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

# Overview

`projectLSA` provides a unified Shiny-based environment for conducting **Latent Structure Analysis (LSA)**, including:

- Latent Profile Analysis (LPA)
- Latent Class Analysis (LCA)
- Latent Trait Analysis (LTA / IRT)
- Exploratory Factor Analysis (EFA)
- Confirmatory Factor Analysis (CFA)

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("projectLSA")     # CRAN version (once released)
library(projectLSA)
```

---

# Launching the Application

```r
library(projectLSA)
run_projectLSA()
```

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

---

# Modules Included

## 1. Latent Profile Analysis (LPA)
- Fit multiple models with varying profile numbers
- Compare AIC, BIC, entropy, and class sizes
- Visualize best model with custom profile names

## 2. Latent Class Analysis (LCA)
- Designed for categorical indicators
- Simulated and uploaded datasets supported
- Class probability tables and interactive visualizations

## 3. Latent Trait Analysis (LTA / IRT)
- Supports dichotomous and polytomous items
- Rasch / 2PL / 3PL and graded models
- Item information curves, test information, and multi-dimensional visualizations

## 4. Exploratory Factor Analysis (EFA)
- KMO test, Bartlett test, and parallel analysis
- Loading matrices, rotation, and factor scores
- Auto-generated interpretation summaries

## 5. Confirmatory Factor Analysis (CFA)
- Lavaan syntax editor
- Fit indices, loadings, modification indices
- SEM path diagram with customizable styles

---

# Example Workflow

Below is a simple workflow using the built-in datasets.

```r
library(projectLSA)

# Launch the GUI
run_projectLSA()
```

Once inside the GUI:

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

---

# Built-in Example Datasets

The package includes several example datasets:

- **pisaUSA15** — student motivation indicators
- **curry_mac** — moral relevance & judgment (simulated)
- **id_edu** — longitudinal educational identity (simulated)

These are accessible from within the Shiny interface.

---

# Reproducibility and Reporting

`projectLSA` 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., Retnawati, H., Hadi, S., & Haryanto (2025).
*projectLSA: An R Shiny application for latent structure analysis with a graphical user interface.*

---

# Session Info

```r
sessionInfo()
```