# Fitting Mixed Effects Models - Python, Julia or R?

**URL:** https://discourse.julialang.org/t/fitting-mixed-effects-models-python-julia-or-r/74955
**Category:** Statistics
**Tags:** blog-post
**Created:** [January 20, 2022, 8:13pm UTC](https://discourse.julialang.org/t/fitting-mixed-effects-models-python-julia-or-r/74955 "2022-01-20T20:13:36Z")
**Posts on this page:** 1
**Page:** 1

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### Author: ![dm13450](https://avatars.discourse-cdn.com/v4/letter/d/eada6e/32.png) [@dm13450](https://discourse.julialang.org/u/dm13450)
#### Post date: [January 20, 2022, 8:13pm UTC](https://discourse.julialang.org/t/fitting-mixed-effects-models-python-julia-or-r/74955/1 "2022-01-20T20:13:36Z")

</div>

I’ve written a blog post bench-marking Julia, Python and R when fitting a mixed effects model. Julia comes out top (by quite a bit!), so if you ever need to convince someone, hopefully this will help!

> **[Fitting Mixed Effects Models - Python, Julia or R?](https://dm13450.github.io/2022/01/06/Mixed-Models-Benchmarking.html)**
>
> I’m benchmarking how long it takes to fit a mixed effects model using lme4 in R, statsmodels in Python, plus showing how MixedModels.jl in Julia is also a viable option.