In plain English
The ggpower app is a wide-screen research workspace. A left sidebar switches between modules so you can run general statistical power analyses, biomarker discovery workflows, or clinical trial designs without leaving the same session.
Sidebar modules
| Module | Use when |
|---|---|
| Power Workspace | Classical test families (t, F, chi-square, exact, z, nonparametric) |
| Biomarker Discovery | ROC/AUC, diagnostic accuracy, survival, Cox, FDR screening |
| Clinical Trials | Superiority, NI, equivalence, Simon two-stage, cluster RCT |
| Calculator | Distribution-function expressions and calculator scripts |
| Protocol | Download a log of every analysis from the session |
| Help | Links to vignettes and reference articles |
Launch the app
run_app()On a 1080p display you get a two-column analysis grid. At 1920px and 2560px width the layout expands to a three-column grid with larger plot panels.
Typical workflow
- Pick a module from the sidebar.
- Choose test family, statistical test, and analysis mode.
- Enter inputs in the parameter panel.
- Click Calculate — results appear as metric cards with full detail below.
- Review the distribution plot and sample-size power curve.
- Open Protocol to download the session log.
Worked example (script)
The same calculation is available programmatically:
power_compute(
"t_two_sample",
analysis = "a_priori",
d = 0.5,
alpha = 0.05,
power = 0.8,
tails = "two"
)
#> ggpower result
#> Test: t test: Means - difference between two independent means (two groups)
#> Analysis: a_priori
#>
#> Input parameters
#> tails: two
#> effect_size_d: 0.5
#> alpha: 0.05
#> sample_size_group_1: 64
#> sample_size_group_2: 64
#> target_power: 0.8
#>
#>
#> Output parameters
#> noncentrality_parameter: 2.828427
#> critical_t: -1.978971, 1.978971
#> df: 126
#> total_sample_size: 128
#> actual_power: 0.8014596
#>
#>
#> Notes
#> - A priori sample sizes are rounded up to integer values and actual power is recomputed.