Learning Causal Cyclic Graphs from Unknown Shift Interventions

Documentation for package ‘backShift’ version 0.1.4.3

backShift	Estimate connectivity matrix of a directed graph with linear effects and hidden variables.
bootstrapBackShift	Computes a simple model-based bootstrap confidence interval for success of joint diagonalization procedure. The model-based bootstrap approach assumes normally distributed error terms; the parameters of the noise distribution are estimated with maximum likelihood.
computeDiagonalization	Computes the matrix Delta Sigma_{c,j} resulting from the joint diagonalization for a given environment (cf. Eq.(7) in the paper). If the joint diagonalization was successful the matrix should be diagonal for all environments $j$.
exampleAdjacencyMatrix	Example adjacency matrix
generateA	Generates a connectivity matrix A.
metricsThreshold	Performance metrics for estimate of connectiviy matrix A.
plotDiagonalization	Plots the joint diagonalization. I.e. if it was successful the matrices should all be diagonal.
plotGraphEdgeAttr	Plotting function to visualize directed graphs
plotInterventionVars	Plots the estimated intervention variances.
simulateInterventions	Simulate data of a causal cyclic model under shift interventions.