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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 (covariatesand distal outcomes). In many applications in social sciences, the mainobjective is not only to cluster individuals into latent classes, but also touse these classes to develop more complex statistical models. These modelsgenerally divide into a measurement model that relates the latent classes toobserved indicators, and a structural model that relates covariates and outcomevariables to the latent classes. The measurement and structural models can beestimated jointly using the so-called one-step approach or sequentially usingstepwise methods, which present significant advantages for practitionersregarding the interpretability of the estimated latent classes. In addition tothe one-step approach, StepMix implements the most important stepwiseestimation methods from the literature, including the bias-adjusted three-stepmethods with Bolk-Croon-Hagenaars and maximum likelihood corrections and themore recent two-step approach. These pseudo-likelihood estimators are presentedin this paper under a unified framework as specific expectation-maximizationsubroutines. To facilitate and promote their adoption among the data sciencecommunity, StepMix follows the object-oriented design of the scikit-learnlibrary and provides an additional R wrapper.

StepMix: A Python Package for Pseudo-Likelihood Estimation of Generalized Mixture Models with External Variables