Package {remverse}

Title: A Meta-Package for Relational Event History Analysis
Version: 0.1.0
Maintainer: Joris Mulder <j.mulder3@tilburguniversity.edu>
Description: A unified workflow for relational event modeling by re-exporting core functions from 'remify', 'remstats', and 'remstimate'. Supports tie-oriented and actor-oriented modeling with frequentist and Bayesian estimation. Methods are described in Butts (2008) <doi:10.1111/j.1467-9531.2008.00203.x> and Stadtfeld and Block (2017) <doi:10.1177/0081175017709295>.
License: MIT + file LICENSE
Encoding: UTF-8
RoxygenNote: 7.3.3
Depends: R (≥ 4.0), remify (≥ 4.0.0), remstats (≥ 4.0.0), remstimate (≥ 3.0.0)
Suggests: knitr, rmarkdown
LazyData: true
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2026-05-22 09:21:07 UTC; jorismulder
Author: Joris Mulder [aut, cre], Giuseppe Arena [aut], Roger Leenders [aut], Marlyne Meijerink-Bosman [aut], Rumana Lakdawala [aut], Fabio Generoso Vieira [aut], Mahdi Shafiee Kamalabad [ctb], Diana Karimova [ctb]
Repository: CRAN
Date/Publication: 2026-05-28 13:30:08 UTC

remverse: A Collection of R Packages for Relational Event Modeling

Description

The remverse package loads an ensemble of R packages for working with Relational Event Histories (REH): preprocessing event sequences (remify), computing network statistics (remstats), and estimating relational event models (remstimate).

Author(s)

Maintainer: Joris Mulder j.mulder3@tilburguniversity.edu

Authors:

Other contributors:

Examples

library(remverse)

# Load example data
data("edgelist0")
data("edgelist0_actors")

# Preprocess data
reh <- remify(edgelist = edgelist0,
              model = "tie",
              directed = TRUE,
              event_type = "setting",
              extend_riskset_by_type = TRUE)

# Compute statistics
stats <- remstats(reh,
                  tie_effects = ~ inertia(scaling="std", consider_type = "ignore") +
                    reciprocity(scaling="std", consider_type = "separate") +
                    same("job", attr_actors = edgelist0_actors),
                  start = 10)

# Fit model
fit <- remstimate(reh, stats)
summary(fit)

# Check diagnostics
diag_fit <- diagnostics(fit, reh, stats)
print(diag_fit)
plot(fit, reh, diag_fit)

Simulated relational event history

Description

A simulated event sequence among 5 actors using the endogenous effects: inertia, reciprocity, and itp.

Usage

data(edgelist0)

Format

A dataframe with 1000 rows and 3 variables:

time

time of the event

actor1

the first actor involved in the event

actor2

the second actor involved in the event

setting

the setting of an event: X or Y

Source

Simulated relational event sequence among 5 actors in a social network.

Examples

data(edgelist0)

Simulated relational event history

Description

Actor attributes of actors in event sequence edgelist0

Usage

data(edgelist0_actors)

Format

A dataframe with 1000 rows and 3 variables:

name

label of the actor

time

the time of measurement of the attribute

job

the job of the actor

Source

Simulated attributes of 5 actors in a social network.

Examples

data(edgelist0_actors)