class com.imsl.stat.distributions.GeometricPD: Returns the upper bound of the parameter.
getParameterUpperBounds() - Method in class com.imsl.stat.distributions.InverseGaussianPD: Returns the upper bounds of the parameters.
getParameterUpperBounds() - Method in class com.imsl.stat.distributions.LogisticPD: Returns the upper bounds of the parameters.
getParameterUpperBounds() - Method in class com.imsl.stat.distributions.LogLogisticPD: Returns the upper bounds of the parameters.
getParameterUpperBounds() - Method in class com.imsl.stat.distributions.LogNormalPD: Returns the upper bounds of the parameters.
getParameterUpperBounds() - Method in class com.imsl.stat.distributions.NegativeBinomialPD: Returns the upper bound of the parameter.
getParameterUpperBounds() - Method in class com.imsl.stat.distributions.NormalPD: Returns the upper bounds of the parameters.
getParameterUpperBounds() - Method in class com.imsl.stat.distributions.ParetoPD: Returns the upper bounds of the parameters.
getParameterUpperBounds() - Method in class com.imsl.stat.distributions.PoissonPD: Returns the upper bound of the parameter.
getParameterUpperBounds() - Method in class com.imsl.stat.distributions.ProbabilityDistribution: Returns the upper bounds of the parameters.
getParameterUpperBounds() - Method in class com.imsl.stat.distributions.RayleighPD: Returns the upper bound for the parameter.
getParameterUpperBounds() - Method in class com.imsl.stat.distributions.WeibullPD: Returns the upper bounds of the parameters.
getParameterUpperBounds() - Method in class com.imsl.stat.ExtendedGARCH: Returns the parameter upper bounds.
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.BinomialPD: Returns the analytic gradient of the pdf.
getPDFGradient(double, double...) - Method in class com.imsl.stat.distributions.ContinuousUniformPD: Returns the analytic gradient of the pdf.
getPDFGradient(double, double...) - Method in class com.imsl.stat.distributions.DiscreteUniformPD: Returns the analytic gradient of the pdf evaluated at x.
getPDFGradient(double, double...) - Method in class com.imsl.stat.distributions.ExponentialPD: Returns the analytic gradient of the pdf.
getPDFGradient(double, double...) - Method in class com.imsl.stat.distributions.ExtremeValuePD: Returns the analytic gradient of the pdf.
getPDFGradient(double, double...) - Method in class com.imsl.stat.distributions.GeometricPD: Returns the analytic gradient of the pdf evaluated at x.
getPDFGradient(double, double...) - Method in class com.imsl.stat.distributions.InverseGaussianPD: Returns the analytic gradient of the pdf.
getPDFGradient(double, double...) - Method in class com.imsl.stat.distributions.LogisticPD: Returns the analytic gradient of the pdf.
getPDFGradient(double, double...) - Method in class com.imsl.stat.distributions.LogLogisticPD: Returns the analytic gradient of the pdf.
getPDFGradient(double, double...) - Method in class com.imsl.stat.distributions.LogNormalPD: Returns the analytic gradient of the pdf evaluated at x.
getPDFGradient(double, double...) - Method in class com.imsl.stat.distributions.NegativeBinomialPD: Returns the analytic gradient of the pdf.
getPDFGradient(double, double...) - Method in class com.imsl.stat.distributions.NormalPD: Returns the analytic gradient of the pdf.
getPDFGradient(double, double...) - Method in class com.imsl.stat.distributions.ParetoPD: Returns the analytic gradient of the pdf.
getPDFGradient(double, double...) - Method in interface com.imsl.stat.distributions.PDFGradientInterface: Returns the gradient of the probability density function.
getPDFGradient(double, double...) - Method in class com.imsl.stat.distributions.PoissonPD: Returns the analytic gradient of the pdf.
getPDFGradient(double, double...) - Method in class com.imsl.stat.distributions.RayleighPD: Returns the analytic gradient of the pdf.
getPDFGradient(double, double...) - Method in class com.imsl.stat.distributions.WeibullPD: Returns the analytic gradient of the pdf.
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.BinomialPD: Returns the analytic Hessian matrix of the pdf.
getPDFHessian(double, double...) - Method in class com.imsl.stat.distributions.ContinuousUniformPD: Returns the analytic Hessian of the pdf.
getPDFHessian(double, double...) - Method in class com.imsl.stat.distributions.DiscreteUniformPD: Returns the analytic Hessian matrix evaluated at x.
getPDFHessian(double, double...) - Method in class com.imsl.stat.distributions.ExponentialPD: Returns the analytic Hessian of the pdf.
getPDFHessian(double, double...) - Method in class com.imsl.stat.distributions.ExtremeValuePD: Returns the analytic Hessian of the pdf.
getPDFHessian(double, double...) - Method in class com.imsl.stat.distributions.GeometricPD: Returns the analytic Hessian matrix evaluated at x.
getPDFHessian(double, double...) - Method in class com.imsl.stat.distributions.InverseGaussianPD: Returns the analytic Hessian matrix of the pdf.
getPDFHessian(double, double...) - Method in class com.imsl.stat.distributions.LogisticPD: Returns the analytic Hessian of the pdf.
getPDFHessian(double, double...) - Method in class com.imsl.stat.distributions.LogLogisticPD: Returns the analytic Hessian of the pdf.
getPDFHessian(double, double...) - Method in class com.imsl.stat.distributions.LogNormalPD: Returns the analytic Hessian of the pdf evaluated at x.
getPDFHessian(double, double...) - Method in class com.imsl.stat.distributions.NegativeBinomialPD: Returns the analytic Hessian matrix of the pdf.
getPDFHessian(double, double...) - Method in class com.imsl.stat.distributions.NormalPD: Returns the analytic Hessian matrix of the pdf.
getPDFHessian(double, double...) - Method in class com.imsl.stat.distributions.ParetoPD: Returns the analytic Hessian of the pdf.
getPDFHessian(double, double...) - Method in interface com.imsl.stat.distributions.PDFHessianInterface: Returns the hessian of the probability density function.
getPDFHessian(double, double...) - Method in class com.imsl.stat.distributions.PoissonPD: Returns the analytic Hessian matrix of the pdf.
getPDFHessian(double, double...) - Method in class com.imsl.stat.distributions.RayleighPD: Returns the analytic Hessian matrix of the pdf.
getPDFHessian(double, double...) - Method in class com.imsl.stat.distributions.WeibullPD: Returns the analytic Hessian of the pdf.
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.
getPerformanceIndex() - Method in class com.imsl.math.ComplexEigen: Returns the performance index of the complex eigensystem.
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.
getPermutation(int) - Method in class com.imsl.stat.RandomSamples: Returns a permutation array of integers.
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.
getPermute() - Method in class com.imsl.stat.LinearRegression: Returns an integer vector containing information about the permutation of the columns of the matrix of regressors during QR factorization.
getPhi() - Method in class com.imsl.stat.ContingencyTable: Returns phi.
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.
getPkgProperties() - Static method in class com.imsl.Version: Get the version information about this library.
getPooledCovariances() - Method in class com.imsl.stat.PooledCovariances: Computes and returns the pooled covariances.
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.
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.datamining.BootstrapAggregation: Returns the mean squared prediction error for regression problems, or the classification percentage error for classification problems.
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.
getPredictions(double[][]) - Method in class com.imsl.datamining.decisionTree.Tree: Returns the predicted values on testData using the tree.
getPredictorIndexes() - Method in class com.imsl.datamining.PredictiveModel: Returns the column indices of xy in which 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.
getPrimalSolution() - Method in class com.imsl.math.LinearProgramming: Deprecated.

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.GradientBoostingModelObject: 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 $\psi$ weights of the infinite order moving average form of the model.
getPsiWeights() - Method in class com.imsl.stat.AutoARIMA: Returns the $\psi$ 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.CrossValidation: Returns the random object being used in the permutation of the observations.
getRandomObject() - Method in class com.imsl.datamining.PredictiveModel: Returns the random object being used in the permutation of the observations.
getRandomObject() - Method in class com.imsl.math.NelderMead: Returns the random object being used in the computation of the vertices of the initial complex.
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.ComplexSVD: Returns the rank of the matrix used to construct this instance.
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.
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.
getRegressors() - Method in class com.imsl.stat.RegressorsForGLM: Returns the regressor array.
getRegularizationParameter() - Method in class com.imsl.datamining.supportvectormachine.SupportVectorMachine: Returns the value of the regularization parameter, C.
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() - 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.
getRelativeError(double) - Method in class com.imsl.math.ConjugateGradient: Returns the relative error used for stopping the algorithm.
getRelativeErrorTolerances() - Method in class com.imsl.math.FeynmanKac: Returns relative error tolerances.
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.
getResiduals() - Method in class com.imsl.stat.MultidimensionalScaling: Returns the observation residuals.
getResidualSeries() - Method in class com.imsl.stat.ExtendedGARCH: Returns the epsilon series (the current 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.LogisticRegressionModelObject: Returns the variable type of the response variable.
getResponseVariableType() - Method in class com.imsl.datamining.PredictiveModel: Returns the variable type of the response variable.
getRHS() - Method in class com.imsl.stat.LinearRegression: Returns the right hand side of the regression problem.
getRightEndTangent() - Method in class com.imsl.math.CsTCB: Returns the value of the tangent at the rightmost endpoint.
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() - Method in class com.imsl.stat.Dissimilarities: Returns a boolean indicating whether distances are computed between rows or columns of x.
getRow(int) - Method in class com.imsl.io.MPSReader: Returns a row of the constraint matrix or a free row.
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.ComplexSVD: Returns the singular values.
getS() - Method in class com.imsl.math.SVD: Returns the singular values.
getSampleIndices(int, int) - Method in class com.imsl.stat.RandomSamples: Computes and returns an array of sampled indices.
getSamples(double[][], int) - Method in class com.imsl.stat.RandomSamples: Generates a pseudorandom sample from a given population matrix, without replacement.
getSamples(double[], int) - Method in class com.imsl.stat.RandomSamples: Generates a pseudorandom sample from a given population array, without replacement.
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.
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.
getSequenceData() - Method in class com.imsl.datamining.SequenceDatabase: Returns the sequence data.
getSeriesValues() - Method in class com.imsl.stat.TimeSeries: Returns the data values of the time series.
getSeriesValues(int) - Method in class com.imsl.stat.TimeSeries: Returns the data values of the k-th time series.
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.
getShrinkage() - Method in class com.imsl.datamining.GradientBoosting: Returns the shrinkage parameter.
getShrinkageParameter() - Method in class com.imsl.datamining.GradientBoosting: Returns the current shrinkage parameter.
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.
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.
getSlopes() - Method in class com.imsl.stat.MultidimensionalScaling: Returns the slope for each subject.
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.NelderMead: Returns the 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.
getSolutionMethod() - Method in class com.imsl.math.Transport: Returns the algorithm used to solve the transportation 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() - Method in class com.imsl.datamining.LogisticRegression: Returns the standard errors of the estimated coefficients.
getStandardErrors() - Method in class com.imsl.datamining.LogisticRegressionModelObject: Returns the standard errors of the coefficients.
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.
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.
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.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.
getStdDevX() - Method in class com.imsl.stat.WelchsTTest: Returns the standard deviation of the first sample.
getStdDevY() - Method in class com.imsl.stat.NormTwoSample: Returns the standard deviation of the second sample.
getStdDevY() - Method in class com.imsl.stat.WelchsTTest: 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(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.
getSubjectWeights() - Method in class com.imsl.stat.MultidimensionalScaling: Returns the subject weights.
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.
getSumOfWeights() - Method in class com.imsl.stat.PooledCovariances: Returns the sum of the weights times the frequencies in the groups.
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.
getSupportVector() - Method in class com.imsl.datamining.SequenceDatabase: Returns the support vector for the sequences in this SequenceDatabase.
getSupportVectorCoef() - Method in class com.imsl.datamining.supportvectormachine.SVModel: Returns the nonzero coefficients of the support vector classifier or the approximate function, in the case of regression.
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() - Method in class com.imsl.datamining.decisionTree.TreeNode: Returns the surrogate split information array.
getSurrogateInfo(int) - Method in class com.imsl.datamining.decisionTree.TreeNode: Returns a value from 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.
getSysProperties() - Static method in class com.imsl.Version: Get the operating system and Java versions.
getT() - Method in class com.imsl.stat.ExtendedGARCH: Returns the length of the GARCH time series.
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.
getTestLossValue() - Method in class com.imsl.datamining.GradientBoostingModelObject: Returns the value of the loss function 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 $D = \max(D^{+}, D^{-})$.
getTheta() - Method in class com.imsl.stat.PoissonDistribution: Returns the mean number of successes in a given time period of the Poisson probability distribution.
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(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) - 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, 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(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) - 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(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).
getTo() - Method in class com.imsl.datamining.neural.Link: Returns the destination Node for this Link.
getTolerance() - Method in class com.imsl.datamining.GradientBoosting: Returns the tolerance level.
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.
getTotalCost() - Method in class com.imsl.math.Transport: Returns the total cost of the optimal routing.
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.
getTotalNumberOfObservations() - Method in class com.imsl.stat.PooledCovariances: Returns the total number of observations used in the analysis.
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.datamining.neural.UnsupervisedOrdinalFilter: Retrieves the transform flag used for encoding and decoding.
getTransformedTimeSeries() - Method in class com.imsl.stat.ARSeasonalFit: Returns the transformed series, $W_t(s,d)$.
getTransformType() - Method in class com.imsl.stat.ClusterHierarchical: Returns the type of transformation.
getTreeList() - Method in class com.imsl.datamining.GradientBoosting: Returns the list of boosted trees.
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.
getTTest() - Method in class com.imsl.stat.WelchsTTest: Returns the calculated test statistic for Welch's t-test.
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.
getTTestDF() - Method in class com.imsl.stat.WelchsTTest: Returns the degrees of freedom used in the test.
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 observing a larger value of the t-statistic given the null hypothesis is true (i.e.,the p-value for the test).
getTTestP() - Method in class com.imsl.stat.WelchsTTest: Returns the approximate probability of observing a more extreme value of the t-statistic given the null hypothesis is true (i.e, the approximate p-value of the test).
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.io.AbstractFlatFile: Returns the type of this ResultSet object.
getTypeCode() - Method in class com.imsl.test.example.math.SparseMatrixEx2.MTXReader
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.ComplexSVD: Returns the left singular vectors.
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.
getU() - Method in class com.imsl.stat.PooledCovariances: Returns the lower matrix U, the lower triangular for the pooled sample crossproducts matrix.
getUnbalancedTable() - Method in class com.imsl.stat.TableMultiWay: Returns an object containing the unbalanced table.
getUnicodeStream(int) - Method in class com.imsl.io.AbstractFlatFile: Deprecated.
Use AbstractFlatFile.getCharacterStream(int) instead.
getUnicodeStream(String) - Method in class com.imsl.io.AbstractFlatFile: Deprecated.
Use AbstractFlatFile.getCharacterStream(String) instead.
getUnion(Itemsets...) - Static method in class com.imsl.datamining.Apriori: Return the union of a sequence of sets of itemsets.
getUpperBound() - Method in class com.imsl.math.SparseLP: Returns the upper bound on the variables.
getUpperBound(int) - Method in class com.imsl.io.MPSReader: Returns the upper bound for a variable.
getUpperCICommonVariance() - Method in class com.imsl.stat.NormTwoSample: Returns the upper confidenceVariance $*100%$ confidence limit for the common variance.
getUpperCIDiff() - Method in class com.imsl.stat.NormTwoSample: Returns the upper confidence limit for the difference, $\mu_x - \mu_y$ for equal or unequal variances depending on the value set by setUnequalVariances.
getUpperCIDiff() - Method in class com.imsl.stat.WelchsTTest: Returns the (approximate) upper confidenceMean*100% confidence limit for the difference in population means, $\mu_x - \mu_y$.
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 in a confidence interval for the ratio of variances, $\sigma_1^2/\sigma_2^2$.
getUpperCIVariance() - Method in class com.imsl.stat.NormOneSample: Returns the upper confidence limits for the variance.
getUpperLimit() - Method in class com.imsl.math.SparseLP: Returns the upper limit of the constraints that have both a lower and an upper bound.
getUpperRange(int) - Method in class com.imsl.io.MPSReader: Returns the upper range value for a constraint equation.
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.
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.
getUseBackPropagation() - Method in class com.imsl.datamining.neural.QuasiNewtonTrainer: Returns the use back propagation setting.
getV() - Method in class com.imsl.math.ComplexSVD: Returns the right singular vectors.
getV() - Method in class com.imsl.math.SVD: Returns the right singular vectors.
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.datamining.supportvectormachine.DataNode: Returns the value of the node.
getValue() - Method in class com.imsl.io.MPSReader.Element: Returns the value of the element.
getValues() - Method in class com.imsl.math.ComplexEigen: Returns the eigenvalues of a matrix of type Complex.
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.
getVariableImportance() - Method in class com.imsl.datamining.BootstrapAggregation: Returns the variable importance measure based on the out-of-bag prediction error.
getVariableImportance() - Method in class com.imsl.datamining.decisionTree.RandomTrees: Returns the variable importance measure based on the out-of-bag prediction error.
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: Returns 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.ComplexEigen: Returns the eigenvectors of the input matrix.
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.
getViolation() - Method in class com.imsl.math.SparseLP: Returns the violation of the variable bounds.
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.
getWeight() - Method in class com.imsl.datamining.neural.Link: Returns the weight for this Link.
getWeightedOptimizedCriterionFunctionValue() - Method in class com.imsl.stat.MultidimensionalScaling: Returns the value of the summed optimized stress function.
getWeightedOptimizedCriterionValues() - Method in class com.imsl.stat.MultidimensionalScaling: Returns the value of the optimized stress function for each subject.
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.
getWeights(int, int) - Method in class com.imsl.datamining.KohonenSOM: Returns the weights of the node at (i, j) in the node grid.
getWorkingArraySize() - Method in class com.imsl.datamining.supportvectormachine.SupportVectorMachine: Returns the setting for the work array size.
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.
getXInteractionsIndex() - Method in class com.imsl.datamining.LogisticRegression: Returns the array containing the indices specifying interaction terms.
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.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 the 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.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.
gFunction(double, double, int, double[], double[]) - Method in class com.imsl.stat.EGARCH: Returns the value of $ g(\epsilon,\sigma) $ evaluated at the input parameter values.
gFunction(double, double, int, double[], double...) - Method in class com.imsl.stat.ExtendedGARCH: Abstract method for the function $g(\epsilon_t,\sigma_t)$.
GINI_INDEX - Enum constant in enum class com.imsl.datamining.decisionTree.DecisionTreeInfoGain.GainCriteria: A measure of statistical dispersion.
gradient(double[]) - Method in class com.imsl.math.RadialBasis: Returns the gradient of the radial basis approximation at a point.
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[], double[]) - Method in interface com.imsl.math.MinUnconMultiVar.Gradient: Public interface for the gradient of the multivariate function to be minimized.
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[], int, double[]) - Method in class com.imsl.test.example.math.MinConNLPEx2: Defines the gradients of the objective and the constraints.
gradient(int, int, double[], boolean[], double, double[], double[], double[][]) - Method in interface com.imsl.math.MinConNonlin.Gradient: Deprecated.

Computes the value of the gradient of the function at the given 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(GradientBoosting) - Constructor for class com.imsl.datamining.GradientBoosting: Constructs a copy of the input GradientBoosting predictive model.
GradientBoosting(PredictiveModel) - Constructor for class com.imsl.datamining.GradientBoosting: Constructs a GradientBoosting object.
GradientBoosting.LossFunctionType - Enum Class in com.imsl.datamining: The loss function type as specified by the error measure.
GradientBoostingEx1 - Class in com.imsl.test.example.datamining: Predicts a regression response variable based on 6 predictor variables.
GradientBoostingEx1() - Constructor for class com.imsl.test.example.datamining.GradientBoostingEx1
GradientBoostingEx2 - Class in com.imsl.test.example.datamining: Predicts a binary response variable based on 4 predictor variables.
GradientBoostingEx2() - Constructor for class com.imsl.test.example.datamining.GradientBoostingEx2
GradientBoostingEx3 - Class in com.imsl.test.example.datamining: Selects the number of iterations using cross-validation.
GradientBoostingEx3() - Constructor for class com.imsl.test.example.datamining.GradientBoostingEx3
GradientBoostingEx4 - Class in com.imsl.test.example.datamining: Uses an input model to set the configuration of the base learner.
GradientBoostingEx4() - Constructor for class com.imsl.test.example.datamining.GradientBoostingEx4
GradientBoostingModelObject - Class in com.imsl.datamining: Predicts a data set using a trained gradient boosting model.
GradientBoostingModelObject(GradientBoosting) - Constructor for class com.imsl.datamining.GradientBoostingModelObject: Constructs the GradientBoostingModelObject.
GradientBoostingModelObjectEx1 - Class in com.imsl.test.example.datamining: Uses a trained gradient boosting model to predict a new data set.
GradientBoostingModelObjectEx1() - Constructor for class com.imsl.test.example.datamining.GradientBoostingModelObjectEx1
GradientBoostingModelObjectEx2 - Class in com.imsl.test.example.datamining: Reads in a trained gradient boosting model object to predict a new data set.
GradientBoostingModelObjectEx2() - Constructor for class com.imsl.test.example.datamining.GradientBoostingModelObjectEx2
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.

H

HardyMultiquadric(double) - Constructor for class com.imsl.math.RadialBasis.HardyMultiquadric: Creates a Hardy multiquadric basis function $\sqrt{r^2+ \delta^2}$.
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.
HERMITIAN - Enum constant in enum class com.imsl.math.ComplexMatrix.MatrixType: Matrix is square Hermitian.
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.
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.
HoltWintersExponentialSmoothingEx1 - Class in com.imsl.test.example.stat: Applies Holt-Winter's exponential smoothing to a series.
HoltWintersExponentialSmoothingEx1() - Constructor for class com.imsl.test.example.stat.HoltWintersExponentialSmoothingEx1
HUBER_M - Enum constant in enum class com.imsl.datamining.GradientBoosting.LossFunctionType: The loss criteria is the Huber-M weighted squared error and absolute deviation error with parameter $\alpha$.
Hyperbolic - Class in com.imsl.math: Pure Java implementation of the hyperbolic functions and their inverses.
HyperbolicEx1 - Class in com.imsl.test.example.math: Evaluates the hyperbolic functions.
HyperbolicEx1() - Constructor for class com.imsl.test.example.math.HyperbolicEx1
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.
hypergeometricProb(int, int, int, int) - Static method in class com.imsl.stat.Cdf: Deprecated.
Use Pdf.hypergeometric(int, int, int, int) instead.
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.
HyperRectangleQuadratureEx1 - Class in com.imsl.test.example.math: Evaluates a multi-dimensional integral.
HyperRectangleQuadratureEx1() - Constructor for class com.imsl.test.example.math.HyperRectangleQuadratureEx1

I

i - Static variable in class com.imsl.math.Complex: The imaginary unit.
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(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.
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 - Enum constant in enum class com.imsl.datamining.PredictiveModel.VariableType: The associated variable should be ignored.
ignoreMissingValues(boolean) - Method in class com.imsl.datamining.NaiveBayesClassifier: Specifies whether or not missing values will be ignored during the training process.
IllConditionedException(String) - Constructor for exception com.imsl.math.MinConNLP.IllConditionedException: Constructs a IllConditionedException object.
IllConditionedException(String) - Constructor for exception com.imsl.stat.ARMA.IllConditionedException: Constructs an IllConditionedException with the specified detail message.
IllConditionedException(String, Object[]) - Constructor for exception com.imsl.math.MinConNLP.IllConditionedException: Constructs a IllConditionedException object.
IllConditionedException(String, Object[]) - Constructor for exception com.imsl.stat.ARMA.IllConditionedException: Constructs an IllConditionedException with the specified detail message.
IllDefinedHessianException(String) - Constructor for exception com.imsl.stat.MultidimensionalScaling.IllDefinedHessianException: Constructs an IllDefinedHessianException with the specified detail message.
IllDefinedHessianException(String, Object[]) - Constructor for exception com.imsl.stat.MultidimensionalScaling.IllDefinedHessianException: Constructs an IllDefinedHessianException with the specified detail message.
IllegalBoundsException(String) - Constructor for exception com.imsl.math.SparseLP.IllegalBoundsException: The lower bound is greater than the upper bound.
IllegalBoundsException(String, Object[]) - Constructor for exception com.imsl.math.SparseLP.IllegalBoundsException: The lower bound is greater than the upper bound.
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 - Enum constant in enum class com.imsl.stat.FactorAnalysis.ScoreMethod: Use image method.
IMAGE_FACTOR_ANALYSIS - Static variable in class com.imsl.stat.FactorAnalysis: Indicates image factor analysis.
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.
InconsistentSystemException() - Constructor for exception com.imsl.math.QuadraticProgramming.InconsistentSystemException: The system of constraints is inconsistent.
InconsistentSystemException(String) - Constructor for exception com.imsl.math.QuadraticProgramming.InconsistentSystemException: The system of constraints is inconsistent.
IncorrectlyActiveException(String) - Constructor for exception com.imsl.math.SparseLP.IncorrectlyActiveException: One or more LP variables are falsely characterized by the internal presolver.
IncorrectlyActiveException(String, Object[]) - Constructor for exception com.imsl.math.SparseLP.IncorrectlyActiveException: One or more LP variables are falsely characterized by the internal presolver.
IncorrectlyEliminatedException(String) - Constructor for exception com.imsl.math.SparseLP.IncorrectlyEliminatedException: One or more LP variables are falsely characterized by the internal presolver.
IncorrectlyEliminatedException(String, Object[]) - Constructor for exception com.imsl.math.SparseLP.IncorrectlyEliminatedException: One or more LP variables are falsely characterized by the internal presolver.
IncreaseErrRelException(String) - Constructor for exception com.imsl.stat.ARMA.IncreaseErrRelException: Constructs an IncreaseErrRelException with the specified detail message.
IncreaseErrRelException(String, Object[]) - Constructor for exception com.imsl.stat.ARMA.IncreaseErrRelException: Constructs an IncreaseErrRelException with the specified detail message.
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 ($L_\infty$ norm) distance method.
infinityNorm() - Method in class com.imsl.math.ComplexSparseMatrix: Returns the infinity norm of the matrix.
infinityNorm() - Method in class com.imsl.math.SparseMatrix: 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(Complex[][]) - Static method in class com.imsl.math.ComplexMatrix: Return the infinity norm of a Complex matrix.
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.
InitialConstraintsException(String, Object[]) - Constructor for exception com.imsl.math.FeynmanKac.InitialConstraintsException: The constraints at the initial point are inconsistent.
InitialSolutionInfeasibleException(String) - Constructor for exception com.imsl.math.SparseLP.InitialSolutionInfeasibleException: The initial solution for the one-row linear program is infeasible.
InitialSolutionInfeasibleException(String, Object[]) - Constructor for exception com.imsl.math.SparseLP.InitialSolutionInfeasibleException: The initial solution for the one-row linear program is infeasible.
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.
innerproduct(double[], double[]) - Method in class com.imsl.test.example.math.GenMinResEx2: Computes the inner product of x and y.
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.
InteriorPointMethod - Enum constant in enum class com.imsl.math.Transport.SolutionMethod: Uses an interior-point method.
INTERSECTION - Enum constant in enum 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.
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.
InvalidMPSFileException(String) - Constructor for exception com.imsl.io.MPSReader.InvalidMPSFileException: Constructs a InvalidMPSFileException object.
InvalidMPSFileException(String, Object[]) - Constructor for exception com.imsl.io.MPSReader.InvalidMPSFileException: Constructs a InvalidMPSFileException object.
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.
InvCdf - Class in com.imsl.stat: Inverse cumulative probability distribution functions.
InvCdfEx1 - Class in com.imsl.test.example.stat: Evaluates the inverse CDF for the beta and chi-squared random variables.
InvCdfEx1() - Constructor for class com.imsl.test.example.stat.InvCdfEx1
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: Deprecated.
Method name does not adhere to the Java naming conventions. Use SVD.getInverse() instead.
inverseBeta(double, double, double) - Static method in class com.imsl.stat.Cdf: Deprecated.
Use InvCdf.beta(double, double, double) instead.
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
InverseCdfEx1 - Class in com.imsl.test.example.stat: Computes the inverse of a user-supplied CDF at a probability value.
InverseCdfEx1() - Constructor for class com.imsl.test.example.stat.InverseCdfEx1
inverseChi(double, double) - Static method in class com.imsl.stat.Cdf: Deprecated.
Use InvCdf.chi(double, double) instead.
inverseDiscreteUniform(double, int) - Static method in class com.imsl.stat.Cdf: Deprecated.
Use InvCdf.discreteUniform(double, int) instead.
inverseExponential(double, double) - Static method in class com.imsl.stat.Cdf: Deprecated.
Use InvCdf.exponential(double, double) instead.
inverseExtremeValue(double, double, double) - Static method in class com.imsl.stat.Cdf: Deprecated.
Use InvCdf.extremeValue(double, double, double) instead.
inverseF(double, double, double) - Static method in class com.imsl.stat.Cdf: Deprecated.
Use InvCdf.F(double, double, double) instead.
inverseGamma(double, double) - Static method in class com.imsl.stat.Cdf: Deprecated.
Use InvCdf.gamma(double, double) instead.
inverseGaussian(double, double, double) - Static method in class com.imsl.stat.Pdf: Evaluates the inverse Gaussian probability density function.
InverseGaussianPD - Class in com.imsl.stat.distributions: The inverse Gaussian (Wald) probability distribution.
InverseGaussianPD() - Constructor for class com.imsl.stat.distributions.InverseGaussianPD: Constructor for the inverse Gaussian probability distribution.
InverseGaussianPDEx1 - Class in com.imsl.test.example.stat.distributions: Evaluates the inverse Gaussian (Wald) probability distribution.
InverseGaussianPDEx1() - Constructor for class com.imsl.test.example.stat.distributions.InverseGaussianPDEx1
inverseGeometric(double, double) - Static method in class com.imsl.stat.Cdf: Deprecated.
Use InvCdf.geometric(double, double) instead.
inverseLogNormal(double, double, double) - Static method in class com.imsl.stat.Cdf: Deprecated.
Use InvCdf.logNormal(double, double, double) instead.
inverseLowerTriangular(double[][]) - Static method in class com.imsl.math.Matrix: Returns the inverse of the lower triangular matrix a.
inverseNoncentralchi(double, double, double) - Static method in class com.imsl.stat.Cdf: Deprecated.
Use InvCdf.noncentralchi(double, double, double) instead.
inverseNoncentralstudentsT(double, int, double) - Static method in class com.imsl.stat.Cdf: Deprecated.
Use InvCdf.noncentralstudentsT(double, int, double) instead.
inverseNormal(double) - Static method in class com.imsl.stat.Cdf: Deprecated.
Use InvCdf.normal(double) instead.
inverseRayleigh(double, double) - Static method in class com.imsl.stat.Cdf: Deprecated.
Use InvCdf.Rayleigh(double, double) instead.
inverseStudentsT(double, double) - Static method in class com.imsl.stat.Cdf: Deprecated.
Use InvCdf.studentsT(double, double) instead.
inverseUniform(double, double, double) - Static method in class com.imsl.stat.Cdf: Deprecated.
Use InvCdf.uniform(double, double, double) instead.
inverseUpperTriangular(double[][]) - Static method in class com.imsl.math.Matrix: Returns the inverse of the upper triangular matrix a.
inverseWeibull(double, double, double) - Static method in class com.imsl.stat.Cdf: Deprecated.
Use InvCdf.Weibull(double, double, double) instead.
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.
isAutoPruningFlag() - Method in class com.imsl.datamining.decisionTree.DecisionTree: Returns the current setting of the boolean to automatically prune the decision tree.
isBeforeFirst() - Method in class com.imsl.io.AbstractFlatFile: Indicates whether the cursor is before the first row in this ResultSet object.
isCalculateVariableImportance() - Method in class com.imsl.datamining.BootstrapAggregation: Returns the boolean indicating whether or not to calculate variable importance during bootstrap aggregation.
isCalculateVariableImportance() - Method in class com.imsl.datamining.decisionTree.RandomTrees: Returns the current setting of the boolean to calculate variable importance.
isClosed() - Method in class com.imsl.io.FlatFile: Retrieves whether this ResultSet object has been closed.
isConstantSeries() - Method in class com.imsl.datamining.PredictiveModel: Returns the current value of the constantSeries flag.
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.
isEvenOrder() - Method in class com.imsl.test.example.math.RadialBasisEx2.PolyHarmonicSpline
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.
isHermitian(Complex[][]) - Static method in class com.imsl.math.ComplexMatrix: Check if the Complex matrix is Hermitian.
isIncludeIntercept() - Method in class com.imsl.datamining.LogisticRegression: Returns the current setting of the flag to include or not include an intercept in the model.
isIncludeMean() - Method in class com.imsl.stat.ExtendedGARCH: Returns the flag to include or not include an ARMA model for the mean.
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.
isMustEstimate() - Method in class com.imsl.stat.ExtendedGARCH: Returns the value of mustEstimateFlag.
isMustFitModel() - 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.
isNuFormulation() - Method in class com.imsl.datamining.supportvectormachine.SupportVectorMachine: Returns the boolean to perform the $\nu$-formulation of the optimization problem.
isProbability() - Method in class com.imsl.datamining.supportvectormachine.SupportVectorMachine: Returns the boolean to calculate probability estimates.
isRandomFeatureSelection() - Method in class com.imsl.datamining.decisionTree.DecisionTree: Returns the current setting of the boolean to perform random feature selection.
isShrinking() - Method in class com.imsl.datamining.supportvectormachine.SupportVectorMachine: Returns the boolean to perform shrinking during optimization.
isStationary(double[]) - Method in class com.imsl.stat.ARMAMaxLikelihood: Tests whether the coefficients in ar are stationary.
isStratifiedCrossValidation() - Method in class com.imsl.datamining.CrossValidation: Returns the flag to perform stratified cross-validation for a categorical response variable.
isSymmetric(double[][]) - Static method in class com.imsl.math.Matrix: Check if the matrix is symmetric.
isSymmetric(Complex[][]) - Static method in class com.imsl.math.ComplexMatrix: Check if the Complex matrix is symmetric.
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.
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.
IterationMatrixSingularException(String, Object[]) - Constructor for exception com.imsl.math.FeynmanKac.IterationMatrixSingularException: Iteration matrix is singular.
iterator() - Method in class com.imsl.io.MPSReader.Row: Returns an iterator over the elements in this row.

J

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.
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.
jacobian(double[]) - Method in interface com.imsl.math.NumericalDerivatives.Jacobian: User-supplied function to compute the Jacobian.
jacobian(double[], double[][]) - Method in interface com.imsl.math.NonlinLeastSquares.Jacobian: Public interface for the nonlinear least squares function.
jacobian(double[], double[][]) - Method in interface com.imsl.math.ZeroSystem.Jacobian: Returns the value of the Jacobian at the given point.
jacobian(double, double[], double[]) - Method in interface com.imsl.math.OdeAdamsGear.Jacobian: Used to compute the Jacobian of the function at t.
JMath - Class in com.imsl.math: Pure Java implementation of the standard java.lang.Math class.

K

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.
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.
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.
KalmanFilter(double[], double[], int, double, double) - Constructor for class com.imsl.stat.KalmanFilter: Deprecated.
Use KalmanFilter(double[], double[][], int, double, double) instead.
KalmanFilterEx1 - Class in com.imsl.test.example.stat: Computes the filtered estimates and the one-step-ahead estimates using the Kalman filter.
KalmanFilterEx1() - Constructor for class com.imsl.test.example.stat.KalmanFilterEx1
KalmanFilterEx2 - Class in com.imsl.test.example.stat: Estimates a moving average model $\text{MA}(1)$ using the Kalman filter.
KalmanFilterEx2() - Constructor for class com.imsl.test.example.stat.KalmanFilterEx2
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.
KaplanMeierECDFEx1 - Class in com.imsl.test.example.stat: Computes the survival curve for units under life-testing.
KaplanMeierECDFEx1() - Constructor for class com.imsl.test.example.stat.KaplanMeierECDFEx1
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 censoring.
KaplanMeierEstimates(double[][]) - Constructor for class com.imsl.stat.KaplanMeierEstimates: Constructor for KaplanMeierEstimates.
KaplanMeierEstimatesEx1 - Class in com.imsl.test.example.stat: Computes the Kaplan-Meier probability estimates for censored data.
KaplanMeierEstimatesEx1() - Constructor for class com.imsl.test.example.stat.KaplanMeierEstimatesEx1
kappa(double, double) - Method in interface com.imsl.math.FeynmanKac.PdeCoefficients: Returns the value of the $\kappa$ coefficient at the given point.
Kernel - Class in com.imsl.datamining.supportvectormachine: Abstract class to specify a kernel function for support vector machines.
Kernel(int) - Constructor for class com.imsl.datamining.supportvectormachine.Kernel: Creates a Kernel and specifies the number of kernel parameters for a specific Kernel.
kernelFunction(DataNode[][], int, int) - Method in class com.imsl.datamining.supportvectormachine.Kernel: Abstract method to calculate the kernel function between two DataNode arrays.
kernelFunction(DataNode[][], int, int) - Method in class com.imsl.datamining.supportvectormachine.LinearKernel: Calculates the kernel function between two DataNodes.
kernelFunction(DataNode[][], int, int) - Method in class com.imsl.datamining.supportvectormachine.PolynomialKernel: Calculates the kernel function between two DataNodes.
kernelFunction(DataNode[][], int, int) - Method in class com.imsl.datamining.supportvectormachine.RadialBasisKernel: Calculates the kernel function between two DataNodes.
kernelFunction(DataNode[][], int, int) - Method in class com.imsl.datamining.supportvectormachine.SigmoidKernel: Calculates the kernel function between two DataNodes.
kernelFunction(DataNode[], DataNode[]) - Method in class com.imsl.datamining.supportvectormachine.Kernel: Abstract method to calculate the kernel function between two DataNode arrays.
kernelFunction(DataNode[], DataNode[]) - Method in class com.imsl.datamining.supportvectormachine.LinearKernel: Calculates the kernel function between two DataNodes.
kernelFunction(DataNode[], DataNode[]) - Method in class com.imsl.datamining.supportvectormachine.PolynomialKernel: Calculates the kernel function between two DataNodes.
kernelFunction(DataNode[], DataNode[]) - Method in class com.imsl.datamining.supportvectormachine.RadialBasisKernel: Calculates the kernel function between two DataNodes.
kernelFunction(DataNode[], DataNode[]) - Method in class com.imsl.datamining.supportvectormachine.SigmoidKernel: Calculates the kernel function between two DataNodes.
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.
KohonenSOMEx1 - Class in com.imsl.test.example.datamining: Creates and trains a Kohonen self-organizing map.
KohonenSOMEx1() - Constructor for class com.imsl.test.example.datamining.KohonenSOMEx1
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.
KolmogorovOneSampleEx1 - Class in com.imsl.test.example.stat: Performs a Kolmogorov one-sample test.
KolmogorovOneSampleEx1() - Constructor for class com.imsl.test.example.stat.KolmogorovOneSampleEx1
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.
KolmogorovTwoSampleEx1 - Class in com.imsl.test.example.stat: Performs a Kolmogorov two-sample test.
KolmogorovTwoSampleEx1() - Constructor for class com.imsl.test.example.stat.KolmogorovTwoSampleEx1
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 $L_1$ norm method.
L1_NORM - Static variable in class com.imsl.stat.Dissimilarities: Indicates the sum of the absolute differences ($L_1$ norm) distance method.
L2_NORM - Static variable in class com.imsl.stat.ClusterKNN: Indicates the distance is computed using the $L_2$ norm, or Euclidean distance measurement.
L2_NORM - Static variable in class com.imsl.stat.Dissimilarities: Indicates the Euclidean distance method ($L_2$ norm).
LackOfFit - Class in com.imsl.stat: Performs lack-of-fit test for a univariate time series or transfer function given the appropriate correlation function.
LackOfFitEx1 - Class in com.imsl.test.example.stat: Performs a lack-of-fit test between an $\text{ARMA}(2,1)$ and Wolfer's sunspot data.
LackOfFitEx1() - Constructor for class com.imsl.test.example.stat.LackOfFitEx1
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 - Enum constant in enum class com.imsl.datamining.GradientBoosting.LossFunctionType: The loss criteria is least absolute deviation error.
LEAST_SQUARES - Enum constant in enum class com.imsl.datamining.GradientBoosting.LossFunctionType: The loss criteria is least squared error.
LEAST_SQUARES - Enum constant in enum class com.imsl.stat.FactorAnalysis.ScoreMethod: Use Least-squares method.
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.
LEFT_SIDED - Enum constant in enum class com.imsl.stat.WelchsTTest.Hypothesis: The LEFT_SIDED test corresponds to $$ H_0: \mu_x - \mu_y \ge c \,\,\,\,\,\mbox{vs.}\,\,\,\,\, H_1: \mu_x - \mu_y < c$$
leftBoundaries(double, double[][]) - Method in interface com.imsl.math.FeynmanKac.Boundaries: Returns the coefficient values of the left boundary conditions.
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.
LicenseException - Exception in com.imsl: A LicenseException 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.
LifeTablesEx1 - Class in com.imsl.test.example.stat: Computes a cohort life table.
LifeTablesEx1() - Constructor for class com.imsl.test.example.stat.LifeTablesEx1
LillieforsTest() - Method in class com.imsl.stat.NormalityTest: Performs the Lilliefors test.
LimitingAccuracyException(String) - Constructor for exception com.imsl.math.MinConNLP.LimitingAccuracyException: Constructs a LimitingAccuracyException object.
LimitingAccuracyException(String, Object[]) - Constructor for exception com.imsl.math.MinConNLP.LimitingAccuracyException: Constructs a LimitingAccuracyException object.
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.
LinearKernel - Class in com.imsl.datamining.supportvectormachine: Specifies the linear kernel for support vector machines.
LinearKernel() - Constructor for class com.imsl.datamining.supportvectormachine.LinearKernel: Constructs a LinearKernel with default parameters.
LinearKernel(LinearKernel) - Constructor for class com.imsl.datamining.supportvectormachine.LinearKernel: Constructs a copy of the input LinearKernel kernel.
LinearlyDependentGradientsException(String) - Constructor for exception com.imsl.math.MinConNLP.LinearlyDependentGradientsException: Constructs a LinearlyDependentGradientsException object.
LinearlyDependentGradientsException(String, Object[]) - Constructor for exception com.imsl.math.MinConNLP.LinearlyDependentGradientsException: Constructs a LinearlyDependentGradientsException object.
LinearProgramming - Class in com.imsl.math: Deprecated.
LinearProgramming has been replaced by DenseLP.
LinearProgramming(double[][], double[], double[]) - Constructor for class com.imsl.math.LinearProgramming: Deprecated.

Constructor variables of type double.
LinearProgramming.BoundsInconsistentException - Exception in com.imsl.math: Deprecated.
LinearProgramming.NumericDifficultyException - Exception in com.imsl.math: Deprecated.
LinearProgramming.ProblemInfeasibleException - Exception in com.imsl.math: Deprecated.
LinearProgramming.ProblemUnboundedException - Exception in com.imsl.math: Deprecated.
LinearProgramming.WrongConstraintTypeException - Exception in com.imsl.math: Deprecated.
No longer used, replaced with an IllegalArgumentException.
LinearProgrammingEx1 - Class in com.imsl.test.example.math: Deprecated.
LinearProgramming class has been deprecated.
LinearProgrammingEx1() - Constructor for class com.imsl.test.example.math.LinearProgrammingEx1: Deprecated.
LinearProgrammingEx2 - Class in com.imsl.test.example.math: Deprecated.
LinearProgramming class has been deprecated.
LinearProgrammingEx2() - Constructor for class com.imsl.test.example.math.LinearProgrammingEx2: Deprecated.
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.
LinearRegressionEx1 - Class in com.imsl.test.example.stat: Computes a simple linear regression model.
LinearRegressionEx1() - Constructor for class com.imsl.test.example.stat.LinearRegressionEx1
LinearRegressionEx2 - Class in com.imsl.test.example.stat: Computes case statistics in a simple linear regression.
LinearRegressionEx2() - Constructor for class com.imsl.test.example.stat.LinearRegressionEx2
LineSearchException(String) - Constructor for exception com.imsl.math.MinConNonlin.LineSearchException: Deprecated.
LineSearchException(String, Object[]) - Constructor for exception com.imsl.math.MinConNonlin.LineSearchException: Deprecated.
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() - 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.
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.
log(double) - Static method in class com.imsl.math.JMath: Returns the natural logarithm of a double.
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.
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.
logGammaCorrection(double) - Static method in class com.imsl.math.Sfun: Deprecated.
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 - Static variable in interface com.imsl.datamining.neural.Activation: The logistic activation function, $g(x)=\frac{1}{1+e^{-x}} $.
LOGISTIC_TABLE - Static variable in interface com.imsl.datamining.neural.Activation: The logistic activation function computed using a table.
LogisticPD - Class in com.imsl.stat.distributions: The logistic probability distribution.
LogisticPD() - Constructor for class com.imsl.stat.distributions.LogisticPD: Constructor for the logistic probability distribution.
LogisticPDEx1 - Class in com.imsl.test.example.stat.distributions: Evaluates the logistic probability distribution.
LogisticPDEx1() - Constructor for class com.imsl.test.example.stat.distributions.LogisticPDEx1
LogisticRegression - Class in com.imsl.datamining: Performs binomial or multinomial logistic regression.
LogisticRegression(double[][], double[][], PredictiveModel.VariableType[], PredictiveModel.VariableType) - Constructor for class com.imsl.datamining.LogisticRegression: Constructs a logistic regression predictive model.
LogisticRegression(double[][], int, PredictiveModel.VariableType[]) - Constructor for class com.imsl.datamining.LogisticRegression: Constructs a logistic regression predictive model.
LogisticRegression(LogisticRegression) - Constructor for class com.imsl.datamining.LogisticRegression: Constructs a copy of the input LogisticRegression predictive model.
LogisticRegressionEx1 - Class in com.imsl.test.example.datamining: Trains a logistic regression model for a binomial response variable.
LogisticRegressionEx1() - Constructor for class com.imsl.test.example.datamining.LogisticRegressionEx1
LogisticRegressionEx2 - Class in com.imsl.test.example.datamining: Trains a logistic regression model for a multinomial response.
LogisticRegressionEx2() - Constructor for class com.imsl.test.example.datamining.LogisticRegressionEx2
LogisticRegressionEx3 - Class in com.imsl.test.example.datamining: Trains a logistic regression model for multinomial count data.
LogisticRegressionEx3() - Constructor for class com.imsl.test.example.datamining.LogisticRegressionEx3
LogisticRegressionModelObject - Class in com.imsl.datamining: Predicts a data set using a previously trained logistic regression model object.
LogisticRegressionModelObject(LogisticRegression) - Constructor for class com.imsl.datamining.LogisticRegressionModelObject: Constructs a LogisticRegressionModelObject.
LogisticRegressionModelObjectEx1 - Class in com.imsl.test.example.datamining: Uses a trained logistic regression model to predict new data.
LogisticRegressionModelObjectEx1() - Constructor for class com.imsl.test.example.datamining.LogisticRegressionModelObjectEx1
LogisticRegressionModelObjectEx2 - Class in com.imsl.test.example.datamining: Aggregates two separate fits of logistic regression.
LogisticRegressionModelObjectEx2() - Constructor for class com.imsl.test.example.datamining.LogisticRegressionModelObjectEx2
logLogistic(double, double, double) - Static method in class com.imsl.stat.Pdf: Evaluates the Log-logistic probability density function.
LogLogisticPD - Class in com.imsl.stat.distributions: The log-logistic probability distribution.
LogLogisticPD() - Constructor for class com.imsl.stat.distributions.LogLogisticPD: Constructor for the log-logistic probability distribution.
LogLogisticPDEx1 - Class in com.imsl.test.example.stat.distributions: Evaluates the log-logistic probability distribution.
LogLogisticPDEx1() - Constructor for class com.imsl.test.example.stat.distributions.LogLogisticPDEx1
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
LogNormalPD - Class in com.imsl.stat.distributions: The log-normal probability distribution.
LogNormalPD() - Constructor for class com.imsl.stat.distributions.LogNormalPD: Constructor for the log-normal probability distribution.
LogNormalPDEx1 - Class in com.imsl.test.example.stat.distributions: Evaluates the log normal probability distribution.
LogNormalPDEx1() - Constructor for class com.imsl.test.example.stat.distributions.LogNormalPDEx1
logNormalProb(double, double, double) - Static method in class com.imsl.stat.Cdf: Deprecated.
Use Pdf.logNormal(double, double, double) instead.
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.
LUEx1 - Class in com.imsl.test.example.math: Performs the LU factorization of a matrix.
LUEx1() - Constructor for class com.imsl.test.example.math.LUEx1

M

MAHALANOBIS - Static variable in class com.imsl.stat.Dissimilarities: Indicates the Mahalanobis distance method.
main(String[]) - Static method in class com.imsl.test.example.datamining.AprioriEx1
main(String[]) - Static method in class com.imsl.test.example.datamining.AprioriEx2
main(String[]) - Static method in class com.imsl.test.example.datamining.BootstrapAggregationEx1
main(String[]) - Static method in class com.imsl.test.example.datamining.BootstrapAggregationEx2
main(String[]) - Static method in class com.imsl.test.example.datamining.CrossValidationEx1: The main method of the example.
main(String[]) - Static method in class com.imsl.test.example.datamining.decisionTree.DecisionTreeEx1
main(String[]) - Static method in class com.imsl.test.example.datamining.decisionTree.DecisionTreeEx2
main(String[]) - Static method in class com.imsl.test.example.datamining.decisionTree.DecisionTreeEx3
main(String[]) - Static method in class com.imsl.test.example.datamining.decisionTree.DecisionTreeEx4
main(String[]) - Static method in class com.imsl.test.example.datamining.decisionTree.DecisionTreeEx5
main(String[]) - Static method in class com.imsl.test.example.datamining.decisionTree.RandomTreesEx1
main(String[]) - Static method in class com.imsl.test.example.datamining.decisionTree.RandomTreesEx2
main(String[]) - Static method in class com.imsl.test.example.datamining.decisionTree.RandomTreesEx3
main(String[]) - Static method in class com.imsl.test.example.datamining.GradientBoostingEx1
main(String[]) - Static method in class com.imsl.test.example.datamining.GradientBoostingEx2
main(String[]) - Static method in class com.imsl.test.example.datamining.GradientBoostingEx3: The main method for the example.
main(String[]) - Static method in class com.imsl.test.example.datamining.GradientBoostingEx4
main(String[]) - Static method in class com.imsl.test.example.datamining.GradientBoostingModelObjectEx1
main(String[]) - Static method in class com.imsl.test.example.datamining.GradientBoostingModelObjectEx2
main(String[]) - Static method in class com.imsl.test.example.datamining.KohonenSOMEx1
main(String[]) - Static method in class com.imsl.test.example.datamining.LogisticRegressionEx1
main(String[]) - Static method in class com.imsl.test.example.datamining.LogisticRegressionEx2
main(String[]) - Static method in class com.imsl.test.example.datamining.LogisticRegressionEx3
main(String[]) - Static method in class com.imsl.test.example.datamining.LogisticRegressionModelObjectEx1
main(String[]) - Static method in class com.imsl.test.example.datamining.LogisticRegressionModelObjectEx2
main(String[]) - Static method in class com.imsl.test.example.datamining.NaiveBayesClassifierEx1
main(String[]) - Static method in class com.imsl.test.example.datamining.NaiveBayesClassifierEx2
main(String[]) - Static method in class com.imsl.test.example.datamining.NaiveBayesClassifierEx3: The main method for the example.
main(String[]) - Static method in class com.imsl.test.example.datamining.neural.BinaryClassificationEx1
main(String[]) - Static method in class com.imsl.test.example.datamining.neural.EpochTrainerEx1
main(String[]) - Static method in class com.imsl.test.example.datamining.neural.MultiClassificationEx1
main(String[]) - Static method in class com.imsl.test.example.datamining.neural.ScaleFilterEx1
main(String[]) - Static method in class com.imsl.test.example.datamining.neural.TimeSeriesClassFilterEx1
main(String[]) - Static method in class com.imsl.test.example.datamining.neural.TimeSeriesFilterEx1
main(String[]) - Static method in class com.imsl.test.example.datamining.neural.UnsupervisedNominalFilterEx1
main(String[]) - Static method in class com.imsl.test.example.datamining.neural.UnsupervisedOrdinalFilterEx1
main(String[]) - Static method in class com.imsl.test.example.datamining.PrefixSpanEx1
main(String[]) - Static method in class com.imsl.test.example.datamining.PrefixSpanEx2
main(String[]) - Static method in class com.imsl.test.example.datamining.PrefixSpanEx3
main(String[]) - Static method in class com.imsl.test.example.datamining.supportvectormachine.SupportVectorMachineEx1
main(String[]) - Static method in class com.imsl.test.example.datamining.supportvectormachine.SupportVectorMachineEx2
main(String[]) - Static method in class com.imsl.test.example.datamining.supportvectormachine.SupportVectorMachineEx3
main(String[]) - Static method in class com.imsl.test.example.datamining.supportvectormachine.SupportVectorMachineEx4
main(String[]) - Static method in class com.imsl.test.example.datamining.supportvectormachine.SupportVectorMachineEx5
main(String[]) - Static method in class com.imsl.test.example.finance.BondAccruedInterestEx1
main(String[]) - Static method in class com.imsl.test.example.finance.BondAccruedInterestEx2
main(String[]) - Static method in class com.imsl.test.example.finance.BondConvexityEx1
main(String[]) - Static method in class com.imsl.test.example.finance.BondCoupdaysbsEx1
main(String[]) - Static method in class com.imsl.test.example.finance.BondCoupdaysEx1
main(String[]) - Static method in class com.imsl.test.example.finance.BondCoupdaysncEx1
main(String[]) - Static method in class com.imsl.test.example.finance.BondCoupncdEx1
main(String[]) - Static method in class com.imsl.test.example.finance.BondCoupnumEx1
main(String[]) - Static method in class com.imsl.test.example.finance.BondCouppcdEx1
main(String[]) - Static method in class com.imsl.test.example.finance.BondDepreciationEx1
main(String[]) - Static method in class com.imsl.test.example.finance.BondDepreciationEx2
main(String[]) - Static method in class com.imsl.test.example.finance.BondDiscEx1
main(String[]) - Static method in class com.imsl.test.example.finance.BondDurationEx1
main(String[]) - Static method in class com.imsl.test.example.finance.BondIntrateEx1
main(String[]) - Static method in class com.imsl.test.example.finance.BondMdurationEx1
main(String[]) - Static method in class com.imsl.test.example.finance.BondPricediscEx1
main(String[]) - Static method in class com.imsl.test.example.finance.BondPriceEx1
main(String[]) - Static method in class com.imsl.test.example.finance.BondPriceEx2
main(String[]) - Static method in class com.imsl.test.example.finance.BondPriceEx3
main(String[]) - Static method in class com.imsl.test.example.finance.BondPriceEx4
main(String[]) - Static method in class com.imsl.test.example.finance.BondPriceEx5
main(String[]) - Static method in class com.imsl.test.example.finance.BondPriceEx6
main(String[]) - Static method in class com.imsl.test.example.finance.BondPriceEx7
main(String[]) - Static method in class com.imsl.test.example.finance.BondPricematEx1
main(String[]) - Static method in class com.imsl.test.example.finance.BondPriceyieldEx1
main(String[]) - Static method in class com.imsl.test.example.finance.BondReceivedEx1
main(String[]) - Static method in class com.imsl.test.example.finance.BondTbilleqEx1
main(String[]) - Static method in class com.imsl.test.example.finance.BondTbillpriceEx1
main(String[]) - Static method in class com.imsl.test.example.finance.BondTbillyieldEx1
main(String[]) - Static method in class com.imsl.test.example.finance.BondYearfracEx1
main(String[]) - Static method in class com.imsl.test.example.finance.BondYielddiscEx1
main(String[]) - Static method in class com.imsl.test.example.finance.BondYieldEx1
main(String[]) - Static method in class com.imsl.test.example.finance.BondYieldEx2
main(String[]) - Static method in class com.imsl.test.example.finance.BondYieldEx3
main(String[]) - Static method in class com.imsl.test.example.finance.BondYieldEx4
main(String[]) - Static method in class com.imsl.test.example.finance.BondYieldEx5
main(String[]) - Static method in class com.imsl.test.example.finance.BondYieldEx6
main(String[]) - Static method in class com.imsl.test.example.finance.BondYieldEx7
main(String[]) - Static method in class com.imsl.test.example.finance.BondYieldmatEx1
main(String[]) - Static method in class com.imsl.test.example.finance.FinanceCumipmtEx1
main(String[]) - Static method in class com.imsl.test.example.finance.FinanceCumprincEx1
main(String[]) - Static method in class com.imsl.test.example.finance.FinanceDbEx1
main(String[]) - Static method in class com.imsl.test.example.finance.FinanceDdbEx1
main(String[]) - Static method in class com.imsl.test.example.finance.FinanceDollardeEx1
main(String[]) - Static method in class com.imsl.test.example.finance.FinanceDollarfrEx1
main(String[]) - Static method in class com.imsl.test.example.finance.FinanceEffectEx1
main(String[]) - Static method in class com.imsl.test.example.finance.FinanceFvEx1
main(String[]) - Static method in class com.imsl.test.example.finance.FinanceFvscheduleEx1
main(String[]) - Static method in class com.imsl.test.example.finance.FinanceIpmtEx1
main(String[]) - Static method in class com.imsl.test.example.finance.FinanceIrrEx1
main(String[]) - Static method in class com.imsl.test.example.finance.FinanceMirrEx1
main(String[]) - Static method in class com.imsl.test.example.finance.FinanceNominalEx1
main(String[]) - Static method in class com.imsl.test.example.finance.FinanceNperEx1
main(String[]) - Static method in class com.imsl.test.example.finance.FinanceNpvEx1
main(String[]) - Static method in class com.imsl.test.example.finance.FinancePmtEx1
main(String[]) - Static method in class com.imsl.test.example.finance.FinancePpmtEx1
main(String[]) - Static method in class com.imsl.test.example.finance.FinancePvEx1
main(String[]) - Static method in class com.imsl.test.example.finance.FinanceRateEx1
main(String[]) - Static method in class com.imsl.test.example.finance.FinanceSlnEx1
main(String[]) - Static method in class com.imsl.test.example.finance.FinanceSydEx1
main(String[]) - Static method in class com.imsl.test.example.finance.FinanceVdbEx1
main(String[]) - Static method in class com.imsl.test.example.finance.FinanceXirrEx1
main(String[]) - Static method in class com.imsl.test.example.finance.FinanceXnpvEx1
main(String[]) - Static method in class com.imsl.test.example.io.FlatFileEx1
main(String[]) - Static method in class com.imsl.test.example.io.FlatFileEx2
main(String[]) - Static method in class com.imsl.test.example.io.MPSReaderEx1
main(String[]) - Static method in class com.imsl.test.example.math.BesselEx1
main(String[]) - Static method in class com.imsl.test.example.math.BoundedLeastSquaresEx1
main(String[]) - Static method in class com.imsl.test.example.math.BoundedLeastSquaresEx2
main(String[]) - Static method in class com.imsl.test.example.math.BoundedVariableLeastSquaresEx1
main(String[]) - Static method in class com.imsl.test.example.math.BsInterpolateEx1
main(String[]) - Static method in class com.imsl.test.example.math.BsLeastSquaresEx1
main(String[]) - Static method in class com.imsl.test.example.math.CholeskyEx1
main(String[]) - Static method in class com.imsl.test.example.math.ComplexEigenEx1
main(String[]) - Static method in class com.imsl.test.example.math.ComplexEx1
main(String[]) - Static method in class com.imsl.test.example.math.ComplexFFTEx1
main(String[]) - Static method in class com.imsl.test.example.math.ComplexLUEx1
main(String[]) - Static method in class com.imsl.test.example.math.ComplexMatrixEx1
main(String[]) - Static method in class com.imsl.test.example.math.ComplexSparseCholeskyEx1
main(String[]) - Static method in class com.imsl.test.example.math.ComplexSparseMatrixEx1
main(String[]) - Static method in class com.imsl.test.example.math.ComplexSuperLUEx1
main(String[]) - Static method in class com.imsl.test.example.math.ComplexSVDEx1
main(String[]) - Static method in class com.imsl.test.example.math.ConjugateGradientEx1
main(String[]) - Static method in class com.imsl.test.example.math.ConjugateGradientEx2
main(String[]) - Static method in class com.imsl.test.example.math.CsAkimaEx1
main(String[]) - Static method in class com.imsl.test.example.math.CsInterpolateEx1
main(String[]) - Static method in class com.imsl.test.example.math.CsPeriodicEx1
main(String[]) - Static method in class com.imsl.test.example.math.CsShapeEx1
main(String[]) - Static method in class com.imsl.test.example.math.CsSmoothC2Ex1
main(String[]) - Static method in class com.imsl.test.example.math.CsSmoothEx1
main(String[]) - Static method in class com.imsl.test.example.math.CsTCBEx1
main(String[]) - Static method in class com.imsl.test.example.math.DenseLPEx1
main(String[]) - Static method in class com.imsl.test.example.math.DenseLPEx2
main(String[]) - Static method in class com.imsl.test.example.math.DenseLPEx3
main(String[]) - Static method in class com.imsl.test.example.math.EigenEx1
main(String[]) - Static method in class com.imsl.test.example.math.EpsilonAlgorithmEx1
main(String[]) - Static method in class com.imsl.test.example.math.FeynmanKacEx1
main(String[]) - Static method in class com.imsl.test.example.math.FeynmanKacEx2
main(String[]) - Static method in class com.imsl.test.example.math.FeynmanKacEx3
main(String[]) - Static method in class com.imsl.test.example.math.FeynmanKacEx4
main(String[]) - Static method in class com.imsl.test.example.math.FeynmanKacEx5
main(String[]) - Static method in class com.imsl.test.example.math.FFTEx1
main(String[]) - Static method in class com.imsl.test.example.math.GenMinResEx1
main(String[]) - Static method in class com.imsl.test.example.math.GenMinResEx2
main(String[]) - Static method in class com.imsl.test.example.math.GenMinResEx3
main(String[]) - Static method in class com.imsl.test.example.math.GenMinResEx4
main(String[]) - Static method in class com.imsl.test.example.math.GenMinResEx5
main(String[]) - Static method in class com.imsl.test.example.math.GenMinResEx6
main(String[]) - Static method in class com.imsl.test.example.math.GenMinResEx7
main(String[]) - Static method in class com.imsl.test.example.math.HyperbolicEx1
main(String[]) - Static method in class com.imsl.test.example.math.HyperRectangleQuadratureEx1
main(String[]) - Static method in class com.imsl.test.example.math.LinearProgrammingEx1: Deprecated.
main(String[]) - Static method in class com.imsl.test.example.math.LinearProgrammingEx2: Deprecated.
main(String[]) - Static method in class com.imsl.test.example.math.LUEx1
main(String[]) - Static method in class com.imsl.test.example.math.MatrixEx1
main(String[]) - Static method in class com.imsl.test.example.math.MinConGenLinEx1
main(String[]) - Static method in class com.imsl.test.example.math.MinConGenLinEx2
main(String[]) - Static method in class com.imsl.test.example.math.MinConNLPEx1
main(String[]) - Static method in class com.imsl.test.example.math.MinConNLPEx2
main(String[]) - Static method in class com.imsl.test.example.math.MinConNLPEx3
main(String[]) - Static method in class com.imsl.test.example.math.MinUnconEx1
main(String[]) - Static method in class com.imsl.test.example.math.MinUnconEx2
main(String[]) - Static method in class com.imsl.test.example.math.MinUnconMultiVarEx1
main(String[]) - Static method in class com.imsl.test.example.math.MinUnconMultiVarEx2
main(String[]) - Static method in class com.imsl.test.example.math.MinUnconMultiVarEx3
main(String[]) - Static method in class com.imsl.test.example.math.NelderMeadEx1
main(String[]) - Static method in class com.imsl.test.example.math.NelderMeadEx2
main(String[]) - Static method in class com.imsl.test.example.math.NonlinLeastSquaresEx1
main(String[]) - Static method in class com.imsl.test.example.math.NonlinLeastSquaresEx2
main(String[]) - Static method in class com.imsl.test.example.math.NonNegativeLeastSquaresEx1
main(String[]) - Static method in class com.imsl.test.example.math.NumericalDerivativesEx1
main(String[]) - Static method in class com.imsl.test.example.math.NumericalDerivativesEx2
main(String[]) - Static method in class com.imsl.test.example.math.NumericalDerivativesEx3
main(String[]) - Static method in class com.imsl.test.example.math.NumericalDerivativesEx4
main(String[]) - Static method in class com.imsl.test.example.math.NumericalDerivativesEx5
main(String[]) - Static method in class com.imsl.test.example.math.NumericalDerivativesEx6
main(String[]) - Static method in class com.imsl.test.example.math.OdeAdamsGearEx1
main(String[]) - Static method in class com.imsl.test.example.math.OdeRungeKuttaEx1
main(String[]) - Static method in class com.imsl.test.example.math.PhysicalEx1
main(String[]) - Static method in class com.imsl.test.example.math.PrintMatrixEx1
main(String[]) - Static method in class com.imsl.test.example.math.PrintMatrixFormatEx1
main(String[]) - Static method in class com.imsl.test.example.math.PrintMatrixFormatEx2
main(String[]) - Static method in class com.imsl.test.example.math.QREx1
main(String[]) - Static method in class com.imsl.test.example.math.QuadraticProgrammingEx1
main(String[]) - Static method in class com.imsl.test.example.math.QuadraticProgrammingEx2
main(String[]) - Static method in class com.imsl.test.example.math.QuadraticProgrammingEx3
main(String[]) - Static method in class com.imsl.test.example.math.QuadratureEx1
main(String[]) - Static method in class com.imsl.test.example.math.QuadratureEx2
main(String[]) - Static method in class com.imsl.test.example.math.QuadratureEx3
main(String[]) - Static method in class com.imsl.test.example.math.QuadratureEx4
main(String[]) - Static method in class com.imsl.test.example.math.RadialBasisEx1
main(String[]) - Static method in class com.imsl.test.example.math.RadialBasisEx2
main(String[]) - Static method in class com.imsl.test.example.math.RadialBasisEx3
main(String[]) - Static method in class com.imsl.test.example.math.RadialBasisEx4
main(String[]) - Static method in class com.imsl.test.example.math.SfunEx1
main(String[]) - Static method in class com.imsl.test.example.math.SparseCholeskyEx1
main(String[]) - Static method in class com.imsl.test.example.math.SparseLPEx1
main(String[]) - Static method in class com.imsl.test.example.math.SparseLPEx2
main(String[]) - Static method in class com.imsl.test.example.math.SparseMatrixEx1
main(String[]) - Static method in class com.imsl.test.example.math.SparseMatrixEx2
main(String[]) - Static method in class com.imsl.test.example.math.Spline2DInterpolateEx1
main(String[]) - Static method in class com.imsl.test.example.math.Spline2DInterpolateEx2
main(String[]) - Static method in class com.imsl.test.example.math.Spline2DInterpolateEx3
main(String[]) - Static method in class com.imsl.test.example.math.Spline2DInterpolateEx4
main(String[]) - Static method in class com.imsl.test.example.math.Spline2DLeastSquaresEx1
main(String[]) - Static method in class com.imsl.test.example.math.SuperLUEx1
main(String[]) - Static method in class com.imsl.test.example.math.SVDEx1
main(String[]) - Static method in class com.imsl.test.example.math.SymEigenEx1
main(String[]) - Static method in class com.imsl.test.example.math.TransportEx1
main(String[]) - Static method in class com.imsl.test.example.math.TransportEx2
main(String[]) - Static method in class com.imsl.test.example.math.ZeroFunctionEx1: Deprecated.
main(String[]) - Static method in class com.imsl.test.example.math.ZeroPolynomialEx1
main(String[]) - Static method in class com.imsl.test.example.math.ZeroPolynomialEx2
main(String[]) - Static method in class com.imsl.test.example.math.ZerosFunctionEx1
main(String[]) - Static method in class com.imsl.test.example.math.ZeroSystemEx1
main(String[]) - Static method in class com.imsl.test.example.math.ZeroSystemEx2
main(String[]) - Static method in class com.imsl.test.example.stat.ANCOVAEx1
main(String[]) - Static method in class com.imsl.test.example.stat.ANCOVAEx2
main(String[]) - Static method in class com.imsl.test.example.stat.ANOVAEx1
main(String[]) - Static method in class com.imsl.test.example.stat.ANOVAFactorialEx1
main(String[]) - Static method in class com.imsl.test.example.stat.ANOVAFactorialEx2
main(String[]) - Static method in class com.imsl.test.example.stat.ANOVAFactorialEx3
main(String[]) - Static method in class com.imsl.test.example.stat.ARAutoUnivariateEx1
main(String[]) - Static method in class com.imsl.test.example.stat.ARAutoUnivariateEx2
main(String[]) - Static method in class com.imsl.test.example.stat.ARMAEstimateMissingEx1
main(String[]) - Static method in class com.imsl.test.example.stat.ARMAEx1
main(String[]) - Static method in class com.imsl.test.example.stat.ARMAEx2
main(String[]) - Static method in class com.imsl.test.example.stat.ARMAEx3
main(String[]) - Static method in class com.imsl.test.example.stat.ARMAMaxLikelihoodEx1
main(String[]) - Static method in class com.imsl.test.example.stat.ARMAOutlierIdentificationEx1
main(String[]) - Static method in class com.imsl.test.example.stat.ARMAOutlierIdentificationEx2
main(String[]) - Static method in class com.imsl.test.example.stat.ARMAOutlierIdentificationEx3
main(String[]) - Static method in class com.imsl.test.example.stat.ARSeasonalFitEx1
main(String[]) - Static method in class com.imsl.test.example.stat.AutoARIMAEx1
main(String[]) - Static method in class com.imsl.test.example.stat.AutoARIMAEx2
main(String[]) - Static method in class com.imsl.test.example.stat.AutoARIMAEx3
main(String[]) - Static method in class com.imsl.test.example.stat.AutoCorrelationEx1
main(String[]) - Static method in class com.imsl.test.example.stat.CategoricalGenLinModelEx1
main(String[]) - Static method in class com.imsl.test.example.stat.CategoricalGenLinModelEx2
main(String[]) - Static method in class com.imsl.test.example.stat.CdfEx1
main(String[]) - Static method in class com.imsl.test.example.stat.ChiSquaredTestEx1
main(String[]) - Static method in class com.imsl.test.example.stat.ClusterHierarchicalEx1
main(String[]) - Static method in class com.imsl.test.example.stat.ClusterKMeansEx1
main(String[]) - Static method in class com.imsl.test.example.stat.ClusterKMeansEx2
main(String[]) - Static method in class com.imsl.test.example.stat.ClusterKNNEx1
main(String[]) - Static method in class com.imsl.test.example.stat.ContingencyTableEx1
main(String[]) - Static method in class com.imsl.test.example.stat.ContingencyTableEx2
main(String[]) - Static method in class com.imsl.test.example.stat.CovariancesEx1
main(String[]) - Static method in class com.imsl.test.example.stat.CrossCorrelationEx1
main(String[]) - Static method in class com.imsl.test.example.stat.DBSCANEx1
main(String[]) - Static method in class com.imsl.test.example.stat.DBSCANEx2
main(String[]) - Static method in class com.imsl.test.example.stat.DifferenceEx1
main(String[]) - Static method in class com.imsl.test.example.stat.DifferenceEx2
main(String[]) - Static method in class com.imsl.test.example.stat.DiscriminantAnalysisEx1
main(String[]) - Static method in class com.imsl.test.example.stat.DissimilaritiesEx1
main(String[]) - Static method in class com.imsl.test.example.stat.distributions.BetaPDEx1
main(String[]) - Static method in class com.imsl.test.example.stat.distributions.BinomialPDEx1
main(String[]) - Static method in class com.imsl.test.example.stat.distributions.ContinuousUniformPDEx1
main(String[]) - Static method in class com.imsl.test.example.stat.distributions.DiscreteUniformPDEx1
main(String[]) - Static method in class com.imsl.test.example.stat.distributions.ExponentialPDEx1
main(String[]) - Static method in class com.imsl.test.example.stat.distributions.ExtremeValuePDEx1
main(String[]) - Static method in class com.imsl.test.example.stat.distributions.GammaPDEx1
main(String[]) - Static method in class com.imsl.test.example.stat.distributions.GeneralizedExtremeValuePDEx1
main(String[]) - Static method in class com.imsl.test.example.stat.distributions.GeneralizedParetoPDEx1
main(String[]) - Static method in class com.imsl.test.example.stat.distributions.GeometricPDEx1
main(String[]) - Static method in class com.imsl.test.example.stat.distributions.InverseGaussianPDEx1
main(String[]) - Static method in class com.imsl.test.example.stat.distributions.LogisticPDEx1
main(String[]) - Static method in class com.imsl.test.example.stat.distributions.LogLogisticPDEx1
main(String[]) - Static method in class com.imsl.test.example.stat.distributions.LogNormalPDEx1
main(String[]) - Static method in class com.imsl.test.example.stat.distributions.MaximumLikelihoodEstimationEx1
main(String[]) - Static method in class com.imsl.test.example.stat.distributions.MaximumLikelihoodEstimationEx2
main(String[]) - Static method in class com.imsl.test.example.stat.distributions.MaximumLikelihoodEstimationEx3
main(String[]) - Static method in class com.imsl.test.example.stat.distributions.MaximumLikelihoodEstimationEx4
main(String[]) - Static method in class com.imsl.test.example.stat.distributions.MaximumLikelihoodEstimationEx5
main(String[]) - Static method in class com.imsl.test.example.stat.distributions.MaximumLikelihoodEstimationEx6
main(String[]) - Static method in class com.imsl.test.example.stat.distributions.MaximumLikelihoodEstimationEx7
main(String[]) - Static method in class com.imsl.test.example.stat.distributions.MaximumLikelihoodEstimationEx8
main(String[]) - Static method in class com.imsl.test.example.stat.distributions.MaximumLikelihoodEstimationEx9
main(String[]) - Static method in class com.imsl.test.example.stat.distributions.NegativeBinomialPDEx1
main(String[]) - Static method in class com.imsl.test.example.stat.distributions.NormalPDEx1
main(String[]) - Static method in class com.imsl.test.example.stat.distributions.ParetoPDEx1
main(String[]) - Static method in class com.imsl.test.example.stat.distributions.PoissonPDEx1
main(String[]) - Static method in class com.imsl.test.example.stat.distributions.RayleighPDEx1
main(String[]) - Static method in class com.imsl.test.example.stat.distributions.WeibullPDEx1
main(String[]) - Static method in class com.imsl.test.example.stat.EGARCHEx1
main(String[]) - Static method in class com.imsl.test.example.stat.EGARCHEx2
main(String[]) - Static method in class com.imsl.test.example.stat.EGARCHEx3
main(String[]) - Static method in class com.imsl.test.example.stat.EmpiricalQuantilesEx1
main(String[]) - Static method in class com.imsl.test.example.stat.FactorAnalysisEx1
main(String[]) - Static method in class com.imsl.test.example.stat.FactorAnalysisEx2
main(String[]) - Static method in class com.imsl.test.example.stat.FaureSequenceEx1
main(String[]) - Static method in class com.imsl.test.example.stat.GARCHEx1
main(String[]) - Static method in class com.imsl.test.example.stat.HoltWintersExponentialSmoothingEx1
main(String[]) - Static method in class com.imsl.test.example.stat.InvCdfEx1
main(String[]) - Static method in class com.imsl.test.example.stat.InverseCdfEx1
main(String[]) - Static method in class com.imsl.test.example.stat.KalmanFilterEx1
main(String[]) - Static method in class com.imsl.test.example.stat.KalmanFilterEx2
main(String[]) - Static method in class com.imsl.test.example.stat.KaplanMeierECDFEx1
main(String[]) - Static method in class com.imsl.test.example.stat.KaplanMeierEstimatesEx1
main(String[]) - Static method in class com.imsl.test.example.stat.KolmogorovOneSampleEx1
main(String[]) - Static method in class com.imsl.test.example.stat.KolmogorovTwoSampleEx1
main(String[]) - Static method in class com.imsl.test.example.stat.LackOfFitEx1
main(String[]) - Static method in class com.imsl.test.example.stat.LifeTablesEx1
main(String[]) - Static method in class com.imsl.test.example.stat.LinearRegressionEx1
main(String[]) - Static method in class com.imsl.test.example.stat.LinearRegressionEx2
main(String[]) - Static method in class com.imsl.test.example.stat.MersenneTwister64Ex1
main(String[]) - Static method in class com.imsl.test.example.stat.MersenneTwisterEx1
main(String[]) - Static method in class com.imsl.test.example.stat.MultiCrossCorrelationEx1
main(String[]) - Static method in class com.imsl.test.example.stat.MultidimensionalScalingEx1
main(String[]) - Static method in class com.imsl.test.example.stat.MultidimensionalScalingEx2
main(String[]) - Static method in class com.imsl.test.example.stat.MultipleComparisonsEx1
main(String[]) - Static method in class com.imsl.test.example.stat.NGARCHEx1
main(String[]) - Static method in class com.imsl.test.example.stat.NonlinearRegressionEx1
main(String[]) - Static method in class com.imsl.test.example.stat.NonlinearRegressionEx2
main(String[]) - Static method in class com.imsl.test.example.stat.NonlinearRegressionEx3
main(String[]) - Static method in class com.imsl.test.example.stat.NormalityTestEx1
main(String[]) - Static method in class com.imsl.test.example.stat.NormOneSampleEx1
main(String[]) - Static method in class com.imsl.test.example.stat.NormTwoSampleEx1
main(String[]) - Static method in class com.imsl.test.example.stat.NormTwoSampleEx2
main(String[]) - Static method in class com.imsl.test.example.stat.PartialCovariancesEx1
main(String[]) - Static method in class com.imsl.test.example.stat.PartialCovariancesEx2
main(String[]) - Static method in class com.imsl.test.example.stat.PdfEx1
main(String[]) - Static method in class com.imsl.test.example.stat.PooledCovariancesEx1
main(String[]) - Static method in class com.imsl.test.example.stat.PooledCovariancesEx2
main(String[]) - Static method in class com.imsl.test.example.stat.ProportionalHazardsEx1
main(String[]) - Static method in class com.imsl.test.example.stat.RandomEx1
main(String[]) - Static method in class com.imsl.test.example.stat.RandomEx2
main(String[]) - Static method in class com.imsl.test.example.stat.RandomEx3
main(String[]) - Static method in class com.imsl.test.example.stat.RandomEx4
main(String[]) - Static method in class com.imsl.test.example.stat.RandomSamplesEx1
main(String[]) - Static method in class com.imsl.test.example.stat.RandomSamplesEx2
main(String[]) - Static method in class com.imsl.test.example.stat.RandomSamplesEx3
main(String[]) - Static method in class com.imsl.test.example.stat.RandomSamplesEx4
main(String[]) - Static method in class com.imsl.test.example.stat.RandomSamplesEx5
main(String[]) - Static method in class com.imsl.test.example.stat.RanksEx1
main(String[]) - Static method in class com.imsl.test.example.stat.RegressorsForGLMEx1
main(String[]) - Static method in class com.imsl.test.example.stat.RegressorsForGLMEx2
main(String[]) - Static method in class com.imsl.test.example.stat.SelectionRegressionEx1
main(String[]) - Static method in class com.imsl.test.example.stat.SelectionRegressionEx2
main(String[]) - Static method in class com.imsl.test.example.stat.SignTestEx1
main(String[]) - Static method in class com.imsl.test.example.stat.SignTestEx2
main(String[]) - Static method in class com.imsl.test.example.stat.SortEx1
main(String[]) - Static method in class com.imsl.test.example.stat.SortEx2
main(String[]) - Static method in class com.imsl.test.example.stat.StepwiseRegressionEx1
main(String[]) - Static method in class com.imsl.test.example.stat.SummaryEx1
main(String[]) - Static method in class com.imsl.test.example.stat.TableMultiWayEx1
main(String[]) - Static method in class com.imsl.test.example.stat.TableMultiWayEx2
main(String[]) - Static method in class com.imsl.test.example.stat.TableMultiWayEx3
main(String[]) - Static method in class com.imsl.test.example.stat.TableOneWayEx1
main(String[]) - Static method in class com.imsl.test.example.stat.TableTwoWayEx1
main(String[]) - Static method in class com.imsl.test.example.stat.TimeSeriesEx1
main(String[]) - Static method in class com.imsl.test.example.stat.TimeSeriesEx2
main(String[]) - Static method in class com.imsl.test.example.stat.TimeSeriesOperationsEx1
main(String[]) - Static method in class com.imsl.test.example.stat.TimeSeriesOperationsEx2
main(String[]) - Static method in class com.imsl.test.example.stat.TimeSeriesOperationsEx3
main(String[]) - Static method in class com.imsl.test.example.stat.TimeSeriesOperationsEx4
main(String[]) - Static method in class com.imsl.test.example.stat.UserBasisRegressionEx1
main(String[]) - Static method in class com.imsl.test.example.stat.UserBasisRegressionEx2
main(String[]) - Static method in class com.imsl.test.example.stat.VectorAutoregressionEx1
main(String[]) - Static method in class com.imsl.test.example.stat.WelchsTTestEx1
main(String[]) - Static method in class com.imsl.test.example.stat.WilcoxonRankSumEx1
main(String[]) - Static method in class com.imsl.test.example.stat.WilcoxonRankSumEx2
main(String[]) - Static method in class com.imsl.test.example.WarningEx1
main(String[]) - Static method in class com.imsl.Version: Print the version information about the environment and this library.
MALLOWS_CP_CRITERION - Static variable in class com.imsl.stat.SelectionRegression: Indicates Mallow's $C_p$ criterion regression.
MASS - Static variable in class com.imsl.math.Physical
Matrix - Class in com.imsl.math: Manipulation methods for real-valued rectangular matrices.
Matrix.MatrixType - Enum Class in com.imsl.math: Indicates which matrix type is used.
MatrixEx1 - Class in com.imsl.test.example.math: Calculates the 1-norm of a simple matrix.
MatrixEx1() - Constructor for class com.imsl.test.example.math.MatrixEx1
MatrixSingularException(String) - Constructor for exception com.imsl.stat.ARMA.MatrixSingularException: Constructs an MatrixSingularException with the specified detail message.
MatrixSingularException(String, Object[]) - Constructor for exception com.imsl.stat.ARMA.MatrixSingularException: Constructs an MatrixSingularException with the specified detail message.
max(double, double) - Static method in class com.imsl.math.JMath: Returns the larger of two doubles.
max(float, float) - Static method in class com.imsl.math.JMath: Returns the larger of two floats.
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 - Enum constant in enum 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.
MaxFcnEvalsExceededException(String) - Constructor for exception com.imsl.math.OdeAdamsGear.MaxFcnEvalsExceededException: Constructs a MaxFcnEvalsExceededException with the specified detailed message.
MaxFcnEvalsExceededException(String, Object[]) - Constructor for exception com.imsl.math.OdeAdamsGear.MaxFcnEvalsExceededException: Constructs a MaxFcnEvalsExceededException with the specified detailed message.
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
MaximumLikelihoodEstimation.OptimizationMethod - Enum Class in com.imsl.stat.distributions: Indicates which optimization method to use in maximizing the likelihood.
MaximumLikelihoodEstimationEx1 - Class in com.imsl.test.example.stat.distributions: Estimates the parameters of a beta probability distribution.
MaximumLikelihoodEstimationEx1() - Constructor for class com.imsl.test.example.stat.distributions.MaximumLikelihoodEstimationEx1
MaximumLikelihoodEstimationEx2 - Class in com.imsl.test.example.stat.distributions: Estimates the parameters of a gamma probability distribution.
MaximumLikelihoodEstimationEx2() - Constructor for class com.imsl.test.example.stat.distributions.MaximumLikelihoodEstimationEx2
MaximumLikelihoodEstimationEx3 - Class in com.imsl.test.example.stat.distributions: Estimates the parameters of the normal probability distribution.
MaximumLikelihoodEstimationEx3() - Constructor for class com.imsl.test.example.stat.distributions.MaximumLikelihoodEstimationEx3
MaximumLikelihoodEstimationEx4 - Class in com.imsl.test.example.stat.distributions: Estimates the parameters of the generalized Gaussian distribution.
MaximumLikelihoodEstimationEx4() - Constructor for class com.imsl.test.example.stat.distributions.MaximumLikelihoodEstimationEx4
MaximumLikelihoodEstimationEx5 - Class in com.imsl.test.example.stat.distributions: Estimates the parameters of the log-logistic probability distribution.
MaximumLikelihoodEstimationEx5() - Constructor for class com.imsl.test.example.stat.distributions.MaximumLikelihoodEstimationEx5
MaximumLikelihoodEstimationEx6 - Class in com.imsl.test.example.stat.distributions: Estimates the parameters of the inverse Gaussian (Wald) probability distribution.
MaximumLikelihoodEstimationEx6() - Constructor for class com.imsl.test.example.stat.distributions.MaximumLikelihoodEstimationEx6
MaximumLikelihoodEstimationEx7 - Class in com.imsl.test.example.stat.distributions: Estimates the parameters of a discrete uniform probability distribution.
MaximumLikelihoodEstimationEx7() - Constructor for class com.imsl.test.example.stat.distributions.MaximumLikelihoodEstimationEx7
MaximumLikelihoodEstimationEx8 - Class in com.imsl.test.example.stat.distributions: Estimates the parameter (probability) of a binomial probability distribution.
MaximumLikelihoodEstimationEx8() - Constructor for class com.imsl.test.example.stat.distributions.MaximumLikelihoodEstimationEx8
MaximumLikelihoodEstimationEx9 - Class in com.imsl.test.example.stat.distributions: Estimates the parameter (probability) of a negative binomial probability distribution.
MaximumLikelihoodEstimationEx9() - Constructor for class com.imsl.test.example.stat.distributions.MaximumLikelihoodEstimationEx9
MaxIterationsException(String) - Constructor for exception com.imsl.math.MinUnconMultiVar.MaxIterationsException: Constructs a MaxIterationsException object.
MaxIterationsException(String, Object[]) - Constructor for exception com.imsl.math.MinUnconMultiVar.MaxIterationsException: Constructs a MaxIterationsException object.
MaxTreeSizeExceededException(String) - Constructor for exception com.imsl.datamining.decisionTree.DecisionTree.MaxTreeSizeExceededException: Constructs a MaxTreeSizeExceededException and issues the specified message.
MaxTreeSizeExceededException(String, Object[]) - Constructor for exception com.imsl.datamining.decisionTree.DecisionTree.MaxTreeSizeExceededException: Constructs a MaxTreeSizeExceededException with the specified detail message.
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(double[]) - Static method in class com.imsl.stat.Summary: Returns the median of the given data set.
median(double[], double[]) - Static method in class com.imsl.stat.Summary: Returns the weighted median of the given data set and 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.
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.
MersenneTwister64Ex1 - Class in com.imsl.test.example.stat: Generates a pseudorandom sequence using the Mersenne64 Twister.
MersenneTwister64Ex1() - Constructor for class com.imsl.test.example.stat.MersenneTwister64Ex1
MersenneTwisterEx1 - Class in com.imsl.test.example.stat: Generates a pseudorandom sequence using the Mersenne Twister.
MersenneTwisterEx1() - Constructor for class com.imsl.test.example.stat.MersenneTwisterEx1
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(double, double) - Static method in class com.imsl.math.JMath: Returns the smaller of two doubles.
min(float, float) - Static method in class com.imsl.math.JMath: Returns the smaller of two floats.
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 - Enum constant in enum class com.imsl.stat.TimeSeriesOperations.CombineMethod: Uses the minimum of the two values.
MIN_CON_NLP - Enum constant in enum class com.imsl.stat.distributions.MaximumLikelihoodEstimation.OptimizationMethod: The MinConNLP method as described in MinConNLP.
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.ConstraintsNotSatisfiedException - Exception in com.imsl.math: No vector x satisfies all of the constraints.
MinConGenLin.EqualityConstraintsException - Exception in com.imsl.math: the variables are determined by the equality constraints.
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.
MinConGenLinEx1 - Class in com.imsl.test.example.math: Solves a general minimization problem with constraints.
MinConGenLinEx1() - Constructor for class com.imsl.test.example.math.MinConGenLinEx1
MinConGenLinEx2 - Class in com.imsl.test.example.math: Minimizes a nonlinear function with constraints.
MinConGenLinEx2() - Constructor for class com.imsl.test.example.math.MinConGenLinEx2
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.ConstraintEvaluationException - Exception in com.imsl.math: Constraint evaluation returns an error with current point.
MinConNLP.Formatter - Class in com.imsl.math: Deprecated.
Use IMSLFormatter instead.
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.LimitingAccuracyException - Exception in com.imsl.math: Limiting accuracy reached for a singular problem.
MinConNLP.LinearlyDependentGradientsException - Exception in com.imsl.math: Working set gradients are linearly dependent.
MinConNLP.NoAcceptableStepsizeException - Exception in com.imsl.math: No acceptable stepsize in [SIGMA,SIGLA].
MinConNLP.ObjectiveEvaluationException - Exception in com.imsl.math: Objective evaluation returns an error with current point.
MinConNLP.PenaltyFunctionPointInfeasibleException - Exception in com.imsl.math: Penalty function point infeasible.
MinConNLP.QPInfeasibleException - Exception in com.imsl.math: QP problem seemingly infeasible.
MinConNLP.SingularException - Exception in com.imsl.math: Problem is singular.
MinConNLP.TerminationCriteriaNotSatisfiedException - Exception in com.imsl.math: Termination criteria are not satisfied.
MinConNLP.TooManyIterationsException - Exception in com.imsl.math: Maximum number of iterations exceeded.
MinConNLP.TooMuchTimeException - Exception in com.imsl.math: Maximum time allowed for solve exceeded.
MinConNLP.WorkingSetSingularException - Exception in com.imsl.math: Working set is singular in dual extended QP.
MinConNLPEx1 - Class in com.imsl.test.example.math: Solves a nonlinear programming problem using a finite difference gradient.
MinConNLPEx1() - Constructor for class com.imsl.test.example.math.MinConNLPEx1
MinConNLPEx2 - Class in com.imsl.test.example.math: MinConNLP Example 2: Solves a general nonlinear programming problem with a user supplied gradient.
MinConNLPEx2() - Constructor for class com.imsl.test.example.math.MinConNLPEx2
MinConNLPEx3 - Class in com.imsl.test.example.math: MinConNLP Example 3: Solves a general nonlinear programming problem using a finite difference gradient.
MinConNLPEx3() - Constructor for class com.imsl.test.example.math.MinConNLPEx3
MinConNonlin - Class in com.imsl.math: Deprecated.
MinConNonlin has been replaced by MinConNLP.
MinConNonlin(int, int, int) - Constructor for class com.imsl.math.MinConNonlin: Deprecated.

Nonlinear programming solver constructor.
MinConNonlin.Function - Interface in com.imsl.math: Deprecated.
MinConNonlin has been replaced by MinConNLP.
MinConNonlin.Gradient - Interface in com.imsl.math: Deprecated.
MinConNonlin has been replaced by MinConNLP.
MinConNonlin.LineSearchException - Exception in com.imsl.math: Deprecated.
MinConNonlin has been replaced by MinConNLP.
MinConNonlin.QPConstraintsException - Exception in com.imsl.math: Deprecated.
MinConNonlin has been replaced by MinConNLP.
MinConNonlin.TooManyIterationsException - Exception in com.imsl.math: Deprecated.
MinConNonlin has been replaced by MinConNLP.
MinConNonlin.UphillSearchCalcException - Exception in com.imsl.math: Deprecated.
MinConNonlin has been replaced by MinConNLP.
MinConNonlin.ZeroSearchDirectionException - Exception in com.imsl.math: Deprecated.
MinConNonlin has been replaced by MinConNLP.
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.
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.
MinUnconEx1 - Class in com.imsl.test.example.math: MinUncon Example 1: Minimizes a single variable function.
MinUnconEx1() - Constructor for class com.imsl.test.example.math.MinUnconEx1
MinUnconEx2 - Class in com.imsl.test.example.math: MinUncon Example 2: Minimizes a single variable function using the analytic derivative.
MinUnconEx2() - Constructor for class com.imsl.test.example.math.MinUnconEx2
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.FalseConvergenceException - Exception in com.imsl.math: False convergence error; the iterates appear to be converging to a noncritical point.
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.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.
MinUnconMultiVarEx1 - Class in com.imsl.test.example.math: MinUnconMultiVar Example 1: Minimizes a multivariate function.
MinUnconMultiVarEx1() - Constructor for class com.imsl.test.example.math.MinUnconMultiVarEx1
MinUnconMultiVarEx2 - Class in com.imsl.test.example.math: MinUnconMultiVar Example 2: Minimizes a multivariate function with a user supplied gradient.
MinUnconMultiVarEx2() - Constructor for class com.imsl.test.example.math.MinUnconMultiVarEx2
MinUnconMultiVarEx3 - Class in com.imsl.test.example.math: MinUnconMultiVar Example 3: Minimizes a multivariate function with a user supplied Hessian.
MinUnconMultiVarEx3() - Constructor for class com.imsl.test.example.math.MinUnconMultiVarEx3
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.
MoreObsDelThanEnteredException(String) - Constructor for exception com.imsl.stat.Covariances.MoreObsDelThanEnteredException: Deprecated.

Constructs a MoreObsDelThanEnteredException object.
MoreObsDelThanEnteredException(String, Object[]) - Constructor for exception com.imsl.stat.Covariances.MoreObsDelThanEnteredException: Deprecated.

Constructs a MoreObsDelThanEnteredException object.
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.Row - Class in com.imsl.io: A row either in the constraint matrix or a free row.
MPSReaderEx1 - Class in com.imsl.test.example.io: Reads an MPS file.
MPSReaderEx1() - Constructor for class com.imsl.test.example.io.MPSReaderEx1
MTXReader() - Constructor for class com.imsl.test.example.math.SparseMatrixEx2.MTXReader
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.
MultiClassificationEx1 - Class in com.imsl.test.example.datamining.neural: Trains a 3-layer network to Fisher's iris data.
MultiClassificationEx1() - Constructor for class com.imsl.test.example.datamining.neural.MultiClassificationEx1
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.
MultiCrossCorrelationEx1 - Class in com.imsl.test.example.stat: Computes cross-correlations for a three-channel time series.
MultiCrossCorrelationEx1() - Constructor for class com.imsl.test.example.stat.MultiCrossCorrelationEx1
MultidimensionalScaling - Class in com.imsl.stat: Performs metric multidimensional scaling using the Euclidean or individual differences model.
MultidimensionalScaling(double[][][], int) - Constructor for class com.imsl.stat.MultidimensionalScaling: Constructor for class MultidimensionalScaling.
MultidimensionalScaling(MultidimensionalScaling) - Constructor for class com.imsl.stat.MultidimensionalScaling: Copy constructor for class MultidimensionalScaling.
MultidimensionalScaling.IllDefinedHessianException - Exception in com.imsl.stat: A Hessian matrix is ill-defined.
MultidimensionalScaling.NotEnoughPositiveEigenvaluesException - Exception in com.imsl.stat: The number of positive eigenvalues of the double-centered distance matrix is too small.
MultidimensionalScalingEx1 - Class in com.imsl.test.example.stat: Applies multidimensional scaling to a distance matrix.
MultidimensionalScalingEx1() - Constructor for class com.imsl.test.example.stat.MultidimensionalScalingEx1
MultidimensionalScalingEx2 - Class in com.imsl.test.example.stat: Applies multidimensional scaling to rectangles of different size.
MultidimensionalScalingEx2() - Constructor for class com.imsl.test.example.stat.MultidimensionalScalingEx2
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_COUNTS - Enum constant in enum class com.imsl.datamining.PredictiveModel.VariableType: The associated variable is binomial or multinomial counts, given in nClasses-1 consecutive columns.
MULTINOMIAL_COUNTS_ALL - Enum constant in enum class com.imsl.datamining.PredictiveModel.VariableType: The associated variable is binomial or multinomial counts, given in nClasses consecutive columns.
MULTINOMIAL_DEVIANCE - Enum constant in enum class com.imsl.datamining.GradientBoosting.LossFunctionType: The loss criteria is the (K-class) multinomial negative log-likelihood, or multinomial deviance.
MULTINOMIAL_LABELS - Enum constant in enum class com.imsl.datamining.PredictiveModel.VariableType: The associated variable is binomial or multinomial class labels.
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.
MultipleComparisonsEx1 - Class in com.imsl.test.example.stat: Performs the Student-Newman-Keuls multiple comparison test on a small set of means.
MultipleComparisonsEx1() - Constructor for class com.imsl.test.example.stat.MultipleComparisonsEx1
MultipleSolutionsException() - Constructor for exception com.imsl.math.DenseLP.MultipleSolutionsException: The problem has multiple solutions giving essentially the same minimum.
MultipleSolutionsException(String) - Constructor for exception com.imsl.math.DenseLP.MultipleSolutionsException: The problem has multiple solutions giving essentially the same minimum.
MultipleSolutionsException(String, Object[]) - Constructor for exception com.imsl.math.DenseLP.MultipleSolutionsException: The problem has multiple solutions giving essentially the same minimum.
MULTIPLICATION - Static variable in class com.imsl.stat.ClusterHierarchical: Indicates transformation by multiplication by -1.0.
multiply(double[]) - Method in class com.imsl.math.SparseMatrix: Multiply the matrix by a vector.
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(double[][], Matrix.MatrixType, double[]) - Static method in class com.imsl.math.Matrix: Multiply the rectangular array a and the column array x.
multiply(double[][], Matrix.MatrixType, double[][], Matrix.MatrixType, int) - Static method in class com.imsl.math.Matrix: Multiply two rectangular arrays, a * b.
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[][], Matrix.MatrixType) - Static method in class com.imsl.math.Matrix: Return the product of the row array x and the rectangular array a.
multiply(double[], double[][], Matrix.MatrixType, double[], boolean) - Static method in class com.imsl.math.Matrix: Compute vector-matrix-vector product trans(x) * a * y.
multiply(double[], SparseMatrix) - Static method in class com.imsl.math.SparseMatrix: Multiply row array x and sparse matrix A, $x^TA $.
multiply(double, Complex) - Static method in class com.imsl.math.Complex: Returns the product of a double and a Complex object, x * y.
multiply(double, Physical) - Static method in class com.imsl.math.Physical: Multiply a double and a Physical object
multiply(Complex[]) - Method in class com.imsl.math.ComplexSparseMatrix: Multiply the matrix by a vector.
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[][], ComplexMatrix.MatrixType, Complex[]) - Static method in class com.imsl.math.ComplexMatrix: Multiply the rectangular array a and the column vector x, both Complex.
multiply(Complex[][], ComplexMatrix.MatrixType, Complex[][], ComplexMatrix.MatrixType, int) - Static method in class com.imsl.math.ComplexMatrix: Multiply two Complex rectangular arrays of type MatrixType, a * b, using multiple java.lang.Threads.
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[][], ComplexMatrix.MatrixType) - 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[][], ComplexMatrix.MatrixType, Complex[], boolean) - Static method in class com.imsl.math.ComplexMatrix: Compute vector-matrix-vector product trans(conj(x)) * a * y.
multiply(Complex[], ComplexSparseMatrix) - Static method in class com.imsl.math.ComplexSparseMatrix: Multiply row array x and sparse matrix A, $x^TA $.
multiply(Complex, double) - Static method in class com.imsl.math.Complex: Returns the product of a Complex object and a double, x * y.
multiply(Complex, Complex) - Static method in class com.imsl.math.Complex: Returns the product of two Complex objects, x * y.
multiply(ComplexSparseMatrix, Complex[]) - Static method in class com.imsl.math.ComplexSparseMatrix: Multiply sparse matrix A and column array x, $A x$.
multiply(ComplexSparseMatrix, ComplexSparseMatrix) - Static method in class com.imsl.math.ComplexSparseMatrix: Multiply two sparse complex matrices A and B, $ C \leftarrow AB$.
multiply(Physical, double) - Static method in class com.imsl.math.Physical: Multiply a Physical object and a double
multiply(Physical, Physical) - Static method in class com.imsl.math.Physical: Multiply two Physical objects.
multiply(SparseMatrix, double[]) - Static method in class com.imsl.math.SparseMatrix: Multiply sparse matrix A and column array x, $A x$.
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(double, Complex) - Static method in class com.imsl.math.Complex: Returns the product of a pure imaginary double and a Complex object, ix * y.
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.
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
NaiveBayesClassifierEx1 - Class in com.imsl.test.example.datamining: Trains a classifier to Fisher's Iris data.
NaiveBayesClassifierEx1() - Constructor for class com.imsl.test.example.datamining.NaiveBayesClassifierEx1
NaiveBayesClassifierEx2 - Class in com.imsl.test.example.datamining: Trains a classifier on nominal (categorical) attributes.
NaiveBayesClassifierEx2() - Constructor for class com.imsl.test.example.datamining.NaiveBayesClassifierEx2
NaiveBayesClassifierEx3 - Class in com.imsl.test.example.datamining: Trains a classifier with a user supplied probability function.
NaiveBayesClassifierEx3() - Constructor for class com.imsl.test.example.datamining.NaiveBayesClassifierEx3
NaiveBayesClassifierEx3.TestGaussFcn1 - Class in com.imsl.test.example.datamining: Defines the user supplied probability distribution.
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.
negativeBinomial(int, double, double) - Static method in class com.imsl.stat.Pdf: Evaluates the negative binomial probability density function.
NegativeBinomialPD - Class in com.imsl.stat.distributions: The negative binomial probability distribution.
NegativeBinomialPD() - Constructor for class com.imsl.stat.distributions.NegativeBinomialPD: Constructor for the binomial probability distribution.
NegativeBinomialPDEx1 - Class in com.imsl.test.example.stat.distributions: Evaluates the negative binomial probability distribution.
NegativeBinomialPDEx1() - Constructor for class com.imsl.test.example.stat.distributions.NegativeBinomialPDEx1
NegativeFreqException(int, int, double) - Constructor for exception com.imsl.stat.NonlinearRegression.NegativeFreqException: Constructs a NegativeFreqException.
NegativeWeightException(int, int, double) - Constructor for exception com.imsl.stat.NonlinearRegression.NegativeWeightException: Constructs a NegativeWeightException.
NELDER_MEAD - Enum constant in enum class com.imsl.stat.distributions.MaximumLikelihoodEstimation.OptimizationMethod: The Nelder-Mead method as described in NelderMead.
NELDER_MEAD - Enum constant in enum class com.imsl.stat.ExtendedGARCH.Solver: The Nelder-Mead direct search polytope algorithm.
NelderMead - Class in com.imsl.math: Minimizes a function of n variables with or without box constraints using a direct search polytope algorithm.
NelderMead(NelderMead.Function, double[], double[]) - Constructor for class com.imsl.math.NelderMead: Constructor for constrained NelderMead.
NelderMead(NelderMead.Function, int) - Constructor for class com.imsl.math.NelderMead: Constructor for unconstrained NelderMead.
NelderMead.Function - Interface in com.imsl.math: Public interface for the user-supplied function to evaluate the objective function of the minimization problem.
NelderMeadEx1 - Class in com.imsl.test.example.math: Solves an unconstrained optimization problem using the simplex method of Nelder and Mead.
NelderMeadEx1() - Constructor for class com.imsl.test.example.math.NelderMeadEx1
NelderMeadEx2 - Class in com.imsl.test.example.math: Solves a constrained optimization problem using a direct search complex method.
NelderMeadEx2() - Constructor for class com.imsl.test.example.math.NelderMeadEx2
Network - Class in com.imsl.datamining.neural: Neural network base class.
Network() - Constructor for class com.imsl.datamining.neural.Network: Default constructor for Network.
NewInitialGuessException(String) - Constructor for exception com.imsl.stat.ARMA.NewInitialGuessException: Constructs an NewInitialGuessException with the specified detail message.
NewInitialGuessException(String, Object[]) - Constructor for exception com.imsl.stat.ARMA.NewInitialGuessException: Constructs an NewInitialGuessException with the specified detail message.
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.
nextContinuousUniform(double, double) - Method in class com.imsl.stat.Random: Generate a pseudorandom number from a continuous uniform 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(int, Cholesky) - Method in class com.imsl.stat.Random: Deprecated.
Use Random.nextGaussianCopula(Cholesky) instead.
nextGaussianCopula(Cholesky) - Method in class com.imsl.stat.Random: Generate pseudorandom numbers from a Gaussian Copula distribution.
nextGeneralizedExtremeValue(double, double, double) - Method in class com.imsl.stat.Random: Generates a pseudorandom number from a generalized extreme value distribution.
nextGeneralizedGaussian(double, double, double) - Method in class com.imsl.stat.Random: Generates a pseudorandom number from a generalized Gaussian distribution.
nextGeneralizedPareto(double, double, double) - Method in class com.imsl.stat.Random: Generates a pseudorandom number from a generalized Pareto 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.
nextLogistic(double, double) - Method in class com.imsl.stat.Random: Generates a pseudorandom number from a logistic 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(int, Cholesky) - Method in class com.imsl.stat.Random: Deprecated.
Use Random.nextMultivariateNormal(Cholesky) instead.
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.
nextNormalAR() - Method in class com.imsl.stat.Random: Deprecated.
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.
nextStudentsTCopula(int, double, Cholesky) - Method in class com.imsl.stat.Random: Deprecated.
Use Random.nextStudentsTCopula(double, Cholesky) instead.
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
NGARCHEx1 - Class in com.imsl.test.example.stat
NGARCHEx1() - Constructor for class com.imsl.test.example.stat.NGARCHEx1
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.
NoAcceptableModelFoundException(String) - Constructor for exception com.imsl.stat.AutoARIMA.NoAcceptableModelFoundException: Constructs a NoAcceptableModelFoundException exception with the specified detail message.
NoAcceptableModelFoundException(String, Object[]) - Constructor for exception com.imsl.stat.AutoARIMA.NoAcceptableModelFoundException: Constructs a NoAcceptableModelFoundException exception with the specified detail message.
NoAcceptablePivotException() - Constructor for exception com.imsl.math.DenseLP.NoAcceptablePivotException: No acceptable pivot could be found.
NoAcceptablePivotException(String) - Constructor for exception com.imsl.math.DenseLP.NoAcceptablePivotException: No acceptable pivot could be found.
NoAcceptablePivotException(String, Object[]) - Constructor for exception com.imsl.math.DenseLP.NoAcceptablePivotException: No acceptable pivot could be found.
NoAcceptableStepsizeException(String) - Constructor for exception com.imsl.math.MinConNLP.NoAcceptableStepsizeException: Constructs a NoAcceptableStepsizeException object.
NoAcceptableStepsizeException(String, Object[]) - Constructor for exception com.imsl.math.MinConNLP.NoAcceptableStepsizeException: Constructs a NoAcceptableStepsizeException object.
nObs - Variable in class com.imsl.datamining.neural.QuasiNewtonTrainer.GradObjective
nObs - Variable in class com.imsl.datamining.neural.QuasiNewtonTrainer.Objective
NoConstraintsAvailableException() - Constructor for exception com.imsl.math.DenseLP.NoConstraintsAvailableException: The LP problem has no constraints.
NoConstraintsAvailableException(String) - Constructor for exception com.imsl.math.DenseLP.NoConstraintsAvailableException: The LP problem has no constraints.
NoConstraintsAvailableException(String, Object[]) - Constructor for exception com.imsl.math.DenseLP.NoConstraintsAvailableException: The LP problem has no constraints.
NoConvergenceException(String) - Constructor for exception com.imsl.math.ConjugateGradient.NoConvergenceException: Constructs a NoConvergenceException object.
NoConvergenceException(String) - Constructor for exception com.imsl.stat.ClusterKMeans.NoConvergenceException: Constructs a NoConvergenceException object.
NoConvergenceException(String, Object[]) - Constructor for exception com.imsl.math.ConjugateGradient.NoConvergenceException: Constructs a NoConvergenceException object.
NoConvergenceException(String, Object[]) - Constructor for exception com.imsl.stat.ClusterKMeans.NoConvergenceException: Constructs a NoConvergenceException object.
Node - Class in com.imsl.datamining.neural: A Node in a neural network.
NoLPSolutionException() - Constructor for exception com.imsl.math.QuadraticProgramming.NoLPSolutionException: No solution for the LP problem with h = 0 was found by DenseLP.
NoLPSolutionException(String) - Constructor for exception com.imsl.math.QuadraticProgramming.NoLPSolutionException: No solution for the LP problem with h = 0 was found by DenseLP.
NoLPSolutionException(String, Object[]) - Constructor for exception com.imsl.math.QuadraticProgramming.NoLPSolutionException: No solution for the LP problem with h = 0 was found by DenseLP.
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: Returns 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: Returns 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: Returns 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: Returns 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.stat.ClusterHierarchical: Indicates no transformation.
NonInvertibleException(String) - Constructor for exception com.imsl.stat.ARMAMaxLikelihood.NonInvertibleException: Constructs a NonInvertible exception with the specified detail message.
NonInvertibleException(String, Object[]) - Constructor for exception com.imsl.stat.ARMAMaxLikelihood.NonInvertibleException: Constructs a NonInvertibleException exception with the specified detail message.
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.NegativeWeightException - Exception in com.imsl.stat: A negative weight was encountered.
NonlinearRegression.TooManyIterationsException - Exception in com.imsl.stat: The number of iterations has exceeded the maximum allowed.
NonlinearRegressionEx1 - Class in com.imsl.test.example.stat: Fits a nonlinear regression using finite differences for the derivatives.
NonlinearRegressionEx1() - Constructor for class com.imsl.test.example.stat.NonlinearRegressionEx1
NonlinearRegressionEx2 - Class in com.imsl.test.example.stat: Fits a nonlinear regression using user supplied derivatives.
NonlinearRegressionEx2() - Constructor for class com.imsl.test.example.stat.NonlinearRegressionEx2
NonlinearRegressionEx3 - Class in com.imsl.test.example.stat: Fits a nonlinear regression on scaled data.
NonlinearRegressionEx3() - Constructor for class com.imsl.test.example.stat.NonlinearRegressionEx3
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.
NonlinLeastSquaresEx1 - Class in com.imsl.test.example.math: Solves a nonlinear least squares problem using a finite difference Jacobian.
NonlinLeastSquaresEx1() - Constructor for class com.imsl.test.example.math.NonlinLeastSquaresEx1
NonlinLeastSquaresEx2 - Class in com.imsl.test.example.math: NonlinLeastSquares Example 2: Solves a nonlinear least squares problem with a user supplied Jacobian.
NonlinLeastSquaresEx2() - Constructor for class com.imsl.test.example.math.NonlinLeastSquaresEx2
NonnegativeFreqException(String) - Constructor for exception com.imsl.stat.ClusterKMeans.NonnegativeFreqException: Deprecated.

Constructs a NonnegativeFreqException object.
NonnegativeFreqException(String) - Constructor for exception com.imsl.stat.Covariances.NonnegativeFreqException: Constructs a NonnegativeFreqException object.
NonnegativeFreqException(String, Object[]) - Constructor for exception com.imsl.stat.ClusterKMeans.NonnegativeFreqException: Deprecated.

Constructs a NonnegativeFreqException object.
NonnegativeFreqException(String, Object[]) - Constructor for exception com.imsl.stat.Covariances.NonnegativeFreqException: Constructs a NonnegativeFreqException 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.TooMuchTimeException - Exception in com.imsl.math: Maximum time allowed for solve is exceeded.
NonNegativeLeastSquaresEx1 - Class in com.imsl.test.example.math: Solves a nonnegative least squares problem.
NonNegativeLeastSquaresEx1() - Constructor for class com.imsl.test.example.math.NonNegativeLeastSquaresEx1
NonnegativeWeightException(String) - Constructor for exception com.imsl.stat.ClusterKMeans.NonnegativeWeightException: Deprecated.

Constructs a NonnegativeWeightException object.
NonnegativeWeightException(String) - Constructor for exception com.imsl.stat.Covariances.NonnegativeWeightException: Constructs a NonnegativeWeightException object.
NonnegativeWeightException(String, Object[]) - Constructor for exception com.imsl.stat.ClusterKMeans.NonnegativeWeightException: Deprecated.

Constructs a NonnegativeWeightException object.
NonnegativeWeightException(String, Object[]) - Constructor for exception com.imsl.stat.Covariances.NonnegativeWeightException: Constructs a NonnegativeWeightException object.
NonPositiveEigenvalueException(String) - Constructor for exception com.imsl.stat.FactorAnalysis.NonPositiveEigenvalueException: Constructs a NonPositiveEigenvalueException object.
NonPositiveEigenvalueException(String, Object[]) - Constructor for exception com.imsl.stat.FactorAnalysis.NonPositiveEigenvalueException: Constructs a NonPositiveEigenvalueException object.
NonPosVariancesException(String) - Constructor for exception com.imsl.stat.AutoCorrelation.NonPosVariancesException: Constructs an NonPosVariancesException with the specified detail message.
NonPosVariancesException(String) - Constructor for exception com.imsl.stat.CrossCorrelation.NonPosVariancesException: Constructs a NonPosVariancesException object.
NonPosVariancesException(String) - Constructor for exception com.imsl.stat.MultiCrossCorrelation.NonPosVariancesException: Constructs a NonPosVariancesException object.
NonPosVariancesException(String, Object[]) - Constructor for exception com.imsl.stat.AutoCorrelation.NonPosVariancesException: Constructs an NonPosVariancesException with the specified detail message.
NonPosVariancesException(String, Object[]) - Constructor for exception com.imsl.stat.CrossCorrelation.NonPosVariancesException: Constructs a NonPosVariancesException object.
NonPosVariancesException(String, Object[]) - Constructor for exception com.imsl.stat.MultiCrossCorrelation.NonPosVariancesException: Constructs a NonPosVariancesException object.
NonStationaryException(String) - Constructor for exception com.imsl.stat.ARMAMaxLikelihood.NonStationaryException: Constructs a NonStationary exception with the specified detail message.
NonStationaryException(String, Object[]) - Constructor for exception com.imsl.stat.ARMAMaxLikelihood.NonStationaryException: Constructs a NonStationary exception with the specified detail message.
NoObservationsException(String, Object[]) - Constructor for exception com.imsl.stat.ChiSquaredTest.NoObservationsException: Constructs a NoObservationsException object.
NoPositiveVarianceException() - Constructor for exception com.imsl.stat.Dissimilarities.NoPositiveVarianceException: Constructs a NoPositiveVarianceException.
norm(double[]) - Method in interface com.imsl.math.GenMinRes.Norm: Used to compute the norm $ \Vert X \Vert $ in the Gram-Schmidt implementation.
norm(double[]) - Method in class com.imsl.test.example.math.GenMinResEx2: Computes the Euclidean norm of x.
normal(double) - Static method in class com.imsl.stat.Cdf: Evaluates the standard normal (Gaussian) cumulative probability distribution function.
normal(double) - Static method in class com.imsl.stat.InvCdf: Returns 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.
NormalityTestEx1 - Class in com.imsl.test.example.stat: Performs a test of normality.
NormalityTestEx1() - Constructor for class com.imsl.test.example.stat.NormalityTestEx1
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.
NormalPDEx1 - Class in com.imsl.test.example.stat.distributions: Evaluates the normal probability distribution.
NormalPDEx1() - Constructor for class com.imsl.test.example.stat.distributions.NormalPDEx1
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.
NormOneSampleEx1 - Class in com.imsl.test.example.stat: Performs a hypothesis test for the mean of a normal distribution.
NormOneSampleEx1() - Constructor for class com.imsl.test.example.stat.NormOneSampleEx1
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.
NormTwoSampleEx1 - Class in com.imsl.test.example.stat: Performs a hypothesis test for the difference in means of two normal distributions.
NormTwoSampleEx1() - Constructor for class com.imsl.test.example.stat.NormTwoSampleEx1
NormTwoSampleEx2 - Class in com.imsl.test.example.stat: Performs a difference in means test with incremental updates.
NormTwoSampleEx2() - Constructor for class com.imsl.test.example.stat.NormTwoSampleEx2
NOT_A_KNOT - Static variable in class com.imsl.math.CsInterpolate
NotCDFException(String, Object[]) - Constructor for exception com.imsl.stat.ChiSquaredTest.NotCDFException: Constructs a NotCDFException object.
NotDefiniteAMatrixException(String) - Constructor for exception com.imsl.math.ConjugateGradient.NotDefiniteAMatrixException: Constructs a NotDefiniteAMatrixException object.
NotDefiniteAMatrixException(String, Object[]) - Constructor for exception com.imsl.math.ConjugateGradient.NotDefiniteAMatrixException: Constructs a NotDefiniteAMatrixException object.
NotDefiniteJacobiPreconditionerException(String) - Constructor for exception com.imsl.math.ConjugateGradient.NotDefiniteJacobiPreconditionerException: Constructs a NotDefiniteJacobiPreconditionerException object.
NotDefiniteJacobiPreconditionerException(String, Object[]) - Constructor for exception com.imsl.math.ConjugateGradient.NotDefiniteJacobiPreconditionerException: Constructs a NotDefiniteJacobiPreconditionerException object.
NotDefinitePreconditionMatrixException(String) - Constructor for exception com.imsl.math.ConjugateGradient.NotDefinitePreconditionMatrixException: Constructs a NotDefinitePreconditionMatrixException object.
NotDefinitePreconditionMatrixException(String, Object[]) - Constructor for exception com.imsl.math.ConjugateGradient.NotDefinitePreconditionMatrixException: Constructs a NotDefinitePreconditionMatrixException object.
NotEnoughPositiveEigenvaluesException(String) - Constructor for exception com.imsl.stat.MultidimensionalScaling.NotEnoughPositiveEigenvaluesException: Constructs a NotEnoughPositiveEigenvaluesException with the specified detail message.
NotEnoughPositiveEigenvaluesException(String, Object[]) - Constructor for exception com.imsl.stat.MultidimensionalScaling.NotEnoughPositiveEigenvaluesException: Constructs a NotEnoughPositiveEigenvaluesException with the specified detail message.
NotPositiveDefiniteException(String) - Constructor for exception com.imsl.stat.FactorAnalysis.NotPositiveDefiniteException: Constructs a NotPositiveDefiniteException object.
NotPositiveDefiniteException(String, Object[]) - Constructor for exception com.imsl.stat.FactorAnalysis.NotPositiveDefiniteException: Constructs a NotPositiveDefiniteException object.
NotPositiveSemiDefiniteException(String) - Constructor for exception com.imsl.stat.FactorAnalysis.NotPositiveSemiDefiniteException: Constructs a NotPositiveSemiDefiniteException object.
NotPositiveSemiDefiniteException(String, Object[]) - Constructor for exception com.imsl.stat.FactorAnalysis.NotPositiveSemiDefiniteException: Constructs a NotPositiveSemiDefiniteException object.
NotSemiDefiniteException(String) - Constructor for exception com.imsl.stat.FactorAnalysis.NotSemiDefiniteException: Constructs a NotSemiDefiniteException object.
NotSemiDefiniteException(String, Object[]) - Constructor for exception com.imsl.stat.FactorAnalysis.NotSemiDefiniteException: Constructs a NotSemiDefiniteException object.
NotSPDException() - Constructor for exception com.imsl.math.Cholesky.NotSPDException: Constructs a NotSPDException object.
NotSPDException() - Constructor for exception com.imsl.math.ComplexSparseCholesky.NotSPDException: Constructs a NotSPDException object.
NotSPDException() - Constructor for exception com.imsl.math.SparseCholesky.NotSPDException: Constructs a NotSPDException object.
NoVariablesEnteredException() - Constructor for exception com.imsl.stat.StepwiseRegression.NoVariablesEnteredException: Constructs a NoVariablesEnteredException.
NoVariablesException() - Constructor for exception com.imsl.stat.SelectionRegression.NoVariablesException: Constructs a NoVariablesException.
NoVariationInputException(String) - Constructor for exception com.imsl.stat.NormalityTest.NoVariationInputException: Constructs a NoVariationInputException object.
NoVariationInputException(String, Object[]) - Constructor for exception com.imsl.stat.NormalityTest.NoVariationInputException: Constructs a NoVariationInputException object.
NoVectorXException(String) - Constructor for exception com.imsl.stat.GARCH.NoVectorXException: Constructs a NoVectorXException object.
NoVectorXException(String, Object[]) - Constructor for exception com.imsl.stat.GARCH.NoVectorXException: Constructs a NoVectorXException object.
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.
NumericalDerivativesEx1 - Class in com.imsl.test.example.math: NumericalDerivatives Example 1: Approximates the gradient of a function of two variables using numerical differentiation.
NumericalDerivativesEx1() - Constructor for class com.imsl.test.example.math.NumericalDerivativesEx1
NumericalDerivativesEx2 - Class in com.imsl.test.example.math: Approximates one component of the gradient using numerical differentiation.
NumericalDerivativesEx2() - Constructor for class com.imsl.test.example.math.NumericalDerivativesEx2
NumericalDerivativesEx3 - Class in com.imsl.test.example.math: Approximates the gradient with a combination of numerical derivatives and analytic derivatives.
NumericalDerivativesEx3() - Constructor for class com.imsl.test.example.math.NumericalDerivativesEx3
NumericalDerivativesEx4 - Class in com.imsl.test.example.math: Approximates the gradient using central divided differences.
NumericalDerivativesEx4(NumericalDerivatives.Function) - Constructor for class com.imsl.test.example.math.NumericalDerivativesEx4
NumericalDerivativesEx5 - Class in com.imsl.test.example.math: Approximates the Hessian of a function using numerical differentiation.
NumericalDerivativesEx5(NumericalDerivatives.Function) - Constructor for class com.imsl.test.example.math.NumericalDerivativesEx5
NumericalDerivativesEx6 - Class in com.imsl.test.example.math: Solves an optimization problem with supplied numerical gradients.
NumericalDerivativesEx6() - Constructor for class com.imsl.test.example.math.NumericalDerivativesEx6
NumericDifficultyException(String) - Constructor for exception com.imsl.math.LinearProgramming.NumericDifficultyException: Deprecated.
NumericDifficultyException(String, Object[]) - Constructor for exception com.imsl.math.LinearProgramming.NumericDifficultyException: Deprecated.
nY - Variable in class com.imsl.datamining.neural.QuasiNewtonTrainer.GradObjective
nY - Variable in class com.imsl.datamining.neural.QuasiNewtonTrainer.Objective

O

ObjectiveEvaluationException(String) - Constructor for exception com.imsl.math.MinConNLP.ObjectiveEvaluationException: Constructs a ObjectiveEvaluationException object.
ObjectiveEvaluationException(String, Object[]) - Constructor for exception com.imsl.math.MinConNLP.ObjectiveEvaluationException: Constructs a ObjectiveEvaluationException object.
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.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.SingularMatrixException - Exception in com.imsl.math: The interpolation matrix is singular.
OdeAdamsGear.ToleranceTooSmallException - Exception in com.imsl.math: Tolerance is too small or the problem is stiff.
OdeAdamsGearEx1 - Class in com.imsl.test.example.math: Solves an ODE using the Adams-Gear method.
OdeAdamsGearEx1() - Constructor for class com.imsl.test.example.math.OdeAdamsGearEx1
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.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.
OdeRungeKuttaEx1 - Class in com.imsl.test.example.math: Solves an ODE using the Runge-Kutta-Verner method.
OdeRungeKuttaEx1() - Constructor for class com.imsl.test.example.math.OdeRungeKuttaEx1
ONE_AT_A_TIME - Static variable in class com.imsl.stat.ANOVA: The One-at-a-Time (Fisher's LSD) method
ONE_CLASS - Enum constant in enum class com.imsl.datamining.PredictiveModel.VariableType: The associated variable takes a single value.
ONE_SIDED - Static variable in class com.imsl.math.NumericalDerivatives: Indicates one sided differences.
oneNorm() - Method in class com.imsl.math.ComplexSparseMatrix: Returns the matrix one norm of the sparse matrix.
oneNorm() - Method in class com.imsl.math.SparseMatrix: 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(Complex[][]) - Static method in class com.imsl.math.ComplexMatrix: Return the Complex matrix one norm.
optimize(DataNode[][], double[], double[], int, Kernel) - Method in class com.imsl.datamining.supportvectormachine.SupportVectorMachine: Abstract method to perform the support vector machine optimization.
optimize(DataNode[][], double[], double[], int, Kernel) - Method in class com.imsl.datamining.supportvectormachine.SVClassification: Performs the classification support vector machine optimization problem.
optimize(DataNode[][], double[], double[], int, Kernel) - Method in class com.imsl.datamining.supportvectormachine.SVOneClass: Performs the one class support vector machine optimization problem.
optimize(DataNode[][], double[], double[], int, Kernel) - Method in class com.imsl.datamining.supportvectormachine.SVRegression: Performs the regression support vector machine optimization problem.
order - Variable in class com.imsl.math.BSpline: Order of the spline.
ORDERED_DISCRETE - Enum constant in enum class com.imsl.datamining.PredictiveModel.VariableType: The associated variable can assume a limited number of discrete, ordered values.
out - Variable in class com.imsl.WarningObject: The warning stream.
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

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.
ParetoPD - Class in com.imsl.stat.distributions: The Pareto probability distribution.
ParetoPD() - Constructor for class com.imsl.stat.distributions.ParetoPD: Constructor for the Pareto probability distribution.
ParetoPDEx1 - Class in com.imsl.test.example.stat.distributions: Evaluates the Pareto probability distribution.
ParetoPDEx1() - Constructor for class com.imsl.test.example.stat.distributions.ParetoPDEx1
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.
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 mix of dependent and independent variables.
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.InvalidMatrixException - Exception in com.imsl.stat: Exception 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.
PartialCovariancesEx1 - Class in com.imsl.test.example.stat: Computes the partial covariances for a set of 9 variables.
PartialCovariancesEx1() - Constructor for class com.imsl.test.example.stat.PartialCovariancesEx1
PartialCovariancesEx2 - Class in com.imsl.test.example.stat: Computes partial covariances after adjusting for specific variables.
PartialCovariancesEx2() - Constructor for class com.imsl.test.example.stat.PartialCovariancesEx2
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.BinomialPD: Returns the value of the binomial probability density function.
pdf(double, double...) - Method in class com.imsl.stat.distributions.ContinuousUniformPD: Returns the value of the continuous uniform probability density function.
pdf(double, double...) - Method in class com.imsl.stat.distributions.DiscreteUniformPD: Evaluates the discrete uniform probability density function.
pdf(double, double...) - Method in class com.imsl.stat.distributions.ExponentialPD: Returns the value of the exponential probability density function.
pdf(double, double...) - Method in class com.imsl.stat.distributions.ExtremeValuePD: Returns the value of the extreme value 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.GeneralizedGaussianPD: Returns the value of the generalized Gaussian probability density function.
pdf(double, double...) - Method in class com.imsl.stat.distributions.GeometricPD: Returns the value of the geometric probability density function.
pdf(double, double...) - Method in class com.imsl.stat.distributions.InverseGaussianPD: Returns the value of the inverse Gaussian probability density function.
pdf(double, double...) - Method in class com.imsl.stat.distributions.LogisticPD: Returns the value of the logistic probability density function.
pdf(double, double...) - Method in class com.imsl.stat.distributions.LogLogisticPD: Returns the value of the log-logistic probability density function.
pdf(double, double...) - Method in class com.imsl.stat.distributions.LogNormalPD: Returns the value of the probability density function.
pdf(double, double...) - Method in class com.imsl.stat.distributions.NegativeBinomialPD: Returns the value of the negative binomial 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.ParetoPD: Returns the value of the Pareto probability density function.
pdf(double, double...) - Method in class com.imsl.stat.distributions.PoissonPD: Returns the value of the Poisson probability density function.
pdf(double, double...) - Method in class com.imsl.stat.distributions.ProbabilityDistribution: Returns the value of the probability density function.
pdf(double, double...) - Method in class com.imsl.stat.distributions.RayleighPD: Returns the value of the Rayleigh probability density function.
pdf(double, double...) - Method in class com.imsl.stat.distributions.WeibullPD: Returns the value of the Weibull 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.
PdfEx1 - Class in com.imsl.test.example.stat: Evaluates probability density functions.
PdfEx1() - Constructor for class com.imsl.test.example.stat.PdfEx1
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
PenaltyFunctionPointInfeasibleException(String) - Constructor for exception com.imsl.math.MinConNLP.PenaltyFunctionPointInfeasibleException: Constructs a PenaltyFunctionPointInfeasibleException object.
PenaltyFunctionPointInfeasibleException(String, Object[]) - Constructor for exception com.imsl.math.MinConNLP.PenaltyFunctionPointInfeasibleException: Constructs a PenaltyFunctionPointInfeasibleException object.
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(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.
Physical(double, String) - Constructor for class com.imsl.math.Physical: Constructs a new Physical object and initializes this object to a double value.
Physical(Physical) - Constructor for class com.imsl.math.Physical: Constructs a copy of a Physical object.
PhysicalEx1 - Class in com.imsl.test.example.math: Displays the physical constant PI.
PhysicalEx1() - Constructor for class com.imsl.test.example.math.PhysicalEx1
PI - Static variable in class com.imsl.math.JMath
plusEquals(int, int, double) - Method in class com.imsl.math.SparseMatrix: Adds a value to an element in the matrix.
plusEquals(int, int, Complex) - Method in class com.imsl.math.ComplexSparseMatrix: 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.
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
PoissonPD - Class in com.imsl.stat.distributions: The Poisson probability distribution.
PoissonPD() - Constructor for class com.imsl.stat.distributions.PoissonPD: Constructor for the Poisson probability distribution.
PoissonPDEx1 - Class in com.imsl.test.example.stat.distributions: Evaluates the Poisson probability distribution.
PoissonPDEx1() - Constructor for class com.imsl.test.example.stat.distributions.PoissonPDEx1
poissonProb(int, double) - Static method in class com.imsl.stat.Cdf: Deprecated.
Use Pdf.poisson(int, double) instead.
PolyHarmonicSpline(int) - Constructor for class com.imsl.test.example.math.RadialBasisEx2.PolyHarmonicSpline
PolynomialKernel - Class in com.imsl.datamining.supportvectormachine: Specifies the polynomial kernel for support vector machines.
PolynomialKernel() - Constructor for class com.imsl.datamining.supportvectormachine.PolynomialKernel: Constructor for the polynomial kernel.
PolynomialKernel(double, double, int) - Constructor for class com.imsl.datamining.supportvectormachine.PolynomialKernel: Constructs a polynomial kernel.
PolynomialKernel(PolynomialKernel) - Constructor for class com.imsl.datamining.supportvectormachine.PolynomialKernel: Constructs a copy of the input PolynomialKernel kernel.
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.
PooledCovariances - Class in com.imsl.stat: Computes a pooled variance-covariance matrix from one or more sets of observations.
PooledCovariances(int) - Constructor for class com.imsl.stat.PooledCovariances: Constructor for PooledCovariances.
PooledCovariancesEx1 - Class in com.imsl.test.example.stat: Computes a pooled variance-covariance matrix involving 2 groups.
PooledCovariancesEx1() - Constructor for class com.imsl.test.example.stat.PooledCovariancesEx1
PooledCovariancesEx2 - Class in com.imsl.test.example.stat: Computes pooled variance-covariance for Fisher's iris data.
PooledCovariancesEx2() - Constructor for class com.imsl.test.example.stat.PooledCovariancesEx2
pow(double, double) - Static method in class com.imsl.math.JMath: Returns x to the power y.
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.
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.
preconditioner(double[], double[]) - Method in class com.imsl.test.example.math.ConjugateGradientEx2
preconditioner(double[], double[]) - Method in class com.imsl.test.example.math.GenMinResEx4: Defines a preconditioning operation for the problem.
preconditioner(double[], double[]) - Method in class com.imsl.test.example.math.GenMinResEx6: Solve the tridiagonal preconditioning matrix problem for z.
predict() - Method in class com.imsl.datamining.decisionTree.DecisionTree: Predicts the training examples (in-sample predictions) using the most recently grown tree.
predict() - Method in class com.imsl.datamining.decisionTree.RandomTrees: Returns the predicted values generated by the random forest on the training data.
predict() - Method in class com.imsl.datamining.GradientBoosting: Returns the predicted values on the training data.
predict() - Method in class com.imsl.datamining.LogisticRegression: Returns the fitted values on the training data.
predict() - Method in class com.imsl.datamining.PredictiveModel: Predicts the response variable using the most recent fit.
predict() - Method in class com.imsl.datamining.supportvectormachine.SupportVectorMachine: Returns the predicted values on the training data, i.e., returns the fitted values.
predict(double[][]) - Method in class com.imsl.datamining.decisionTree.DecisionTree: Predicts new data using the most recently grown decision tree.
predict(double[][]) - Method in class com.imsl.datamining.decisionTree.RandomTrees: Returns the predicted values on the input test data.
predict(double[][]) - Method in class com.imsl.datamining.GradientBoosting: Returns the predicted values on the input test data.
predict(double[][]) - Method in class com.imsl.datamining.GradientBoostingModelObject: Returns the predictions on the test data.
predict(double[][]) - Method in class com.imsl.datamining.LogisticRegression: Returns the predicted values on the test data.
predict(double[][]) - Method in class com.imsl.datamining.LogisticRegressionModelObject: Returns the predictions on the test data.
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[][]) - Method in class com.imsl.datamining.supportvectormachine.SupportVectorMachine: Returns the predicted values on the input test data.
predict(double[][], double[]) - Method in class com.imsl.datamining.decisionTree.DecisionTree: Predicts new weighted data using the most recently grown decision tree.
predict(double[][], double[]) - Method in class com.imsl.datamining.decisionTree.RandomTrees: Returns the predicted values on the input test data and the test data weights.
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(double[][], double[]) - Method in class com.imsl.datamining.GradientBoostingModelObject: Returns the predictions on the test data using data weights.
predict(double[][], double[]) - Method in class com.imsl.datamining.LogisticRegression: Returns predicted values on the test data using the given weights.
predict(double[][], double[]) - Method in class com.imsl.datamining.LogisticRegressionModelObject: Returns predictions on the given test data based on the given weights.
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: Specifies a predictive model.
PredictiveModel(double[][], double[][], PredictiveModel.VariableType[], PredictiveModel.VariableType) - Constructor for class com.imsl.datamining.PredictiveModel: Constructs a PredictiveModel object for a single response variable and multiple predictor variables.
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(PredictiveModel) - Constructor for class com.imsl.datamining.PredictiveModel: Constructs a PredictiveModel from an existing instance.
PredictiveModel.CloneNotSupportedException - Exception in com.imsl.datamining: Wraps the java.lang.CloneNotSupportedException to indicate that the clone method in class Object has been called to clone an object, but that the object's class does not implement the Cloneable interface.
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.StateChangeException - Exception in com.imsl.datamining: Exception thrown when an input parameter has changed that might affect the model estimates or predictions.
PredictiveModel.SumOfProbabilitiesNotOneException - Exception in com.imsl.datamining: Exception thrown when the sum of probabilities is not approximately one.
PredictiveModel.VariableType - Enum Class in com.imsl.datamining: Enumerates different variable types.
PredictiveModelException(String) - Constructor for exception com.imsl.datamining.PredictiveModel.PredictiveModelException: Constructs a PredictiveModelException and issues the specified message.
PredictiveModelException(String, String, Object[]) - Constructor for exception com.imsl.datamining.PredictiveModel.PredictiveModelException
predictValues(SVModel, double[][]) - Method in class com.imsl.datamining.supportvectormachine.SupportVectorMachine: Abstract method for generating the predicted values using the fitted support vector machine model.
predictValues(SVModel, double[][]) - Method in class com.imsl.datamining.supportvectormachine.SVClassification: Generates the predicted values on the attribute data using the given support vector machine model.
predictValues(SVModel, double[][]) - Method in class com.imsl.datamining.supportvectormachine.SVOneClass: Generates the predicted values on the attribute data using the given support vector machine model.
predictValues(SVModel, double[][]) - Method in class com.imsl.datamining.supportvectormachine.SVRegression: Generates the predicted values on the attribute data using the given support vector machine model.
PrefixSpan - Class in com.imsl.datamining: Performs the PrefixSpan algorithm for sequential pattern mining.
PrefixSpan(double[][], int[]) - Constructor for class com.imsl.datamining.PrefixSpan: Constructs a PrefixSpan object from a transaction database.
PrefixSpan(SequenceDatabase) - Constructor for class com.imsl.datamining.PrefixSpan: Constructs a PrefixSpan object from a SequenceDatabase.
PrefixSpanEx1 - Class in com.imsl.test.example.datamining: Finds sequential patterns in a sequence database.
PrefixSpanEx1() - Constructor for class com.imsl.test.example.datamining.PrefixSpanEx1
PrefixSpanEx2 - Class in com.imsl.test.example.datamining: Finds sequential patterns in a sequence database.
PrefixSpanEx2() - Constructor for class com.imsl.test.example.datamining.PrefixSpanEx2
PrefixSpanEx3 - Class in com.imsl.test.example.datamining: Creates a sequence database from a transaction database.
PrefixSpanEx3() - Constructor for class com.imsl.test.example.datamining.PrefixSpanEx3
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, 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.
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.
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.
PrimalInfeasibleException(String) - Constructor for exception com.imsl.math.SparseLP.PrimalInfeasibleException: The primal problem is infeasible.
PrimalInfeasibleException(String, Object[]) - Constructor for exception com.imsl.math.SparseLP.PrimalInfeasibleException: The primal problem is infeasible.
PrimalUnboundedException(String) - Constructor for exception com.imsl.math.SparseLP.PrimalUnboundedException: The primal problem is unbounded.
PrimalUnboundedException(String, Object[]) - Constructor for exception com.imsl.math.SparseLP.PrimalUnboundedException: The primal problem is unbounded.
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() - Method in class com.imsl.datamining.AssociationRule: Print the member data in this object.
print() - Method in class com.imsl.datamining.Itemsets: Prints a standard representation of the members of this object.
print() - Method in class com.imsl.datamining.SequenceDatabase: Prints the sequence database to the standard output.
print(AssociationRule[]) - Static method in class com.imsl.datamining.AssociationRule: Print out the association rules in ar.
print(PrintMatrixFormat, Object) - Method in class com.imsl.math.PrintMatrix: Prints a matrix with specified format.
print(Object) - Method in class com.imsl.math.PrintMatrix: Prints a matrix with a default 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.
print(String) - Method in class com.imsl.math.PrintMatrix: Print a string.
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 using labels.
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(PrintStream, String) - Constructor for class com.imsl.math.PrintMatrix: Creates a PrintMatrix object with the specified PrintStream and sets its title.
PrintMatrix(String) - Constructor for class com.imsl.math.PrintMatrix: Creates a PrintMatrix object and sets its title.
PrintMatrixEx1 - Class in com.imsl.test.example.math: Prints a simple matrix.
PrintMatrixEx1() - Constructor for class com.imsl.test.example.math.PrintMatrixEx1
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.
PrintMatrixFormatEx1 - Class in com.imsl.test.example.math: Prints a matrix with and without row and column labels.
PrintMatrixFormatEx1() - Constructor for class com.imsl.test.example.math.PrintMatrixFormatEx1
PrintMatrixFormatEx2 - Class in com.imsl.test.example.math: Prints a matrix in CSV format.
PrintMatrixFormatEx2(int) - Constructor for class com.imsl.test.example.math.PrintMatrixFormatEx2
printProperties(Set) - Static method in class com.imsl.Version
printTree() - Method in class com.imsl.datamining.decisionTree.Tree: Prints the tree structure.
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.
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.
probabilities(double[], int[]) - Method in class com.imsl.datamining.NaiveBayesClassifier: Predicts the classification probabilities for the input pattern using the trained Naive Bayes classifier.
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 - Interface in com.imsl.stat: Public interface for a user-supplied probability distribution.
ProbabilityDistribution(int) - Constructor for class com.imsl.stat.distributions.ProbabilityDistribution: Constructor for the probability distribution
ProblemInfeasibleException() - Constructor for exception com.imsl.math.LinearProgramming.ProblemInfeasibleException: Deprecated.
ProblemInfeasibleException(String) - Constructor for exception com.imsl.math.LinearProgramming.ProblemInfeasibleException: Deprecated.
ProblemUnboundedException() - Constructor for exception com.imsl.math.DenseLP.ProblemUnboundedException: The problem is unbounded.
ProblemUnboundedException() - Constructor for exception com.imsl.math.LinearProgramming.ProblemUnboundedException: Deprecated.
ProblemUnboundedException() - Constructor for exception com.imsl.math.QuadraticProgramming.ProblemUnboundedException: The objective value for the problem is unbounded.
ProblemUnboundedException(String) - Constructor for exception com.imsl.math.DenseLP.ProblemUnboundedException: The problem is unbounded.
ProblemUnboundedException(String) - Constructor for exception com.imsl.math.LinearProgramming.ProblemUnboundedException: Deprecated.
ProblemUnboundedException(String) - Constructor for exception com.imsl.math.QuadraticProgramming.ProblemUnboundedException: The objective value for the problem is unbounded.
ProblemUnboundedException(String) - Constructor for exception com.imsl.math.SparseLP.ProblemUnboundedException: The problem is unbounded.
ProblemUnboundedException(String, Object[]) - Constructor for exception com.imsl.math.DenseLP.ProblemUnboundedException: The problem is unbounded.
ProblemUnboundedException(String, Object[]) - Constructor for exception com.imsl.math.SparseLP.ProblemUnboundedException: The problem is unbounded.
ProblemVacuousException() - Constructor for exception com.imsl.math.DenseLP.ProblemVacuousException: The problem is vacuous.
ProblemVacuousException(String) - Constructor for exception com.imsl.math.DenseLP.ProblemVacuousException: The problem is vacuous.
ProblemVacuousException(String, Object[]) - Constructor for exception com.imsl.math.DenseLP.ProblemVacuousException: The problem is vacuous.
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.
ProportionalHazardsEx1 - Class in com.imsl.test.example.stat: Performs proportional-hazards data analysis on lung cancer data.
ProportionalHazardsEx1() - Constructor for class com.imsl.test.example.stat.ProportionalHazardsEx1
pruneTree(double) - Method in class com.imsl.datamining.decisionTree.DecisionTree: Finds the minimum cost-complexity decision tree for the cost-complexity value, gamma.
PruningFailedToConvergeException(String) - Constructor for exception com.imsl.datamining.decisionTree.DecisionTree.PruningFailedToConvergeException: Constructs a PruningFailedToConvergeException with the specified detail message.
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.
PureNodeException(String) - Constructor for exception com.imsl.datamining.decisionTree.DecisionTree.PureNodeException: Constructs a PureNodeException with the specified detail message.
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

QPConstraintsException(String) - Constructor for exception com.imsl.math.MinConNonlin.QPConstraintsException: Deprecated.
QPConstraintsException(String, Object[]) - Constructor for exception com.imsl.math.MinConNonlin.QPConstraintsException: Deprecated.
QPInfeasibleException(String) - Constructor for exception com.imsl.math.MinConNLP.QPInfeasibleException: Constructs a QPInfeasibleException object.
QPInfeasibleException(String, Object[]) - Constructor for exception com.imsl.math.MinConNLP.QPInfeasibleException: Constructs a QPInfeasibleException object.
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.
QREx1 - Class in com.imsl.test.example.math: Performs the QR factorization of a matrix.
QREx1() - Constructor for class com.imsl.test.example.math.QREx1
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.NoLPSolutionException - Exception in com.imsl.math: No solution for the LP problem with h = 0 was found by DenseLP.
QuadraticProgramming.ProblemUnboundedException - Exception in com.imsl.math: The objective value for the problem is unbounded.
QuadraticProgramming.SolutionNotFoundException - Exception in com.imsl.math: A solution was not found.
QuadraticProgrammingEx1 - Class in com.imsl.test.example.math: Solves a quadratic programming problem in 4 variables.
QuadraticProgrammingEx1() - Constructor for class com.imsl.test.example.math.QuadraticProgrammingEx1
QuadraticProgrammingEx2 - Class in com.imsl.test.example.math: Solves a quadratic programming problem with equality constraints.
QuadraticProgrammingEx2() - Constructor for class com.imsl.test.example.math.QuadraticProgrammingEx2
QuadraticProgrammingEx3 - Class in com.imsl.test.example.math: Illustrates the exception thrown by the solver when it encounters inconsistent style constraints.
QuadraticProgrammingEx3() - Constructor for class com.imsl.test.example.math.QuadraticProgrammingEx3
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.
QuadratureEx1 - Class in com.imsl.test.example.math: Quadrature Example 1: Approximates an integral.
QuadratureEx1() - Constructor for class com.imsl.test.example.math.QuadratureEx1
QuadratureEx2 - Class in com.imsl.test.example.math: Quadrature Example 2: Approximates the integral of $e^{-x}$.
QuadratureEx2() - Constructor for class com.imsl.test.example.math.QuadratureEx2
QuadratureEx3 - Class in com.imsl.test.example.math: Quadrature Example 3: Approximates the integral of the entire real line.
QuadratureEx3() - Constructor for class com.imsl.test.example.math.QuadratureEx3
QuadratureEx4 - Class in com.imsl.test.example.math: Quadrature Example 4: Approximates a trigonometric integral.
QuadratureEx4() - Constructor for class com.imsl.test.example.math.QuadratureEx4
quantile(double[], double[], double) - Static method in class com.imsl.stat.Summary
QUANTITATIVE_CONTINUOUS - Enum constant in enum class com.imsl.datamining.PredictiveModel.VariableType: The associated variable can assume any real value within a range of values.
QUARTERLY - Static variable in class com.imsl.finance.Bond: Coupon payments are made quarterly.
QUASI_NEWTON - Enum constant in enum class com.imsl.stat.ExtendedGARCH.Solver: A quasi-Newton type solver based on Powell's TOLMIN method.
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.BlockObjective - Class in com.imsl.datamining.neural
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: Constructs a QUEST object for a single response variable and multiple predictor variables.
QUEST(QUEST) - Constructor for class com.imsl.datamining.decisionTree.QUEST: Constructs a copy of the input QUEST decision tree.

R

R_SQUARED_CRITERION - Static variable in class com.imsl.stat.SelectionRegression: Indicates $R^2$ criterion regression.
r9lgmc(double) - Static method in class com.imsl.math.Sfun: Deprecated.
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.HardyMultiquadric - Class in com.imsl.math: The Hardy multiquadric basis function, $\sqrt{r^2+\delta^2} $.
RadialBasisEx1 - Class in com.imsl.test.example.math: RadialBasis Example 1: Approximates a function with a Hardy multiquadric radial basis function.
RadialBasisEx1() - Constructor for class com.imsl.test.example.math.RadialBasisEx1
RadialBasisEx2 - Class in com.imsl.test.example.math: Approximates a function with a polyharmonic spline radial basis function.
RadialBasisEx2() - Constructor for class com.imsl.test.example.math.RadialBasisEx2
RadialBasisEx2.PolyHarmonicSpline - Class in com.imsl.test.example.math: RadialBasis Example 2b: Defines a polyharmonic spline radial basis function.
RadialBasisEx3 - Class in com.imsl.test.example.math: Approximates a function using a Hardy multiquadric radial basis function.
RadialBasisEx3() - Constructor for class com.imsl.test.example.math.RadialBasisEx3
RadialBasisEx4 - Class in com.imsl.test.example.math: Approximates a function with a Gaussian radial basis function.
RadialBasisEx4() - Constructor for class com.imsl.test.example.math.RadialBasisEx4
RadialBasisKernel - Class in com.imsl.datamining.supportvectormachine: Specifies the radial basis kernel for support vector machines.
RadialBasisKernel() - Constructor for class com.imsl.datamining.supportvectormachine.RadialBasisKernel: Constructs a radial basis kernel with a $\gamma$ value of 1.0.
RadialBasisKernel(double) - Constructor for class com.imsl.datamining.supportvectormachine.RadialBasisKernel: Constructs a radial basis kernel.
RadialBasisKernel(RadialBasisKernel) - Constructor for class com.imsl.datamining.supportvectormachine.RadialBasisKernel: Constructs a copy of the input RadialBasisKernel kernel.
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.
RandomEx1 - Class in com.imsl.test.example.stat: Generates a pseudorandom sample from a normal distribution and performs a goodness of fit test.
RandomEx1() - Constructor for class com.imsl.test.example.stat.RandomEx1
RandomEx2 - Class in com.imsl.test.example.stat: Generates a pseudorandom multivariate sequence with user defined marginal distributions.
RandomEx2() - Constructor for class com.imsl.test.example.stat.RandomEx2
RandomEx3 - Class in com.imsl.test.example.stat: Generates a pseudorandom sample from a discrete distribution.
RandomEx3() - Constructor for class com.imsl.test.example.stat.RandomEx3
RandomEx4 - Class in com.imsl.test.example.stat: Generates a pseudorandom sample from a discrete uniform distribution.
RandomEx4() - Constructor for class com.imsl.test.example.stat.RandomEx4
RandomSamples - Class in com.imsl.stat: Generates a simple pseudorandom sample from a finite population, a sample of indices, or a permutation of an array of indices.
RandomSamples() - Constructor for class com.imsl.stat.RandomSamples: Constructor for the RandomSamples class.
RandomSamples(Random) - Constructor for class com.imsl.stat.RandomSamples: Constructor for the RandomSamples class.
RandomSamplesEx1 - Class in com.imsl.test.example.stat: Generates a pseudorandom permutation.
RandomSamplesEx1() - Constructor for class com.imsl.test.example.stat.RandomSamplesEx1
RandomSamplesEx2 - Class in com.imsl.test.example.stat: Generates a set of pseudorandom indices.
RandomSamplesEx2() - Constructor for class com.imsl.test.example.stat.RandomSamplesEx2
RandomSamplesEx3 - Class in com.imsl.test.example.stat: Selects a sample from a data set.
RandomSamplesEx3() - Constructor for class com.imsl.test.example.stat.RandomSamplesEx3
RandomSamplesEx4 - Class in com.imsl.test.example.stat: Selects a pseudorandom sample from a million records.
RandomSamplesEx4() - Constructor for class com.imsl.test.example.stat.RandomSamplesEx4
RandomSamplesEx5 - Class in com.imsl.test.example.stat: Selects a pseudorandom sample from Fisher's iris data.
RandomSamplesEx5() - Constructor for class com.imsl.test.example.stat.RandomSamplesEx5
RandomSequence - Interface in com.imsl.stat: Interface implemented by generators of random or quasi-random multidimensional sequences.
RandomTrees - Class in com.imsl.datamining.decisionTree: Generates predictions using a random forest of decision trees.
RandomTrees(double[][], int, PredictiveModel.VariableType[]) - Constructor for class com.imsl.datamining.decisionTree.RandomTrees: Constructs a RandomTrees random forest of ALACART decision trees.
RandomTrees(DecisionTree) - Constructor for class com.imsl.datamining.decisionTree.RandomTrees: Constructs a RandomTrees random forest of the input decision tree.
RandomTrees(RandomTrees) - Constructor for class com.imsl.datamining.decisionTree.RandomTrees: Constructs a copy of the input RandomTrees predictive model.
RandomTrees.ReflectiveOperationException - Exception in com.imsl.datamining.decisionTree: Class that wraps exceptions thrown by reflective operations in core reflection.
RandomTreesEx1 - Class in com.imsl.test.example.datamining.decisionTree: Fits a random forest to the Kyphosis data using ALACART decision trees and generates predictions on a test set.
RandomTreesEx1() - Constructor for class com.imsl.test.example.datamining.decisionTree.RandomTreesEx1
RandomTreesEx2 - Class in com.imsl.test.example.datamining.decisionTree: Fits a random forest to Fisher's Iris data using ALACART decision trees.
RandomTreesEx2() - Constructor for class com.imsl.test.example.datamining.decisionTree.RandomTreesEx2
RandomTreesEx3 - Class in com.imsl.test.example.datamining.decisionTree: Fits a random forest using C45 decision trees and calculates variable importance.
RandomTreesEx3() - Constructor for class com.imsl.test.example.datamining.decisionTree.RandomTreesEx3
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.
RankDeficientException(int) - Constructor for exception com.imsl.stat.CategoricalGenLinModel.RankDeficientException: Constructs a RankDeficientException.
RankException(String) - Constructor for exception com.imsl.stat.FactorAnalysis.RankException: Constructs a RankException object.
RankException(String, Object[]) - Constructor for exception com.imsl.stat.FactorAnalysis.RankException: Constructs a RankException object.
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.
RanksEx1 - Class in com.imsl.test.example.stat: Analyzes the ranks of a data set.
RanksEx1() - Constructor for class com.imsl.test.example.stat.RanksEx1
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.
RayleighPD - Class in com.imsl.stat.distributions: The Rayleigh probability distribution.
RayleighPD() - Constructor for class com.imsl.stat.distributions.RayleighPD: Constructor for the Rayleigh probability distribution.
RayleighPDEx1 - Class in com.imsl.test.example.stat.distributions: Evaluates the Rayleigh probability distribution.
RayleighPDEx1() - Constructor for class com.imsl.test.example.stat.distributions.RayleighPDEx1
RayleighProb(double, double) - Static method in class com.imsl.stat.Cdf: Deprecated.
Use Pdf.Rayleigh(double, double) instead.
read(Reader) - Method in class com.imsl.io.MPSReader: Reads and parses the MPS file.
read(String) - Method in class com.imsl.test.example.math.SparseMatrixEx2.MTXReader: Reads a 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.
ReflectiveOperationException(String) - Constructor for exception com.imsl.datamining.decisionTree.RandomTrees.ReflectiveOperationException: Constructs a ReflectiveOperationException and issues the specified message.
ReflectiveOperationException(String) - Constructor for exception com.imsl.datamining.supportvectormachine.SupportVectorMachine.ReflectiveOperationException: Constructs a ReflectiveOperationException and issues the specified message.
ReflectiveOperationException(String, Object[]) - Constructor for exception com.imsl.datamining.decisionTree.RandomTrees.ReflectiveOperationException: Constructs a ReflectiveOperationException with the specified detail message.
ReflectiveOperationException(String, Object[]) - Constructor for exception com.imsl.datamining.supportvectormachine.SupportVectorMachine.ReflectiveOperationException: Constructs a ReflectiveOperationException with the specified detail message.
refreshRow() - Method in class com.imsl.io.AbstractFlatFile: Refreshes the current row with its most recent value in the database.
REGRESSION - Enum constant in enum class com.imsl.stat.FactorAnalysis.ScoreMethod: Use regression method.
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.
RegressorsForGLMEx1 - Class in com.imsl.test.example.stat: Generates binary regressors for classification variables.
RegressorsForGLMEx1() - Constructor for class com.imsl.test.example.stat.RegressorsForGLMEx1
RegressorsForGLMEx2 - Class in com.imsl.test.example.stat: Sets up data for a two-way analysis of covariance.
RegressorsForGLMEx2() - Constructor for class com.imsl.test.example.stat.RegressorsForGLMEx2
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(Link) - Method in class com.imsl.datamining.neural.FeedForwardNetwork: Removes a Link from the network.
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.
ResidualsTooLargeException(String) - Constructor for exception com.imsl.stat.ARMA.ResidualsTooLargeException: Constructs an ResidualsTooLargeException with the specified detail message.
ResidualsTooLargeException(String, Object[]) - Constructor for exception com.imsl.stat.ARMA.ResidualsTooLargeException: Constructs a ResidualsTooLargeException with the specified detail message.
RevisedSimplexMethod - Enum constant in enum class com.imsl.math.Transport.SolutionMethod: Uses the revised simplex method.
RIGHT_SIDED - Enum constant in enum class com.imsl.stat.WelchsTTest.Hypothesis: The RIGHT_SIDED test corresponds to $$ H_0: \mu_x - \mu_y \le c \,\,\,\,\,\mbox{vs.}\,\,\,\,\, H_1: \mu_x - \mu_y >c$$
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(double) - Static method in class com.imsl.math.JMath: Returns the long closest to a given double.
round(float) - Static method in class com.imsl.math.JMath: Returns the integer closest to a given float.
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.
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.
scalbn(double, int) - Static method in class com.imsl.math.IEEE: Returns $x\,2^n$ 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.
ScaleFactorZeroException(int) - Constructor for exception com.imsl.stat.Dissimilarities.ScaleFactorZeroException: Constructs a ScaleFactorZeroException.
ScaleFactorZeroException(int) - Constructor for exception com.imsl.stat.EmpiricalQuantiles.ScaleFactorZeroException: Constructs a ScaleFactorZeroException.
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.
ScaleFilterEx1 - Class in com.imsl.test.example.datamining.neural: Applies scaling methods to three data sets.
ScaleFilterEx1() - Constructor for class com.imsl.test.example.datamining.neural.ScaleFilterEx1
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.Statistics - Class in com.imsl.stat: Statistics contains statistics related to the regression coefficients.
SelectionRegressionEx1 - Class in com.imsl.test.example.stat: Finds the best regressions using the $R^2$ criterion.
SelectionRegressionEx1() - Constructor for class com.imsl.test.example.stat.SelectionRegressionEx1
SelectionRegressionEx2 - Class in com.imsl.test.example.stat: Finds the best regressions using Mallow's $C_p$ criterion.
SelectionRegressionEx2() - Constructor for class com.imsl.test.example.stat.SelectionRegressionEx2
selectSplitVariable(double[][], 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[], 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[], 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[], 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[], 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[], 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).
SequenceDatabase - Class in com.imsl.datamining: Defines a sequence database for use with the PrefixSpan algorithm.
SequenceDatabase() - Constructor for class com.imsl.datamining.SequenceDatabase
set(int, int, double) - Method in class com.imsl.math.SparseMatrix: Sets the value of an element in the matrix.
set(int, int, Complex) - Method in class com.imsl.math.ComplexSparseMatrix: 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.ZeroFunction: Deprecated.

Sets first stopping criterion.
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.
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.MinConNonlin: Deprecated.

Set the final accuracy.
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.
setAlpha(double) - Method in class com.imsl.stat.MultipleComparisons: Sets the significance level of the test
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.
setAutoPruningFlag(boolean) - Method in class com.imsl.datamining.decisionTree.DecisionTree: Sets the flag to automatically prune the tree during the fitting procedure.
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.
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.
setBounds(double, double) - Method in class com.imsl.math.ZerosFunction: Sets the closed interval in which to search for the roots.
setBounds(double, double, double, double) - Method in class com.imsl.datamining.neural.ScaleFilter: Sets bounds to be used during bounded scaling and unscaling.
setBoundViolationBound(double) - Method in class com.imsl.math.MinConNLP: Set the amount by which bounds may be violated during numerical differentiation.
setCalculateVariableImportance(boolean) - Method in class com.imsl.datamining.BootstrapAggregation: Sets the boolean to calculate variable importance.
setCalculateVariableImportance(boolean) - Method in class com.imsl.datamining.decisionTree.RandomTrees: Sets the boolean to calculate variable importance.
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(boolean) - Method in class com.imsl.stat.ARMA: Sets center option.
setCenter(boolean) - Method in class com.imsl.stat.VectorAutoregression: Sets the flag to center the data.
setCenter(double) - Method in class com.imsl.datamining.neural.ScaleFilter: Set the measure of center to be used during z-score scaling.
setCenter(int) - Method in class com.imsl.stat.ARSeasonalFit: Controls centering of the differenced series.
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.
setClassLabels(int[]) - Method in class com.imsl.datamining.supportvectormachine.SVModel: Sets the class labels.
setClassLabels(String[]) - Method in class com.imsl.datamining.PredictiveModel: Sets the class names or labels for a categorical response variable.
setClassPenaltyWeights(int[], double[]) - Method in class com.imsl.datamining.supportvectormachine.SVClassification: Sets the class penalty weights.
setClassProbabilities(double[][]) - Method in class com.imsl.datamining.PredictiveModel: Sets the class probabilities.
setClassVarColumns(int[]) - Method in class com.imsl.stat.ProportionalHazards: Sets the column indices of x that are the classification variables.
setClosedForm(boolean) - Method in class com.imsl.stat.distributions.MaximumLikelihoodEstimation: Sets the flag indicating whether or not the closed form solution should be used.
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.
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.
setConfidenceLevel(double) - Method in class com.imsl.datamining.LogisticRegression: Sets the confidence level for calculating the confidence limits for the predictions.
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 confidence interval for the difference in means, $\mu_x - \mu_y$.
setConfidenceMean(double) - Method in class com.imsl.stat.WelchsTTest: Sets the confidence level for a two-sided confidence interval for the difference in population means, $\mu_x - \mu_y$.
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 for a two-sided interval estimate for the common variance and for the ratios of variances.
setConfiguration(PredictiveModel) - Method in class com.imsl.datamining.decisionTree.ALACART
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.decisionTree.RandomTrees: Sets the configuration of RandomTrees 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.
setConfiguration(PredictiveModel) - Method in class com.imsl.datamining.supportvectormachine.SupportVectorMachine: Sets the configuration of PredictiveModel to that of the input model.
setConfiguration(PredictiveModel) - Method in class com.imsl.datamining.supportvectormachine.SVClassification: Sets the configuration to that of the input PredictiveModel.
setConfiguration(PredictiveModel) - Method in class com.imsl.datamining.supportvectormachine.SVRegression: Sets the configuration to that of the input PredictiveModel.
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 $w_i$ 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.LinearProgramming: Deprecated.

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.
setContractionCoefficient(double) - Method in class com.imsl.math.NelderMead: Sets the value for the contraction coefficient.
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.datamining.supportvectormachine.SupportVectorMachine: Sets 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.
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.
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.
setCutpoints(double[]) - Method in class com.imsl.stat.ChiSquaredTest: Sets the cutpoints.
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.
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.
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.
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.
setDissimilarityConversion(int) - Method in class com.imsl.stat.MultidimensionalScaling: Sets the option for conversion of input similarity matrices to dissimilarity matrices.
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.
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[][]) - Method in class com.imsl.stat.RegressorsForGLM: Set the effects.
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.
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(double) - Method in class com.imsl.math.ZerosFunction: Sets the first convergence criterion.
setError(QuasiNewtonTrainer.Error) - Method in class com.imsl.datamining.neural.QuasiNewtonTrainer: Sets the function used to compute the network error.
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: Deprecated.
use MaximumLikelihoodEstimation.setUseAnalytic(boolean) instead
setExclude(boolean) - Method in class com.imsl.stat.ARSeasonalFit: Controls whether to exclude or replace the inital values in the transformed series.
setExpansionCoefficient(double) - Method in class com.imsl.math.NelderMead: Sets the value for the expansion coefficient.
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.
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.
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.
setForce(int) - Method in class com.imsl.stat.StepwiseRegression: Forces independent variables into the model based on their level assigned from setlevels(int[]).
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.
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.MinUncon: Set the initial guess of the minimum point of the input function.
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.MinConNonlin: Deprecated.

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.NelderMead: Sets an initial guess of the solution.
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
setHessianOption(boolean) - Method in class com.imsl.stat.ProportionalHazards: Set the option to have the Hessian and gradient be computed at the initial estimates.
setHypothesis(WelchsTTest.Hypothesis) - Method in class com.imsl.stat.WelchsTTest: Sets the direction of the null/alternative test.
setIhess(int) - Method in class com.imsl.math.MinUnconMultiVar: Set the Hessian initialization parameter.
setIncludeIntercept(boolean) - Method in class com.imsl.datamining.LogisticRegression: Sets the flag to include or not include an intercept in the model.
setIndex(int) - Method in class com.imsl.datamining.supportvectormachine.DataNode: Sets the index of the node.
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.
setInitialComplex(double[][]) - Method in class com.imsl.math.NelderMead: Defines the initial complex.
setInitialConditionalVariance(double) - Method in class com.imsl.stat.ExtendedGARCH: Sets the initial value of the conditional variance.
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[]) - Method in class com.imsl.stat.ProportionalHazards: Sets the initial parameter estimates.
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.
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.
setInsensitivityBand(double) - Method in class com.imsl.datamining.supportvectormachine.SVRegression: Sets the insensitivity band parameter, $\epsilon$, in the standard formulation of the SVM regression problem.
setIntegrationMethod(int) - Method in class com.imsl.math.OdeAdamsGear: Indicates which integration method is to be used.
setInteractionIndex(int[]) - Method in class com.imsl.datamining.LogisticRegression: Sets the column indices for pairwise interactions to include in the model.
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.
setKernel(Kernel) - Method in class com.imsl.datamining.supportvectormachine.SupportVectorMachine: Sets the kernel to be used in the optimization.
setKernelParameters(double[]) - Method in class com.imsl.datamining.supportvectormachine.SupportVectorMachine: Sets the kernel parameters.
setLeftEndTangent(double) - Method in class com.imsl.math.CsTCB: Sets the value of the tangent at the left endpoint.
setLevels(int[]) - Method in class com.imsl.stat.StepwiseRegression: Sets the levels of priority for variables entering and leaving the regression.
setLossFunctionType(GradientBoosting.LossFunctionType) - Method in class com.imsl.datamining.GradientBoosting: Sets the loss function type for the gradient boosting algorithm.
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.LinearProgramming: Deprecated.

Sets the lower bound 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, ($ \alpha$, $\beta$, $\gamma $).
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.
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: Sets the maximum tree depth allowed.
setMaxEvaluations(int) - Method in class com.imsl.math.ZerosFunction: Sets the maximum number of function evaluations allowed.
setMaxFunctionEvaluations(int) - Method in class com.imsl.stat.ARMA: Sets the maximum number of function evaluations.
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.
setMaximumIteration(int) - Method in class com.imsl.math.LinearProgramming: Deprecated.

Sets the maximum number of iterations.
setMaximumJacobianEvals(int) - Method in class com.imsl.math.BoundedLeastSquares: Sets the maximum number of Jacobian evaluations.
setMaximumNumberOfFunctionEvaluations(int) - Method in class com.imsl.math.NelderMead: Sets the maximum number of allowed function iterations.
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.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.
setMaximumStepSize(double) - Method in class com.imsl.math.BoundedLeastSquares: Sets the maximum allowable step size.
setMaximumSubsetSize(int) - Method in class com.imsl.stat.SelectionRegression: Sets the maximum subset size if $R^2$ 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.
setMaxIterations(int) - Method in class com.imsl.math.BoundedVariableLeastSquares: Sets the maximum number of iterations.
setMaxIterations(int) - Method in class com.imsl.math.ComplexEigen: Set the maximum number of iterations allowed.
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.MinConNonlin: Deprecated.

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.Transport: Sets the maximum number of iterations in the solver for the transportation problem.
setMaxIterations(int) - Method in class com.imsl.math.ZeroFunction: Deprecated.

Sets the maximum number of iterations allowed per root.
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.distributions.MaximumLikelihoodEstimation: Sets the maximum number of iterations for the solver to use.
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 and response variables.
setMaxNumberOfEvaluations(int) - Method in class com.imsl.stat.ExtendedGARCH: Sets the maximum number of function evaluations to allow in the Nelder-Mead method.
setMaxNumberOfIterations(int) - Method in class com.imsl.datamining.PredictiveModel: Sets the maximum number of iterations allowed for the fitting procedure or training algorithm.
setMaxNumberOfSequences(int) - Method in class com.imsl.datamining.PrefixSpan: Sets the maximum number of sequences.
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.
setMaxSequenceLength(int) - Method in class com.imsl.datamining.PrefixSpan: Sets the maximum sequence length.
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.
setMeanEstimate(double) - Method in class com.imsl.stat.ARMA: Deprecated.
Use ARMA.setMean(double) instead.
setMeanModel(int[]) - Method in class com.imsl.stat.ExtendedGARCH: Sets the mean ARMA model specification as an integer array $\{p,d,q\}$, where $p$ is the number of AR lags and $q$ is the number of MA lags.
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.
setMinObsPerChildNode(int) - Method in class com.imsl.datamining.decisionTree.DecisionTree: Sets the minimum number of observations that a child node must have in order to split.
setMinObsPerNode(int) - Method in class com.imsl.datamining.decisionTree.DecisionTree: Sets the minimum number of observations a node must have to allow a split.
setMissingTestYFlag(boolean) - Method in class com.imsl.datamining.GradientBoosting: Sets the flag indicating whether the test data is missing the response variable data.
setMissingTestYFlag(boolean) - Method in class com.imsl.datamining.GradientBoostingModelObject: Sets the flag for whether or not the test data has missing response values.
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.
setModel(int) - Method in class com.imsl.stat.MultidimensionalScaling: Sets the model option parameter.
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.
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.
setMustEstimate(boolean) - Method in class com.imsl.stat.ExtendedGARCH: Sets the value of mustEstimateFlag.
setMustFitModel(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.
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.
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.
setNuFormulation(boolean) - Method in class com.imsl.datamining.supportvectormachine.SupportVectorMachine: Sets the boolean to perform the $\nu$-formulation of the optimization problem.
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, ($\alpha $, $\beta$, $\gamma$).
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.
setNumberOfClasses(int) - Method in class com.imsl.datamining.PredictiveModel: Sets the number of distinct classes or categories the response variable may assume.
setNumberOfCustomers(int) - Method in class com.imsl.datamining.SequenceDatabase: Sets the number of customers represented in this SequenceDatabase.
setNumberOfEpochs(int) - Method in class com.imsl.datamining.neural.EpochTrainer: Sets the number of epochs.
setNumberOfItems(int) - Method in class com.imsl.datamining.SequenceDatabase: Sets the number of items expected to occur in the sequence data.
setNumberOfIterations(int) - Method in class com.imsl.datamining.GradientBoosting: Sets the number of iterations.
setNumberOfObservations(int) - Method in class com.imsl.stat.HoltWintersExponentialSmoothing: Sets the number of equally spaced series values.
setNumberOfOriginalSequences(int) - Method in class com.imsl.datamining.SequenceDatabase: Sets the number of original sequences.
setNumberOfParameters(int) - Method in class com.imsl.stat.ExtendedGARCH: Sets the number of parameters in the Extended GARCH model.
setNumberOfRandomFeatures(int) - Method in class com.imsl.datamining.decisionTree.DecisionTree: Sets the number of predictors in the random subset to select from at each node.
setNumberOfRandomFeatures(int) - Method in class com.imsl.datamining.decisionTree.RandomTrees: Sets the number of random features used in the splitting rules.
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.
setNumberOfSamples(int) - Method in class com.imsl.datamining.BootstrapAggregation: Sets the number of bootstrap samples.
setNumberOfSuccesses(int) - Method in class com.imsl.stat.distributions.NegativeBinomialPD: Sets the number of successes.
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.
setNumberOfThreads(int) - Method in class com.imsl.datamining.BootstrapAggregation: Sets the maximum number of threads for multithreaded runs.
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.decisionTree.RandomTrees: 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.NelderMead: 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.math.Transport: 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.
setNumberOfThreads(int) - Method in class com.imsl.stat.DBSCAN: Sets the number of java.lang.Thread instances to be used for parallel processing.
setNumberOfThreads(int) - Method in class com.imsl.stat.ExtendedGARCH: Sets the number of java.lang.Thread instances to be used for parallel processing.
setNumberOfTrees(int) - Method in class com.imsl.datamining.decisionTree.RandomTrees: Sets the number of trees to generate in the random forest.
setNumberOfTrials(int) - Method in class com.imsl.stat.distributions.BinomialPD: Sets the number of independent Bernoulli trials.
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.
setNuParameter(double) - Method in class com.imsl.datamining.supportvectormachine.SupportVectorMachine: Sets the value of $\nu$ in the $\nu$-formulation of the optimization problem.
setObservationMax(int) - Method in class com.imsl.stat.CategoricalGenLinModel: Sets the maximum number of observations that can be handled in the linear programming.
setOptionalDistributionParameterColumn(int) - Method in class com.imsl.stat.CategoricalGenLinModel: Sets the column number in x that contains an optional distribution parameter for each observation.
setOrders(int[]) - Method in class com.imsl.stat.Difference: Sets the orders for the Difference object
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.
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.
setParameterLowerBounds(double[]) - Method in class com.imsl.stat.ExtendedGARCH: Sets the lower bounds for the parameter estimates.
setParameters(double...) - Method in class com.imsl.datamining.supportvectormachine.Kernel: Sets the kernel parameters.
setParameters(double...) - Method in class com.imsl.datamining.supportvectormachine.PolynomialKernel: Sets the parameters for the polynomial kernel.
setParameters(double...) - Method in class com.imsl.datamining.supportvectormachine.RadialBasisKernel: Sets the parameters for the radial basis kernel.
setParameters(double...) - Method in class com.imsl.datamining.supportvectormachine.SigmoidKernel: Sets the parameters for the sigmoid kernel.
setParameters(double[]) - Method in class com.imsl.stat.ExtendedGARCH: Sets the values of the parameters.
setParameters(double[]) - Method in class com.imsl.stat.HoltWintersExponentialSmoothing: Sets the values of the smoothing parameters for the level ($ \alpha$), the trend ($\beta$), and the seasonal ($\gamma$) component sequences.
setParameterUpperBounds(double[]) - Method in class com.imsl.stat.ExtendedGARCH: Sets the upper bounds for the parameter estimates.
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.
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.
setPredictorIndex(int[]) - Method in class com.imsl.datamining.PredictiveModel: Sets the column indices of xy in which 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.GradientBoostingModelObject: Sets the print level.
setPrintLevel(int) - Method in class com.imsl.datamining.PredictiveModel: Sets the print level for a PredictiveModel.
setPrintLevel(int) - Method in class com.imsl.datamining.PrefixSpan: Sets the print level.
setPrintLevel(int) - Method in class com.imsl.math.SparseLP: Sets the print level.
setPrintLevel(int) - Method in class com.imsl.stat.MultidimensionalScaling: Sets the print level.
setPrior(double[]) - Method in class com.imsl.stat.DiscriminantAnalysis: Specifies user supplied prior probabilities.
setPrior(int) - Method in class com.imsl.stat.DiscriminantAnalysis: Specifies the prior probabilities to be calculated as either equal or proportional priors.
setPriorProbabilities(double[]) - Method in class com.imsl.datamining.PredictiveModel: Sets the prior probabilities for class membership.
setProbability(boolean) - Method in class com.imsl.datamining.supportvectormachine.SupportVectorMachine: Sets the boolean to calculate probability estimates.
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.
setRandomFeatureSelection(boolean) - Method in class com.imsl.datamining.decisionTree.DecisionTree: Sets the flag to select split variables from a random subset of the features.
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.
setRandomObject(Random) - Method in class com.imsl.math.NelderMead: Sets the random object to be used in the computation of the vertices of the complex.
setRandomObject(Random) - Method in class com.imsl.stat.distributions.MaximumLikelihoodEstimation: Sets the random object to be used in the Nelder-Mead method.
setRandomObject(Random) - Method in class com.imsl.stat.ExtendedGARCH: Sets the random object.
setRandomObject(Random) - Method in class com.imsl.stat.RandomSamples: Sets the seed for the random number generator.
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.
setRefinementType(int) - Method in class com.imsl.math.DenseLP: Set the type of refinement used.
setReflectionCoefficient(double) - Method in class com.imsl.math.NelderMead: Sets the value for the reflection coefficient.
setRegularizationParameter(double) - Method in class com.imsl.datamining.supportvectormachine.SupportVectorMachine: Sets the regularization parameter, C.
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.ZeroFunction: Deprecated.

Sets second stopping criterion is the relative error.
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.
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.
setResponseColumnIndex(int) - Method in class com.imsl.datamining.PredictiveModel: Sets the column index in xy containing the response variable.
setRightEndTangent(double) - Method in class com.imsl.math.CsTCB: Sets the value of the tangent at the right endpoint.
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.
setSampleSizeProportion(double) - Method in class com.imsl.datamining.GradientBoosting: Sets the sample size proportion.
setSaveTrees(boolean) - Method in class com.imsl.datamining.GradientBoosting: Sets the flag to save the boosted trees.
setScale(boolean) - Method in class com.imsl.stat.VectorAutoregression: Sets the flag to scale the data.
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.
setScaledStepTol(double) - Method in class com.imsl.math.BoundedLeastSquares: Sets the scaled step tolerance.
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.
setScalingVariable(double) - Method in class com.imsl.math.MinConNonlin: Deprecated.

Set the scaling variable for the problem function.
setScalingVector(double[]) - Method in class com.imsl.math.BoundedLeastSquares: Sets the scaling vector for the variables.
setSeed(long) - Method in class com.imsl.stat.Random: Sets the seed.
setSequenceData(int[]) - Method in class com.imsl.datamining.SequenceDatabase: Sets the sequence data.
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[][]) - Method in class com.imsl.stat.TimeSeries: Sets the values of the TimeSeries.
setSeriesValues(double[], int) - Method in class com.imsl.stat.TimeSeries: Sets the values of a multivariate time series and initializes the time index array.
setShrinkageParameter(double) - Method in class com.imsl.datamining.GradientBoosting: Sets the value of the shrinkage parameter.
setShrinking(boolean) - Method in class com.imsl.datamining.supportvectormachine.SupportVectorMachine: Sets the boolean to perform shrinking during optimization.
setSolutionMethod(Transport.SolutionMethod) - Method in class com.imsl.math.Transport: Sets the algorithm used to solve the transportation problem.
setSolveMethod(int) - Method in class com.imsl.math.OdeAdamsGear: Indicates which method to use for solving the formula equations.
setSolverMethod(ExtendedGARCH.Solver) - Method in class com.imsl.stat.ExtendedGARCH: Sets the solver to use for parameter estimation.
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.
setSpread(double) - Method in class com.imsl.math.ZeroFunction: Deprecated.

Sets the spread.
setSpreadTolerance(double) - Method in class com.imsl.math.ZeroFunction: Deprecated.

Sets the spread criteria for multiple zeros.
setStartDate(Date) - Method in class com.imsl.stat.TimeSeries: Sets the start date of the series.
setStartingCoefficients(double[]) - Method in class com.imsl.datamining.LogisticRegression: Sets the starting values for the coefficients.
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.
setStratifiedCrossValidation(boolean) - Method in class com.imsl.datamining.CrossValidation: Sets the flag to perform stratified cross-validation.
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.
setStressFormula(int) - Method in class com.imsl.stat.MultidimensionalScaling: Sets the stress formula option.
setSupportVector(int[]) - Method in class com.imsl.datamining.SequenceDatabase: Sets the support vector for the sequences in this SequenceDatabase.
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.
setTestData(double[][]) - Method in class com.imsl.datamining.BootstrapAggregation: Sets the test data to be predicted.
setTestData(double[][], double[]) - Method in class com.imsl.datamining.BootstrapAggregation: Sets the test data to be predicted using bootstrap aggregation along with weights for each row in the test data.
setTestData(double[][], double[][]) - Method in class com.imsl.datamining.BootstrapAggregation: Sets the test data to be predicted using bootstrap aggregation.
setTestData(double[][], double[][], double[]) - Method in class com.imsl.datamining.BootstrapAggregation: Sets the test data to be predicted using bootstrap aggregation along with weights for each row in the test data.
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(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.
setTimeZone(TimeZone) - Method in class com.imsl.stat.TimeSeries: Sets the time zone for the time series to the given TimeZone.
setTitle(String) - Method in class com.imsl.math.PrintMatrix: Sets matrix title
setTolerance(double) - Method in class com.imsl.datamining.LogisticRegression: Sets the error tolerance criteria for convergence.
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.NelderMead: Sets the convergence tolerance.
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.ExtendedGARCH: Sets the convergence tolerance.
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.
setTrainingData(double[][], int, PredictiveModel.VariableType[]) - Method in class com.imsl.datamining.GradientBoosting: Sets up the training data for the predictive model.
setTrainingData(double[][], int, PredictiveModel.VariableType[]) - Method in class com.imsl.datamining.PredictiveModel: Sets up the training data for the predictive model.
setTransformationFormula(int) - Method in class com.imsl.stat.MultidimensionalScaling: Defines the transformation used when computing the criterion function.
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.
setTTestNull(double) - Method in class com.imsl.stat.WelchsTTest: Sets the null hypothesis value.
setUnequalVariances(boolean) - Method in class com.imsl.stat.NormTwoSample: Specifies whether to return statistics based on equal or unequal variances.
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.LinearProgramming: Deprecated.

Sets the upper bound 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, ($ \alpha$, $\beta$, $\gamma $).
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.LinearProgramming: Deprecated.

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.
setUseAnalytic(boolean) - Method in class com.imsl.stat.distributions.MaximumLikelihoodEstimation: Sets the flag indicating whether or not the PDF supplies the analytic gradient and Hessian.
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.datamining.neural.InputNode: Sets the value of this Node.
setValue(double) - Method in class com.imsl.datamining.supportvectormachine.DataNode: Sets the value of the node.
setValues(double[]) - Method in class com.imsl.stat.ExtendedGARCH: Sets the optional values needed in the specification for gFunction.
setVariableType(PredictiveModel.VariableType[]) - Method in class com.imsl.datamining.PredictiveModel: Sets the variable types for the data.
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 initial values of the unique 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.
setViolationBound(double) - Method in class com.imsl.math.MinConNLP: Set the scalar which defines allowable constraint violations of the final accepted result.
setWarning(WarningObject) - Static method in class com.imsl.Warning: Sets a new WarningObject.
setWarning(SQLWarning) - Method in class com.imsl.io.AbstractFlatFile: Sets a SQLWarning.
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(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.
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(Random) - Method in class com.imsl.datamining.KohonenSOM: Sets the weights of the nodes using a Random object.
setWorkArraySize(double) - Method in class com.imsl.datamining.supportvectormachine.SupportVectorMachine: Sets the work array size.
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.
setXlowerBound(double[]) - Method in class com.imsl.math.MinConNonlin: Deprecated.

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.MinConNonlin: Deprecated.

Set the diagonal scaling matrix for 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 scaling in the x-coordinate.
setXupperBound(double[]) - Method in class com.imsl.math.MinConNLP: Set the upper bounds on the variables.
setXupperBound(double[]) - Method in class com.imsl.math.MinConNonlin: Deprecated.

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.
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.
setYWeights(double[]) - Method in class com.imsl.math.Spline2DLeastSquares: Sets the weights for the least-squares fit in the y-direction.
setZDistribution(ExtendedGARCH.zDistribution) - Method in class com.imsl.stat.ExtendedGARCH: Sets the distribution to use for the random variable, $z$, in the extended GARCH model.
setZeroCorrection(double) - Method in class com.imsl.datamining.NaiveBayesClassifier: Specifies the replacement value to be used for conditional probabilities equal to zero.
Sfun - Class in com.imsl.math: Collection of special functions.
SfunEx1 - Class in com.imsl.test.example.math: Calculates various special functions.
SfunEx1() - Constructor for class com.imsl.test.example.math.SfunEx1
SHANNON_ENTROPY - Enum constant in enum 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.
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.
SigmoidKernel - Class in com.imsl.datamining.supportvectormachine: Specifies the sigmoid kernel for support vector machines.
SigmoidKernel() - Constructor for class com.imsl.datamining.supportvectormachine.SigmoidKernel: Constructor for the sigmoid kernel.
SigmoidKernel(double, double) - Constructor for class com.imsl.datamining.supportvectormachine.SigmoidKernel: Constructs a sigmoid kernel.
SigmoidKernel(SigmoidKernel) - Constructor for class com.imsl.datamining.supportvectormachine.SigmoidKernel: Constructs a copy of the input SigmoidKernel kernel.
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.
SignTestEx1 - Class in com.imsl.test.example.stat: Performs the sign test on a small data set.
SignTestEx1() - Constructor for class com.imsl.test.example.stat.SignTestEx1
SignTestEx2 - Class in com.imsl.test.example.stat: Performs the sign test on a small data set.
SignTestEx2() - Constructor for class com.imsl.test.example.stat.SignTestEx2
sin(double) - Static method in class com.imsl.math.JMath: Returns the sine of a double.
sin(Complex) - Static method in class com.imsl.math.Complex: Returns the sine of a Complex.
SingularException(String) - Constructor for exception com.imsl.math.MinConNLP.SingularException: Constructs a SingularException object.
SingularException(String) - Constructor for exception com.imsl.stat.FactorAnalysis.SingularException: Constructs a SingularException object.
SingularException(String, Object[]) - Constructor for exception com.imsl.math.MinConNLP.SingularException: Constructs a SingularException object.
SingularException(String, Object[]) - Constructor for exception com.imsl.stat.FactorAnalysis.SingularException: Constructs a SingularException object.
SingularMatrixException - Exception in com.imsl.math: The matrix is singular.
SingularMatrixException() - Constructor for exception com.imsl.math.SingularMatrixException
SingularMatrixException(String) - Constructor for exception com.imsl.math.OdeAdamsGear.SingularMatrixException: Constructs a SingularMatrixException with the specified detailed message.
SingularMatrixException(String, Object[]) - Constructor for exception com.imsl.math.OdeAdamsGear.SingularMatrixException: Constructs a SingularMatrixException with the specified detailed message.
SingularPreconditionMatrixException(String) - Constructor for exception com.imsl.math.ConjugateGradient.SingularPreconditionMatrixException: Constructs a SingularPreconditionMatrixException object.
SingularPreconditionMatrixException(String, Object[]) - Constructor for exception com.imsl.math.ConjugateGradient.SingularPreconditionMatrixException: Constructs a SingularPreconditionMatrixException object.
sinh(double) - Static method in class com.imsl.math.Hyperbolic: Returns the hyperbolic sine of its argument.
sinh(Complex) - Static method in class com.imsl.math.Complex: Returns the hyperbolic sine of a Complex.
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(int) - Method in class com.imsl.stat.Random: Resets the seed to skip ahead in the base linear congruential generator.
SKIP - Static variable in class com.imsl.math.NumericalDerivatives: Indicates a variable to be skipped.
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.
SolutionNotFoundException() - Constructor for exception com.imsl.math.QuadraticProgramming.SolutionNotFoundException: A solution was not found.
SolutionNotFoundException(String) - Constructor for exception com.imsl.math.QuadraticProgramming.SolutionNotFoundException: A solution was not found.
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() - Method in class com.imsl.math.DenseLP: Solves the problem using an active set method.
solve() - Method in class com.imsl.math.LinearProgramming: Deprecated.

Solves the program using the revised simplex algorithm.
solve() - Method in class com.imsl.math.MinConGenLin: Minimizes a general objective function subject to linear equality/inequality constraints.
solve() - Method in class com.imsl.math.NelderMead: Solves a minimization problem using a Nelder-Mead type algorithm.
solve() - Method in class com.imsl.math.NonNegativeLeastSquares: Finds the solution to the problem for the current constraints.
solve() - Method in class com.imsl.math.SparseLP: Solves the sparse linear programming problem by an infeasible primal-dual interior-point method.
solve() - Method in class com.imsl.math.Transport: Solves a transportation problem using the revised simplex method or an interior-point method.
solve() - Method in class com.imsl.stat.CategoricalGenLinModel: Returns the parameter estimates and associated statistics for a CategoricalGenLinModel object.
solve(boolean) - Method in class com.imsl.math.ComplexEigen: Solves for the eigenvalues and (optionally) the eigenvectors of a complex square matrix.
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(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(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[]) - Method in class com.imsl.math.QR: Returns the solution to the least-squares problem Ax = b.
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(double[]) - Method in class com.imsl.math.SuperLU: Computation of the solution vector for the system $ Ax = b$.
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[][], double[]) - Static method in class com.imsl.math.LU: Solve Ax = b for x using the LU factorization of A.
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, 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(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[]) - 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(Complex[][], Complex[]) - Static method in class com.imsl.math.ComplexLU: Solve Ax = b for x using the LU factorization of A.
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(MinConNonlin.Function) - Method in class com.imsl.math.MinConNonlin: Deprecated.

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(ZeroSystem.Function) - Method in class com.imsl.math.ZeroSystem: Solve a system of nonlinear equations using the modified Powell hybrid algorithm
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(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$.
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$.
SomeConstraintsDiscardedException() - Constructor for exception com.imsl.math.DenseLP.SomeConstraintsDiscardedException: Some constraints were discarded because they were too linearly dependent on other active constraints.
SomeConstraintsDiscardedException(String) - Constructor for exception com.imsl.math.DenseLP.SomeConstraintsDiscardedException: Some constraints were discarded because they were too linearly dependent on other active constraints.
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.
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.
SortEx1 - Class in com.imsl.test.example.stat: Sorts an array and computes the permutation.
SortEx1() - Constructor for class com.imsl.test.example.stat.SortEx1
SortEx2 - Class in com.imsl.test.example.stat: Sorts a matrix using columns as keys.
SortEx2() - Constructor for class com.imsl.test.example.stat.SortEx2
SparseArray() - Constructor for class com.imsl.math.ComplexSparseMatrix.SparseArray
SparseArray() - Constructor for class com.imsl.math.SparseMatrix.SparseArray
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.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.
SparseCholeskyEx1 - Class in com.imsl.test.example.math: SparseCholesky Example 1: Computes the Cholesky factorization of a sparse matrix.
SparseCholeskyEx1() - Constructor for class com.imsl.test.example.math.SparseCholeskyEx1
SparseLP - Class in com.imsl.math: Solves a sparse linear programming problem by an infeasible primal-dual interior-point method.
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(MPSReader) - Constructor for class com.imsl.math.SparseLP: Constructs a SparseLP object using an MPSReader object.
SparseLP(SparseMatrix, double[], double[]) - Constructor for class com.imsl.math.SparseLP: Constructs a SparseLP object.
SparseLP.CholeskyFactorizationAccuracyException - Exception in com.imsl.math: 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.DualInfeasibleException - Exception in com.imsl.math: The dual problem is infeasible.
SparseLP.IllegalBoundsException - Exception in com.imsl.math: 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.IncorrectlyEliminatedException - Exception in com.imsl.math: 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.PrimalInfeasibleException - Exception in com.imsl.math: The primal problem is infeasible.
SparseLP.PrimalUnboundedException - Exception in com.imsl.math: The primal problem is unbounded.
SparseLP.ProblemUnboundedException - Exception in com.imsl.math: The problem is unbounded.
SparseLP.TooManyIterationsException - Exception in com.imsl.math: 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.ZeroRowException - Exception in com.imsl.math: A row of the constraint matrix has no entries.
SparseLPEx1 - Class in com.imsl.test.example.math: Solves a linear programming problem with sparse representation.
SparseLPEx1() - Constructor for class com.imsl.test.example.math.SparseLPEx1
SparseLPEx2 - Class in com.imsl.test.example.math: SparseLP Example 2: Solves a linear programming problem defined in an MPS file.
SparseLPEx2() - Constructor for class com.imsl.test.example.math.SparseLPEx2
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(int, int, int[][], double[][]) - Constructor for class com.imsl.math.SparseMatrix: Constructs a sparse matrix from SparseArray (Java Sparse Array) data.
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.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.
SparseMatrixEx1 - Class in com.imsl.test.example.math: SparseMatrix Example 1: Computes the matrix product of two sparse matrices.
SparseMatrixEx1() - Constructor for class com.imsl.test.example.math.SparseMatrixEx1
SparseMatrixEx2 - Class in com.imsl.test.example.math: SparseMatrix Example 2: Converts a matrix in market format to a sparse matrix format.
SparseMatrixEx2() - Constructor for class com.imsl.test.example.math.SparseMatrixEx2
SparseMatrixEx2.MTXReader - Class in com.imsl.test.example.math: Reads a file containing Market format data.
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.
Spline2DInterpolateEx1 - Class in com.imsl.test.example.math: Spline2DInterpolate Example 1: Computes a tensor-product spline interpolant.
Spline2DInterpolateEx1() - Constructor for class com.imsl.test.example.math.Spline2DInterpolateEx1
Spline2DInterpolateEx2 - Class in com.imsl.test.example.math: Spline2DInterpolate Example 2: Computes the tensor-product spline interpolant.
Spline2DInterpolateEx2() - Constructor for class com.imsl.test.example.math.Spline2DInterpolateEx2
Spline2DInterpolateEx3 - Class in com.imsl.test.example.math: Computes a spline interpolant on a function and evaluates the partial derivatives.
Spline2DInterpolateEx3() - Constructor for class com.imsl.test.example.math.Spline2DInterpolateEx3
Spline2DInterpolateEx4 - Class in com.imsl.test.example.math: Spline2DInterpolate Example 4: Integrates a tensor-product spline.
Spline2DInterpolateEx4() - Constructor for class com.imsl.test.example.math.Spline2DInterpolateEx4
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.
Spline2DLeastSquaresEx1 - Class in com.imsl.test.example.math: Computes a tensor-product cubic spline least squares fit to a function.
Spline2DLeastSquaresEx1() - Constructor for class com.imsl.test.example.math.Spline2DLeastSquaresEx1
sqrt(double) - Static method in class com.imsl.math.JMath: Returns the square root of a double.
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.
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_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.
StateChangeException(String) - Constructor for exception com.imsl.datamining.PredictiveModel.StateChangeException: Constructs a StateChangeException and issues the specified message.
StateChangeException(String, Object[]) - Constructor for exception com.imsl.datamining.PredictiveModel.StateChangeException: Constructs a StateChangeException with the specified detail message.
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.NoVariablesEnteredException - Exception in com.imsl.stat: No Variables can enter the model.
StepwiseRegressionEx1 - Class in com.imsl.test.example.stat: Performs stepwise regression variable selection.
StepwiseRegressionEx1() - Constructor for class com.imsl.test.example.stat.StepwiseRegressionEx1
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.
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(double[][], double[][]) - Static method in class com.imsl.math.Matrix: Subtract two rectangular arrays, a - b.
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(Complex, double) - Static method in class com.imsl.math.Complex: Returns the difference of a Complex object and a double, x-y.
subtract(Complex, Complex) - Static method in class com.imsl.math.Complex: Returns the difference of two Complex objects, x-y.
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[]...) - Static method in class com.imsl.datamining.Apriori: Sums up the itemset frequencies.
SUM - Enum constant in enum 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 $n-1$ 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.
SummaryEx1 - Class in com.imsl.test.example.stat: Computes summary statistics for a small data set.
SummaryEx1() - Constructor for class com.imsl.test.example.stat.SummaryEx1
SumOfProbabilitiesNotOneException(String) - Constructor for exception com.imsl.datamining.PredictiveModel.SumOfProbabilitiesNotOneException: Constructs a SumOfProbabilitiesNotOneException and issues the specified message
SumOfProbabilitiesNotOneException(String, Object[]) - Constructor for exception com.imsl.datamining.PredictiveModel.SumOfProbabilitiesNotOneException: Constructs a SumOfProbabilitiesNotOneException with the specified detail message.
SumOfWeightsNegException(String) - Constructor for exception com.imsl.stat.DiscriminantAnalysis.SumOfWeightsNegException: The sum of the weights have become negative.
SumOfWeightsNegException(String, Object[]) - Constructor for exception com.imsl.stat.DiscriminantAnalysis.SumOfWeightsNegException: The sum of the weights have become negative.
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.
SuperLUEx1 - Class in com.imsl.test.example.math: SuperLU Example 1: Computes the LU factorization of a sparse matrix.
SuperLUEx1() - Constructor for class com.imsl.test.example.math.SuperLUEx1
SupportVectorMachine - Class in com.imsl.datamining.supportvectormachine: Abstract class for generating a support vector machine.
SupportVectorMachine(double[][], int, PredictiveModel.VariableType[]) - Constructor for class com.imsl.datamining.supportvectormachine.SupportVectorMachine: Constructs a support vector machine for a single response variable and multiple predictor variables.
SupportVectorMachine(double[][], int, PredictiveModel.VariableType[], Kernel) - Constructor for class com.imsl.datamining.supportvectormachine.SupportVectorMachine: Constructs a support vector machine for a single response variable and multiple predictor variables.
SupportVectorMachine.ReflectiveOperationException - Exception in com.imsl.datamining.supportvectormachine: Class that wraps exceptions thrown by reflective operations in core reflection.
SupportVectorMachineEx1 - Class in com.imsl.test.example.datamining.supportvectormachine: Trains a support vector machine on Fisher's iris data.
SupportVectorMachineEx1() - Constructor for class com.imsl.test.example.datamining.supportvectormachine.SupportVectorMachineEx1
SupportVectorMachineEx2 - Class in com.imsl.test.example.datamining.supportvectormachine: Classifies Fisher's iris data after first selecting parameter values using cross-validation.
SupportVectorMachineEx2() - Constructor for class com.imsl.test.example.datamining.supportvectormachine.SupportVectorMachineEx2
SupportVectorMachineEx3 - Class in com.imsl.test.example.datamining.supportvectormachine: Performs goodness-of-fit using the one-class support vector machine.
SupportVectorMachineEx3() - Constructor for class com.imsl.test.example.datamining.supportvectormachine.SupportVectorMachineEx3
SupportVectorMachineEx4 - Class in com.imsl.test.example.datamining.supportvectormachine: Compares a regression and a classification support vector machine for predicting a categorical response.
SupportVectorMachineEx4() - Constructor for class com.imsl.test.example.datamining.supportvectormachine.SupportVectorMachineEx4
SupportVectorMachineEx5 - Class in com.imsl.test.example.datamining.supportvectormachine: Illustrates the use of case weights on the training data.
SupportVectorMachineEx5() - Constructor for class com.imsl.test.example.datamining.supportvectormachine.SupportVectorMachineEx5
SVClassification - Class in com.imsl.datamining.supportvectormachine: Specifies a support vector machine for classification (SVC).
SVClassification(double[][], int, PredictiveModel.VariableType[]) - Constructor for class com.imsl.datamining.supportvectormachine.SVClassification: Constructs a support vector machine for classification (SVC).
SVClassification(double[][], int, PredictiveModel.VariableType[], Kernel) - Constructor for class com.imsl.datamining.supportvectormachine.SVClassification: Constructs a support vector machine for classification (SVC).
SVClassification(SVClassification) - Constructor for class com.imsl.datamining.supportvectormachine.SVClassification: Copy constructor.
SVD - Class in com.imsl.math: Singular Value Decomposition (SVD) of a rectangular matrix of type double.
SVD(double[][]) - Constructor for class com.imsl.math.SVD: Construct the singular value decomposition of a rectangular matrix with default tolerance.
SVD(double[][], double) - Constructor for class com.imsl.math.SVD: Construct the singular value decomposition of a rectangular matrix with a given tolerance.
SVD.DidNotConvergeException - Exception in com.imsl.math: The iteration did not converge
SVDEx1 - Class in com.imsl.test.example.math: Computes the SVD factorization of a matrix.
SVDEx1() - Constructor for class com.imsl.test.example.math.SVDEx1
SVModel - Class in com.imsl.datamining.supportvectormachine: Class to contain model estimates after training a support vector machine.
SVModel() - Constructor for class com.imsl.datamining.supportvectormachine.SVModel
SVOneClass - Class in com.imsl.datamining.supportvectormachine: Specifies a support vector machine for the one class problem.
SVOneClass(double[][], int, PredictiveModel.VariableType[]) - Constructor for class com.imsl.datamining.supportvectormachine.SVOneClass: Constructs a one class support vector machine.
SVOneClass(double[][], int, PredictiveModel.VariableType[], Kernel) - Constructor for class com.imsl.datamining.supportvectormachine.SVOneClass: Constructs a one class support vector machine.
SVOneClass(SVOneClass) - Constructor for class com.imsl.datamining.supportvectormachine.SVOneClass: Constructs a copy of the input SVOneClass predictive model.
SVRegression - Class in com.imsl.datamining.supportvectormachine: Specifies a support vector machine for regression (SVR).
SVRegression(double[][], int, PredictiveModel.VariableType[]) - Constructor for class com.imsl.datamining.supportvectormachine.SVRegression: Constructs a support vector machine for regression (SVR).
SVRegression(double[][], int, PredictiveModel.VariableType[], Kernel) - Constructor for class com.imsl.datamining.supportvectormachine.SVRegression: Constructs a support vector machine for regression (SVR).
SVRegression(SVRegression) - Constructor for class com.imsl.datamining.supportvectormachine.SVRegression: Constructs a copy of the input SVRegression predictive model.
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.
SymEigenEx1 - Class in com.imsl.test.example.math: Computes the eigenvalues and eigenvectors of a symmetric matrix.
SymEigenEx1() - Constructor for class com.imsl.test.example.math.SymEigenEx1
SYMMETRIC - Enum constant in enum class com.imsl.math.ComplexMatrix.MatrixType: Matrix is square symmetric.
SYMMETRIC - Enum constant in enum class com.imsl.math.Matrix.MatrixType: Matrix is square symmetric.

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.
TableMultiWayEx1 - Class in com.imsl.test.example.stat: Computes a two-way table in the presence of missing values.
TableMultiWayEx1() - Constructor for class com.imsl.test.example.stat.TableMultiWayEx1
TableMultiWayEx2 - Class in com.imsl.test.example.stat: Computes a two-way table and displays the balanced table.
TableMultiWayEx2() - Constructor for class com.imsl.test.example.stat.TableMultiWayEx2
TableMultiWayEx3 - Class in com.imsl.test.example.stat: Computes a two-way table and displays the unbalanced table.
TableMultiWayEx3() - Constructor for class com.imsl.test.example.stat.TableMultiWayEx3
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.
TableOneWayEx1 - Class in com.imsl.test.example.stat: Computes a one-way table for a continuous scale variable.
TableOneWayEx1() - Constructor for class com.imsl.test.example.stat.TableOneWayEx1
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.
TableTwoWayEx1 - Class in com.imsl.test.example.stat: Computes a two-way table for continuous scale data.
TableTwoWayEx1() - Constructor for class com.imsl.test.example.stat.TableTwoWayEx1
tan(double) - Static method in class com.imsl.math.JMath: Returns the tangent of a double.
tan(Complex) - Static method in class com.imsl.math.Complex: Returns the tangent of a Complex.
tanh(double) - Static method in class com.imsl.math.Hyperbolic: Returns the hyperbolic tangent of its argument.
tanh(Complex) - Static method in class com.imsl.math.Complex: Returns the hyperbolic tanh of a Complex.
TANH - Static variable in interface com.imsl.datamining.neural.Activation: The hyperbolic tangent activation function, $g(x)=\tanh{x}= \frac{e^x-e^{-x}}{e^x+e^{-x}}$.
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.
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.
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.
TEqualsToutException(String, Object[]) - Constructor for exception com.imsl.math.FeynmanKac.TEqualsToutException: The current integration point in time and the end point are equal.
terminal(double) - Method in interface com.imsl.math.FeynmanKac.Boundaries: Returns the terminal condition value.
TerminationCriteriaNotSatisfiedException(String) - Constructor for exception com.imsl.math.MinConNLP.TerminationCriteriaNotSatisfiedException: Constructs a TerminationCriteriaNotSatisfiedException object.
TerminationCriteriaNotSatisfiedException(String, Object[]) - Constructor for exception com.imsl.math.MinConNLP.TerminationCriteriaNotSatisfiedException: Constructs a TerminationCriteriaNotSatisfiedException object.
TestGaussFcn1(double, double) - Constructor for class com.imsl.test.example.datamining.NaiveBayesClassifierEx3.TestGaussFcn1
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
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.
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.
TimeSeriesClassFilterEx1 - Class in com.imsl.test.example.datamining.neural: Applies a time series filter to a classification variable.
TimeSeriesClassFilterEx1() - Constructor for class com.imsl.test.example.datamining.neural.TimeSeriesClassFilterEx1
TimeSeriesEx1 - Class in com.imsl.test.example.stat: Sets up a time series object.
TimeSeriesEx1() - Constructor for class com.imsl.test.example.stat.TimeSeriesEx1
TimeSeriesEx2 - Class in com.imsl.test.example.stat: Sets up a time series with a different time zone.
TimeSeriesEx2() - Constructor for class com.imsl.test.example.stat.TimeSeriesEx2
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.
TimeSeriesFilterEx1 - Class in com.imsl.test.example.datamining.neural: Applies a time series filter.
TimeSeriesFilterEx1() - Constructor for class com.imsl.test.example.datamining.neural.TimeSeriesFilterEx1
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 - Enum 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 - Enum Class in com.imsl.stat: Public enum of merge rules that defines how two time series should be merged.
TimeSeriesOperationsEx1 - Class in com.imsl.test.example.stat: Merges two time series using different merging rules.
TimeSeriesOperationsEx1() - Constructor for class com.imsl.test.example.stat.TimeSeriesOperationsEx1
TimeSeriesOperationsEx2 - Class in com.imsl.test.example.stat: Merges two time series using different combining methods.
TimeSeriesOperationsEx2() - Constructor for class com.imsl.test.example.stat.TimeSeriesOperationsEx2
TimeSeriesOperationsEx3 - Class in com.imsl.test.example.stat: Performs the backshift operation on a time series.
TimeSeriesOperationsEx3() - Constructor for class com.imsl.test.example.stat.TimeSeriesOperationsEx3
TimeSeriesOperationsEx4 - Class in com.imsl.test.example.stat: Performs the stacking or vectorizing operation on a time series.
TimeSeriesOperationsEx4() - Constructor for class com.imsl.test.example.stat.TimeSeriesOperationsEx4
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.
ToleranceTooSmallException(String) - Constructor for exception com.imsl.math.OdeAdamsGear.ToleranceTooSmallException: Constructs a ToleranceTooSmallException with the specified detailed message.
ToleranceTooSmallException(String) - Constructor for exception com.imsl.math.OdeRungeKutta.ToleranceTooSmallException: Constructs a ToleranceTooSmallException with the specified detailed message.
ToleranceTooSmallException(String, Object[]) - Constructor for exception com.imsl.math.FeynmanKac.ToleranceTooSmallException: Tolerance is too small.
ToleranceTooSmallException(String, Object[]) - Constructor for exception com.imsl.math.OdeAdamsGear.ToleranceTooSmallException: Constructs a ToleranceTooSmallException with the specified detailed message.
ToleranceTooSmallException(String, Object[]) - Constructor for exception com.imsl.math.OdeRungeKutta.ToleranceTooSmallException: Constructs a ToleranceTooSmallException with the specified detailed message.
ToleranceTooSmallException(String, Object[]) - Constructor for exception com.imsl.math.ZeroSystem.ToleranceTooSmallException
TooManyCallsException(String) - Constructor for exception com.imsl.stat.ARMA.TooManyCallsException: Constructs an TooManyCallsException with the specified detail message.
TooManyCallsException(String, Object[]) - Constructor for exception com.imsl.stat.ARMA.TooManyCallsException: Constructs an TooManyCallsException with the specified detail message.
TooManyFcnEvalException(String) - Constructor for exception com.imsl.stat.ARMA.TooManyFcnEvalException: Constructs an TooManyFcnEvalException with the specified detail message.
TooManyFcnEvalException(String, Object[]) - Constructor for exception com.imsl.stat.ARMA.TooManyFcnEvalException: Constructs an TooManyFcnEvalException with the specified detail message.
TooManyIterationsException() - Constructor for exception com.imsl.math.CsShape.TooManyIterationsException: Constructs a TooManyIterationsException object.
TooManyIterationsException() - Constructor for exception com.imsl.math.NonlinLeastSquares.TooManyIterationsException: Constructs a TooManyIterationsException object.
TooManyIterationsException() - Constructor for exception com.imsl.math.ZeroSystem.TooManyIterationsException
TooManyIterationsException() - Constructor for exception com.imsl.stat.NonlinearRegression.TooManyIterationsException: Constructs a TooManyIterationsException.
TooManyIterationsException(Object[]) - Constructor for exception com.imsl.math.CsShape.TooManyIterationsException: Constructs a TooManyIterationsException object.
TooManyIterationsException(Object[]) - Constructor for exception com.imsl.math.NonlinLeastSquares.TooManyIterationsException: Constructs a TooManyIterationsException object.
TooManyIterationsException(Object[]) - Constructor for exception com.imsl.math.ZeroSystem.TooManyIterationsException
TooManyIterationsException(String) - Constructor for exception com.imsl.math.GenMinRes.TooManyIterationsException: Constructs a TooManyIterationsException object.
TooManyIterationsException(String) - Constructor for exception com.imsl.math.MinConNLP.TooManyIterationsException: Constructs a TooManyIterationsException object.
TooManyIterationsException(String) - Constructor for exception com.imsl.math.MinConNonlin.TooManyIterationsException: Deprecated.
TooManyIterationsException(String) - Constructor for exception com.imsl.math.SparseLP.TooManyIterationsException: The maximum number of iterations has been exceeded.
TooManyIterationsException(String) - Constructor for exception com.imsl.math.Transport.TooManyIterationsException: Constructs a TooManyIterationsException object.
TooManyIterationsException(String) - Constructor for exception com.imsl.stat.GARCH.TooManyIterationsException: Constructs a TooManyIterationsException object.
TooManyIterationsException(String, Object[]) - Constructor for exception com.imsl.math.CsShape.TooManyIterationsException: Constructs a TooManyIterationsException object.
TooManyIterationsException(String, Object[]) - Constructor for exception com.imsl.math.FeynmanKac.TooManyIterationsException: Too many iterations required by the DAE solver.
TooManyIterationsException(String, Object[]) - Constructor for exception com.imsl.math.GenMinRes.TooManyIterationsException: Constructs a TooManyIterationsException object.
TooManyIterationsException(String, Object[]) - Constructor for exception com.imsl.math.MinConNLP.TooManyIterationsException: Constructs a TooManyIterationsException object.
TooManyIterationsException(String, Object[]) - Constructor for exception com.imsl.math.MinConNonlin.TooManyIterationsException: Deprecated.
TooManyIterationsException(String, Object[]) - Constructor for exception com.imsl.math.NonlinLeastSquares.TooManyIterationsException: Constructs a TooManyIterationsException object.
TooManyIterationsException(String, Object[]) - Constructor for exception com.imsl.math.SparseLP.TooManyIterationsException: The maximum number of iterations has been exceeded.
TooManyIterationsException(String, Object[]) - Constructor for exception com.imsl.math.Transport.TooManyIterationsException: Constructs a TooManyIterationsException object.
TooManyIterationsException(String, Object[]) - Constructor for exception com.imsl.math.ZeroSystem.TooManyIterationsException
TooManyIterationsException(String, Object[]) - Constructor for exception com.imsl.stat.GARCH.TooManyIterationsException: Constructs a TooManyIterationsException object.
TooManyIterException(String) - Constructor for exception com.imsl.math.BoundedVariableLeastSquares.TooManyIterException: The maximum number of iterations has exceeded.
TooManyIterException(String) - Constructor for exception com.imsl.math.NonNegativeLeastSquares.TooManyIterException: The maximum number of iterations has been exceeded.
TooManyIterException(String, Object[]) - Constructor for exception com.imsl.math.BoundedVariableLeastSquares.TooManyIterException: The maximum number of iterations has exceeded.
TooManyIterException(String, Object[]) - Constructor for exception com.imsl.math.NonNegativeLeastSquares.TooManyIterException: The maximum number of iterations has been exceeded.
TooManyITNException(String) - Constructor for exception com.imsl.stat.ARMA.TooManyITNException: Constructs an TooManyITNException with the specified detail message.
TooManyITNException(String, Object[]) - Constructor for exception com.imsl.stat.ARMA.TooManyITNException: Constructs an TooManyITNException with the specified detail message.
TooManyJacobianEvalException(String) - Constructor for exception com.imsl.stat.ARMA.TooManyJacobianEvalException: Constructs an TooManyJacobianEvalException with the specified detail message.
TooManyJacobianEvalException(String, Object[]) - Constructor for exception com.imsl.stat.ARMA.TooManyJacobianEvalException: Constructs an TooManyJacobianEvalException with the specified detail message.
TooManyObsDeletedException(String) - Constructor for exception com.imsl.stat.Covariances.TooManyObsDeletedException: Deprecated.

Constructs a TooManyObsDeletedException object.
TooManyObsDeletedException(String, Object[]) - Constructor for exception com.imsl.stat.Covariances.TooManyObsDeletedException: Deprecated.

Constructs a TooManyObsDeletedException object.
TooMuchTimeException(long) - Constructor for exception com.imsl.math.MinConNLP.TooMuchTimeException: Constructs a TooMuchTimeException object.
TooMuchTimeException(String) - Constructor for exception com.imsl.math.NonNegativeLeastSquares.TooMuchTimeException: The maximum time allowed for solve is exceeded.
TooMuchTimeException(String, Object[]) - Constructor for exception com.imsl.math.NonNegativeLeastSquares.TooMuchTimeException: The maximum time allowed for solve is exceeded.
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.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.
Trace - Class in com.imsl.datamining.neural: Standardizes the output format for the neural network log files.
Trace() - Constructor for class com.imsl.datamining.neural.Trace
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(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(int[][], int[]) - Method in class com.imsl.datamining.NaiveBayesClassifier: Trains a Naive Bayes classifier for classifying data into one of nClasses target classifications.
train(KohonenSOM, double[][]) - Method in class com.imsl.datamining.KohonenSOMTrainer: Trains a Kohonen network.
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(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.
train(Trainer, double[][], int[]) - Method in class com.imsl.datamining.neural.BinaryClassification: Trains the classification neural network using supplied trainer and patterns.
train(Trainer, double[][], int[]) - Method in class com.imsl.datamining.neural.MultiClassification: Trains the classification neural network using supplied training patterns.
Trainer - Interface in com.imsl.datamining.neural: Interface implemented by classes used to train a network.
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_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.
Transport - Class in com.imsl.math: Solves a Transportation problem.
Transport(double[], double[], double[][]) - Constructor for class com.imsl.math.Transport: Construct the transportation problem from given sources, destinations and costs.
Transport(Transport) - Constructor for class com.imsl.math.Transport: Copy constructor for the transportation problem.
Transport.SolutionMethod - Enum Class in com.imsl.math: Indicates which algorithm is used to solve the transportation problem.
Transport.TooManyIterationsException - Exception in com.imsl.math: Maximum number of iterations exceeded.
Transport.UnexpectedErrorException - Exception in com.imsl.math: An unexpected error occurred.
TransportEx1 - Class in com.imsl.test.example.math: Solves a transportation problem using the revised Simplex method.
TransportEx1() - Constructor for class com.imsl.test.example.math.TransportEx1
TransportEx2 - Class in com.imsl.test.example.math: Solves a random transportation problem using both the simplex and the interior-point method.
TransportEx2() - Constructor for class com.imsl.test.example.math.TransportEx2
transpose() - Method in class com.imsl.math.SparseMatrix: Returns the transpose of the matrix.
transpose(double[][]) - Static method in class com.imsl.math.Matrix: Return the transpose of a matrix.
transpose(Complex[][]) - Static method in class com.imsl.math.ComplexMatrix: Return the transpose of a Complex 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: Deprecated.
Update to the other constructor that accepts the predictor indices in argument 10. Creates 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[], 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.
TreeNode - Class in com.imsl.datamining.decisionTree: A DecisionTree node that is a child node of Tree.
TreeNode() - Constructor for class com.imsl.datamining.decisionTree.TreeNode: Constructs a DecisionTreeNode object.
TriangularMatrixSingularException(String) - Constructor for exception com.imsl.stat.ARAutoUnivariate.TriangularMatrixSingularException: Constructs a TriangularMatrixSingularException object.
TriangularMatrixSingularException(String, Object[]) - Constructor for exception com.imsl.stat.ARAutoUnivariate.TriangularMatrixSingularException: Constructs a TriangularMatrixSingularException object.
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
TWO_SIDED - Enum constant in enum class com.imsl.stat.WelchsTTest.Hypothesis: The TWO_SIDED test corresponds to $$ H_0: \mu_x - \mu_y = c \,\,\,\,\,\mbox{vs.}\,\,\,\,\, H_1: \mu_x - \mu_y \ne c$$
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

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.
UnboundedBelowException(String) - Constructor for exception com.imsl.math.MinUnconMultiVar.UnboundedBelowException: Constructs a UnboundedBelowException object.
UnboundedBelowException(String, Object[]) - Constructor for exception com.imsl.math.MinUnconMultiVar.UnboundedBelowException: Constructs a UnboundedBelowException object.
UnexpectedErrorException(String) - Constructor for exception com.imsl.math.Transport.UnexpectedErrorException: Constructs an UnexpectedErrorException object.
UnexpectedErrorException(String, Object[]) - Constructor for exception com.imsl.math.Transport.UnexpectedErrorException: Constructs an UnexpectedErrorException object.
uniform(double, double, double) - Static method in class com.imsl.stat.Cdf: Deprecated.
use Cdf.continuousUniform(double, double, double) instead
uniform(double, double, double) - Static method in class com.imsl.stat.InvCdf: Deprecated.
use InvCdf.continuousUniform(double, double, double) instead
UNION - Enum constant in enum 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 - Enum constant in enum 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.
UnsupervisedNominalFilterEx1 - Class in com.imsl.test.example.datamining.neural: Filters a small data set on a nominal type variable.
UnsupervisedNominalFilterEx1() - Constructor for class com.imsl.test.example.datamining.neural.UnsupervisedNominalFilterEx1
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.
UnsupervisedOrdinalFilterEx1 - Class in com.imsl.test.example.datamining.neural: Encodes a small ordinal data set using the arcsin square root transform.
UnsupervisedOrdinalFilterEx1() - Constructor for class com.imsl.test.example.datamining.neural.UnsupervisedOrdinalFilterEx1
UNWEIGHTED_LEAST_SQUARES - Static variable in class com.imsl.stat.FactorAnalysis: Indicates unweighted least squares method.
unwrap(Class<T>) - 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(double) - Method in class com.imsl.stat.ChiSquaredTest: Adds a new observation to the test.
update(double) - Method in class com.imsl.stat.Summary: Adds an observation to the Summary object.
update(double[]) - Method in class com.imsl.math.Cholesky: Updates the factorization by adding a rank-1 matrix.
update(double[]) - Method in class com.imsl.stat.ChiSquaredTest: Adds new observations to the test.
update(double[]) - Method in class com.imsl.stat.Summary: Adds a set of observations to the Summary object.
update(double[][]) - Method in class com.imsl.stat.DiscriminantAnalysis: Deprecated.
Use DiscriminantAnalysis.update(double[][], int[]) instead.
update(double[][]) - Method in class com.imsl.stat.PooledCovariances: Updates the pooled covariances with new observations from one group.
update(double[][], double[]) - Method in class com.imsl.math.RadialBasis: Adds a set of data points, all with weight = 1.
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.math.RadialBasis: Adds a set of data points with user-specified weights.
update(double[][], double[], double[]) - Method in class com.imsl.stat.DiscriminantAnalysis: Deprecated.
Use DiscriminantAnalysis.update(double[][], int[], int[], double[]) instead.
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[][], int) - Method in class com.imsl.stat.DiscriminantAnalysis: Deprecated.
Use DiscriminantAnalysis.update(double[][], int[]) instead.
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[]) - Method in class com.imsl.stat.PooledCovariances: Updates the pooled covariances with new group observations.
update(double[][], int[], double[], double) - Method in class com.imsl.stat.PooledCovariances: Updates the pooled covariances with new group observations, frequencies and a scalar weight.
update(double[][], int[], double[], double[]) - Method in class com.imsl.stat.DiscriminantAnalysis: Deprecated.
Use DiscriminantAnalysis.update(double[][], int[], int[], int[], double[]) instead.
update(double[][], int[], double[], double[]) - Method in class com.imsl.stat.PooledCovariances: Updates the pooled covariances with new group observations, frequencies and weights.
update(double[][], int[], double, double) - Method in class com.imsl.stat.PooledCovariances: Updates the pooled covariances with new group observations and a scalar frequency and weight.
update(double[][], int[], double, double[]) - Method in class com.imsl.stat.PooledCovariances: Updates the pooled covariances with new group observations, a scalar frequency and weights.
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[][], int, double[], double[]) - Method in class com.imsl.stat.DiscriminantAnalysis: Deprecated.
Use DiscriminantAnalysis.update(double[][], int[], int[], double[]) instead.
update(double[][], int, int[]) - Method in class com.imsl.stat.DiscriminantAnalysis: Deprecated.
Use DiscriminantAnalysis.update(double[][], int[], int[]) instead.
update(double[][], int, int[], double[], double[]) - Method in class com.imsl.stat.DiscriminantAnalysis: Deprecated.
Use DiscriminantAnalysis.update(double [][], int[], int[], int[], double[]) instead.
update(double[], double) - Method in class com.imsl.math.RadialBasis: Adds a data point with weight = 1.
update(double[], double) - Method in class com.imsl.stat.LinearRegression: Updates the regression object with a new observation.
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.NormTwoSample: Concatenates the data in x and y with the samples provided in the constructor.
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[]) - Method in class com.imsl.stat.WelchsTTest: Concatenates the data in x and y with the samples provided in the constructor.
update(double[], double[][], double[][]) - Method in class com.imsl.stat.KalmanFilter: Performs computation of the update equations.
update(double[], double, double) - Method in class com.imsl.math.RadialBasis: Adds a data point with a specified weight.
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.ChiSquaredTest: Adds a new observation to the test.
update(double, double) - Method in class com.imsl.stat.Summary: Adds an observation and associated weight 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(int, Array) - Method in class com.imsl.io.AbstractFlatFile: Updates the designated column with an Array value.
updateArray(String, Array) - Method in class com.imsl.io.AbstractFlatFile: Updates the designated column with an Array 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.AbstractFlatFile: 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.AbstractFlatFile: 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) - 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.AbstractFlatFile: 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.AbstractFlatFile: 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, 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(int, 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(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.
updateBlob(String, 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.FlatFile: Updates the designated column with a java.sql.Blob value.
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) - 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.AbstractFlatFile: 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.AbstractFlatFile: 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(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(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(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.
updateClob(String, Clob) - Method in class com.imsl.io.AbstractFlatFile: Updates the designated column with an java.sql.Clob value.
updateClob(String, Clob) - Method in class com.imsl.io.FlatFile: Updates the designated column with a java.sql.Clob value.
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, 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(int, 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.
updateNClob(String, NClob) - Method in class com.imsl.io.FlatFile: Updates the designated column with a java.sql.NClob value.
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) - Method in class com.imsl.io.AbstractFlatFile: Updates the designated column with an Object value.
updateObject(int, 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.
updateObject(String, Object, int) - Method in class com.imsl.io.AbstractFlatFile: Updates the designated column with an Object value.
updateRef(int, Ref) - Method in class com.imsl.io.AbstractFlatFile: Updates the designated column with an java.sql.Ref value.
updateRef(String, 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 data in x with the first sample.
updateX(double[]) - Method in class com.imsl.stat.WelchsTTest: Concatenates the data in x with the first sample.
updateY(double[]) - Method in class com.imsl.stat.NormTwoSample: Concatenates the data in y with the second sample.
updateY(double[]) - Method in class com.imsl.stat.WelchsTTest: Concatenates the data in y with the second sample.
UphillSearchCalcException(String) - Constructor for exception com.imsl.math.MinConNonlin.UphillSearchCalcException: Deprecated.
UphillSearchCalcException(String, Object[]) - Constructor for exception com.imsl.math.MinConNonlin.UphillSearchCalcException: Deprecated.
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 $y = c_0 + c_1 f_1 (x) + c_2 f_2 (x) + \cdots + c_k f_k (x) + \varepsilon$, where $f_1 (x),f_2 (x), \cdots ,f_k (x)$ are the user basis functions $f_i (x)$ evaluated at index values $i = 1,2, \ldots ,k,c_0 $ is the intercept, $c_1 ,c_2 , \cdots ,c_k$ 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
UserBasisRegressionEx1 - Class in com.imsl.test.example.stat: Fits a regression to a function without noise with user defined basis functions.
UserBasisRegressionEx1() - Constructor for class com.imsl.test.example.stat.UserBasisRegressionEx1
UserBasisRegressionEx2 - Class in com.imsl.test.example.stat: Fits a regression to a polynomial with user defined basis functions.
UserBasisRegressionEx2() - Constructor for class com.imsl.test.example.stat.UserBasisRegressionEx2

V

validateLink(Node, Node) - Method in class com.imsl.datamining.neural.FeedForwardNetwork: Checks that a Link between two Nodes is valid.
value - Variable in class com.imsl.math.Physical
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.Spline: Returns the value of the 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(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.Spline: Returns the value of the spline at each point of an array.
value(double[][]) - Method in class com.imsl.math.RadialBasis: Returns the value of the radial basis at a point.
value(double[], double[]) - Method in class com.imsl.math.Spline2D: Returns the values of the tensor-product spline of an array of points.
value(double, double) - Method in class com.imsl.math.Spline2D: Returns the value of the tensor-product spline at the point (x, y).
valueOf(String) - Static method in enum class com.imsl.datamining.decisionTree.DecisionTreeInfoGain.GainCriteria: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class com.imsl.datamining.GradientBoosting.LossFunctionType: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class com.imsl.datamining.PredictiveModel.VariableType: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in class com.imsl.math.Complex: Parses a String into a Complex.
valueOf(String) - Static method in enum class com.imsl.math.ComplexMatrix.MatrixType: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class com.imsl.math.Matrix.MatrixType: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class com.imsl.math.Transport.SolutionMethod: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class com.imsl.stat.distributions.MaximumLikelihoodEstimation.OptimizationMethod: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class com.imsl.stat.ExtendedGARCH.Solver: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class com.imsl.stat.FactorAnalysis.ScoreMethod: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class com.imsl.stat.TimeSeriesOperations.CombineMethod: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class com.imsl.stat.TimeSeriesOperations.MergeRule: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class com.imsl.stat.WelchsTTest.Hypothesis: Returns the enum constant of this class with the specified name.
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 enum class com.imsl.datamining.decisionTree.DecisionTreeInfoGain.GainCriteria: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class com.imsl.datamining.GradientBoosting.LossFunctionType: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class com.imsl.datamining.PredictiveModel.VariableType: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class com.imsl.math.ComplexMatrix.MatrixType: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class com.imsl.math.Matrix.MatrixType: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class com.imsl.math.Transport.SolutionMethod: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class com.imsl.stat.distributions.MaximumLikelihoodEstimation.OptimizationMethod: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class com.imsl.stat.ExtendedGARCH.Solver: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class com.imsl.stat.FactorAnalysis.ScoreMethod: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class com.imsl.stat.TimeSeriesOperations.CombineMethod: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class com.imsl.stat.TimeSeriesOperations.MergeRule: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class com.imsl.stat.WelchsTTest.Hypothesis: Returns an array containing the constants of this enum class, in the order they are declared.
VarBoundsInconsistentException(String) - Constructor for exception com.imsl.math.MinConGenLin.VarBoundsInconsistentException: Constructs a VarBoundsInconsistentException object.
VarBoundsInconsistentException(String, Object[]) - Constructor for exception com.imsl.math.MinConGenLin.VarBoundsInconsistentException: Constructs a VarBoundsInconsistentException object.
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.
VarsDeterminedException(String) - Constructor for exception com.imsl.stat.GARCH.VarsDeterminedException: Constructs a VarsDeterminedException object.
VarsDeterminedException(String, Object[]) - Constructor for exception com.imsl.stat.GARCH.VarsDeterminedException: Constructs a VarsDeterminedException object.
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.
VectorAutoregressionEx1 - Class in com.imsl.test.example.stat: Fits a vector autoregression to a time series.
VectorAutoregressionEx1() - Constructor for class com.imsl.test.example.stat.VectorAutoregressionEx1
Version - Class in com.imsl: Print the version information.
Version() - Constructor for class com.imsl.Version
vnorm(double[], double[], double[]) - Method in class com.imsl.math.ODE: Returns the norm of a vector.

W

Warning - Class in com.imsl: Handle warning messages.
Warning() - Constructor for class com.imsl.Warning
WarningEx1 - Class in com.imsl.test.example: Captures a warning message and reprints the message later.
WarningEx1() - Constructor for class com.imsl.test.example.WarningEx1
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.
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.
WeibullPD - Class in com.imsl.stat.distributions: The Weibull probability distribution.
WeibullPD() - Constructor for class com.imsl.stat.distributions.WeibullPD: Constructor for the Weibull probability distribution.
WeibullPDEx1 - Class in com.imsl.test.example.stat.distributions: Evaluates the Weibull probability distribution.
WeibullPDEx1() - Constructor for class com.imsl.test.example.stat.distributions.WeibullPDEx1
WeibullProb(double, double, double) - Static method in class com.imsl.stat.Cdf: Deprecated.
Use Pdf.Weibull(double, double, double) instead.
weight - Variable in class com.imsl.math.BsLeastSquares: The weight array of length n, where n is the number of data points fit.
WelchsTTest - Class in com.imsl.stat: Performs Welch's t-test for testing the difference in means between two normal populations with unequal variances.
WelchsTTest(double[], double[]) - Constructor for class com.imsl.stat.WelchsTTest: Constructor for the class.
WelchsTTest.Hypothesis - Enum Class in com.imsl.stat: The form of the alternate hypothesis.
WelchsTTestEx1 - Class in com.imsl.test.example.stat: Performs Welch's t-test for three example data sets.
WelchsTTestEx1() - Constructor for class com.imsl.test.example.stat.WelchsTTestEx1
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.
WilcoxonRankSumEx1 - Class in com.imsl.test.example.stat: Performs a rank sum test.
WilcoxonRankSumEx1() - Constructor for class com.imsl.test.example.stat.WilcoxonRankSumEx1
WilcoxonRankSumEx2 - Class in com.imsl.test.example.stat: Performs a rank sum test and displays all the statistics.
WilcoxonRankSumEx2() - Constructor for class com.imsl.test.example.stat.WilcoxonRankSumEx2
WorkingSetSingularException(String) - Constructor for exception com.imsl.math.MinConNLP.WorkingSetSingularException: Constructs a WorkingSetSingularException object.
WorkingSetSingularException(String, Object[]) - Constructor for exception com.imsl.math.MinConNLP.WorkingSetSingularException: Constructs a WorkingSetSingularException object.
wrapAround() - Method in class com.imsl.datamining.KohonenSOM: Sets a flag to indicate the map should wrap around or connect opposite edges.
write(Object, String) - Static method in class com.imsl.test.example.datamining.neural.EpochTrainerEx1
write(String) - Method in class com.imsl.datamining.SequenceDatabase: Serializes the sequence database to a file.
WriteCutString(String, int) - Static method in class com.imsl.test.example.math.QuadraticProgrammingEx3
WrongConstraintTypeException(String) - Constructor for exception com.imsl.math.DenseLP.WrongConstraintTypeException: Deprecated.
WrongConstraintTypeException(String) - Constructor for exception com.imsl.math.LinearProgramming.WrongConstraintTypeException: Deprecated.
WrongConstraintTypeException(String, Object[]) - Constructor for exception com.imsl.math.DenseLP.WrongConstraintTypeException: Deprecated.
WrongConstraintTypeException(String, Object[]) - Constructor for exception com.imsl.math.LinearProgramming.WrongConstraintTypeException: Deprecated.

X

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.
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, 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.
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.
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

ZeroColumnException(String) - Constructor for exception com.imsl.math.SparseLP.ZeroColumnException: A column of the constraint matrix has no entries.
ZeroColumnException(String, Object[]) - Constructor for exception com.imsl.math.SparseLP.ZeroColumnException: A column of the constraint matrix has no entries.
ZeroFunction - Class in com.imsl.math: Deprecated.
ZeroFunction has been replaced by ZerosFunction.
ZeroFunction() - Constructor for class com.imsl.math.ZeroFunction: Deprecated.

Creates an instance of the solver.
ZeroFunction.Function - Interface in com.imsl.math: Deprecated.
ZeroFunction has been replaced by ZerosFunction.
ZeroFunctionEx1 - Class in com.imsl.test.example.math: Deprecated.
ZeroFunction class has been deprecated.
ZeroFunctionEx1() - Constructor for class com.imsl.test.example.math.ZeroFunctionEx1: Deprecated.
ZeroNormException(int) - Constructor for exception com.imsl.stat.Dissimilarities.ZeroNormException: Constructs a ZeroNormException.
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
ZeroPolynomialEx1 - Class in com.imsl.test.example.math: Finds the zeros of a polynomial.
ZeroPolynomialEx1() - Constructor for class com.imsl.test.example.math.ZeroPolynomialEx1
ZeroPolynomialEx2 - Class in com.imsl.test.example.math: Finds the zeros of a polynomial with complex coefficients.
ZeroPolynomialEx2() - Constructor for class com.imsl.test.example.math.ZeroPolynomialEx2
ZeroRowException(String) - Constructor for exception com.imsl.math.SparseLP.ZeroRowException: A row of the constraint matrix has no entries.
ZeroRowException(String, Object[]) - Constructor for exception com.imsl.math.SparseLP.ZeroRowException: A row of the constraint matrix has no entries.
ZeroSearchDirectionException(String) - Constructor for exception com.imsl.math.MinConNonlin.ZeroSearchDirectionException: Deprecated.
ZeroSearchDirectionException(String, Object[]) - Constructor for exception com.imsl.math.MinConNonlin.ZeroSearchDirectionException: Deprecated.
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.
ZerosFunctionEx1 - Class in com.imsl.test.example.math: Finds zeros of the $\sin$ function.
ZerosFunctionEx1() - Constructor for class com.imsl.test.example.math.ZerosFunctionEx1
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.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.TooManyIterationsException - Exception in com.imsl.math: Too many iterations.
ZeroSystemEx1 - Class in com.imsl.test.example.math: Solves a system of nonlinear equations.
ZeroSystemEx1() - Constructor for class com.imsl.test.example.math.ZeroSystemEx1
ZeroSystemEx2 - Class in com.imsl.test.example.math: ZeroSystem Example 2: Solves a system of nonlinear equations with logging enabled.
ZeroSystemEx2() - Constructor for class com.imsl.test.example.math.ZeroSystemEx2
zPdf(double) - Method in interface com.imsl.stat.ExtendedGARCH.zDistribution: Returns the probability density function for $z$.

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