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()
Portions of this package were prepared with assistance from large language model tooling for
narrowly defined, non-authorial tasks: copyediting, prose smoothing, Markdown/LaTeX formatting,
scaffolding of boilerplate files (CI configs, build scripts), code refactoring. The tools used were Chat AI,
the LLM service of KISSKI (GWDG), and a self-hosted Mistral Small (24B, Apache-2.0) run locally via
Ollama and the ollamar R package — local inference only, with no data sent to
third parties for the self-hosted model.
All scientific claims, methodological choices, analyses, interpretations, and conclusions are the author's own. No LLM-generated text was incorporated without review and revision, and every reference was verified against its DOI, arXiv ID, or ISBN.
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