Social networks are tied to population dynamics; interactions are driven by population density and demographic structure, while social relationships can be key determinants of survival and reproductive success. However, difficulties integrating models used in demography and network analysis have limited research at this interface. We introduce the R package genNetDem for simulating integrated network-demographic datasets. It can be used to create longitudinal social networks and/or capture-recapture datasets with known properties. It incorporates the ability to generate populations and their social networks, generate grouping events using these networks, simulate social network effects on individual survival, and flexibly sample these longitudinal datasets of social associations. By generating co-capture data with known statistical relationships it provides functionality for methodological research. We demonstrate its use with case studies testing how imputation and sampling design influence the success of adding network traits to conventional Cormack-Jolly-Seber (CJS) models. We show that incorporating social network effects in CJS models generates qualitatively accurate results, but with downward-biased parameter estimates when network position influences survival. Biases are greater when fewer interactions are sampled or fewer individuals are observed in each interaction. While our results indicate the potential of incorporating social effects within demographic models, they show that imputing missing network measures alone is insufficient to accurately estimate social effects on survival, pointing to the importance of incorporating network imputation approaches. genNetDem provides a flexible tool to aid these methodological advancements and help researchers test other sampling considerations in social network studies.

The dataset and code stored here is for Case Studies 1 and 2 in the paper. Datsets were generated using simulations in R. Here we provide 1) the R code used for the simulations; 2) the simulation outputs (as .RDS files); and 3) the R code to analyse simulation outputs and generate the tables and figures in the paper.

All simulations and analysis are conducted in R using the following (additional) packages: genNetDem, igraph, asnipe, sna, tnet, car, boot, Matrix, MASS, MCMCvis, nimble, tidyr, readr, viridis, HDInterval, emdist, knitr, kableExtra, magrittr, and flextable.

Case Study 1:

The CreateParameters.R script generates the parameters1.RDS file used in the simulations.

Simulations are conducted using the SimulationScript3.R script and simulation results used in the paper are stored in the Results folder.

Data analysis and visualization are done using the datavis_final.R script, and tables are produced are stored in the Outputs folder.

Case Study 2:

The CreateParameters.R script generates the parameters2.RDS file used in the simulations.

Simulations are conducted using the SimulationScript2.R script and simulation results used in the paper are stored in the Results folder.

Data analysis and visualization are done using the datavis_final.R script, and tables are produced are stored in the Outputs folder.