Type:	Package
Title:	Simulate and Analyse Social Interaction Data
Version:	0.1.6
Description:	Provides tools to simulate and analyse datasets of social interactions between individuals using hierarchical Bayesian models implemented in Stan. The package interacts with Stan via 'cmdstanr' (available from https://mc-stan.org/r-packages/) or 'rstan', depending on user setup. Users can generate realistic interaction data where individual phenotypes influence and respond to those of their partners, with control over sampling design parameters such as the number of individuals, partners, and repeated dyads. The simulation framework allows flexible control over variation and correlation in mean trait values, social responsiveness, and social impact, making it suitable for research on interacting phenotypes and on direct and indirect genetic effects ('DGEs' and 'IGEs'). The package also includes functions to fit and compare alternative models of social effects, including impact–responsiveness, variance–partitioning, and trait-based models, and to summarise model performance in terms of bias and dispersion. For more details on the study of social interactions and impact-responsiveness, see Moore et al. (1997) <doi:10.1111/j.1558-5646.1997.tb01458.x> and de Groot et al. (2022) <doi:10.1016/j.neubiorev.2022.104996>.
License:	MIT + file LICENSE
Encoding:	UTF-8
Language:	en-GB
RoxygenNote:	7.3.3
Depends:	R (≥ 4.2)
Imports:	MASS, stats, future, future.apply,
Suggests:	rstan, cmdstanr, devtools, testthat, rmarkdown, knitr
VignetteBuilder:	knitr
Config/testthat/edition:	3
URL:	https://github.com/RoriWijnhorst/socialSim
BugReports:	https://github.com/RoriWijnhorst/socialSim/issues
Additional_repositories:	https://stan-dev.r-universe.dev
NeedsCompilation:	no
Packaged:	2025-10-26 21:36:19 UTC; Wijnhorst
Author:	Rori Efrain Wijnhorst [aut, cre]
Maintainer:	Rori Efrain Wijnhorst <roriwijnhorst@gmail.com>
Repository:	CRAN
Date/Publication:	2025-10-30 14:20:02 UTC

Fit one of the available Stan models to simulated datasets

Description

Fit one of the available Stan models to simulated datasets

Usage

run_model(sim, model = NULL, iter = 2000, seed = 1234, cores = 1)

Arguments

Value

A list of fitted model summaries, one per dataset.

Examples

if (requireNamespace("cmdstanr", quietly = TRUE) ||
    requireNamespace("rstan", quietly = TRUE)) {
  sim <- simulate_data(ind = 100, Valpha = 0.2, Vepsilon = 0.1, iterations = 2)
  res <- run_model(sim, model = "Trait.stan", iter = 500, cores = 2)
  summary(res)
} else {
  message("CmdStanR or rstan not available; example skipped.")
}

Simulate social interaction datasets

Description

This function generates datasets where individual phenotypes are influenced by both direct and indirect (social) effects, under a specified sampling design.

Usage

simulate_data(
  ind = 200,
  partners = 4,
  repeats = 1,
  iterations = 100,
  B_0 = 0,
  psi = NULL,
  Valpha,
  Vepsilon = NULL,
  Vpsi = 0,
  Vx = 1,
  Ve = 0.6,
  Vxe = 0,
  r_alpha_epsilon = 0,
  r_alpha_psi = 0,
  r_epsilon_psi = 0,
  r_alpha_x = 0,
  r_psi_x = 0,
  r_epsilon_x = 0,
  fix_total_var = TRUE
)

Arguments

Value

A list with:

• data: list of datasets

• params: named list of effect sizes

• design: sample design (n_ind, partners, repeats, iterations)

Examples

sim <- simulate_data(ind =1200, partners = 4, iterations = 100, B_0 = 1, Valpha=0.2, Vepsilon = 0.1)

Summarise bias and dispersion (MADm) across simulated fits

Description

Summarise bias and dispersion (MADm) across simulated fits

Usage

summarise_results(results)

Arguments

Value

Data frame with only parameters that were estimated in the Stan model.

Examples

if (requireNamespace("cmdstanr", quietly = TRUE) ||
    requireNamespace("rstan", quietly = TRUE)) {
  sim <- simulate_data(ind = 100, Valpha = 0.2, Vepsilon = 0.1, iterations = 2)
  res <- run_model(sim, model = "Trait.stan", iter = 500, cores = 2)
  summary(res)
} else {
  message("CmdStanR or rstan not available; example skipped.")
}