---
title: "Getting Started with the GUI"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Getting Started with the GUI}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r setup, include = FALSE}
knitr::opts_chunk$set(collapse = TRUE, comment = "#>")
library(ggpower)
```

## 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

```{r launch, eval=FALSE}
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:

```{r example}
power_compute(
  "t_two_sample",
  analysis = "a_priori",
  d = 0.5,
  alpha = 0.05,
  power = 0.8,
  tails = "two"
)
```

## Related analyses

- [Choosing a power analysis](choosing-a-power-analysis.html)
- [Support matrix](support-matrix.html)
