Causal network analysis methods

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Documentation for package ‘CausalR’ version 1.41.0

• DESCRIPTION file.
• User guides, package vignettes and other documentation.

Help Pages

CausalR-package	The CausalR package
AddIDsToVertices	add IDs to vertices
AddWeightsToEdges	add weights to edges
AnalyseExperimentalData	analyse experimental data
AnalysePredictionsList	analyse predictions list
CalculateEnrichmentPValue	calculates an enrichment p-value
CalculateSignificance	calculate overall significance p-value
CalculateSignificanceUsingCubicAlgorithm	calculate significance using the cubic algorithm
CalculateSignificanceUsingCubicAlgorithm1b	Calculate Significance Using Cubic Algorithm
CalculateSignificanceUsingQuarticAlgorithm	calculate significance using the quartic algorithm
CalculateTotalWeightForAllContingencyTables	calculate total weight for all contingency tables
CalculateWeightGivenValuesInThreeByThreeContingencyTable	calculate weight given values in three-by-three contingency table
CausalR	The CausalR package
CheckPossibleValuesAreValid	check possible values are valid
CheckRowAndColumnSumValuesAreValid	check row and column sum values are valid
CompareHypothesis	compare hypothesis
ComputeFinalDistribution	compute final distribution
ComputePValueFromDistributionTable	compute a p-value from the distribution table
CreateCCG	create a Computational Causal Graph (CCG)
CreateCG	create a Computational Graph (CG)
CreateNetworkFromTable	create network from table
DetermineInteractionTypeOfPath	determine interaction type of path
FindApproximateValuesThatWillMaximiseDValue	find approximate values that will maximise D value
FindIdsOfConnectedNodesInSubgraph	find Ids of connected nodes in subgraph
FindMaximumDValue	find maximum D value
GetAllPossibleRoundingCombinations	get score for numbers of correct and incorrect predictions
GetApproximateMaximumDValueFromThreeByTwoContingencyTable	returns approximate maximum D value or weight for a 3x2 superfamily
GetApproximateMaximumDValueFromTwoByTwoContingencyTable	computes an approximate maximum D value or weight
GetCombinationsOfCorrectandIncorrectPredictions	returns table of correct and incorrect predictions
GetExplainedNodesOfCCG	Get explained nodes of CCG
GetInteractionInformation	returns interaction information from input data
GetMatrixOfCausalRelationships	compute causal relationships matrix
GetMaxDValueForAFamily	get maximun D value for a family
GetMaxDValueForAThreeByTwoFamily	get maximum D value for three-by-two a family
GetMaximumDValueFromTwoByTwoContingencyTable	get maximum D value from two-by-two contingency table
GetNodeID	get CCG node ID
GetNodeName	get node name
GetNumberOfPositiveAndNegativeEntries	counts the number of positive and negative entries
GetPathsInSifFormat	Get paths in Sif format
GetRegulatedNodes	get regulated nodes
GetRowAndColumnSumValues	get row and column sum values
GetScoreForNumbersOfCorrectandIncorrectPredictions	returns the score for a given number of correct and incorrect predictions
GetScoresForSingleNode	Get scores for single node
GetScoresWeightsMatrix	get scores weight matrix
GetScoresWeightsMatrixByCubicAlg	get scores weights matrix by the cubic algorithm
GetSetOfDifferentiallyExpressedGenes	get set of differientially expressed genes
GetSetOfSignificantPredictions	get set of significant predictions
GetShortestPathsFromCCG	get shortest paths from CCG
GetWeightForNumbersOfCorrectandIncorrectPredictions	get weight for numbers of correct and incorrect predictions
GetWeightsAboveHypothesisScoreAndTotalWeights	get weights above hypothesis score and total weights
GetWeightsAboveHypothesisScoreForAThreeByTwoTable	updates weights for contingency table and produce values for p-value calculation
GetWeightsFromInteractionInformation	get weights from interaction information
MakePredictions	make predictions
MakePredictionsFromCCG	make predictions from CCG
MakePredictionsFromCG	make predictions from CG
OrderHypotheses	order hypotheses
PlotGraphWithNodeNames	plot graph with node names
PopulateTheThreeByThreeContingencyTable	populate the three-by-three contingency table
PopulateTwoByTwoContingencyTable	Populate Two by Two Contingency Table
ProcessExperimentalData	process experimental data
RankTheHypotheses	rank the hypotheses
ReadExperimentalData	read experimental data
ReadSifFileToTable	read .sif to Table
RemoveIDsNotInExperimentalData	remove IDs not in experimental data
runRankHypothesis	run rank the hypothesis
runSCANR	run ScanR
ScoreHypothesis	score hypothesis
ValidateFormatOfDataTable	validate format of the experimental data table
ValidateFormatOfTable	validate format of table
WriteAllExplainedNodesToSifFile	Write all explained nodes to Sif file
WriteExplainedNodesToSifFile	Write explained nodes to Sif file