A modular Python framework for fitting finite mixture EM models to longitudinal data. Includes variants with QP decomposition (within–between effects) and covariate-dependent priors (CRE models). Designed for simulation studies and model comparison (AIC/BIC-based selection).
python statistics expectation-maximization longitudinal-data finite-mixture-models latent-class-models
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Updated
Jul 15, 2026 - Python