Jstacs 2.1 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	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.classifiers.utils	Provides some useful classes for working with classifiers.
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.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. The implementations of sequences currently include DiscreteSequences prepared for alphabets of different sizes, and ArbitrarySequences that may contain continuous values as well. As sub-package provides the facilities to annotate 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.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 Storables to an XML-representation.
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	The package provides all interfaces and classes for states used in hidden Markov models.
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.sequenceScores.statisticalModels.trainable.phylo.parser
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.