GitHub / 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.
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PURL: pkg:github/Labo-Lacourse/stepmix
Stars: 72
Forks: 6
Open issues: 15
License: mit
Language: Python
Size: 479 KB
Dependencies parsed at: Pending
Created at: over 3 years ago
Updated at: 14 days ago
Pushed at: 14 days ago
Last synced at: 13 days ago
Commit Stats
Commits: 399
Authors: 5
Mean commits per author: 79.8
Development Distribution Score: 0.07
More commit stats: https://commits.ecosyste.ms/hosts/GitHub/repositories/Labo-Lacourse/stepmix
Topics: classification, clustering, expectation-maximization, latent-class-analysis, lca, machine-learning, mixture-models, supervised-learning