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

Packages that use IGraphSearch
Package
Description
Contains classes for searching for (mostly structural) causal models given data.
Contains some utility classes for search algorithms.
Contains some classes that aren't ready for prime time.
 
 
  • Uses of IGraphSearch in edu.cmu.tetrad.search

    Modifier and Type
    Interface
    Description
    interface 
    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 
    Uses BOSS in place of FGES for the initial step in the GFCI algorithm.
    final class 
    Implemented the Cyclic Causal Discovery (CCD) algorithm by Thomas Richardson.
    final class 
    Adjusts FCI (see) to use conservative orientation as in CPC (see).
    final class 
    Implements a convervative version of PC, in which the Markov condition is assumed but faithfulness is tested locally.
    class 
    Implements the Fast Adjacency Search (FAS), which is the adjacency search of the PC algorithm (see).
    class 
    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 
    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 
    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 
    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 
    Implements the Fast Greedy Equivalence Search (FGES) algorithm.
    final class 
    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 
    Uses GRaSP in place of FGES for the initial step in the GFCI algorithm.
    class 
    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 
    Modifies the PC algorithm to handle the deterministic case.
    final class 
    Searches for a CPDAG representing all the Markov blankets for a given target T consistent with the given independence information.
    final class 
    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 
    Uses SP in place of FGES for the initial step in the GFCI algorithm.
    class 
    Adapts FAS for the time series setting, assuming the data is generated by a SVAR (structural vector autoregression).
    final class 
    Adapts FCI for the time series setting, assuming the data is generated by a SVAR (structural vector autoregression).
    final class 
    Adapts FGES for the time series setting, assuming the data is generated by a SVAR (structural vector autoregression).
    final class 
    Adapts GFCI for the time series setting, assuming the data is generated by a SVAR (structural vector autoregression).
  • 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 
    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 
    Prodies some common implementation pieces of variaous 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

    Modifier and Type
    Class
    Description
    class 
    Implements the "fast adjacency search" used in several causal algorithm in this package.
    class 
    Implements the Fast Adjacency Search (FAS), which is the adjacency search of the PC algorithm (see).
    class 
    Implements the "fast adjacency search" used in several causal algorithm in this package.
    final class 
    Runs Fast Adjacency Search (FAS) and then orients each edge using the robust skew orientation algorithm.
    class 
    Kernelized PC algorithm.
    class 
    Implements the MMHC algorithm.
    final class 
    Implements a conservative version of PC, in which the Markov condition is assumed but faithfulness is tested locally.
    final class 
    Implements a conservative version of PC, in which the Markov condition is assumed but faithfulness is tested locally.
    final class 
    Implements a convervative version of PC, in which the Markov condition is assumed but faithfulness is tested locally.
    final class 
    Implements a convervative version of PC, in which the Markov condition is assumed but faithfulness is tested locally.
    final class 
    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 
    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

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