Package edu.cmu.tetrad.algcomparison.statistic
package edu.cmu.tetrad.algcomparison.statistic
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ClassDescriptionThe adjacency precision.The adjacency precision.The adjacency true positive rate.The adjacency precision.The adjacency recall.The adjacency precision.The adjacency precision.The adjacency true positive rate.Calculates the F1 statistic for adjacencies.Ancestor precision.Ancestor recall.The bidirected true positives.The bidirected true positives.The arrow precision.The arrow precision.The adjacency true positive rate.The arrow precision.The arrow precision.The arrow recall.The arrow recall.The arrow precision.The arrow precision.The adjacency precision.The adjacency precision.Difference between the true and estimated BIC scores.Difference between the true and estiamted BIC scores.Estimated BIC score.True BIC score.The bidirected true positives.The bidirected false negatives.The BidirectedLatentPrecision class implements the Statistic interface and represents a statistic that calculates the percentage of bidirected edges in an estimated graph for which a latent confounder exists in the true graph.The bidirected edge precision.The bidirected edge precision.The bidirected true positives.The bidirected true positives.The bidirected true positives.The bidirected true positives.The bidirected true positives.The bidirected true positives.Outputs 1 if the skeleton is correct, 0 if not..The bidirected true positives.The bidirected true positives.The adjacency precision.The adjacency precision.Records the elapsed time of the algorithm in seconds.Calculates the F1 statistic for adjacencies.Calculates the F1 statistic for adjacencies.Calculates the F1 statistic for arrowheads.The bidirected true positives.The bidirected true positives.Calculates the F1 statistic for adjacencies.Estimates whether the p-values under the null are Uniform usign the Markov Checker.Estimates whether the p-values under the null are Uniform usign the Markov Checker.Return a 1 if the graph is exactly right, 0 otherwise.The IDA average squared distance.IdaCheckAvgMaxSquaredDiffEstTrue calculates the average maximum squared difference between the estimated and true values for a given data model and graphs.Represents a statistic that calculates the average minimum squared difference between the estimated and true values for a given data model and graphs.IdaCheckAvgSquaredDifference is a class that implements the Statistic interface.IdaMaximumSquaredDifference is a statistic that calculates the "IDA Average Maximum Squared Difference" between a true graph and an estimated graph.IdaMinimumSquaredDifference is a statistic that calculates the "IDA Average Minimum Squared Difference" between a true graph and an estimated graph.The Implied Arrow Orientation Ratio Est statistic calculates the ratio of the number of implied arrows to the number of arrows in unshielded colliders in the estimated graph.The Implied Arrow Orientation Ratio Est statistic calculates the ratio of the number of implied arrows to the number of arrows in unshielded colliders in the estimated graph.The Implied Arrow Orientation Ratio Est statistic calculates the ratio of the number of implied arrows to the number of arrows in unshielded colliders in the estimated graph.Implies Legal MAGImplementation of the KnowledgeSatisfied class.The bidirected true positives.The bidirected true positives.The bidirected true positives.The bidirected true positives.Legal PAGThe LocalGraphPrecision class implements the Statistic interface and represents the Local Graph Precision statistic.LocalGraphRecall implements the Statistic interface and represents the local graph recall statistic.Takes a MAG in a PAG using Zhang's method and then reports the MAG DG BIC score for it.Takes a MAG in a PAG using Zhang's method and then reports the MAG DG BIC score for it.Takes a MAG in a PAG using Zhang's method and then reports the MAG SEM BIC score for it.Calculates the Anderson Darling P value for the Markov check of whether the p-values for the estimated graph are distributed as U(0, 1).Calculates the Anderson Darling P value for the Markov check of whether the p-values for the estimated graph are distributed as U(0, 1).Calculates the Anderson Darling P value for the Markov check of whether the p-values for the estimated graph are distributed as U(0, 1).Calculates the Anderson Darling P value for the Markov check of whether the p-values for the estimated graph are distributed as U(0, 1).Represents a markov check statistic that calculates the Binomial P value for whether the p-values for the estimated graph are distributed as U(0, 1).Represents a markov check statistic that calculates the Binomial P value for whether the p-values for the estimated graph are distributed as U(0, 1).Represents a markov check statistic that calculates the Kolmogorov-Smirnoff P value for whether the p-values for the estimated graph are distributed as U(0, 1).Represents a markov check statistic that calculates the Kolmogorov-Smirnoff P value for whether the p-values for the estimated graph are distributed as U(0, 1).Represents a markov check statistic that calculates the Kolmogorov-Smirnoff P value for whether the p-values for the estimated graph are distributed as U(0, 1).Represents a markov check statistic that calculates the Kolmogorov-Smirnoff P value for whether the p-values for the estimated graph are distributed as U(0, 1).Calculates the Matthew's correlation coefficient for adjacencies.Calculates the Matthew's correlation coefficient for adjacencies.Checks whether a PAG is maximal.MaximalMag statistic.No almost cyclic paths condition.No cyclic paths condition.The adjacency precision.The adjacency precision.Number of NOT X~~>Y in true graph for which also NOT X~~>Y in estimated graph.Number of NOT X~~>Y in true graph for which also NOT X~~>Y in estimated graph.Calculates the F1 statistic for adjacencies.The bidirected true positives.The bidirected true positives.The adjacency precision.Represents the NumberEdgesEst statistic, which calculates the number of arrows in the estimated graph.Represents the NumberEdgesEst statistic, which calculates the number of arrows not in unshielded colliders in the estimated graph.Represents the NumberEdgesEst statistic, which calculates the number of unshielded colliders in the estimated graph.Represents the NumberEdgesEst statistic, which calculates the number of edges in the estimated graph.Represents the NumberEdgesEst statistic, which calculates the number of edges in colliders in the estimated graph.Represents the NumberEdgesEst statistic, which calculates the number of edges in unshielded colliders in the estimated graph.The NumberEdgesTrue class is an implementation of the Statistic interface.Prints the number of edges in the estimated graph.Prints the number of edges in the true graph.Represents the NumberEdgesEst statistic, which calculates the number of tails in the estimated graph.Represents the NumberEdgesEst statistic, which calculates the number of unshielded colliders in the estimated graph.The bidirected edge precision.The adjacency precision.The adjacency precision.The bidirected true positives.The bidirected true positives.The bidirected true positives.The bidirected true positives.The bidirected true positives.The bidirected true positives.The bidirected true positives.The bidirected true positives.The bidirected true positives.The bidirected true positives.The bidirected true positives.The bidirected true positives.Counts the number of X<->Y edges for which a latent confounder of X and Y exists.The bidirected true positives.The bidirected true positives.Represents a statistic that calculates the number of correct visible ancestors in the true graph that are also visible ancestors in the estimated graph.The number of covering adjacencies in an estimated PAG compared to the true PAG.The bidirected true positives.The bidirected true positives.The bidirected true positives.The bidirected true positives.The bidirected true positives.The bidirected true positives.The bidirected true positives.Number of X-->Y in est.The bidirected true positives.The bidirected true positives.The bidirected true positives.The number of adjacencies in the estimated graph but not in the true graph.The number of genuine adjacencies in an estimated PAG compared to the true PAG.The bidirected true positives.The bidirected true positives.The bidirected true positives.The bidirected true positives.Number of X---Y in est.Number of parameters for a discrete Bayes model of the data.Number of Xo->Y in est.The bidirected true positives.Number of X---Y in est.NumVisibleEdgeEst is a class that implements the Statistic interface.Represents a statistic that calculates the number of correct visible ancestors in the true graph that are also visible ancestors in the estimated graph.A class that implements the Statistic interface to calculate the number of visible edges in the true PAG.The orientation precision.Represents an implementation of the Statistic interface that calculates the Orientation Recall.The PagAdjacencyPrecision class implements the Statistic interface and represents the adjacency precision compared to the true PAG (Partially Ancestral Graph).A class that implements the PagAdjacencyRecall statistic.Adds a column to the output table in which values for the given parameter are listed.The adjacency precision.The adjacency precision.Proportion of semi(X, Y) in estimated graph for which there is no semi(Y, X) in true graph.Proportion of semi(X, Y) in true graph for which there is no semi(Y, Z) in estimated graph.Estimates whether the p-values under the null are Uniform usign the Markov Checker.Estimates whether the p-values under the null are Uniform using the Markov Checker.Calculates the F1 statistic for adjacencies.The bidirected true positives.A class implementing the Semidirected-Rec statistic.The interface that each statistic needs to implement.A list of statistics and their utility weights.Calculates the structural Hamming distance (SHD) between the estimated graph and the true graph.TailPrecision is a class that implements the Statistic interface.Implements the TailRecall statistic, which calculates the tail recall value for a given true graph, estimated graph, and data model.Represents the statistic of False Negatives for Arrows compared to the true DAG.The class TrueDagFalseNegativesTails implements the Statistic interface to calculate the number of false negatives for tails compared to the true Directed Acyclic Graph (DAG).Represents a statistic that calculates the false positives for arrows compared to the true directed acyclic graph (DAG).TrueDagFalsePositiveTails is a class that implements the Statistic interface.The proportion of X*->Y in the estimated graph for which there is no path Y~~>X in the true graph.A class that implements the Statistic interface to calculate the proportion of X-->Y edges in the estimated graph for which there is a path X~~>Y in the true graph.The bidirected true positives.The bidirected true positives.The bidirected true positives.The bidirected true positives.The bidirected true positives.The 2-cycle precision.The 2-cycle precision.The 2-cycle precision.The 2-cycle recall.The 2-cycle precision.