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.

翻译：潜类模型（LCM）的传统应用通常聚焦于未观测类别定义清晰且易于区分的场景。然而，在众多实际应用中，这些类别往往呈现弱分离特征且难以区分，导致显著的计算难题。为解决这些问题，我们开发了R包ddtlcm，该包提供综合分析及可视化工具，旨在增强LCM在弱类别分离场景下的稳健性和可解释性，尤其适用于小样本量情况。该包实现了树正则化贝叶斯LCM，通过利用潜类之间的统计关联性，从有限数据中做出更优估计。为提升用户交互性，我们还开发了Shiny应用程序。本文通过模拟数据展示了使用ddtlcm的典型分析流程。所有软件已在CRAN和GitHub上公开。