A quick note on how cgmm relates to existing tools: * scikit-learn's GaussianMixture models the unconditional distribution of data. cgmm, on the other hand, mo

A quick note on how cgmm relates to existing tools: * scikit-learn's GaussianMixture models the unconditional distribution of data. cgmm, on the other hand, models conditional distributions (p(y|x)), which makes it more suitable for regression and forecasting tasks. * Compared to linear or generalized linear models, cgmm can capture multi-modal outputs, non-Gaussian behavior, and input-dependent variance. * Compared to Bayesian frameworks (like PyMC or Stan), cgmm is more focused and lightweight: it provides efficient EM-based algorithms and scikit-learn–style APIs rather than full Bayesian inference. So I see cgmm as complementary, a middle ground between simple regression models and full probabilistic programming frameworks, with a focus on conditional mixture models that are easy to drop into existing Python/ML pipelines.