Learning Causal Cyclic Graphs from Unknown Shift Interventions

Code for 'backShift', an algorithm to estimate the connectivity matrix of a directed (possibly cyclic) graph with hidden variables. The underlying system is required to be linear and we assume that observations under different shift interventions are available. For more details, see .


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install.packages("backShift")

0.1.4.2 by Christina Heinze-Deml, 2 months ago


https://github.com/christinaheinze/backShift


Report a bug at https://github.com/christinaheinze/backShift/issues


Browse source code at https://github.com/cran/backShift


Authors: Christina Heinze-Deml <heinzedeml@stat.math.ethz.ch>


Documentation:   PDF Manual  


GPL license


Imports methods, clue, igraph, matrixcalc, reshape2, ggplot2, mvnmle, MASS

Suggests knitr, pander, fields, testthat, pcalg, rmarkdown


Suggested by CompareCausalNetworks.


See at CRAN