This contribution presents a guide to the R package multilevLCA, which offers a complete and innovative set of technical tools for the latent class analysis of single-level and multilevel categorical data. We describe the available model specifications, mainly falling within the fixed-effect or random-effect approaches. Maximum likelihood estimation of the model parameters, enhanced by a refined initialization strategy, is implemented either simultaneously, i.e., in one-step, or by means of the more advantageous two-step estimator. The package features i) semi-automatic model selection when a priori information on the number of classes is lacking, ii) predictors of class membership, and iii) output visualization tools for any of the available model specifications. All functionalities are illustrated by means of a real application on citizenship norms data, which are available in the package.

翻译：摘要：本文介绍了R包multilevLCA的使用指南，该包为单层与多层分类数据的潜在类别分析提供了一套完整且创新的技术工具。我们描述了可用的模型规格，主要分为固定效应或随机效应方法。模型参数的最大似然估计通过精炼的初始化策略得到增强，既可同步实现（即一步法），也可通过更具优势的两步估计器完成。该包具备以下功能：i）在缺乏类别数量先验信息时实现半自动模型选择；ii）类别成员关系的预测变量；iii）针对任何可用模型规格的输出可视化工具。所有功能均通过包内提供的公民规范数据实际应用案例进行演示。