Vincent Arel-Bundock
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vincentab.bsky.social
Vincent Arel-Bundock
@vincentab.bsky.social
6.2K followers 1.4K following 1K posts
Prof. Most tweets about R. “Polisci, it’s all about what’s going on.”

http://arelbundock.com
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vincentab.bsky.social
Vincent Arel-Bundock @vincentab.bsky.social · Sep 17
Whoa—my book is up for pre-order!

𝐌𝐨𝐝𝐞𝐥 𝐭𝐨 𝐌𝐞𝐚𝐧𝐢𝐧𝐠: 𝐇𝐨𝐰 𝐭𝐨 𝐈𝐧𝐭𝐞𝐫𝐩𝐫𝐞𝐭 𝐒𝐭𝐚𝐭 & 𝐌𝐋 𝐌𝐨𝐝𝐞𝐥𝐬 𝐢𝐧 #Rstats 𝐚𝐧𝐝 #PyData

The book presents an ultra-simple and powerful workflow to make sense of ± any model you fit

The web version will stay free forever and my proceeds go to charity.

tinyurl.com/4fk56fc8
Reposted by Vincent Arel-Bundock
yiqingxu.bsky.social
1/ Sorry for double-posting from X. Sharing a new working paper for the Year of the Horce 🐎:

"An AI-assisted workflow that scales reproducibility in empirical research" (bit.ly/repro-ai) w/ Leo Yang Yang
February 18, 2026 at 7:22 PM
vincentab.bsky.social
I wrote a #RStats wrapper around the Hydra config management system. It's a super powerful and convenient tool to organize and use configs like keys, tuning parameters, paths, etc.

I'd love it if you could tell me what works well and what doesn't. Thanks!

vincentarelbundock.github.io/hydraR
February 18, 2026 at 7:36 PM
vincentab.bsky.social
Noah is an *excellent* R package developer. This looks very interesting.
February 18, 2026 at 6:18 PM
Reposted by Vincent Arel-Bundock
andrew.heiss.phd
This is the most current one! I'm on team {tinytable} for pretty much everything nowadays
Week 5 FAQs – Data Visualization with R
Use R, ggplot2, and the principles of graphic design to create beautiful and truthful visualizations of data
datavizf25.classes.andrewheiss.com
February 16, 2026 at 11:56 PM
vincentab.bsky.social
You can now use LLMs to learn about any chapter of my 𝑀𝑜𝑑𝑒𝑙 𝑡𝑜 𝑀𝑒𝑎𝑛𝑖𝑛𝑔 book, or any function in the `marginaleffects` package for R or Python.

Check out the cool demo video below (sound on).

Install here: github.com/vincentarelb...

#marginaleffects #RStats #pydata
February 13, 2026 at 3:52 PM
vincentab.bsky.social
"Model to Meaning" just received a super generous review in JASA.

Check it out! (I'm blushing 😊)

Reminder: You can get the paper copy from CRC, and the full **free** version will remain online forever at marginaleffects.com

www.tandfonline.com/doi/full/10....
Model to Meaning: How to Interpret Statistical Models with R and Python
Published in Journal of the American Statistical Association (Just accepted, 2026)
www.tandfonline.com
February 12, 2026 at 4:25 PM
Reposted by Vincent Arel-Bundock
ryancbriggs.net
We actually show in the model that because our best estimates of statistical power are fairly low, even if literally 100% of our hypotheses are in fact correct we still must have extreme selection against nulls
tngrine.bsky.social Tangerine @tngrine.bsky.social · 6d
Everyone’s hypotheses are sooooo good
February 12, 2026 at 1:42 PM
vincentab.bsky.social
Check out the new paper! And please please please send us both gut reactions and harsh criticism.
ryancbriggs.net Ryan Briggs @ryancbriggs.net · 7d
I have a new paper. We look at ~all stats articles in political science post-2010 & show that 94% have abstracts that claim to reject a null. Only 2% present only null results. This is hard to explain unless the research process has a filter that only lets rejections through.
It must be very hard to publish null results Publication practices in the social sciences act as a filter that favors statistically significant results over null findings. While the problem of selection on significance (SoS) is well-known in theory, it has been difficult to measure its scope empirically, and it has been challenging to determine how selection varies across contexts. In this article, we use large language models to extract granular and validated data on about 100,000 articles published in over 150 political science journals from 2010 to 2024. We show that fewer than 2% of articles that rely on statistical methods report null-only findings in their abstracts, while over 90% of papers highlight significant results. To put these findings in perspective, we develop and calibrate a simple model of publication bias. Across a range of plausible assumptions, we find that statistically significant results are estimated to be one to two orders of magnitude more likely to enter the published record than null results. Leveraging metadata extracted from individual articles, we show that the pattern of strong SoS holds across subfields, journals, methods, and time periods. However, a few factors such as pre-registration and randomized experiments correlate with greater acceptance of null results. We conclude by discussing implications for the field and the potential of our new dataset for investigating other questions about political science.
February 11, 2026 at 5:10 PM
Reposted by Vincent Arel-Bundock
dingdingpeng.the100.ci
New preprint! So, what's a multiverse analysis good for anyway?>

With @jessicahullman.bsky.social and @statmodeling.bsky.social

juliarohrer.com/wp-content/u...
What’s a multiverse good for anyway? Julia M. Rohrer, Jessica Hullman, and Andrew Gelman Multiverse analysis has become a fairly popular approach, as indicated by the present special issue on the matter. Here, we take one step back and ask why one would conduct a multiverse analysis in the first place. We discuss various ways in which a multiverse may be employed – as a tool for reflection and critique, as a persuasive tool, as a serious inferential tool – as well as potential problems that arise depending on the specific purpose. For example, it fails as a persuasive tool when researchers disagree about which variations should be included in the analysis, and it fails as a serious inferential tool when the included analyses do not target a coherent estimand. Then, we take yet another step back and ask what the multiverse discourse has been good for and whether any broader lessons can be drawn. Ultimately, we conclude that the multiverse does remain a valuable tool; however, we urge against taking it too seriously.
February 4, 2026 at 10:24 AM
Reposted by Vincent Arel-Bundock
gsimpson.bsky.social
🚨 GAMs have moved on—so it’s time for an update.

On March 3, 2026 (17:00–19:00 CET) I’ll be livestreaming an updated introduction to Generalized Additive Models in R

📺 YouTube livestream link: youtube.com/live/A9U8e1K...

#RStats #mgcv #GAMs #gratia #statistics 🧪
What's new in the world of Generalized Additive Models
YouTube video by Bottom of the Heap
youtube.com
February 3, 2026 at 12:57 PM
Reposted by Vincent Arel-Bundock
paulgp.com
One of the most important statistical packages made in Econ in the last decade
paperposterbot.bsky.social ArXiv Paperboy (Stat.ME+Econ.EM) @paperposterbot.bsky.social · 19d
arXiv📈🤖
Fast and user-friendly econometrics estimations: The R package fixest
By Berg\'e, Butts, McDermott
fixest is an R package for fast and flexible econometric estimation, providing a comprehensive toolkit for applied researchers. The package particularly excels at fixed-effects estimation, supported by a novel fixed-point acceleration algorithm implemented in C++. This algorithm achieves rapid convergence across a broad class of data contexts and further enables estimation of complex models, including those with varying slopes, in a highly efficient manner. Beyond computational speed, fixest provides a unified syntax for a wide variety of models: ordinary least squares, instrumental variables, generalized linear models, maximum likelihood, and difference-in-differences estimators. An expressive formula interface enables multiple estimations, stepwise regressions, and variable interpolation in a single call, while users can make on-the-fly inference adjustments using a variety of built-in robust standard errors. Finally, fixest provides methods for publication-ready regression tables and coefficient plots. Benchmarks against leading alternatives in R, Python, and Julia demonstrate best-in-class performance, and the paper includes many worked examples illustrating the core functionality.
January 30, 2026 at 5:03 PM
Reposted by Vincent Arel-Bundock
gmcd.bsky.social
Things are grim. But in more frivolous news...

@jamesbrandecon.bsky.social and I have been chipping away at `dbreg`, a 📦 for running big regression models on database backends. For the right kinds of problems, the speed-ups are near magical.

Website: grantmcdermott.com/dbreg/

#rstats

[1/2]
dbreg
grantmcdermott.com
January 26, 2026 at 4:57 PM
Reposted by Vincent Arel-Bundock
mattansb.msbstats.info
Finished teaching my new Advance Stats for Psych graduate course today with a heavy emphasis on both DAGs and shifting away from coefficient interpretation and towards models as prediction machines.

Both went great!

The latter was extremely helpful for logistic regression (for obvious reasons 😵‍💫)!
dingdingpeng.the100.ci Julia M. Rohrer @dingdingpeng.the100.ci · Aug 25
Ever stared at a table of regression coefficients & wondered what you're doing with your life?

Very excited to share this gentle introduction to another way of making sense of statistical models (w @vincentab.bsky.social)
Preprint: doi.org/10.31234/osf...
Website: j-rohrer.github.io/marginal-psy...
Models as Prediction Machines: How to Convert Confusing Coefficients into Clear Quantities Abstract Psychological researchers usually make sense of regression models by interpreting coefficient estimates directly. This works well enough for simple linear models, but is more challenging for more complex models with, for example, categorical variables, interactions, non-linearities, and hierarchical structures. Here, we introduce an alternative approach to making sense of statistical models. The central idea is to abstract away from the mechanics of estimation, and to treat models as “counterfactual prediction machines,” which are subsequently queried to estimate quantities and conduct tests that matter substantively. This workflow is model-agnostic; it can be applied in a consistent fashion to draw causal or descriptive inference from a wide range of models. We illustrate how to implement this workflow with the marginaleffects package, which supports over 100 different classes of models in R and Python, and present two worked examples. These examples show how the workflow can be applied across designs (e.g., observational study, randomized experiment) to answer different research questions (e.g., associations, causal effects, effect heterogeneity) while facing various challenges (e.g., controlling for confounders in a flexible manner, modelling ordinal outcomes, and interpreting non-linear models). Figure illustrating model predictions. On the X-axis the predictor, annual gross income in Euro. On the Y-axis the outcome, predicted life satisfaction. A solid line marks the curve of predictions on which individual data points are marked as model-implied outcomes at incomes of interest. Comparing two such predictions gives us a comparison. We can also fit a tangent to the line of predictions, which illustrates the slope at any given point of the curve. A figure illustrating various ways to include age as a predictor in a model. On the x-axis age (predictor), on the y-axis the outcome (model-implied importance of friends, including confidence intervals). Illustrated are 1. age as a categorical predictor, resultings in the predictions bouncing around a lot with wide confidence intervals 2. age as a linear predictor, which forces a straight line through the data points that has a very tight confidence band and 3. age splines, which lies somewhere in between as it smoothly follows the data but has more uncertainty than the straight line.
January 19, 2026 at 6:28 PM
vincentab.bsky.social
My poster got allocated prime real estate at the conference: High traffic, captive audience.
January 6, 2026 at 1:19 PM
Reposted by Vincent Arel-Bundock
s3alfisc.bsky.social
There is a new-ish Python package, moderndid, that implements almost all of the "modern" DiD estimators:
github.com/jordandekler...
GitHub - jordandeklerk/moderndid: Python package implementing modern DiD estimators with diagnostic tools and sensitivity analysis.
Python package implementing modern DiD estimators with diagnostic tools and sensitivity analysis. - jordandeklerk/moderndid
github.com
January 3, 2026 at 10:40 AM
Reposted by Vincent Arel-Bundock
michaelchirico.bsky.social
Look under your tree! 🌲 🎁

There's a major #rstats #rdatatable release waiting!

A tremendous thanks to all involved and especially those contributing to some major (performance-maintaining!!) rewrites around the non-API issue.

cran.r-project.org/web/packages...
December 24, 2025 at 7:00 PM
Reposted by Vincent Arel-Bundock
avehtari.bsky.social
We're doing the final sprint, and I think we're able to send the PDF of the forthcoming Bayesian Workflow book to the publisher in the next two weeks (500+ pages), which would mean it would be published some time next year
December 16, 2025 at 5:21 PM
Reposted by Vincent Arel-Bundock
scientificdiscovery.dev
Big new blogpost!

My guide to data visualization, which includes a very long table of contents, tons of charts, and more.

--> Why data visualization matters and how to make charts more effective, clear, transparent, and sometimes, beautiful.
www.scientificdiscovery.dev/p/salonis-gu...
screenshot of my post
December 9, 2025 at 8:28 PM
Reposted by Vincent Arel-Bundock
lrberge.bsky.social
Any good ressource for LEARNING PROGRAMMING for students who know NOTHING about programming (really nothing)?

It would be great in R/python (final obj. is data analysis).

Any help appreciated! Thanks!

@andrew.heiss.phd @gmcd.bsky.social @kylefbutts.bsky.social @vincentab.bsky.social
December 10, 2025 at 10:08 AM
Reposted by Vincent Arel-Bundock
jmorenocruz.bsky.social
It's happening! Canada launched two programs to recruit international researchers.

Canada Impact+ Research Chairs (1 million/yr for 8 yrs +)
Canada Impact+ Emerging Leaders.

I will do my best to facilitate the process for those interested. Hit me up.

www.canada.ca/en/impact-pl...
The Government of Canada introduces new programs for international researchers - Canada.ca
www.canada.ca
December 9, 2025 at 6:06 PM
Reposted by Vincent Arel-Bundock
jpbrammer.bsky.social
sorry I'm late! I have at last broken my silence on Pantone's Color of the Year johnpaulbrammer.substack.com/p/cloud-danc...
'Cloud Dancer': A Measured Response
Regarding Pantone's decision, and so on, and so forth
johnpaulbrammer.substack.com
December 6, 2025 at 7:55 PM
Reposted by Vincent Arel-Bundock
datavisfriendly.bsky.social
#rstats
It is with profound sadness I heard that my long-time friend and colleague, John Fox passed away this week.
He was the author of {car}, {effects}, {Rcmdr}, ... and numerous influential books. I will miss him greatly.
www.john-fox.ca
John Fox: Books and Software
www.john-fox.ca
November 28, 2025 at 3:26 PM
Reposted by Vincent Arel-Bundock
nrennie.bsky.social
One of the things that has been on my to do list for a very long time, is building a gallery of all of the charts I've made across #TidyTuesday, #30DayChartChallenge, #30DayMapChallenge, and other miscellaneous projects 📊

And it's finally here!

Link: nrennie.rbind.io/viz-gallery/

#DataViz #RStats
Data Visualisation Gallery
Gallery of data visualisations created by Nicola Rennie.
nrennie.rbind.io
November 26, 2025 at 1:33 PM
Reposted by Vincent Arel-Bundock
metahag.bsky.social
Coding tip of the day: make sure you leave informative comments in your code
library (tidybayes) # for wrangling and plotting brms models Library (bayesplot) # for plotting brms models library (marginaleffects) #for marginal effects library (ggdist) library (patchwork)
November 27, 2025 at 4:02 PM
Reposted by Vincent Arel-Bundock
etiennebacher.bsky.social
#rstats Please welcome Jarl, a new R linter.

Jarl is a CLI tool with extensions in VS Code, Positron, and Zed. It can check thousands of lines of R code in milliseconds.

Jarl provides several output formats, a Github Actions workflow, and more.

Blog post: www.etiennebacher.com/posts/2025-1...
November 20, 2025 at 4:50 PM