# Any Julia's equivalent to R's packages mcgv or mixed-effects models larger than memory?

**URL:** <https://discourse.julialang.org/t/any-julias-equivalent-to-rs-packages-mcgv-or-mixed-effects-models-larger-than-memory/11865>\
**Category:** Statistics\
**Created:** [June 21, 2018, 3:52pm UTC](https://discourse.julialang.org/t/any-julias-equivalent-to-rs-packages-mcgv-or-mixed-effects-models-larger-than-memory/11865 "2018-06-21T15:52:18Z")\
**Posts on this page:** 1\
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**Author:** ![Juan](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/juan/32/7657_2.png) [@Juan](https://discourse.julialang.org/u/Juan)\
**Post date:** [November 19, 2018, 12:55am UTC](https://discourse.julialang.org/t/any-julias-equivalent-to-rs-packages-mcgv-or-mixed-effects-models-larger-than-memory/11865/8 "2018-11-19T00:55:21Z")

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I couldn’t find any example on how to run regressions with random effects (repeated measures) with OnlineStats.

I have opened a thread with an example and some benchmarks

> [@GLM is slow on large datasets. Using OnlineStats for regressions? MixedModels?](https://discourse.julialang.org/t/glm-is-slow-on-large-datasets-using-onlinestats-for-regressions-mixedmodels/17695):
>
> Hello. I’m planning to move from R to Julia and doing some tests about how to properly deal with large datasets and do simple tasks like regressions or survival analysis. I’ve done a benchmark with R (microbenchmark) for the regressions. N ← 3000 x1 ← rep(1:N, N) x2 ← rep(1:N, each = N) x3 ← sqrt(rep(1:N^2)) x1x2 ← x1x2 gg ← rep(1:5, each=N^2/5) y ← 1-2x1+3x2+0.5x1x2+rnorm(N^2)+x3\*rnorm(N^2) dat ← data.frame(y,x1,x2,x1x2,x3, gg) dat2 ← cbind(1,x1,x2,x1x2,x3,gg) lm(y ~ x1 + x2 + x1…

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