Package edu.cmu.tetrad.search

package edu.cmu.tetrad.search

Contains classes for searching for (mostly structural) causal models given data.

Related Packages

Package	Description
edu.cmu.tetrad.search.ntad_test
edu.cmu.tetrad.search.score	Contains classes for various sorts of scores for running score-based algorithms.
edu.cmu.tetrad.search.test	Contains classes for running conditional independence tests for various sorts of data.
edu.cmu.tetrad.search.utils	Contains some utility classes for search algorithms.
edu.cmu.tetrad.search.work_in_progress	Contains some classes that aren't ready for prime time.

Class	Description
Boss	Implements Best Order Score Search (BOSS).
BossDot	BOSS-POD is a class that implements the IGraphSearch interface.
BossFci	Uses BOSS in the *-FCI algorithm.
Bpc	Implements the Build Pure Clusters (BPC) algorithm, which allows one to identify clusters of measured variables in a dataset that are explained by a single latent.
Ccd	Implemented the Cyclic Causal Discovery (CCD) algorithm by Thomas Richardson.
Cfci	Adjusts FCI (see) to use conservative orientation as in CPC (see).
CheckKnowledge	Identifies violations of knowledge for a given graph.
CompositeIndependenceTest	CompositeIndependenceTest class.
ConditioningSetType	The type of conditioning set to use for the Markov check.
Cpc	Implements a convervative version of PC, in which the Markov condition is assumed but faithfulness is tested locally.
CpdagParentDistancesFromTrue	CpdagParentDistancesFromTrue computes the distances between true edge strengths in a true DAG and the range of estimated edge strengths in an output CPDAG.
CpdagParentDistancesFromTrue.DistanceType	The type of distance to calculate.
Cstar	Implements the CStaR algorithm (Stekhoven et al., 2012), which finds a CPDAG of that data and then tries all orientations of the undirected edges about a variable in the CPDAG to estimate a minimum bound on the effect for a given edge.
Cstar.CpdagAlgorithm	An enumeration of the options available for determining the CPDAG used for the algorithm.
Cstar.Record	Represents a single record in the returned table for CSTaR.
Cstar.SampleStyle	An enumeration of the methods for selecting samples from the full dataset.
Dagma	Implements the DAGMA algorithm.
DirectLingam	Implements the Direct-LiNGAM algorithm.
DmPc	The Detect-Mimic-PC (DM-PC) algorithm.
EffectiveSampleSizeSettable	Gives an interface for classes where the effective sample size can be set by the user.
FactorAnalysis	Implements the classical Factor Analysis algorithm.
Fas	Implements the Fast Adjacency Search (FAS), which is the adjacency search of the PC algorithm (see).
Fasd	Adjusts FAS (see) for the deterministic case by refusing to removed edges based on conditional independence tests that are judged to be deterministic.
Fask	Implements the FASK (Fast Adjacency Skewness) algorithm, which makes decisions for adjacency and orientation using a combination of conditional independence testing, judgments of nonlinear adjacency, and pairwise orientation due to non-Gaussianity.
Fask.LeftRight	Enumerates the options left-right rules to use for FASK.
FaskOrig	Implements the FASK (Fast Adjacency Skewness) algorithm, which makes decisions for adjacency and orientation using a combination of conditional independence testing, judgments of nonlinear adjacency, and pairwise orientation due to non-Gaussianity.
FaskOrig.AdjacencyMethod	Enumerates the alternatives to use for finding the initial adjacencies for FASK.
FaskOrig.LeftRight	Enumerates the options left-right rules to use for FASK.
FastIca	Translates a version of the FastICA algorithm used in R from Fortran into Java for use in Tetrad.
FastIca.IcaResult	A list containing the following components
Fci	Implements the Fast Causal Inference (FCI) algorithm due to Peter Spirtes, which addressed the case where latent common causes cannot be assumed not to exist with respect to the data set being analyzed.
FciMax	Modifies FCI to do orientation of unshielded colliders (X*-*Y*-*Z with X and Z not adjacent) using the max-P rule (see the PC-Max algorithm).
FciOrientDijkstra	A simple implementation of Dijkstra's algorithm for finding the shortest path in a graph.
FciOrientDijkstra.DijkstraEdge	Represents a node in Dijkstra's algorithm.
FciOrientDijkstra.Graph	Represents a graph for Dijkstra's algorithm.
Fcit	The FCI Targeted Testing (FCIT) algorithm implements a search algorithm for learning the structure of a graphical model from observational data with latent variables.
Fcit.START_WITH	Enumeration representing different start options.
Fges	Implements the Fast Greedy Equivalence Search (FGES) algorithm.
FgesFci	Uses FGES for the initial step of *-FCI.
FgesMb	Implements the Fast Greedy Equivalence Search (FGES) algorithm.
Fofc	Implements the Find One Factor Clusters (FOFC) algorithm by Erich Kummerfeld, which uses reasoning about vanishing tetrads of algorithms to infer clusters of the measured variables in a dataset that each be explained by a single latent variable.
Ftfc	Implements the Find Two Factor Clusters (FOFC) algorithm, which uses reasoning about vanishing tetrads of algorithms to infer clusters of the measured variables in a dataset that each be explained by a single latent variable.
Gfci	*-FCI implements a template modification of GFCI that starts with a given Markov CPDAG and then fixes that result to be correct for latent variables models.
Grasp	Implements the GRaSP algorithms, which uses a certain procedure to search in the space of permutations of variables for ones that imply CPDAGs that are especially close to the CPDAG of the true model.
GraspFci	Uses GRaSP in the *-FCI algorithm.
GrowShrink	Implements the Grow-Shrink algorithm of Margaritis and Thrun, a simple yet correct and useful Markov blanket search.
IcaLingam	Implements the ICA-LiNGAM algorithm.
IcaLingD	Implements the ICA-LiNG-D algorithm as well as a some auxiliary methods for ICA-LiNG-D and ICA-LiNGAM.
Ida	Implements the IDA algorithm.
Ida.NodeEffects	Gives a list of nodes (parents or children) and corresponding minimum effects for the IDA algorithm.
IdaCheck	This calculates total effects and absolute total effects for an MPDAG G for all pairs distinct (x, y) of variables, where the total effect is obtained by regressing y on x ∪ S and reporting the regression coefficient.
IFas	Gives an interface for fast adjacency searches (i.e., PC adjacency searches).
IGraphSearch	Gives an interface for a search method that searches and returns a graph.
IMbSearch	Gives an interface for Markov blanket searches.
IndependenceTest	Gives an interface that can be implemented by classes that do conditional independence testing.
IndTestIod	Checks independence results by listing all tests with those variables, testing each one, and returning the resolution of these test results.
Lofs	Implements a number of methods which take a fixed graph as input and use linear, non-Gaussian methods to orient the edges in the graph.
Lofs.Rule	Give a list of options for rules for doing the non-Gaussian orientations.
Lofs.Score	Gives a list of options for non-Gaussian transformations that can be used for some scores.
MarkovCheck	Checks whether a graph is Markov given a data set.
MarkovCheck.AllSubsetsIndependenceFacts	Stores the set of m-separation facts and the set of m-connection facts for a graph, for the global check.
MarkovCheck.MarkovCheckRecord	A single record for the results of the Markov check.
Mimbuild	Provides an implementation of Mimbuild, an algorithm that takes a clustering of variables, each of which is explained by a single latent, then forms the implied covariance matrix over the latent variables, then runs a CPDAG search to in the structure over the latent themselves.
MimbuildTrek	Implements Mimbuild using the theory of treks and ranks.
ModelObserver	The ModelObserver interface is implemented by classes that want to observe changes in a model.
OrderedLocalMarkovProperty	Utility class for computing the ordered local Markov property of a maximal ancestral graph (MAG).
Pc	Implements the Peter/Clark (PC) algorithm, which uses conditional independence testing as an oracle to do three things: (1) Remove extraneous edges from a complete graph, (2) orient the unshielded colliders in the graph.
Pcd	Modifies the PC algorithm to handle the deterministic case.
PcMb	Searches for a CPDAG representing all the Markov blankets for a given target T consistent with the given independence information.
PermutationSearch	Implements common elements of a permutation search.
RecursiveBlocking	The RecursiveBlocking class provides methods for assessing the blockability of paths in a graph based on specified conditions.
RecursiveBlocking.Blockable	Enum representing the blocking status for graph-path-related operations in the RecursiveBlocking class.
RecursiveBlockingChokePointA	This class provides methods for identifying a minimal set of blocking nodes (a "choke point") that prevent all paths between two specified nodes in a graph using recursive and iterative mechanisms.
RecursiveBlockingChokePointB	Option B, but – completely ignores the direct edge x *-* y while searching, and – returns null if it proves impossible to block the remaining x–y paths with eligible non-colliders.
RecursiveDiscriminatingPathRule	Implements the R4 Discriminating Path rule in the final FCI orientation rules (Zhang 2008) using the block_paths_recursively method as a optimization.
Rfci	Implements the Really Fast Causal Inference (RFCI) algorithm, which aims to do a correct inference of inferrable causal structure under the assumption that unmeasured common causes of variables in the data may exist.
SepsetFinder	This class provides methods for finding sepsets in a given graph.
Sp	Implements the SP (Sparsest Permutation) algorithm.
SpFci	Uses SP in place of FGES for the initial step in the *-FCI algorithm.
StarFci	*-FCI implements a template modification of GFCI that starts with a given Markov CPDAG and then fixes that result to be correct for latent variables models.
SuborderSearch	An interface for suborder searches for various types of permutation algorithms.
SvarFas	Adapts FAS for the time series setting, assuming the data is generated by a SVAR (structural vector autoregression).
SvarFci	Adapts FCI for the time series setting, assuming the data is generated by a SVAR (structural vector autoregression).
SvarFges	Adapts FGES for the time series setting, assuming the data is generated by a SVAR (structural vector autoregression).
SvarGfci	Represents a FGES-FCI search algorithm for structure learning in causal discovery.