- MachineAccuracy - Class in com.itemanalysis.psychometrics.measurement
-
determines machine accuracy
- MachineAccuracy() - Constructor for class com.itemanalysis.psychometrics.measurement.MachineAccuracy
-
- machineEpsilon() - Method in class com.itemanalysis.psychometrics.analysis.AbstractDiffFunction
-
- machineEpsilon() - Method in class com.itemanalysis.psychometrics.analysis.AbstractMultivariateFunction
-
- main(String[]) - Static method in class com.itemanalysis.psychometrics.irt.estimation.MMLEsimulation
-
- main(String[]) - Static method in class com.itemanalysis.psychometrics.optimization.ArrayMath
-
For testing only.
- main(String[]) - Static method in class com.itemanalysis.psychometrics.optimization.SloppyMath
-
Tests the hypergeometric distribution code, or other functions
provided in this module.
- mapEstimate(double, double, double, double, int, double) - Method in class com.itemanalysis.psychometrics.irt.estimation.IrtExaminee
-
Maximum a Posteriori (MAP) estimate of examinee ability using a normal prior
distribution.
- mapEstimate(double, double, double, double) - Method in class com.itemanalysis.psychometrics.irt.estimation.IrtExaminee
-
- mapStandardErrorAt(double) - Method in class com.itemanalysis.psychometrics.irt.estimation.IrtExaminee
-
- MarginalMaximumLikelihoodEstimation - Class in com.itemanalysis.psychometrics.irt.estimation
-
Marginal maximum likelihood estimation (MMLE) for item parameters in Item Response Theory (IRT).
- MarginalMaximumLikelihoodEstimation(ItemResponseVector[], ItemResponseModel[], DistributionApproximation) - Constructor for class com.itemanalysis.psychometrics.irt.estimation.MarginalMaximumLikelihoodEstimation
-
- matmat_f77(double[][], double[][], double[][], int, int, int) - Static method in class com.itemanalysis.psychometrics.uncmin.Blas_f77
-
This method multiplies an n x p matrix by a p x r matrix.
- matrix - Variable in class com.itemanalysis.psychometrics.reliability.AbstractScoreReliability
-
- MatrixToVector - Class in com.itemanalysis.psychometrics.statistics
-
- MatrixToVector(RealMatrix) - Constructor for class com.itemanalysis.psychometrics.statistics.MatrixToVector
-
- MatrixUtils - Class in com.itemanalysis.psychometrics.factoranalysis
-
- MatrixUtils() - Constructor for class com.itemanalysis.psychometrics.factoranalysis.MatrixUtils
-
- mattran_f77(double[][], double[][], int, int) - Static method in class com.itemanalysis.psychometrics.uncmin.Blas_f77
-
This method obtains the transpose of an n x p matrix.
- matvec_f77(double[][], double[], double[], int, int) - Static method in class com.itemanalysis.psychometrics.uncmin.Blas_f77
-
This method multiplies an n x p matrix by a p x 1 vector.
- max - Variable in class com.itemanalysis.psychometrics.distribution.AbstractDistributionApproximation
-
- max - Variable in class com.itemanalysis.psychometrics.histogram.AbstractBinCalculation
-
Largest observe value.
- max() - Method in class com.itemanalysis.psychometrics.histogram.AbstractBinCalculation
-
Gets the largest observed data point.
- max(double[]) - Static method in class com.itemanalysis.psychometrics.optimization.ArrayMath
-
- max(Collection<Double>) - Static method in class com.itemanalysis.psychometrics.optimization.ArrayMath
-
- max(float[]) - Static method in class com.itemanalysis.psychometrics.optimization.ArrayMath
-
- max(int[]) - Static method in class com.itemanalysis.psychometrics.optimization.ArrayMath
-
- max(int[][]) - Static method in class com.itemanalysis.psychometrics.optimization.ArrayMath
-
Returns the smallest element of the matrix
- max(int, int, int) - Static method in class com.itemanalysis.psychometrics.optimization.SloppyMath
-
max() that works on three integers.
- max(Collection<Integer>) - Static method in class com.itemanalysis.psychometrics.optimization.SloppyMath
-
- max(int, int) - Static method in class com.itemanalysis.psychometrics.optimization.SloppyMath
-
Returns the greater of two int values.
- max(float, float) - Static method in class com.itemanalysis.psychometrics.optimization.SloppyMath
-
Returns the greater of two float values.
- max(double, double) - Static method in class com.itemanalysis.psychometrics.optimization.SloppyMath
-
Returns the greater of two double values.
- maxCategory - Variable in class com.itemanalysis.psychometrics.irt.model.AbstractItemResponseModel
-
- maxCategory - Variable in class com.itemanalysis.psychometrics.irt.model.AbstractItemResponseModelWithGradient
-
- maximumLikelihoodEstimate(double, double, int, double) - Method in class com.itemanalysis.psychometrics.irt.estimation.IrtExaminee
-
Maximum likelihood estimate (MLE) of examinee ability.
- maximumLikelihoodEstimate(double, double) - Method in class com.itemanalysis.psychometrics.irt.estimation.IrtExaminee
-
- MaximumLikelihoodFunction() - Constructor for class com.itemanalysis.psychometrics.polycor.PolychoricMaximumLikelihood.MaximumLikelihoodFunction
-
- MaximumLikelihoodMethod - Class in com.itemanalysis.psychometrics.factoranalysis
-
Exploratory factor analysis by maximum likelihood estimation.
- MaximumLikelihoodMethod(RealMatrix, int, RotationMethod) - Constructor for class com.itemanalysis.psychometrics.factoranalysis.MaximumLikelihoodMethod
-
- maximumPossibleScore() - Method in class com.itemanalysis.psychometrics.measurement.DefaultItemScoring
-
- maximumPossibleScore() - Method in interface com.itemanalysis.psychometrics.measurement.ItemScoring
-
- maxPossibleScore - Variable in class com.itemanalysis.psychometrics.scaling.ScoreBounds
-
- maxSip() - Method in class com.itemanalysis.psychometrics.irt.estimation.ItemResponseSummary
-
Maximum possible raw item score
- maxWeight - Variable in class com.itemanalysis.psychometrics.irt.model.AbstractItemResponseModel
-
- maxWeight - Variable in class com.itemanalysis.psychometrics.irt.model.AbstractItemResponseModelWithGradient
-
- mean() - Method in class com.itemanalysis.psychometrics.measurement.ConditionalNumericItemResponseSummary
-
- mean() - Method in class com.itemanalysis.psychometrics.measurement.ConditionalTextItemResponseSummary
-
- mean() - Method in class com.itemanalysis.psychometrics.measurement.TextItemResponseSummary
-
Computes a weighted sample mean.
- mean(double[]) - Static method in class com.itemanalysis.psychometrics.optimization.ArrayMath
-
- meanDifference() - Method in class com.itemanalysis.psychometrics.cmh.CmhTable
-
Table calculation for standardized p-dif
- MeanMeanMethod - Class in com.itemanalysis.psychometrics.irt.equating
-
- MeanMeanMethod(LinkedHashMap<String, ItemResponseModel>, LinkedHashMap<String, ItemResponseModel>) - Constructor for class com.itemanalysis.psychometrics.irt.equating.MeanMeanMethod
-
- MeanSigmaMethod - Class in com.itemanalysis.psychometrics.irt.equating
-
- MeanSigmaMethod(LinkedHashMap<String, ItemResponseModel>, LinkedHashMap<String, ItemResponseModel>, boolean) - Constructor for class com.itemanalysis.psychometrics.irt.equating.MeanSigmaMethod
-
- meanSquareError() - Method in class com.itemanalysis.psychometrics.irt.estimation.RaschScaleQualityStatistics
-
Mean square error of the estimate.
- meanValue() - Method in class com.itemanalysis.psychometrics.scaling.RawScore
-
- median(double[]) - Static method in class com.itemanalysis.psychometrics.optimization.ArrayMath
-
- min - Variable in class com.itemanalysis.psychometrics.distribution.AbstractDistributionApproximation
-
- min - Variable in class com.itemanalysis.psychometrics.histogram.AbstractBinCalculation
-
Smallest observed value.
- min() - Method in class com.itemanalysis.psychometrics.histogram.AbstractBinCalculation
-
Gets the smallest observed data points.
- min(double[]) - Static method in class com.itemanalysis.psychometrics.optimization.ArrayMath
-
- min(float[]) - Static method in class com.itemanalysis.psychometrics.optimization.ArrayMath
-
- min(int[]) - Static method in class com.itemanalysis.psychometrics.optimization.ArrayMath
-
- min(int[][]) - Static method in class com.itemanalysis.psychometrics.optimization.ArrayMath
-
Returns the smallest element of the matrix
- min(int, int, int) - Static method in class com.itemanalysis.psychometrics.optimization.SloppyMath
-
Returns the minimum of three int values.
- min(float, float) - Static method in class com.itemanalysis.psychometrics.optimization.SloppyMath
-
Returns the smaller of two float values.
- min(double, double) - Static method in class com.itemanalysis.psychometrics.optimization.SloppyMath
-
Returns the smaller of two double values.
- minCategory - Variable in class com.itemanalysis.psychometrics.irt.model.AbstractItemResponseModel
-
- minCategory - Variable in class com.itemanalysis.psychometrics.irt.model.AbstractItemResponseModelWithGradient
-
- minExpectedCellCount - Variable in class com.itemanalysis.psychometrics.irt.estimation.AbstractItemFitStatistic
-
- minimize(DiffFunction, double, double[]) - Method in class com.itemanalysis.psychometrics.optimization.CGMinimizer
-
- minimize(DiffFunction, double, double[], int) - Method in class com.itemanalysis.psychometrics.optimization.CGMinimizer
-
- minimize(T, double, double[]) - Method in interface com.itemanalysis.psychometrics.optimization.Minimizer
-
Attempts to find an unconstrained minimum of the objective
function starting at initial, within
functionTolerance.
- minimize(T, double, double[], int) - Method in interface com.itemanalysis.psychometrics.optimization.Minimizer
-
- minimize(DiffFloatFunction, float, float[]) - Method in class com.itemanalysis.psychometrics.optimization.QNMinimizer
-
- minimize(DiffFunction, double, double[]) - Method in class com.itemanalysis.psychometrics.optimization.QNMinimizer
-
- minimize(DiffFunction, double, double[], int) - Method in class com.itemanalysis.psychometrics.optimization.QNMinimizer
-
- minimize(DiffFunction, double, double[], int, QNMinimizer.QNInfo) - Method in class com.itemanalysis.psychometrics.optimization.QNMinimizer
-
- minimize(Uncmin_methods, double[]) - Method in class com.itemanalysis.psychometrics.uncmin.DefaultUncminOptimizer
-
This wrapper will use the same configuration as Uncmin_f77.optif0_f77.
- minimize(Uncmin_methods, double[], boolean, boolean, int, double) - Method in class com.itemanalysis.psychometrics.uncmin.DefaultUncminOptimizer
-
Wrapper for calling in Uncmin_f77.optif9_f77.
- Minimizer<T extends Function> - Interface in com.itemanalysis.psychometrics.optimization
-
The interface for unconstrained function minimizers.
- MINIMUM_PROBLEM_DIMENSION - Static variable in class com.itemanalysis.psychometrics.optimization.BOBYQAOptimizer
-
Minimum dimension of the problem: 2
- minimumPossibleScore() - Method in class com.itemanalysis.psychometrics.measurement.DefaultItemScoring
-
- minimumPossibleScore() - Method in interface com.itemanalysis.psychometrics.measurement.ItemScoring
-
- minPossibleScore - Variable in class com.itemanalysis.psychometrics.scaling.ScoreBounds
-
- MINRESmethod - Class in com.itemanalysis.psychometrics.factoranalysis
-
Harman's minimum residual (MINRES) method of factor analysis.
- MINRESmethod(RealMatrix, int, RotationMethod) - Constructor for class com.itemanalysis.psychometrics.factoranalysis.MINRESmethod
-
- minSip() - Method in class com.itemanalysis.psychometrics.irt.estimation.ItemResponseSummary
-
Minimum possible raw item score
- minWeight - Variable in class com.itemanalysis.psychometrics.irt.model.AbstractItemResponseModel
-
- minWeight - Variable in class com.itemanalysis.psychometrics.irt.model.AbstractItemResponseModelWithGradient
-
- missingResponseAt(int) - Method in class com.itemanalysis.psychometrics.irt.estimation.IrtExaminee
-
- MixedCorrelationMatrix - Class in com.itemanalysis.psychometrics.polycor
-
- MixedCorrelationMatrix(ArrayList<VariableAttributes>, boolean) - Constructor for class com.itemanalysis.psychometrics.polycor.MixedCorrelationMatrix
-
- MixedCorrelationMatrix.CorrelationType - Enum in com.itemanalysis.psychometrics.polycor
-
- MixtureModel - Interface in com.itemanalysis.psychometrics.mixture
-
- mleStandardErrorAt(double) - Method in class com.itemanalysis.psychometrics.irt.estimation.IrtExaminee
-
- MMLEsimulation - Class in com.itemanalysis.psychometrics.irt.estimation
-
- MMLEsimulation() - Constructor for class com.itemanalysis.psychometrics.irt.estimation.MMLEsimulation
-
- monitorX(double[]) - Method in class com.itemanalysis.psychometrics.optimization.QNMinimizer.Record
-
- mStep() - Method in interface com.itemanalysis.psychometrics.mixture.MixtureModel
-
- mStep() - Method in class com.itemanalysis.psychometrics.mixture.MvNormalMixtureModel
-
- MstepParallel - Class in com.itemanalysis.psychometrics.irt.estimation
-
Mstep of the EM algorithm for estimating item parameters in Item Response Theory.
- MstepParallel(ItemResponseModel[], DistributionApproximation, EstepEstimates, DensityEstimationType, int, int) - Constructor for class com.itemanalysis.psychometrics.irt.estimation.MstepParallel
-
- multipleRandomStarts() - Method in interface com.itemanalysis.psychometrics.mixture.MixtureModel
-
- multipleRandomStarts() - Method in class com.itemanalysis.psychometrics.mixture.MvNormalMixtureModel
-
- multiply(double[], double) - Static method in class com.itemanalysis.psychometrics.optimization.ArrayMath
-
Scales the values in this array by c.
- multiply(float[], float) - Static method in class com.itemanalysis.psychometrics.optimization.ArrayMath
-
Scales the values in this array by c.
- multiplyElements(RealMatrix, RealMatrix) - Static method in class com.itemanalysis.psychometrics.factoranalysis.MatrixUtils
-
Elementwise multiplication of elements in two arrays.
- multiplyElementsBy(RealMatrix, RealMatrix) - Static method in class com.itemanalysis.psychometrics.factoranalysis.MatrixUtils
-
Elementwise multiplication of two matrices.
- multiplyInPlace(double[], double) - Static method in class com.itemanalysis.psychometrics.optimization.ArrayMath
-
Scales the values in this array by b.
- multiplyInPlace(float[], double) - Static method in class com.itemanalysis.psychometrics.optimization.ArrayMath
-
Scales the values in this array by b.
- multiplyInto(double[], double[], double) - Static method in class com.itemanalysis.psychometrics.optimization.ArrayMath
-
- mvmltl_f77(int, double[][], double[], double[]) - Method in class com.itemanalysis.psychometrics.uncmin.Uncmin
-
The mvmltl_f77 method computes y = Lx where L is a lower
triangular matrix stored in A.
- mvmltl_f77(int, double[][], double[], double[]) - Method in class com.itemanalysis.psychometrics.uncmin.Uncmin_f77
-
The mvmltl_f77 method computes y = Lx where L is a lower
triangular matrix stored in A.
- mvmlts_f77(int, double[][], double[], double[]) - Method in class com.itemanalysis.psychometrics.uncmin.Uncmin
-
The mvmlts_f77 method computes y = Ax where A is a symmetric matrix
stored in its lower triangular part.
- mvmlts_f77(int, double[][], double[], double[]) - Method in class com.itemanalysis.psychometrics.uncmin.Uncmin_f77
-
The mvmlts_f77 method computes y = Ax where A is a symmetric matrix
stored in its lower triangular part.
- mvmltu_f77(int, double[][], double[], double[]) - Method in class com.itemanalysis.psychometrics.uncmin.Uncmin
-
The mvmltu_f77 method computes Y = (L transpose)X where L is a
lower triangular matrix stored in A (L transpose
is taken implicitly).
- mvmltu_f77(int, double[][], double[], double[]) - Method in class com.itemanalysis.psychometrics.uncmin.Uncmin_f77
-
The mvmltu_f77 method computes Y = (L transpose)X where L is a
lower triangular matrix stored in A (L transpose
is taken implicitly).
- MvNormalComponentDistribution - Class in com.itemanalysis.psychometrics.mixture
-
- MvNormalComponentDistribution(int) - Constructor for class com.itemanalysis.psychometrics.mixture.MvNormalComponentDistribution
-
- MvNormalMixtureModel - Class in com.itemanalysis.psychometrics.mixture
-
- MvNormalMixtureModel(RealMatrix, int) - Constructor for class com.itemanalysis.psychometrics.mixture.MvNormalMixtureModel
-