backShift: 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 <doi:10.48550/arXiv.1506.02494>.
| Version: | 
0.1.4.3 | 
| Depends: | 
R (≥ 3.1.0) | 
| Imports: | 
methods, clue, igraph, matrixcalc, reshape2, ggplot2, MASS | 
| Suggests: | 
knitr, pander, fields, testthat, pcalg, rmarkdown | 
| Published: | 
2020-05-06 | 
| DOI: | 
10.32614/CRAN.package.backShift | 
| Author: | 
Christina Heinze-Deml | 
| Maintainer: | 
Christina Heinze-Deml  <heinzedeml at stat.math.ethz.ch> | 
| BugReports: | 
https://github.com/christinaheinze/backShift/issues | 
| License: | 
GPL-2 | GPL-3 [expanded from: GPL] | 
| URL: | 
https://github.com/christinaheinze/backShift | 
| NeedsCompilation: | 
yes | 
| CRAN checks: | 
backShift results | 
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