projectLSA is an R package that provides a complete graphical user interface (GUI) for conducting Latent Structure Analysis (LSA) through a Shiny application. It integrates multiple latent variable methods, including:

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

All analyses can be performed without writing any code, making the package accessible for researchers, students, and applied analysts.

# Install from CRAN (when available)
install.packages("projectLSA")

# Install development version from GitHub (optional)
remotes::install_github("hdmeasure/projectLSA")

library(projectLSA)
run_projectLSA()

This opens the full Shiny application, including all LSA modules, data upload, built-in datasets, interactive plots, and reporting features.

🎬 Click the image to watch the installation and quick-start tutorial for projectLSA.

• Upload your own dataset or use built-in examples.
• Fit multiple LPA models automatically.
• Compare AIC, BIC, entropy, and class size.
• Visualize the best model with customizable class names.

• Supports categorical indicators.
• Fits multiple class solutions.
• Interactive plots with ggiraph.
• Probability tables and class membership export.

• 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.

• KMO, Bartlett test, parallel analysis.
• Factor extraction with rotation.
• Factor scores and loading matrix export.
• Clean HTML summaries for clearer interpretation.

• Lavaan model editor.
• Fit measures, loadings, factor scores.
• Fully customized SEM path diagrams.

All features of projectLSA can be explored through an interactive Shiny web application.

👉 Launch the live application:
https://measure.shinyapps.io/ProjectLSA/

The web interface provides access to Latent Profile Analysis (LPA), Latent Class Analysis (LCA), Confirmatory Factor Analysis (CFA), Structural Equation Modeling (SEM), and Latent Trait Analysis (IRT), allowing users to explore the full workflow without local installation.

If you use projectLSA in publications, please cite:

Djidu, H., Retnawati, H., Hadi, S., & Haryanto (2026). projectLSA:Shiny application for latent structure analysis with a graphical user interface. https://doi.org/10.32614/CRAN.package.projectLSA

Bug reports and feature requests are welcome:

https://github.com/hdmeasure/projectLSA/issues

MIT License © 2026 Hasan Djidu