Type:	Package
Title:	Simulate Dynamic Networks from Relational Event Models
Version:	2.1.0
Date:	2025-03-25
Maintainer:	Rumana Lakdawala <rumanalakdawala@gmail.com>
Description:	Model based simulation of dynamic networks under tie-oriented (Butts, C., 2008, <doi:10.1111/j.1467-9531.2008.00203.x>) and actor-oriented (Stadtfeld, C., & Block, P., 2017, <doi:10.15195/v4.a14>) relational event models. Supports simulation from a variety of relational event model extensions, including temporal variability in effects, heterogeneity through dyadic latent class relational event models (DLC-REM), random effects, blockmodels, and memory decay in relational event models (Lakdawala, R., 2024 <doi:10.48550/arXiv.2403.19329>). 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).
License:	MIT + file LICENSE
URL:	https://github.com/TilburgNetworkGroup/remulate
Depends:	R (≥ 4.0.0),
Imports:	Rcpp, stats
LinkingTo:	Rcpp, RcppArmadillo
Suggests:	tinytest
RoxygenNote:	7.3.2
Encoding:	UTF-8
NeedsCompilation:	yes
Packaged:	2025-04-11 07:54:55 UTC; rumana
Author:	Rumana Lakdawala [aut, cre], Marlyne Meijerink-Bosman [ctb], Giuseppe Arena [ctb], Diana Karimova [ctb], Mahdi Shafiee Kamalabad [ctb], Fabio Generoso Vieira [ctb], Roger Leenders [ctb], Joris Mulder [ctb]
Repository:	CRAN
Date/Publication:	2025-04-16 13:40:03 UTC

remulate: Simulate Dynamic Networks from Relational Event Models

Description

Model based simulation of dynamic networks under tie-oriented (Butts, C., 2008, doi:10.1111/j.1467-9531.2008.00203.x) and actor-oriented (Stadtfeld, C., & Block, P., 2017, doi:10.15195/v4.a14) relational event models. Supports simulation from a variety of relational event model extensions, including temporal variability in effects, heterogeneity through dyadic latent class relational event models (DLC-REM), random effects, blockmodels, and memory decay in relational event models (Lakdawala, R., 2024 doi:10.48550/arXiv.2403.19329). 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).

Author(s)

Maintainer: Rumana Lakdawala rumanalakdawala@gmail.com (ORCID)

Other contributors:

• Marlyne Meijerink-Bosman m.l.meijerink@tilburguniversity.edu [contributor]

• Giuseppe Arena g.arena@tilburguniversity.edu [contributor]

• Diana Karimova d.karimova@tilburguniversity.edu [contributor]

• Mahdi Shafiee Kamalabad m.shafiee@tilburguniversity.edu [contributor]

• Fabio Generoso Vieira f.v.generosovieira@tilburguniversity.edu [contributor]

• Roger Leenders r.t.a.j.leenders@tilburguniversity.edu [contributor]

• Joris Mulder j.mulder3@tilburguniversity.edu [contributor]

See Also

Useful links:

• https://github.com/TilburgNetworkGroup/remulate

average

Description

This function specifies the input for the average effect in the formula argument for the function remulateTie or remulateActor. Not to be used independently

Usage

average(param = NULL, variable, attr_actors, scaling = c("none", "std"))

Arguments

Details

Dyadic covariate: average attribute value for dyad (i,j) is the average of the attribute values for actors i, j

if param is a data frame, it must have three columns: sender, receiver, and value (numeric), representing the parameter value for thay dyadic pair. The data.frame must contain all pairs of actors or dyads corresponding to the riskset.

if param is a function, it's first argument must be 't', corresponding to the time. The function may have additional arguments.

Value

List with all information required by ‘remulate::remulateTie()' or ’remulate::remulateActor()' to compute the statistic.

baseline

Description

This function specified the input for the baseline effect in the formula argument for the function remulateTie or remulateActor.

Usage

baseline(param = NULL)

Arguments

Details

if param is a data frame, it must have three columns: sender, receiver, and value (numeric), representing the parameter value for thay dyadic pair. The data.frame must contain all pairs of actors or dyads corresponding to the riskset.

if param is a function, it's first argument must be 't', corresponding to the time. The function may have additional arguments.

Value

List with all information required by ‘remulate::remulateTie()' or ’remulate::remulateActor()' to compute the statistic.

difference

Description

This function specifies the input for the difference effect in the formula argument for the function remulateTie or remulateActor. Not to be used independently

Usage

difference(param = NULL, variable, attr_actors, scaling = c("none", "std"))

Arguments

Details

Dyadic covariate: (Heterophily) is the tendency to create an event i->j if actors i and j have a high absolute difference in attribute values

if param is a data frame, it must have three columns: sender, receiver, and value (numeric), representing the parameter value for thay dyadic pair. The data.frame must contain all pairs of actors or dyads corresponding to the riskset.

if param is a function, it's first argument must be 't', corresponding to the time. The function may have additional arguments.

Value

List with all information required by ‘remulate::remulateTie()' or ’remulate::remulateActor()' to compute the statistic.

dyad

Description

This function specifies the input for the dyad effect in the formula argument for the function remulateTie or remulateActor. Not to be used independently

Usage

dyad(param = NULL, variable, attr_dyads, scaling = c("none", "std"))

Arguments

Details

Dyadic covariate: dyad attribute value is the tendency to create an event i -> j when (i,j) has a high attribute value.

if param is a data frame, it must have three columns: sender, receiver, and value (numeric), representing the parameter value for thay dyadic pair. The data.frame must contain all pairs of actors or dyads corresponding to the riskset.

if param is a function, it's first argument must be 't', corresponding to the time. The function may have additional arguments.

Value

List with all information required by ‘remulate::remulateTie()' or ’remulate::remulateActor()' to compute the statistic.

indegreeReceiver

Description

This function specifies the input for the indegreeReceiver effect in the formula argument for the function remulateTie or remulateActor. Not to be used independently

Usage

indegreeReceiver(param = NULL, scaling = c("none", "std", "prop"))

Arguments

Details

if param is a data frame, it must have three columns: sender, receiver, and value (numeric), representing the parameter value for thay dyadic pair. The data.frame must contain all pairs of actors or dyads corresponding to the riskset.

if param is a function, it's first argument must be 't', corresponding to the time. The function may have additional arguments.

Value

List with all information required by ‘remulate::remulateTie()' or ’remulate::remulateActor()' to compute the statistic.

indegreeSender

Description

This function specifies the input for the indegreeSender effect in the formula argument for the function remulateTie or remulateActor. Not to be used independently

Usage

indegreeSender(param = NULL, scaling = c("none", "std", "prop"))

Arguments

Details

if param is a data frame, it must have three columns: sender, receiver, and value (numeric), representing the parameter value for thay dyadic pair. The data.frame must contain all pairs of actors or dyads corresponding to the riskset.

if param is a function, it's first argument must be 't', corresponding to the time. The function may have additional arguments.

Value

List with all information required by ‘remulate::remulateTie()' or ’remulate::remulateActor()' to compute the statistic.

inertia

Description

This function specifies the input for the inertia effect in the formula argument for the function remulateTie or remulateActor. Not to be used independently

Usage

inertia(param = NULL, scaling = c("none", "std", "prop"))

Arguments

Details

if param is a data frame, it must have three columns: sender, receiver, and value (numeric), representing the parameter value for thay dyadic pair. The data.frame must contain all pairs of actors or dyads corresponding to the riskset.

if param is a function, it's first argument must be 't', corresponding to the time. The function may have additional arguments.

Value

List with all information required by ‘remulate::remulateTie()' or ’remulate::remulateActor()' to compute the statistic.

interact

Description

This function specifies the input for the interact effect in the formula argument for the function remulateTie or remulateActor. Not to be used independently

Usage

interact(param = NULL, indices, scaling = c("none", "std"))

Arguments

Details

if param is a data frame, it must have three columns: sender, receiver, and value (numeric), representing the parameter value for thay dyadic pair. The data.frame must contain all pairs of actors or dyads corresponding to the riskset.

if param is a function, it's first argument must be 't', corresponding to the time. The function may have additional arguments.

Value

List with all information required by ‘remulate::remulateTie()' or ’remulate::remulateActor()' to compute the statistic.

isp

Description

This function specifies the input for the isp effect in the formula argument for the function remulateTie or remulateActor. Not to be used independently

Usage

isp(param = NULL, scaling = c("none", "std"))

Arguments

Details

if param is a data frame, it must have three columns: sender, receiver, and value (numeric), representing the parameter value for thay dyadic pair. The data.frame must contain all pairs of actors or dyads corresponding to the riskset.

if param is a function, it's first argument must be 't', corresponding to the time. The function may have additional arguments.

Value

List with all information required by ‘remulate::remulateTie()' or ’remulate::remulateActor()' to compute the statistic.

itp

Description

This function specifies the input for the itp effect in the formula argument for the function remulateTie or remulateActor. Not to be used independently

Usage

itp(param = NULL, scaling = c("none", "std"))

Arguments

Details

if param is a data frame, it must have three columns: sender, receiver, and value (numeric), representing the parameter value for thay dyadic pair. The data.frame must contain all pairs of actors or dyads corresponding to the riskset.

if param is a function, it's first argument must be 't', corresponding to the time. The function may have additional arguments.

Value

List with all information required by ‘remulate::remulateTie()' or ’remulate::remulateActor()' to compute the statistic.

maximum

Description

This function specifies the input for the maximum effect in the formula argument for the function remulateTie or remulateActor. Not to be used independently

Usage

maximum(param = NULL, variable, attr_actors, scaling = c("none", "std"))

Arguments

Details

Dyadic covariate: maximum attribute value for dyad (i,j) is the bigger of the attribute values for actors i , j

if param is a data frame, it must have three columns: sender, receiver, and value (numeric), representing the parameter value for thay dyadic pair. The data.frame must contain all pairs of actors or dyads corresponding to the riskset.

if param is a function, it's first argument must be 't', corresponding to the time. The function may have additional arguments.

Value

List with all information required by ‘remulate::remulateTie()' or ’remulate::remulateActor()' to compute the statistic.

minimum

Description

This function specifies the input for the minimum effect in the formula argument for the function remulateTie or remulateActor. Not to be used independently

Usage

minimum(param = NULL, variable, attr_actors, scaling = c("none", "std"))

Arguments

Details

Dyadic covariate: minimum attribute value for dyad (i,j) is the smaller of the attribute values for actors i , j

if param is a data frame, it must have three columns: sender, receiver, and value (numeric), representing the parameter value for thay dyadic pair. The data.frame must contain all pairs of actors or dyads corresponding to the riskset.

if param is a function, it's first argument must be 't', corresponding to the time. The function may have additional arguments.

Value

List with all information required by ‘remulate::remulateTie()' or ’remulate::remulateActor()' to compute the statistic.

osp

Description

This function specifies the input for the osp effect in the formula argument for the function remulateTie or remulateActor. Not to be used independently

Usage

osp(param = NULL, scaling = c("none", "std"))

Arguments

Details

if param is a data frame, it must have three columns: sender, receiver, and value (numeric), representing the parameter value for thay dyadic pair. The data.frame must contain all pairs of actors or dyads corresponding to the riskset.

if param is a function, it's first argument must be 't', corresponding to the time. The function may have additional arguments.

Value

List with all information required by ‘remulate::remulateTie()' or ’remulate::remulateActor()' to compute the statistic.

otp sender

Description

This function specified the input for the baseline effect in the formula argument for the function remulateActor. Not to be used independently.

Usage

ospSender(param = NULL, scaling = c("none", "std", "prop"))

Arguments

Details

if param is a data frame, it must have three columns: sender, receiver, and value (numeric), representing the parameter value for thay dyadic pair. The data.frame must contain all pairs of actors or dyads corresponding to the riskset.

if param is a function, it's first argument must be 't', corresponding to the time. The function may have additional arguments.

Value

List with all information required by 'remulate::remulateActor()' to compute the statistic.

otp

Description

This function specifies the input for the otp effect in the formula argument for the function remulateTie or remulateActor. Not to be used independently

Usage

otp(param = NULL, scaling = c("none", "std"))

Arguments

Details

if param is a data frame, it must have three columns: sender, receiver, and value (numeric), representing the parameter value for thay dyadic pair. The data.frame must contain all pairs of actors or dyads corresponding to the riskset.

if param is a function, it's first argument must be 't', corresponding to the time. The function may have additional arguments.

Value

List with all information required by ‘remulate::remulateTie()' or ’remulate::remulateActor()' to compute the statistic.

osp sender This function specifies the input for the osp sender effect in the s_formula argument for the function remulateActor. Not to be used independently.

Description

osp sender

This function specifies the input for the osp sender effect in the s_formula argument for the function remulateActor. Not to be used independently.

Usage

otpSender(param = NULL, scaling = c("none", "std", "prop"))

Arguments

Details

if param is a data frame, it must have three columns: sender, receiver, and value (numeric), representing the parameter value for thay dyadic pair. The data.frame must contain all pairs of actors or dyads corresponding to the riskset.

if param is a function, it's first argument must be 't', corresponding to the time. The function may have additional arguments.

Value

List with all information required by 'remulate::remulateActor()' to compute the statistic.

outdegreeReceiver

Description

This function specifies the input for the outdegreeReceiver effect in the formula argument for the function remulateTie or remulateActor. Not to be used independently

Usage

outdegreeReceiver(param = NULL, scaling = c("none", "std", "prop"))

Arguments

Details

if param is a data frame, it must have three columns: sender, receiver, and value (numeric), representing the parameter value for thay dyadic pair. The data.frame must contain all pairs of actors or dyads corresponding to the riskset.

if param is a function, it's first argument must be 't', corresponding to the time. The function may have additional arguments.

Value

List with all information required by ‘remulate::remulateTie()' or ’remulate::remulateActor()' to compute the statistic.

outdegreeSender

Description

This function specifies the input for the outdegreeSender effect in the formula argument for the function remulateTie or remulateActor. Not to be used independently

Usage

outdegreeSender(param = NULL, scaling = c("none", "std", "prop"))

Arguments

Details

if param is a data frame, it must have three columns: sender, receiver, and value (numeric), representing the parameter value for thay dyadic pair. The data.frame must contain all pairs of actors or dyads corresponding to the riskset.

if param is a function, it's first argument must be 't', corresponding to the time. The function may have additional arguments.

Value

List with all information required by ‘remulate::remulateTie()' or ’remulate::remulateActor()' to compute the statistic.

psABAY

Description

This function specifies the input for the psABAY effect in the formula argument for the function remulateTie or remulateActor. Not to be used independently

Usage

psABAY(param = NULL, scaling = c("none", "std"))

Arguments

Details

if param is a data frame, it must have three columns: sender, receiver, and value (numeric), representing the parameter value for thay dyadic pair. The data.frame must contain all pairs of actors or dyads corresponding to the riskset.

if param is a function, it's first argument must be 't', corresponding to the time. The function may have additional arguments.

Value

List with all information required by ‘remulate::remulateTie()' or ’remulate::remulateActor()' to compute the statistic.

psABBA

Description

This function specifies the input for the psABBA effect in the formula argument for the function remulateTie or remulateActor. Not to be used independently

Usage

psABBA(param = NULL, scaling = c("none", "std"))

Arguments

Details

if param is a data frame, it must have three columns: sender, receiver, and value (numeric), representing the parameter value for thay dyadic pair. The data.frame must contain all pairs of actors or dyads corresponding to the riskset.

if param is a function, it's first argument must be 't', corresponding to the time. The function may have additional arguments.

Value

List with all information required by ‘remulate::remulateTie()' or ’remulate::remulateActor()' to compute the statistic.

psABBY

Description

This function specifies the input for the psABBY effect in the formula argument for the function remulateTie or remulateActor. Not to be used independently

Usage

psABBY(param = NULL, scaling = c("none", "std"))

Arguments

Details

if param is a data frame, it must have three columns: sender, receiver, and value (numeric), representing the parameter value for thay dyadic pair. The data.frame must contain all pairs of actors or dyads corresponding to the riskset.

if param is a function, it's first argument must be 't', corresponding to the time. The function may have additional arguments.

Value

List with all information required by ‘remulate::remulateTie()' or ’remulate::remulateActor()' to compute the statistic.

psABXA

Description

This function specifies the input for the psABXA effect in the formula argument for the function remulateTie or remulateActor. Not to be used independently

Usage

psABXA(param = NULL, scaling = c("none", "std"))

Arguments

Details

if param is a data frame, it must have three columns: sender, receiver, and value (numeric), representing the parameter value for thay dyadic pair. The data.frame must contain all pairs of actors or dyads corresponding to the riskset.

if param is a function, it's first argument must be 't', corresponding to the time. The function may have additional arguments.

Value

List with all information required by ‘remulate::remulateTie()' or ’remulate::remulateActor()' to compute the statistic.

psABXB

Description

This function specifies the input for the psABXB effect in the formula argument for the function remulateTie or remulateActor. Not to be used independently

Usage

psABXB(param = NULL, scaling = c("none", "std"))

Arguments

Details

if param is a data frame, it must have three columns: sender, receiver, and value (numeric), representing the parameter value for thay dyadic pair. The data.frame must contain all pairs of actors or dyads corresponding to the riskset.

if param is a function, it's first argument must be 't', corresponding to the time. The function may have additional arguments.

Value

List with all information required by ‘remulate::remulateTie()' or ’remulate::remulateActor()' to compute the statistic.

psABXY

Description

This function specifies the input for the psABXY effect in the formula argument for the function remulateTie or remulateActor. Not to be used independently

Usage

psABXY(param = NULL, scaling = c("none", "std"))

Arguments

Details

if param is a data frame, it must have three columns: sender, receiver, and value (numeric), representing the parameter value for thay dyadic pair. The data.frame must contain all pairs of actors or dyads corresponding to the riskset.

if param is a function, it's first argument must be 't', corresponding to the time. The function may have additional arguments.

Value

List with all information required by ‘remulate::remulateTie()' or ’remulate::remulateActor()' to compute the statistic.

receive

Description

This function specifies the input for the receive effect in the formula argument for the function remulateTie or remulateActor. Not to be used independently

Usage

receive(param = NULL, variable, attr_actors, scaling = c("none", "std"))

Arguments

Details

Receiver covariate: The tendency to create an event i->j when j has a high attribute value.

if param is a data frame, it must have three columns: sender, receiver, and value (numeric), representing the parameter value for thay dyadic pair. The data.frame must contain all pairs of actors or dyads corresponding to the riskset.

if param is a function, it's first argument must be 't', corresponding to the time. The function may have additional arguments.

Value

List with all information required by ‘remulate::remulateTie()' or ’remulate::remulateActor()' to compute the statistic.

recencyContinue

Description

This function specifies the input for the recencyContinue effect in the formula argument for the function remulateTie. Not to be used independently

Usage

recencyContinue(param = NULL)

Arguments

Details

if param is a data frame, it must have three columns: sender, receiver, and value (numeric), representing the parameter value for thay dyadic pair. The data.frame must contain all pairs of actors or dyads corresponding to the riskset.

if param is a function, it's first argument must be 't', corresponding to the time. The function may have additional arguments.

Value

List with all information required by ‘remulate::remulateTie()' or ’remulate::remulateActor()' to compute the statistic.

recencyReceiveReceiver

Description

This function specifies the input for the recencyReceiveReceiver effect in the formula argument for the function remulateTie. Not to be used independently

Usage

recencyReceiveReceiver(param = NULL)

Arguments

Details

if param is a data frame, it must have three columns: sender, receiver, and value (numeric), representing the parameter value for thay dyadic pair. The data.frame must contain all pairs of actors or dyads corresponding to the riskset.

if param is a function, it's first argument must be 't', corresponding to the time. The function may have additional arguments.

Value

List with all information required by ‘remulate::remulateTie()' or ’remulate::remulateActor()' to compute the statistic.

recencyReceiveSender

Description

This function specifies the input for the recencyReceiveSender effect in the formula argument for the function remulateTie. Not to be used independently

Usage

recencyReceiveSender(param = NULL)

Arguments

Details

if param is a data frame, it must have three columns: sender, receiver, and value (numeric), representing the parameter value for thay dyadic pair. The data.frame must contain all pairs of actors or dyads corresponding to the riskset.

if param is a function, it's first argument must be 't', corresponding to the time. The function may have additional arguments.

Value

List with all information required by ‘remulate::remulateTie()' or ’remulate::remulateActor()' to compute the statistic.

recencySendReceiver

Description

This function specifies the input for the recencySendReceiver effect in the formula argument for the function remulateTie. Not to be used independently

Usage

recencySendReceiver(param = NULL)

Arguments

Details

if param is a data frame, it must have three columns: sender, receiver, and value (numeric), representing the parameter value for thay dyadic pair. The data.frame must contain all pairs of actors or dyads corresponding to the riskset.

if param is a function, it's first argument must be 't', corresponding to the time. The function may have additional arguments.

Value

List with all information required by ‘remulate::remulateTie()' or ’remulate::remulateActor()' to compute the statistic.

recencySendSender

Description

This function specifies the input for the recencySendSender effect in the formula argument for the function remulateTie. Not to be used independently

Usage

recencySendSender(param = NULL)

Arguments

Details

if param is a data frame, it must have three columns: sender, receiver, and value (numeric), representing the parameter value for thay dyadic pair. The data.frame must contain all pairs of actors or dyads corresponding to the riskset.

if param is a function, it's first argument must be 't', corresponding to the time. The function may have additional arguments.

Value

List with all information required by ‘remulate::remulateTie()' or ’remulate::remulateActor()' to compute the statistic.

reciprocity

Description

This function specifies the input for the reciprocity effect in the formula argument for the function remulateTie or remulateActor. Not to be used independently

Usage

reciprocity(param = NULL, scaling = c("none", "std", "prop"))

Arguments

Details

if param is a data frame, it must have three columns: sender, receiver, and value (numeric), representing the parameter value for thay dyadic pair. The data.frame must contain all pairs of actors or dyads corresponding to the riskset.

if param is a function, it's first argument must be 't', corresponding to the time. The function may have additional arguments.

Value

List with all information required by ‘remulate::remulateTie()' or ’remulate::remulateActor()' to compute the statistic.

Simulate Relational Event Data - Actor-Oriented model

Description

A function to simulate relational event data by sampling from an actor-oriented relational event model.

Usage

remulateActor(
  rateEffects,
  choiceEffects,
  actors,
  endTime,
  events = NULL,
  startTime = 0,
  initial = 0,
  riskset = NULL,
  memory = c("full", "window", "window_m", "decay"),
  memoryParam = NULL
)

Arguments

Details

#' If time is irrelevant and only a specific number of events are desired, set time to Inf. If both time and events are supplied then the function stops simulating whenever the first stop condition is met

A list of available statistics for actor rate model. See remulateActorEffects for details on effects:

• baseline()

• indegreeSender()

• outdegreeSender()

• totaldegreeSender()

• ospSender()

• otpSender()

• send()

• interact()

A list of available statistics for receiver choice model. See remulateActorEffects for details on effects: :

• inertia()

• reciprocity()

• indegreeReceiver()

• outdegreeReceiver()

• totaldegreeReceiver()

• otp()

• itp()

• osp()

• isp()

• psABBA()

• psABBY()

• psABXA()

• psABXB()

• psABXY()

• psABAY()

• recencyContinue()

• recencySendReceiver()

• recencyReceiveReceiver()

• rrankReceive()

• receive()

• dyad()

• same()

• average()

• difference()

• minimum()

• maximum()

• interact()

Value

An object of class "remulateActor". A data.frame containing the simulated event sequence with columns (time, sender, receiver). The "remulateActor" object has the following attributes::

evls

A matrix containing the event list with columns (dyad, time), where dyad represents the index of the (sender, receiver) pair in the risk set.

rateStatistics

An array of dimensions M x N x P, where: - M is the number of events, - N is the number of actors, - P is the number of sender rate statistics.

choiceStatistics

An array of dimensions M x D x Q, where: - M is the number of events, - D is the number of dyads in the risk set, - Q is the number of receiver choice statistics.

rateParams

A named list of rate model parameters corresponding to the specified rate statistics.

choiceParams

A named list of choice model parameters corresponding to the specified choice statistics.

riskset

A matrix with columns (sender, receiver) representing the risk set used in the simulation.

actors

A data.frame mapping the actor names provided by the user to the integer IDs used in internal computations.

#'

initial

A A numeric or data.frame object representing the network initialization, which can be a number of random initialization events or a data.frame specifying pre-existing ties.

rateEffects

A formula object specifying the effects included in the rate sub-model.

choiceEffects

A formula object specifying the effects included in the choice sub-model.

density

A numeric value indicating the density of the generated network, defined as the number of observed ties divided by N*(N-1), where N is the number of actors.

References

Lakdawala, R., Mulder, J., & Leenders, R. (2025). *Simulating Relational Event Histories: Why and How*. arXiv:2403.19329.

Examples

 # To generate events up to time '50' in a network of 25 actors with 
 # 200 random initial events
 
 # Exogenous attributes data.frame
 cov <- data.frame(
   id   = 1:25, 
   time = rep(0, 25), 
   sex  = sample(c(0,1), 25, replace = TRUE, prob = c(0.4, 0.6)), 
   age  = sample(20:30, 25, replace = TRUE) 
 )
 
 # Effects specification
 rateform <- ~ remulate::baseline(-6) + 
              remulate::indegreeSender(0.01) + 
              remulate::send(0.02, variable = "age", attr_actors = cov) + 
              remulate::interact(0.01, indices = c(2, 3))
 
 choiceform <- ~ remulate::inertia(0.01) + 
               remulate::reciprocity(-0.03) + 
               remulate::interact(0.01, indices = c(2, 1))
 
 # Calling remulateActor
 remulate::remulateActor(
   rateform, 
   choiceform, 
   actors  = 1:25, 
   endTime = 100, 
   initial = 200, 
   events  = 500, 
 )
  
 # To predict events, given an edgelist of initial events
 initialREH <- data.frame(
   time = seq(0.5, 100, 0.5), 
   sender = sample(1:25, 200, TRUE), 
   receiver = sample(1:25, 200, TRUE)
 )
 
 remulate::remulateActor(
   rateform, 
   choiceform, 
   actors  = 1:25, 
   endTime = 200, 
   initial = initialREH, 
   events  = 500
 )

Remulate Actor Effects

Description

This page lists the effects that are available in the remulate package for the actor-oriented relational event model.

Usage

remulateActorEffects(rateEffects = TRUE, choiceEffects = TRUE)

Arguments

Details

The attr_actors object contains at least three columns (actor,time,attribute). It should be constructed as follows: Each row refers to the attribute value of actor i at timepoint t. The first column contains the actor names (corresponding to the vector of names in the actors argument of remulateActor). The second column contains the time when attributes change (set to zero if the attributes do not vary over time). Subsequent columns contain the attributes that are called in the specifications of exogenous statistics. The same attr_actors object can be used with multiple exogenous statistics.

Value

Returns a character vector of available effects for the rateEffects or choiceEffects argument for the function remulateActor.

remulateActor Rate Effects

Endogenous effects:

baseline

Baseline tendency for actors to create events. The statistic equals to 1 for all actors in the riskset. The parameter for baseline controls the average number of events per unit time.

indegreeSender

In degree effect of the sender is the tendency for actor i to create an event when i has received more events in the past. The statistic at timepoint t for dyad (i,j) is equal to the number of events received by actor i before timepoint t. Note: if scaling is "prop" for indegreeSender, the statistic for dyad (i,j) at time t is divided by the total degree of the sender i at time t.

outdegreeSender

Out degree effect of sender is the tendency for actor i to create an event when i has sent more events in the past. Note: if scaling is "prop" for outdegreeSender, the statistic for dyad (i,j) at time t is divided by the total degree of the sender i at time t.

totaldegreeSender

Total degree effect of sender is the tendency for actor i to create an event when i has sent and received more events in the past.

ospSender

Outgoing Shared Partners actor effect is the tendency for actor i to create an event if actor i is the source in a transitive structure (i->h<-j<-i).

otpSender

Outgoing Two Path actor effect is the tendency for sender i to create an event if actor i is the source in a transitive structure (i->h->j<-i).

Exogenous effects:

send

The tendency for actor i to create an event when i has a high attribute value.

remulateActor Choice Effects

Endogenous effects (Dyad statistics):

inertia

Inertia is the tendency to create an event i->j if the event i->j occurred in the past. The statistic at timepoint t for dyad (i,j) is equal to the number of (i,j) events before timepoint t. Note: if scaling is "prop" for inertia, the statistic for dyad (i,j) at time t is divided by the out degree of the sender i at time t.

reciprocity

Reciprocity is the tendency to create an event i->j if j->i occurred in the past.The statistic at timepoint t for dyad (i,j) is equal to the number of (j,i) events before timepoint t. Note: if scaling is "prop" for inertia, the statistic for dyad (i,j) at time t is divided by the in degree of the sender i at time t.

tie

Tie effect is the tendency to create an event i->j if the event i->j occurred at least once in the past. The statistic at timepoint t for dyad (i,j) is equal to 1 if a an event i->j occurred before timepoint t

Endogenous effects (Triadic statistics):

otp

Outgoing Two Path effect is the tendency to create an event i->j if they have past outgoing two-paths between them (i->h->j). The statistic for dyad (i,j) at timepoint t is equal to the minimum of past (i,h), (h,j) events, summed over all h.

itp

Incoming Two Path effect is the tendency to create an event i->j if they have past incoming two-paths between them (i<-h<-j). The statistic for dyad (i,j) at timepoint t is equal to the minimum of past (j,h), (h,i) events, summed over all h.

osp

Outgoing Shared Partners effect is the tendency to create an event i->j if they have past outgoing shared partners between them (i->h<-j). The statistic for dyad (i,j) at timepoint t is equal to the minimum of past (i,h), (j,h) events, summed over all h.

isp

Incoming Shared Partners effect is the tendency to create an event i->j if they have past incoming shared partners between them (i<-h->j). The statistic for dyad (i,j) at timepoint t is equal to the minimum of past (h,i), (h,j) events, summed over all h.

Endogenous effects (Node statistics):

indegreeReceiver

In degree effect of receiver is the tendency to create an event i->j if j has received more events in the past. The statistic at timepoint t for dyad (i,j) is equal to the number of events received by actor j before timepoint t. Note: if scaling is "prop" for indegreeReceiver, the statistic for dyad (i,j) at time t is divided by the total degree of the receiver j at time t.

outdegreeReceiver

Out degree effect of receiver is the tendency to create an event i->j if j has sent more events in the past. Note: if scaling is "prop" for outdegreeReceiver, the statistic for dyad (i,j) at time t is divided by the total degree of the receiver j at time t.

totaldegreeReceiver

Total degree effect of receiver is the tendency to create an event i->j if j has sent and received more events in the past.

Exogenous effects:

dyad

Dyadic attribute value is tendency to create an event i -> j when (i,j) has a high attribute value.

receive

Receiver attribute value is the tendency to create an event i->j when j has a high attribute value.

same

Same attribute value (Homophily) is the tendency to create an event i->j if actors i and j have the same attribute values

Difference

difference attribute value (Heterophily) is the tendency to create an event i->j if actors i and j have a high absolute difference in attribute values

Examples

#To specify an exogenous effect (example: same)

cov <- data.frame(
  actor = 1:10,
  time = rep(0, 10),
  gender = sample(c(0, 1), replace = TRUE, 10),
  age = sample(20:30, 10, replace = TRUE)
)

effects <- ~ same(0.2, variable = "gender", attr_actors = cov)

#Rate Effects:

#If parameter is constant

rateEffects <- ~ outdegreeSender(0.3) + 
  send(0.1, variable = "age", attr_actors = cov)

#If parameter varies with time

rateEffects <- ~ outdegreeSender(param = function(t) exp(-t)) + 
  send(0.1, variable = "age", attr_actors = cov)

#Choice Effects:

#If parameter is constant

choiceEffects <- ~ inertia(0.4) + 
  reciprocity(-0.1) + 
  same(0.2, variable = "gender", attr_actors = cov) + 
  receive(0.1, variable = "age", attr_actors = cov)

#If parameter varies with time

choiceEffects <- ~ inertia(param = function(t) exp(-t)) +  
  reciprocity(-0.1) + 
  same(0.2, variable = "gender", attr_actors = cov) + 
  receive(0.1, variable = "age", attr_actors = cov)

Simulate Relational Event Data - Tie-Oriented model

Description

A function to simulate relational event data by sampling from a tie-oriented relational event model.

Usage

remulateTie(
  effects,
  actors,
  endTime,
  events = NULL,
  startTime = 0,
  initial = 0,
  riskset = NULL,
  memory = c("full", "window", "window_m", "decay"),
  memoryParam = NULL
)

Arguments

Details

If time is irrelevant and only a specific number of events are desired, set time to Inf. If both time and events are supplied then the function stops simulating whenever the first stop condition is met

If effects is a 'remstimate' object, then the params are extracted automatically. Furthermore if exogenous statistics are used, the data.frames corresponding to the 'attr_actors' argument of the 'remstats' formula must be loaded in the environment.

A list of available statistics. See remulateTieEffects for details:

• baseline(param)

• send()

• receive()

• dyad()

• same()

• difference()

• average()

• minimum()

• maximum()

• inertia()

• reciprocity()

• tie()

• indegreeSender()

• indegreeReceiver()

• outdegreeSender()

• outdegreeReceiver()

• totaldegreeSender()

• totaldegreeReceiver()

• otp()

• itp()

• osp()

• isp()

• psABBA()

• psABBY()

• psABXA()

• psABXB()

• psABXY()

• psABAY()

• recencyContinue()

• recencySendSender()

• recencySendReceiver()

• recencyReceiveSender()

• recencyReceiveReceiver()

• rrankSend()

• rrankReceive()

• interact()

Value

An object of class "remulateTie". A data.frame containing the simulated event sequence with columns (time, sender, receiver). The "remulateTie" object has the following attributes:

statistics

An array with dimensions M x D x P, where M is the number of events, D is the number of dyads in the risk set, and P is the number of computed statistics.

evls

A matrix containing the event list with columns (dyad, time), where dyad represents the index of the (sender, receiver) pair in the risk set.

actors

A data.frame mapping the actor names provided by the user to the integer IDs used in internal computations.

riskset

A data.frame with columns (sender, receiver) containing the risk set used for dyad indices in the computed statistics and event list.

initial

A A numeric or data.frame object representing the network initialization, which can be a number of random initialization events or a data.frame specifying pre-existing ties.

effects

A formula object specifying the effects included in the model.

density

A numeric value indicating the density of the generated network, defined as the number of observed ties divided by N*(N-1), where N is the number of actors.

References

Lakdawala, R., Mulder, J., & Leenders, R. (2025). *Simulating Relational Event Histories: Why and How*. arXiv:2403.19329.

Examples

 # To generate events up to time '50' in a network of 25 actors with 
 # 200 random initial events
 # Exogenous attributes data.frame

 cov <- data.frame(
   id = 1:25, 
   time = rep(0, 25), 
   sex = sample(c(0, 1), 25, replace = TRUE, prob = c(0.4, 0.6)), 
   age = sample(20:30, 25, replace = TRUE) 
 )

 #Effects specification
 effects <- ~ remulate::baseline(-5) + 
             remulate::inertia(0.01) + 
             remulate::reciprocity(-0.04) + 
             remulate::itp(0.01, scaling = "std") + 
             remulate::same(0.02, variable = "sex", attr_actors = cov) + 
             remulate::interact(0.01, indices = c(2, 5))
 # Calling remulateTie
 remulate::remulateTie(
   effects, 
   actors  = 1:25, 
   endTime = 50, 
   events  = 500, 
   initial = 200
 )

 # To predict events, given an edgelist of initial events
 initialREH <- data.frame(
   time = seq(0.5, 100, 0.5), 
   sender = sample(1:25, 200, TRUE), 
   receiver = sample(1:25, 200, TRUE)
 )

 remulate::remulateTie(
   effects, 
   actors  = 1:25, 
   endTime = 150, 
   events  = 500, 
   initial = initialREH
 )

 # Custom risk set
 rs <- as.matrix(expand.grid(1:25, 1:25))
 rs <- rs[rs[, 1] != rs[, 2], ]

 custom_rs <- rs[sample(1:90, 50), ]

 remulate::remulateTie(
   effects, 
   actors  = 1:25, 
   endTime = 150, 
   events  = 500, 
   riskset = custom_rs
 )

Remulate Tie Effects

Description

This page lists the effects that are available in the remulate package for the tie oriented relational event model.

Usage

remulateTieEffects(endogenous = NULL)

Arguments

Details

The attr_actors object for exogenous effects based on actor covariates (send, receive, same, difference, average, max, min) contains at least three columns (actor,time,attribute). It should be constructed as follows: Each row refers to the attribute value of actor i at timepoint t. The first column contains the actor names (corresponding to the vector of names in the actors argument of remulateTie). The second column contains the time when attr_actors change (set to zero if the attr_actors do not vary over time). At least one of the subsequent columns must contain values for the attr_actors with column name corresponding to variable name specified in the effect specification.

The attribute object for exogenous effect dyad contains at least three columns (sender_id,receiver_id,attribute). It should be constructed as follows: First column must contain sender id, second column receiver id, at least one of the subsequent columns must contain values for the attr_actors with column name corresponding to variable name specified in the effect specification.

if param is a data frame, it must have three columns: sender, receiver, and value (numeric), representing the parameter value for thay dyadic pair. The data.frame must contain all pairs of actors or dyads corresponding to the riskset.

if param is a function, it's first argument must be 't', corresponding to the time. The function may have additional arguments.

The indices aregument in the interact effect corresponds to the position of the specified effects in the effects argument of remulateTie for which the interaction needs to be computed. The individual constitutive effects for an interaction must be specified before the interact term in the effects argument. To omit the individual constitutive effects in the generation, specify the param arugment to zero.

Value

Returns a character vector of available effects for the effects argument for the function remulateTie.

Remulate Effects

baseline

Baseline tendency for dyads to create events. The statistic equals to 1 for all dyads (i,j) in the riskset. The parameter for baseline controls the average number of events per unit time.

Endogenous effects (Dyad statistics):

inertia

Inertia is the tendency to create an event i->j if the event i->j occurred in the past. The statistic at timepoint t for dyad (i,j) is equal to the number of (i,j) events before timepoint t. Note: if scaling is "prop" for inertia, the statistic for dyad (i,j) at time t is divided by the out degree of the sender i at time t.

reciprocity

Reciprocity is the tendency to create an event i->j if j->i occurred in the past.The statistic at timepoint t for dyad (i,j) is equal to the number of (j,i) events before timepoint t. Note: if scaling is "prop" for reciprocity, the statistic for dyad (i,j) at time t is divided by the in degree of the sender i at time t.

tie

Tie effect is the tendency to create an event i->j if the event i->j occurred at least once in the past. The statistic at timepoint t for dyad (i,j) is equal to 1 if a an event i->j occurred before timepoint t

Endogenous effects (Triadic statistics):

otp

Outgoing Two Path effect is the tendency to create an event i->j if they have past outgoing two-paths between them (i->h->j). The statistic for dyad (i,j) at timepoint t is equal to the minimum of past (i,h), (h,j) events, summed over all h.

itp

Incoming Two Path effect is the tendency to create an event i->j if they have past incoming two-paths between them (i<-h<-j). The statistic for dyad (i,j) at timepoint t is equal to the minimum of past (j,h), (h,i) events, summed over all h.

osp

Outgoing Shared Partners effect is the tendency to create an event i->j if they have past outgoing shared partners between them (i->h<-j). The statistic for dyad (i,j) at timepoint t is equal to the minimum of past (i,h), (j,h) events, summed over all h.

isp

Incoming Shared Partners effect is the tendency to create an event i->j if they have past incoming shared partners between them (i<-h->j). The statistic for dyad (i,j) at timepoint t is equal to the minimum of past (h,i), (h,j) events, summed over all h.

Endogenous effects (Node statistics):

indegreeSender

In degree effect of the sender is the tendency to create an event i->j if i has received more events in the past. The statistic at timepoint t for dyad (i,j) is equal to the number of events received by actor i before timepoint t. Note: if scaling is "prop" for indegreeSender, the statistic for dyad (i,j) at time t is divided by the number of past events until time t.

indegreeReceiver

In degree effect of receiver is the tendency to create an event i->j if j has received more events in the past. The statistic at timepoint t for dyad (i,j) is equal to the number of events received by actor j before timepoint t. Note: if scaling is "prop" for indegreeReceiver, the statistic for dyad (i,j) at time t is divided by the number of past events until time t.

outdegreeSender

Out degree effect of sender is the tendency to create an event i->j if i has sent more events in the past. Note: if scaling is "prop" for outdegreeSender, the statistic for dyad (i,j) at time t is divided by by the number of past events until time t.

outdegreeReceiver

Out degree effect of receiver is the tendency to create an event i->j if j has sent more events in the past. Note: if scaling is "prop" for outdegreeReceiver, the statistic for dyad (i,j) at time t is divided by the number of past events until time t.

totaldegreeSender

Total degree effect of sender is the tendency to create an event i->j if i has sent and received more events in the past.

totaldegreeReceiver

Total degree effect of receiver is the tendency to create an event i->j if j has sent and received more events in the past.

Endogenous effects (Participating Shifts):

psABBA

AB-BA Pacticipating shift (turn receiving) is the tendency to create an event j->i at timepoint t if event i->j occurred at timepoint t-1. The psABBA statistic is equal to one for the dyad (j.i) that will create the participation shift at timepoint t.

psABBY

AB-BY Participating shift (turn receiving) is the tendency to create an event j->h at timepoint t if event i->j occurred at timepoint t-1. The psABBY statistic is equal to one for the dyads (j,h) for all h not equal to i, that will create the participation shift at timepoint t.

PSABAY

AB-AY Participating shifts (turn continuing) is the tendency to create an event i->h at timepoint t if event i->j occurred at timepoint t-1. The PSABAY statistic is equal to one for the dyads (i,h) for all h not equal to j, that will create the participation shift at timepoint t.

psABXA

AB-XA Participating shifts (turn usurping) is the tendency to create an event h->i at timepoint t if event i->j occurred at timepoint t-1. The psABXA statistic is equal to one for the dyads (h,i) for all h not equal to j, that will create the participation shift at timepoint t.

psABXB

AB-XB Participating shifts (turn usurping) is the tendency to create an event h->j at timepoint t if event i->j occurred at timepoint t-1. The psABXB statistic is equal to one for the dyads (h,j) for all h not equal to i, that will create the participation shift at timepoint t.

psABXY

AB-XY Participating shifts (turn usurping) is the tendency to create an event h->k at timepoint t if event i->j occurred at timepoint t-1. The psABXB statistic is equal to one for the dyads (h,k) for all h not equal to i and k not equal to j, that will create the participation shift at timepoint t.

Endogenous effects (Recency statistics):

recencyContinue

The recencyContinue effect refers to a recency statistic similar to what is described in Vu et al. (2017) and Mulder and Leenders (2019). For each timepoint t, for directed dyad (i,j) the statistic is equal to 1/(the time that has past since the dyad was last active + 1).

recencySendSender

The recencySendSender effect refers to a recency statistic similar to what is described in Vu et al. (2017) and Mulder and Leenders (2019). For each timepoint t, for directed dyad (i,j) the statistic is equal to 1/(the time that has past since sender i was last active as sender + 1).

recencySendReceiver

The recencySendReceiver effect refers to a recency statistic similar to what is described in Vu et al. (2017) and Mulder and Leenders (2019). For each timepoint t, for directed dyad (i,j) the statistic is equal to 1/(the time that has past since receiver j was last active as sender + 1).

recencyReceiveSender

The recencyReceiveSender effect refers to a recency statistic similar to what is described in Vu et al. (2017) and Mulder and Leenders (2019). For each timepoint t, for directed dyad (i,j) the statistic is equal to 1/(the time that has past since sender i was last active as receiver + 1).

recencyReceiveReceiver

The recencyReceiveReceiver effect refers to a recency statistic similar to what is described in Vu et al. (2017) and Mulder and Leenders (2019). For each timepoint t, for directed dyad (i,j) the statistic is equal to 1/(the time that has past since receiver j was last active as receiver + 1).

Exogenous effects (Node attr_actors):

send

Sender covariate: The tendency to create an event i->j when i has a high attribute value.

receive

Receiver covariate: The tendency to create an event i->j when j has a high attribute value.

Exogenous effects (Tie Attribute):

dyad

dyad attribute value is the tendency to create an event i -> j when (i,j) has a high attribute value.

same

(Homophily) is the tendency to create an event i->j if actors i and j have the same attribute values

difference

(Heterophily) is the tendency to create an event i->j if actors i and j have a high absolute difference in attribute values

average

average attribute value for dyad (i,j) is the average of the attribute values for actors i, j

minimum

minimum attribute value for dyad (i,j) is the smaller of the attribute values for actors i , j

maximum

maximum attribute value for dyad (i,j) is the bigger of the attribute values for actors i , j

Examples

#To specify an endogenous effect (example: inertia)

effects <- ~ inertia(0.1, scaling = "std")

#To specify an exogenous effect (example: same)

cov <- data.frame(
  actor = 1:10,
  time = rep(0, 10),
  gender = sample(c(0, 1), replace = TRUE, 10),
  age = sample(20:30, 10, replace = TRUE)
)

effects <- ~ same(0.2, variable = "gender", attr_actors = cov)

#To specify an exogenous dyadic effect (example: dyad)

cov <- expand.grid(1:10, 1:10)
cov <- cov[cov[, 1] != cov[, 2], ]
cov$grade <- runif(90, 1, 10)

effects <- ~ dyad(0.2, variable = "grade", attr_actors = cov)

#If parameter is constant

effects <- ~ inertia(0.3) + 
  same(0.2, variable = "gender", attr_actors = cov) + 
  reciprocity(-0.1) + 
  itp(0.01)

#If parameter varies with time

effects <- ~ inertia(param = function(t) exp(-t)) + 
  same(0.2, variable = "gender", attr_actors = cov) +
  reciprocity(-0.1) + 
  itp(0.01)

 #If parameter varies across dyads or actors
 rs <- expand.grid(1:10,1:10)
 rs <- rs[rs[,1] != rs[, 2],]

 param_df <- as.data.frame(rs)
 param_df$beta = runif(nrow(rs),-0.1,0.1)

 effects <- ~ remulate::baseline(-3)+
    remulate::inertia(param_df) +
    remulate::reciprocity(0.1)

 
#To specify an interaction (example: between inertia and same constitutive effects)

effects <- ~ inertia(0.3) + 
  same(0.2, variable = "gender", attr_actors = cov) + 
  reciprocity(-0.1) + 
  itp(0.01) + 
  interact(0.1, indices = c(1, 2))

rrankReceive

Description

This function specifies the input for the rrankReceive effect in the formula argument for the function remulateTie. Not to be used independently

Usage

rrankReceive(param = NULL)

Arguments

Details

if param is a data frame, it must have three columns: sender, receiver, and value (numeric), representing the parameter value for thay dyadic pair. The data.frame must contain all pairs of actors or dyads corresponding to the riskset.

if param is a function, it's first argument must be 't', corresponding to the time. The function may have additional arguments.

Value

List with all information required by ‘remulate::remulateTie()' or ’remulate::remulateActor()' to compute the statistic.

rrankSend

Description

This function specifies the input for the rrankSend effect in the formula argument for the function remulateTie. Not to be used independently

Usage

rrankSend(param = NULL)

Arguments

Details

if param is a data frame, it must have three columns: sender, receiver, and value (numeric), representing the parameter value for thay dyadic pair. The data.frame must contain all pairs of actors or dyads corresponding to the riskset.

if param is a function, it's first argument must be 't', corresponding to the time. The function may have additional arguments.

Value

List with all information required by ‘remulate::remulateTie()' or ’remulate::remulateActor()' to compute the statistic.

same

Description

This function specifies the input for the same effect in the formula argument for the function remulateTie or remulateActor. Not to be used independently

Usage

same(param = NULL, variable, attr_actors, scaling = c("none", "std"))

Arguments

Details

Dyadic covariate: (Homophily) is the tendency to create an event i->j if actors i and j have the same attribute values

if param is a data frame, it must have three columns: sender, receiver, and value (numeric), representing the parameter value for thay dyadic pair. The data.frame must contain all pairs of actors or dyads corresponding to the riskset.

if param is a function, it's first argument must be 't', corresponding to the time. The function may have additional arguments.

Value

List with all information required by ‘remulate::remulateTie()' or ’remulate::remulateActor()' to compute the statistic.

send

Description

This function specifies the input for the send effect in the formula argument for the function remulateTie or remulateActor. Not to be used independently

Usage

send(param = NULL, variable, attr_actors, scaling = c("none", "std"))

Arguments

Details

Sender covariate: The tendency to create an event i->j when i has a high attribute value.

if param is a data frame, it must have three columns: sender, receiver, and value (numeric), representing the parameter value for thay dyadic pair. The data.frame must contain all pairs of actors or dyads corresponding to the riskset.

if param is a function, it's first argument must be 't', corresponding to the time. The function may have additional arguments.

Value

List with all information required by ‘remulate::remulateTie()' or ’remulate::remulateActor()' to compute the statistic.

tie

Description

This function specifies the input for the tie effect in the formula argument for the function remulateTie or remulateActor. Not to be used independently

Usage

tie(param = NULL, scaling = c("none", "std"))

Arguments

Details

if param is a data frame, it must have three columns: sender, receiver, and value (numeric), representing the parameter value for thay dyadic pair. The data.frame must contain all pairs of actors or dyads corresponding to the riskset.

if param is a function, it's first argument must be 't', corresponding to the time. The function may have additional arguments.

Value

List with all information required by ‘remulate::remulateTie()' or ’remulate::remulateActor()' to compute the statistic.

totaldegreeReceiver

Description

This function specifies the input for the totaldegreeReceiver effect in the formula argument for the function remulateTie or remulateActor. Not to be used independently

Usage

totaldegreeReceiver(param = NULL, scaling = c("none", "std", "prop"))

Arguments

Details

if param is a data frame, it must have three columns: sender, receiver, and value (numeric), representing the parameter value for thay dyadic pair. The data.frame must contain all pairs of actors or dyads corresponding to the riskset.

if param is a function, it's first argument must be 't', corresponding to the time. The function may have additional arguments.

Value

List with all information required by ‘remulate::remulateTie()' or ’remulate::remulateActor()' to compute the statistic.

totaldegreeSender

Description

This function specifies the input for the totaldegreeSender effect in the formula argument for the function remulateTie or remulateActor. Not to be used independently

Usage

totaldegreeSender(param = NULL, scaling = c("none", "std", "prop"))

Arguments

Details

if param is a data frame, it must have three columns: sender, receiver, and value (numeric), representing the parameter value for thay dyadic pair. The data.frame must contain all pairs of actors or dyads corresponding to the riskset.

if param is a function, it's first argument must be 't', corresponding to the time. The function may have additional arguments.

Value

List with all information required by ‘remulate::remulateTie()' or ’remulate::remulateActor()' to compute the statistic.