Calculate endogenous network effects in event sequences and fit relational event models (REM): Using network event sequences (where each tie between a sender and a target in a network is time-stamped), REMs can measure how networks form and evolve over time. Endogenous patterns such as popularity effects, inertia, similarities, cycles or triads can be calculated and analyzed over time.
| Version: | 1.3.1 |
| Depends: | R (≥ 2.14.0) |
| Imports: | Rcpp, foreach, doParallel |
| LinkingTo: | Rcpp |
| Suggests: | texreg, statnet, ggplot2 |
| Published: | 2018-10-25 |
| Author: | Laurence Brandenberger |
| Maintainer: | Laurence Brandenberger <lbrandenberger at ethz.ch> |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: | yes |
| Citation: | rem citation info |
| CRAN checks: | rem results |
| Reference manual: | rem.pdf |
| Package source: | rem_1.3.1.tar.gz |
| Windows binaries: | r-devel: rem_1.3.1.zip, r-release: rem_1.3.1.zip, r-oldrel: rem_1.3.1.zip |
| macOS binaries: | r-release (arm64): rem_1.3.1.tgz, r-oldrel (arm64): rem_1.3.1.tgz, r-release (x86_64): rem_1.3.1.tgz, r-oldrel (x86_64): rem_1.3.1.tgz |
| Old sources: | rem archive |
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