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，用于估计带有外部变量（协变量和结果）的广义混合模型（潜在类别分析和潜在剖面分析）的伪似然估计方法（单步、两步和三步方法）。在社会科学的许多应用中，主要目标不仅是将个体聚类成为潜在的类别，而且使用这些类别来发展更复杂的统计模型。这些模型通常分为测量模型和结构模型两部分，前者将潜在类别与观察指标相关，后者将协变量和结果变量与潜在类别相关。测量模型和结构模型可以同时估计，也可以使用分步方法逐步估计。分步方法对于从业人员来说具有重要的优势，可以轻松解释估计的潜在类别。除了一步方法，StepMix还实现了文献中最重要的逐步估计方法，包括带有BCH和ML校正的偏差调整的三步方法和较新的两步方法。这些伪似然估计器在本文中以统一框架在特定的期望最大化子程序下进行介绍。为了方便和推广在数据科学社区的采用，StepMix遵循scikit-learn库的面向对象设计，并提供Python和R两种接口。