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.
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.
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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.
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Language
Python
License
MIT
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Last pushed
Jul 14, 2025
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