# Incorporating Splines and Offset in MixedModels.jl

**URL:** <https://discourse.julialang.org/t/incorporating-splines-and-offset-in-mixedmodels-jl/136025>\
**Category:** Statistics\
**Tags:** mixed-models\
**Created:** [March 5, 2026, 1:38am UTC](https://discourse.julialang.org/t/incorporating-splines-and-offset-in-mixedmodels-jl/136025 "2026-03-05T01:38:20Z")\
**Posts on this page:** 1\
**Showing post:** 2

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**Author:** ![technocrat](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/technocrat/32/220947_2.png) [@technocrat](https://discourse.julialang.org/u/technocrat)\
**Post date:** [March 5, 2026, 2:59am UTC](https://discourse.julialang.org/t/incorporating-splines-and-offset-in-mixedmodels-jl/136025/2 "2026-03-05T02:59:03Z")

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[Post from four years ago discusses offsets.](https://discourse.julialang.org/t/how-do-i-fit-generalised-linear-multilevel-models-including-offsets/74286/10). [Offsets are available.](https://github.com/JuliaStats/MixedModels.jl/pull/482) Where you will have to make a decision is whether to use `Poisson()` as an approximation to the negative binomial if overdispersion is mild. `Splines2` provides an equivalent to R’s `ns`. If negative binomial is a must-have, there’s `Turing` but performance will be slow.

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