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## Uses of Class
de.jstacs.models.AbstractModel

Packages that use AbstractModel
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.inhomogeneous | This package contains various inhomogeneous models.
de.jstacs.models.discrete.inhomogeneous.shared |
de.jstacs.models.mixture | This package is the super package for any mixture model.
de.jstacs.models.mixture.gibbssampling | This package contains many classes that can be used while a Gibbs sampling.
de.jstacs.models.mixture.motif |

Uses of AbstractModel in de.jstacs.models

Subclasses of AbstractModel in de.jstacs.models
`class` | `CompositeModel` This class is for modelling sequences by modelling the different positions of the each sequence by different models.
`class` | `NormalizableScoringFunctionModel` This model can be used to use a NormalizableScoringFunction as model.
`class` | `UniformModel` This class represents a uniform model.
`class` | `VariableLengthWrapperModel` This class allows to train any `Model` on `Sample`s of `Sequence`s with variable length if each individual length is at least `Model.getLength()`.

Methods in de.jstacs.models that return AbstractModel
`AbstractModel` | `AbstractModel.clone()` Follows the conventions of `Object`'s `clone()`-method.

Uses of AbstractModel in de.jstacs.models.discrete

Subclasses of AbstractModel in de.jstacs.models.discrete
`class` | `DiscreteGraphicalModel` This is the main class for all discrete graphical models (DGM).

Uses of AbstractModel in de.jstacs.models.discrete.homogeneous

Subclasses of AbstractModel in de.jstacs.models.discrete.homogeneous
`class` | `HomogeneousMM` This class implements homogeneous Markov models (hMM) of arbitrary order.
`class` | `HomogeneousModel` This class implements homogeneous models of arbitrary order.

Uses of AbstractModel in de.jstacs.models.discrete.inhomogeneous

Subclasses of AbstractModel in de.jstacs.models.discrete.inhomogeneous
`class` | `BayesianNetworkModel` The class implements a Bayesian network ( `StructureLearner.ModelType.BN` ) of fixed order.
`class` | `DAGModel` The abstract class for directed acyclic graphical models (`DAGModel`).
`class` | `FSDAGModel` This class can be used for any discrete fixed structure directed acyclic graphical model ( `FSDAGModel`).
`class` | `InhomogeneousDGM` This class is the main class for all inhomogeneous discrete graphical models (`InhomogeneousDGM`).

Uses of AbstractModel in de.jstacs.models.discrete.inhomogeneous.shared

Subclasses of AbstractModel in de.jstacs.models.discrete.inhomogeneous.shared
`class` | `SharedStructureMixture` This class handles a mixture of models with the same structure that is learned via EM.

Uses of AbstractModel in de.jstacs.models.mixture

Subclasses of AbstractModel in de.jstacs.models.mixture
`class` | `AbstractMixtureModel` This is the abstract class for all kinds of mixture models.
`class` | `MixtureModel` The class for a mixture model of any `Model`s.
`class` | `StrandModel` This model handles sequences that can either lie on the forward strand or on the reverse complementary strand.

Uses of AbstractModel in de.jstacs.models.mixture.gibbssampling

Subclasses of AbstractModel in de.jstacs.models.mixture.gibbssampling
`class` | `FSDAGModelForGibbsSampling` This is the class for a fixed structure directed acyclic graphical model (see `FSDAGModel`) that can be used in a Gibbs sampling.

Uses of AbstractModel in de.jstacs.models.mixture.motif

Subclasses of AbstractModel in de.jstacs.models.mixture.motif
`class` | `HiddenMotifMixture` This is the main class that every generative motif discoverer should implement.
`class` | `SingleHiddenMotifMixture` This class enables the user to search for a single motif in a sequence.

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