The {tidylog} #rstats 📦 generates in-line additional data about your dplyr and tidyr operations. "It provides simple wrapper functions for almost all dplyr and tidyr functions, such as filter, mutate, select, full_join, and group_by." By Benjamin Elbers.
(1/2) Shiny Apps for demystifying statistical models and methods 🚀
This is a cool website that explains different statistical concepts with the use of interactive Shiny Apps. Ben Prytherch made this website from the Department of Statistics at Colorado State University.
(2/2) It covers the following topics:
✅ Factorial ANOVA
✅ Mixed effect ANOVA
✅ Mixed effect with random slopes
✅ Logistic regression
✅ ANCOVA
✅ One-way ANOVA
✅ Odds ratio vs relative risk
✅ Correlation coefficient vs slope
✅ Sampling
The {geotargets} extends the {targets} package to work with geospatial data formats. This release provides support for {terra} formats.
It simple would not have been possible to have this package without Eric Scott @LeafyEricScott and Andrew Brown @humus_rocks - it's been a really fun project to work on together, looking forward to future iterations!
The {mrup} #rstats 📦 is an “RStudio addin for searching for all local projects and editing the most recently used project list.
“Easily open any previous project. Add projects to, or remove projects from, the recent project drop-down menu.” By John Wilson, teaching fellow at Edinburgh University https://github.com/jmcvw/mrup #RStudio@rstats
If you are somewhere between disillusioned & enraged with the direction that StackOverflow has taken, but like me you still periodically have the “answer programming questions on the internet" itch, here's a small suggestion that I’ve found rewarding.
Follow the GitHub issues of an open source project you use a lot. I 💯 guarantee you that they periodically get issues that are just confused users asking for help, or “bug reports” that are just a simple misunderstanding of the tool…
When writing guides and documentation, I always try to write what I would have liked to have read, so here is a guide on how to contribute to #Nix from the perspective of an #RStats user:
📣 I launched my first newsletter! It's free and I'll be using it to send a little email note any time I publish a blog post, publication, talk, or project on my #quarto site.
(1/4) TIL about the plotnine library- the grammar of graphics in Python 🚀
I had never heard about the Plotnine library until I came across the Posit Plotnine contest (see the link below). The plotnine is a Python implementation of a grammar of graphics based on the ggplot2 library.
For #API client packages, I want to make sure args are in the form the API expects. Other packages ({checkmate}, {vctrs}) didn't QUITE do what I want, so I https://xkcd.com/927/ 'ed it and made my own!
There's still a lot missing, and I'll likely tweak the errors to make things clearer, but it was done-enough to put it into the world. Enjoy!
For model calibration (esp via logistic regression), does anyone know of a statistical investigation of the properties of the resulting calibrated predictions?
IOW, if we use predictions from one model as inputs to another model, do we know the probability distribution of the final predictions?