Getting started with dynasimR

library(dynasimR)

Load data

sim <- load_example_data()
print(sim)
#> 
#> ── dynasimR_data ───────────────────────────────────────────────────────────────
#> • Scenarios: 4
#> • Profile: "Profile_A"
#> • Summary rows: 200
#> • Entity events: 16000
#> • Loaded: "2026-11-03 18:08"
#> • Path: '/tmp/RtmpCBw96w/Rinst188a71273f10/dynasimR/extdata'

Time-to-event analysis

km <- km_estimate(sim, endpoint = "stage2",
                  stratify_by = "scenario")
plot_km(km, title = "Time-to-stage-2")

Policy effect

pol <- policy_effect(sim,
  policy_a_scenario = "A-S08",
  policy_b_scenario = "A-S07",
  n_bootstrap       = 200)
cat(pol$narrative)
#> The policy A-minus-B event-rate difference (scenario A-S08 vs. A-S07) was -7.4 percentage points (95% CI: -9.1 to -5.6); the event rate was lower under policy A (Wilcoxon test: W = 215, adjusted p < 0.001). The Compliance Index was higher under policy A (0.919 vs. 0.658).

Autonomy trade-off

Only two AL points are present in the shipped example data, but the machinery is the same for a full AL sweep.

al <- al_efficiency(
  sim,
  al_scenarios         = c("0" = "A-S00", "1" = "A-S01"),
  compliance_threshold = 0.80,
  n_bootstrap          = 200
)
plot_al_tradeoff(al)

Manuscript-ready export

export_latex_table(
  data     = pol$effect_sizes,
  filename = "policy_table.tex",
  caption  = "Policy effect sizes.",
  label    = "policy"
)

Launching the dashboard

launch_app()                                        # example data
launch_app(data_dir = "~/my-simulation/data/raw")