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 |
DOI: | 10.32614/CRAN.package.rem |
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 |
Please use the canonical form https://CRAN.R-project.org/package=rem to link to this page.