Mixed Models

MixedModels.jl fits statistical mixed effects models using the Julia programming language.

github.com/dmbates/MixedModels.jl
Mixed Models homepage screenshot

About Mixed Models

MixedModels.jl is a Julia package for fitting statistical mixed-effects models. It provides LinearMixedModel for linear models and GeneralizedLinearMixedModel for models where the conditional mean relates to the linear predictor through a link function, including Bernoulli for binary data and Poisson for count data.

Users build models with formulas, fit them with fit and fit!, and query fitted models for results such as likelihood and variance components. Use requires Julia, and version 5.0.0 removed constrained optimization support and multithreading options for the bootstrap.

Key features

  • Fits linear mixed models with LinearMixedModel type
  • Fits generalized linear mixed models with link functions
  • Defines fixed effects and grouped random effects using formulas
  • Builds, fits, and queries models via statistical model API
  • Handles Bernoulli responses for binary data and Poisson for counts
  • Keeps fit log as Tables.jl-compatible column table
Best for: Julia programmers who need to build and fit linear or generalized linear mixed-effects models.
Good for
  • Supports both linear and generalized linear mixed-effects models
  • Uses formula syntax with fit, fit!, and query functions for fitted models
  • Tested on Linux, macOS, and Windows with specified Julia versions
Watch out for
  • Requires Julia rather than working as a standalone program
  • Version 5.0 removed support for constrained optimization
  • Version 5.0 removed multithreading options for the bootstrap

Mixed Models at a glance

Type
Open-source project
Pricing
Not confirmed. Check the site
Open source
Yes · Source code
Language
English
Last checked
Oct 5, 2026 · Online when checked
Listed
Oct 5, 2026

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Mixed Models FAQ

What model types does the package fit?
Linear mixed models and generalized linear mixed models, including Bernoulli models for binary data and Poisson models for count data.
How are random effects modeled?
As independent samples from a multivariate Gaussian distribution of the form B ~ N(0, Σ).
Which platforms and Julia versions are currently tested?
Linux Ubuntu 22.04 and 24.04, macOS Sonoma 14 and Sequoia 15, and Windows Server 2022, using Julia v1.10, current release, or nightly depending on the platform.
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