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Constraint-based causal discovery using the PC algorithm while accounting for a partial node ordering, for example a partial temporal ordering when the data were collected in different waves of a cohort study. Andrews RM, Foraita R, Didelez V, Witte J (2021) <doi:10.48550/arXiv.2108.13395> provide a guide how to use tpc to analyse cohort data.
Version: | 1.0 |
Depends: | pcalg, R (≥ 3.5.0) |
Imports: | graph, graphics, methods, parallel, utils |
Suggests: | Rgraphviz, testthat (≥ 3.0.0) |
Published: | 2023-02-20 |
DOI: | 10.32614/CRAN.package.tpc |
Author: | Janine Witte [aut], Ronja Foraita [cre, ctb], DFG [fnd] |
Maintainer: | Ronja Foraita <foraita at leibniz-bips.de> |
BugReports: | https://github.com/bips-hb/tpc/issues |
License: | GPL (≥ 3) |
URL: | https://github.com/bips-hb/tpc |
NeedsCompilation: | no |
Materials: | README |
CRAN checks: | tpc results |
Reference manual: | tpc.pdf |
Package source: | tpc_1.0.tar.gz |
Windows binaries: | r-devel: tpc_1.0.zip, r-release: tpc_1.0.zip, r-oldrel: tpc_1.0.zip |
macOS binaries: | r-release (arm64): tpc_1.0.tgz, r-oldrel (arm64): tpc_1.0.tgz, r-release (x86_64): tpc_1.0.tgz, r-oldrel (x86_64): tpc_1.0.tgz |
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.