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## Uses of Interface
de.jstacs.parameters.GalaxyConvertible

Packages that use GalaxyConvertible
de.jstacs.algorithms.optimization.termination | Provides classes for termination conditions that can be used in algorithms
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.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.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.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.sampling | This package contains many classes that can be used while a sampling.
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.trainable.discrete |
de.jstacs.sequenceScores.statisticalModels.trainable.discrete.homogeneous.parameters |
de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.parameters |
de.jstacs.sequenceScores.statisticalModels.trainable.hmm.training | The package provides all classes used to determine the training algorithm of a hidden Markov model

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

Classes in de.jstacs.algorithms.optimization.termination that implement GalaxyConvertible
`static class` | `AbsoluteValueCondition.AbsoluteValueConditionParameterSet` Deprecated. This class implements the parameter set for a `AbsoluteValueCondition`.
`static class` | `AbstractTerminationCondition.AbstractTerminationConditionParameterSet` This class implements the super class of all parameter sets of instances from `AbstractTerminationCondition`.
`static class` | `CombinedCondition.CombinedConditionParameterSet` This class implements the parameter set for a `CombinedCondition`.
`static class` | `IterationCondition.IterationConditionParameterSet` This class implements the parameter set for a `IterationCondition`.
`static class` | `SmallDifferenceOfFunctionEvaluationsCondition.SmallDifferenceOfFunctionEvaluationsConditionParameterSet` This class implements the parameter set for a `SmallDifferenceOfFunctionEvaluationsCondition`.
`static class` | `SmallGradientConditon.SmallGradientConditonParameterSet` This class implements the parameter set for a `SmallStepCondition`.
`static class` | `SmallStepCondition.SmallStepConditionParameterSet` This class implements the parameter set for a `SmallStepCondition`.
`static class` | `TimeCondition.TimeConditionParameterSet` This class implements the parameter set for a `TimeCondition`.

Uses of GalaxyConvertible in de.jstacs.classifiers.assessment

Classes in de.jstacs.classifiers.assessment that implement GalaxyConvertible
`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 GalaxyConvertible in de.jstacs.classifiers.differentiableSequenceScoreBased

Classes in de.jstacs.classifiers.differentiableSequenceScoreBased that implement GalaxyConvertible
`class` | `ScoreClassifierParameterSet` A set of `Parameter`s for any `ScoreClassifier`.

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

Classes in de.jstacs.classifiers.differentiableSequenceScoreBased.gendismix that implement GalaxyConvertible
`class` | `GenDisMixClassifierParameterSet` This class contains the parameters for the `GenDisMixClassifier`.

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

Classes in de.jstacs.classifiers.differentiableSequenceScoreBased.sampling that implement GalaxyConvertible
`class` | `SamplingGenDisMixClassifierParameterSet` `ParameterSet` to instantiate a `SamplingGenDisMixClassifier`.
`class` | `SamplingScoreBasedClassifierParameterSet` `ParameterSet` to instantiate a `SamplingScoreBasedClassifier`.

Uses of GalaxyConvertible in de.jstacs.classifiers.performanceMeasures

Classes in de.jstacs.classifiers.performanceMeasures that implement GalaxyConvertible
`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 GalaxyConvertible in de.jstacs.data

Classes in de.jstacs.data that implement GalaxyConvertible
`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 GalaxyConvertible in de.jstacs.data.alphabets

Classes in de.jstacs.data.alphabets that implement GalaxyConvertible
`static class` | `Alphabet.AlphabetParameterSet<T extends Alphabet>` The super class for the `InstanceParameterSet` of any `Alphabet`.
`static class` | `ContinuousAlphabet.ContinuousAlphabetParameterSet` Class for the `ParameterSet` of a `ContinuousAlphabet`.
`static class` | `DiscreteAlphabet.DiscreteAlphabetParameterSet` Class for the `ParameterSet` of a `DiscreteAlphabet`.
`static class` | `DNAAlphabet.DNAAlphabetParameterSet` The parameter set for a `DNAAlphabet`.
`static class` | `DNAAlphabetContainer.DNAAlphabetContainerParameterSet` This class implements a singleton for the `ParameterSet` of a `DNAAlphabetContainer`.
`static class` | `GenericComplementableDiscreteAlphabet.GenericComplementableDiscreteAlphabetParameterSet` This class is used as container for the parameters of a `GenericComplementableDiscreteAlphabet`.
`static class` | `ProteinAlphabet.ProteinAlphabetParameterSet` The parameter set for a `ProteinAlphabet`.

Uses of GalaxyConvertible in de.jstacs.parameters

Classes in de.jstacs.parameters that implement GalaxyConvertible
`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.
`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` | `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 GalaxyConvertible in de.jstacs.parameters.validation

Classes in de.jstacs.parameters.validation that implement GalaxyConvertible
`class` | `NumberValidator<E extends Comparable<? extends Number>>` Class that validates all subclasses of `Number` that implement `Comparable` (e.g.

Uses of GalaxyConvertible in de.jstacs.sampling

Classes in de.jstacs.sampling that implement GalaxyConvertible
`class` | `AbstractBurnInTestParameterSet` Class for the parameters of a `AbstractBurnInTest`.
`class` | `VarianceRatioBurnInTestParameterSet` Class for the parameters of a `VarianceRatioBurnInTest`.

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

Classes in de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels that implement GalaxyConvertible
`class` | `BayesianNetworkDiffSMParameterSet` Class for the parameters of a `BayesianNetworkDiffSM`.

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

Classes in de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.structureLearning.measures that implement GalaxyConvertible
`static class` | `InhomogeneousMarkov.InhomogeneousMarkovParameterSet` Class for an `InstanceParameterSet` that defines the parameters of an `InhomogeneousMarkov` structure `Measure`.
`static class` | `Measure.MeasureParameterSet` This class is the super class of any `ParameterSet` that can be used to instantiate a `Measure`.

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

Classes in de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.structureLearning.measures.btMeasures that implement GalaxyConvertible
`static class` | `BTExplainingAwayResidual.BTExplainingAwayResidualParameterSet` Class for the parameters of a `BTExplainingAwayResidual` structure `Measure`.
`static class` | `BTMutualInformation.BTMutualInformationParameterSet` Class for the parameters of a `BTMutualInformation` structure `Measure`.

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

Classes in de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.structureLearning.measures.pmmMeasures that implement GalaxyConvertible
`static class` | `PMMExplainingAwayResidual.PMMExplainingAwayResidualParameterSet` Class for the parameters of a `PMMExplainingAwayResidual` structure `Measure`.
`static class` | `PMMMutualInformation.PMMMutualInformationParameterSet` Class for the parameters of a `PMMMutualInformation` structure `Measure`.

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

Classes in de.jstacs.sequenceScores.statisticalModels.trainable.discrete that implement GalaxyConvertible
`class` | `DGTrainSMParameterSet<T extends DiscreteGraphicalTrainSM>` The super `ParameterSet` for any parameter set of a `DiscreteGraphicalTrainSM`.

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

Classes in de.jstacs.sequenceScores.statisticalModels.trainable.discrete.homogeneous.parameters that implement GalaxyConvertible
`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 GalaxyConvertible in de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.parameters

Classes in de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.parameters that implement GalaxyConvertible
`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 GalaxyConvertible in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.training

Classes in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.training that implement GalaxyConvertible
`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`.

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