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Methods and utilities for causal emergence. Used to explore and compute various information theory metrics for networks, such as effective information, effectiveness and causal emergence.
| Version: | 0.1.0 |
| Depends: | R (≥ 3.2.0) |
| Imports: | assertthat, igraph, magrittr, shiny, entropy |
| Suggests: | testthat, RColorBrewer, knitr, rmarkdown, bench |
| Published: | 2020-04-23 |
| Author: | Travis Byrum [aut, cre], Anshuman Swain [aut], Brennan Klein [aut], William Fagan [aut] |
| Maintainer: | Travis Byrum <tbyrum at terpmail.umd.edu> |
| BugReports: | https://github.com/travisbyrum/einet/issues |
| License: | MIT + file LICENSE |
| URL: | https://github.com/travisbyrum/einet |
| NeedsCompilation: | no |
| Materials: | README |
| CRAN checks: | einet results |
| Reference manual: | einet.pdf |
| Vignettes: |
Introduction |
| Package source: | einet_0.1.0.tar.gz |
| Windows binaries: | r-devel: einet_0.1.0.zip, r-release: einet_0.1.0.zip, r-oldrel: einet_0.1.0.zip |
| macOS binaries: | r-release (arm64): einet_0.1.0.tgz, r-oldrel (arm64): einet_0.1.0.tgz, r-release (x86_64): einet_0.1.0.tgz, r-oldrel (x86_64): einet_0.1.0.tgz |
Please use the canonical form https://CRAN.R-project.org/package=einet to link to this page.
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.