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## Uses of Interface
de.jstacs.Storable

Packages that use Storable
de.jstacs | This package is the root package for the most and important packages.
de.jstacs.algorithms.optimization.termination | Provides classes for termination conditions that can be used in algorithms
de.jstacs.classifiers | This package provides the framework for any classifier.
de.jstacs.classifiers.assessment | This package allows to assess classifiers.
de.jstacs.classifiers.differentiableSequenceScoreBased | Provides the classes for `Classifier`s that are based on `SequenceScore`s.
de.jstacs.classifiers.differentiableSequenceScoreBased.gendismix | Provides an implementation of a classifier that allows to train the parameters of a set of `DifferentiableStatisticalModel`s by a unified generative-discriminative learning principle
de.jstacs.classifiers.differentiableSequenceScoreBased.logPrior | Provides a general definition of a parameter log-prior and a number of implementations of Laplace and Gaussian priors
de.jstacs.classifiers.differentiableSequenceScoreBased.msp | Provides an implementation of a classifier that allows to train the parameters of a set of `DifferentiableStatisticalModel`s either by maximum supervised posterior (MSP) or by maximum conditional likelihood (MCL)
de.jstacs.classifiers.differentiableSequenceScoreBased.sampling | Provides the classes for `AbstractScoreBasedClassifier`s that are based on `SamplingDifferentiableStatisticalModel`s and that sample parameters using the Metropolis-Hastings algorithm.
de.jstacs.classifiers.performanceMeasures | This package provides the implementations of performance measures that can be used to assess any classifier
de.jstacs.classifiers.trainSMBased | Provides the classes for `Classifier`s that are based on `TrainableStatisticalModel`s
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.motifDiscovery | This package provides the framework including the interface for any de novo motif discoverer
de.jstacs.motifDiscovery.history |
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.sampling | This package contains many classes that can be used while a sampling.
de.jstacs.sequenceScores | Provides all `SequenceScore`s, which can be used to score a `Sequence`, typically using some model assumptions.
de.jstacs.sequenceScores.differentiable |
de.jstacs.sequenceScores.differentiable.logistic |
de.jstacs.sequenceScores.statisticalModels | Provides all `StatisticalModel`s, which can compute a proper (i.e., normalized) likelihood over the input space of sequences.
de.jstacs.sequenceScores.statisticalModels.differentiable | Provides all `DifferentiableStatisticalModel`s, which can compute the gradient with respect to their parameters for a given input `Sequence`.
de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels | Provides `DifferentiableStatisticalModel`s that are directed graphical models.
de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.structureLearning.measures | Provides the facilities to learn the structure of a `BayesianNetworkDiffSM`.
de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.structureLearning.measures.btMeasures | Provides the facilities to learn the structure of a `BayesianNetworkDiffSM` as a Bayesian tree using a number of measures to define a rating of structures
de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.structureLearning.measures.pmmMeasures | Provides the facilities to learn the structure of a `BayesianNetworkDiffSM` as a permuted Markov model using a number of measures to define a rating of structures
de.jstacs.sequenceScores.statisticalModels.differentiable.homogeneous | Provides `DifferentiableStatisticalModel`s that are homogeneous, i.e. model probabilities or scores independent of the position within a sequence
de.jstacs.sequenceScores.statisticalModels.differentiable.mixture | Provides `DifferentiableSequenceScore`s that are mixtures of other `DifferentiableSequenceScore`s.
de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif |
de.jstacs.sequenceScores.statisticalModels.trainable | Provides all `TrainableStatisticalModel`s, which can be learned from a single `DataSet`.
de.jstacs.sequenceScores.statisticalModels.trainable.discrete |
de.jstacs.sequenceScores.statisticalModels.trainable.discrete.homogeneous |
de.jstacs.sequenceScores.statisticalModels.trainable.discrete.homogeneous.parameters |
de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous | This package contains various inhomogeneous models.
de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.parameters |
de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.shared |
de.jstacs.sequenceScores.statisticalModels.trainable.hmm | The package provides all interfaces and classes for a hidden Markov model (HMM).
de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models | The package provides different implementations of hidden Markov models based on `AbstractHMM`
de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions |
de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.continuous |
de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.discrete |
de.jstacs.sequenceScores.statisticalModels.trainable.hmm.training | The package provides all classes used to determine the training algorithm of a hidden Markov model
de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions | The package provides all interfaces and classes for transitions used in hidden Markov models.
de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.elements |
de.jstacs.sequenceScores.statisticalModels.trainable.mixture | This package is the super package for any mixture model.
de.jstacs.sequenceScores.statisticalModels.trainable.mixture.motif |
de.jstacs.sequenceScores.statisticalModels.trainable.mixture.motif.positionprior |
de.jstacs.sequenceScores.statisticalModels.trainable.phylo |
de.jstacs.utils | This package contains a bundle of useful classes and interfaces like ...
de.jstacs.utils.galaxy |

Uses of Storable in de.jstacs

Classes in de.jstacs that implement Storable
`class` | `AnnotatedEntity` Superclass for all Jstacs entities that have a name, a comment, and a data type as annotations.

Uses of Storable in de.jstacs.algorithms.optimization.termination

Subinterfaces of Storable in de.jstacs.algorithms.optimization.termination
`interface` | `TerminationCondition` This interface can be used in any iterative algorithm for determining the end of the algorithm.

Classes in de.jstacs.algorithms.optimization.termination that implement Storable
`class` | `AbsoluteValueCondition` Deprecated. use of the absolute value condition is not recommended and it may be removed in future releases
`static class` | `AbsoluteValueCondition.AbsoluteValueConditionParameterSet` Deprecated. This class implements the parameter set for a `AbsoluteValueCondition`.
`class` | `AbstractTerminationCondition` This class is the abstract super class of many `TerminationCondition`s.
`static class` | `AbstractTerminationCondition.AbstractTerminationConditionParameterSet` This class implements the super class of all parameter sets of instances from `AbstractTerminationCondition`.
`class` | `CombinedCondition` This class allows to use many `TerminationCondition`s at once.
`static class` | `CombinedCondition.CombinedConditionParameterSet` This class implements the parameter set for a `CombinedCondition`.
`class` | `IterationCondition` This class will stop an optimization if the number of iteration reaches a given number.
`static class` | `IterationCondition.IterationConditionParameterSet` This class implements the parameter set for a `IterationCondition`.
`class` | `SmallDifferenceOfFunctionEvaluationsCondition` This class implements a `TerminationCondition` that stops an optimization if the difference of the current and the last function evaluations will be small, i.e., .
`static class` | `SmallDifferenceOfFunctionEvaluationsCondition.SmallDifferenceOfFunctionEvaluationsConditionParameterSet` This class implements the parameter set for a `SmallDifferenceOfFunctionEvaluationsCondition`.
`class` | `SmallGradientConditon` This class implements a `TerminationCondition` that allows no further iteration in an optimization if the the gradient becomes small, i.e., .
`static class` | `SmallGradientConditon.SmallGradientConditonParameterSet` This class implements the parameter set for a `SmallStepCondition`.
`class` | `SmallStepCondition` This class implements a `TerminationCondition` that allows no further iteration in an optimization if the scalar product of the current and the last values of `x` will be small, i.e., .
`static class` | `SmallStepCondition.SmallStepConditionParameterSet` This class implements the parameter set for a `SmallStepCondition`.
`class` | `TimeCondition` This class implements a `TerminationCondition` that stops the optimization if the elapsed time in seconds is greater than a given value.
`static class` | `TimeCondition.TimeConditionParameterSet` This class implements the parameter set for a `TimeCondition`.

Uses of Storable in de.jstacs.classifiers

Classes in de.jstacs.classifiers 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 `Result`s given as a table of `double` s.
`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.

Uses of Storable in de.jstacs.classifiers.assessment

Classes in de.jstacs.classifiers.assessment that implement Storable
`class` | `ClassifierAssessmentAssessParameterSet` This class is the superclass used by all `ClassifierAssessmentAssessParameterSet`s.
`class` | `KFoldCrossValidationAssessParameterSet` 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 `RepeatedHoldOutExperiment`.
`class` | `RepeatedSubSamplingAssessParameterSet` This class implements a `ClassifierAssessmentAssessParameterSet` that must be used to call method `assess( ... )` of a `RepeatedSubSamplingExperiment`.
`class` | `Sampled_RepeatedHoldOutAssessParameterSet` This class implements a `ClassifierAssessmentAssessParameterSet` that must be used to call the method `assess( ... )` of a `Sampled_RepeatedHoldOutExperiment`.

Uses of Storable in de.jstacs.classifiers.differentiableSequenceScoreBased

Classes in de.jstacs.classifiers.differentiableSequenceScoreBased that implement Storable
`class` | `ScoreClassifier` This abstract class implements the main functionality of a `DifferentiableSequenceScore` based classifier.
`class` | `ScoreClassifierParameterSet` A set of `Parameter`s for any `ScoreClassifier`.

Uses of Storable in de.jstacs.classifiers.differentiableSequenceScoreBased.gendismix

Classes in de.jstacs.classifiers.differentiableSequenceScoreBased.gendismix that implement Storable
`class` | `GenDisMixClassifier` This class implements a classifier the optimizes the following function The weights have to sum to 1.
`class` | `GenDisMixClassifierParameterSet` This class contains the parameters for the `GenDisMixClassifier`.

Uses of Storable in de.jstacs.classifiers.differentiableSequenceScoreBased.logPrior

Classes in de.jstacs.classifiers.differentiableSequenceScoreBased.logPrior that implement Storable
`class` | `CompositeLogPrior` This class implements a composite prior that can be used for DifferentiableStatisticalModel.
`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 `DifferentiableStatisticalModel`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 `DifferentiableStatisticalModel`s and a set of class parameters.
`class` | `SeparateLogPrior` Abstract class for priors that penalize each parameter value independently and have some variances (and possible means) as hyperparameters.
`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.classifiers.differentiableSequenceScoreBased.msp

Classes in de.jstacs.classifiers.differentiableSequenceScoreBased.msp that implement Storable
`class` | `MSPClassifier` This class implements a classifier that allows the training via MCL or MSP principle.

Uses of Storable in de.jstacs.classifiers.differentiableSequenceScoreBased.sampling

Classes in de.jstacs.classifiers.differentiableSequenceScoreBased.sampling that implement Storable
`class` | `SamplingGenDisMixClassifier` A classifier that samples its parameters from a `LogGenDisMixFunction` using the Metropolis-Hastings algorithm.
`class` | `SamplingGenDisMixClassifierParameterSet` `ParameterSet` to instantiate a `SamplingGenDisMixClassifier`.
`class` | `SamplingScoreBasedClassifier` A classifier that samples the parameters of `SamplingDifferentiableStatisticalModel`s by the Metropolis-Hastings algorithm.
`class` | `SamplingScoreBasedClassifierParameterSet` `ParameterSet` to instantiate a `SamplingScoreBasedClassifier`.

Uses of Storable in de.jstacs.classifiers.performanceMeasures

Classes in de.jstacs.classifiers.performanceMeasures that implement Storable
`class` | `AbstractPerformanceMeasure` This class is the abstract super class of any performance measure used to evaluate an `AbstractClassifier`.
`class` | `AucPR` This class implements the area under curve of the precision-recall curve.
`class` | `AucROC` This class implements the area under curve of the Receiver Operating Characteristics curve.
`class` | `ClassificationRate` This class implements the classification rate, i.e.
`class` | `ConfusionMatrix` This class implements the performance measure confusion matrix.
`class` | `FalsePositiveRateForFixedSensitivity` This class implements the false positive rate for a fixed sensitivity.
`class` | `MaximumCorrelationCoefficient` This class implements the maximum of the correlation coefficient .
`class` | `MaximumFMeasure` Computes the maximum of the general F-measure given a positive real parameter .
`class` | `MaximumNumericalTwoClassMeasure` This class prepares everything for an easy implementation of a maximum of any numerical performance measure.
`class` | `NumericalPerformanceMeasureParameterSet` This class implements a container for `NumericalPerformanceMeasure`s that can be used, for instance, in an repeated assessment, (cf.
`class` | `PerformanceMeasureParameterSet` This class implements a container of `AbstractPerformanceMeasure`s that can be used in `AbstractClassifier.evaluate(PerformanceMeasureParameterSet, boolean, de.jstacs.data.DataSet...)`.
`class` | `PositivePredictiveValueForFixedSensitivity` This class implements the positive predictive value for a fixed sensitivity.
`class` | `PRCurve` This class implements the precision-recall curve and its area under the curve.
`class` | `ROCCurve` This class implements the Receiver Operating Characteristics curve and the area under the curve.
`class` | `SensitivityForFixedSpecificity` This class implements the sensitivity for a fixed specificity.
`class` | `TwoClassAbstractPerformanceMeasure` This class is the abstract super class of any performance measure that can only be computed for binary classifiers.

Uses of Storable in de.jstacs.classifiers.trainSMBased

Classes in de.jstacs.classifiers.trainSMBased that implement Storable
`class` | `TrainSMBasedClassifier` Classifier that works on `TrainableStatisticalModel`s for each of the different classes.

Uses of Storable in de.jstacs.data

Classes in de.jstacs.data that implement Storable
`class` | `AlphabetContainer` The container for `Alphabet`s used in a `Sequence`, `DataSet`, `AbstractTrainableStatisticalModel` or ... .
`static class` | `AlphabetContainer.AbstractAlphabetContainerParameterSet<T extends AlphabetContainer>` This class is the super class of any `InstanceParameterSet` for `AlphabetContainer`.
`class` | `AlphabetContainerParameterSet` Class for the `AlphabetContainerParameterSet.SectionDefinedAlphabetParameterSet` 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 parameter set 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` | `Alphabet` Class for a set of symbols, i.e. an `Alphabet`.
`static class` | `Alphabet.AlphabetParameterSet<T extends Alphabet>` The super class for the `InstanceParameterSet` of any `Alphabet`.
`class` | `ComplementableDiscreteAlphabet` This abstract class indicates that an alphabet can be used 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 `String`s.
`static class` | `DiscreteAlphabet.DiscreteAlphabetParameterSet` Class for the `ParameterSet` of a `DiscreteAlphabet`.
`class` | `DiscreteAlphabetMapping` This class implements the transformation of discrete values to other discrete values which define a `DiscreteAlphabet`.
`class` | `DNAAlphabet` This class implements the discrete alphabet that is used for DNA.
`static class` | `DNAAlphabet.DNAAlphabetParameterSet` The parameter set for a `DNAAlphabet`.
`class` | `DNAAlphabetContainer` This class implements a singleton for an `AlphabetContainer` that can be used for DNA.
`static class` | `DNAAlphabetContainer.DNAAlphabetContainerParameterSet` This class implements a singleton for the `ParameterSet` of a `DNAAlphabetContainer`.
`class` | `GenericComplementableDiscreteAlphabet` This class implements an generic complementable discrete alphabet.
`static class` | `GenericComplementableDiscreteAlphabet.GenericComplementableDiscreteAlphabetParameterSet` This class is used as container for the parameters of a `GenericComplementableDiscreteAlphabet`.
`class` | `ProteinAlphabet` This class implements the discrete alphabet that is used for proteins (one letter code).
`static class` | `ProteinAlphabet.ProteinAlphabetParameterSet` The parameter set for a `ProteinAlphabet`.

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` | `ReferenceSequenceAnnotation` This class implements a `SequenceAnnotation` that contains a reference sequence.
`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.motifDiscovery

Subinterfaces of Storable in de.jstacs.motifDiscovery
`interface` | `MotifDiscoverer` This is the interface that any tool for de-novo motif discovery should implement.
`interface` | `MutableMotifDiscoverer` This is the interface that any tool for de-novo motif discovery should implement that allows any modify-operations like shift, shrink and expand.

Uses of Storable in de.jstacs.motifDiscovery.history

Subinterfaces of Storable in de.jstacs.motifDiscovery.history
`interface` | `History` This interface is used to manage the history of some process.

Classes in de.jstacs.motifDiscovery.history that implement Storable
`class` | `CappedHistory` This class combines a threshold on the number of steps which can be performed with any other `History`.
`class` | `NoRevertHistory` This class implements a history that allows operations, that are not a priorily forbidden and do not create a configuration that has already be considered.
`class` | `RestrictedRepeatHistory` This class implements a history that allows operations (i.e. a pair of `int`), that are not a priorily forbidden and that are done before less than a specified threshold.
`class` | `SimpleHistory` This class implements a simple history that has a limited memory that will be used cyclicly.

Uses of Storable in de.jstacs.parameters

Classes in de.jstacs.parameters that implement Storable
`class` | `AbstractSelectionParameter` Class for a collection parameter, i.e. a parameter that provides some collection of possible values the user can choose from.
`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` | `EnumParameter` This class implements a `SelectionParameter` 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<T extends InstantiableFromParameterSet>` Container class for a set of `Parameter`s that can be used to instantiate another class.
`class` | `MultiSelectionParameter` 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 `ParameterSetContainer` 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` | `SelectionParameter` Class for a collection parameter, i.e. a parameter that provides some collection of possible values the user can choose from.
`class` | `SequenceScoringParameterSet<T extends InstantiableFromParameterSet>` Abstract class for a `ParameterSet` containing all parameters necessary to construct an `Object` that implements `InstantiableFromParameterSet`.
`class` | `SimpleParameter` Class for a "simple" parameter.
`class` | `SimpleParameterSet` Class for a `ParameterSet` that is constructed from an array of `Parameter`s.

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 values.

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` | `SimpleStaticConstraint` Class for a `Constraint` that checks values against static values using the comparison operators defined in the interface `Constraint`.
`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 `StorableValidator` for a subclass of `Storable`.
`StorableValidator(Class<? extends Storable> clazz, boolean trained)` Creates a new `StorableValidator` for a subclass of `AbstractTrainableStatisticalModel` 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 `String`s.
`class` | `DataSetResult` `Result` that contains a `DataSet`.
`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` | `SimpleResult` Abstract class for a `Result` with a value of a primitive data type or `String`.
`class` | `StorableResult` Class for `Result`s 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 `StorableResult`.

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.sampling

Subinterfaces of Storable in de.jstacs.sampling
`interface` | `BurnInTest` This is the abstract super class for any test of the length of the burn-in phase.

Classes in de.jstacs.sampling that implement Storable
`class` | `AbstractBurnInTest` This abstract class implements some of the methods of `BurnInTest` to alleviate the implementation of efficient and new burn-in tests.
`class` | `AbstractBurnInTestParameterSet` Class for the parameters of a `AbstractBurnInTest`.
`class` | `SimpleBurnInTest` Deprecated. since this burn test ignore the data coming from the sampling, it might be problematic to use this test
`class` | `VarianceRatioBurnInTest` In this class the Variance-Ratio method of Gelman and Rubin is implemented to test the length of the burn-in phase of a multi-chain Gibbs Sampling (number of chains >2).
`class` | `VarianceRatioBurnInTestParameterSet` Class for the parameters of a `VarianceRatioBurnInTest`.

Uses of Storable in de.jstacs.sequenceScores

Subinterfaces of Storable in de.jstacs.sequenceScores
`interface` | `SequenceScore` This interface defines a scoring function that assigns a score to each input sequence.

Uses of Storable in de.jstacs.sequenceScores.differentiable

Subinterfaces of Storable in de.jstacs.sequenceScores.differentiable
`interface` | `DifferentiableSequenceScore` This interface is the main part of any `ScoreClassifier`.

Classes in de.jstacs.sequenceScores.differentiable that implement Storable
`class` | `AbstractDifferentiableSequenceScore` This class is the main part of any `ScoreClassifier`.
`class` | `IndependentProductDiffSS` This class enables the user to model parts of a sequence independent of each other.
`class` | `UniformDiffSS` This `DifferentiableSequenceScore` does nothing.

Uses of Storable in de.jstacs.sequenceScores.differentiable.logistic

Subinterfaces of Storable in de.jstacs.sequenceScores.differentiable.logistic
`interface` | `LogisticConstraint` This interface defines the function for sequence which can be used in `LogisticDiffSS`.

Classes in de.jstacs.sequenceScores.differentiable.logistic that implement Storable
`class` | `LogisticDiffSS` This class implements a logistic function.
`class` | `ProductConstraint` This class implements product constraints, i.e., the method `ProductConstraint.getValue(Sequence,int)` returns the product of the values for the positions defined in the constructor.

Uses of Storable in de.jstacs.sequenceScores.statisticalModels

Subinterfaces of Storable in de.jstacs.sequenceScores.statisticalModels
`interface` | `StatisticalModel` This interface declares methods of a statistical model, i.e., a `SequenceScore` that defines a proper likelihood over the input `Sequence`s.

Uses of Storable in de.jstacs.sequenceScores.statisticalModels.differentiable

Subinterfaces of Storable in de.jstacs.sequenceScores.statisticalModels.differentiable
`interface` | `DifferentiableStatisticalModel` The interface for normalizable `DifferentiableSequenceScore`s.
`interface` | `SamplingDifferentiableStatisticalModel` Interface for `DifferentiableStatisticalModel`s that can be used for Metropolis-Hastings sampling in a `SamplingScoreBasedClassifier`.
`interface` | `VariableLengthDiffSM` This is an interface for all `DifferentiableStatisticalModel`s that allow to score subsequences of arbitrary length.

Classes in de.jstacs.sequenceScores.statisticalModels.differentiable that implement Storable
`class` | `AbstractDifferentiableStatisticalModel` This class is the main part of any `ScoreClassifier`.
`class` | `AbstractVariableLengthDiffSM` This abstract class implements some methods declared in `DifferentiableStatisticalModel` based on the declaration of methods in `VariableLengthDiffSM`.
`class` | `CyclicMarkovModelDiffSM` This scoring function implements a cyclic Markov model of arbitrary order and periodicity for any sequence length.
`class` | `IndependentProductDiffSM` This class enables the user to model parts of a sequence independent of each other.
`class` | `MappingDiffSM` This class implements a `DifferentiableStatisticalModel` that works on mapped `Sequence`s.
`class` | `MarkovRandomFieldDiffSM` This class implements the scoring function for any MRF (Markov Random Field).
`class` | `MultiDimensionalSequenceWrapperDiffSM` This class implements a simple wrapper for multidimensional sequences.
`class` | `NormalizedDiffSM` This class makes an unnormalized `DifferentiableStatisticalModel` to a normalized `DifferentiableStatisticalModel`.
`class` | `UniformDiffSM` This `DifferentiableStatisticalModel` does nothing.

Uses of Storable in de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels

Classes in de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels that implement Storable
`class` | `BayesianNetworkDiffSM` This class implements a scoring function that is a moral directed graphical model, i.e. a moral Bayesian network.
`class` | `BayesianNetworkDiffSMParameterSet` Class for the parameters of a `BayesianNetworkDiffSM`.
`class` | `BNDiffSMParameter` Class for the parameters of a `BayesianNetworkDiffSM`.
`class` | `BNDiffSMParameterTree` Class for the tree that represents the context of a `BNDiffSMParameter` in a `BayesianNetworkDiffSM`.
`class` | `BNDiffSMParameterTree.TreeElement` Class for the nodes of a `BNDiffSMParameterTree`
`class` | `MarkovModelDiffSM` This class implements a `AbstractDifferentiableStatisticalModel` for an inhomogeneous Markov model.

Uses of Storable in de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.structureLearning.measures

Classes in de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.structureLearning.measures that implement Storable
`class` | `InhomogeneousMarkov` Class for a network structure of a `BayesianNetworkDiffSM` that is an inhomogeneous Markov model.
`static class` | `InhomogeneousMarkov.InhomogeneousMarkovParameterSet` Class for an `InstanceParameterSet` that defines the parameters of an `InhomogeneousMarkov` structure `Measure`.
`class` | `Measure` Class for structure measures that derive an optimal structure with respect to some criterion within a class of possible structures from data.
`static class` | `Measure.MeasureParameterSet` This class is the super class of any `ParameterSet` that can be used to instantiate a `Measure`.

Uses of Storable in de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.structureLearning.measures.btMeasures

Classes in de.jstacs.sequenceScores.statisticalModels.differentiable.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 `BayesianNetworkDiffSM` .
`static class` | `BTExplainingAwayResidual.BTExplainingAwayResidualParameterSet` Class for the parameters of a `BTExplainingAwayResidual` structure `Measure`.
`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 `BayesianNetworkDiffSM` .
`static class` | `BTMutualInformation.BTMutualInformationParameterSet` Class for the parameters of a `BTMutualInformation` structure `Measure`.

Uses of Storable in de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.structureLearning.measures.pmmMeasures

Classes in de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.structureLearning.measures.pmmMeasures that implement Storable
`class` | `PMMExplainingAwayResidual` Class for the network structure of a `BayesianNetworkDiffSM` that is a permuted Markov model based on the explaining away residual.
`static class` | `PMMExplainingAwayResidual.PMMExplainingAwayResidualParameterSet` Class for the parameters of a `PMMExplainingAwayResidual` structure `Measure`.
`class` | `PMMMutualInformation` Class for the network structure of a `BayesianNetworkDiffSM` that is a permuted Markov model based on mutual information.
`static class` | `PMMMutualInformation.PMMMutualInformationParameterSet` Class for the parameters of a `PMMMutualInformation` structure `Measure`.

Uses of Storable in de.jstacs.sequenceScores.statisticalModels.differentiable.homogeneous

Classes in de.jstacs.sequenceScores.statisticalModels.differentiable.homogeneous that implement Storable
`class` | `HomogeneousDiffSM` This is the main class for all homogeneous `DifferentiableSequenceScore`s.
`class` | `HomogeneousMM0DiffSM` This scoring function implements a homogeneous Markov model of order zero (hMM(0)) for a fixed sequence length.
`class` | `HomogeneousMMDiffSM` This scoring function implements a homogeneous Markov model of arbitrary order for any sequence length.
`class` | `UniformHomogeneousDiffSM` This scoring function does nothing.

Uses of Storable in de.jstacs.sequenceScores.statisticalModels.differentiable.mixture

Classes in de.jstacs.sequenceScores.statisticalModels.differentiable.mixture that implement Storable
`class` | `AbstractMixtureDiffSM` This main abstract class for any mixture scoring function (e.g.
`class` | `MixtureDiffSM` This class implements a real mixture model.
`class` | `StrandDiffSM` This class enables the user to search on both strand.
`class` | `VariableLengthMixtureDiffSM` This class implements a mixture of `VariableLengthDiffSM` by extending `MixtureDiffSM` and implementing the methods of `VariableLengthDiffSM`.

Uses of Storable in de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif

Classes in de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif that implement Storable
`class` | `DurationDiffSM` This class is the super class for all one dimensional position scoring functions that can be used as durations for semi Markov models.
`class` | `ExtendedZOOPSDiffSM` This class handles mixtures with at least one hidden motif.
`class` | `MixtureDurationDiffSM` This class implements a mixture of `DurationDiffSM`s.
`class` | `PositionDiffSM` This class implements a position scoring function that enables the user to get a score without using a Sequence object.
`class` | `SkewNormalLikeDurationDiffSM` This class implements a skew normal like discrete truncated distribution.
`class` | `UniformDurationDiffSM` This scoring function implements a uniform distribution for positions.

Uses of Storable in de.jstacs.sequenceScores.statisticalModels.trainable

Subinterfaces of Storable in de.jstacs.sequenceScores.statisticalModels.trainable
`interface` | `TrainableStatisticalModel` This interface defines all methods for a probabilistic model.

Classes in de.jstacs.sequenceScores.statisticalModels.trainable that implement Storable
`class` | `AbstractTrainableStatisticalModel` Abstract class for a model for pattern recognition.
`class` | `CompositeTrainSM` This class is for modelling sequences by modelling the different positions of the each sequence by different models.
`class` | `DifferentiableStatisticalModelWrapperTrainSM` This model can be used to use a DifferentiableStatisticalModel as model.
`class` | `UniformTrainSM` This class represents a uniform model.
`class` | `VariableLengthWrapperTrainSM` This class allows to train any `TrainableStatisticalModel` on `DataSet`s of `Sequence`s with variable length if each individual length is at least `SequenceScore.getLength()`.

Uses of Storable in de.jstacs.sequenceScores.statisticalModels.trainable.discrete

Classes in de.jstacs.sequenceScores.statisticalModels.trainable.discrete that implement Storable
`class` | `Constraint` The main class for all constraints on models.
`class` | `DGTrainSMParameterSet<T extends DiscreteGraphicalTrainSM>` The super `ParameterSet` for any parameter set of a `DiscreteGraphicalTrainSM`.
`class` | `DiscreteGraphicalTrainSM` This is the main class for all discrete graphical models (DGM).

Uses of Storable in de.jstacs.sequenceScores.statisticalModels.trainable.discrete.homogeneous

Classes in de.jstacs.sequenceScores.statisticalModels.trainable.discrete.homogeneous that implement Storable
`class` | `HomogeneousMM` This class implements homogeneous Markov models (hMM) of arbitrary order.
`class` | `HomogeneousTrainSM` This class implements homogeneous models of arbitrary order.
`protected class` | `HomogeneousTrainSM.HomCondProb` This class handles the (conditional) probabilities of a homogeneous model in a fast way.

Uses of Storable in de.jstacs.sequenceScores.statisticalModels.trainable.discrete.homogeneous.parameters

Classes in de.jstacs.sequenceScores.statisticalModels.trainable.discrete.homogeneous.parameters that implement Storable
`class` | `HomMMParameterSet` This class implements a container for all parameters of a homogeneous Markov model.
`class` | `HomogeneousTrainSMParameterSet` This class implements a container for all parameters of any homogeneous model.

Uses of Storable in de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous

Classes in de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous that implement Storable
`class` | `BayesianNetworkTrainSM` The class implements a Bayesian network ( `StructureLearner.ModelType.BN` ) of fixed order.
`class` | `DAGTrainSM` The abstract class for directed acyclic graphical models (`DAGTrainSM`).
`class` | `FSDAGModelForGibbsSampling` This is the class for a fixed structure directed acyclic graphical model (see `FSDAGTrainSM`) that can be used in a Gibbs sampling.
`class` | `FSDAGTrainSM` This class can be used for any discrete fixed structure directed acyclic graphical model ( `FSDAGTrainSM`).
`class` | `InhCondProb` This class handles (conditional) probabilities of sequences for inhomogeneous models.
`class` | `InhConstraint` This class is the superclass for all inhomogeneous constraints.
`class` | `InhomogeneousDGTrainSM` This class is the main class for all inhomogeneous discrete graphical models (`InhomogeneousDGTrainSM`).
`class` | `MEMConstraint` This constraint can be used for any maximum entropy model (MEM) application.

Uses of Storable in de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.parameters

Classes in de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.parameters that implement Storable
`class` | `BayesianNetworkTrainSMParameterSet` The `ParameterSet` for the class `BayesianNetworkTrainSM`.
`class` | `FSDAGModelForGibbsSamplingParameterSet` The class for the parameters of a `FSDAGModelForGibbsSampling`.
`class` | `FSDAGTrainSMParameterSet` The class for the parameters of a `FSDAGTrainSM` (fixed structure directed acyclic graphical model).
`class` | `IDGTrainSMParameterSet` This is the abstract container of parameters that is a root container for all inhomogeneous discrete graphical model parameter containers.

Uses of Storable in de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.shared

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

Uses of Storable in de.jstacs.sequenceScores.statisticalModels.trainable.hmm

Classes in de.jstacs.sequenceScores.statisticalModels.trainable.hmm that implement Storable
`class` | `AbstractHMM` This class is the super class of all implementations hidden Markov models (HMMs) in Jstacs.

Uses of Storable in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models

Classes in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models that implement Storable
`class` | `DifferentiableHigherOrderHMM` This class combines an `HigherOrderHMM` and a `DifferentiableStatisticalModel` by implementing some of the declared methods.
`class` | `HigherOrderHMM` This class implements a higher order hidden Markov model.
`class` | `SamplingHigherOrderHMM`
`class` | `SamplingPhyloHMM` This class implements an (higher order) HMM that contains multi-dimensional emissions described by a phylogenetic tree.

Uses of Storable in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions

Subinterfaces of Storable in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions
`interface` | `DifferentiableEmission` This interface declares all methods needed in an emission during a numerical optimization of HMM.
`interface` | `Emission` This interface declares all method for an emission of a state.
`interface` | `SamplingEmission`

Classes in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions that implement Storable
`class` | `MixtureEmission` This class implements a mixture of `Emission`s.
`class` | `SilentEmission` This class implements a silent emission which is used to create silent states.
`class` | `UniformEmission` This class implements a simple uniform emission.

Uses of Storable in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.continuous

Classes in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.continuous that implement Storable
`class` | `GaussianEmission` Emission for continuous values following a Gaussian distribution.
`class` | `PluginGaussianEmission` Basic Gaussian emission distribution without random initialization of parameters.

Uses of Storable in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.discrete

Classes in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.discrete that implement Storable
`class` | `AbstractConditionalDiscreteEmission` The abstract super class of discrete emissions.
`class` | `DiscreteEmission` This class implements a simple discrete emission without any condition.
`class` | `PhyloDiscreteEmission` Phylogenetic discrete emission This class uses a phylogenetic tree to describe multidimensional data It implements Felsensteins model for nucleotide substitution (F81)
`class` | `ReferenceSequenceDiscreteEmission` This class implements a discrete emission that depends on some `ReferenceSequenceAnnotation` at a certain reference position.

Uses of Storable in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.training

Classes in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.training that implement Storable
`class` | `BaumWelchParameterSet` This class implements an `HMMTrainingParameterSet` for the Baum-Welch training of an `AbstractHMM`.
`class` | `HMMTrainingParameterSet` This class implements an abstract `ParameterSet` that is used for the training of an `AbstractHMM`.
`class` | `MaxHMMTrainingParameterSet` This class is the super class for any `HMMTrainingParameterSet` that is used for a maximizing training algorithm of a hidden Markov model.
`class` | `MultiThreadedTrainingParameterSet` This class is the super class for any `MaxHMMTrainingParameterSet` that is used for a multi-threaded maximizing training algorithm of a hidden Markov model.
`class` | `NumericalHMMTrainingParameterSet` This class implements an `ParameterSet` for numerical training of an `AbstractHMM`.
`class` | `SamplingHMMTrainingParameterSet` This class contains the parameters for training training an `AbstractHMM` using a sampling strategy.
`class` | `ViterbiParameterSet` This class implements an `ParameterSet` for the viterbi training of an `AbstractHMM`.

Uses of Storable in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions

Subinterfaces of Storable in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions
`interface` | `DifferentiableTransition` This class declares methods that allow for optimizing the parameters numerically using the `Optimizer`.
`interface` | `SamplingTransition` This interface declares all method used during a sampling.
`interface` | `TrainableAndDifferentiableTransition` This interface unifies the interfaces `TrainableTransition` and `DifferentiableTransition`.
`interface` | `TrainableTransition` This class declares methods that allow for estimating the parameters from a sufficient statistic, as for instance done in the (modified) Baum-Welch algorithm, viterbi training, or Gibbs sampling.
`interface` | `Transition` This interface declares the methods of the transition used in a hidden Markov model.
`interface` | `TransitionWithSufficientStatistic` This interface defines method for reseting and filling an internal sufficient statistic.

Classes in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions that implement Storable
`class` | `BasicHigherOrderTransition` This class implements the basic transition that allows to be trained using the viterbi or the Baum-Welch algorithm.
`static class` | `BasicHigherOrderTransition.AbstractTransitionElement` This class declares the probability distribution for a given context, i.e. it contains all possible transition and the corresponding probabilities for a given set offset previously visited states.
`class` | `HigherOrderTransition` This class can be used in any `AbstractHMM` allowing to use gradient based or sampling training algorithm.

Uses of Storable in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.elements

Classes in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.elements that implement Storable
`class` | `BasicPluginTransitionElement` Basic transition element without random initialization of parameters.
`class` | `BasicTransitionElement` This class implements the probability distribution for a given context, i.e. it contains all possible transition and the corresponding probabilities for a given set of previously visited states.
`class` | `DistanceBasedScaledTransitionElement` Distance-based scaled transition element for an HMM with distance-scaled transition matrices (DSHMM).
`class` | `ReferenceBasedTransitionElement` This class implements transition elements that utilize a reference sequence to determine the transition probability.
`class` | `ScaledTransitionElement` Scaled transition element for an HMM with scaled transition matrices (SHMM).
`class` | `TransitionElement` This class implements an transition element implements method used for training via sampling or gradient based optimization approach.

Uses of Storable in de.jstacs.sequenceScores.statisticalModels.trainable.mixture

Classes in de.jstacs.sequenceScores.statisticalModels.trainable.mixture that implement Storable
`class` | `AbstractMixtureTrainSM` This is the abstract class for all kinds of mixture models.
`class` | `MixtureTrainSM` The class for a mixture model of any `TrainableStatisticalModel`s.
`class` | `StrandTrainSM` This model handles sequences that can either lie on the forward strand or on the reverse complementary strand.

Uses of Storable in de.jstacs.sequenceScores.statisticalModels.trainable.mixture.motif

Classes in de.jstacs.sequenceScores.statisticalModels.trainable.mixture.motif that implement Storable
`class` | `HiddenMotifMixture` This is the main class that every generative motif discoverer should implement.
`class` | `ZOOPSTrainSM` This class enables the user to search for a single motif in a sequence.

Uses of Storable in de.jstacs.sequenceScores.statisticalModels.trainable.mixture.motif.positionprior

Classes in de.jstacs.sequenceScores.statisticalModels.trainable.mixture.motif.positionprior that implement Storable
`class` | `GaussianLikePositionPrior` This class implements a gaussian like discrete truncated prior.
`class` | `PositionPrior` This is the main class for any position prior that can be used in a motif discovery.
`class` | `UniformPositionPrior` This prior implements a uniform distribution for the start position.

Uses of Storable in de.jstacs.sequenceScores.statisticalModels.trainable.phylo

Classes in de.jstacs.sequenceScores.statisticalModels.trainable.phylo that implement Storable
`class` | `PhyloNode` This class implements a node in a `PhyloTree` A PhyloNode contains some basic informations of itself and the incoming edge Furthermore it contains a list of `PhyloNode`s that represent the children nodes
`class` | `PhyloTree` This class implements a simple (phylogenetic) tree.

Uses of Storable in de.jstacs.utils

Classes in de.jstacs.utils that implement Storable
`class` | `DoubleList` A simple list of primitive type `double`.

Uses of Storable in de.jstacs.utils.galaxy

Classes in de.jstacs.utils.galaxy that implement Storable
`static class` | `GalaxyAdaptor.FileResult` `Result` for files that are results of some computation.
`static class` | `GalaxyAdaptor.LinkedImageResult` Class for an `ImageResult` that is linked to a file that can be downloaded.

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