Vincent Arel-Bundock
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vincentab.bsky.social
Vincent Arel-Bundock
@vincentab.bsky.social
6.2K followers 1.4K following 950 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
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 · 8d
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
Reposted by Vincent Arel-Bundock
s3alfisc.bsky.social
Our py-econometrics org has a new package: `maketables` is now installable from PyPi! You can create regression tables for PyFixest, statsmodels, linearmodels, and save them as html, latex, or word.

github: github.com/py-econometr...
docs: py-econometrics.github.io/maketables/
November 20, 2025 at 7:49 PM
Reposted by Vincent Arel-Bundock
joshmccrain.bsky.social
new paper by Sean Westwood:

With current technology, it is impossible to tell whether survey respondents are real or bots. Among other things, makes it easy for bad actors to manipulate outcomes. No good news here for the future of online-based survey research
November 18, 2025 at 7:16 PM
Reposted by Vincent Arel-Bundock
s3alfisc.bsky.social
I am very happy that 𝗣𝘆𝗙𝗶𝘅𝗲𝘀𝘁 𝟬.𝟰𝟬.𝟬/𝟬.𝟰𝟬.𝟭 is now available on PyPi! The release includes a few technical changes and exciting new features =)
LinkedIn
This link will take you to a page that’s not on LinkedIn
lnkd.in
November 13, 2025 at 6:37 PM
Reposted by Vincent Arel-Bundock
johnholbein1.bsky.social
Look at the distribution of z-values from medical research!
November 4, 2025 at 10:36 PM
vincentab.bsky.social
What are the chances of winning by a single vote in an election with 11K voters?

It happened last night in our local municipal election!
November 3, 2025 at 7:03 PM
Reposted by Vincent Arel-Bundock
rmkubinec.bsky.social
I just ordered @vincentab.bsky.social 's epic new book Model to Meaning.

This project is one of the most exciting recent developments in data science. The book & #rstats / #python package show how to understand your model *within* its real-world context/use case.

www.routledge.com/Model-to-Mea...
Model to Meaning: How to Interpret Statistical Models with R and Python
Our world is complex. To make sense of it, data analysts routinely fit sophisticated statistical or machine learning models. Interpreting the results produced by such models can be challenging, and re...
www.routledge.com
October 31, 2025 at 1:19 PM