Simon Couch
@simonpcouch
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he/him - writing statistical software @posit_pbc (née RStudio)🥑 on the other sites @ simonpcouch
Chicago, IL, U.S.A.
Joined September 2019
Some news: I've just open-sourced the draft of a book I'm working on about how #rstats tidymodels users can make their code run faster without sacrificing predictive performance!.
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RT @frankiethull: Released a cauldron of R code today 🎃👻. Introducing Kolmogorov-Arnold Networks for Time Series in R! . It's an R package….
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RT @jumping_uk: Monitoring Models in Production is essential to ensure accuracy, detect drift, and maintain fairness over time. For more, c….
jumpingrivers.com
Part 3 in our series of blogs on vetiver for MLOps. Having previously introduced the modelling and deployment steps of the MLOps workflow, we now consider the maintenance of a model in production....
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I'll be giving a free virtual talk on speeding up your .#rstats tidymodels code at R/Pharma 2024 this Tuesday and would love to see you there! I'll also be announcing a new project.👀. Register here:
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The #rstats tidymodels team is hard at work implementing postprocessing! There will be changes in almost all of our core packages as well as an entirely new package included in this set of releases. We value your opinion—let us know what you think:
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RT @rlbarter: 📢 Tomorrow is the official release of @bbiinnyyuu and my book, Veridical Data Science, with @mitpress. I'm so excited to for….
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RT @frankiethull: Updated the documentation for {maize} 🌽 . String kernels are the newest addition which were a bit tricky to plant 👨🏻🌾. A….
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RT @winston_chang: Announcing Shiny Assistant, an AI-powered tool that can help you build Shiny applications!.
shiny.posit.co
Shiny is a package that makes it easy to create interactive web apps using R and Python.
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Postprocessing is coming to tidymodels! We've got a set of changes for updating model predictions coming to the next version of our #rstats packages. Let us know what you think:
tidyverse.org
The tidymodels team has been hard at work on postprocessing, a set of features to adjust model predictions. The functionality includes a new package as well as changes across the framework.
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RT @frankiethull: {maize} works with {stacks} too! 🌽🥞 . {stacks} is part of the tidymodels ecosystem for combining many models into a new….
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RT @jimhester_: Today marks the 10 year anniversary of my first lintr commit (. Hard to believe it was 10 years ago….
github.com
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A new release of #rstats broom made it to CRAN yesterday! v1.0.7 includes changes to tidiers for objects from survival, boot, car, and (base) stats. Read more:
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Slides and #QuartoPub source code:
github.com
Source code and slides for "Fair machine learning" at Cascadia R Conf 2024 - simonpcouch/cascadia-24
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The tidymodels team recently released a set of features for assessing model fairness. I got to drop by the Cascadia #rstats Conf this summer to share about our process in putting that toolkit together, and a recording of the talk is now live!.
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RT @dvaughan32: cpp11 0.5.0 is on CRAN 🥳 . The main thing to note is that we've removed all non-API R calls, so if you use cpp11 you should….
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RT @brodriguesco: Let me take this opportunity to shill the infer #RStats package. read the vignette which explain….
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RT @frankiethull: This R repo wouldn't be possible without {modeltime} and {tidymodels} 🙌🏻 . It shows how easy conformal prediction is in R….
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RT @StanfordAILab: arXiv -> alphaXiv. Students at Stanford have built alphaXiv, an open discussion forum for arXiv papers. @askalphaxiv. Yo….
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