Uses of Interface
edu.cmu.tetrad.util.TetradSerializable

Packages that use TetradSerializable

Package	Description
edu.cmu.tetrad.algcomparison.algorithm
edu.cmu.tetrad.algcomparison.algorithm.cluster
edu.cmu.tetrad.algcomparison.algorithm.continuous.dag
edu.cmu.tetrad.algcomparison.algorithm.external
edu.cmu.tetrad.algcomparison.algorithm.mixed
edu.cmu.tetrad.algcomparison.algorithm.multi
edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag
edu.cmu.tetrad.algcomparison.algorithm.oracle.pag
edu.cmu.tetrad.algcomparison.algorithm.oracle.pattern
edu.cmu.tetrad.algcomparison.algorithm.other
edu.cmu.tetrad.algcomparison.algorithm.pairwise
edu.cmu.tetrad.algcomparison.graph
edu.cmu.tetrad.algcomparison.independence
edu.cmu.tetrad.algcomparison.score
edu.cmu.tetrad.algcomparison.simulation
edu.cmu.tetrad.bayes
edu.cmu.tetrad.calculator.expression
edu.cmu.tetrad.data
edu.cmu.tetrad.data.simulation
edu.cmu.tetrad.gene.tetrad.gene.graph	Contains an editable display graph for (small) lag graphs.
edu.cmu.tetrad.gene.tetrad.gene.history	Implements a time-series simulation engine suitable for time-series gene expression studies.
edu.cmu.tetrad.gene.tetrad.gene.simulation	Contains classes for generating simulations of expression levels over a collection of genes.
edu.cmu.tetrad.gene.tetradapp.model
edu.cmu.tetrad.graph
edu.cmu.tetrad.regression
edu.cmu.tetrad.search
edu.cmu.tetrad.sem
edu.cmu.tetrad.session
edu.cmu.tetrad.util
edu.cmu.tetrad.util.dist

Uses of TetradSerializable in edu.cmu.tetrad.algcomparison.algorithm

Subinterfaces of TetradSerializable in edu.cmu.tetrad.algcomparison.algorithm

Modifier and Type	Interface	Description
interface	Algorithm	Interface that algorithm must implement.
interface	MultiDataSetAlgorithm	Implements an algorithm that takes multiple data sets as input.

Classes in edu.cmu.tetrad.algcomparison.algorithm that implement TetradSerializable

Modifier and Type	Class	Description
class	ExternalAlgorithm	Tags an an algorithm that loads up external graphs for inclusion in reports.
class	FirstInflection	First inflection point.
class	StabilitySelection	Stability selection.
class	StARS	StARS

Uses of TetradSerializable in edu.cmu.tetrad.algcomparison.algorithm.cluster

Classes in edu.cmu.tetrad.algcomparison.algorithm.cluster that implement TetradSerializable

Modifier and Type	Class	Description
class	Bpc	Build Pure Clusters.
class	Fofc	Find One Factor Clusters.
class	Ftfc	FTFC.

Uses of TetradSerializable in edu.cmu.tetrad.algcomparison.algorithm.continuous.dag

Classes in edu.cmu.tetrad.algcomparison.algorithm.continuous.dag that implement TetradSerializable

Modifier and Type	Class	Description
class	Ling	LiNGAM.
class	Lingam	LiNGAM.

Uses of TetradSerializable in edu.cmu.tetrad.algcomparison.algorithm.external

Classes in edu.cmu.tetrad.algcomparison.algorithm.external that implement TetradSerializable

Modifier and Type	Class	Description
class	ExternalAlgorithmBnlearnMmhc	An API to allow results from external algorithms to be included in a report through the algrorithm comparison tool.
class	ExternalAlgorithmBNTPc	An API to allow results from external algorithms to be included in a report through the algrorithm comparison tool.
class	ExternalAlgorithmIntersection	An API to allow results from external algorithms to be included in a report through the algrorithm comparison tool.
class	ExternalAlgorithmPcalgGes	An API to allow results from external algorithms to be included in a report through the algrorithm comparison tool.
class	ExternalAlgorithmPcalgPc	An API to allow results from external algorithms to be included in a report through the algrorithm comparison tool.
class	ExternalAlgorithmTetrad	An API to allow results from external algorithms to be included in a report through the algrorithm comparison tool.

Uses of TetradSerializable in edu.cmu.tetrad.algcomparison.algorithm.mixed

Classes in edu.cmu.tetrad.algcomparison.algorithm.mixed that implement TetradSerializable

Modifier and Type	Class	Description
class	Mgm

Uses of TetradSerializable in edu.cmu.tetrad.algcomparison.algorithm.multi

Classes in edu.cmu.tetrad.algcomparison.algorithm.multi that implement TetradSerializable

Modifier and Type	Class	Description
class	CcdMaxConcatenated	Requires that the parameter 'randomSelectionSize' be set to indicate how many datasets should be taken at a time (randomly).
class	Fask	Wraps the IMaGES algorithm for continuous variables.
class	FaskConcatenated	Wraps the IMaGES algorithm for continuous variables.
class	FaskLofsConcatenated	Wraps the IMaGES algorithm for continuous variables.
class	FaskVote	Wraps the MultiFask algorithm for continuous variables.
class	FasLofs	Wraps the IMaGES algorithm for continuous variables.
class	FgesConcatenated	Requires that the parameter 'randomSelectionSize' be set to indicate how many datasets should be taken at a time (randomly).
class	Images	Wraps the IMaGES algorithm for continuous variables.

Uses of TetradSerializable in edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag

Classes in edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag that implement TetradSerializable

Modifier and Type	Class	Description
class	Bdce	BOSS-DC (Best Order Score Search Divide and Conquer Experimental)
class	Boss	BOSS-DC (Best Order Score Search Divide and Conquer)
class	BossMb	BOSS-MB.
class	BossMb2
class	Bridges	FGES (the heuristic version).
class	Bridges2	FGES (the heuristic version).
class	Cpc	CPC.
class	CpcStable	PC.
class	Fas	FAS.
class	Fges	FGES (the heuristic version).
class	FgesMb	FGES (the heuristic version).
class	FgesMeasurement	FGES (the heuristic version).
class	GesMe	FGES (the heuristic version).
class	Grasp	GRaSP (Greedy Relaxations of Sparsest Permutation)
class	GraspTol	GRaSP (Greedy Relaxations of Sparsest Permutation)
class	Pc	PC.
class	Pcd	PC.
class	PcMax	PC-Max.
class	PcMb	PC.
class	PcStable	PC-Stable.
class	SingleGraphAlg	PC.
class	Sp	SP (Sparsest Permutation)

Uses of TetradSerializable in edu.cmu.tetrad.algcomparison.algorithm.oracle.pag

Classes in edu.cmu.tetrad.algcomparison.algorithm.oracle.pag that implement TetradSerializable

Modifier and Type	Class	Description
class	Bfci	Adjusts GFCI to use a permutation algorithm (such as BOSS-Tuck) to do the initial steps of finding adjacencies and unshielded colliders.
class	BfciTr	Adjusts GFCI to use a permutation algorithm (such as BOSS-Tuck) to do the initial steps of finding adjacencies and unshielded colliders.
class	Ccd	CCD (Cyclic Causal Discovery)
class	CcdMax	FGES (the heuristic version).
class	Cfci	Conserative FCI.
class	Fci	FCI.
class	FciMax	FCI.
class	Gfci	GFCI.
class	GraspFci	Adjusts GFCI to use a permutation algorithm (such as BOSS-Tuck) to do the initial steps of finding adjacencies and unshielded colliders.
class	PagSampleRfci	Jan 29, 2023 3:45:09 PM
class	Rfci	RFCI.
class	RfciBsc	Jan 4, 2019 4:32:05 PM
class	SpFci	Adjusts GFCI to use a permutation algorithm (in this case SP) to do the initial steps of finding adjacencies and unshielded colliders.
class	SvarFci	SvarFCI.
class	SvarGfci	SvarFCI.

Uses of TetradSerializable in edu.cmu.tetrad.algcomparison.algorithm.oracle.pattern

Classes in edu.cmu.tetrad.algcomparison.algorithm.oracle.pattern that implement TetradSerializable

Modifier and Type	Class	Description
class	Cstar

Uses of TetradSerializable in edu.cmu.tetrad.algcomparison.algorithm.other

Classes in edu.cmu.tetrad.algcomparison.algorithm.other that implement TetradSerializable

Modifier and Type	Class	Description
class	FactorAnalysis
class	Glasso

Uses of TetradSerializable in edu.cmu.tetrad.algcomparison.algorithm.pairwise

Classes in edu.cmu.tetrad.algcomparison.algorithm.pairwise that implement TetradSerializable

Modifier and Type	Class	Description
class	Eb	EB.
class	FaskPw	FASK-PW (pairwise).
class	R1	R1.
class	R2	R2.
class	R3	R3.
class	Rskew	RSkew.
class	RskewE	RSkewE.
class	Skew	Skew.
class	SkewE	SkewE.
class	Tanh	Tanh.

Uses of TetradSerializable in edu.cmu.tetrad.algcomparison.graph

Subinterfaces of TetradSerializable in edu.cmu.tetrad.algcomparison.graph

Modifier and Type	Interface	Description
interface	RandomGraph	An interface to represent a random graph of some sort.

Classes in edu.cmu.tetrad.algcomparison.graph that implement TetradSerializable

Modifier and Type	Class	Description
class	Cyclic	Returns a cyclic graph build up from small cyclic graph components.
class	ErdosRenyi	Creates a random graph by the Erdos-Renyi method (probabiliy of edge fixed, # edges not).
class	RandomForward	Creates a random graph by adding forward edges.
class	RandomSingleFactorMim	Creates a random graph by adding forward edges.
class	RandomTwoFactorMim	Creates a random graph by adding forward edges.
class	ScaleFree	Returns a scale free graph.
class	SingleGraph	Stores a single graph for use in simulations, etc.

Uses of TetradSerializable in edu.cmu.tetrad.algcomparison.independence

Subinterfaces of TetradSerializable in edu.cmu.tetrad.algcomparison.independence

Modifier and Type	Interface	Description
interface	IndependenceWrapper	Interface that algorithm must implement.

Classes in edu.cmu.tetrad.algcomparison.independence that implement TetradSerializable

Modifier and Type	Class	Description
class	BdeuTest	Wrapper for Fisher Z test.
class	CciLingamTest	Wrapper for Daudin Conditional Independence test.
class	CciTest	Wrapper for Daudin Conditional Independence test.
class	ChiSquare	Wrapper for Fisher Z test.
class	Codec	Wrapper for Fisher Z test.
class	ConditionalGaussianLRT	Wrapper for Fisher Z test.
class	DegenerateGaussianLRT	Wrapper for DG LRT.
class	DiscreteBicTest	Wrapper for Fisher Z test.
class	DSeparationTest	Wrapper for D-separation test.
class	FisherZ	Wrapper for Fisher Z test.
class	Gsquare	Wrapper for Fisher Z test.
class	Kci	Wrapper for KCI test.
class	KimEtAlScoreTests	Wrapper for Fisher Z test.
class	MagSemBicTest	Wrapper for Fisher Z test.
class	Mnlrlrt	Wrapper for Fisher Z test.
class	MultinomialLogisticRegressionWald	Wrapper for Fisher Z test.
class	Mvplrt	Wrapper for Fisher Z test.
class	PositiveCorr	Wrapper for Fisher Z test.
class	ProbabilisticTest	Dec 17, 2018 3:44:46 PM
class	SemBicDTest	Wrapper for Fisher Z test.
class	SemBicTest	Wrapper for Fisher Z test.

Uses of TetradSerializable in edu.cmu.tetrad.algcomparison.score

Subinterfaces of TetradSerializable in edu.cmu.tetrad.algcomparison.score

Modifier and Type	Interface	Description
interface	ScoreWrapper	Interface that algorithm must implement.

Classes in edu.cmu.tetrad.algcomparison.score that implement TetradSerializable

Modifier and Type	Class	Description
class	BdeuScore	Wrapper for Fisher Z test.
class	CciScore
class	ConditionalGaussianBicScore	Wrapper for Fisher Z test.
class	ConditionalGaussianOtherBicScore	Wrapper for Fisher Z test.
class	DegenerateGaussianBicScore	Wrapper for degenerate Gaussian BIC score
class	DiscreteBicScore	Wrapper for Discrete BIC test.
class	DiscreteMixedBicScore	Wrapper for Fisher Z test.
class	DSeparationScore	Wrapper for Fisher Z test.
class	EbicScore	Wrapper for linear, Gaussian Extended BIC score (Chen and Chen).
class	FisherZScore	Wrapper for Fisher Z test.
class	KimEtAlScores	Wrapper for linear, Gaussian Extended BIC score (Chen and Chen).
class	MagSemBicScore	Wrapper for linear, Gaussian SEM BIC score.
class	MNLRBicScore	Wrapper for MVP BIC Score.
class	MVPBicScore	Wrapper for MVP BIC Score.
class	PoissonPriorScore	Wrapper for the Poisson prior score (Bryan)
class	PositiveCorrScore	Wrapper for Fisher Z test.
class	SemBicScore	Wrapper for linear, Gaussian SEM BIC score.
class	SemBicScoreDeterministic	Wrapper for Fisher Z test.
class	ZhangShenBoundScore	Wrapper for linear, Gaussian Extended BIC score (Chen and Chen).

Uses of TetradSerializable in edu.cmu.tetrad.algcomparison.simulation

Subinterfaces of TetradSerializable in edu.cmu.tetrad.algcomparison.simulation

Modifier and Type	Interface	Description
interface	Simulation	The interface that simulations must implement.

Classes in edu.cmu.tetrad.algcomparison.simulation that implement TetradSerializable

Modifier and Type	Class	Description
class	BayesNetSimulation
class	BooleanGlassSimulation	A version of the Lee and Hastic simulation which is guaranteed to generate a discrete data set.
class	ConditionalGaussianSimulation	A simulation method based on the conditional Gaussian assumption.
class	GeneralSemSimulation
class	GeneralSemSimulationSpecial1	This was used for a simulation to test the FTFC and FOFC algorithm and contains some carefully selected functions to test nonlinearity and non-Gaussianity.
class	LeeHastieSimulation	A version of the Lee and Hastic simulation which is guaranteed ot generate a discrete data set.
class	LinearFisherModel
class	LinearSineSimulation	A simulation method based on the mixed variable polynomial assumption.
class	SemSimulation
class	SemThenDiscretize
class	StandardizedSemSimulation
class	TimeSeriesSemSimulation

Uses of TetradSerializable in edu.cmu.tetrad.bayes

Subinterfaces of TetradSerializable in edu.cmu.tetrad.bayes

Modifier and Type	Interface	Description
interface	BayesIm	Interface implemented by Bayes instantiated models.
interface	BayesUpdater	Interface for a discrete Bayes updating algorithm.
interface	ManipulatingBayesUpdater	Interface for a Bayes updating algorithm that's capable of doing manipulation.

Classes in edu.cmu.tetrad.bayes that implement TetradSerializable

Modifier and Type	Class	Description
final class	ApproximateUpdater	Calculates updated marginals for a Bayes net by simulating data and calculating likelihood ratios.
final class	BayesImProbs	Calculates cell probabilities from conditional BayesIm probabilities on the fly without constructing the entire table.
final class	BayesPm	Implements a discrete Bayes parametric model--that is, a DAG together with a map from the nodes in the graph to a set of discrete variables, specifying the number of categories for each variable and the name of each category for each variable.
final class	CptInvariantMarginalCalculator	Calculates marginals of the form P(V=v') for an updated Bayes net for purposes of the CPT Invariant Updater.
final class	CptInvariantUpdater	Calculates updated probabilities for variables conditional on their parents as well as single-variable updated marginals for a Bayes IM using an algorithm that restricts expensive updating summations only to conditional probabilities of variables with respect to their parents that change from non-updated to updated values.
final class	DirichletBayesIm	Stores Dirichlet pseudocounts for the distributions of each variable conditional on particular combinations of its parent values and, together with Bayes Pm and Dag, provides methods to manipulate these tables.
final class	Evidence	Stores information for a variable source about evidence we have for each variable as well as whether each variable has been manipulated.
final class	Identifiability	Identifiability, based on RowSummingExactUpdater
class	JunctionTreeAlgorithm	Junction Tree Algorithm.
class	JunctionTreeUpdater	Jan 21, 2020 11:03:09 AM
final class	Manipulation	Stores information for a variable source about evidence we have for each variable as well as whether each variable has been manipulated.
final class	MlBayesIm	Stores a table of probabilities for a Bayes net and, together with BayesPm and Dag, provides methods to manipulate this table.
final class	MlBayesImObs	Stores a table of probabilities for a Bayes net and, together with BayesPm and Dag, provides methods to manipulate this table.
final class	Proposition	Represents propositions over the variables of a particular BayesIm describing an event of a fairly general sort--namely, conjunctions of propositions that particular variables take on values from a particular disjunctive list of categories.
final class	RowSummingExactUpdater	Performs updating operations on a BayesIm by summing over cells in the joint probability table for the BayesIm.
final class	StoredCellProbs	Creates a table of stored cell probabilities for the given list of variables.
final class	StoredCellProbsObs	Creates a table of stored cell probabilities for the given list of variables.
final class	UpdatedBayesIm	Represents a Bayes IM in which all of the conditional probability tables have been updated to take into account evidence.

Uses of TetradSerializable in edu.cmu.tetrad.calculator.expression

Subinterfaces of TetradSerializable in edu.cmu.tetrad.calculator.expression

Modifier and Type	Interface	Description
interface	Expression	Represents a mathematical expression.
interface	ExpressionDescriptor	Represents a definition for some expression.
interface	ExpressionSignature	Represents the signature of the expression, for example sqrt(number).

Classes in edu.cmu.tetrad.calculator.expression that implement TetradSerializable

Modifier and Type	Class	Description
class	ConstantExpression	Represents a constant expression, that is an expression that always evaluates to the same value.
class	EvaluationExpression	An equation expression.
static enum	ExpressionDescriptor.Position
class	VariableExpression	An Expression for a variable.

Uses of TetradSerializable in edu.cmu.tetrad.data

Subinterfaces of TetradSerializable in edu.cmu.tetrad.data

Modifier and Type	Interface	Description
interface	DataBox	Stores a 2D array of data.
interface	DataModel	Interface implemented by classes, instantiations of which can serve as data models in Tetrad.
interface	DataSet	Implements a rectangular data set, in the sense of being a dataset with a fixed number of columns and a fixed number of rows, the length of each column being constant.
interface	ICovarianceMatrix	Interface for covariance matrices.
interface	KnowledgeBoxInput	Tags classes that can yield graphs as input to knowledge.
interface	KnowledgeTransferable	Interface implemented by classes that are capable of participating in the transfer of knowledge objects.
interface	Simulator	Created by jdramsey on 12/22/15.
interface	Variable	Interface implemented by classes, instantiations of which are capable of serving as variables for columns in a DataSet.
interface	VariableSource	Inteface implemented by classes, instantiations of which are associated with lists of variables.

Classes in edu.cmu.tetrad.data that implement TetradSerializable

Modifier and Type	Class	Description
class	AbstractVariable	Base class for variable specifications for DataSet.
final class	BoxDataSet	Wraps a DataBox in such a way that mixed data sets can be stored.
class	ByteDataBox	Stores a 2D array of byte data.
final class	Clusters	Stores clusters of variables for MimBuild, Purify, etc.
final class	ContinuousDiscretizationSpec	Specifies how a column (continuous or discrete) should be discretized.
final class	ContinuousVariable	Represents a real-valued variable.
final class	CorrelationMatrix	Stores a correlation matrix together with variable names and sample size; intended as a representation of a data set.
class	CorrelationMatrixOnTheFly	Stores a covariance matrix together with variable names and sample size, intended as a representation of a data set.
class	CovarianceMatrix	Stores a covariance matrix together with variable names and sample size, intended as a representation of a data set.
class	CovarianceMatrixOnTheFly	Stores a covariance matrix together with variable names and sample size, intended as a representation of a data set.
final class	DataModelList	Stores a list of data models and keeps track of which one is selected.
final class	DelimiterType	Type-safe enum of delimiter types for parsing data.
final class	DiscreteDiscretizationSpec	Specifies how a column (continuous or discrete) should be discretized.
final class	DiscreteVariable	Represents a discrete variable as a range of integer-valued categories 0, 1, ..., m - 1, where m is the number of categories for the variable.
final class	DiscreteVariableType	Type-safe enum of discrete variable types.
class	DoubleDataBox	Stores a 2D array of double data.
class	FloatDataBox	Stores a 2D array of float data.
class	IndependenceFacts	Stores a list of independence facts.
class	IntDataBox	Stores a 2D array of integer data.
final class	Knowledge	Stores information about required and forbidden edges and common causes for use in algorithm.
final class	KnowledgeEdge	Implements a knowledge edge X-->Y as a simple ordered pair of strings.
final class	KnowledgeGroup	Represents a "Other Group" in Knowledge, which can be understood as: Group1 -> Group2 where there are edges between all members of Group1 to Group2.
class	LongDataBox	Stores a 2D array of long data.
class	MixedDataBox	Stores a 2D array of double continuousData.
final class	NumberObjectDataSet	Wraps a 2D array of Number objects in such a way that mixed data sets can be stored.
class	ShortDataBox	Stores a 2D array of short data.
final class	SplitCasesSpec	Specifies how a column (continuous or discrete) should be discretized.
final class	TimeSeriesData	Stores time series data as a list of continuous columns.
class	VerticalDoubleDataBox	Stores a 2D array of double data.
class	VerticalIntDataBox	Stores a 2D array of int data.

Uses of TetradSerializable in edu.cmu.tetrad.data.simulation

Classes in edu.cmu.tetrad.data.simulation that implement TetradSerializable

Modifier and Type	Class	Description
class	LoadContinuousDataAndGraphs
class	LoadContinuousDataAndSingleGraph
class	LoadContinuousDataSmithSim
class	LoadDataAndGraphs
class	LoadDataFromFileWithoutGraph

Uses of TetradSerializable in edu.cmu.tetrad.gene.tetrad.gene.graph

Classes in edu.cmu.tetrad.gene.tetrad.gene.graph that implement TetradSerializable

Modifier and Type	Class	Description
class	ActiveLagGraph	Adds Javabean property change events so that it can be used in a MVC type architecture.
class	ManualActiveLagGraph	Constructs as a (manual) update graph.
final class	ManualLagGraph	Constructs as a (manual) update graph.
class	ManualLagGraphParams	Stores the parameters needed to generate a new lag graph, whether randomized or manually constructed.
class	RandomActiveLagGraph	Constructs as a randomized update workbench.
class	StoredLagGraphParams	Stores a file for reading in a lag graph from a file.

Uses of TetradSerializable in edu.cmu.tetrad.gene.tetrad.gene.history

Subinterfaces of TetradSerializable in edu.cmu.tetrad.gene.tetrad.gene.history

Modifier and Type	Interface	Description
interface	GraphRandomizer	Instantiations of this interface know how to randomize update graphs in particular ways.
interface	Initializer	Initializes a history array.
interface	LagGraph
interface	RandomDistribution	A distribution from which noise values are drawn.
interface	UpdateFunction	Implements a function from the previous time steps of a history array to the getModel time step.

Classes in edu.cmu.tetrad.gene.tetrad.gene.history that implement TetradSerializable

Modifier and Type	Class	Description
class	BasalInitializer	Initializes a history array by setting the value of each variable to basal if it is unregulated (has no parents other than itself one time step back) and to a random value chosen from a N(basal, initStDev) distribution otherwise.
final class	BasicLagGraph	Stores a time series in the "update" (rather than, say, the "repeated") form--that is, for a given set of factors (the word "factor" is being used here to avoid ambiguity), only lags behind the getModel time step are recorded temporally, with causal edges extending from lagged factors with lags >= 1 to factors in the getModel time step (lag = 0) only.
class	BooleanFunction	Stores a boolean function from a set of boolean-valued parents to a single boolean-valued column.
class	BooleanGlassFunction	Updates a gene given a history using the formula Gi.0 = max(Gi.1 - decayRate * -Gi.1 + booleanInfluenceRate * F(Parents(Gi) in the graph \ Gi.1), lowerBound), as described in Edwards and Glass, (2000), "Combinatorial explosion in model gene networks", American Institute of Physics.
class	DishModel	Models the manner in which gene models are initialized differentially depending on the dishes that the cells are in.
class	GeneHistory	Implements the basic machinery used by all history objects.
class	IndexedConnectivity	Stores a "shapshot" of the indexedConnectivity of a lag graph, using indices rather than Strings to refer to factors.
class	IndexedLagGraph	Stores a "shapshot" of the indexedLagGraph of a lag graph, using indices rather than Strings to refer to factors.
final class	IndexedParent	Holds an ordered pair (index, lag) to represent a causal parent of a factor, where the factor at the given index is independently known.
class	LaggedEdge	wrapper class for passing factor+edge via a propertyChange event
class	LaggedFactor	Identifies a particular factor (by name) at a particular lag (integer).
class	LinearFunction	Implements a linear update function, Gi.0 = L(Parents(G0.0)) + ei, where P is a polynomial function and ei is a random noise term.
final class	Polynomial	Implements a polynomial as a sum of a list of terms whose variables are identified as integers in the set {0, 1, 2, ...}.
class	PolynomialFunction	Implements a polynomial update function, Gi.0 = P(Parents(G0.0)) + ei, where P is a polynomial function and ei is a random noise term.
class	PolynomialTerm	Implements a term in a polymonial whose variables are mapped to indices in in the set {0, 1, 2, ...}.

Uses of TetradSerializable in edu.cmu.tetrad.gene.tetrad.gene.simulation

Classes in edu.cmu.tetrad.gene.tetrad.gene.simulation that implement TetradSerializable

Modifier and Type	Class	Description
class	MeasurementSimulator	Simulates measurement genetic data using an underlying GeneHistory object to generate individual cell data.

Uses of TetradSerializable in edu.cmu.tetrad.gene.tetradapp.model

Classes in edu.cmu.tetrad.gene.tetradapp.model that implement TetradSerializable

Modifier and Type	Class	Description
class	BooleanGlassGeneIm	Wraps the MeasurementSimulator class as an instantiated model.
class	BooleanGlassGenePm	Implements a parametric model for Boolean Glass gene PM's, which in this case just presents the underlying workbench.
class	GenePm	Implements a parametric gene model.
class	MeasurementSimulatorParams	Wraps MeasurementSimulator so that it may be used as a parameter object.

Uses of TetradSerializable in edu.cmu.tetrad.graph

Subinterfaces of TetradSerializable in edu.cmu.tetrad.graph

Modifier and Type	Interface	Description
interface	Graph	Implements a graph capable of storing edges of type N1 *-# N2 where * and # are endpoints of type Endpoint.
interface	Node	Represents an object with a name, node type, and position that can serve as a node in a graph.

Classes in edu.cmu.tetrad.graph that implement TetradSerializable

Modifier and Type	Class	Description
final class	Dag	Represents a directed acyclic graph--that is, a graph containing only directed edges, with no cycles.
class	Edge	Represents an edge node1 *-# node2 where * and # are endpoints of type Endpoint--that is, Endpoint.TAIL, Endpoint.ARROW, or Endpoint.CIRCLE.
class	EdgeListGraph	Stores a graph a list of lists of edges adjacent to each node in the graph, with an additional list storing all of the edges in the graph.
class	EdgeTypeProbability	Apr 13, 2017 3:56:46 PM
enum	Endpoint	A typesafe enumeration of the types of endpoints that are permitted in Tetrad-style graphs: null (-), arrow (->), circle (-o), start (-*), and null (no endpoint).
class	GraphNode	Implements a basic node in a graph--that is, a node that is not itself a variable.
final class	IndependenceFact	Stores a triple (x, y, z) of nodes.
class	LagGraph	Implements a graph allowing nodes in the getModel time lag to have parents taken from previous time lags.
final class	NodeType	A typesafe enum of the types of the types of nodes in a graph (MEASURED, LATENT, ERROR).
class	OrderedPair<E>	An ordered pair of objects.
class	Paths
final class	SemGraph	Represents the graphical structure of a structural equation model.
class	TimeLagGraph	Represents a time series graph--that is, a graph with a fixed number S of lags, with edges into initial lags only--that is, into nodes in the first R lags, for some R.
final class	Triple	Stores a triple (x, y, z) of nodes.
class	Underlines

Uses of TetradSerializable in edu.cmu.tetrad.regression

Classes in edu.cmu.tetrad.regression that implement TetradSerializable

Modifier and Type	Class	Description
class	LogisticRegression	Implements a logistic regression algorithm based on a Javascript implementation by John Pezzullo.
static class	LogisticRegression.Result
class	RegressionResult	Stores the various components of a regression result so they can be passed around together more easily.

Uses of TetradSerializable in edu.cmu.tetrad.search

Classes in edu.cmu.tetrad.search that implement TetradSerializable

Modifier and Type	Class	Description
final class	BayesUpdaterClassifier	This class contains a method classify which uses an instantiated Bayes net (BayesIm) provided in the constructor.
enum	BpcAlgorithmType	An enumeration of the test types for BuildPureClusters, and Purify.
static class	Cstar.Record
final class	IndependenceResult
final class	IndTestType	A typesafe enumeration of the types of independence tests that are used for basic search algorithm in this package.
class	IntSextad
class	ScoredGraph	Stores a graph with a score.
final class	SepsetMap	Stores a map from pairs of nodes to separating sets--that is, for each unordered pair of nodes {node1, node2} in a graph, stores a set of nodes conditional on which node1 and node2 are independent (where the nodes are considered as variables) or stores null if the pair was not judged to be independent.
class	Sextad	Represents a sextad of variables.
enum	TestType	An enumeration of the test types for BuildPureClusters, and Purify.

Uses of TetradSerializable in edu.cmu.tetrad.sem

Subinterfaces of TetradSerializable in edu.cmu.tetrad.sem

Modifier and Type	Interface	Description
interface	ISemIm	An interface for SemIM's; see implementations.
interface	SemOptimizer	Interface for algorithm that optimize the fitting function of a SemIm model by adjusting its freeParameters in search of a global maximum.

Classes in edu.cmu.tetrad.sem that implement TetradSerializable

Modifier and Type	Class	Description
final class	DagScorer	Estimates a SemIm given a CovarianceMatrix and a SemPm.
class	GeneralizedSemIm	Represents a generalized SEM instantiated model.
final class	GeneralizedSemPm	Parametric model for Generalized SEM model.
class	Mapping	Maps a parameter to the matrix element where its value is stored in the model.
class	ParamConstraint	A class for implementing constraints on the values of the freeParameters of of instances of the SemIm class.
class	ParamConstraintType	A typesafe enum of the types of the types of constraints on freeParameters for SEM models (LT, GT, EQ).
final class	Parameter	Stores information about the identity of a SEM parameter--its name, its type (COEF, COVAR), and the node(s) it is associated with.
class	ParameterPair	Implements an ordered pair of objects (a, b) suitable for storing in HashSets.
class	ParamType	A typesafe enum of the types of the types of freeParameters for SEM models (COEF, MEAN, VAR, COVAR).
final class	SemEstimator	Estimates a SemIm given a CovarianceMatrix and a SemPm.
final class	SemEstimatorGibbsParams	Stores the freeParameters for an instance of a SemEstimatorGibbs.
final class	SemEvidence	Stores information for a SemIm about evidence we have for each variable as well as whether each variable has been manipulated.
final class	SemIm	Stores an instantiated structural equation model (SEM), with error covariance terms, possibly cyclic, suitable for estimation and simulation.
final class	SemManipulation	Stores information for a BayesIm about evidence we have for each variable as well as whether each variable has been manipulated.
class	SemOptimizerEm	Optimizes a DAG SEM with hidden variables using expectation-maximization.
class	SemOptimizerPowell	Optimizes a SEM using Powell's method from the Apache library.
class	SemOptimizerRegression	Optimizes a DAG SEM with no hidden variables using closed formula regressions.
class	SemOptimizerRicf	Optimizes a SEM using RICF (see that class).
class	SemOptimizerScattershot	Optimizes a SEM by randomly selecting points in cubes of decreasing size about a given point.
final class	SemPm	Parametric model for Structural Equation Models.
final class	SemProposition	Represents propositions over the variables of a particular BayesIm describing and event of a fairly general sort--namely, conjunctions of propositions that particular variables take on values from a particular disjunctive list of categories.
class	SemUpdater	Calculates updated structural equation models given evidence of the form X1=x1',...,The main task of such and algorithm is to calculate P(X = x' | evidence), where evidence takes the form of a Proposition over the variables in the Bayes net, possibly with additional information about which variables in the Bayes net have been manipulated.
class	StandardizedSemIm	A special SEM model in which variances of variables are always 1 and means of variables are always 0.
static final class	StandardizedSemIm.ParameterRange	Stores a coefficient range--i.e.

Uses of TetradSerializable in edu.cmu.tetrad.session

Subinterfaces of TetradSerializable in edu.cmu.tetrad.session

Modifier and Type	Interface	Description
interface	ExecutionRestarter	Interface indicating that a parameter object can and should be reinitialized at each new execution in a simulation edu.cmu.tetrad.study.
interface	SessionModel	Interface implemented by all session models in Tetrad.

Classes in edu.cmu.tetrad.session that implement TetradSerializable

Modifier and Type	Class	Description
final class	Session	Stores a directed graph over models of specific types, where the possible parent relationships between the models are given in the constructors of the model classes themselves.
class	SessionNode	Represents a node in a session for a model in a particular class.
class	Type1	A sample class to be wrapped in a SessionNode as a model.
class	Type10	A sample class to be wrapped in a SessionNode as a model.
class	Type11	A sample class to be wrapped in a SessionNode as a model.
class	Type12	A sample class to be wrapped in a SessionNode as a model.
class	Type2	A sample class to be wrapped in a SessionNode as a model.
class	Type3	A sample class to be wrapped in a SessionNode as a model.
class	Type4	A sample class to be wrapped in a SessionNode as a model.
class	Type5	A sample class to be wrapped in a SessionNode as a model.
class	Type6	A sample class to be wrapped in a SessionNode as a model.
class	Type7	A sample class to be wrapped in a SessionNode as a model.
class	Type8	A sample class to be wrapped in a SessionNode as a model.
class	Type9	A sample class to be wrapped in a SessionNode as a model.

Uses of TetradSerializable in edu.cmu.tetrad.util

Subinterfaces of TetradSerializable in edu.cmu.tetrad.util

Modifier and Type	Interface	Description
interface	Im	Tagging interface for instantiated models.
interface	TetradLoggerConfig	Represents the configuration for the logger.
static interface	TetradLoggerConfig.Event	Represents an event which is just an id and a description.

Classes in edu.cmu.tetrad.util that implement TetradSerializable

Modifier and Type	Class	Description
class	DefaultTetradLoggerConfig	Logger configuration.
static class	DefaultTetradLoggerConfig.DefaultEvent
class	Matrix	Wraps the Apache math3 linear algebra library for most uses in Tetrad.
class	Parameters	Stores a list of named parameters with their values.
class	PartialCorrelationPdf	Frequency function of partial correlation r(12|34...k), assuming that the true partial correlation is equal to zero.
class	PointXy	Stores a (x, y) point without having to use awt classes.
static class	TetradLogger.EmptyConfig	A empty config, where no event is active.
class	Vector	Vector wrapping matrix library.
class	Version	Represents the getModel Tetrad version.

Uses of TetradSerializable in edu.cmu.tetrad.util.dist

Subinterfaces of TetradSerializable in edu.cmu.tetrad.util.dist

Modifier and Type	Interface	Description
interface	Distribution	Interface for a statistical distribution from which random values can be drawn.

Classes in edu.cmu.tetrad.util.dist that implement TetradSerializable

Modifier and Type	Class	Description
class	Beta	Implements a Beta distribution for purposes of drawing random numbers.
class	ChiSquare	Wraps a chi square distribution for purposes of drawing random samples.
class	Discrete	Wraps a chi square distribution for purposes of drawing random samples.
class	Exponential	Wraps a chi square distribution for purposes of drawing random samples.
class	Gamma	Wraps a chi square distribution for purposes of drawing random samples.
class	GaussianPower
class	Indicator	Created by IntelliJ IDEA.
class	LogNormal	Represents a lognormal distribution for purposes of sampling.
class	MixtureOfGaussians	Wraps a chi square distribution for purposes of drawing random samples.
class	Normal	A normal distribution that allows its parameters to be set and allows random sampling.
class	Poisson	Wraps a chi square distribution for purposes of drawing random samples.
class	SingleValue	A pretend distribution that always returns the given value when nextRandom() is called.
class	Split	Wraps a chi square distribution for purposes of drawing random samples.
class	TruncatedNormal	A normal distribution that allows its parameters to be set and allows random sampling.
class	Uniform	For given a, b (a < b), returns a point chosen uniformly from [a, b].