R: Learning Causal Cyclic Graphs from Unknown Shift Interventions

# Learning Causal Cyclic Graphs from Unknown Shift Interventions

## Documentation for package ‘backShift’ version 0.1.4.3

- DESCRIPTION file
- User guides, package vignettes and other documentation

## Help Pages

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.