# Getting Started with the GUI

## 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

1. Pick a module from the sidebar.
2. Choose test family, statistical test, and analysis mode.
3. Enter inputs in the parameter panel.
4. Click Calculate — results appear as metric cards with full detail below.
5. Review the distribution plot and sample-size power curve.
6. 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.

## Related analyses

- Choosing a power analysis
- Support matrix