# Introduction to projectLSA

# 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

    install.packages("projectLSA")     # CRAN version (once released)
    library(projectLSA)

# Launching the Application

    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.

    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

    sessionInfo()