Uses of Interface
edu.cmu.tetrad.algcomparison.statistic.Statistic

Packages that use Statistic

Package	Description
edu.cmu.tetrad.algcomparison.statistic

Uses of Statistic in edu.cmu.tetrad.algcomparison.statistic

Classes in edu.cmu.tetrad.algcomparison.statistic that implement Statistic

Modifier and Type	Class	Description
class	AdjacencyFn	The adjacency precision.
class	AdjacencyFp	The adjacency precision.
class	AdjacencyFpr	The adjacency true positive rate.
class	AdjacencyPrecision	The adjacency precision.
class	AdjacencyRecall	The adjacency recall.
class	AdjacencyTn	The adjacency precision.
class	AdjacencyTp	The adjacency precision.
class	AdjacencyTpr	The adjacency true positive rate.
class	AncestorF1	Calculates the F1 statistic for adjacencies.
class	AncestorPrecision	Ancestor precision.
class	AncestorRecall	Ancestor recall.
class	AncestralPrecision	The bidirected true positives.
class	AncestralRecall	The bidirected true positives.
class	ArrowheadFn	The arrow precision.
class	ArrowheadFp	The arrow precision.
class	ArrowheadFpr	The adjacency true positive rate.
class	ArrowheadPrecision	The arrow precision.
class	ArrowheadPrecisionCommonEdges	The arrow precision.
class	ArrowheadRecall	The arrow recall.
class	ArrowheadRecallCommonEdges	The arrow recall.
class	ArrowheadTn	The arrow precision.
class	ArrowheadTp	The arrow precision.
class	AverageDegreeEst	The adjacency precision.
class	AverageDegreeTrue	The adjacency precision.
class	BicDiff	Difference between the true and estimated BIC scores.
class	BicDiffPerRecord	Difference between the true and estiamted BIC scores.
class	BicEst	Estimated BIC score.
class	BicTrue	True BIC score.
class	BidirectedEst	The bidirected true positives.
class	BidirectedFP	The bidirected false negatives.
class	BidirectedLatentPrecision	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.
class	BidirectedPrecision	The bidirected edge precision.
class	BidirectedRecall	The bidirected edge precision.
class	BidirectedTP	The bidirected true positives.
class	BidirectedTrue	The bidirected true positives.
class	CirclePrecision	CirclePrecision is a class that implements the Statistic interface.
class	CircleRecall	Implements the CircleRecall statistic, which calculates the circle recall value for a given true graph, estimated graph, and data model.
class	CommonAncestorFalseNegativeBidirected	The bidirected true positives.
class	CommonAncestorFalsePositiveBidirected	The bidirected true positives.
class	CommonAncestorTruePositiveBidirected	The bidirected true positives.
class	CommonMeasuredAncestorRecallBidirected	The bidirected true positives.
class	CorrectSkeleton	Outputs 1 if the skeleton is correct, 0 if not..
class	DefiniteDirectedPathPrecision	The bidirected true positives.
class	DefiniteDirectedPathRecall	The bidirected true positives.
class	DensityEst	The adjacency precision.
class	DensityTrue	The adjacency precision.
class	ElapsedCpuTime	Records the elapsed time of the algorithm in seconds.
class	F1Adj	Calculates the F1 statistic for adjacencies.
class	F1All	Calculates the F1 statistic for adjacencies.
class	F1Arrow	Calculates the F1 statistic for arrowheads.
class	F1Circle	Calculates the F1 statistic for circles.
class	F1Tail	Calculates the F1 statistic for tails.
class	FalseNegativesAdjacencies	The bidirected true positives.
class	FalsePositiveAdjacencies	The bidirected true positives.
class	FBetaAdj	Calculates the F1 statistic for adjacencies.
class	FractionDependentUnderAlternative	Estimates whether the p-values under the null are Uniform usign the Markov Checker.
class	FractionDependentUnderNull	Estimates whether the p-values under the null are Uniform usign the Markov Checker.
class	GraphExactlyRight	Return a 1 if the graph is exactly right, 0 otherwise.
class	IdaAverageSquaredDistance	The IDA average squared distance.
class	IdaCheckAvgMaxSquaredDiffEstTrue	IdaCheckAvgMaxSquaredDiffEstTrue calculates the average maximum squared difference between the estimated and true values for a given data model and graphs.
class	IdaCheckAvgMinSquaredDiffEstTrue	Represents a statistic that calculates the average minimum squared difference between the estimated and true values for a given data model and graphs.
class	IdaCheckAvgSquaredDifference	IdaCheckAvgSquaredDifference is a class that implements the Statistic interface.
class	IdaMaximumSquaredDifference	IdaMaximumSquaredDifference is a statistic that calculates the "IDA Average Maximum Squared Difference" between a true graph and an estimated graph.
class	IdaMinimumSquaredDifference	IdaMinimumSquaredDifference is a statistic that calculates the "IDA Average Minimum Squared Difference" between a true graph and an estimated graph.
class	ImpliedArrowOrientationRatioEst	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.
class	ImpliedArrowOrientationRatioEst2	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.
class	ImpliedOrientationRatioEst	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.
class	ImpliesLegalMag	Implies Legal MAG
class	KnowledgeSatisfied	Implementation of the KnowledgeSatisfied class.
class	LatentCommonAncestorFalseNegativeBidirected	The bidirected true positives.
class	LatentCommonAncestorFalsePositiveBidirected	The bidirected true positives.
class	LatentCommonAncestorRecallBidirected	The bidirected true positives.
class	LatentCommonAncestorTruePositiveBidirected	The bidirected true positives.
class	LegalCpdag	The LegalCpdag class implements the Statistic interface and provides methods to evaluate whether an estimated graph is a Legal CPDAG.
class	LegalPag	Legal PAG
class	LocalGraphPrecision	The LocalGraphPrecision class implements the Statistic interface and represents the Local Graph Precision statistic.
class	LocalGraphRecall	LocalGraphRecall implements the Statistic interface and represents the local graph recall statistic.
class	MagCgScore	Takes a MAG in a PAG using Zhang's method and then reports the MAG DG BIC score for it.
class	MagDgScore	Takes a MAG in a PAG using Zhang's method and then reports the MAG DG BIC score for it.
class	MagSemScore	Takes a MAG in a PAG using Zhang's method and then reports the MAG SEM BIC score for it.
class	MarkovCheckAdPasses	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).
class	MarkovCheckAdPassesBestOf10	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).
class	MarkovCheckAndersonDarlingP	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).
class	MarkovCheckAndersonDarlingPBestOf10	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).
class	MarkovCheckBinomialP	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).
class	MarkovCheckBinomialPBestOf10	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).
class	MarkovCheckFractionDependentH0	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).
class	MarkovCheckFractionDependentH1	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).
class	MarkovCheckKolmogorovSmirnoffP	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).
class	MarkovCheckKolmogorovSmirnoffPBestOf10	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).
class	MarkovCheckKsPasses	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).
class	MarkovCheckKsPassesBestOf10	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).
class	MathewsCorrAdj	Calculates the Matthew's correlation coefficient for adjacencies.
class	MathewsCorrArrow	Calculates the Matthew's correlation coefficient for adjacencies.
class	Maximal	Checks whether a PAG is maximal.
class	MaximalityCondition	MaximalMag statistic.
class	McGetNumTestsH0	Calculates the number of tests Kolmogorov-Smirnoff under the null hypothesis H0 of independence for the Markov check of whether the p-values for the estimated graph are distributed as U(0, 1).
class	McGetNumTestsH1	Calculates the number of tests Kolmogorov-Smirnoff under the alternative hypothesis H1 of dependennce for the Markov check of whether the p-values for the estimated graph are distributed as U(0, 1).
class	NoAlmostCyclicPathsCondition	No almost cyclic paths condition.
class	NoCyclicPathsCondition	No cyclic paths condition.
class	NodesInCyclesPrecision	The adjacency precision.
class	NodesInCyclesRecall	The adjacency precision.
class	NonancestorPrecision	Number of NOT X~~>Y in true graph for which also NOT X~~>Y in estimated graph.
class	NonancestorRecall	Number of NOT X~~>Y in true graph for which also NOT X~~>Y in estimated graph.
class	NoSemidirectedF1	Calculates the F1 statistic for adjacencies.
class	NoSemidirectedPrecision	The bidirected true positives.
class	NoSemidirectedRecall	The bidirected true positives.
class	NumAmbiguousTriples	The adjacency precision.
class	NumberArrowsEst	Represents the NumberEdgesEst statistic, which calculates the number of arrows in the estimated graph.
class	NumberArrowsNotInUnshieldedCollidersEst	Represents the NumberEdgesEst statistic, which calculates the number of arrows not in unshielded colliders in the estimated graph.
class	NumberCollidersEst	Represents the NumberEdgesEst statistic, which calculates the number of unshielded colliders in the estimated graph.
class	NumberEdgesEst	Represents the NumberEdgesEst statistic, which calculates the number of edges in the estimated graph.
class	NumberEdgesInCollidersEst	Represents the NumberEdgesEst statistic, which calculates the number of edges in colliders in the estimated graph.
class	NumberEdgesInUnshieldedCollidersEst	Represents the NumberEdgesEst statistic, which calculates the number of edges in unshielded colliders in the estimated graph.
class	NumberEdgesTrue	The NumberEdgesTrue class is an implementation of the Statistic interface.
class	NumberOfEdgesEst	Prints the number of edges in the estimated graph.
class	NumberOfEdgesTrue	Prints the number of edges in the true graph.
class	NumberTailsEst	Represents the NumberEdgesEst statistic, which calculates the number of tails in the estimated graph.
class	NumberUnshieldedCollidersEst	Represents the NumberEdgesEst statistic, which calculates the number of unshielded colliders in the estimated graph.
class	NumBidirectedBothNonancestorAncestor	The bidirected edge precision.
class	NumBidirectedEdgesEst	The adjacency precision.
class	NumBidirectedEdgesTrue	The adjacency precision.
class	NumColoredDD	The bidirected true positives.
class	NumColoredNL	The bidirected true positives.
class	NumColoredPD	The bidirected true positives.
class	NumColoredPL	The bidirected true positives.
class	NumCommonMeasuredAncestorBidirected	The bidirected true positives.
class	NumCompatibleDefiniteDirectedEdgeAncestors	The bidirected true positives.
class	NumCompatibleDirectedEdgeConfounded	The bidirected true positives.
class	NumCompatibleDirectedEdgeNonAncestors	The bidirected true positives.
class	NumCompatibleEdges	The bidirected true positives.
class	NumCompatiblePossiblyDirectedEdgeAncestors	The bidirected true positives.
class	NumCompatiblePossiblyDirectedEdgeNonAncestors	The bidirected true positives.
class	NumCompatibleVisibleNonancestors	The bidirected true positives.
class	NumCorrectBidirected	Counts the number of X<->Y edges for which a latent confounder of X and Y exists.
class	NumCorrectDDAncestors	The bidirected true positives.
class	NumCorrectPDAncestors	The bidirected true positives.
class	NumCorrectVisibleEdges	Represents a statistic that calculates the number of correct visible ancestors in the true graph that are also visible ancestors in the estimated graph.
class	NumCoveringAdjacenciesInPag	The number of covering adjacencies in an estimated PAG compared to the true PAG.
class	NumDefinitelyDirected	The bidirected true positives.
class	NumDefinitelyNotDirectedPaths	The bidirected true positives.
class	NumDirectedEdgeAncestors	The bidirected true positives.
class	NumDirectedEdgeBnaMeasuredCounfounded	The bidirected true positives.
class	NumDirectedEdgeNoMeasureAncestors	The bidirected true positives.
class	NumDirectedEdgeNotAncNotRev	The bidirected true positives.
class	NumDirectedEdgeReversed	The bidirected true positives.
class	NumDirectedEdges	Number of X-->Y in est.
class	NumDirectedPathsEst	The bidirected true positives.
class	NumDirectedPathsTrue	The bidirected true positives.
class	NumDirectedShouldBePartiallyDirected	The bidirected true positives.
class	NumEdgeInEstInTrue	The number of adjacencies in the estimated graph but not in the true graph.
class	NumGenuineAdjacenciesInPag	The number of genuine adjacencies in an estimated PAG compared to the true PAG.
class	NumIncorrectDDAncestors	The bidirected true positives.
class	NumIncorrectPDAncestors	The bidirected true positives.
class	NumIncorrectVisibleAncestors	The bidirected true positives.
class	NumLatentCommonAncestorBidirected	The bidirected true positives.
class	NumNondirectedEdges	Number of X---Y in est.
class	NumParametersEst	Number of parameters for a discrete Bayes model of the data.
class	NumPartiallyOrientedEdges	Number of Xo->Y in est.
class	NumPossiblyDirected	The bidirected true positives.
class	NumUndirectedEdges	Number of X---Y in est.
class	NumVisibleEdgeEst	NumVisibleEdgeEst is a class that implements the Statistic interface.
class	NumVisibleEdges	Represents a statistic that calculates the number of correct visible ancestors in the true graph that are also visible ancestors in the estimated graph.
class	NumVisibleEdgeTrue	A class that implements the Statistic interface to calculate the number of visible edges in the true PAG.
class	OrientationPrecision	The orientation precision.
class	OrientationRecall	Represents an implementation of the Statistic interface that calculates the Orientation Recall.
class	PagAdjacencyPrecision	The PagAdjacencyPrecision class implements the Statistic interface and represents the adjacency precision compared to the true PAG (Partially Ancestral Graph).
class	PagAdjacencyRecall	A class that implements the PagAdjacencyRecall statistic.
class	ParameterColumn	Adds a column to the output table in which values for the given parameter are listed.
class	PercentAmbiguous	The adjacency precision.
class	PercentBidirectedEdges	The adjacency precision.
class	ProportionSemidirectedPathsNotReversedEst	Proportion of semi(X, Y) in estimated graph for which there is no semi(Y, X) in true graph.
class	ProportionSemidirectedPathsNotReversedTrue	Proportion of semi(X, Y) in true graph for which there is no semi(Y, Z) in estimated graph.
class	PvalueDistanceToAlpha	Estimates whether the p-values under the null are Uniform usign the Markov Checker.
class	PvalueUniformityUnderNull	Estimates whether the p-values under the null are Uniform using the Markov Checker.
class	SemidirectedPathF1	Calculates the F1 statistic for adjacencies.
class	SemidirectedPrecision	The bidirected true positives.
class	SemidirectedRecall	A class implementing the Semidirected-Rec statistic.
class	StructuralHammingDistance	Calculates the structural Hamming distance (SHD) between the estimated graph and the true graph.
class	TailPrecision	TailPrecision is a class that implements the Statistic interface.
class	TailRecall	Implements the TailRecall statistic, which calculates the tail recall value for a given true graph, estimated graph, and data model.
class	TrueDagFalseNegativesArrows	Represents the statistic of False Negatives for Arrows compared to the true DAG.
class	TrueDagFalseNegativesTails	The class TrueDagFalseNegativesTails implements the Statistic interface to calculate the number of false negatives for tails compared to the true Directed Acyclic Graph (DAG).
class	TrueDagFalsePositiveArrow	Represents a statistic that calculates the false positives for arrows compared to the true directed acyclic graph (DAG).
class	TrueDagFalsePositiveTails	TrueDagFalsePositiveTails is a class that implements the Statistic interface.
class	TrueDagPrecisionArrow	The proportion of X*->Y in the estimated graph for which there is no path Y~~>X in the true graph.
class	TrueDagPrecisionTails	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.
class	TrueDagRecallArrows	The bidirected true positives.
class	TrueDagRecallTails	The bidirected true positives.
class	TrueDagTruePositiveArrow	The bidirected true positives.
class	TrueDagTruePositiveDirectedPathNonancestor	The bidirected true positives.
class	TrueDagTruePositiveTails	The bidirected true positives.
class	TwoCycleFalseNegative	The 2-cycle precision.
class	TwoCycleFalsePositive	The 2-cycle precision.
class	TwoCyclePrecision	The 2-cycle precision.
class	TwoCycleRecall	The 2-cycle recall.
class	TwoCycleTruePositive	The 2-cycle precision.

Methods in edu.cmu.tetrad.algcomparison.statistic that return types with arguments of type Statistic

Modifier and Type	Method	Description
List<Statistic>	Statistics.getStatistics()	Return the list of statistics.

Methods in edu.cmu.tetrad.algcomparison.statistic with parameters of type Statistic

Modifier and Type	Method	Description
void	Statistics.add(Statistic statistic)	Adds a statistic.
double	Statistics.getWeight(Statistic statistic)	The utility weight for the statistic.