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  1. @njtierney @kellybodwin @PipingHotData Having added tests, @maelle looked at the source and suggested using `withr::local_seed()` (as opposed to `set.seed()`). A good reason why it’s worth following package good-practice, even for silly packages, you get to learn about package better-practice!

    Also, this was very much the theme of @fontikar’s talk at userconf2024.sched.com/event/1

  2. Postscript: At the dev day (the day after the main conf), organised by @R_Contributors, @kellybodwin and I submitted a patch to fix the alignment bug in the stem() plot, which I discussed in the talk. It was a really fun day, and a nice result! Huge thanks to Paul Murrell and Martin Maechler from R Core for their friendly and helpful support.

  3. useR! 2024, the global R user conference, will be taking place in Salzburg, Austria (as well as virtually) in July 2024. We have a full lineup of giants in the field of data science. Thank you, Dr.,
    @kellybodwin for being a part of the conference!
    Kelly Bodwin is an Associate Professor of Statistics and Data Science at Cal Poly in San Luis Obispo, CA.
    events.linuxfoundation.org/use

    #rstats #rlanguage #coding #statistics #data #analytics #rprogramming #data #opensourcesoftware
    #rstats

  4. Slides from my talk today on designing an Intermediate course:

    kbodwin.github.io/Talks-and-Presentations/SDSS_2025/Intermediate_R.html

    (Skip to the end if you want to get on a mailing list for sharing the materials we're making!)

    Thank you to all who attended an early Friday talk and asked great questions! 🤩

  5. My Keynote talk "Keep R Weird" from is now on YouTube!

    youtu.be/KOQBfC1WPwM?si=zUR7uo

    Thank you so much, @useR_conf for having me - it was truly a special conference and a joy to learn new things about alongside my fellow weiRdos.

  6. ☑️ in Richmond
    ☑️ in Austria
    ☑️ Get married
    🔲 in Portland (on my way!)
    🔲 in Seattle (woo!)

    Hype level: High
    Exhaustion level: Also high

    Let's do this thing.

  7. ok how did I not know until now that you can add se.fit = TRUE to the predict() function to get errors?

    and of course, I now see there is a std_error option and several others in the version

    what do these do for nonparametric models, I wonder?

    No matter how much I think I know, there is always so much more to learn... 🤓