Deprecated API

Contents

• Interfaces
• Classes
• Exceptions
• Methods
• Constructors

Deprecated Interfaces

Interface	Description
com.imsl.math.MinConNonlin.Function	MinConNonlin has been replaced by MinConNLP.
com.imsl.math.MinConNonlin.Gradient	MinConNonlin has been replaced by MinConNLP.
com.imsl.math.ZeroFunction.Function	ZeroFunction has been replaced by ZerosFunction.

Deprecated Classes

Class	Description
com.imsl.math.GenMinRes.Formatter	Use IMSLFormatter instead.
com.imsl.math.LinearProgramming	LinearProgramming has been replaced by DenseLP.
com.imsl.math.MinConNLP.Formatter	Use IMSLFormatter instead.
com.imsl.math.MinConNonlin	MinConNonlin has been replaced by MinConNLP.
com.imsl.math.ZeroFunction	ZeroFunction has been replaced by ZerosFunction.
com.imsl.stat.ARAutoUnivariate.Formatter	Use IMSLFormatter instead.
com.imsl.test.example.math.LinearProgrammingEx1	LinearProgramming class has been deprecated.
com.imsl.test.example.math.LinearProgrammingEx2	LinearProgramming class has been deprecated.
com.imsl.test.example.math.ZeroFunctionEx1	ZeroFunction class has been deprecated.

Deprecated Exceptions

Exceptions	Description
com.imsl.math.DenseLP.WrongConstraintTypeException	No longer used, replaced with an IllegalArgumentException.
com.imsl.math.LinearProgramming.BoundsInconsistentException
com.imsl.math.LinearProgramming.NumericDifficultyException
com.imsl.math.LinearProgramming.ProblemInfeasibleException
com.imsl.math.LinearProgramming.ProblemUnboundedException
com.imsl.math.LinearProgramming.WrongConstraintTypeException	No longer used, replaced with an IllegalArgumentException.
com.imsl.math.MinConNonlin.LineSearchException	MinConNonlin has been replaced by MinConNLP.
com.imsl.math.MinConNonlin.QPConstraintsException	MinConNonlin has been replaced by MinConNLP.
com.imsl.math.MinConNonlin.TooManyIterationsException	MinConNonlin has been replaced by MinConNLP.
com.imsl.math.MinConNonlin.UphillSearchCalcException	MinConNonlin has been replaced by MinConNLP.
com.imsl.math.MinConNonlin.ZeroSearchDirectionException	MinConNonlin has been replaced by MinConNLP.
com.imsl.stat.ClusterKMeans.NonnegativeFreqException	No longer used, replaced with an IllegalArgumentException.
com.imsl.stat.ClusterKMeans.NonnegativeWeightException	No longer used, replaced with an IllegalArgumentException.
com.imsl.stat.Covariances.DiffObsDeletedException
com.imsl.stat.Covariances.MoreObsDelThanEnteredException
com.imsl.stat.Covariances.TooManyObsDeletedException

Deprecated Methods

Method	Description
com.imsl.datamining.BootstrapAggregation.getMeanSquaredPredictionError()	Renamed to BootstrapAggregation.getPredictionError().
com.imsl.finance.BasisPart.getDaysInYear(GregorianCalendar, GregorianCalendar)	Use BasisPart.getDaysInYear(GregorianCalendar) instead.
com.imsl.io.AbstractFlatFile.getArray(int)
com.imsl.io.AbstractFlatFile.getBigDecimal(int, int)
com.imsl.io.AbstractFlatFile.getBigDecimal(String, int)
com.imsl.io.AbstractFlatFile.getUnicodeStream(int)	Use AbstractFlatFile.getCharacterStream(int) instead.
com.imsl.io.AbstractFlatFile.getUnicodeStream(String)	Use AbstractFlatFile.getCharacterStream(String) instead.
com.imsl.math.ComplexMatrix.CheckMatrix(Complex[][])	Use ComplexMatrix.checkMatrix(Complex[][]) instead.
com.imsl.math.ComplexMatrix.CheckSquareMatrix(Complex[][])	Use ComplexMatrix.checkSquareMatrix(Complex[][]) instead.
com.imsl.math.Matrix.CheckMatrix(double[][])	Use Matrix.checkMatrix(double[][]) instead.
com.imsl.math.Matrix.CheckSquareMatrix(double[][])	Use Matrix.checkSquareMatrix(double[][]) instead.
com.imsl.math.MinConGenLin.getLagrangeMultiplerEst()	Method name misspelled. Use MinConGenLin.getLagrangeMultiplierEst() instead.
com.imsl.math.Sfun.logGammaCorrection(double)
com.imsl.math.Sfun.r9lgmc(double)
com.imsl.math.SVD.inverse()	Method name does not adhere to the Java naming conventions. Use SVD.getInverse() instead.
com.imsl.stat.ANOVA.getDunnSidak(int, int)	Use ANOVA.getConfidenceInterval(double, int, int, int) instead.
com.imsl.stat.ARMA.getMeanEstimate()	Use ARMA.getMean() instead.
com.imsl.stat.ARMA.setMeanEstimate(double)	Use ARMA.setMean(double) instead.
com.imsl.stat.Cdf.betaProb(double, double, double)	Use Pdf.beta(double, double, double) instead.
com.imsl.stat.Cdf.binomialProb(int, int, double)	Use Pdf.binomial(int, int, double) instead.
com.imsl.stat.Cdf.chiProb(double, double)	Use Pdf.chi(double, double) instead.
com.imsl.stat.Cdf.discreteUniformProb(int, int)	Use Pdf.discreteUniform(int, int) instead.
com.imsl.stat.Cdf.exponentialProb(double, double)	Use Pdf.exponential(double, double) instead.
com.imsl.stat.Cdf.extremeValueProb(double, double, double)	Use Pdf.extremeValue(double, double, double) instead.
com.imsl.stat.Cdf.FProb(double, double, double)	Use Pdf.F(double, double, double) instead.
com.imsl.stat.Cdf.gammaProb(double, double, double)	Use Pdf.gamma(double, double, double) instead.
com.imsl.stat.Cdf.geometricProb(int, double)	Use Pdf.geometric(int, double) instead.
com.imsl.stat.Cdf.hypergeometricProb(int, int, int, int)	Use Pdf.hypergeometric(int, int, int, int) instead.
com.imsl.stat.Cdf.inverseBeta(double, double, double)	Use InvCdf.beta(double, double, double) instead.
com.imsl.stat.Cdf.inverseChi(double, double)	Use InvCdf.chi(double, double) instead.
com.imsl.stat.Cdf.inverseDiscreteUniform(double, int)	Use InvCdf.discreteUniform(double, int) instead.
com.imsl.stat.Cdf.inverseExponential(double, double)	Use InvCdf.exponential(double, double) instead.
com.imsl.stat.Cdf.inverseExtremeValue(double, double, double)	Use InvCdf.extremeValue(double, double, double) instead.
com.imsl.stat.Cdf.inverseF(double, double, double)	Use InvCdf.F(double, double, double) instead.
com.imsl.stat.Cdf.inverseGamma(double, double)	Use InvCdf.gamma(double, double) instead.
com.imsl.stat.Cdf.inverseGeometric(double, double)	Use InvCdf.geometric(double, double) instead.
com.imsl.stat.Cdf.inverseLogNormal(double, double, double)	Use InvCdf.logNormal(double, double, double) instead.
com.imsl.stat.Cdf.inverseNoncentralchi(double, double, double)	Use InvCdf.noncentralchi(double, double, double) instead.
com.imsl.stat.Cdf.inverseNoncentralstudentsT(double, int, double)	Use InvCdf.noncentralstudentsT(double, int, double) instead.
com.imsl.stat.Cdf.inverseNormal(double)	Use InvCdf.normal(double) instead.
com.imsl.stat.Cdf.inverseRayleigh(double, double)	Use InvCdf.Rayleigh(double, double) instead.
com.imsl.stat.Cdf.inverseStudentsT(double, double)	Use InvCdf.studentsT(double, double) instead.
com.imsl.stat.Cdf.inverseUniform(double, double, double)	Use InvCdf.uniform(double, double, double) instead.
com.imsl.stat.Cdf.inverseWeibull(double, double, double)	Use InvCdf.Weibull(double, double, double) instead.
com.imsl.stat.Cdf.logNormalProb(double, double, double)	Use Pdf.logNormal(double, double, double) instead.
com.imsl.stat.Cdf.poissonProb(int, double)	Use Pdf.poisson(int, double) instead.
com.imsl.stat.Cdf.RayleighProb(double, double)	Use Pdf.Rayleigh(double, double) instead.
com.imsl.stat.Cdf.uniform(double, double, double)	use Cdf.continuousUniform(double, double, double) instead
com.imsl.stat.Cdf.WeibullProb(double, double, double)	Use Pdf.Weibull(double, double, double) instead.
com.imsl.stat.DiscriminantAnalysis.getNRowsMissing()	Use DiscriminantAnalysis.getNumberOfRowsMissing() instead.
com.imsl.stat.DiscriminantAnalysis.update(double[][])	Use DiscriminantAnalysis.update(double[][], int[]) instead.
com.imsl.stat.DiscriminantAnalysis.update(double[][], double[], double[])	Use DiscriminantAnalysis.update(double[][], int[], int[], double[]) instead.
com.imsl.stat.DiscriminantAnalysis.update(double[][], int)	Use DiscriminantAnalysis.update(double[][], int[]) instead.
com.imsl.stat.DiscriminantAnalysis.update(double[][], int[], double[], double[])	Use DiscriminantAnalysis.update(double[][], int[], int[], int[], double[]) instead.
com.imsl.stat.DiscriminantAnalysis.update(double[][], int, double[], double[])	Use DiscriminantAnalysis.update(double[][], int[], int[], double[]) instead.
com.imsl.stat.DiscriminantAnalysis.update(double[][], int, int[])	Use DiscriminantAnalysis.update(double[][], int[], int[]) instead.
com.imsl.stat.DiscriminantAnalysis.update(double[][], int, int[], double[], double[])	Use DiscriminantAnalysis.update(double [][], int[], int[], int[], double[]) instead.
com.imsl.stat.distributions.MaximumLikelihoodEstimation.setExact(boolean)	use MaximumLikelihoodEstimation.setUseAnalytic(boolean) instead
com.imsl.stat.distributions.NormalPD.getMLEs(double[])	Use NormalPD.getClosedFormMLE(double[]) instead.
com.imsl.stat.GARCH.getAR()	Use GARCH.getGARCH() instead.
com.imsl.stat.GARCH.getMA()	Use GARCH.getARCH() instead.
com.imsl.stat.InvCdf.uniform(double, double, double)	use InvCdf.continuousUniform(double, double, double) instead
com.imsl.stat.LinearRegression.CaseStatistics.getCaseStatistics()	Recommend using specific getter methods.
com.imsl.stat.Random.canonicalCorrelationSTC(double, double[][], double[][])
com.imsl.stat.Random.nextGaussianCopula(int, Cholesky)	Use Random.nextGaussianCopula(Cholesky) instead.
com.imsl.stat.Random.nextMultivariateNormal(int, Cholesky)	Use Random.nextMultivariateNormal(Cholesky) instead.
com.imsl.stat.Random.nextNormalAR()
com.imsl.stat.Random.nextStudentsTCopula(int, double, Cholesky)	Use Random.nextStudentsTCopula(double, Cholesky) instead.

Deprecated Constructors

Constructor	Description
com.imsl.datamining.decisionTree.Tree(int, int, int, int, int, int, PredictiveModel.VariableType, int[], PredictiveModel.VariableType[], int)	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.
com.imsl.math.Eigen(double[][])	Use Eigen() instead.
com.imsl.math.Eigen(double[][], boolean)	Use Eigen() instead.
com.imsl.stat.ClusterHierarchical(double[][], int, int)	Use ClusterHierarchical(double[][]) instead.
com.imsl.stat.Dissimilarities(double[][], int, int, int)	Use Dissimilarities(double[][]) instead.
com.imsl.stat.Dissimilarities(double[][], int, int, int, int[])	Use Dissimilarities(double[][]) instead.
com.imsl.stat.KalmanFilter(double[], double[], int, double, double)	Use KalmanFilter(double[], double[][], int, double, double) instead.
com.imsl.stat.LinearRegression.CaseStatistics(double[], double)	The CaseStatistics constructors have been deprecated in favor of getter methods in LinearRegression.
com.imsl.stat.LinearRegression.CaseStatistics(double[], double, double)	The CaseStatistics constructors have been deprecated in favor of getter methods in LinearRegression.
com.imsl.stat.LinearRegression.CaseStatistics(double[], double, double, int)	The CaseStatistics constructors have been deprecated in favor of getter methods in LinearRegression.
com.imsl.stat.LinearRegression.CaseStatistics(double[], double, int)	The CaseStatistics constructors have been deprecated in favor of getter methods in LinearRegression.