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.
We have this in scripts/run_real_example.py but it includes a lot of boilerplate to reproduce all the tables from the paper. I suggest we design a simpler example (e.g., with only 3-step BCH) and include it in the notebooks.
@ericlacourse @FelixLaliberte I think we discussed this already and it has been requested by users a few times. I don't have cycles to take care of this these days, but I'm happy to share access to the notebooks
We have this in
scripts/run_real_example.py
but it includes a lot of boilerplate to reproduce all the tables from the paper. I suggest we design a simpler example (e.g., with only 3-step BCH) and include it in the notebooks.