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. It contains the class ClassifierAssessment that is used as a super-class of all implemented methodologies of an assessment to assess classifiers.
de.jstacs.classifiers.differentiableSequenceScoreBased	Provides the classes for Classifiers that are based on SequenceScores. It includes a sub-package for discriminative objective functions, namely conditional likelihood and supervised posterior, and a separate sub-package for the parameter priors, that can be used for the supervised posterior.
de.jstacs.classifiers.differentiableSequenceScoreBased.gendismix	Provides an implementation of a classifier that allows to train the parameters of a set of DifferentiableStatisticalModels 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 DifferentiableStatisticalModels either by maximum supervised posterior (MSP) or by maximum conditional likelihood (MCL).
de.jstacs.classifiers.differentiableSequenceScoreBased.sampling	Provides the classes for AbstractScoreBasedClassifiers that are based on SamplingDifferentiableStatisticalModels 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 Classifiers that are based on TrainableStatisticalModels.
de.jstacs.data	Provides classes for the representation of data. The base classes to represent data are Alphabet and AlphabetContainer for representing alphabets, Sequence and its sub-classes to represent continuous and discrete sequences, and DataSet to represent data sets comprising a set of sequences.
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 Sequences 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 SequenceScores, 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 StatisticalModels, which can compute a proper (i.e., normalized) likelihood over the input space of sequences. StatisticalModels can be further differentiated into TrainableStatisticalModels, which can be learned from a single input DataSet, and DifferentiableStatisticalModels, which define a proper likelihood but can also compute gradients like DifferentiableSequenceScores.
de.jstacs.sequenceScores.statisticalModels.differentiable	Provides all DifferentiableStatisticalModels, which can compute the gradient with respect to their parameters for a given input Sequence.
de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels	Provides DifferentiableStatisticalModels 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 DifferentiableStatisticalModels that are homogeneous, i.e.
de.jstacs.sequenceScores.statisticalModels.differentiable.mixture	Provides DifferentiableSequenceScores that are mixtures of other DifferentiableSequenceScores.
de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif
de.jstacs.sequenceScores.statisticalModels.trainable	Provides all TrainableStatisticalModels, 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 TerminationConditions.
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 TerminationConditions 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	MultipleIterationsCondition This TerminationCondition requires another provided TerminationCondition to fail a contiguous specified number of times before the optimization is terminated.
static class	MultipleIterationsCondition.MultipleIterationsConditionParameterSet This class implements the parameter set for a MultipleIterationsCondition.
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 Results 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 ClassifierAssessmentAssessParameterSets.
class	KFoldCrossValidationAssessParameterSet This class implements a ClassifierAssessmentAssessParameterSet that must be used to call method assess( ...
class	RepeatedHoldOutAssessParameterSet This class implements a ClassifierAssessmentAssessParameterSet that must be used to call method assess( ...
class	RepeatedSubSamplingAssessParameterSet This class implements a ClassifierAssessmentAssessParameterSet that must be used to call method assess( ...
class	Sampled_RepeatedHoldOutAssessParameterSet This class implements a ClassifierAssessmentAssessParameterSet that must be used to call the method assess( ...

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 Parameters 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.
class	SeparateGaussianLogPrior Class for a LogPrior that defines a Gaussian prior on the parameters of a set of DifferentiableStatisticalModels and a set of class parameters.
class	SeparateLaplaceLogPrior Class for a LogPrior that defines a Laplace prior on the parameters of a set of DifferentiableStatisticalModels 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 SamplingDifferentiableStatisticalModels 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	AbstractNumericalTwoClassPerformanceMeasure This class is the abstract super class of any performance measure that can only be computed for binary classifiers.
class	AbstractPerformanceMeasure This class is the abstract super class of any performance measure used to evaluate an AbstractClassifier.
class	AbstractPerformanceMeasureParameterSet<T extends PerformanceMeasure> This class implements a container of PerformanceMeasures that can be used in AbstractClassifier.evaluate(AbstractPerformanceMeasureParameterSet, boolean, de.jstacs.data.DataSet...).
class	AbstractTwoClassPerformanceMeasure This class is the abstract super class of any performance measure that can only be computed for binary classifiers.
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 NumericalPerformanceMeasures that can be used, for instance, in an repeated assessment, (cf.
class	PerformanceMeasureParameterSet This class implements a container of AbstractPerformanceMeasures that can be used in AbstractClassifier.evaluate(AbstractPerformanceMeasureParameterSet, 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.

Uses of Storable in de.jstacs.classifiers.trainSMBased

Classes in de.jstacs.classifiers.trainSMBased that implement Storable

class

TrainSMBasedClassifier Classifier that works on TrainableStatisticalModels 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 Alphabets 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 Alphabets of defined length.
static class	AlphabetContainerParameterSet.SectionDefinedAlphabetParameterSet Class for the parameter set of an array of Alphabets 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.
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 Strings.
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 MotifAnnotations 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.
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.
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.
class	ArrayParameterSet Class for a ParameterSet that consists of a length-Parameter that defines the length of the array and an array of ParameterSetContainers 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 Parameters 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 Parameters 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 Parameters.
class	ParameterSetContainer Class for a ParameterSetContainer that contains a ParameterSet as value.
class	RangeParameter Class for a parameter wrapper that allows SimpleParameters to be set to a set of values. These values may be given either as a list of values separated by spaces, as a range between a first and a last value with a given number of steps between these values, or a single value.
class	SelectionParameter Class for a collection parameter, i.e.
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 Parameters.

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.

Classes in de.jstacs.parameters.validation that implement Storable

class	ConstraintValidator Class for a ParameterValidator that is based on Constraints.
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.
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 ResultSets.
class	MeanResultSet Class that computes the mean and the standard error of a series of NumericalResultSets.
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 Results which provides methods to access single Results in the set, to retrieve the number of Results in the set, to get a String representation or an XML representation of all the Results in the set.
class	SimpleResult Abstract class for a Result with a value of a primitive data type or String.
class	StorableResult Class for Results that are Storables.

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	MultiDimensionalSequenceWrapperDiffSS This class implements a simple wrapper for multidimensional sequences.
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 Sequences.

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

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

interface	DifferentiableStatisticalModel The interface for normalizable DifferentiableSequenceScores.
interface	SamplingDifferentiableStatisticalModel Interface for DifferentiableStatisticalModels that can be used for Metropolis-Hastings sampling in a SamplingScoreBasedClassifier.
interface	VariableLengthDiffSM This is an interface for all DifferentiableStatisticalModels 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 Sequences.
class	MarkovRandomFieldDiffSM This class implements the scoring function for any MRF (Markov Random Field).
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.
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 DifferentiableSequenceScores.
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 DurationDiffSMs.
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 DataSets of Sequences 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	FSMEManager This class can be used for any discrete fixed structure maximum entropy model (FSMEM).
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	MEM This class represents a maximum entropy model.
class	MEManager This class is the super class for all maximum entropy models
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	ConstraintParameterSet This class enables you to input your own structure defined by some constraints.
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	FSMEMParameterSet The ParameterSet for a FSMEManager.
class	IDGTrainSMParameterSet This is the abstract container of parameters that is a root container for all inhomogeneous discrete graphical model parameter containers.
class	MEManagerParameterSet The ParameterSet for any MEManager.

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 Emissions.
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	MultivariateGaussianEmission Multivariate Gaussian emission density for a Hidden Markov Model.
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.
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.
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 TrainableStatisticalModels.
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 PhyloNodes 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.
class	MultilineSimpleParameter An extension of SimpleParameter that renders as a textarea in Galaxy, which is only suitable for DataType.STRINGs.