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## C

canBeCastedFromTo(DataType, DataType) - Static method in enum de.jstacs.DataType
  Checks if the `DataType` `from` can be casted to the `DataType` `to` without losing information.
CappedHistory - Class in de.jstacs.motifDiscovery.history
  This class combines a threshold on the number of steps which can be performed with any other `History`.
CappedHistory(int, History) - Constructor for class de.jstacs.motifDiscovery.history.CappedHistory
  This constructor creates an instance that allows at most `t` steps using the history `h`.
cast(Object[]) - Static method in class de.jstacs.io.ArrayHandler
  This method creates a new array of the superclass of all elements of the given array and copies the elements.
cast(Class<? extends S>, Object[]) - Static method in class de.jstacs.io.ArrayHandler
  This method returns an array of a user-specified class with all elements in the given array `o`.
CategoricalResult - Class in de.jstacs.results
  A class for categorical results (i.e. non-numerical results) for primitives and `String`s.
CategoricalResult(StringBuffer) - Constructor for class de.jstacs.results.CategoricalResult
  The standard constructor for the interface `Storable`.
CategoricalResult(DataType, String, String, Comparable) - Constructor for class de.jstacs.results.CategoricalResult
  Creates a result of a primitive categorical data type or a `String` .
CategoricalResult(String, String, String) - Constructor for class de.jstacs.results.CategoricalResult
  Creates a result of a `String`.
CategoricalResult(String, String, boolean) - Constructor for class de.jstacs.results.CategoricalResult
  Creates a result of a `boolean`.
CDFOfNormal - Class in de.jstacs.scoringFunctions.mix.motifSearch
  This class enables to compute the
  The code is a simplified version of pnorm.c from R (version 2.8.0 downloaded at 07.12.2008 about 19:30).
CDFOfNormal() - Constructor for class de.jstacs.scoringFunctions.mix.motifSearch.CDFOfNormal
check(Sample) - Method in class de.jstacs.classifier.AbstractScoreBasedClassifier
  This method checks if the given `Sample` can be used.
check(Sequence) - Method in class de.jstacs.classifier.AbstractScoreBasedClassifier
  This method checks if the given `Sequence` can be used.
check(Sequence, int, int) - Method in class de.jstacs.models.discrete.DiscreteGraphicalModel
  Checks some conditions on a `Sequence`.
check(Sequence, int, int) - Method in class de.jstacs.models.discrete.homogeneous.HomogeneousModel
  Checks some constraints, these are in general conditions on the `AlphabetContainer` of a (sub)`Sequence` between `startpos` und `endpos`.
check(Sequence, int, int) - Method in class de.jstacs.models.discrete.inhomogeneous.InhomogeneousDGM
check(Object) - Method in interface de.jstacs.parameters.validation.Constraint
  Checks `value` for the constraint defined in the `Constraint`.
check(Object) - Method in class de.jstacs.parameters.validation.ReferenceConstraint
check(Object) - Method in class de.jstacs.parameters.validation.SimpleReferenceConstraint
check(Object) - Method in class de.jstacs.parameters.validation.SimpleStaticConstraint
checkAcyclic(int, int[][]) - Static method in class de.jstacs.models.discrete.inhomogeneous.DAGModel
  This method checks whether a given graph is acyclic.
checkConsistency(Alphabet) - Method in class de.jstacs.data.Alphabet
  Checks if this `Alphabet` is consistent consistent with another `Alphabet`, i.e. both `Alphabet`s use the same symbols.
checkConsistency(AlphabetContainer) - Method in class de.jstacs.data.AlphabetContainer
  Checks if this `AlphabetContainer` is consistent consistent with another `AlphabetContainer`.
checkDatatype(DataType, Object) - Static method in class de.jstacs.results.Result
  This method provides the possibility to check the compliance of some result `value` with one of the pre-defined `DataType`s before creating a new `Result` and possibly running into an `Exception`.
checkLength(int, int) - Method in class de.jstacs.models.mixture.AbstractMixtureModel
  This method checks if the length `l` of the model with index `index` is capable for the current instance.
checkLength(int, int) - Method in class de.jstacs.models.mixture.motif.HiddenMotifMixture
checkModelsForGibbsSampling() - Method in class de.jstacs.models.mixture.AbstractMixtureModel
  This method can be used to check whether the necessary models have implemented the `GibbsSamplingComponent`.
checkValue(Object) - Method in class de.jstacs.parameters.CollectionParameter
  Returns `true` if the key specified by `value` is in the set of keys of this `CollectionParameter`.
checkValue(Object) - Method in class de.jstacs.parameters.FileParameter
checkValue(Object) - Method in class de.jstacs.parameters.MultiSelectionCollectionParameter
checkValue(Object) - Method in class de.jstacs.parameters.Parameter
  Checks the value for correctness, e.g. for numerical parameters this might be checking if the value is within specified bounds.
checkValue(Object) - Method in class de.jstacs.parameters.ParameterSetContainer
checkValue(Object) - Method in class de.jstacs.parameters.RangeParameter
checkValue(Object) - Method in class de.jstacs.parameters.SimpleParameter
checkValue(Object) - Method in class de.jstacs.parameters.validation.ConstraintValidator
checkValue(Object) - Method in class de.jstacs.parameters.validation.NumberValidator
checkValue(Object) - Method in interface de.jstacs.parameters.validation.ParameterValidator
  Returns `true` if the value is valid and `false` otherwise.
checkValue(Object) - Method in class de.jstacs.parameters.validation.StorableValidator
  Checks the value of `value`.
checkWeights(double[]) - Static method in enum de.jstacs.classifier.scoringFunctionBased.gendismix.LearningPrinciple
  This method checks the values of the `weights` array.
chooseFromDistr(Constraint, int, int, double) - Method in class de.jstacs.models.discrete.homogeneous.HomogeneousModel
  Chooses a value in `[0,end-start]` according to the distribution encoded in the frequencies of `distr` between the indices `start` and `end`.
Chu_Liu_Edmonds - Class in de.jstacs.algorithms.graphs
  This class implements the algorithm of Chu_Liu_Edmonds to determine an optimal branching (optimal directed graph of order 1).
CisRegulatoryModuleAnnotation - Class in de.jstacs.data.sequences.annotation
  Annotation for a cis-regulatory module as defined by a set of `MotifAnnotation`s of the motifs in the module.
CisRegulatoryModuleAnnotation(String, MotifAnnotation[], Result...) - Constructor for class de.jstacs.data.sequences.annotation.CisRegulatoryModuleAnnotation
  Creates a new `CisRegulatoryModuleAnnotation` from a set of motifs and possibly additional annotations.
CisRegulatoryModuleAnnotation(StringBuffer) - Constructor for class de.jstacs.data.sequences.annotation.CisRegulatoryModuleAnnotation
  The standard constructor for the interface `Storable`.
cl - Variable in class de.jstacs.classifier.scoringFunctionBased.AbstractOptimizableFunction
  The number of different classes.
ClassDimensionException - Exception in de.jstacs.classifier
  This class indicates that a classifier is defined for less than 2 classes or is defined over a different number of classes than given (e.g. by an array of `Sample`s).
ClassDimensionException() - Constructor for exception de.jstacs.classifier.ClassDimensionException
  This constructor creates a `ClassDimensionException` with the default error message ("The number of classes in the classfier differs from the given number.
ClassDimensionException(String) - Constructor for exception de.jstacs.classifier.ClassDimensionException
  This constructor creates a `ClassDimensionException` with given error message.
ClassificationVisualizer - Class in de.jstacs.classifier.utils
  This class enables you to visualize some classifier results.
ClassifierAssessment - Class in de.jstacs.classifier.assessment
  Class defining an assessment of classifiers.
ClassifierAssessment(AbstractClassifier[], Model[][], boolean, boolean) - Constructor for class de.jstacs.classifier.assessment.ClassifierAssessment
  Creates a new `ClassifierAssessment` from an array of `AbstractClassifier`s and a two-dimensional array of `Model` s, which are combined to additional classifiers.
ClassifierAssessment(AbstractClassifier...) - Constructor for class de.jstacs.classifier.assessment.ClassifierAssessment
  Creates a new `ClassifierAssessment` from a set of `AbstractClassifier`s.
ClassifierAssessment(boolean, Model[]...) - Constructor for class de.jstacs.classifier.assessment.ClassifierAssessment
  Creates a new `ClassifierAssessment` from a set of `Model`s.
ClassifierAssessment(AbstractClassifier[], boolean, Model[]...) - Constructor for class de.jstacs.classifier.assessment.ClassifierAssessment
  This constructor allows to assess a collection of given `AbstractClassifier`s and, in addition, classifiers that will be constructed using the given `AbstractModel`s.
ClassifierAssessmentAssessParameterSet - Class in de.jstacs.classifier.assessment
  This class is the superclass used by all `ClassifierAssessmentAssessParameterSet`s.
ClassifierAssessmentAssessParameterSet() - Constructor for class de.jstacs.classifier.assessment.ClassifierAssessmentAssessParameterSet
  Constructs a new `ClassifierAssessmentAssessParameterSet` with empty parameter values.
ClassifierAssessmentAssessParameterSet(StringBuffer) - Constructor for class de.jstacs.classifier.assessment.ClassifierAssessmentAssessParameterSet
  The standard constructor for the interface `Storable`.
ClassifierAssessmentAssessParameterSet(int, boolean) - Constructor for class de.jstacs.classifier.assessment.ClassifierAssessmentAssessParameterSet
  Constructs a new `ClassifierAssessmentAssessParameterSet` with given parameter values.
classify(Sequence) - Method in class de.jstacs.classifier.AbstractClassifier
  This method classifies a sequence and returns the index `i` of the class to which the sequence is assigned with `0 < i < getNumberOfClasses()`.
classify(Sample) - Method in class de.jstacs.classifier.AbstractClassifier
  This method classifies all sequences of a sample and returns an array of indices of the classes to which the respective sequences are assigned with for each index `i` in the array `0 < i < getNumberOfClasses()`.
classify(Sequence) - Method in class de.jstacs.classifier.AbstractScoreBasedClassifier
classify(Sequence, boolean) - Method in class de.jstacs.classifier.AbstractScoreBasedClassifier
  This method classifies a `Sequence`.
classify(Sample) - Method in class de.jstacs.classifier.modelBased.ModelBasedClassifier
classMus - Variable in class de.jstacs.classifier.scoringFunctionBased.logPrior.SeparateLogPrior
  The means for the class parameters, as specified by the user.
classVars - Variable in class de.jstacs.classifier.scoringFunctionBased.logPrior.SeparateLogPrior
  The variances for the class parameters, as specified by the user.
clazz - Variable in class de.jstacs.classifier.scoringFunctionBased.AbstractOptimizableFunction
  The class parameters.
clear() - Method in class de.jstacs.motifDiscovery.history.CappedHistory
clear() - Method in interface de.jstacs.motifDiscovery.history.History
  This method clears the history, i.e. it removes all operations from the history.
clear() - Method in class de.jstacs.motifDiscovery.history.NoRevertHistory
clear() - Method in class de.jstacs.motifDiscovery.history.RestrictedRepeatHistory
clear() - Method in class de.jstacs.motifDiscovery.history.SimpleHistory
clear() - Method in class de.jstacs.utils.DoubleList
  Removes all elements from the list.
clear() - Method in class de.jstacs.utils.IntList
  Removes all elements from the list.
clearAnnotation() - Method in class de.jstacs.data.sequences.annotation.NullSequenceAnnotationParser
clearAnnotation() - Method in interface de.jstacs.data.sequences.annotation.SequenceAnnotationParser
  This method reset the current `SequenceAnnotation`.
clearAnnotation() - Method in class de.jstacs.data.sequences.annotation.SimpleSequenceAnnotationParser
clearAnnotation() - Method in class de.jstacs.data.sequences.annotation.SplitSequenceAnnotationParser
clearHistoryArray(History[][]) - Static method in class de.jstacs.motifDiscovery.MutableMotifDiscovererToolbox
  This method clears all elements of an History-array, so that it can be used again.
clone() - Method in class de.jstacs.algorithms.graphs.Edge
clone() - Method in class de.jstacs.classifier.AbstractClassifier
clone() - Method in class de.jstacs.classifier.AbstractScoreBasedClassifier
clone() - Method in class de.jstacs.classifier.modelBased.ModelBasedClassifier
clone() - Method in class de.jstacs.classifier.scoringFunctionBased.gendismix.GenDisMixClassifier
clone() - Method in class de.jstacs.classifier.scoringFunctionBased.ScoreClassifier
clone() - Method in class de.jstacs.data.Alphabet.AlphabetParameterSet
clone() - Method in class de.jstacs.data.AlphabetContainerParameterSet.AlphabetArrayParameterSet
clone() - Method in class de.jstacs.data.AlphabetContainerParameterSet
clone() - Method in class de.jstacs.data.AlphabetContainerParameterSet.SectionDefinedAlphabetParameterSet
clone(T...) - Static method in class de.jstacs.io.ArrayHandler
  This method returns a deep copy of a (multi-dimensional) array of `Cloneable`s or primitives.
clone() - Method in class de.jstacs.models.AbstractModel
  Follows the conventions of `Object`'s `clone()`-method.
clone() - Method in class de.jstacs.models.CompositeModel
clone() - Method in class de.jstacs.models.discrete.Constraint
clone() - Method in class de.jstacs.models.discrete.DGMParameterSet
clone() - Method in class de.jstacs.models.discrete.DiscreteGraphicalModel
clone() - Method in class de.jstacs.models.discrete.homogeneous.HomogeneousMM
clone() - Method in class de.jstacs.models.discrete.inhomogeneous.BayesianNetworkModel
clone() - Method in class de.jstacs.models.discrete.inhomogeneous.DAGModel
clone() - Method in class de.jstacs.models.discrete.inhomogeneous.InhCondProb
clone() - Method in class de.jstacs.models.discrete.inhomogeneous.InhConstraint
clone() - Method in class de.jstacs.models.discrete.inhomogeneous.InhomogeneousDGM
clone() - Method in class de.jstacs.models.discrete.inhomogeneous.MEMConstraint
clone() - Method in class de.jstacs.models.discrete.inhomogeneous.SequenceIterator
clone() - Method in class de.jstacs.models.discrete.inhomogeneous.shared.SharedStructureClassifier
clone() - Method in class de.jstacs.models.discrete.inhomogeneous.shared.SharedStructureMixture
clone() - Method in class de.jstacs.models.mixture.AbstractMixtureModel
clone() - Method in class de.jstacs.models.mixture.gibbssampling.BurnInTest
clone() - Method in class de.jstacs.models.mixture.gibbssampling.FSDAGModelForGibbsSampling
  In this method the `reader` is set to `null` and the `paramsFile` is cloned.
clone() - Method in class de.jstacs.models.mixture.motif.HiddenMotifMixture
clone() - Method in class de.jstacs.models.mixture.motif.positionprior.PositionPrior
clone() - Method in interface de.jstacs.models.Model
  Creates a clone (deep copy) of the current `Model` instance.
clone() - Method in class de.jstacs.models.NormalizableScoringFunctionModel
clone() - Method in class de.jstacs.models.UniformModel
clone() - Method in class de.jstacs.models.VariableLengthWrapperModel
clone() - Method in class de.jstacs.motifDiscovery.history.CappedHistory
clone() - Method in interface de.jstacs.motifDiscovery.history.History
  This method returns a deep copy of the instance
clone() - Method in class de.jstacs.motifDiscovery.history.NoRevertHistory
clone() - Method in class de.jstacs.motifDiscovery.history.RestrictedRepeatHistory
clone() - Method in class de.jstacs.motifDiscovery.history.SimpleHistory
clone() - Method in class de.jstacs.parameters.CollectionParameter
clone() - Method in class de.jstacs.parameters.ExpandableParameterSet
clone() - Method in class de.jstacs.parameters.FileParameter
clone() - Method in class de.jstacs.parameters.FileParameter.FileRepresentation
clone() - Method in class de.jstacs.parameters.MultiSelectionCollectionParameter
clone() - Method in class de.jstacs.parameters.Parameter
clone() - Method in class de.jstacs.parameters.ParameterSet
  Creates a full clone (deep copy) of this `ParameterSet`.
clone() - Method in class de.jstacs.parameters.ParameterSetContainer
clone() - Method in class de.jstacs.parameters.RangeParameter
clone() - Method in class de.jstacs.parameters.SequenceScoringParameterSet
clone() - Method in class de.jstacs.parameters.SimpleParameter
clone() - Method in class de.jstacs.parameters.SimpleParameterSet
clone() - Method in interface de.jstacs.parameters.validation.Constraint
  This method returns a deep copy of the current instance.
clone() - Method in class de.jstacs.parameters.validation.ConstraintValidator
clone() - Method in class de.jstacs.parameters.validation.NumberValidator
clone() - Method in interface de.jstacs.parameters.validation.ParameterValidator
  This method returns a deep copy of the current instance.
clone() - Method in class de.jstacs.parameters.validation.ReferenceConstraint
clone() - Method in class de.jstacs.parameters.validation.SimpleReferenceConstraint
clone() - Method in class de.jstacs.parameters.validation.SimpleStaticConstraint
clone() - Method in class de.jstacs.parameters.validation.StorableValidator
clone() - Method in class de.jstacs.scoringFunctions.AbstractNormalizableScoringFunction
clone() - Method in class de.jstacs.scoringFunctions.CMMScoringFunction
clone() - Method in class de.jstacs.scoringFunctions.directedGraphicalModels.BayesianNetworkScoringFunction
clone() - Method in class de.jstacs.scoringFunctions.directedGraphicalModels.Parameter
clone() - Method in class de.jstacs.scoringFunctions.directedGraphicalModels.ParameterTree
clone() - Method in class de.jstacs.scoringFunctions.directedGraphicalModels.structureLearning.measures.btMeasures.BTExplainingAwayResidual
clone() - Method in class de.jstacs.scoringFunctions.directedGraphicalModels.structureLearning.measures.btMeasures.BTMutualInformation
clone() - Method in class de.jstacs.scoringFunctions.directedGraphicalModels.structureLearning.measures.InhomogeneousMarkov
clone() - Method in class de.jstacs.scoringFunctions.directedGraphicalModels.structureLearning.measures.Measure
clone() - Method in class de.jstacs.scoringFunctions.directedGraphicalModels.structureLearning.measures.pmmMeasures.PMMExplainingAwayResidual
clone() - Method in class de.jstacs.scoringFunctions.directedGraphicalModels.structureLearning.measures.pmmMeasures.PMMMutualInformation
clone() - Method in class de.jstacs.scoringFunctions.homogeneous.HMM0ScoringFunction
clone() - Method in class de.jstacs.scoringFunctions.homogeneous.HMMScoringFunction
clone() - Method in class de.jstacs.scoringFunctions.IndependentProductScoringFunction
clone() - Method in class de.jstacs.scoringFunctions.mix.AbstractMixtureScoringFunction
clone() - Method in class de.jstacs.scoringFunctions.mix.MixtureScoringFunction
clone() - Method in class de.jstacs.scoringFunctions.mix.motifSearch.HiddenMotifsMixture
clone() - Method in class de.jstacs.scoringFunctions.mix.motifSearch.MixtureDuration
clone() - Method in class de.jstacs.scoringFunctions.mix.motifSearch.PositionScoringFunction
clone() - Method in class de.jstacs.scoringFunctions.mix.motifSearch.SkewNormalLikeScoringFunction
clone() - Method in class de.jstacs.scoringFunctions.MRFScoringFunction
clone() - Method in class de.jstacs.scoringFunctions.NormalizedScoringFunction
clone() - Method in interface de.jstacs.scoringFunctions.ScoringFunction
  Creates a clone (deep copy) of the current `ScoringFunction` instance.
cloneFunctions(NormalizableScoringFunction[]) - Method in class de.jstacs.scoringFunctions.mix.AbstractMixtureScoringFunction
  This method clones the given array of functions and enables the user to do some post-processing.
cloneHomProb(HomogeneousModel.HomCondProb[]) - Method in class de.jstacs.models.discrete.homogeneous.HomogeneousModel
  Clones the given array of conditional probabilities.
close() - Method in class de.jstacs.utils.NullProgressUpdater
  Closes the supervision.
close() - Method in class de.jstacs.utils.REnvironment
  Closes the `REnvironment` and removes all files from the server.
close() - Method in class de.jstacs.utils.SafeOutputStream
  Closes the `SafeOutputStream` by closing the `OutputStream` this stream was constructed of.
CMMScoringFunction - Class in de.jstacs.scoringFunctions
  This scoring function implements a cyclic Markov model of arbitrary order and periodicity for any sequence length.
CMMScoringFunction(AlphabetContainer, int, int, double, double[], boolean, boolean, int, int) - Constructor for class de.jstacs.scoringFunctions.CMMScoringFunction
  The main constructor.
CMMScoringFunction(StringBuffer) - Constructor for class de.jstacs.scoringFunctions.CMMScoringFunction
  This is the constructor for `Storable`.
CollectionParameter - Class in de.jstacs.parameters
  Class for a collection parameter, i.e. a parameter that provides some collection of possible values the user can choose from.
CollectionParameter(ParameterSet, int, int, boolean, String, String, boolean, DataType, String, boolean) - Constructor for class de.jstacs.parameters.CollectionParameter
  Creates a new `CollectionParameter` from the necessary field.
CollectionParameter(DataType, String[], Object[], String, String, boolean) - Constructor for class de.jstacs.parameters.CollectionParameter
  Constructor for a `CollectionParameter`.
CollectionParameter(DataType, String[], Object[], String[], String, String, boolean) - Constructor for class de.jstacs.parameters.CollectionParameter
  Constructor for a `CollectionParameter`.
CollectionParameter(ParameterSet[], String[], String[], String, String, boolean) - Constructor for class de.jstacs.parameters.CollectionParameter
  Constructor for a `CollectionParameter` from an array of `ParameterSet`s.
CollectionParameter(InstanceParameterSet[], String, String, boolean) - Constructor for class de.jstacs.parameters.CollectionParameter
  Constructor for a `CollectionParameter` from an array of `ParameterSet`s.
CollectionParameter(StringBuffer) - Constructor for class de.jstacs.parameters.CollectionParameter
  The standard constructor for the interface `Storable`.
CollectionParameter.InconsistentCollectionException - Exception in de.jstacs.parameters
  This exception is thrown if the `CollectionParameter` is inconsistent for some reason.
CollectionParameter.InconsistentCollectionException(String) - Constructor for exception de.jstacs.parameters.CollectionParameter.InconsistentCollectionException
  Constructs a new `CollectionParameter.InconsistentCollectionException` with message `message`.
CombinationIterator - Class in de.jstacs.models.discrete.inhomogeneous
  This class can be used for iterating over all possible combinations (in the sense of combinatorics).
CombinationIterator(int, int) - Constructor for class de.jstacs.models.discrete.inhomogeneous.CombinationIterator
  Creates a new `CombinationIterator` with `n` elements and at most `max` selected elements.
CombinedCondition - Class in de.jstacs.algorithms.optimization.termination
  This class allows to use many `TerminationCondition`s at once.
CombinedCondition(int, TerminationCondition...) - Constructor for class de.jstacs.algorithms.optimization.termination.CombinedCondition
  This constructor creates an instance that allows to use many `TerminationCondition`s at one.
comment - Variable in class de.jstacs.parameters.SimpleParameter
  A comment on the parameter
comment - Variable in class de.jstacs.results.Result
  The comment for the result.
commentTemplate - Variable in class de.jstacs.parameters.ExpandableParameterSet
  A template for the comment of the enclosing `ParameterSetContainer`
ComparableElement<E,C extends Comparable> - Class in de.jstacs.utils
  This class is a container for any objects that have to be compared.
ComparableElement(E, C) - Constructor for class de.jstacs.utils.ComparableElement
  Creates a new `ComparableElement`.
compare(Map.Entry<K, V>, Map.Entry<K, V>) - Method in class de.jstacs.parameters.ParameterSetTagger.KeyEntryComparator
compare(Map.Entry<K, ComparableElement<V, Integer>>, Map.Entry<K, ComparableElement<V, Integer>>) - Method in class de.jstacs.parameters.ParameterSetTagger.RankEntryComparator
compareTo(Edge) - Method in class de.jstacs.algorithms.graphs.Edge
compareTo(AlphabetContainer) - Method in class de.jstacs.data.AlphabetContainer
compareTo(Alphabet) - Method in class de.jstacs.data.alphabets.ContinuousAlphabet
compareTo(Alphabet) - Method in class de.jstacs.data.alphabets.DiscreteAlphabet
compareTo(Sequence) - Method in class de.jstacs.data.Sequence
compareTo(SimpleResult) - Method in class de.jstacs.results.SimpleResult
compareTo(ComparableElement<E, C>) - Method in class de.jstacs.utils.ComparableElement
complement() - Method in class de.jstacs.data.Sequence
  This method returns a new instance of `Sequence.CompositeSequence` containing the complementary current `Sequence.CompositeSequence`.
complement(int, int) - Method in class de.jstacs.data.Sequence
  This method returns a new instance of `Sequence.CompositeSequence` containing a part of the complementary current `Sequence.CompositeSequence`.
complement(int, int) - Method in class de.jstacs.data.sequences.SparseSequence
ComplementableDiscreteAlphabet - Class in de.jstacs.data.alphabets
  This abstract class indicates that an alphabet can be used to compute the complement.
ComplementableDiscreteAlphabet(StringBuffer) - Constructor for class de.jstacs.data.alphabets.ComplementableDiscreteAlphabet
  The standard constructor for the interface `Storable`.
ComplementableDiscreteAlphabet(boolean, String...) - Constructor for class de.jstacs.data.alphabets.ComplementableDiscreteAlphabet
  Creates a new `ComplementableDiscreteAlphabet` from a given array of symbols.
componentHyperParams - Variable in class de.jstacs.models.mixture.AbstractMixtureModel
  The hyperparameters for estimating the probabilities of the components.
componentScore - Variable in class de.jstacs.scoringFunctions.mix.AbstractMixtureScoringFunction
  This array is used while computing the score.
CompositeLogPrior - Class in de.jstacs.classifier.scoringFunctionBased.logPrior
  This class implements a composite prior that can be used for NormalizableScoringFunction.
CompositeLogPrior() - Constructor for class de.jstacs.classifier.scoringFunctionBased.logPrior.CompositeLogPrior
  The main constructor.
CompositeLogPrior(StringBuffer) - Constructor for class de.jstacs.classifier.scoringFunctionBased.logPrior.CompositeLogPrior
  The constructor for the `Storable` interface.
CompositeModel - Class in de.jstacs.models
  This class is for modelling sequences by modelling the different positions of the each sequence by different models.
CompositeModel(AlphabetContainer, int[], Model...) - Constructor for class de.jstacs.models.CompositeModel
  Creates a new `CompositeModel`.
CompositeModel(StringBuffer) - Constructor for class de.jstacs.models.CompositeModel
  The standard constructor for the interface `Storable`.
compProb - Variable in class de.jstacs.models.mixture.AbstractMixtureModel
  This array is used while training to avoid creating many new objects.
computeFreqs(double, Constraint...) - Static method in class de.jstacs.models.discrete.ConstraintManager
  This method computes the (smoothed) relative frequencies.
computeGammaNorm() - Method in class de.jstacs.scoringFunctions.directedGraphicalModels.ParameterTree
  Computes the Gamma-normalization for the prior.
computeHiddenParameter(double[]) - Method in class de.jstacs.scoringFunctions.mix.AbstractMixtureScoringFunction
  This method has to be invoked during an initialization.
computeLengthOfBurnIn() - Method in class de.jstacs.models.mixture.gibbssampling.AbstractBurnInTest
  Computes and returns the length of the burn-in phase using the values from `BurnInTest.setValue(double)`.
computeLengthOfBurnIn() - Method in class de.jstacs.models.mixture.gibbssampling.VarianceRatioBurnInTest
  Computes and returns the length of the burn-in phase given by the Variance-Ratio burn-in test.
computeLogGammaSum() - Method in class de.jstacs.scoringFunctions.mix.AbstractMixtureScoringFunction
  This method is used to pre-compute the sum of the logarithm of the gamma functions that is used in the prior.
computeMaximalHP(Tensor) - Static method in class de.jstacs.algorithms.graphs.DAG
  The method computes the HP(k) (see `DAG`).
computeMaximalKDAG(Tensor) - Static method in class de.jstacs.algorithms.graphs.DAG
  Computes the maximal k-DAG (see `DAG`), i.e. the k-DAG that maximizes the score given by a `Tensor`.
computeMaxKDAG(SymmetricTensor) - Static method in class de.jstacs.algorithms.graphs.DAG
  Computes the maximal k-DAG (see `DAG`), i.e. the k-DAG that maximizes the score given by a `SymmetricTensor`.
CONDITIONAL_LIKELIHOOD_INDEX - Static variable in enum de.jstacs.classifier.scoringFunctionBased.gendismix.LearningPrinciple
  This constant is the array index of the weighting factor for the conditional likelihood.
ConfusionMatrix - Class in de.jstacs.classifier
  This class holds the confusion matrix of a classifier.
ConfusionMatrix(int) - Constructor for class de.jstacs.classifier.ConfusionMatrix
  Creates a new `ConfusionMatrix` with a given number of classes.
ConfusionMatrix(StringBuffer) - Constructor for class de.jstacs.classifier.ConfusionMatrix
  The standard constructor for the interface `Storable`.
CONJUGATE_GRADIENTS_FR - Static variable in class de.jstacs.algorithms.optimization.Optimizer
  This constant can be used to specify that conjugate gradients (by Fletcher and Reeves) should be used in the `optimize`-method.
CONJUGATE_GRADIENTS_PRP - Static variable in class de.jstacs.algorithms.optimization.Optimizer
  This constant can be used to specify that conjugate gradients (by Polak and Ribière should be used in the `optimize`-method.
conjugateGradientsFR(DifferentiableFunction, double[], TerminationCondition, double, StartDistanceForecaster, OutputStream, Time) - Static method in class de.jstacs.algorithms.optimization.Optimizer
  The conjugate gradient algorithm by Fletcher and Reeves.
conjugateGradientsPR(DifferentiableFunction, double[], TerminationCondition, double, StartDistanceForecaster, OutputStream, Time) - Static method in class de.jstacs.algorithms.optimization.Optimizer
  The conjugate gradient algorithm by Polak and Ribière.
conjugateGradientsPRP(DifferentiableFunction, double[], TerminationCondition, double, StartDistanceForecaster, OutputStream, Time) - Static method in class de.jstacs.algorithms.optimization.Optimizer
  The conjugate gradient algorithm by Polak and Ribière called "Polak-Ribière-Positive".
ConstantStartDistance - Class in de.jstacs.algorithms.optimization
  The most simple `StartDistanceForecaster` that returns always the same value.
ConstantStartDistance(double) - Constructor for class de.jstacs.algorithms.optimization.ConstantStartDistance
  This constructor creates an instance of `ConstantStartDistance` that returns always the given `value`.
Constraint - Class in de.jstacs.models.discrete
  The main class for all constraints on models.
Constraint(int[], int) - Constructor for class de.jstacs.models.discrete.Constraint
  The main constructor.
Constraint(StringBuffer) - Constructor for class de.jstacs.models.discrete.Constraint
  The standard constructor for the interface `Storable`.
Constraint - Interface in de.jstacs.parameters.validation
  Interface for a constraint that must be fulfilled in a `ConstraintValidator`.
ConstraintManager - Class in de.jstacs.models.discrete
  This class manipulates and manages some constraints.
constraintParameter - Variable in class de.jstacs.parameters.validation.ReferenceConstraint
  The reference to the `Parameter` that is part of the condition.
constraints - Variable in class de.jstacs.models.discrete.inhomogeneous.DAGModel
  The constraints for the model.
ConstraintValidator - Class in de.jstacs.parameters.validation
  Class for a `ParameterValidator` that is based on `Constraint`s.
ConstraintValidator() - Constructor for class de.jstacs.parameters.validation.ConstraintValidator
  Creates a new `ConstraintValidator` having an empty list of `Constraint`s, i.e. the constraints of this `ConstraintValidator` are always fulfilled before additional `Constraint`s are added using `ConstraintValidator.addConstraint(Constraint)`.
ConstraintValidator(StringBuffer) - Constructor for class de.jstacs.parameters.validation.ConstraintValidator
  The standard constructor for the interface `Storable`.
CONTAINS_ALWAYS_A_MOTIF - Static variable in class de.jstacs.scoringFunctions.mix.motifSearch.HiddenMotifsMixture
  This constant indicates that in each sequence has one binding site of a motif instance (similar to OOPS).
CONTAINS_SOMETIMES_A_MOTIF - Static variable in class de.jstacs.scoringFunctions.mix.motifSearch.HiddenMotifsMixture
  This constant indicates that a sequence possibly has one binding site of a motif instance (similar to ZOOPS).
content - Variable in class de.jstacs.data.sequences.RecursiveSequence
  The internal sequence.
context - Variable in class de.jstacs.scoringFunctions.directedGraphicalModels.Parameter
  The context of this parameter.
continueIterations(double[], double[][]) - Method in class de.jstacs.models.mixture.AbstractMixtureModel
  This method will run the train algorithm for the current model on the internal sample.
continueIterations(double[], double[][], int, int) - Method in class de.jstacs.models.mixture.AbstractMixtureModel
  This method will run the train algorithm for the current model on the internal sample.
ContinuousAlphabet - Class in de.jstacs.data.alphabets
  Class for a continuous alphabet.
ContinuousAlphabet(ContinuousAlphabet.ContinuousAlphabetParameterSet) - Constructor for class de.jstacs.data.alphabets.ContinuousAlphabet
  The constructor for the `InstantiableFromParameterSet` interface.
ContinuousAlphabet(double, double) - Constructor for class de.jstacs.data.alphabets.ContinuousAlphabet
  Creates a new `ContinuousAlphabet` from a minimal and a maximal value.
ContinuousAlphabet() - Constructor for class de.jstacs.data.alphabets.ContinuousAlphabet
  Creates a new `ContinuousAlphabet` with minimum and maximum value being -`Double.MAX_VALUE` and `Double.MAX_VALUE`, respectively.
ContinuousAlphabet(StringBuffer) - Constructor for class de.jstacs.data.alphabets.ContinuousAlphabet
  The standard constructor for the interface `Storable`.
ContinuousAlphabet.ContinuousAlphabetParameterSet - Class in de.jstacs.data.alphabets
  Class for the `ParameterSet` of a `ContinuousAlphabet`.
ContinuousAlphabet.ContinuousAlphabetParameterSet() - Constructor for class de.jstacs.data.alphabets.ContinuousAlphabet.ContinuousAlphabetParameterSet
  Creates a new `ContinuousAlphabet.ContinuousAlphabetParameterSet` with empty values.
ContinuousAlphabet.ContinuousAlphabetParameterSet(double, double) - Constructor for class de.jstacs.data.alphabets.ContinuousAlphabet.ContinuousAlphabetParameterSet
  Creates a new `ContinuousAlphabet.ContinuousAlphabetParameterSet` from a minimum and a maximum value.
ContinuousAlphabet.ContinuousAlphabetParameterSet(StringBuffer) - Constructor for class de.jstacs.data.alphabets.ContinuousAlphabet.ContinuousAlphabetParameterSet
  The standard constructor for the interface `Storable` .
continuousVal(int) - Method in class de.jstacs.data.Sequence
  Returns the continuous value at position `pos` of the `Sequence.CompositeSequence`.
continuousVal(int) - Method in class de.jstacs.data.sequences.ArbitrarySequence
continuousVal(int) - Method in class de.jstacs.data.sequences.DiscreteSequence
continuousVal(int) - Method in class de.jstacs.data.sequences.RecursiveSequence
copy(String, String) - Static method in class de.jstacs.io.FileManager
  This method copies a `File` in a faster manner.
copy(String, String, byte[]) - Static method in class de.jstacs.io.FileManager
  This method copies a `File` in a faster manner using a specified buffer.
copy(ParameterTree) - Method in class de.jstacs.scoringFunctions.directedGraphicalModels.ParameterTree
  Copies the values of the parameters from another `ParameterTree`.
copyDiff(File, File, Date, boolean, FileFilter) - Static method in class de.jstacs.io.FileManager
  This method copies all `File`s and directories, if selected, from a `source` `File`, i.e. directory, to a `target` `File`, i.e. directory, that have been modified after a predefined `date`.
copyFileFromServer(String, String, boolean) - Method in class de.jstacs.utils.REnvironment
  Copies a file from the server to the local machine.
copyFileFromServer(String, String, RConnection) - Static method in class de.jstacs.utils.RUtils
  This method copies a file from the server to the client.
copyFileToServer(String, String, boolean) - Method in class de.jstacs.utils.REnvironment
  Copies a file from the local machine to the server.
copyFileToServer(File, String, RConnection) - Static method in class de.jstacs.utils.RUtils
  Copies a file to the R side.
copyFileToServer(String, String, RConnection) - Static method in class de.jstacs.utils.RUtils
  Copies a file to the R side.
count(int[][], byte) - Method in class de.jstacs.models.discrete.inhomogeneous.BayesianNetworkModel
  Counts the occurrence of the different indegrees and checks if the conventions are met.
count - Variable in class de.jstacs.scoringFunctions.directedGraphicalModels.Parameter
  The counts for this parameter.
countElements() - Method in class de.jstacs.io.SymbolExtractor
  Counts the number of elements (symbols) that can be received initially.
counter - Variable in class de.jstacs.models.mixture.AbstractMixtureModel
  The current index of the parameter set while adjustment (optimization).
counter - Variable in class de.jstacs.models.mixture.gibbssampling.FSDAGModelForGibbsSampling
  The counter for the sampling steps of each sampling.
countInhomogeneous(AlphabetContainer, int, Sample, double[], boolean, Constraint...) - Static method in class de.jstacs.models.discrete.ConstraintManager
  Fills the (inhomogeneous) `Constraint` `constr` with the weighted absolute frequencies of the `Sample` `data`.
counts - Variable in class de.jstacs.models.discrete.Constraint
  The counts for each specific constraint.
create(GenDisMixClassifierParameterSet, LogPrior, double[], NormalizableScoringFunction[]...) - Static method in class de.jstacs.classifier.scoringFunctionBased.gendismix.GenDisMixClassifier
  This method creates an array of GenDisMixClassifiers by using the cross-product of the given NormalizableScoringFunctions.
create(AlphabetContainer, String) - Static method in class de.jstacs.data.Sequence
  Creates a `Sequence.CompositeSequence` from a `String` based on the given `AlphabetContainer` using the standard delimiter for this `AlphabetContainer`.
create(AlphabetContainer, String, String) - Static method in class de.jstacs.data.Sequence
  Creates a `Sequence.CompositeSequence` from a `String` based on the given `AlphabetContainer` using the given delimiter `delim`.
create(AlphabetContainer, SequenceAnnotation[], String, String) - Static method in class de.jstacs.data.Sequence
  Creates a `Sequence.CompositeSequence` from a `String` based on the given `AlphabetContainer` using the given delimiter `delim` and some `annotation` for the `Sequence.CompositeSequence`.
createArrayOf(T, int) - Static method in class de.jstacs.io.ArrayHandler
  This method returns an array of length `l` that has at each position a clone of `t`.
createConstraints(AbstractList<int[]>, int[], int[]) - Static method in class de.jstacs.models.discrete.ConstraintManager
  Creates the constraints for a part of a model.
createConstraints(AbstractList<int[]>, int[]) - Static method in class de.jstacs.models.discrete.ConstraintManager
  Creates the constraints of a model.
createConstraints(int[][]) - Method in class de.jstacs.models.discrete.inhomogeneous.DAGModel
  This method creates the constraints for a given structure.
createDefaultClassWeights(int, double) - Method in class de.jstacs.classifier.AbstractScoreBasedClassifier
  This method creates new class weights.
createHistoryArray(ScoringFunction[], History) - Static method in class de.jstacs.motifDiscovery.MutableMotifDiscovererToolbox
  This method creates a History-array that can be used in an optimization.
createMatrix(String, int[][]) - Method in class de.jstacs.utils.REnvironment
  Creates a matrix of `int`s.
createMatrix(String, double[][]) - Method in class de.jstacs.utils.REnvironment
  Creates a matrix of `double`s.
createMinimalNewLengthArray(ScoringFunction[]) - Static method in class de.jstacs.motifDiscovery.MutableMotifDiscovererToolbox
  This method creates a minimalNewLength-array that can be used in an optimization.
createParameterSet(Object[], String[], String[]) - Method in class de.jstacs.parameters.CollectionParameter
  Creates a new `ParameterSet` from an array of values, an array of names and an array of comments.
createResult(String, String, DataType, Object) - Static method in class de.jstacs.results.Result
  Factory method to create a new `Result`.
createSeqWeightsArray() - Method in class de.jstacs.models.mixture.AbstractMixtureModel
  Creates an array that can be used for weighting sequences in the algorithm.
createSeqWeightsArray() - Method in class de.jstacs.models.mixture.motif.SingleHiddenMotifMixture
createStructure(Sample[], double[][], boolean) - Method in class de.jstacs.classifier.scoringFunctionBased.ScoreClassifier
  Creates the structure that will be used in the optimization.
createStructure(Sample[], double[][]) - Method in class de.jstacs.classifier.scoringFunctionBased.ScoreClassifier
  Creates the structure that will be used in the optimization.
createTrees(Sample[], double[][]) - Method in class de.jstacs.scoringFunctions.directedGraphicalModels.BayesianNetworkScoringFunction
  Creates the tree structures that represent the context (array `BayesianNetworkScoringFunction.trees`) and the parameter objects `BayesianNetworkScoringFunction.parameters` using the given `Measure` `BayesianNetworkScoringFunction.structureMeasure`.
createVector(String, String[]) - Method in class de.jstacs.utils.REnvironment
  Creates a vector of `String`s.
createVector(String, int[]) - Method in class de.jstacs.utils.REnvironment
  Creates a vector of `int`s.
createVector(String, long[]) - Method in class de.jstacs.utils.REnvironment
  Creates a vector of `long`s.
createVector(String, double[]) - Method in class de.jstacs.utils.REnvironment
  Creates a vector of `double`s.

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