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.
