A Python package following the scikit-learn API for model-based clustering and generalized mixture modeling (latent class/profile analysis) of continuous and categorical data. StepMix handles missing values through Full Information Maximum Likelihood (FIML) and provides multiple stepwise Expectation-Maximization (EM) estimation methods.
Users should be able to specify which parameters to start optimizing from.
StepMix can already be provided with parameters (see examples in
datasets.py), but we need to make sure they don't get reinitialized when callingfit.