Labo-Lacourse/stepmix

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.

35
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Emerging

This tool helps researchers and analysts uncover hidden groups within their data, whether it's survey responses, patient characteristics, or customer behaviors. You input your raw data, which can include both numerical and categorical information, even with missing entries, and it identifies distinct profiles or segments. The output consists of these identified groups, along with insights into their characteristics, which is useful for social scientists, market researchers, and medical professionals.

No commits in the last 6 months.

Use this if you need to understand underlying segments or profiles in complex datasets that contain a mix of different data types and potentially missing information.

Not ideal if you're looking for a simple, out-of-the-box solution without any programming or statistical knowledge.

latent-profile-analysis market-segmentation survey-analysis patient-stratification social-science-research
Stale 6m No Package No Dependents
Maintenance 2 / 25
Adoption 9 / 25
Maturity 16 / 25
Community 8 / 25

How are scores calculated?

Stars

83

Forks

5

Language

Python

License

MIT

Last pushed

Jul 14, 2025

Commits (30d)

0

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