Uses of Interface
edu.cmu.tetrad.util.TetradSerializable
Packages that use TetradSerializable
Package
Description
Contains classes for searching for (mostly structural) causal models given data.
Contains classes for various sorts of scores for running score-based algorithms.
Contains classes for running conditional independence tests for various sorts of data.
Contains some utility classes for search algorithms.
Contains some classes that aren't ready for prime time.
Contains an editable display graph for (small) lag graphs.
Implements a time-series simulation engine suitable for time-series gene
expression
studies.
Contains classes for generating simulations of expression levels over a
collection
of genes.
-
Uses of TetradSerializable in edu.cmu.tetrad.algcomparison
Classes in edu.cmu.tetrad.algcomparison that implement TetradSerializableModifier and TypeClassDescriptionclass
Script to do a comparison of a list of algorithms using a list of statistics and a list of parameters and their values. -
Uses of TetradSerializable in edu.cmu.tetrad.algcomparison.algorithm
Subinterfaces of TetradSerializable in edu.cmu.tetrad.algcomparison.algorithmModifier and TypeInterfaceDescriptioninterface
Interface that algorithm must implement.interface
Implements an algorithm that takes multiple data sets as input.Classes in edu.cmu.tetrad.algcomparison.algorithm that implement TetradSerializableModifier and TypeClassDescriptionclass
This is a base class for bootstrap algorithms.class
A list of algorithm to be compared.class
Tags an an algorithm that loads up external graphs for inclusion in reports.class
First inflection point.class
Stability selection.class
StARS -
Uses of TetradSerializable in edu.cmu.tetrad.algcomparison.algorithm.cluster
Classes in edu.cmu.tetrad.algcomparison.algorithm.cluster that implement TetradSerializable -
Uses of TetradSerializable in edu.cmu.tetrad.algcomparison.algorithm.continuous.dag
Classes in edu.cmu.tetrad.algcomparison.algorithm.continuous.dag that implement TetradSerializableModifier and TypeClassDescriptionclass
Implements the DAGMA algorithm.class
Direct LiNGAM.class
FASK algorithm.class
The FaskOrig class is an implementation of the FASK-Orig algorithm for causal discovery.class
IcaLingam class implements the Algorithm and ReturnsBootstrapGraphs interface.class
IcaLingD is an implementation of the Algorithm interface that performs the ICA-LiNG-D algorithm for discovering causal models for the linear non-Gaussian case where the underlying model might be cyclic. -
Uses of TetradSerializable in edu.cmu.tetrad.algcomparison.algorithm.mixed
Classes in edu.cmu.tetrad.algcomparison.algorithm.mixed that implement TetradSerializable -
Uses of TetradSerializable in edu.cmu.tetrad.algcomparison.algorithm.multi
Classes in edu.cmu.tetrad.algcomparison.algorithm.multi that implement TetradSerializableModifier and TypeClassDescriptionclass
Wraps the IMaGES algorithm for continuous variables.class
Wraps the IMaGES algorithm for continuous variables.class
Wraps the MultiFask algorithm for continuous variables.class
Wraps the IMaGES algorithm for continuous variables.class
Runs FCI on multiple datasets using the IOD pooled dataset independence test.class
Requires that the parameter 'randomSelectionSize' be set to indicate how many datasets should be taken at a time (randomly).class
Wraps the IMaGES algorithm for continuous variables.class
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 TetradSerializableModifier and TypeClassDescriptionclass
BOSS (Best Order Score Search)class
BOSS-LiNGAM algorithm.class
Conservative PC (CPC).class
Cstar class.class
Fast Adjacency Search (FAS)--i.e., the PC adjacency step, which is used in many algorithms.class
FGES (the heuristic version).class
FGES-MB (the heuristic version).class
FGES (the heuristic version).class
GRaSP (Greedy Relaxations of Sparsest Permutation)class
Peter/Clark algorithm (PC).class
PC.class
PC.class
BOSS-DC (Best Order Score Search Divide and Conquer)class
PC.class
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 TetradSerializableModifier and TypeClassDescriptionclass
Adjusts GFCI to use a permutation algorithm (such as BOSS-Tuck) to do the initial steps of finding adjacencies and unshielded colliders.class
This class represents the LV-Lite algorithm, which is an implementation of the GFCI algorithm for learning causal structures from observational data using the BOSS algorithm as an initial CPDAG and using all score-based steps afterward.class
This class represents the LV-Lite algorithm, which is an implementation of the GFCI algorithm for learning causal structures from observational data using the BOSS algorithm as an initial CPDAG and using all score-based steps afterward.class
CCD (Cyclic Causal Discovery)class
Conservative FCI.class
The Fast Causal Inference (FCI) algorithm.class
FCI-Max algorithm.class
The Gfci class represents the Greedy Fast Causal Inference algorithm.class
Adjusts GFCI to use a permutation algorithm (such as BOSS-Tuck) to do the initial steps of finding adjacencies and unshielded colliders.class
This class represents the LV-Lite algorithm, which is an implementation of the GFCI algorithm for learning causal structures from observational data using the BOSS algorithm as an initial CPDAG and using all score-based steps afterward.class
Jan 29, 2023 3:45:09 PMclass
RFCI.class
Runs RFCI-BSC, which is RFCI with bootstrap sampling of PAGs.class
Adjusts GFCI to use a permutation algorithm (in this case SP) to do the initial steps of finding adjacencies and unshielded colliders.class
The SvarFci class is an implementation of the SVAR Fast Causal Inference algorithm.class
SvarGfci class is an implementation of the SVAR GFCI algorithm. -
Uses of TetradSerializable in edu.cmu.tetrad.algcomparison.algorithm.other
Classes in edu.cmu.tetrad.algcomparison.algorithm.other that implement TetradSerializable -
Uses of TetradSerializable in edu.cmu.tetrad.algcomparison.algorithm.pairwise
Classes in edu.cmu.tetrad.algcomparison.algorithm.pairwise that implement TetradSerializable -
Uses of TetradSerializable in edu.cmu.tetrad.algcomparison.graph
Subinterfaces of TetradSerializable in edu.cmu.tetrad.algcomparison.graphModifier and TypeInterfaceDescriptioninterface
An interface to represent a random graph of some sort.Classes in edu.cmu.tetrad.algcomparison.graph that implement TetradSerializableModifier and TypeClassDescriptionclass
Returns a cyclic graph build up from small cyclic graph components.class
Creates a random graph by the Erdos-Renyi method (probabiliy of edge fixed, # edges not).class
Creates a random graph by adding forward edges.class
Creates a random graph by adding forward edges.class
Creates a random graph by adding forward edges.class
Returns a scale free graph.class
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.independenceModifier and TypeInterfaceDescriptioninterface
Interface that algorithm must implement.Classes in edu.cmu.tetrad.algcomparison.independence that implement TetradSerializableModifier and TypeClassDescriptionclass
class
Wrapper for Fisher Z test.class
Wrapper for Daudin Conditional Independence test.class
Wrapper for Fisher Z test.class
Wrapper for Fisher Z test.class
Wrapper for DG LRT.class
Wrapper for Fisher Z test.class
Wrapper for Fisher Z test.class
Represents a class for Generalized Information Criterion Score Tests.class
Wrapper for Fisher Z test.class
Wrapper for KCI test.class
Wrapper for Fisher Z test.class
Wrapper for Fisher Z test.class
Wrapper for M-separation test.class
Wrapper for Fisher Z test.class
Wrapper for Fisher Z test.class
class
Wrapper for Fisher Z test.class
Dec 17, 2018 3:44:46 PMclass
The SemBicDTest class implements the IndependenceWrapper interface and represents a test for independence based on SEM BIC algorithm.class
-
Uses of TetradSerializable in edu.cmu.tetrad.algcomparison.score
Subinterfaces of TetradSerializable in edu.cmu.tetrad.algcomparison.scoreModifier and TypeInterfaceDescriptioninterface
Interface that algorithm must implement.Classes in edu.cmu.tetrad.algcomparison.score that implement TetradSerializableModifier and TypeClassDescriptionclass
Wrapper for Basis Function BIC Score (Basis-BIC).class
Wrapper for Fisher Z test.class
Wrapper for Fisher Z test.class
Wrapper for degenerate Gaussian BIC scoreclass
Wrapper for Discrete BIC test.class
Wrapper for linear, Gaussian Extended BIC score (Chen and Chen).class
Wrapper for Fisher Z test.class
The GicScores class is an implementation of the ScoreWrapper interface that calculates the Generalized Information Criterion (GIC) scores for a given data model.class
Wrapper for degenerate Gaussian BIC scoreclass
Wrapper for Fisher Z test.class
Wrapper for MVP BIC Score.class
Wrapper for the Poisson prior score (Bryan)class
Wrapper for Fisher Z test.class
Wrapper for linear, Gaussian SEM BIC score.class
SemBicScoreDeterministic is a class that implements the ScoreWrapper interface.class
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.simulationModifier and TypeInterfaceDescriptioninterface
The interface that simulations must implement.Classes in edu.cmu.tetrad.algcomparison.simulation that implement TetradSerializableModifier and TypeClassDescriptionclass
Bayes net simulation.class
A simulation method based on the conditional Gaussian assumption.class
The GeneralSemSimulation class represents a simulation using a generalized structural equation model (SEM).class
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
A version of the Lee and Hastic simulation which is guaranteed ot generate a discrete data set.class
Linear Fisher Model.class
A simulation method based on the mixed variable polynomial assumption.class
NL SEM simulation.class
This class represents a Simulation using Structural Equation Modeling (SEM).class
SEM the discretize.class
A list of simulations to be compared.class
ASimulation
implementation that returns a single supplied data set.class
Standardized SEM simulation.class
Time series SEM simulation. -
Uses of TetradSerializable in edu.cmu.tetrad.algcomparison.statistic
Classes in edu.cmu.tetrad.algcomparison.statistic that implement TetradSerializableModifier and TypeClassDescriptionclass
A list of statistics and their utility weights. -
Uses of TetradSerializable in edu.cmu.tetrad.bayes
Subinterfaces of TetradSerializable in edu.cmu.tetrad.bayesModifier and TypeInterfaceDescriptioninterface
Interface implemented by Bayes instantiated models.interface
Interface for a discrete Bayes updating algorithm.interface
An interface representing a map of probabilities or counts for nodes in a Bayesian network.interface
Interface for a Bayes updating algorithm that's capable of doing manipulation.Classes in edu.cmu.tetrad.bayes that implement TetradSerializableModifier and TypeClassDescriptionfinal class
Calculates updated marginals for a Bayes net by simulating data and calculating likelihood ratios.final class
Calculates cell probabilities from conditional BayesIm probabilities on the fly without constructing the entire table.final class
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
Calculates marginals of the form P(V=v') for an updated Bayes net for purposes of the CPT Invariant Updater.final class
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.class
Represents a conditional probability table (CPT) in a Bayes net.class
Represents a conditional probability table (CPT) in a Bayes net.final class
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
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, based on RowSummingExactUpdaterclass
Junction Tree Algorithm.class
Jan 21, 2020 11:03:09 AMfinal class
Stores information for a variable source about evidence we have for each variable as well as whether each variable has been manipulated.final class
Stores a table of probabilities for a Bayes net and, together with BayesPm and Dag, provides methods to manipulate this table.final class
Stores a table of probabilities for a Bayes net and, together with BayesPm and Dag, provides methods to manipulate this table.final class
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
Performs updating operations on a BayesIm by summing over cells in the joint probability table for the BayesIm.final class
Creates a table of stored cell probabilities for the given list of variables.final class
Creates a table of stored cell probabilities for the given list of variables.final class
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.expressionModifier and TypeInterfaceDescriptioninterface
Represents a mathematical expression.interface
Represents a definition for some expression.interface
Represents the signature of the expression, for example sqrt(number).Classes in edu.cmu.tetrad.calculator.expression that implement TetradSerializableModifier and TypeClassDescriptionclass
Represents a constant expression, that is an expression that always evaluates to the same value.class
An equation expression.static enum
An enum of positions that an expression can occur in.class
An Expression for a variable. -
Uses of TetradSerializable in edu.cmu.tetrad.classify
Classes in edu.cmu.tetrad.classify that implement TetradSerializableModifier and TypeClassDescriptionfinal class
This class contains a method classify which uses an instantiated Bayes net (BayesIm) provided in the constructor. -
Uses of TetradSerializable in edu.cmu.tetrad.data
Subinterfaces of TetradSerializable in edu.cmu.tetrad.dataModifier and TypeInterfaceDescriptioninterface
Stores a 2D array of data.interface
Interface implemented by classes, instantiations of which can serve as data models in Tetrad.interface
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
Interface for covariance matrices.interface
Interface implemented by classes that are capable of participating in the transfer of knowledge objects.interface
Created by jdramsey on 12/22/15.interface
Interface implemented by classes, instantiations of which are capable of serving as variables for columns in a DataSet.interface
Inteface implemented by classes, instantiations of which are associated with lists of variables.Classes in edu.cmu.tetrad.data that implement TetradSerializableModifier and TypeClassDescriptionclass
Base class for variable specifications for DataSet.final class
Wraps a DataBox in such a way that mixed data sets can be stored.class
Stores a 2D array of byte data.final class
Stores clusters of variables for MimBuild, Purify, etc.final class
Specifies how a column (continuous or discrete) should be discretized.final class
Represents a real-valued variable.final class
Stores a correlation matrix together with variable names and sample size; intended as a representation of a data set.class
Stores a covariance matrix together with variable names and sample size, intended as a representation of a data set.class
Stores a covariance matrix together with variable names and sample size, intended as a representation of a data set.class
Stores a covariance matrix together with variable names and sample size, intended as a representation of a data set.final class
Stores a list of data models and keeps track of which one is selected.final class
Type-safe enum of delimiter types for parsing data.final class
Specifies how a column (continuous or discrete) should be discretized.final class
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
Type-safe enum of discrete variable types.class
Stores a 2D array of double data.class
Stores a 2D array of float data.class
Stores a list of independence facts.class
Stores a 2D array of integer data.final class
Stores information about required and forbidden edges and common causes for use in algorithm.final class
Implements a knowledge edge X-->Y as a simple ordered pair of strings.final class
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
Stores a 2D array of long data.class
Stores a 2D array of double continuousData.final class
Wraps a 2D array of Number objects in such a way that mixed data sets can be stored.class
Stores a 2D array of short data.final class
Specifies how a column (continuous or discrete) should be discretized.final class
Stores time series data as a list of continuous columns.class
Stores a 2D array of double data.class
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 TetradSerializableModifier and TypeClassDescriptionclass
Load data sets and graphs from a directory.class
Load data sets and graphs from a directory.class
Load data sets and graphs from a directory.class
Load data sets and graphs from a directory.class
Load data sets and graphs from a directory. -
Uses of TetradSerializable in edu.cmu.tetrad.graph
Subinterfaces of TetradSerializable in edu.cmu.tetrad.graphModifier and TypeInterfaceDescriptioninterface
Implements a graph capable of storing edges of type N1 *-# N2 where * and # are endpoints of type Endpoint.interface
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 TetradSerializableModifier and TypeClassDescriptionfinal class
Represents a directed acyclic graph--that is, a graph containing only directed edges, with no cycles.class
Represents an edge node1 *-# node2 where * and # are endpoints of type Endpoint--that is, Endpoint.TAIL, Endpoint.ARROW, or Endpoint.CIRCLE.class
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
Apr 13, 2017 3:56:46 PMenum
A enumeration of the endpoint types that are permitted in Tetrad-style graphs: null (-), arrow (->), circle (-o), start (-*), and null (no endpoint).class
Implements a basic node in a graph--that is, a node that is not itself a variable.final class
Stores a triple (x, y, z) of nodes.class
Implements a graph allowing nodes in the getModel time lag to have parents taken from previous time lags.class
OrderedPair<E>
An ordered pair of objects.class
Paths class.final class
Represents the graphical structure of a structural equation model.class
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
Stores a triple (x, y, z) of nodes.class
Underlines class. -
Uses of TetradSerializable in edu.cmu.tetrad.regression
Classes in edu.cmu.tetrad.regression that implement TetradSerializableModifier and TypeClassDescriptionclass
Implements a logistic regression algorithm based on a Javascript implementation by John Pezzullo.static class
The result of a logistic regression.class
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 TetradSerializableModifier and TypeClassDescriptionstatic class
Represents a single record in the returned table for CSTaR. -
Uses of TetradSerializable in edu.cmu.tetrad.search.score
Classes in edu.cmu.tetrad.search.score that implement TetradSerializable -
Uses of TetradSerializable in edu.cmu.tetrad.search.test
Classes in edu.cmu.tetrad.search.test that implement TetradSerializableModifier and TypeClassDescriptionfinal class
Stores a single conditional independence result, e.g., whether X _||_ Y | Z1,...,Zn holds or does not, and the p-value of the test. -
Uses of TetradSerializable in edu.cmu.tetrad.search.utils
Classes in edu.cmu.tetrad.search.utils that implement TetradSerializableModifier and TypeClassDescriptionclass
A class for heuristically removing almost cycles from a PAG to avoid unfaithfulness in an estimated PAG.enum
Enumerates the algorithm types for BuildPureClusters, and Purify.enum
Enumerates the test types for BuildPureClusters, and Purify.final class
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
Represents an ordered sextad of nodes. -
Uses of TetradSerializable in edu.cmu.tetrad.search.work_in_progress
Classes in edu.cmu.tetrad.search.work_in_progress that implement TetradSerializable -
Uses of TetradSerializable in edu.cmu.tetrad.sem
Subinterfaces of TetradSerializable in edu.cmu.tetrad.semModifier and TypeInterfaceDescriptioninterface
An interface for SemIM's; see implementations.interface
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 TetradSerializableModifier and TypeClassDescriptionfinal class
Estimates a SemIm given a CovarianceMatrix and a SemPm.class
Represents a generalized SEM-instantiated model.final class
Parametric model for a Generalized SEM model.class
Maps a parameter to the matrix element where its value is stored in the model.class
A class for implementing constraints on the values of the freeParameters of of instances of the SemIm class.final class
Stores information about the identity of a SEM parameter--its name, its type (COEF, COVAR), and the node(s) it is associated with.class
Implements an ordered pair of objects (a, b) suitable for storing in HashSets.final class
Estimates a SemIm given a CovarianceMatrix and a SemPm.final class
Stores the freeParameters for an instance of a SemEstimatorGibbs.final class
Stores information for a SemIm about evidence we have for each variable as well as whether each variable has been manipulated.final class
Stores an instantiated structural equation model (SEM), with error covariance terms, possibly cyclic, suitable for estimation and simulation.final class
Stores information for a BayesIm about evidence we have for each variable as well as whether each variable has been manipulated.class
Optimizes a DAG SEM with hidden variables using expectation-maximization.class
Optimizes a SEM using Powell's method from the Apache library.class
Optimizes a DAG SEM by regressing each varaible onto its parents using a linear regression.class
Optimizes a SEM using RICF (see that class).class
Optimizes a SEM by randomly selecting points in cubes of decreasing size about a given point.final class
Parametric model for Structural Equation Models.final class
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
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
A special SEM model in which variances of variables are always 1 and means of variables are always 0.static final class
Stores a coefficient range--i.e. -
Uses of TetradSerializable in edu.cmu.tetrad.study.gene.tetrad.gene.graph
Classes in edu.cmu.tetrad.study.gene.tetrad.gene.graph that implement TetradSerializableModifier and TypeClassDescriptionclass
Adds Javabean property change events so that it can be used in a MVC type architecture.final class
Constructs as a (manual) update graph.class
Stores the parameters needed to generate a new lag graph, whether randomized or manually constructed.class
Stores a file for reading in a lag graph from a file. -
Uses of TetradSerializable in edu.cmu.tetrad.study.gene.tetrad.gene.history
Subinterfaces of TetradSerializable in edu.cmu.tetrad.study.gene.tetrad.gene.historyModifier and TypeInterfaceDescriptioninterface
Instantiations of this interface know how to randomize update graphs in particular ways.interface
Initializes a history array.interface
Lag graph.interface
A distribution from which noise values are drawn.interface
Implements a function from the previous time steps of a history array to the getModel time step.Classes in edu.cmu.tetrad.study.gene.tetrad.gene.history that implement TetradSerializableModifier and TypeClassDescriptionclass
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
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
Stores a boolean function from a set of boolean-valued parents to a single boolean-valued column.class
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
Models the manner in which gene models are initialized differentially depending on the dishes that the cells are in.class
Implements the basic machinery used by all history objects.class
Stores a "shapshot" of the indexedConnectivity of a lag graph, using indices rather than Strings to refer to factors.class
Stores a "shapshot" of the indexedLagGraph of a lag graph, using indices rather than Strings to refer to factors.final class
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
wrapper class for passing factor+edge via a propertyChange eventclass
Identifies a particular factor (by name) at a particular lag (integer).class
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
Implements a polynomial as a sum of a list of terms whose variables are identified as integers in the set {0, 1, 2, ...}.class
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
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.study.gene.tetrad.gene.simulation
Classes in edu.cmu.tetrad.study.gene.tetrad.gene.simulation that implement TetradSerializableModifier and TypeClassDescriptionclass
Simulates measurement genetic data using an underlying GeneHistory object to generate individual cell data. -
Uses of TetradSerializable in edu.cmu.tetrad.study.gene.tetradapp.model
Classes in edu.cmu.tetrad.study.gene.tetradapp.model that implement TetradSerializableModifier and TypeClassDescriptionclass
Implements a parametric gene model.class
Wraps MeasurementSimulator so that it may be used as a parameter object. -
Uses of TetradSerializable in edu.cmu.tetrad.util
Subinterfaces of TetradSerializable in edu.cmu.tetrad.utilModifier and TypeInterfaceDescriptioninterface
Tagging interface for instantiated models.interface
Represents the configuration for the logger.static interface
Represents an event which is just an id and a description.Classes in edu.cmu.tetrad.util that implement TetradSerializableModifier and TypeClassDescriptionclass
Logger configuration.static class
A default implementation of the event.class
Wraps the Apache math3 linear algebra library for most uses in Tetrad.class
Stores a list of named parameters with their values.class
Frequency function of partial correlation r(12|34...k), assuming that the true partial correlation is equal to zero.class
Stores a (x, y) point without having to use awt classes.static class
Represents an empty configuration for the logger.class
Vector wrapping matrix library.class
Represents the getModel Tetrad version. -
Uses of TetradSerializable in edu.cmu.tetrad.util.dist
Subinterfaces of TetradSerializable in edu.cmu.tetrad.util.distModifier and TypeInterfaceDescriptioninterface
Interface for a statistical distribution from which random values can be drawn.Classes in edu.cmu.tetrad.util.dist that implement TetradSerializableModifier and TypeClassDescriptionclass
Implements a Beta distribution for purposes of drawing random numbers.class
Wraps a chi square distribution for purposes of drawing random samples.class
Wraps a chi square distribution for purposes of drawing random samples.class
Wraps a chi square distribution for purposes of drawing random samples.class
Wraps a chi square distribution for purposes of drawing random samples.class
GaussianPower class.class
Created by IntelliJ IDEA.class
Represents a lognormal distribution for purposes of sampling.class
Wraps a chi square distribution for purposes of drawing random samples.class
A normal distribution that allows its parameters to be set and allows random sampling.class
Wraps a chi square distribution for purposes of drawing random samples.class
A pretend distribution that always returns the given value when nextRandom() is called.class
Wraps a chi square distribution for purposes of drawing random samples.class
A normal distribution that allows its parameters to be set and allows random sampling.class
For given a, b (a < b), returns a point chosen uniformly from [a, b].