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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