Package edu.cmu.tetrad.search.test
package edu.cmu.tetrad.search.test
Contains classes for running conditional independence tests for various sorts of data.
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ClassDescriptionCalculates marginal chi square test results for a discrete dataset.Simple class to store the parameters of the result returned by the G Square test.Checks conditional independence of variable in a continuous data set using Daudin's method.Gives a choice of basis functions to use for judgments of independence for conditional correlation independence.Gives a choice of kernels to use for the independence judgments for conditional correlation independence.Performs conditional independence tests of discrete data using the G Square method.Stores the parameters of the result returned by the G Square test and its p-value.Stores a single conditional independence result, e.g., whether X _||_ Y | Z1,..,Zn holds or does not, and the p-value of the test.Checks the conditional independence X _||_ Y | S, where S is a set of discrete variable, and X and Y are discrete variable not in S, by applying a conditional Chi Square test.Checks conditional independence of variable in a continuous data set using a conditional correlation test for the nonlinear nonGaussian with the additive error case.Performs a test of conditional independence X _||_ Y | Z1...Zn where all searchVariables are either continuous or discrete.Implements a degenerate Gaussian score as a LRT.Stores a return value for a likelihood--i.e., a likelihood value and the degrees of freedom for it.Checks conditional independence of variable in a continuous data set using Fisher's Z test.Calculates independence from pooled residuals using the Fisher Z method.Calculates independence from multiple datasets from using the Fisher method of pooling independence results.Checks the conditional independence X _||_ Y | S, where S is a set of discrete variable, and X and Y are discrete variable not in S, by applying a conditional G Square test.Checks the conditional independence X _||_ Y | S, where S is a set of continuous variable, and X and Y are discrete variable not in S, using the Hilbert-Schmidth Independence Criterion (HSIC), a kernel based nonparametric test for conditional independence.Checks conditional independence against a list of conditional independence facts, manually entered.Pools together a set of independence tests using a specified method.Performs a test of conditional independence X _||_ Y | Z1...Zn where all variables are either continuous or discrete.Uses BCInference by Cooper and Bui to calculate probabilistic conditional independence judgments.Checks independence of X _||_ Y | Z for variables X and Y and list Z of variables by regressing X on {Y} U Z and testing whether the coefficient for Y is zero.Checks d-separations in structural model using t-separations over indicators.Gives an implementation of the Kernal Independence Test (KCI) by Kun Zhang, which is a general test of conditional independence.Checks independence facts for variables associated with the nodes in a given graph by checking m-separation facts on the underlying nodes.Gives a way of interpreting a score as an independence test.