ddtlcm: An R package for overcoming weak separation in Bayesian latent class analysis via tree-regularization

TL;DR

The paper introduces the ddtlcm R package that employs tree-regularized Bayesian latent class models to improve analysis robustness and interpretability in scenarios with weak class separation and small sample sizes.

Contribution

It presents a novel R package implementing tree-regularized Bayesian LCMs, enhancing analysis of weakly separated classes with visualization and interactivity tools.

Findings

Effective analysis of weakly separated classes demonstrated

Improved robustness with small sample sizes shown

Software available on CRAN and GitHub

Abstract

Traditional applications of latent class models (LCMs) often focus on scenarios where a set of unobserved classes are well-defined and easily distinguishable. However, in numerous real-world applications, these classes are weakly separated and difficult to distinguish, creating significant numerical challenges. To address these issues, we have developed an R package ddtlcm that provides comprehensive analysis and visualization tools designed to enhance the robustness and interpretability of LCMs in the presence of weak class separation, particularly useful for small sample sizes. This package implements a tree-regularized Bayesian LCM that leverages statistical strength between latent classes to make better estimates using limited data. A Shiny app has also been developed to improve user interactivity. In this paper, we showcase a typical analysis pipeline with simulated data using ddtlcm. All software has been made publicly available on CRAN and GitHub.