remulate

remulate is an R package designed to simulate dynamic temporal networks based on Relational Event Models (REM).

Key Features

remulate provides a range of features to facilitate the simulation of relational event data:

• Support for Dyadic and Actor-Oriented Models: Simulate both dyadic relational event models and actor-oriented relational event models.
• Rich Collection of Endogenous Statistics: Pre-computed endogenous statistics with multiple normalization and standardization options, along with support for interaction terms.
• Flexible Memory Decay Functions: Includes exponential decay, step-wise decay, and other functions to model the impact of past events on future interactions.
• Custom Risk Sets: Enables simulations with custom risksets where certain actor pairs cannot interact.
• Time-Varying Network Parameters: Allows for variations in network effects over time.
• Time-Varying Nodal Covariates: Allows for variations in node attributes over time.
• Support for Advanced REM Variants:
  • Dyadic latent class relational event models (DLC-REM)
  • Frailty relational event models
  • Relational event block models

Programming Languages

The package contains code written in:

• R (>= 4.0.0)
• Rcpp (>= 1.0.4.6) and RcppArmadillo (>= 0.9.860.2.0)
• C++11 (Compiler Version: GCC-8.1.0)

Installation

To install the package in R using devtools:

library(devtools)
# install_github("TilburgNetworkGroup/remulate")
install.packages("remulate", repos = c('https://tilburgnetworkgroup.r-universe.dev', 'https://cloud.r-project.org'))

#load the package
library(remulate)

Usage

effects <- ~ baseline(-4) + inertia(0.01) + reciprocity(-0.04) + itp(0.01,scaling="std")

remulateTie(effects, actors = 1:25, time = 20, events = 500, initial = 200)

Support

#To view all help files in the remulate package
help(package='remulate')

#To view available effects for remulateTie
help('remulateTieEffects')

#To view available effects for remulateActor
help('remulateActorEffects')

Citing this Work

If you use this R package in your research or in any publications, please cite it as follows to help support our work:

@article{lakdawalaSimulatingRelationalEvent2025,
  title = {Simulating Relational Event History Data: Why and How},
  shorttitle = {Simulating Relational Event History Data},
  author = {Lakdawala, Rumana and Mulder, Joris and Leenders, Roger},
  year = {2025},
  month = aug,
  journal = {Journal of Computational Social Science},
  volume = {8},
  number = {4},
  pages = {92},
  issn = {2432-2725},
  doi = {10.1007/s42001-025-00427-2},
  urldate = {2025-08-28},
  langid = {english},
  keywords = {Actor-oriented models,Dyadic interaction models,Interventions,Model fit assessment,Relational events,Simulation techniques,Temporal social networks}
}

Contributing

Pull requests and bug reports are welcome. For major changes, please open an issue first to discuss what you would like to change.

Acknowledgement

The development of this package was supported by a Vidi Grant (452-17-006) awarded by the Netherlands Organization for Scientific Research (NWO) Grant and an ERC Starting Grant (758791).