#julialang — Public Fediverse posts
Live and recent posts from across the Fediverse tagged #julialang, aggregated by home.social.
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Nerding out at #SDSL2026 today
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https://DSLC.io welcomes you to week 37 of #TidyTuesday! We're exploring Dead Sea Scrolls Manuscripts!
📂 https://tidytues.day/2026/2026-09-15
📰 https://www.deadseascrolls.org.il/Submit a dataset! https://github.com/rfordatascience/tidytuesday/blob/main/.github/CONTRIBUTING.md
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Hello Everyone! I have been experimenting with using #Quarto to call #julialang to run #multilevel models with #MixedModels. Unfortunately, my document is taking about 10-15 minutes to render with small data sets. I've found it difficult to understand the #julialang documentation on this issue, so would appreciate any "Explain to me like I'm 5" explanations of how to speed up #julialang. Code is here: https://agrogan1.github.io/multilevel-multilingual/. I am grateful for #julialang, just wish I could figure out the speed.
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Hello Everyone! I have been experimenting with using #Quarto to call #julialang to run #multilevel models with #MixedModels. Unfortunately, my document is taking about 10-15 minutes to render with small data sets. I've found it difficult to understand the #julialang documentation on this issue, so would appreciate any "Explain to me like I'm 5" explanations of how to speed up #julialang. Code is here: https://agrogan1.github.io/multilevel-multilingual/. I am grateful for #julialang, just wish I could figure out the speed.
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Hello Everyone! I have been experimenting with using #Quarto to call #julialang to run #multilevel models with #MixedModels. Unfortunately, my document is taking about 10-15 minutes to render with small data sets. I've found it difficult to understand the #julialang documentation on this issue, so would appreciate any "Explain to me like I'm 5" explanations of how to speed up #julialang. Code is here: https://agrogan1.github.io/multilevel-multilingual/. I am grateful for #julialang, just wish I could figure out the speed.
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Hello Everyone! I have been experimenting with using #Quarto to call #julialang to run #multilevel models with #MixedModels. Unfortunately, my document is taking about 10-15 minutes to render with small data sets. I've found it difficult to understand the #julialang documentation on this issue, so would appreciate any "Explain to me like I'm 5" explanations of how to speed up #julialang. Code is here: https://agrogan1.github.io/multilevel-multilingual/. I am grateful for #julialang, just wish I could figure out the speed.
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Julia's terse #NamedTuple syntax with a semi-colon is ... cryptic on first exposure. And to me, not documented where I expected it to be, thus https://github.com/JuliaLang/julia/issues/53490 #JuliaLang
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Nice to see Ole presenting Javis.jl online soon!
November 6
https://www.meetup.com/julia-user-group-munich/events/296834151
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#MakieCon starts today, follow the event here: https://zoom.us/j/94184124746?pwd=Wm1RQ2xXT0pROVNYVkwxWmsrTU5MZz09 and updates at https://www.bgc-jena.mpg.de/5632072/makiecon #julialang @makie
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Next week, starting Tuesday 18th, the Makie conference, MakieCon.
https://www.youtube.com/watch?v=anpmViaVEtU
More details here:
https://www.bgc-jena.mpg.de/5632072/makiecon.html
#makie #makiecon #makiecon2023 #julia #julialang #julialanguage
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"Pluto making scientific computing accessible and fun"
The Inspiring Computing podcast (at https://www.inspiringcomputing.com) this week is about Pluto notebooks, and we hear from Fons and Gerhard about the history and philosophy of Pluto and a sneak preview of what's to come in the future.
#julia #julialang #julialanguage #inspiringcomputing #pluto #plutojl #plutonian #podcast
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Ok, went down a slight rabbit hole... You can use #nftables to log #packets to the nflog facility. #tcpdump or #tshark can read from the nflog queue and report info on the packets. Nftables can sample the packets randomly using numgen random mod 1000 < 50 I can sample 5% of packets (or whatever) if the output is easily readable by #Julialang I can turn the network visibility issue into a data analysis issue. What's the best way to read the output? tshark json?
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If you want to control for the "repeat" in a repeated measures design using LMMs - you have to model that random slope!
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y ~ 1 + cond + (1|subject)
does *not* control for within condition effects (except if you have only 1 trial per level per subject)
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If this sounds relevant to you, I could prepare a blog-post + interactive demo
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If you want to control for the "repeat" in a repeated measures design using LMMs - you have to model that random slope!
---
y ~ 1 + cond + (1|subject)
does *not* control for within condition effects (except if you have only 1 trial per level per subject)
---
If this sounds relevant to you, I could prepare a blog-post + interactive demo
-
If you want to control for the "repeat" in a repeated measures design using LMMs - you have to model that random slope!
---
y ~ 1 + cond + (1|subject)
does *not* control for within condition effects (except if you have only 1 trial per level per subject)
---
If this sounds relevant to you, I could prepare a blog-post + interactive demo
-
If you want to control for the "repeat" in a repeated measures design using LMMs - you have to model that random slope!
---
y ~ 1 + cond + (1|subject)
does *not* control for within condition effects (except if you have only 1 trial per level per subject)
---
If this sounds relevant to you, I could prepare a blog-post + interactive demo
-
If you want to control for the "repeat" in a repeated measures design using LMMs - you have to model that random slope!
---
y ~ 1 + cond + (1|subject)
does *not* control for within condition effects (except if you have only 1 trial per level per subject)
---
If this sounds relevant to you, I could prepare a blog-post + interactive demo
