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## M

mapDataSet(DataSet[]) - Method in class de.jstacs.classifiers.MappingClassifier
  This method maps the given `DataSet`s to the internal classes.
MappedDiscreteSequence - Class in de.jstacs.data.sequences
  This class allows to map a discrete `Sequence` to an new `Sequence` using some `DiscreteAlphabetMapping`s.
MappedDiscreteSequence(AlphabetContainer, SequenceAnnotation[], DiscreteAlphabetMapping...) - Constructor for class de.jstacs.data.sequences.MappedDiscreteSequence
  This method allows to create an empty `MappedDiscreteSequence`.
MappedDiscreteSequence(SimpleDiscreteSequence, DiscreteAlphabetMapping...) - Constructor for class de.jstacs.data.sequences.MappedDiscreteSequence
  This method allows to create a `MappedDiscreteSequence` from a given `Sequence` and some given `DiscreteAlphabetMapping`s.
MappingClassifier - Class in de.jstacs.classifiers
  This class allows the user to train the classifier on a given number of classes and to evaluate the classifier on a smaller number of classes by mapping classes together.
MappingClassifier(AbstractScoreBasedClassifier, int...) - Constructor for class de.jstacs.classifiers.MappingClassifier
  Creates a new `MappingClassifier` from a given classifier and a class mapping.
MappingClassifier(StringBuffer) - Constructor for class de.jstacs.classifiers.MappingClassifier
  The standard constructor for the interface `Storable`.
MappingDiffSM - Class in de.jstacs.sequenceScores.statisticalModels.differentiable
  This class implements a `DifferentiableStatisticalModel` that works on mapped `Sequence`s.
MappingDiffSM(AlphabetContainer, DifferentiableStatisticalModel, DiscreteAlphabetMapping...) - Constructor for class de.jstacs.sequenceScores.statisticalModels.differentiable.MappingDiffSM
  The main constructor creating a `MappingDiffSM`.
MappingDiffSM(StringBuffer) - Constructor for class de.jstacs.sequenceScores.statisticalModels.differentiable.MappingDiffSM
  This is the constructor for `Storable`.
MarkovModelDiffSM - Class in de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels
  This class implements a `AbstractDifferentiableStatisticalModel` for an inhomogeneous Markov model.
MarkovModelDiffSM(AlphabetContainer, int, double, boolean, int, DurationDiffSM) - Constructor for class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.MarkovModelDiffSM
  This constructor creates an instance with an prior for the modeled length.
MarkovModelDiffSM(AlphabetContainer, int, double, boolean, InhomogeneousMarkov) - Constructor for class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.MarkovModelDiffSM
  This constructor creates an instance without any prior for the modeled length.
MarkovModelDiffSM(AlphabetContainer, int, double, boolean, InhomogeneousMarkov, DurationDiffSM) - Constructor for class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.MarkovModelDiffSM
  This constructor creates an instance with an prior for the modeled length.
MarkovModelDiffSM(StringBuffer) - Constructor for class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.MarkovModelDiffSM
  The standard constructor for the interface `Storable`.
MarkovRandomFieldDiffSM - Class in de.jstacs.sequenceScores.statisticalModels.differentiable
  This class implements the scoring function for any MRF (Markov Random Field).
MarkovRandomFieldDiffSM(AlphabetContainer, int, String) - Constructor for class de.jstacs.sequenceScores.statisticalModels.differentiable.MarkovRandomFieldDiffSM
  This constructor creates an instance of a `MarkovRandomFieldDiffSM` with equivalent sample size (ess) 0.
MarkovRandomFieldDiffSM(AlphabetContainer, int, double, String) - Constructor for class de.jstacs.sequenceScores.statisticalModels.differentiable.MarkovRandomFieldDiffSM
  This is the main constructor that creates an instance of a `MarkovRandomFieldDiffSM`.
MarkovRandomFieldDiffSM(StringBuffer) - Constructor for class de.jstacs.sequenceScores.statisticalModels.differentiable.MarkovRandomFieldDiffSM
  This is the constructor for the interface `Storable`.
matches(int, Sequence) - Method in class de.jstacs.data.sequences.Sequence
  This method allows to answer the question whether there is a similar pattern find a match with a given maximal number of mismatches.
MatrixCosts - Class in de.jstacs.algorithms.alignment.cost
  Class for matrix costs, i.e., the cost of any match/mismatch is stored in a matrix allowing a huge degree of freedom.
MatrixCosts(double[][], double) - Constructor for class de.jstacs.algorithms.alignment.cost.MatrixCosts
  Creates a new instance of `MatrixCosts` where the costs for mismatch and match are given in `matrix`.
max - Variable in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif.DurationDiffSM
  The maximal value.
max(double[], int, int) - Static method in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.AbstractMixtureTrainSM
  This method returns the index of a maximal entry in the array `w` between index `start` and `end`.
max - Variable in class de.jstacs.utils.DefaultProgressUpdater
  The maximal number of steps.
max(double...) - Static method in class de.jstacs.utils.ToolBox
  This method returns the maximum of the elements of an `array`.
max(int, int, double[]) - Static method in class de.jstacs.utils.ToolBox
  This method returns the maximum of the elements of an `array` between `start` and `end`.
MaxHMMTrainingParameterSet - Class in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.training
  This class is the super class for any `HMMTrainingParameterSet` that is used for a maximizing training algorithm of a hidden Markov model.
MaxHMMTrainingParameterSet() - Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.training.MaxHMMTrainingParameterSet
  This is the empty constructor that can be used to fill the parameters after creation.
MaxHMMTrainingParameterSet(int, AbstractTerminationCondition) - Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.training.MaxHMMTrainingParameterSet
  This constructor can be used to create an instance with specified parameters.
MaxHMMTrainingParameterSet(StringBuffer) - Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.training.MaxHMMTrainingParameterSet
  The standard constructor for the interface `Storable`.
MAXIMALBRANCHING - Static variable in class de.jstacs.algorithms.graphs.Chu_Liu_Edmonds
  Compute the branching yielding the maximum sum of weights.
maximalMarkovOrder - Variable in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.BasicHigherOrderTransition
  The maximal Markov order of the transition.
MaximumCorrelationCoefficient - Class in de.jstacs.classifiers.performanceMeasures
  This class implements the maximum of the correlation coefficient .
MaximumCorrelationCoefficient() - Constructor for class de.jstacs.classifiers.performanceMeasures.MaximumCorrelationCoefficient
  Constructs a new instance of the performance measure `MaximumCorrelationCoefficient`.
MaximumCorrelationCoefficient(StringBuffer) - Constructor for class de.jstacs.classifiers.performanceMeasures.MaximumCorrelationCoefficient
  The standard constructor for the interface `Storable`.
MaximumFMeasure - Class in de.jstacs.classifiers.performanceMeasures
  Computes the maximum of the general F-measure given a positive real parameter .
MaximumFMeasure() - Constructor for class de.jstacs.classifiers.performanceMeasures.MaximumFMeasure
  Constructs a new instance of the performance measure `MaximumFMeasure` with empty parameters.
MaximumFMeasure(double) - Constructor for class de.jstacs.classifiers.performanceMeasures.MaximumFMeasure
  Constructs a new instance of the performance measure `MaximumFMeasure` with given `beta`.
MaximumFMeasure(StringBuffer) - Constructor for class de.jstacs.classifiers.performanceMeasures.MaximumFMeasure
  The standard constructor for the interface `Storable`.
MaximumNumericalTwoClassMeasure - Class in de.jstacs.classifiers.performanceMeasures
  This class prepares everything for an easy implementation of a maximum of any numerical performance measure.
MaximumNumericalTwoClassMeasure() - Constructor for class de.jstacs.classifiers.performanceMeasures.MaximumNumericalTwoClassMeasure
  Constructs a new instance of the performance measure `MaximumNumericalTwoClassMeasure`.
MaximumNumericalTwoClassMeasure(StringBuffer) - Constructor for class de.jstacs.classifiers.performanceMeasures.MaximumNumericalTwoClassMeasure
  The standard constructor for the interface `Storable`.
maxInDegree - Variable in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.BasicHigherOrderTransition
  The maximal in-degree of any state.
mean(int, int) - Method in class de.jstacs.utils.DoubleList
  This method computes the mean of a part of the list.
MeanResultSet - Class in de.jstacs.results
  Class that computes the mean and the standard error of a series of `NumericalResultSet`s.
MeanResultSet(SimpleResult...) - Constructor for class de.jstacs.results.MeanResultSet
  Constructs a new `MeanResultSet` with an empty set of `NumericalResultSet`s.
MeanResultSet() - Constructor for class de.jstacs.results.MeanResultSet
  Constructs a new `MeanResultSet` with an empty set of `NumericalResultSet`s and no further information.
MeanResultSet(StringBuffer) - Constructor for class de.jstacs.results.MeanResultSet
  The standard constructor for the interface `Storable`.
MeanResultSet.AdditionImpossibleException - Exception in de.jstacs.results
  Class for the exception that is thrown if two `MeanResultSet`s should be added that do not match.
MeanResultSet.AdditionImpossibleException() - Constructor for exception de.jstacs.results.MeanResultSet.AdditionImpossibleException
  Constructs a new `MeanResultSet.AdditionImpossibleException` with an appropriate error message.
MeanResultSet.InconsistentResultNumberException - Exception in de.jstacs.results
  Class for the exception that is thrown if a `NumericalResultSet` is added to the `MeanResultSet` that has a number of results which is not equal to the number of results of the previously added results.
MeanResultSet.InconsistentResultNumberException() - Constructor for exception de.jstacs.results.MeanResultSet.InconsistentResultNumberException
  Constructs a new `MeanResultSet.InconsistentResultNumberException` with an appropriate error message.
meanValue - Variable in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.continuous.PluginGaussianEmission
  Initial mean value.
Measure - Class in de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.structureLearning.measures
  Class for structure measures that derive an optimal structure with respect to some criterion within a class of possible structures from data.
Measure(StringBuffer) - Constructor for class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.structureLearning.measures.Measure
  Creates a new `Measure` from its XML-representation.
Measure(Measure.MeasureParameterSet) - Constructor for class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.structureLearning.measures.Measure
  Creates a new `Measure` from its `Measure.MeasureParameterSet`.
Measure.MeasureParameterSet - Class in de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.structureLearning.measures
  This class is the super class of any `ParameterSet` that can be used to instantiate a `Measure`.
Measure.MeasureParameterSet(Class<? extends Measure>) - Constructor for class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.structureLearning.measures.Measure.MeasureParameterSet
  Creates a new empty `Measure.MeasureParameterSet` for the given sub-class of `Measure`,
Measure.MeasureParameterSet(StringBuffer) - Constructor for class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.structureLearning.measures.Measure.MeasureParameterSet
  The standard constructor for the interface `Storable`.
median(int, int) - Method in class de.jstacs.utils.DoubleList
  This method computes the median of a part of the list.
median(double...) - Static method in class de.jstacs.utils.ToolBox
  This method returns the median of the elements of `array`.
median(int, int, double[]) - Static method in class de.jstacs.utils.ToolBox
  This method returns the medin of the elements of an `array` between `start` and `end`.
MEMConstraint - Class in de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous
  This constraint can be used for any maximum entropy model (MEM) application.
MEMConstraint(int[], int[]) - Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.MEMConstraint
  Creates a `MEMConstraint` as part of a (whole) model.
MEMConstraint(int[], int[], int[]) - Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.MEMConstraint
  Creates a `MEMConstraint` as part of a model.
MEMConstraint(StringBuffer) - Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.MEMConstraint
  The standard constructor for the interface `Storable`.
merge(Hashtable<Sequence, BitSet[]>, int, boolean) - Static method in class de.jstacs.motifDiscovery.KMereStatistic
  This method allows to merge the statistics of k-mers by allowing mismatches.
min - Variable in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif.DurationDiffSM
  The minimal value.
min(int, int) - Method in class de.jstacs.utils.DoubleList
  This method computes the minimum of a part of the list.
min(double...) - Static method in class de.jstacs.utils.ToolBox
  This method returns the minimum of the elements of an `array`.
min(int, int, double[]) - Static method in class de.jstacs.utils.ToolBox
  This method returns the minimum of the elements of an `array` between `start` and `end`.
MINIMALBRANCHING - Static variable in class de.jstacs.algorithms.graphs.Chu_Liu_Edmonds
  Compute the branching yielding the minimum sum of weights.
MixtureDiffSM - Class in de.jstacs.sequenceScores.statisticalModels.differentiable.mixture
  This class implements a real mixture model.
MixtureDiffSM(int, boolean, DifferentiableStatisticalModel...) - Constructor for class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.MixtureDiffSM
  This constructor creates a new `MixtureDiffSM`.
MixtureDiffSM(StringBuffer) - Constructor for class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.MixtureDiffSM
  This is the constructor for the interface `Storable`.
MixtureDurationDiffSM - Class in de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif
  This class implements a mixture of `DurationDiffSM`s.
MixtureDurationDiffSM(int, DurationDiffSM...) - Constructor for class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif.MixtureDurationDiffSM
  The main constructor of a `MixtureDurationDiffSM`.
MixtureDurationDiffSM(StringBuffer) - Constructor for class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif.MixtureDurationDiffSM
  This is the constructor for `Storable`.
MixtureEmission - Class in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions
  This class implements a mixture of `Emission`s.
MixtureEmission(Emission[], double[]) - Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.MixtureEmission
  The main constructor creating a `MixtureEmission` from a set of emissions.
MixtureEmission(StringBuffer) - Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.MixtureEmission
  The standard constructor for the interface `Storable`.
MixtureTrainSM - Class in de.jstacs.sequenceScores.statisticalModels.trainable.mixture
  The class for a mixture model of any `TrainableStatisticalModel`s.
MixtureTrainSM(int, TrainableStatisticalModel[], int, boolean, double[], double[], AbstractMixtureTrainSM.Algorithm, double, TerminationCondition, AbstractMixtureTrainSM.Parameterization, int, int, BurnInTest) - Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.MixtureTrainSM
  Creates a new `MixtureTrainSM`.
MixtureTrainSM(int, TrainableStatisticalModel[], int, double[], double, TerminationCondition, AbstractMixtureTrainSM.Parameterization) - Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.MixtureTrainSM
  Creates an instance using EM and estimating the component probabilities.
MixtureTrainSM(int, TrainableStatisticalModel[], double[], int, double, TerminationCondition, AbstractMixtureTrainSM.Parameterization) - Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.MixtureTrainSM
  Creates an instance using EM and fixed component probabilities.
MixtureTrainSM(int, TrainableStatisticalModel[], int, double[], int, int, BurnInTest) - Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.MixtureTrainSM
  Creates an instance using Gibbs Sampling and sampling the component probabilities.
MixtureTrainSM(int, TrainableStatisticalModel[], double[], int, int, int, BurnInTest) - Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.MixtureTrainSM
  Creates an instance using Gibbs Sampling and fixed component probabilities.
MixtureTrainSM(StringBuffer) - Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.MixtureTrainSM
  The constructor for the interface `Storable`.
model - Variable in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.AbstractMixtureTrainSM
  The model for the sequences.
models - Variable in class de.jstacs.classifiers.trainSMBased.TrainSMBasedClassifier
  The internal `TrainableStatisticalModel`s.
models - Variable in class de.jstacs.sequenceScores.statisticalModels.trainable.CompositeTrainSM
  The models for the components
modify(int, int) - Method in interface de.jstacs.motifDiscovery.Mutable
  Manually modifies the model.
modify(int, int) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.MarkovModelDiffSM
modify(int, int) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.MappingDiffSM
modify(int) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif.DurationDiffSM
  This method modifies the underlying `AlphabetContainer`.
modify(int) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif.MixtureDurationDiffSM
modify(int) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif.SkewNormalLikeDurationDiffSM
modify(int, int) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.StrandDiffSM
modify(int, int) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.NormalizedDiffSM
modify(double[], double[], int, int) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.motif.ZOOPSTrainSM
  This method modifies the computed weights for one sequence and returns the score.
modifyFunctionValue(double) - Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.sampling.SamplingGenDisMixClassifier
modifyFunctionValue(double) - Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.sampling.SamplingScoreBasedClassifier
  Allows for a modification of the value returned by the function obtained by `SamplingScoreBasedClassifier.getFunction(DataSet[], double[][])`.
modifyMotif(int, int, int) - Method in interface de.jstacs.motifDiscovery.MutableMotifDiscoverer
  Manually modifies the motif model with index `motifIndex`.
modifyMotif(int, int, int) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.IndependentProductDiffSM
modifyMotif(int, int, int) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.MappingDiffSM
modifyMotif(int, int, int) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.MixtureDiffSM
modifyMotif(int, int, int) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif.ExtendedZOOPSDiffSM
modifyWeights(double[]) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.AbstractMixtureTrainSM
  This method modifies the computed weights for one sequence and returns the score.
MotifAnnotation - Class in de.jstacs.data.sequences.annotation
  Class for a `StrandedLocatedSequenceAnnotationWithLength` that is a motif.
MotifAnnotation(String, int, int, StrandedLocatedSequenceAnnotationWithLength.Strand, Result...) - Constructor for class de.jstacs.data.sequences.annotation.MotifAnnotation
  Creates a new `MotifAnnotation` of type `type` with identifier `identifier` and additional annotation (that does not fit the `SequenceAnnotation` definitions) given as an array of `Result`s `additionalAnnotation`.
MotifAnnotation(StringBuffer) - Constructor for class de.jstacs.data.sequences.annotation.MotifAnnotation
  The standard constructor for the interface `Storable`.
MotifAnnotationParser - Class in de.jstacs.data.sequences.annotation
  Annotation parser for `MotifAnnotation`s.
MotifAnnotationParser() - Constructor for class de.jstacs.data.sequences.annotation.MotifAnnotationParser
  Creates a new `MotifAnnotationParser` with default delimiters.
MotifAnnotationParser(String, String) - Constructor for class de.jstacs.data.sequences.annotation.MotifAnnotationParser
  Creates a new `MotifAnnotationParser` with the supplied delimiters
MotifDiscoverer - Interface in de.jstacs.motifDiscovery
  This is the interface that any tool for de-novo motif discovery should implement.
MotifDiscoverer.KindOfProfile - Enum in de.jstacs.motifDiscovery
  This `enum` can be used to determine which kind of profile should be returned.
MotifDiscovererToolBox - Class in de.jstacs.motifDiscovery
  This class contains static methods for the `MotifDiscoverer`.
MotifDiscovererToolBox() - Constructor for class de.jstacs.motifDiscovery.MotifDiscovererToolBox
MotifDiscoveryAssessment - Class in de.jstacs.motifDiscovery
  This class enables the user to assess the prediction of motif occurrences
MotifDiscoveryAssessment() - Constructor for class de.jstacs.motifDiscovery.MotifDiscoveryAssessment
motifLength - Variable in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.motif.positionprior.PositionPrior
  The length of the motif.
MRGParams - Class in de.jstacs.utils.random
  The super container for parameter of multivariate random generators.
MRGParams() - Constructor for class de.jstacs.utils.random.MRGParams
MSPClassifier - Class in de.jstacs.classifiers.differentiableSequenceScoreBased.msp
  This class implements a classifier that allows the training via MCL or MSP principle.
MSPClassifier(GenDisMixClassifierParameterSet, DifferentiableSequenceScore...) - Constructor for class de.jstacs.classifiers.differentiableSequenceScoreBased.msp.MSPClassifier
  This convenience constructor creates an `MSPClassifier` that used MCL principle for training.
MSPClassifier(GenDisMixClassifierParameterSet, LogPrior, DifferentiableSequenceScore...) - Constructor for class de.jstacs.classifiers.differentiableSequenceScoreBased.msp.MSPClassifier
  The default constructor that creates a new `MSPClassifier` from a given parameter set, a prior and `DifferentiableSequenceScore`s for the classes.
MSPClassifier(GenDisMixClassifierParameterSet, LogPrior, double, DifferentiableSequenceScore...) - Constructor for class de.jstacs.classifiers.differentiableSequenceScoreBased.msp.MSPClassifier
  This constructor that creates a new `MSPClassifier` from a given parameter set, a prior and `DifferentiableSequenceScore`s for the classes.
MSPClassifier(StringBuffer) - Constructor for class de.jstacs.classifiers.differentiableSequenceScoreBased.msp.MSPClassifier
  This is the constructor for `Storable`.
MST - Class in de.jstacs.algorithms.graphs
  This class enables you to compute the maximal spanning forest for an undirected, weighted graph.
MST() - Constructor for class de.jstacs.algorithms.graphs.MST
MultiDimensionalArbitrarySequence - Class in de.jstacs.data.sequences
  This class is for multidimensional arbitrary sequences.
MultiDimensionalArbitrarySequence(SequenceAnnotation[], ArbitrarySequence...) - Constructor for class de.jstacs.data.sequences.MultiDimensionalArbitrarySequence
  This constructor creates an `MultiDimensionalDiscreteSequence` from a set of individual `Sequence`s.
MultiDimensionalDiscreteSequence - Class in de.jstacs.data.sequences
  This class is for multidimensional discrete sequences that can be used, for instance, for phylogenetic footprinting.
MultiDimensionalDiscreteSequence(SequenceAnnotation[], SimpleDiscreteSequence...) - Constructor for class de.jstacs.data.sequences.MultiDimensionalDiscreteSequence
  This constructor creates an `MultiDimensionalDiscreteSequence` from a set of individual `Sequence`s.
MultiDimensionalSequence<T> - Class in de.jstacs.data.sequences
  This class is for multidimensional sequences that can be used, for instance, for phylogenetic footprinting.
MultiDimensionalSequence(SequenceAnnotation[], Sequence...) - Constructor for class de.jstacs.data.sequences.MultiDimensionalSequence
  This constructor creates an `MultiDimensionalSequence` from a set of individual `Sequence`s.
MultiDimensionalSequenceWrapperDiffSM - Class in de.jstacs.sequenceScores.statisticalModels.differentiable
  This class implements a simple wrapper for multidimensional sequences.
MultiDimensionalSequenceWrapperDiffSM(DifferentiableSequenceScore) - Constructor for class de.jstacs.sequenceScores.statisticalModels.differentiable.MultiDimensionalSequenceWrapperDiffSM
  The main constructor.
MultiDimensionalSequenceWrapperDiffSM(StringBuffer) - Constructor for class de.jstacs.sequenceScores.statisticalModels.differentiable.MultiDimensionalSequenceWrapperDiffSM
  This is the constructor for `Storable`.
multiply(int, int, double) - Method in class de.jstacs.utils.DoubleList
  Multiplies all values in the list from index `start` to `end` with the value `factor`.
multiplyExpLambdaWith(int, double) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.MEMConstraint
  Multiplies the exponential value of with the factor `val`: .
MultiSelectionParameter - Class in de.jstacs.parameters
  Class for a `Parameter` that provides a collection of possible values.
MultiSelectionParameter(DataType, String[], Object[], String, String, boolean) - Constructor for class de.jstacs.parameters.MultiSelectionParameter
  Constructor for a `MultiSelectionParameter`.
MultiSelectionParameter(DataType, String[], Object[], String[], String, String, boolean) - Constructor for class de.jstacs.parameters.MultiSelectionParameter
  Constructor for a `MultiSelectionParameter`.
MultiSelectionParameter(String, String, boolean, ParameterSet...) - Constructor for class de.jstacs.parameters.MultiSelectionParameter
  Creates a new `MultiSelectionParameter` from an array of `ParameterSet`s.
MultiSelectionParameter(String, String, boolean, Class<? extends ParameterSet>...) - Constructor for class de.jstacs.parameters.MultiSelectionParameter
  Creates a new `MultiSelectionParameter` from an array of `Class`es of `ParameterSet`s.
MultiSelectionParameter(StringBuffer) - Constructor for class de.jstacs.parameters.MultiSelectionParameter
  The standard constructor for the interface `Storable`.
MultiThreadedTrainingParameterSet - Class in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.training
  This class is the super class for any `MaxHMMTrainingParameterSet` that is used for a multi-threaded maximizing training algorithm of a hidden Markov model.
MultiThreadedTrainingParameterSet() - Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.training.MultiThreadedTrainingParameterSet
  This is the empty constructor that can be used to fill the parameters after creation.
MultiThreadedTrainingParameterSet(int, AbstractTerminationCondition, int) - Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.training.MultiThreadedTrainingParameterSet
  This constructor can be used to create an instance with specified parameters.
MultiThreadedTrainingParameterSet(StringBuffer) - Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.training.MultiThreadedTrainingParameterSet
  The standard constructor for the interface `Storable`.
MultivariateRandomGenerator - Class in de.jstacs.utils.random
  This class is the abstract super class for any multivariate random generator (MRG).
MultivariateRandomGenerator() - Constructor for class de.jstacs.utils.random.MultivariateRandomGenerator
Mutable - Interface in de.jstacs.motifDiscovery
  This interface allows to modify a motif model.
MutableMotifDiscoverer - Interface in de.jstacs.motifDiscovery
  This is the interface that any tool for de-novo motif discovery should implement that allows any modify-operations like shift, shrink and expand.
MutableMotifDiscovererToolbox - Class in de.jstacs.motifDiscovery
  This class contains some important methods for the initiation and optimization of `MutableMotifDiscoverer`.
MutableMotifDiscovererToolbox() - Constructor for class de.jstacs.motifDiscovery.MutableMotifDiscovererToolbox
MutableMotifDiscovererToolbox.InitMethodForDiffSM - Enum in de.jstacs.motifDiscovery
  This enum defines some constants for the method `MutableMotifDiscovererToolbox.getSortedInitialParameters(DifferentiableSequenceScore[], InitMethodForDiffSM[], DiffSSBasedOptimizableFunction, int, OutputStream, int)`.
myAbstractClassifier - Variable in class de.jstacs.classifiers.assessment.ClassifierAssessment
  This array contains the internal used classifiers.
myModel - Variable in class de.jstacs.classifiers.assessment.ClassifierAssessment
  This array contains for each class the internal used models.
myTempMeanResultSets - Variable in class de.jstacs.classifiers.assessment.ClassifierAssessment
  The temporary result set.

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