StepMix is an open-source Python package for the pseudo-likelihood estimation (one-, two- and three-step approaches) of generalized finite mixture models (latent profile and latent class analysis) with external variables (covariates and distal outcomes). In many applications in social sciences, the main objective is not only to cluster individuals into latent classes, but also to use these classes to develop more complex statistical models. These models generally divide into a measurement model that relates the latent classes to observed indicators, and a structural model that relates covariates and outcome variables to the latent classes. The measurement and structural models can be estimated jointly using the so-called one-step approach or sequentially using stepwise methods, which present significant advantages for practitioners regarding the interpretability of the estimated latent classes. In addition to the one-step approach, StepMix implements the most important stepwise estimation methods from the literature, including the bias-adjusted three-step methods with Bolk-Croon-Hagenaars and maximum likelihood corrections and the more recent two-step approach. These pseudo-likelihood estimators are presented in this paper under a unified framework as specific expectation-maximization subroutines. To facilitate and promote their adoption among the data science community, StepMix follows the object-oriented design of the scikit-learn library and provides an additional R wrapper.

翻译：StepMix是一个开源的Python工具包，用于含外生变量（协变量与远端结果）的广义有限混合模型（潜在剖面分析与潜在类别分析）的伪似然估计（一步法、两步法及三步法）。在社会科学领域的诸多应用中，主要目标不仅包括将个体聚类至潜在类别，更涉及利用这些类别构建更复杂的统计模型。此类模型通常可划分为两部分：测量模型用于关联潜在类别与观测指标，结构模型则用于关联协变量和结果变量与潜在类别。测量模型与结构模型可通过单步法联合估计，也可通过逐步法分步估计——后者在解释估计所得潜在类别方面为实践者提供了显著优势。除单步法外，StepMix实现了文献中最重要的逐步估计方法，包括采用Bolk-Croon-Hagenaars偏差校正与最大似然校正的三步法，以及近年提出的两步法。本文在统一框架下将这些伪似然估计量呈现为特定的期望最大化子程序。为促进数据科学领域的采纳与推广，StepMix遵循scikit-learn库的面向对象设计，并额外提供R语言接口。