pypoLCA – Latent Class Analysis and Regression in Python
Contribute to marcandre259/pypolca development by creating an account on GitHub.
pypoLCA – Latent Class Analysis and Regression in Python launched on June 7, 2026 with 6 votes, #2,066 of 22,647 launches that month and more than 75% of that year's launches.
What it does
Polytomous variable latent class analysis (LCA) for Python, powered by a C++17 backend. pypoLCA is a translation of R's poLCA package by Drew Linzer and Jeffrey Lewis. LCA is a statistical method that discovers latent (unobserved) categorical variables from a set of nominal responses. The core assumption is that observed responses are mutually independent conditionally on the latent variable. LCA's latent variables are categorical (e.g., class 1 = "non-cheaters", class 2 = "chronic cheaters"). This makes LCA the categorical-data analogue of Gaussian Mixture Models (GMM): GMM assumes normally-distributed responses, LCA assumes multinomial responses. Both fit parameters via…from github.com
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