{"categories":[{"label":"Software Development","url":"https://skillfed.io/packages/category/software-development/16"},{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/7"}],"enrichment":{"capability":"Gaussian Mixture Models for clustering and regression, providing expectation-maximization fitting, sampling, and conditional prediction on multivariate data.","skillfed_tags":["probabilistic-modeling","regression","clustering"],"use_cases":["Fit a mixture model to training data and sample new synthetic examples from the learned distribution.","Predict missing feature values given observed features, with uncertainty estimates from the posterior distribution.","Initialize a gmr GMM from a scikit-learn GaussianMixture for regression tasks not supported by sklearn.","Generate trajectory data by sampling from a learned mixture model conditioned on time or state variables.","Model multimodal relationships in data where a single linear or nonlinear regression would be inadequate."],"what_it_does":"gmr is a Python library implementing Gaussian Mixture Models for both clustering and regression tasks. It provides a GMM class that fits mixture models to data via expectation-maximization, samples from the learned distribution, and makes conditional predictions given partial observations. The core use case is probabilistic regression: given values for some features, predict the distribution over remaining features.\n\nThe package depends only on numpy and scipy, making it lightweight to install. It complements scikit-learn's GaussianMixture (which focuses on clustering) by adding regression capability through conditional probability estimation. Models can be saved via pickle or by extracting and storing the learned priors, means, and covariances for interoperability with other GMM implementations.","worth_installing":"Yes, if you need probabilistic regression or conditional prediction on multivariate data. The package is stable, well-documented, has no known vulnerabilities, and low install friction. The aging maintenance status (214 days since release) is not a blocker for a mature library, but verify that the release cadence matches your support expectations. Best suited for research, robotics, and scientific computing workflows where mixture models are standard."},"id":"gmr","links":{"html":"https://skillfed.io/packages/gmr","md":"https://skillfed.io/packages/gmr.md","pypi":"https://pypi.org/project/gmr/"},"maintenance":{"status":"aging"},"meta":{"latest_release":"2026-01-12","license_spdx":"BSD-3-Clause","license_treatment":"permissive","name":"gmr","python_support":"supports_current","summary":"Gaussian Mixture Regression"},"popularity":{"monthly_downloads":110553,"position":12452,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"2.0.3"}