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

Documentation:

Reference manual:	backShift.pdf
Vignettes:	backShift demo

Downloads:

Package source:	backShift_0.1.4.3.tar.gz
Windows binaries:	r-devel: backShift_0.1.4.3.zip, r-release: backShift_0.1.4.3.zip, r-oldrel: backShift_0.1.4.3.zip
macOS binaries:	r-release (arm64): backShift_0.1.4.3.tgz, r-oldrel (arm64): backShift_0.1.4.3.tgz, r-release (x86_64): backShift_0.1.4.3.tgz, r-oldrel (x86_64): backShift_0.1.4.3.tgz
Old sources:	backShift archive

Reverse dependencies:

Reverse suggests:

CompareCausalNetworks

Linking:

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