Package edu.cmu.tetrad.search
package edu.cmu.tetrad.search
Contains classes for searching for (mostly structural) causal models given data.
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ClassDescriptionUses BOSS in place of FGES for the initial step in the GFCI algorithm.Implements Best Order Score Search (BOSS).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.Implemented the Cyclic Causal Discovery (CCD) algorithm by Thomas Richardson.Adjusts FCI (see) to use conservative orientation as in CPC (see).Implements a convervative version of PC, in which the Markov condition is assumed but faithfulness is tested locally.Implements the CStaR algorithm (Steckoven 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.An enumeration of the options available for estiting the CPDAG used for the algorthm.Represents a single record in the returned table for CSTaR.An enumeration of the methods for selecting samples from the full dataset.Implements the classical Factor Analysis algorithm.Implements the adjacency search of the PC algorithm (see), which is a useful algorithm in many contexts, including as the first step of FCI (see).Adjusts FAS (see) for the deterministic case by refusing to removed edges based on conditional independence tests that are judged to be deterministic.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 pairwsie orientation due to non-Gaussianity.Enumerates the alternatives to use for finding the initial adjacencies for FASK.Enumerates the options left-right rules to use for FASK.Translates a version of the FastICA algorithm used in R from Fortran into Java for use in Tetrad.A list containing the following componentsImplements 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.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).Imlements the Fast Greedy Equivalence Search (GGES) algorithm.Restricts the FGES algorithm (see) to the operations needed just to find the graph over the Markov blanket of a variable X (or a graph over the Markov blankets of a list of variables X1,..,Xn), together with the target X (or, respectively, the targets X1,...,Xn).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.Gives the options to be used in FOFC to sort through the various possibilities for forming clusters to find the best options.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.Gives the options to be used in FOFC to sort through the various possibilities for forming clusters to find the best options.Implements a modification of FCI that started by running the FGES algorithm and then fixes that result to be correct for latent variables models.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 especailly close to the CPDAG of the true model.Uses GRaSP in place of FGES for the initial step in the GFCI algorithm.Implements the Grow-Shrink algorithm of Margaritis and Thrun, a simple yet correct and useful Markov blanket search.Implements the ICA-LiNGAM algorithm.Implements the ICA-LiNG-D algorithm as well as a number of ancillary methods for LiNG-D and LiNGAM.Implements the IDA algorithm.Gives a list of nodes (parents or children) and corresponding minimum effects for the IDA algorithm.Gives an interface for fast adjacency searches (i.e.Gives an interface for a search method that searches and returns a graph.Gives an interface for Markov blanket searches.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.Give a list of options for rules for doing the non-Gaussian orientations.Gives a list of options for non-Gaussian transformations that can be used for some scores.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.Implements Mimbuild using the theory of treks and ranks.Implements the PC (Peter and Clark) 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.Modifies the PC algorithm to handle the deterministic case.Implements the PC-LiNGAM algorithm which first finds a CPDAG for the variables and then uses a non-Gaussian orientation method to orient the undirected edges.Modifies the PC algorithm to use the Max P rule for orienting ushielded colliders.Searches for a CPDAG representing all the Markov blankets for a given target T consistent with the given independence information.Implements common elements of a permutation search.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.Implements the SP (Sparsest Permutation) algorithm.Uses SP in place of FGES for the initial step in the GFCI algorithm.An interface for suborder searches for various types of permutation algorithms.Adapts FAS for the time series setting, assuming the data is generated by a SVAR (structural vector autoregression).Adapts FCI for the time series setting, assuming the data is generated by a SVAR (structural vector autoregression).Adapts FGES for the time series setting, assuming the data is generated by a SVAR (structural vector autoregression).Adapts GFCI for the time series setting, assuming the data is generated by a SVAR (structural vector autoregression).