Package: rem 1.3.1
Laurence Brandenberger
rem: Relational Event Models (REM)
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.
Authors:
rem_1.3.1.tar.gz
rem_1.3.1.tar.gz(r-4.5-noble)rem_1.3.1.tar.gz(r-4.4-noble)
rem_1.3.1.tgz(r-4.4-emscripten)rem_1.3.1.tgz(r-4.3-emscripten)
rem.pdf |rem.html✨
rem/json (API)
| # Install 'rem' in R: |
| install.packages('rem', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 6 years agofrom:278408b125. Checks:OK: 2. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Nov 25 2024 |
| R-4.5-linux-x86_64 | OK | Nov 25 2024 |
Exports:createRemDatasetdegreeStateventSequencefourCycleStatinertiaStatreciprocityStatsimilarityStattimeToEventtriadStat
Dependencies:codetoolsdoParallelforeachiteratorsRcpp
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Fit Relational Event Models (REM) | rem-package relational event model relational-event-model rem |
| Create REM data set with dynamic risk sets | createRemDataset |
| Calculate (in/out)-degree statistics | degree degreeStat indegree outdegree |
| Create event sequence | event sequence event.sequence eventSequence |
| Calculate four cycle statistics | fourCycle fourCycleStat |
| Calculate inertia statistics | inertia inertiaStat |
| Calculate reciprocity statistics | reciprocity reciprocityStat |
| Calculate similarity statistics | similarity similarityStat |
| Calculate the time-to-next-event or the time-since-date for a REM data set. | timeToEvent |
| Calculate triad statistics | triad triadStat |