---
title: "Introduction to measureR"
output:
  rmarkdown::html_vignette:
    df_print: paged
vignette: >
  %\VignetteIndexEntry{Introduction to measureR}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

# Overview

`measureR` provides a unified Shiny-based environment for **educational and psychological measurement**, including:

- Content Validity (CV)
- Exploratory Factor Analysis (EFA)
- Confirmatory Factor Analysis (CFA)
- Classical Test Theory (CTT)
- Item Response Theory (IRT)

The package is designed for users who prefer a graphical workflow without writing code, while still leveraging
robust statistical methodologies implemented in well-established R packages.

---

# Installation

```r
install.packages("measureR")
library(measureR)
```

---

# Launching the Application

```r
library(measureR)
run_measureR()
```

This will open the full Shiny interface, where you can upload data, choose an analysis module, and generate results.

---

# Modules Included

## ✔ Content Validity (CV)
- Aiken’s V, CVR (Lawshe), I-CVI, and S-CVI/Ave computation.
- Automatic critical value comparison and interpretation badges.
- Clear tabular summaries and export-ready results.

## ✔ Exploratory Factor Analysis (EFA)
- KMO, Bartlett test, parallel analysis.
- Factor extraction with rotation.
- Factor scores and loading matrix export.
- Clean HTML summaries for clearer interpretation.

## ✔ Confirmatory Factor Analysis (CFA)
- Lavaan model editor.
- Fit measures, loadings, factor scores.
- Fully customized SEM path diagrams.

## ✔ Classical Test Theory (CTT)
- Item difficulty and discrimination indices.
- Test reliability (α), SEM, and score distribution analysis.
- Distractor analysis for multiple-choice items.
- Comprehensive item and test-level summary outputs.

## ✔ Item Response Theory (IRT)
- Supports dichotomous and polytomous items.
- Automatically fits Rasch, 2PL, 3PL (or PCM/GRM/GPCM).
- ICC plots, test information, factor scores.
- Multi-dimensional visualization with 3D surfaces and heatmaps.
---

Once inside the GUI:

1. Choose a module (e.g., IRT)
2. Upload your dataset or select a built-in dataset
3. Choose variables and model settings
4. Fit the models and explore the outputs

---

# Reproducibility and Reporting

`measureR` provides:

- Exportable tables (CSV, Excel)
- Downloadable graphics (PNG)
- Reproducible summaries and model comparisons

This ensures results produced through the GUI can be published or documented with confidence.

---

# Citation

Please cite this package as:

Djidu, H. (2026).
*measureR: Tools for educational and psychological measurement.  https://github.com/hdmeasure/measureR*. R Packages.
---

# Session Info

```r
sessionInfo()
```
