Mixed Models
A Julia package for fitting linear and generalized linear mixed-effects models with maximum likelihood estimation.
What is Mixed Models?
MixedModels.jl is a Julia package for fitting statistical mixed-effects models, which are used to analyze data with both fixed and random effects. It provides implementations of linear mixed models (LMMs) and generalized linear mixed models (GLMMs), allowing users to model hierarchical or grouped data structures commonly found in fields like psychology, linguistics, and biology. The package supports maximum likelihood estimation and parametric bootstrap methods for robust statistical inference.
Researchers, data scientists, and statisticians working with hierarchical, longitudinal, or grouped data who need to fit mixed-effects models in a high-performance computing environment. It is particularly useful for those in psychology, linguistics, and biological sciences.
MixedModels.jl offers a fast, native Julia implementation of mixed-effects models, leveraging Julia's speed for iterative optimization. It integrates seamlessly with Julia's statistical ecosystem, provides a clean API, and includes features like parametric bootstrapping, making it a powerful alternative to R's lme4 or Python's statsmodels for users prioritizing performance and flexibility.
Overview
A Julia package for fitting (statistical) mixed-effects models
Use Cases
Best For
Related Projects
Found a gem we're missing?
Open-Awesome is built by the community, for the community. Submit a project, suggest an awesome list, or help improve the catalog on GitHub.
