Uses of Package
edu.cmu.tetrad.search
Packages that use edu.cmu.tetrad.search
Package
Description
Contains classes for searching for (mostly structural) causal models given data.
Contains classes for various 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.
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Classes in edu.cmu.tetrad.search used by edu.cmu.tetrad.algcomparison.algorithm.multiClassDescriptionGive a list of options for rules for doing the non-Gaussian orientations.
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Classes in edu.cmu.tetrad.search used by edu.cmu.tetrad.algcomparison.algorithm.oracle.pattern
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Classes in edu.cmu.tetrad.search used by edu.cmu.tetrad.algcomparison.independenceClassDescriptionGives an interface that can be implemented by classes that do conditional independence testing.
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Classes in edu.cmu.tetrad.search used by edu.cmu.tetrad.graphClassDescriptionGives an interface that can be implemented by classes that do conditional independence testing.
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Classes in edu.cmu.tetrad.search used by edu.cmu.tetrad.searchClassDescriptionAn enumeration of the options available for determining the CPDAG used for the algorithm.Represents a single record in the returned table for CSTaR.An enumeration of the methods for selecting samples from the full dataset.Enumerates the alternatives to use for finding the initial adjacencies for FASK.Enumerates the options left-right rules to use for FASK.A list containing the following componentsGives the options to be used in FOFC to sort through the various possibilities for forming clusters to find the best options.Gives the options to be used in FOFC to sort through the various possibilities for forming clusters to find the best options.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., PC adjacency searches).Gives an interface for a search method that searches and returns a graph.Gives an interface for Markov blanket searches.Gives an interface that can be implemented by classes that do conditional independence testing.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.The type of conditioning set to use for the Markov check.An interface for suborder searches for various types of permutation algorithms.
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Classes in edu.cmu.tetrad.search used by edu.cmu.tetrad.search.scoreClassDescriptionGives an interface that can be implemented by classes that do conditional independence testing.
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Classes in edu.cmu.tetrad.search used by edu.cmu.tetrad.search.testClassDescriptionGives an interface that can be implemented by classes that do conditional independence testing.
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Classes in edu.cmu.tetrad.search used by edu.cmu.tetrad.search.utilsClassDescriptionGives an interface for a search method that searches and returns a graph.Gives an interface that can be implemented by classes that do conditional independence testing.
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Classes in edu.cmu.tetrad.search used by edu.cmu.tetrad.search.work_in_progressClassDescriptionGives an interface for fast adjacency searches (i.e., PC adjacency searches).Gives an interface for a search method that searches and returns a graph.Gives an interface for Markov blanket searches.Gives an interface that can be implemented by classes that do conditional independence testing.Give a list of options for rules for doing the non-Gaussian orientations.
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Classes in edu.cmu.tetrad.search used by edu.pitt.csb.mgmClassDescriptionGives an interface for a search method that searches and returns a graph.Gives an interface that can be implemented by classes that do conditional independence testing.
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Classes in edu.cmu.tetrad.search used by edu.pitt.dbmi.algo.bayesian.constraint.searchClassDescriptionGives an interface for a search method that searches and returns a graph.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.