Binary ties limit the richness of network analyses as relations are unique. The two-mode structure contains a number of features lost when projection it to a one-mode network. Longitudinal datasets allow for an understanding of the causal relationship among ties, which is not the case in cross-sectional datasets as ties are dependent upon each other.
| Version: | 3.0.16 |
| Depends: | R (≥ 2.13.0), igraph, survival |
| Published: | 2020-02-24 |
| Author: | Tore Opsahl |
| Maintainer: | Tore Opsahl <tore at opsahl.co.uk> |
| License: | GPL-3 |
| URL: | http://toreopsahl.com/tnet/ |
| NeedsCompilation: | no |
| Citation: | tnet citation info |
| Materials: | ChangeLog |
| In views: | CausalInference |
| CRAN checks: | tnet results |
| Reference manual: | tnet.pdf |
| Package source: | tnet_3.0.16.tar.gz |
| Windows binaries: | r-devel: tnet_3.0.16.zip, r-release: tnet_3.0.16.zip, r-oldrel: tnet_3.0.16.zip |
| macOS binaries: | r-release (arm64): tnet_3.0.16.tgz, r-oldrel (arm64): tnet_3.0.16.tgz, r-release (x86_64): tnet_3.0.16.tgz, r-oldrel (x86_64): tnet_3.0.16.tgz |
| Old sources: | tnet archive |
| Reverse imports: | Cascade, ITNr, Patterns, SPONGE |
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