- L1Norm(double[]) - Static method in class com.itemanalysis.psychometrics.optimization.ArrayMath
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- L1normalize(double[]) - Static method in class com.itemanalysis.psychometrics.optimization.ArrayMath
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- L2Norm(double[]) - Static method in class com.itemanalysis.psychometrics.optimization.ArrayMath
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- LeastSquaresCrossValidation - Class in com.itemanalysis.psychometrics.kernel
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A class for computing the bandwidth by least squares cross validation kernel.
- LeastSquaresCrossValidation(double[]) - Constructor for class com.itemanalysis.psychometrics.kernel.LeastSquaresCrossValidation
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- leaveOutDensityAt(double, int) - Method in class com.itemanalysis.psychometrics.kernel.LeastSquaresCrossValidation
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- leftJustifyText(String, int) - Method in class com.itemanalysis.psychometrics.texttable.TextTableRow
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- lgamma(double) - Static method in class com.itemanalysis.psychometrics.optimization.SloppyMath
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- LikelihoodCrossValidation - Class in com.itemanalysis.psychometrics.kernel
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See Silverman (1986, p.
- LikelihoodCrossValidation(KernelFunction, double[]) - Constructor for class com.itemanalysis.psychometrics.kernel.LikelihoodCrossValidation
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- linearTransformation(double, double) - Method in class com.itemanalysis.psychometrics.irt.estimation.JointMaximumLikelihoodEstimation
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Apply linear transformation to person and item parameter estimates.
- linearTransformation(DefaultLinearTransformation, int) - Method in class com.itemanalysis.psychometrics.irt.estimation.RaschScoreTable
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A linear transformation can be applied to teh score table.
- LinearTransformation - Interface in com.itemanalysis.psychometrics.scaling
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- LinearTransformationType - Enum in com.itemanalysis.psychometrics.scaling
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- listwiseSampleSize() - Method in class com.itemanalysis.psychometrics.polycor.CovarianceMatrix
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- lltslv_f77(int, double[][], double[], double[]) - Method in class com.itemanalysis.psychometrics.uncmin.Uncmin
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The lltslv_f77 method solves Ax = b where A has the form L(L transpose)
but only the lower triangular part, L, is stored.
- lltslv_f77(int, double[][], double[], double[]) - Method in class com.itemanalysis.psychometrics.uncmin.Uncmin_f77
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The lltslv_f77 method solves Ax = b where A has the form L(L transpose)
but only the lower triangular part, L, is stored.
- lnsrch_f77(int, double[], double[], double[], double[], double[], double[], Uncmin_methods, boolean[], int[], double[], double[], double[]) - Method in class com.itemanalysis.psychometrics.uncmin.Uncmin
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The lnsrch_f77 method finds a next Newton iterate by line search.
- lnsrch_f77(int, double[], double[], double[], double[], double[], double[], Uncmin_methods, boolean[], int[], double[], double[], double[]) - Method in class com.itemanalysis.psychometrics.uncmin.Uncmin_f77
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The lnsrch_f77 method finds a next Newton iterate by line search.
- LocalLinearRegression - Class in com.itemanalysis.psychometrics.kernel
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- LocalLinearRegression(KernelFunction, Bandwidth, UniformDistributionApproximation) - Constructor for class com.itemanalysis.psychometrics.kernel.LocalLinearRegression
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- log(double[]) - Static method in class com.itemanalysis.psychometrics.optimization.ArrayMath
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- log(double, double) - Static method in class com.itemanalysis.psychometrics.optimization.SloppyMath
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Convenience method for log to a different base
- logAdd(float, float) - Static method in class com.itemanalysis.psychometrics.optimization.SloppyMath
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Returns the log of the sum of two numbers, which are
themselves input in log form.
- logAdd(double, double) - Static method in class com.itemanalysis.psychometrics.optimization.SloppyMath
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Returns the log of the sum of two numbers, which are
themselves input in log form.
- logDensity(double) - Method in interface com.itemanalysis.psychometrics.irt.estimation.ItemParamPrior
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- logDensity(double) - Method in class com.itemanalysis.psychometrics.irt.estimation.ItemParamPriorBeta
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- logDensity(double) - Method in class com.itemanalysis.psychometrics.irt.estimation.ItemParamPriorBeta4
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Only compute part of the log of the density that depends on the parameter
- logDensity(double) - Method in class com.itemanalysis.psychometrics.irt.estimation.ItemParamPriorLogNormal
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Only compute part of the log of the density that depends on the parameter
- logDensity(double) - Method in class com.itemanalysis.psychometrics.irt.estimation.ItemParamPriorNormal
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Only compute part of the log of the density that depends on the parameter
- logDensityDeriv1(double) - Method in interface com.itemanalysis.psychometrics.irt.estimation.ItemParamPrior
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- logDensityDeriv1(double) - Method in class com.itemanalysis.psychometrics.irt.estimation.ItemParamPriorBeta
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- logDensityDeriv1(double) - Method in class com.itemanalysis.psychometrics.irt.estimation.ItemParamPriorBeta4
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First derivative of log density.
- logDensityDeriv1(double) - Method in class com.itemanalysis.psychometrics.irt.estimation.ItemParamPriorLogNormal
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First derivative of log density.
- logDensityDeriv1(double) - Method in class com.itemanalysis.psychometrics.irt.estimation.ItemParamPriorNormal
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First derivative of log density.
- logDensityDeriv2(double) - Method in interface com.itemanalysis.psychometrics.irt.estimation.ItemParamPrior
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- logDensityDeriv2(double) - Method in class com.itemanalysis.psychometrics.irt.estimation.ItemParamPriorBeta
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Second derivative of log density.
- logDensityDeriv2(double) - Method in class com.itemanalysis.psychometrics.irt.estimation.ItemParamPriorBeta4
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Second derivative of log density.
- logDensityDeriv2(double) - Method in class com.itemanalysis.psychometrics.irt.estimation.ItemParamPriorLogNormal
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Second derivative of log density.
- logDensityDeriv2(double) - Method in class com.itemanalysis.psychometrics.irt.estimation.ItemParamPriorNormal
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- logInPlace(double[]) - Static method in class com.itemanalysis.psychometrics.optimization.ArrayMath
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- logLikelihood(double) - Method in class com.itemanalysis.psychometrics.irt.estimation.IrtExaminee
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computes the loglikelihood of a responseVector vector at a given value of theta.
- logLikelihood(double[]) - Method in class com.itemanalysis.psychometrics.irt.estimation.ItemLogLikelihood
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Log-likelihood for an item.
- logLikelihood() - Method in interface com.itemanalysis.psychometrics.irt.estimation.ItemLogLikelihoodFunction
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Item loglikelihood function.
- loglikelihood() - Method in interface com.itemanalysis.psychometrics.mixture.MixtureModel
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- loglikelihood() - Method in class com.itemanalysis.psychometrics.mixture.MvNormalMixtureModel
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- logLikelihood(double[], double[], double) - Method in class com.itemanalysis.psychometrics.polycor.AbstractPolychoricCorrelation
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- logNormalize(double[]) - Static method in class com.itemanalysis.psychometrics.optimization.ArrayMath
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Makes the values in this array sum to 1.0.
- logSum(double...) - Static method in class com.itemanalysis.psychometrics.optimization.ArrayMath
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Returns the log of the sum of an array of numbers, which are
themselves input in log form.
- logSum(double[], int, int) - Static method in class com.itemanalysis.psychometrics.optimization.ArrayMath
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Returns the log of the portion between fromIndex, inclusive, and
toIndex, exclusive, of an array of numbers, which are
themselves input in log form.
- logSum(double[], int, int, int) - Static method in class com.itemanalysis.psychometrics.optimization.ArrayMath
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Returns the log of the portion between fromIndex, inclusive, and
toIndex, exclusive, of an array of numbers, which are
themselves input in log form.
- logSum(List<Double>) - Static method in class com.itemanalysis.psychometrics.optimization.ArrayMath
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- logSum(List<Double>, int, int) - Static method in class com.itemanalysis.psychometrics.optimization.ArrayMath
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- logSum(float[]) - Static method in class com.itemanalysis.psychometrics.optimization.ArrayMath
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Returns the log of the sum of an array of numbers, which are
themselves input in log form.
- lowerInclusive() - Method in class com.itemanalysis.psychometrics.histogram.Bin
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Gets a boolean indicating whether the bin is lower inclusive or not.