A domain-neutral analysis and visualisation layer for discrete-event, agent-based and node-actor simulation outputs. The package is schema-harmonised so that two output profiles (Profile A and Profile B) can be analysed with a single API.
Typical use cases include any system in which entities (actors, nodes, agents) move through states and interact within spatial or network environments — for example flow analyses, state-transition studies, resource allocation and policy comparisons.
# install.packages("remotes")
remotes::install_github("cttir/dynasimR")library(dynasimR)
# 1. Load bundled example data (or point to your own simulation outputs)
sim <- load_example_data()
# sim <- read_simulation("~/my-simulation/data/raw/")
# 2. Time-to-event analysis
km <- km_estimate(sim, endpoint = "stage2", stratify_by = "scenario")
plot_km(km, title = "Time-to-stage-2")
# 3. Policy effect (policy A vs. policy B)
pol <- policy_effect(sim, policy_a_scenario = "A-S08",
policy_b_scenario = "A-S07")
cat(pol$narrative) # ready to drop into a report
# 4. Autonomy-level trade-off
al <- al_efficiency(sim)
plot_al_tradeoff(al)
# 5. Manuscript export
export_latex_table(
data = pol$effect_sizes,
filename = "policy_table.tex",
caption = "Policy effect sizes.",
label = "policy"
)
# 6. Interactive Shiny dashboard
launch_app()| Analysis goal | Key function |
|---|---|
| Entity flow through processing stages | stage_throughput() |
| Time-to-event stratified by scenario | km_estimate(), cox_model() |
| Policy A vs. policy B comparison | policy_effect() |
| Autonomy-level trade-off (AL0-AL5) | al_efficiency() |
| Compliance Index | compute_compliance_index() |
| Profile B progress trajectory | progress_trajectory() |
| Profile B wait-gap index | compute_wait_gap_index() |
| Manuscript placeholder fill | fill_placeholders() |
- R >= 4.1.0 (base pipe
|>used throughout) - Required imports: dplyr, tidyr, purrr, readr, tibble, ggplot2, survival, rlang, cli, glue
- Shiny app requires the
Suggests:dependencies — install withdynasimR::check_app_dependencies()
MIT (c) R. Heller
If you use this software, please cite it as:
Heller, R. (2026). dynasimR: Analysis and visualisation of agent-based and discrete-event simulation output (Version 0.1.0) [Computer software]. Zenodo. https://doi.org/10.5281/zenodo.21889932
Where applicable, LLM tools were used for narrowly defined, non-authorial tasks: copyediting, prose refinement, Markdown/LaTeX formatting, boilerplate scaffolding (such as CI configurations and build scripts), and code refactoring. Tools included Chat AI, the KISSKI (GWDG) service, and self-hosted Mistral Small (24B, Apache-2.0) served through a local vLLM architecture on an Ubuntu Linux workstation with Intel processors and NVIDIA GPUs. The self-hosted model ran locally, with no data sent to third parties.
All scientific claims, methodological choices, analyses, interpretations and conclusions remain the authors' own. Where applicable, LLM-generated material must be reviewed and revised before inclusion, and references must be verified against their DOI, arXiv ID or ISBN.
Local software setup: vLLM with Python/PyTorch, plus R and RStudio.
Editable R artwork and regeneration instructions are in tools/branding.