Uses of Interface
edu.cmu.tetrad.search.IGraphSearch

Packages that use IGraphSearch

Package	Description
edu.cmu.tetrad.search	Contains classes for searching for (mostly structural) causal models given 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.
edu.pitt.csb.mgm
edu.pitt.dbmi.algo.bayesian.constraint.search

Uses of IGraphSearch in edu.cmu.tetrad.search

Subinterfaces of IGraphSearch in edu.cmu.tetrad.search

Modifier and Type	Interface	Description
interface	IFas	Gives an interface for fast adjacency searches (i.e., PC adjacency searches).

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

Modifier and Type	Class	Description
final class	BFci	Uses BOSS in place of FGES for the initial step in the GFCI algorithm.
final class	Ccd	Implemented the Cyclic Causal Discovery (CCD) algorithm by Thomas Richardson.
final class	Cfci	Adjusts FCI (see) to use conservative orientation as in CPC (see).
final class	Cpc	Implements a convervative version of PC, in which the Markov condition is assumed but faithfulness is tested locally.
class	Fas	Implements the Fast Adjacency Search (FAS), which is the adjacency search of the PC algorithm (see).
class	Fasd	Adjusts FAS (see) for the deterministic case by refusing to removed edges based on conditional independence tests that are judged to be deterministic.
final class	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.
final class	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.
final class	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).
final class	Fges	Implements the Fast Greedy Equivalence Search (FGES) algorithm.
final class	GFci	Implements a modification of FCI that started by running the FGES algorithm and then fixes that result to be correct for latent variables models.
final class	GraspFci	Uses GRaSP in place of FGES for the initial step in the GFCI algorithm.
final class	LvDumb	LV-Dumb is a class that implements the IGraphSearch interface.
final class	LvLite	The LV-Lite algorithm (Latent Variable "Lite") algorithm implements a search algorithm for learning the structure of a graphical model from observational data with latent variables.
class	Pc	Implements the Peter/Clark (PC) algorithm, which uses conditional independence testing as an oracle to first of all remove extraneous edges from a complete graph, then to orient the unshielded colliders in the graph, and finally to make any additional orientations that are capable of avoiding additional unshielded colliders in the graph.
class	Pcd	Modifies the PC algorithm to handle the deterministic case.
final class	PcMb	Searches for a CPDAG representing all the Markov blankets for a given target T consistent with the given independence information.
final class	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.
final class	SpFci	Uses SP in place of FGES for the initial step in the GFCI algorithm.
class	SvarFas	Adapts FAS for the time series setting, assuming the data is generated by a SVAR (structural vector autoregression).
final class	SvarFci	Adapts FCI for the time series setting, assuming the data is generated by a SVAR (structural vector autoregression).
final class	SvarFges	Adapts FGES for the time series setting, assuming the data is generated by a SVAR (structural vector autoregression).
final class	SvarGfci	Represents a GFCI search algorithm for structure learning in causal discovery.

Uses of IGraphSearch in edu.cmu.tetrad.search.utils

Classes in edu.cmu.tetrad.search.utils that implement IGraphSearch

Modifier and Type	Class	Description
final class	FgesOrienter	This Orients a given undirected graph such that the edges in the graph are a superset of the edges in the oriented graph, using FGES method.
final class	PcCommon	Provides some common implementation pieces of various PC-like algorithms, with options for collider discovery type, FAS type, and conflict rule.

Uses of IGraphSearch in edu.cmu.tetrad.search.work_in_progress

Classes in edu.cmu.tetrad.search.work_in_progress that implement IGraphSearch

Modifier and Type	Class	Description
class	FasFdr	Implements the "fast adjacency search" used in several causal algorithm in this package.
final class	FasLofs	Runs Fast Adjacency Search (FAS) and then orients each edge using the robust skew orientation algorithm.
final class	IGFci	Instance-specific GFci given in Fattaneh Jabbari's dissertation (Pages 144-147)
final class	ISFges	GesSearch is an implementation of the GES algorithm, as specified in Chickering (2002) "Optimal structure identification with greedy search" Journal of Machine Learning Research.
class	Kpc	Kernelized PC algorithm.
class	Mmhc	Implements the MMHC algorithm.
final class	SampleVcpc	Implements a conservative version of PC, in which the Markov condition is assumed but faithfulness is tested locally.
final class	SampleVcpcFast	Implements a conservative version of PC, in which the Markov condition is assumed but faithfulness is tested locally.
final class	VcPc	Implements a convervative version of PC, in which the Markov condition is assumed but faithfulness is tested locally.
final class	VcPcAlt	Implements a convervative version of PC, in which the Markov condition is assumed but faithfulness is tested locally.
final class	VcPcFast	Implements a convervative version of PC, in which the Markov condition is assumed but faithfulness is tested locally.

Uses of IGraphSearch in edu.pitt.csb.mgm

Classes in edu.pitt.csb.mgm that implement IGraphSearch

Modifier and Type	Class	Description
class	Mgm	Implementation of Lee and Hastie's (2012) pseudolikelihood method for learning Mixed Gaussian-Categorical Graphical Models Created by ajsedgewick on 7/15/15.

Uses of IGraphSearch in edu.pitt.dbmi.algo.bayesian.constraint.search

Classes in edu.pitt.dbmi.algo.bayesian.constraint.search that implement IGraphSearch

Modifier and Type	Class	Description
class	PagSamplingRfci	Jan 29, 2023 4:10:52 PM
class	RfciBsc	Dec 17, 2018 3:28:15 PM