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## Uses of Interface
de.jstacs.Storable

Packages that use Storable
de.jstacs.classifier | This package provides the framework for any classifier.
de.jstacs.classifier.assessment | This package allows to assess classifiers.
de.jstacs.classifier.modelBased | Provides the classes for `Classifier`s that are based on `Model`s
de.jstacs.classifier.scoringFunctionBased | Provides the classes for `Classifier`s that are based on `ScoringFunction`s.
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 | Provides a general definition of a parameter log-prior and a number of implementations of Laplace and Gaussian priors
de.jstacs.data | Provides classes for the representation of data.
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.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 | 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 | Provides the interface `Model` and its abstract implementation `AbstractModel`, which is the super class of all other models.
de.jstacs.models.discrete |
de.jstacs.models.discrete.inhomogeneous | This package contains various inhomogeneous models.
de.jstacs.models.discrete.inhomogeneous.parameters |
de.jstacs.models.discrete.inhomogeneous.shared |
de.jstacs.models.mixture | This package is the super package for any mixture model.
de.jstacs.models.mixture.gibbssampling | This package contains many classes that can be used while a Gibbs sampling.
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 | Provides classes for the validation of `Parameter` values
de.jstacs.results | This package provides classes for results and sets of results.
de.jstacs.scoringFunctions | Provides `ScoringFunction`s that can be used in a `ScoreClassifier`.
de.jstacs.scoringFunctions.directedGraphicalModels | Provides `ScoringFunction`s that are equivalent to directed graphical models.
de.jstacs.scoringFunctions.directedGraphicalModels.structureLearning.measures | Provides the facilities to learn the structure of a `BayesianNetworkScoringFunction`.
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 | 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 | Provides `ScoringFunction`s that are homogeneous, i.e. model probabilities or scores independent of the position within a sequence
de.jstacs.scoringFunctions.mix | Provides `ScoringFunction`s that are mixtures of other `ScoringFunction`s.

Uses of Storable in de.jstacs.classifier

Classes in de.jstacs.classifier that implement Storable
`class` | `AbstractClassifier` The super class for any classifier.
`class` | `AbstractScoreBasedClassifier` This class is the main class for all score based classifiers.
`static class` | `AbstractScoreBasedClassifier.DoubleTableResult` This class is for a table of `double`s.
`class` | `ConfusionMatrix` This class holds the confusion matrix of a classifier.
`class` | `MappingClassifier` 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.
`class` | `MeasureParameters` This class holds the parameters for evaluate-methods of a classifier.

Uses of Storable in de.jstacs.classifier.assessment

Classes in de.jstacs.classifier.assessment that implement Storable
`class` | `ClassifierAssessmentAssessParameterSet` This class is the super-class used by all `ClassifierAssessmentAssessParameterSet`s.
`class` | `KFoldCVAssessParameterSet` This class implements a `ClassifierAssessmentAssessParameterSet` that must be used to call method `assess()` of a `KFoldCrossValidation`.
`class` | `RepeatedHoldOutAssessParameterSet` This class implements a `ClassifierAssessmentAssessParameterSet` that must be used to call method `assess()` of a `RepatedHoldOutExperiment`.
`class` | `RepeatedSubSamplingAssessParameterSet` This class implements a `ClassifierAssessmentAssessParameterSet` that must be used to call method `assess()` of a `RepatedSubSamplingExperiment`.
`class` | `Sampled_RepeatedHoldOutAssessParameterSet`

Uses of Storable in de.jstacs.classifier.modelBased

Classes in de.jstacs.classifier.modelBased that implement Storable
`class` | `ModelBasedClassifier` This class is the main class for all model based classifiers.

Uses of Storable in de.jstacs.classifier.scoringFunctionBased

Classes in de.jstacs.classifier.scoringFunctionBased that implement Storable
`class` | `ScoreClassifier`
`class` | `ScoreClassifierParameterSet` The parameter set for any CL classifier.

Uses of Storable in de.jstacs.classifier.scoringFunctionBased.cll

Classes in de.jstacs.classifier.scoringFunctionBased.cll that implement Storable
`class` | `CLLClassifier` This class implements the conditional log likelihood classifier.
`class` | `CLLClassifierParameterSet` This class contains the parameters for the `CLLClassifier`.

Uses of Storable in de.jstacs.classifier.scoringFunctionBased.logPrior

Classes in de.jstacs.classifier.scoringFunctionBased.logPrior that implement Storable
`class` | `DoesNothingLogPrior` This class defines a `LogPrior` that does not penalize any parameter.
`class` | `LogPrior` The abstract class for any log-prior used e.g. for maximum supervised posterior optimization.
`class` | `SeparateGaussianLogPrior` Class for a `LogPrior` that defines a Gaussian prior on the parameters of a set of `NormalizableScoringFunction`s and a set of class-parameters.
`class` | `SeparateLaplaceLogPrior` Class for a `LogPrior` that defines a Laplace-prior on the parameters of a set of `NormalizableScoringFunction`s and a set of class-parameters.
`class` | `SeparateLogPrior` Abstract class for priors that penalize each parameter value independently and have some variance (and possible mean) as hyper-parameters.
`class` | `SimpleGaussianSumLogPrior` This class implements a prior that is a product of Gaussian distributions with mean 0 and equal variance for each parameter.

Uses of Storable in de.jstacs.data

Classes in de.jstacs.data that implement Storable
`class` | `Alphabet` Class for an Alphabet.
`static class` | `Alphabet.AlphabetParameterSet` The super class for the `ParameterSet` of any `Alphabet`.
`class` | `AlphabetContainer` The container for some alphabets used in a sequence, sample, model or ... .
`class` | `AlphabetContainerParameterSet` Class for the `ParameterSet` of an `AlphabetContainer`.
`static class` | `AlphabetContainerParameterSet.AlphabetArrayParameterSet` Class for the parameters of an array of `Alphabet`s of defined length.
`static class` | `AlphabetContainerParameterSet.SectionDefinedAlphabetParameterSet` Class for the parameters of an array of `Alphabet`s where each alphabet may be used for one or more sections of positions.

Uses of Storable in de.jstacs.data.alphabets

Classes in de.jstacs.data.alphabets that implement Storable
`class` | `ComplementableDiscreteAlphabet` This abstract class indicates that an alphabet can be used for to compute the complement.
`class` | `ContinuousAlphabet` Class for a continuous alphabet.
`static class` | `ContinuousAlphabet.ContinuousAlphabetParameterSet` Class for the `ParameterSet` of a `ContinuousAlphabet`.
`class` | `DiscreteAlphabet` Class for an alphabet that consists of arbitrary Strings.
`static class` | `DiscreteAlphabet.DiscreteAlphabetParameterSet` Class for the `ParameterSet` of a `DiscreteAlphabet`.
`class` | `DNAAlphabet` This class implements the alphabet that is used for DNA.
`static class` | `DNAAlphabet.DNAAlphabetParameterSet` The parameter set for an DNA alphabet.

Uses of Storable in de.jstacs.data.sequences.annotation

Classes in de.jstacs.data.sequences.annotation that implement Storable
`class` | `CisRegulatoryModuleAnnotation` Annotation for a cis-regulatory module as defined by a set of `MotifAnnotation`s of the motifs in the module.
`class` | `IntronAnnotation` Annotation class for an intron as defined by a donor and an acceptor splice site.
`class` | `LocatedSequenceAnnotation` Class for a `SequenceAnnotation` that has a position on the sequence, e.g for transcription start sites or intron-exon junctions.
`class` | `LocatedSequenceAnnotationWithLength` Class for a `SequenceAnnotation` that has a position on the sequence and a length, e.g. for donor splice sites, exons, or genes.
`class` | `MotifAnnotation` Class for a `StrandedLocatedSequenceAnnotationWithLength` that is a motif.
`class` | `SequenceAnnotation` Class for a general annotation of a `Sequence`.
`class` | `SinglePositionSequenceAnnotation` Class for some annotations that consist mainly of one position on a sequence.
`class` | `StrandedLocatedSequenceAnnotationWithLength` Class for a `SequenceAnnotation` that has a position, a length, and an orientation on the strand of a `Sequence`.

Uses of Storable in de.jstacs.io

Methods in de.jstacs.io that return Storable
`static Storable[][]` | `XMLParser.extractStorable2ArrayForTag(StringBuffer source, String tag)` Returns the value between equal start and end tags as a two dimensional `Storable` array.
`static Storable[][]` | `XMLParser.extractStorable2ArrayForTag(StringBuffer source, String startTag, String endTag)` Returns the value between start and end tag as a two dimensional `Storable` array.
`static Storable[][][]` | `XMLParser.extractStorable3ArrayForTag(StringBuffer source, String tag)` Returns the value between equal start and end tags as a three dimensional `Storable` array.
`static Storable[][][]` | `XMLParser.extractStorable3ArrayForTag(StringBuffer source, String startTag, String endTag)` Returns the value between start and end tag as a three dimensional `Storable` array.
`static Storable[]` | `XMLParser.extractStorableArrayForTag(StringBuffer source, String startTag)` Returns the value between equal start and end tags as a Storable array.
`static Storable[]` | `XMLParser.extractStorableArrayForTag(StringBuffer source, String startTag, String endTag)` Returns the value between start and end tag as a Storable array.
`static Storable` | `XMLParser.extractStorableForTag(StringBuffer source, String startTag)` Returns the value between equal start and end tags as `Storable`.
`static Storable` | `XMLParser.extractStorableForTag(StringBuffer source, String startTag, String endTag)` Returns the value between start and end tag as `Storable`.
`static Storable` | `XMLParser.extractStorableOrNullForTag(StringBuffer source, String tag)` Returns the value between equal start and end tags as `Storable` or `null`.
`static Storable` | `XMLParser.extractStorableOrNullForTag(StringBuffer source, String startTag, String endTag)` Returns the value between start and end tag as `Storable` or `null`.

Methods in de.jstacs.io with parameters of type Storable
`static void` | `XMLParser.appendStorable2ArrayWithTags(StringBuffer source, Storable[][] s, String tag)` Appends an encoded two dimensional `Storable` array with equal tags to the StringBuffer `source`.
`static void` | `XMLParser.appendStorable2ArrayWithTags(StringBuffer source, Storable[][] s, String startTag, String endTag)` Appends an encoded two dimensional `Storable` array with start and end tag to the StringBuffer `source`.
`static void` | `XMLParser.appendStorable3ArrayWithTags(StringBuffer source, Storable[][][] s, String tag)` Appends an encoded three dimensional `Storable` array with equal tags to the StringBuffer `source`.
`static void` | `XMLParser.appendStorable3ArrayWithTags(StringBuffer source, Storable[][][] s, String startTag, String endTag)` Appends an encoded three dimensional `Storable` array with start and end tag to the StringBuffer `source`.
`static void` | `XMLParser.appendStorableArrayWithTags(StringBuffer source, Storable[] s, String tag)` Appends an encoded Storable array with equal tags to the StringBuffer `source`.
`static void` | `XMLParser.appendStorableArrayWithTags(StringBuffer source, Storable[] s, String startTag, String endTag)` Appends an encoded Storable array with start and end tag to the StringBuffer `source`.
`static void` | `XMLParser.appendStorableOrNullWithTags(StringBuffer source, Storable s, String tag)` Appends a `Storable` object or "null" with equal tags to the StringBuffer `source`.
`static void` | `XMLParser.appendStorableOrNullWithTags(StringBuffer source, Storable s, String startTag, String endTag)` Appends a `Storable` object or "null" with start and end tag to the StringBuffer `source`.
`static void` | `XMLParser.appendStorableWithTags(StringBuffer source, Storable s, String startTag)` Appends a `Storable` object with equal tags to the StringBuffer `source`.
`static void` | `XMLParser.appendStorableWithTags(StringBuffer source, Storable s, String startTag, String endTag)` Appends a `Storable` object with start and end tag to the StringBuffer `source`.
`protected static StringBuffer` | `XMLParser.StorableArrayWithTags(Storable[] s)` Encodes a Storable array.

Uses of Storable in de.jstacs.models

Subinterfaces of Storable in de.jstacs.models
`interface` | `Model` This interface defines all methods for a probabilistic model.

Classes in de.jstacs.models that implement Storable
`class` | `AbstractModel` Abstract class for a model for pattern recognition.
`class` | `CompositeModel` This class is for modelling sequences by modelling the different positions of the each sequence by different models.
`class` | `UniformModel` This class represents an uniform model.

Uses of Storable in de.jstacs.models.discrete

Classes in de.jstacs.models.discrete that implement Storable
`class` | `Constraint` The main class for all constraints on models.
`class` | `DGMParameterSet` The super ParameterSet for any ParameterSet of a `DiscreteGraphicalModel`.
`class` | `DiscreteGraphicalModel` This is the main class for all discrete graphical models (DGM)

Uses of Storable in de.jstacs.models.discrete.inhomogeneous

Classes in de.jstacs.models.discrete.inhomogeneous that implement Storable
`class` | `BayesianNetworkModel` The class implements a Bayesian network of fixed order.
`class` | `DAGModel` The abstract class for directed acyclic graphical models.
`class` | `FSDAGModel` This class can be used for any discrete fixed structure DAG model (FSDAGModel).
`class` | `InhCondProb` This class handles the (conditional) probabilities.
`class` | `InhConstraint` This class is the super class for all inhomogeneous constraints.
`class` | `InhomogeneousDGM` This class is the main class for all inhomgeneous discrete graphical models (IDGM).
`class` | `MEMConstraint` The constraint can be used for any MEM application.

Uses of Storable in de.jstacs.models.discrete.inhomogeneous.parameters

Classes in de.jstacs.models.discrete.inhomogeneous.parameters that implement Storable
`class` | `BayesianNetworkModelParameterSet` The ParameterSet for the class BayesianNetworkModel.
`class` | `FSDAGMParameterSet` The class for the parameters of a FSDAGModel.
`class` | `IDGMParameterSet` This is the abstract container of parameters that is root container for all inhomogeneous discrete graphical model parameter containers.

Uses of Storable in de.jstacs.models.discrete.inhomogeneous.shared

Classes in de.jstacs.models.discrete.inhomogeneous.shared that implement Storable
`class` | `SharedStructureClassifier` This class enables you to learn the structure on all classes together.
`class` | `SharedStructureMixture` This class handles a mixture of models with the same structure that are learned via EM.

Uses of Storable in de.jstacs.models.mixture

Classes in de.jstacs.models.mixture that implement Storable
`class` | `AbstractMixtureModel` This is the abstract class for all kinds of mixture models.
`class` | `MixtureModel` Class for a mixture model of any `Model`s.
`class` | `StrandModel` This model handles sequences that can either lie on the forward strand or on the backward strand.

Uses of Storable in de.jstacs.models.mixture.gibbssampling

Classes in de.jstacs.models.mixture.gibbssampling that implement Storable
`class` | `BurnInTest` This is the abstract super class for any test of the length of the burn-in phase.
`class` | `FSDAGModelForGibbsSampling` This is the class for a fixed structure directed acyclic graphical model that can be used in a Gibbs sampling.
`class` | `SimpleBurnInTest` This is a very simple test for the length of the burn-in phase.

Uses of Storable in de.jstacs.parameters

Classes in de.jstacs.parameters that implement Storable
`class` | `ArrayParameterSet` Class for a `ParameterSet` that consists of a length-`Parameter` that defines the length of the array and an array of `ParameterSetContainer`s of this length.
`class` | `CollectionParameter` Class for a collection parameter, i.e. a parameter that provides some collection of possible values the user can choose from.
`class` | `EnumParameter` This class implements a `CollectionParameter` based on an Enum.
`class` | `ExpandableParameterSet` A class for a `ParameterSet` that can be expanded by additional `Parameter`s at runtime.
`class` | `FileParameter` Class for a parameter that represents a local file.
`static class` | `FileParameter.FileRepresentation` Class that represents a file
`class` | `InstanceParameterSet` Abstract class for a `ParameterSet` containing all parameters necessary to construct an `Object` that implements `InstantiableFromParameterSet`.
`class` | `MultiSelectionCollectionParameter` Class for a `Parameter` that provides a collection of possible values.
`class` | `Parameter` Abstract class for a parameter that shall be used as the parameter of some method, constructor, etc.
`class` | `ParameterSet` (Container) class for a set of `Parameter`s.
`class` | `ParameterSetContainer` Class for a `Parameter` that contains a `ParameterSet` as value.
`class` | `RangeParameter` Class for a parameter wrapper that allows `SimpleParameter`s to be set to a set of values.
`class` | `SimpleParameter` Class for a "simple" parameter.
`class` | `SimpleParameterSet` Class for a `ParameterSet` that is constructed from an array of `Parameters` and thus does nothing in the `loadParameters()`-method.

Uses of Storable in de.jstacs.parameters.validation

Subinterfaces of Storable in de.jstacs.parameters.validation
`interface` | `Constraint` Interface for a constraint that must be fulfilled in a `ConstraintValidator`
`interface` | `ParameterValidator` Interface for a parameter validator, i.e. a class that can validate some possible parameter value.

Classes in de.jstacs.parameters.validation that implement Storable
`class` | `ConstraintValidator` Class for a `ParameterValidator` that is based on `Constraint`s.
`class` | `NumberValidator<E extends Comparable<? extends Number>>` Class that validates all subclasses of `Number` that implement `Comparable` (e.g.
`class` | `ReferenceConstraint` Class for a `Constraint` that defines a condition on one `Parameter` (the one containing the `ConstraintValidator`) with respect to another `Parameter`.
`class` | `SimpleReferenceConstraint` Class for a `ReferenceConstraint` that checks for "simple" conditions as defined in the `Constraint`-interface.
`class` | `SimpleStaticConstraint` Class for a `Constraint` that checks values against static values using the comparison operators defined in the `Constraint`-interface.
`class` | `StorableValidator` Class for a validator that validates instances and XML-representations for the correct class types (e.g.

Constructor parameters in de.jstacs.parameters.validation with type arguments of type Storable
`StorableValidator(Class<? extends Storable> clazz)` Creates a new `ObjectValidator` for a subclass of `Storable`.
`StorableValidator(Class<? extends Storable> clazz, boolean trained)` Creates a new `ObjectValidator` for a subclass of `AbstractModel` or `AbstractClassifier`.

Uses of Storable in de.jstacs.results

Classes in de.jstacs.results that implement Storable
`class` | `CategoricalResult` A class for categorical results (i.e. non-numerical results) for primitives and `Strings`
`class` | `ImageResult` A class for results that are images of the PNG format.
`class` | `ListResult` Class for a `Result` that contains a list or a matrix, respectively, of `ResultSet`s.
`class` | `MeanResultSet` Class that computes the mean and the standard error of a series of `NumericalResultSet`s.
`class` | `NumericalResult` Class for numerical `Result` values.
`class` | `NumericalResultSet` Class for a set of numerical result values, which are all of the type `NumericalResult`.
`class` | `Result` The abstract class for any result.
`class` | `ResultSet` Class for a set of `Result`s, which provides methods to access single `Result`s in the set, to retrieve the number of `Result`s in the set, to get a `String` representation or an XML-representation of all the `Result`s in the set.
`class` | `SampleResult` Result that contains a `Sample`.
`class` | `SimpleResult` Abstract class for a `Result` with a value of a primitive data type or `String`.
`class` | `StorableResult` Class for results that are `Storable`s.

Methods in de.jstacs.results that return Storable
`Storable` | `StorableResult.getResultInstance()` Returns the instance of the `Storable` that is the result of this `ObjectResult`

Constructors in de.jstacs.results with parameters of type Storable
`StorableResult(String name, String comment, Storable object)` Creates a result for an XML-representation of an object.

Uses of Storable in de.jstacs.scoringFunctions

Subinterfaces of Storable in de.jstacs.scoringFunctions
`interface` | `NormalizableScoringFunction` The interface for normalizable ScoringFunctions.
`interface` | `ScoringFunction` This interface is the main part of any ScoreClassifier.

Classes in de.jstacs.scoringFunctions that implement Storable
`class` | `AbstractNormalizableScoringFunction` This class is the main part of any ScoreClassifier.
`class` | `IndependentProductScoringFunction` This class enables the user to model parts of the sequence independent of each other.
`class` | `MRFScoringFunction` This class implements the scoring function for any MRF.
`class` | `UniformScoringFunction` This scoring function does nothing.
`class` | `VariableLengthScoringFunction` This is the main class for all ScoringFunctions that allow to score subsequences of arbitrary length.

Uses of Storable in de.jstacs.scoringFunctions.directedGraphicalModels

Classes in de.jstacs.scoringFunctions.directedGraphicalModels that implement Storable
`class` | `BayesianNetworkScoringFunction` This class implements a scoring function that is a moral directed graphical model, i.e. a moral Bayesian network.

Uses of Storable in de.jstacs.scoringFunctions.directedGraphicalModels.structureLearning.measures

Classes in de.jstacs.scoringFunctions.directedGraphicalModels.structureLearning.measures that implement Storable
`class` | `InhomogeneousMarkov` Class for a network structure of a `BayesianNetworkScoringFunction` that is an inhomogeneous Markov model.
`class` | `Measure` Class for structure measures that derive an optimal structure with respect to some criterion within a class of possible structures from data.

Uses of Storable in de.jstacs.scoringFunctions.directedGraphicalModels.structureLearning.measures.btMeasures

Classes in de.jstacs.scoringFunctions.directedGraphicalModels.structureLearning.measures.btMeasures that implement Storable
`class` | `BTExplainingAwayResidual` Structure learning `Measure` that computes a maximum spanning tree based on the explaining away residual and uses the resulting tree structure as structure of a Bayesian tree (special case of a Bayesian network) in a `BayesianNetworkScoringFunction`.
`class` | `BTMutualInformation` Structure learning `Measure` that computes a maximum spanning tree based on mutual information and uses the resulting tree structure as structure of a Bayesian tree (special case of a Bayesian network) in a `BayesianNetworkScoringFunction`.

Uses of Storable in de.jstacs.scoringFunctions.directedGraphicalModels.structureLearning.measures.pmmMeasures

Classes in de.jstacs.scoringFunctions.directedGraphicalModels.structureLearning.measures.pmmMeasures that implement Storable
`class` | `PMMExplainingAwayResidual` Class for the network structure of a `BayesianNetworkScoringFunction` that is a permuted Markov model based on the explaining away residual.
`class` | `PMMMutualInformation` Class for the network structure of a `BayesianNetworkScoringFunction` that is a permuted Markov model based on mutual information.

Uses of Storable in de.jstacs.scoringFunctions.homogeneous

Classes in de.jstacs.scoringFunctions.homogeneous that implement Storable
`class` | `HMM0ScoringFunction` This scoring function implements a homogeneous Markov model of order zero (hMM(0)) for a fixed sequence length.
`class` | `HMMScoringFunction` This scoring function implements a homogeneous Markov model of arbitrary order for any sequence length.
`class` | `HomogeneousScoringFunction` This is the main class for all homogeneous ScoringFunctions.
`class` | `UniformHomogeneousScoringFunction` This scoring function does nothing.

Uses of Storable in de.jstacs.scoringFunctions.mix

Classes in de.jstacs.scoringFunctions.mix that implement Storable
`class` | `AbstractMixtureScoringFunction` This main abstract class for any mixture (e.g.
`class` | `MixtureScoringFunction` This class implements a real mixture model.

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