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# Jstacs 1.5 API Specification

Packages
de.jstacs | This package is the root package for the most and important packages.
de.jstacs.algorithms.alignment | Provides classes for alignments
de.jstacs.algorithms.alignment.cost | Provides classes for cost functions used in alignments
de.jstacs.algorithms.graphs | Provides classes for algorithms on graphs.
de.jstacs.algorithms.graphs.tensor | Provides classes to represent symmetric and asymmetric tensors in graphs
de.jstacs.algorithms.optimization | Provides classes for different types of algorithms that are not directly linked to the modelling components of Jstacs: Algorithms on graphs, algorithms for numerical optimization, and a basic alignment algorithm.
de.jstacs.algorithms.optimization.termination |
de.jstacs.classifier | This package provides the framework for any classifier.
de.jstacs.classifier.assessment | This package allows to assess classifiers.
de.jstacs.classifier.modelBased | Provides the classes for `Classifier`s that are based on `Model`s
de.jstacs.classifier.scoringFunctionBased | Provides the classes for `Classifier`s that are based on `ScoringFunction`s.
de.jstacs.classifier.scoringFunctionBased.gendismix | Provides an implementation of a classifier that allows to train the parameters of a set of `NormalizableScoringFunction`s by a unified generative-discriminative learning principle
de.jstacs.classifier.scoringFunctionBased.logPrior | Provides a general definition of a parameter log-prior and a number of implementations of Laplace and Gaussian priors
de.jstacs.classifier.scoringFunctionBased.msp | Provides an implementation of a classifier that allows to train the parameters of a set of `ScoringFunctions` either by maximum supervised posterior (MSP) or by maximum conditional likelihood (MCL)
de.jstacs.classifier.scoringFunctionBased.sampling | Provides the classes for `AbstractScoreBasedClassifier`s that are based on `SamplingScoringFunction`s and that sample parameters using the Metropolis-Hastings algorithm.
de.jstacs.classifier.utils | Provides some useful classes for working with classifiers
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.bioJava | Provides an adapter between the representation of data in BioJava and the representation used in Jstacs.
de.jstacs.data.sequences | Provides classes for representing sequences.
de.jstacs.data.sequences.annotation | Provides the facilities to annotate `Sequence`s using a number of pre-defined annotation types, or additional implementations of the `SequenceAnnotation` class
de.jstacs.io | Provides classes for reading data from and writing to a file and storing a number of datatypes, including all primitives, arrays of primitives, and `Storable`s to an XML-representation
de.jstacs.models | Provides the interface `Model` and its abstract implementation `AbstractModel`, which is the super class of all other models.
de.jstacs.models.discrete |
de.jstacs.models.discrete.homogeneous |
de.jstacs.models.discrete.homogeneous.parameters |
de.jstacs.models.discrete.inhomogeneous | This package contains various inhomogeneous models.
de.jstacs.models.discrete.inhomogeneous.parameters |
de.jstacs.models.discrete.inhomogeneous.shared |
de.jstacs.models.hmm | The package provides all interfaces and classes for a hidden Markov model (HMM).
de.jstacs.models.hmm.models | The package provides different implementations of hidden Markov models based on `AbstractHMM`
de.jstacs.models.hmm.states | The package provides all interfaces and classes for states used in hidden Markov models.
de.jstacs.models.hmm.states.emissions |
de.jstacs.models.hmm.states.emissions.continuous |
de.jstacs.models.hmm.states.emissions.discrete |
de.jstacs.models.hmm.training | The package provides all classes used to determine the training algorithm of a hidden Markov model
de.jstacs.models.hmm.transitions | The package provides all interfaces and classes for transitions used in hidden Markov models.
de.jstacs.models.hmm.transitions.elements |
de.jstacs.models.mixture | This package is the super package for any mixture model.
de.jstacs.models.mixture.motif |
de.jstacs.models.mixture.motif.positionprior |
de.jstacs.models.phylo |
de.jstacs.models.phylo.parser |
de.jstacs.models.utils |
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.scoringFunctions | Provides `ScoringFunction`s that can be used in a `ScoreClassifier`.
de.jstacs.scoringFunctions.directedGraphicalModels | Provides `ScoringFunction`s that are equivalent to directed graphical models.
de.jstacs.scoringFunctions.directedGraphicalModels.structureLearning.measures | Provides the facilities to learn the structure of a `BayesianNetworkScoringFunction`.
de.jstacs.scoringFunctions.directedGraphicalModels.structureLearning.measures.btMeasures | Provides the facilities to learn the structure of a `BayesianNetworkScoringFunction` as a Bayesian tree using a number of measures to define a rating of structures
de.jstacs.scoringFunctions.directedGraphicalModels.structureLearning.measures.pmmMeasures | Provides the facilities to learn the structure of a `BayesianNetworkScoringFunction` as a permuted Markov model using a number of measures to define a rating of structures
de.jstacs.scoringFunctions.homogeneous | Provides `ScoringFunction`s that are homogeneous, i.e. model probabilities or scores independent of the position within a sequence
de.jstacs.scoringFunctions.mix | Provides `ScoringFunction`s that are mixtures of other `ScoringFunction`s.
de.jstacs.scoringFunctions.mix.motifSearch |
de.jstacs.utils | This package contains a bundle of useful classes and interfaces like ...
de.jstacs.utils.galaxy |
de.jstacs.utils.random | This package contains some classes for generating random numbers

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