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## D

DAG - Class in de.jstacs.algorithms.graphs
  This is the main class of the graph library.
DAG() - Constructor for class de.jstacs.algorithms.graphs.DAG
DAGModel - Class in de.jstacs.models.discrete.inhomogeneous
  The abstract class for directed acyclic graphical models (`DAGModel`).
DAGModel(IDGMParameterSet) - Constructor for class de.jstacs.models.discrete.inhomogeneous.DAGModel
  This is the main constructor.
DAGModel(StringBuffer) - Constructor for class de.jstacs.models.discrete.inhomogeneous.DAGModel
  The standard constructor for the interface `Storable`.
data - Variable in class de.jstacs.classifier.scoringFunctionBased.AbstractOptimizableFunction
  The data that is used to evaluate this function.
DataType - Enum in de.jstacs
  This `enum` defines a number of data types that can be used for `Parameter`s and `Result` s.
datatype - Variable in class de.jstacs.parameters.SimpleParameter
  The data type of the parameter value
datatype - Variable in class de.jstacs.results.Result
  The data type of the result.
de.jstacs - package de.jstacs
  This package is the root package for the most and important packages.
de.jstacs.algorithms - package de.jstacs.algorithms
  Provides classes for algorithms on graphs.
de.jstacs.algorithms.graphs - package de.jstacs.algorithms.graphs
  Provides classes for algorithms on graphs.
de.jstacs.algorithms.graphs.tensor - package de.jstacs.algorithms.graphs.tensor
  Provides classes to represent symmetric and asymmetric tensors in graphs
de.jstacs.algorithms.optimization - package de.jstacs.algorithms.optimization
  Provides classes for different types of algorithms that are not directly linked to the modelling components of Jstacs: Algorithms on graphs, algorithms for numerical optimization, and a basic alignment algorithm. de.jstacs.classifier - package de.jstacs.classifier
  This package provides the framework for any classifier.
de.jstacs.classifier.assessment - package de.jstacs.classifier.assessment
  This package allows to assess classifiers.
de.jstacs.classifier.modelBased - package de.jstacs.classifier.modelBased
  Provides the classes for `Classifier`s that are based on `Model`s
de.jstacs.classifier.scoringFunctionBased - package de.jstacs.classifier.scoringFunctionBased
  Provides the classes for `Classifier`s that are based on `ScoringFunction`s.
de.jstacs.classifier.scoringFunctionBased.cll - package de.jstacs.classifier.scoringFunctionBased.cll
  Provides the implementation of the log conditional likelihood as an `OptimizableFunction` and a classifier that uses log conditional likelihood or supervised posterior to learn the parameters of a set of `ScoringFunctions`
de.jstacs.classifier.scoringFunctionBased.logPrior - package de.jstacs.classifier.scoringFunctionBased.logPrior
  Provides a general definition of a parameter log-prior and a number of implementations of Laplace and Gaussian priors
de.jstacs.classifier.utils - package de.jstacs.classifier.utils
  Provides some useful classes for working with classifiers
de.jstacs.data - package de.jstacs.data
  Provides classes for the representation of data.
de.jstacs.data.alphabets - package de.jstacs.data.alphabets
  Provides classes for the representation of discrete and continuous alphabets, including a `DNAAlphabet` for the most common case of DNA-sequences
de.jstacs.data.bioJava - package de.jstacs.data.bioJava
  Provides an adapter between the representation of data in BioJava and the representation used in Jstacs.
de.jstacs.data.sequences - package de.jstacs.data.sequences
  Provides classes for representing sequences.
de.jstacs.data.sequences.annotation - package de.jstacs.data.sequences.annotation
  Provides the facilities to annotate `Sequence`s using a number of pre-defined annotation types, or additional implementations of the `SequenceAnnotation` class
de.jstacs.io - package de.jstacs.io
  Provides classes for reading data from and writing to a file and storing a number of datatypes, including all primitives, arrays of primitives, and `Storable`s to an XML-representation
de.jstacs.models - package de.jstacs.models
  Provides the interface `Model` and its abstract implementation `AbstractModel`, which is the super class of all other models.
de.jstacs.models.discrete - package de.jstacs.models.discrete
de.jstacs.models.discrete.homogeneous - package de.jstacs.models.discrete.homogeneous
de.jstacs.models.discrete.homogeneous.parameters - package de.jstacs.models.discrete.homogeneous.parameters
de.jstacs.models.discrete.inhomogeneous - package de.jstacs.models.discrete.inhomogeneous
  This package contains various inhomogeneous models.
de.jstacs.models.discrete.inhomogeneous.parameters - package de.jstacs.models.discrete.inhomogeneous.parameters
de.jstacs.models.discrete.inhomogeneous.shared - package de.jstacs.models.discrete.inhomogeneous.shared
de.jstacs.models.mixture - package de.jstacs.models.mixture
  This package is the super package for any mixture model.
de.jstacs.models.mixture.gibbssampling - package de.jstacs.models.mixture.gibbssampling
  This package contains many classes that can be used while a Gibbs sampling.
de.jstacs.models.mixture.motif - package de.jstacs.models.mixture.motif
de.jstacs.models.mixture.motif.positionprior - package de.jstacs.models.mixture.motif.positionprior
de.jstacs.models.utils - package de.jstacs.models.utils
de.jstacs.motifDiscovery - package de.jstacs.motifDiscovery
  This package provides the framework including the interface for any de novo motif discoverer
de.jstacs.motifDiscovery.history - package de.jstacs.motifDiscovery.history
de.jstacs.parameters - package de.jstacs.parameters
  This package provides classes for parameters that establish a general convention for the description of parameters as defined in the `Parameter`-interface.
de.jstacs.parameters.validation - package de.jstacs.parameters.validation
  Provides classes for the validation of `Parameter` values
de.jstacs.results - package de.jstacs.results
  This package provides classes for results and sets of results.
de.jstacs.scoringFunctions - package de.jstacs.scoringFunctions
  Provides `ScoringFunction`s that can be used in a `ScoreClassifier`.
de.jstacs.scoringFunctions.directedGraphicalModels - package de.jstacs.scoringFunctions.directedGraphicalModels
  Provides `ScoringFunction`s that are equivalent to directed graphical models.
de.jstacs.scoringFunctions.directedGraphicalModels.structureLearning.measures - package de.jstacs.scoringFunctions.directedGraphicalModels.structureLearning.measures
  Provides the facilities to learn the structure of a `BayesianNetworkScoringFunction`.
de.jstacs.scoringFunctions.directedGraphicalModels.structureLearning.measures.btMeasures - package de.jstacs.scoringFunctions.directedGraphicalModels.structureLearning.measures.btMeasures
  Provides the facilities to learn the structure of a `BayesianNetworkScoringFunction` as a Bayesian tree using a number of measures to define a rating of structures
de.jstacs.scoringFunctions.directedGraphicalModels.structureLearning.measures.pmmMeasures - package de.jstacs.scoringFunctions.directedGraphicalModels.structureLearning.measures.pmmMeasures
  Provides the facilities to learn the structure of a `BayesianNetworkScoringFunction` as a permuted Markov model using a number of measures to define a rating of structures
de.jstacs.scoringFunctions.homogeneous - package de.jstacs.scoringFunctions.homogeneous
  Provides `ScoringFunction`s that are homogeneous, i.e. model probabilities or scores independent of the position within a sequence
de.jstacs.scoringFunctions.mix - package de.jstacs.scoringFunctions.mix
  Provides `ScoringFunction`s that are mixtures of other `ScoringFunction`s.
de.jstacs.scoringFunctions.mix.motifSearch - package de.jstacs.scoringFunctions.mix.motifSearch
de.jstacs.utils - package de.jstacs.utils
  This package contains a bundle of useful classes and interfaces like ...
de.jstacs.utils.random - package de.jstacs.utils.random
  This package contains some classes for generating random numbers
DEFAULT_INSTANCE - Static variable in class de.jstacs.utils.random.DirichletMRG
  This instance shall be used, since quite often two instance of this class return the same values.
DEFAULT_SIGN - Static variable in class de.jstacs.scoringFunctions.mix.motifSearch.HiddenMotifsMixture
  The default significance level that is used in `Mutable.determineNotSignificantPositions(double, double[], double[], double[][][][], double[][][][], double)`.
DEFAULT_STREAM - Static variable in class de.jstacs.models.discrete.inhomogeneous.InhomogeneousDGM
  The default `OutputStream`.
DEFAULT_STREAM - Static variable in class de.jstacs.utils.SafeOutputStream
  This stream can be used as default stream.
defaultInstance - Static variable in class de.jstacs.classifier.scoringFunctionBased.logPrior.DoesNothingLogPrior
  As this prior does not penalize parameters and does not have any parameters itself, this class does not have a constructor, but provides a default instance in order to reduce memory consumption.
DefaultProgressUpdater - Class in de.jstacs.utils
  Simple class that implements `ProgressUpdater` and prints the percentage of iterations that is already done on the screen.
DefaultProgressUpdater() - Constructor for class de.jstacs.utils.DefaultProgressUpdater
  Creates a `DefaultProgressUpdater`.
defaultValue - Variable in class de.jstacs.parameters.SimpleParameter
  The default value of the parameter
deleteAllFilesAtTheServer() - Method in class de.jstacs.utils.REnvironment
  Deletes all files that have been copied to the server or created on the server.
delta - Variable in class de.jstacs.scoringFunctions.mix.motifSearch.DurationScoringFunction
  The difference of maximal and minimal value.
deselectAll() - Method in class de.jstacs.classifier.MeasureParameters
  Deselects all measures, i.e. calls `MeasureParameters.setSelected(Measure, boolean)` for all `MeasureParameters.Measure`s.
determineNotSignificantPositions(double, double[], double[], double[][][][], double[][][][], double) - Method in interface de.jstacs.motifDiscovery.Mutable
  This method determines the number of not significant positions from each side of the motif using the the significance level `sign` and the contrast distributions of the left or right side, `contrastLeft` and `contrastRight`, respectively.
determineNotSignificantPositions(double, double[], double[], double[][][][], double[][][][], double) - Method in class de.jstacs.scoringFunctions.directedGraphicalModels.MutableMarkovModelScoringFunction
determineNotSignificantPositions(double, double[], double[], double[][][][], double[][][][], double) - Method in class de.jstacs.scoringFunctions.mix.StrandScoringFunction
determineNotSignificantPositions(double, double[], double[], double[][][][], double[][][][], double) - Method in class de.jstacs.scoringFunctions.NormalizedScoringFunction
determineNotSignificantPositionsFor(int, Sample[], double[][], int) - Method in interface de.jstacs.motifDiscovery.MutableMotifDiscoverer
  This method determines the number of not significant positions from each side of the motif with index `motif`.
determineNotSignificantPositionsFor(int, Sample[], double[][], int) - Method in class de.jstacs.scoringFunctions.IndependentProductScoringFunction
determineNotSignificantPositionsFor(int, Sample[], double[][], int) - Method in class de.jstacs.scoringFunctions.mix.motifSearch.HiddenMotifsMixture
DGMParameterSet - Class in de.jstacs.models.discrete
  The super `ParameterSet` for any parameter set of a `DiscreteGraphicalModel`.
DGMParameterSet(StringBuffer) - Constructor for class de.jstacs.models.discrete.DGMParameterSet
  The standard constructor for the interface `Storable`.
DGMParameterSet(Class<? extends DiscreteGraphicalModel>, boolean, boolean) - Constructor for class de.jstacs.models.discrete.DGMParameterSet
  An empty constructor.
DGMParameterSet(Class<? extends DiscreteGraphicalModel>, AlphabetContainer, double, String) - Constructor for class de.jstacs.models.discrete.DGMParameterSet
  The constructor for models that can handle variable lengths.
DGMParameterSet(Class<? extends DiscreteGraphicalModel>, AlphabetContainer, int, double, String) - Constructor for class de.jstacs.models.discrete.DGMParameterSet
  The constructor for models that can handle only sequences of fixed length given by `length`.
DifferentiableFunction - Class in de.jstacs.algorithms.optimization
  This class is the framework for any (at least) one time differentiable function `f: R^n -> R`.
DifferentiableFunction() - Constructor for class de.jstacs.algorithms.optimization.DifferentiableFunction
dimension - Variable in class de.jstacs.models.mixture.AbstractMixtureModel
  The number of dimensions.
DimensionException - Exception in de.jstacs.algorithms.optimization
  This class is for `Exception`s depending on wrong dimensions of vectors for a given function.
DimensionException() - Constructor for exception de.jstacs.algorithms.optimization.DimensionException
  Creates a new `DimensionException` with standard error message ("The vector has wrong dimension for this function.
DimensionException(int, int) - Constructor for exception de.jstacs.algorithms.optimization.DimensionException
  Creates a new `DimensionException` with a more detailed error message.
DiMRGParams - Class in de.jstacs.utils.random
  The super container for parameters of Dirichlet multivariate random generators.
DiMRGParams() - Constructor for class de.jstacs.utils.random.DiMRGParams
DirichletMRG - Class in de.jstacs.utils.random
  This class is a multivariate random generator based on a Dirichlet distribution.
DirichletMRGParams - Class in de.jstacs.utils.random
  The container for parameters of a Dirichlet random generator.
DirichletMRGParams(double, int) - Constructor for class de.jstacs.utils.random.DirichletMRGParams
  Constructor which creates a new hyperparameter vector for a Dirichlet random generator.
DirichletMRGParams(double...) - Constructor for class de.jstacs.utils.random.DirichletMRGParams
  Constructor which creates a new hyperparameter vector for a Dirichlet random generator.
DiscreteAlphabet - Class in de.jstacs.data.alphabets
  Class for an alphabet that consists of arbitrary `String`s.
DiscreteAlphabet(StringBuffer) - Constructor for class de.jstacs.data.alphabets.DiscreteAlphabet
  The standard constructor for the interface `Storable`.
DiscreteAlphabet(DiscreteAlphabet.DiscreteAlphabetParameterSet) - Constructor for class de.jstacs.data.alphabets.DiscreteAlphabet
  The constructor for the `InstantiableFromParameterSet` interface.
DiscreteAlphabet(int, int) - Constructor for class de.jstacs.data.alphabets.DiscreteAlphabet
  Creates a new `DiscreteAlphabet` from a minimal and a maximal value, i.e. in `[min,max]`.
DiscreteAlphabet(boolean, String...) - Constructor for class de.jstacs.data.alphabets.DiscreteAlphabet
  Creates a new `DiscreteAlphabet` from a given alphabet as a `String` array.
DiscreteAlphabet.DiscreteAlphabetParameterSet - Class in de.jstacs.data.alphabets
  Class for the `ParameterSet` of a `DiscreteAlphabet`.
DiscreteAlphabet.DiscreteAlphabetParameterSet() - Constructor for class de.jstacs.data.alphabets.DiscreteAlphabet.DiscreteAlphabetParameterSet
  Creates a new `DiscreteAlphabet.DiscreteAlphabetParameterSet` with empty values.
DiscreteAlphabet.DiscreteAlphabetParameterSet(String[], boolean) - Constructor for class de.jstacs.data.alphabets.DiscreteAlphabet.DiscreteAlphabetParameterSet
  Creates a new `DiscreteAlphabet.DiscreteAlphabetParameterSet` from an alphabet given as a `String` array.
DiscreteAlphabet.DiscreteAlphabetParameterSet(char[], boolean) - Constructor for class de.jstacs.data.alphabets.DiscreteAlphabet.DiscreteAlphabetParameterSet
  Creates a new `DiscreteAlphabet.DiscreteAlphabetParameterSet` from an alphabet of symbols given as a `char` array.
DiscreteAlphabet.DiscreteAlphabetParameterSet(StringBuffer) - Constructor for class de.jstacs.data.alphabets.DiscreteAlphabet.DiscreteAlphabetParameterSet
  The standard constructor for the interface `Storable` .
DiscreteGraphicalModel - Class in de.jstacs.models.discrete
  This is the main class for all discrete graphical models (DGM).
DiscreteGraphicalModel(DGMParameterSet) - Constructor for class de.jstacs.models.discrete.DiscreteGraphicalModel
  The default constructor.
DiscreteGraphicalModel(StringBuffer) - Constructor for class de.jstacs.models.discrete.DiscreteGraphicalModel
  The standard constructor for the interface `Storable`.
DiscreteInhomogenousSampleEmitter - Class in de.jstacs.models.utils
  Emits `Sample`s for discrete inhomogeneous models by a naive implementation.
DiscreteInhomogenousSampleEmitter() - Constructor for class de.jstacs.models.utils.DiscreteInhomogenousSampleEmitter
DiscreteSequence - Class in de.jstacs.data.sequences
  This is the main class for any discrete sequence.
DiscreteSequence(AlphabetContainer, SequenceAnnotation[]) - Constructor for class de.jstacs.data.sequences.DiscreteSequence
  This constructor creates a new `DiscreteSequence` with the `AlphabetContainer` `container` and the annotation `annotation` but without the content.
DiscreteSequenceEnumerator - Class in de.jstacs.data
  This class enumerates over all `Sequence`s of a specific `AlphabetContainer` and length.
DiscreteSequenceEnumerator(AlphabetContainer, int, boolean) - Constructor for class de.jstacs.data.DiscreteSequenceEnumerator
  Creates a new `DiscreteSequenceEnumerator` from a given `AlphabetContainer` and a length.
discreteVal(int) - Method in class de.jstacs.data.Sequence
  Returns the discrete value at position `pos` of the `Sequence.CompositeSequence`.
discreteVal(int) - Method in class de.jstacs.data.Sequence.SubSequence
discreteVal(int) - Method in class de.jstacs.data.sequences.ArbitrarySequence
discreteVal(int) - Method in class de.jstacs.data.sequences.ByteSequence
discreteVal(int) - Method in class de.jstacs.data.sequences.IntSequence
discreteVal(int) - Method in class de.jstacs.data.sequences.RecursiveSequence
discreteVal(int) - Method in class de.jstacs.data.sequences.ShortSequence
discreteVal(int) - Method in class de.jstacs.data.sequences.SparseSequence
divideByUnfree() - Method in class de.jstacs.scoringFunctions.directedGraphicalModels.ParameterTree
  Divides each of the normalized parameters on a simplex by the last `Parameter`, which is defined not to be free.
dList - Variable in class de.jstacs.scoringFunctions.mix.AbstractMixtureScoringFunction
  This array contains some `DoubleList`s that are used while computing the partial derivation.
DNAAlphabet - Class in de.jstacs.data.alphabets
  This class implements the discrete alphabet that is used for DNA.
DNAAlphabet(StringBuffer) - Constructor for class de.jstacs.data.alphabets.DNAAlphabet
  The standard constructor for the interface `Storable`.
DNAAlphabet(DNAAlphabet.DNAAlphabetParameterSet) - Constructor for class de.jstacs.data.alphabets.DNAAlphabet
  The constructor for the `InstantiableFromParameterSet` interface.
DNAAlphabet() - Constructor for class de.jstacs.data.alphabets.DNAAlphabet
  The main constructor.
DNAAlphabet.DNAAlphabetParameterSet - Class in de.jstacs.data.alphabets
  The parameter set for a `DNAAlphabet`.
DNAAlphabet.DNAAlphabetParameterSet() - Constructor for class de.jstacs.data.alphabets.DNAAlphabet.DNAAlphabetParameterSet
  Creates a new `DNAAlphabet.DNAAlphabetParameterSet`.
DNAAlphabet.DNAAlphabetParameterSet(StringBuffer) - Constructor for class de.jstacs.data.alphabets.DNAAlphabet.DNAAlphabetParameterSet
  The standard constructor for the interface `Storable` .
doesApplyFor(Sequence) - Method in class de.jstacs.scoringFunctions.directedGraphicalModels.Parameter
  Indicates if the `Sequence` `seq` fulfills all requirements defined in the `Parameter.context`.
doesNothing() - Method in class de.jstacs.utils.SafeOutputStream
  Indicates whether the instance is doing something or not.
DoesNothingLogPrior - Class in de.jstacs.classifier.scoringFunctionBased.logPrior
  This class defines a `LogPrior` that does not penalize any parameter.
doFirstIteration(Sample, double[]) - Method in class de.jstacs.models.mixture.AbstractMixtureModel
  This method will do the first step in the train algorithm for the current model.
doFirstIteration(Sample, double[], MultivariateRandomGenerator, MRGParams[]) - Method in class de.jstacs.models.mixture.AbstractMixtureModel
  This method will do the first step in the train algorithm for the current model.
doFirstIteration(double[], MultivariateRandomGenerator, MRGParams[]) - Method in class de.jstacs.models.mixture.AbstractMixtureModel
  This method will do the first step in the train algorithm for the current model on the internal sample.
doFirstIteration(double[], MultivariateRandomGenerator, MRGParams[]) - Method in class de.jstacs.models.mixture.MixtureModel
doFirstIteration(Sample, double[], double[][]) - Method in class de.jstacs.models.mixture.MixtureModel
  This method enables you to train a mixture model with a fixed start partitioning.
doFirstIteration(double[], MultivariateRandomGenerator, MRGParams[]) - Method in class de.jstacs.models.mixture.motif.SingleHiddenMotifMixture
doFirstIteration(double[], MultivariateRandomGenerator, MRGParams[]) - Method in class de.jstacs.models.mixture.StrandModel
doHeuristicSteps(ScoringFunction[], Sample[], double[][], OptimizableFunction, SafeOutputStream, boolean, History[][], int[][]) - Static method in class de.jstacs.motifDiscovery.MutableMotifDiscovererToolbox
  This method tries to make some heuristic step if at least one `MutableMotifDiscovererToolbox.InitMethodForScoringFunction` is a `MutableMotifDiscoverer`.
doOptimization(Sample[], double[][]) - Method in class de.jstacs.classifier.scoringFunctionBased.ScoreClassifier
  This method does the optimization of the `train`-method
DoubleArrayWithTags(double[]) - Static method in class de.jstacs.io.XMLParser
  Encodes a `double` array.
DoubleList - Class in de.jstacs.utils
  A simple list of primitive type `double`.
DoubleList() - Constructor for class de.jstacs.utils.DoubleList
  This is the default constructor that creates a `DoubleList` with initial length 10.
DoubleList(int) - Constructor for class de.jstacs.utils.DoubleList
  This is the default constructor that creates a `DoubleList` with initial length `size`.
DoubleList(StringBuffer) - Constructor for class de.jstacs.utils.DoubleList
  This is the constructor for the interface `Storable`.
DoubleSymbolException - Exception in de.jstacs.data.alphabets
  A `DoubleSymbolException` is thrown if a symbol occurred more than once in an alphabet.
DoubleSymbolException(String) - Constructor for exception de.jstacs.data.alphabets.DoubleSymbolException
  Constructor for a `DoubleSymbolException` that takes the symbol that occurs more than once in the error message.
draw(double[], int) - Static method in class de.jstacs.models.mixture.AbstractMixtureModel
  This method draws an index of an array corresponding to the probabilities encoded in the entries of the array.
drawFreqs(double, InhCondProb...) - Static method in class de.jstacs.models.discrete.ConstraintManager
  This method draws relative frequencies for the constraints in `constr`.
drawKLDivergences(double, double[], int, int, double[][][], double) - Method in class de.jstacs.scoringFunctions.directedGraphicalModels.ParameterTree
  Draws KL-divergences between the distribution given by `contrast` and `endIdx-startIdx` distributions drawn from a Dirichlet density centered around `contrast`, i.e. the hyper-parameters of the Dirichlet density are the probabilities of `contrast` weighted by `samples`.
drawKLDivergences(double[], double[], double[][][][], double) - Method in class de.jstacs.scoringFunctions.directedGraphicalModels.ParameterTree
  Draws KL-divergences between the distributions given by `contrast[i]` each weighted by `weights[i]` `kls.length` distributions drawn from a Dirichlet density centered around `contrast`, i.e. the hyper-parameters of the Dirichlet density are the probabilities of `contrast` weighted by `samples`.
drawParameters(Sample, double[]) - Method in class de.jstacs.models.discrete.inhomogeneous.DAGModel
  This method draws the parameter of the model from the likelihood or the posterior, respectively.
drawParameters(Sample, double[], int[][]) - Method in class de.jstacs.models.discrete.inhomogeneous.FSDAGModel
  This method draws the parameters of the model from the a posteriori density.
drawParameters(double) - Method in class de.jstacs.models.discrete.inhomogeneous.InhCondProb
  Draws the parameters from a Dirichlet distribution using the counts and the given `ess` (equivalent sample size) as hyperparameters.
drawParameters(Sample, double[]) - Method in class de.jstacs.models.mixture.gibbssampling.FSDAGModelForGibbsSampling
drawParameters(Sample, double[], int[][]) - Method in class de.jstacs.models.mixture.gibbssampling.FSDAGModelForGibbsSampling
drawParameters(Sample, double[]) - Method in interface de.jstacs.models.mixture.gibbssampling.GibbsSamplingComponent
  This method draws the parameters of the model from the a posteriori density.
drawPosition(int[]) - Method in class de.jstacs.scoringFunctions.mix.motifSearch.UniformDurationScoringFunction
  This method draws from the distribution and returns the result in the given array.
drawUnConditional(int, int, double) - Method in class de.jstacs.models.discrete.inhomogeneous.InhCondProb
  This method draws the parameters for a part of this constraint.
DurationScoringFunction - Class in de.jstacs.scoringFunctions.mix.motifSearch
  This class is the super class for all one dimensional position scoring functions that can be used as durations for semi Markov models.
DurationScoringFunction(int, int, double) - Constructor for class de.jstacs.scoringFunctions.mix.motifSearch.DurationScoringFunction
  The default constructor.
DurationScoringFunction(StringBuffer) - Constructor for class de.jstacs.scoringFunctions.mix.motifSearch.DurationScoringFunction
  This is the constructor for `Storable`.

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