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! https://t.co/jizXn03Nf6
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Released a cauldron of R code today 🎃👻 Introducing Kolmogorov-Arnold Networks for Time Series in R! It's an R package called {kantime}, a super experimental port of Nixtla's {neuralforecast} from Python to {modeltime} in R
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Monitoring Models in Production is essential to ensure accuracy, detect drift, and maintain fairness over time. For more, check out our recent blog by Myles Mitchell on "Vetiver: Monitoring Models in Production". #DataScience #MLOps #Rstats
https://t.co/lrS85CqgEV
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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This is tomorrow! #rstats folks, come through!
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: https://t.co/hNec3kzjZH
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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: https://t.co/hNec3kzjZH
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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: https://t.co/ASbr3gFWWt
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📢 Tomorrow is the official release of @bbiinnyyuu and my book, Veridical Data Science, with @mitpress. I'm so excited to for physical copies to be out in the world! Get your copy from https://t.co/KSYIq7jn3T
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Updated the documentation for {maize} 🌽 String kernels are the newest addition which were a bit tricky to plant 👨🏻🌾 And I'm liking how this table turned out, thanks to @gt_package & gtUtils 🙌🏻
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Announcing Shiny Assistant, an AI-powered tool that can help you build Shiny applications! https://t.co/5sJ1EHG2QH
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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{maize} works with {stacks} too! 🌽🥞 {stacks} is part of the tidymodels ecosystem for combining many models into a new ensemble model Here's an ensemble example of three SVMs with different types of kernels 🌽🌽🌽
The dopest parts about {maize}? It jives well with the tidymodels ecosystem! Here, I'm answering a typical question keeping everyone up at night, "what does conformal inference quantile regression prediction intervals look like for a laplacian kernel support vector machine" 🤓
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Today marks the 10 year anniversary of my first lintr commit ( https://t.co/S5jBj13V4a) Hard to believe it was 10 years ago Apparently originally I called it styleR, long before the current styler package came into being 😄
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! https://t.co/GVqQbpB2x6
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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 no longer get any NOTEs when submitting to CRAN! #rstats
https://t.co/lEHWXlmnqs
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Let me take this opportunity to shill the infer #RStats package https://t.co/JT1jCErgQs read the vignette which explains why charts like the one below can be ignored nowadays (we have very good computers these days)
How I pick the right statistical test for my experimental variables
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This R repo wouldn't be possible without {modeltime} and {tidymodels} 🙌🏻 It shows how easy conformal prediction is in R A nice read if you already have @predict_addict's book and Tidy Modeling with R by @topepos & @juliasilge
R fans of conformal prediction, there are some repos translating code for my book into R (courtesy of @frankiethull) I am also going to post a few libraries in R in comments. #confoRmalprediction
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arXiv -> alphaXiv Students at Stanford have built alphaXiv, an open discussion forum for arXiv papers. @askalphaxiv You can post questions and comments directly on top of any arXiv paper by changing arXiv to alphaXiv in any URL!
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