Quarto 2 is coming, and it’s a total rewrite in Rust. 🦀
The headline feature? Native collaborative editing. Don't choose between Google Docs ease and Git rigor! You get real-time, conflict-free collab directly in your .qmd files. #RStats #Python
Coming soon! ✨ opensource.posit.co/blog/2026-04...
How does Posit Assistant compare to Claude Code for data analysis in #RStats?
We put them side-by-side to show how the experience differs on three dimensions:
💻 R session access
📊 Plotting
🔍 Iterative EDA
Watch the full comparison here! youtu.be/7GI6-4J0AXA
The latest Glimpse newsletter is packed with open source #RStats and #Python updates from Posit!
Quarto 1.9, introducing Great Docs, orbital 0.5.0, devtools uses pak, and more.
Read it here: posit.co/blog/glimpse...
fixest is an R package for fast and flexible econometric estimation. It provides a unified framework for applied research, with comprehensive support for a diverse class of models: ordinary least squares, instrumental variables, generalized linear models, maximum likelihood, and difference-in-differences. 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 variety of data contexts and enables efficient estimation of complex models, including those with varying slopes. An expressive formula interface facilitates multiple estimations, stepwise regressions, and variable interpolation in a single call. Users can adjust inference strategies on the fly, choosing from an array of built-in robust standard errors. The package also provides methods for publication-ready regression tables and coefficient plots. Benchmarks demonstrate that fixest offers best-in-class performance against leading alternatives in R, PYTHON, and JULIA.
arXiv📈🤖
fixest: A fast and feature-rich framework for econometric estimations in R
By Berg\'e, Butts, McDermott
And another Quarto announcement; I've alluded to it before, but we're making it "official".
We've started work on Quarto 2. The blog post has an overview: quarto.org/docs/blog/po...
We'll share more in future blog posts, but here's what you can expect from the Quarto 2 dev effort:
(1/)
India's Muslim and Scheduled Caste neighborhoods face systematically worse access to schools, clinics, and basic infrastructure. Asher et al. use new administrative data to document the extent of neighborhood segregation, currently invisible in the aggregate data policymakers use. bit.ly/4saLvJH
Lecturing on discrimination this week and this study will be one discussion point: ideas.repec.org/p/feb/natura...
Big data show that minority drivers are 24–33% more likely to be cited and paid 23–34% higher fines, not explained by differences in offense rates, accident risk, or re-offense rates.
Metapackage that brings together a collection of R packages providing access to APIs functions and curated datasets from Argentina, Brazil, Chile, Colombia, and Peru. While also offering extensive curated collections of open datasets #r #latinamerica #datascience #surveys #API
The 𝙇𝙤𝙣𝙙𝙤𝙣 𝙎𝙚𝙢𝙞𝙣𝙖𝙧 𝙎𝙚𝙧𝙞𝙚𝙨 at NU London begins with Maria Giulia Preti (École Polytechnique Fédérale de Lausanne). Her talk explores how MRI and connectome harmonics reveal brain structure–function links.
Join us in #London (please register) or online #AOE—we’d love to see you!
tinyurl.com/4nt43jaa
For the #spatial folks among you: we exploited random distances between residential locations and large firms within German local labor markets to identify the causal effect of #dropout due to attractive alternatives 🚙💸⬆️
Woo (and I say this as a respectful Belgian) Hoo…
🏙️ Our upcoming #NetSI talk explores urban resilience: why some neighborhoods recover while others struggle.
Prof. 𝗕𝗼𝗴𝗮𝗻𝗴 𝗝𝘂𝗻 examines how economic complexity and amenity networks shape business survival, mobility, and consumption during COVID-19 and extreme heat in Seoul.
🔗 tinyurl.com/238j9adr
I don't know if I agree with this yet but it's easy to see the appeal #linklog
Latin America in one picture...
Is there a student discount?
A dark teal-toned aerial photograph of a coastline with dense forest, overlaid with semi-transparent R code snippets. In the foreground, the Posit AI logo — a white diamond with an inner diamond shape — appears above the text "We don't believe in magic buttons. We believe in code."
We don’t believe in magic buttons. We believe in code.
Posit AI is embedded in RStudio — it sees your session, shows its work, and produces reproducible code.
posit.co/products/ai/...
#RStudio #PositAI
Karmelo Iribarren:
fuck, I’m sorry, I can’t compete with this
We have developed and tested a spatial scan statistic for categorical, functional data (CFSS) - a data structure within which current approaches cannot identify spatial clusters. Our methodology combines an encoding scheme for categorical, functional observations with a nonparametric scan statistic. In a simulation study with three distinct scenarios, the CFSS accurately recovered the simulated spatial clusters and gave very low false positive rates, high true positive rates, and high positive predictive values. We have also used the CFSS to identify and characterize spatial clusters in French air pollution data from the winter of 2024.
arXiv📈🤖
A spatial scan statistical for categorical, functional data
By Fr\'event, Sarr, Dabo-Niang
I find this article cacm.acm.org/opinion/the-... a far more compelling and interesting discussion of the role of agents in the broader economy.
Written by very many smart economists at Microsoft research
Timely contribution! Focus on time-dynamic boundaries is the frontier in segregation/neighbourhood studies.
Example for the two staged unsupervised machine learning algorithm using point data as input. Backlayer maps depict Hamburg. The map shows neighborhoods in different sizes and forms, sometimes following administrative borders (black lines) sometimes not. Three differently colored neighborhood types are displayed, each representing a different social group of residents.
xample for the two staged unsupervised machine learning algorithm using 500x500m grid cells as input. Backlayer maps depict Hamburg. The map shows large neighborhoods in different sizes and forms, sometimes following administrative borders (black lines) sometimes not. Three differently colored neighborhood types are displayed, each representing a different social group of residents.
Looking for a measure of #neighborhoods, micro or macro #segregation?
I've got something for you!
My newly published paper in Sociological Methods & Research presents a machine-learning-based algorithm to delineate neighborhoods with grid-cell or point data:
journals.sagepub.com/doi/10.1177/...
Wouldn’t using a class S7 object be more efficient and less error prone?
Compositional data (proportions that sum to 1) behave in ways standard models aren’t built for
I walk through why Dirichlet regression is often the right tool & what extra insight it gives using a real ex of eyetracking
#Dirichlet #r #brms #guide #eyetracking
open.substack.com/pub/mzlotean...
New blog post about the age-period-cohort identification problem!
In which, for the first time ever, I ask "What's the mechanism?" and also suggest that sometimes you may actually *not* be interested in causal inference.
www.the100.ci/2026/02/13/o...
Results for CEM and neighborhood fixed effects regressions. We find robust and significant positive effects (blue coefficients) of childhood exposure to different ethnicities on the likelihood of interethnic marriage.
Left: georeferenced households in 1880. Right: Ethnic organic neighborhoods in Manhattan in 1880. Six ethnic groups are prevalent: first/second generation Americans, Asians, Germans, Irish and Others (residual category). We use these neighborhoods to account for segregation by applying organic neighborhood fixed effects.
New preprint out with @wendering.bsky.social & Nan Zhang!
We show that childhood exposure to ethnic outgroups increases the prob. of #interethnic marriage decades later, using historical linked US census data (1880–1910) and next-door neighbor comparisons 🏠🌃
Read more here:
shorturl.at/U0IHR
The joke of the week in my causal inference methods course this week 😂
We ran a massive, uncontrolled social experiment on kids with social media. Outcomes: anxiety, comparison, fractured attention. Jon Haidt called the direction of travel. We’re doing it again with AI, outsourcing thinking and social skills, to act surprised later. www.nytimes.com/2026/01/30/o...
Learn linux, that's it...