# Multinomial Logistic Regression Packages | Tips

**URL:** <https://discourse.julialang.org/t/multinomial-logistic-regression-packages-tips/72562>\
**Category:** New to Julia\
**Tags:** question\
**Created:** [December 4, 2021, 7:18am UTC](https://discourse.julialang.org/t/multinomial-logistic-regression-packages-tips/72562 "2021-12-04T07:18:30Z")\
**Posts on this page:** 2\
**Page:** 1

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**Author:** ![YummyPampers2](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/yummypampers2/32/27328_2.png) [@YummyPampers2](https://discourse.julialang.org/u/YummyPampers2)\
**Post date:** [December 4, 2021, 7:18am UTC](https://discourse.julialang.org/t/multinomial-logistic-regression-packages-tips/72562/1 "2021-12-04T07:18:30Z")

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Good Day Julians:

I have looked several places including ATI (Alan Turing Institute)  
to find a package since 2019 that help me perform a multinomial  
logistic regression on the following DF:

```julia
Scale = ["A", "B", "C", "E"]
DF = Dataframe(ID = 1:1:15, M1 = rand(0:5:100,15), M2 = rand(0:5:100,15),
                          M3 = rand(0:5:100,15), Out1 = rand(Scale,15), Out2 = rand(Scale,15))

```

What packages exist that could help me identify  
associations between the exploratory variables  
[“M1”, “M2”, “M3”] and the response variables  
[“Out1”, “Out2”]?

Also, pre-empting performance bottlenecks,  
what are the dimensional limits for any module  
you present? For example, if I had a DF sized  
(10,10) with two categorical response variables  
and float/int typed exploratory variables, might  
I have convergence issues after a certain  
number of iterations or dimensional size?

Thank you,

---

<div class="post-metadata">

**Author:** ![nilshg](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/nilshg/32/2283_2.png) [@nilshg](https://discourse.julialang.org/u/nilshg)\
**Post date:** [December 4, 2021, 8:18am UTC](https://discourse.julialang.org/t/multinomial-logistic-regression-packages-tips/72562/2 "2021-12-04T08:18:54Z")

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Econometrics.jl has multinomial logistic regression I believe.