StepMix is an open-source software 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 BCH and ML 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 interfaces in both Python and R.

翻译：StepMix是一个开源软件包，用于带外部变量（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、二、三步法）。在社会科学应用中，主要目标不仅包括将个体聚类至潜在类别，还涉及利用这些类别构建更复杂的统计模型。此类模型通常分为两部分：将潜在类别与观测指标关联的测量模型，以及将协变量与结果变量关联至潜在类别的结构模型。测量模型与结构模型可通过所谓的一步法进行联合估计，或采用逐步方法进行顺序估计——后者在估计后潜在类别的可解释性方面对实践者具有显著优势。除一步法外，StepMix还实现了文献中最重要的逐步估计方法，包括基于BCH和ML校正的偏差校正三步法，以及较新的两步法。本文将这些伪似然估计量统一框架下解释为特定的期望最大化子程序。为促进数据科学社区的采用与推广，StepMix遵循scikit-learn库的面向对象设计，并提供Python与R接口。