iiinova — where your digital self keeps evolving

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Saloni @scientificdiscovery.dev · 12/08/2026

I wrote briefly on the Ebola situation. Things could be going a lot better than they are. www.abundanceandgrowth.org/i/210835777/...

A keyhole view of the next pandemic — Saloni Dattani
I’ve been following the Ebola outbreak in central Africa, which has just surpassed 2,000 reported deaths and 4,381 confirmed cases. (The real numbers are likely higher due to underdetection.) What’s surprising about the Ebola outbreak is that we actually have an Ebola vaccine, but it was developed and tested against the Ebola Zaire virus during the 2014–2016 outbreak, while this outbreak is caused by Ebola Bundibugyo virus. The two viruses have similarities and some laboratory and animal data suggested there may be cross-protection, although the evidence is still unclear.1 But until last week, the WHO hadn’t decided to test that vaccine in this outbreak; the focus was on developing and testing new Bundibugyo-specific vaccines and antivirals instead.

In a recent STAT opinion piece, Krutika Kuppalli and Placide Mbala argue that pandemic preparedness shouldn’t be so narrow. Rather than focusing so narrowly on each virus, we should invest in broad-spectrum diagnostics, vaccines, and treatments, and should have tested the effects of the Zaire vaccine on Bundibugyo earlier; I agree.

Interestingly, the Ebola Zaire vaccine came about through innovative trials and coordination. It was developed through funding by BARDA and the DoD, tested using ring vaccination trials led by the WHO, incentivized through an advance market commitment, and stockpiled by Gavi, WHO, UNICEF and the Red Cross for future outbreaks. That stockpile has been useful in the years since then to contain outbreaks of Ebola Zaire quickly. But now that a larger, harder to contain outbreak has arrived, we’ve lost expertise and funding to tackle it, and we don’t know if the existing Ebola vaccine protects against this virus too. We could be doing a lot better.

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Vincent Arel-Bundock @vincentab.bsky.social · 17/09/2025

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

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Daniel Lakens @lakens.bsky.social · 26/09/2025

You have not seen second generation p-values used anywhere because they were badly reinvented equivalence tests, and people should use equivalence tests instead. See open.lnu.se/index.php/me...

open.lnu.se

LnuOpen | Meta-Psychology

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The Review of Economic Studies (REStud) @reveconstudies.bsky.social · 17/09/2025

"New study finds salary benchmarking cuts pay gaps by 25%. Pay dispersion partly arises from firms’ uncertainty about market rates, with key implications for pay transparency policy." New paper from Perez-Truglia, Li & Cullen www.restud.com/whats-my-emp...: #econsky #REStud

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Posit @posit.co · 12/09/2025

Posit and AWS have signed a Strategic Collaboration Agreement! This collaboration helps customers modernize their data infrastructure & accelerate their data science journey on the cloud, making it easier for teams to build and run critical data science outcomes. Read now posit.co/blog/posit-s...

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Thomas Lin Pedersen @thomasp85.com · 11/09/2025

I am beyond excited to announce that ggplot2 4.0.0 has just landed on CRAN. It's not every day we have a new major #ggplot2 release but it is a fitting 18 year birthday present for the package. Get an overview of the release in this blog post and be on the lookout for more in-depth posts #rstats

tidyverse.org

ggplot2 4.0.0

A new major version of ggplot2 has been released on CRAN. Find out what is new here.

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Julia M. Rohrer @dingdingpeng.the100.ci · 11/09/2025

PS there’s a (mildly) updated version available now: osf.io/g4s2a_v2

osf.io

OSF

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Julia M. Rohrer @dingdingpeng.the100.ci · 25/08/2025

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.

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Gábor Békés @gaborbekes.bsky.social · 15/08/2025

If interested please test the notebooks. Examples: Describe github.com/gabors-data-... OLS github.com/gabors-data-... Random forest, SHAP github.com/gabors-data-... Panel FE, FD github.com/gabors-data-...

github.com

da_case_studies/ch10-hotels-multiple-reg/ch10-hotels-multiple-reg.ipynb at master · gabors-data-analysis/da_case_studies

Codes for case studies for the Bekes-Kezdi Data Analysis textbook - gabors-data-analysis/da_case_studies

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Scottish Journal of Political Economy @scot-j-pol-econ.bsky.social · 18/06/2025

Very pleased to announce our special issue on Gender Norms, edited by @emmatominey.bsky.social and @nikkishure.bsky.social. onlinelibrary.wiley.com/doi/toc/10.1...

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Rafe Meager (they/them) @economeager.bsky.social · 14/06/2025

this was excellent. a highly recommended way to spend 5 hours.

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Nathaniel B @bechhof.bsky.social · 13/06/2025

I indulged in a bit of fun doing a first pass of a regularized model for homicide declines with ChatGPT, using @jeffasher.bsky.social's data from jasher.substack.com/p/why-i-thin... to estimate "underlying" rates of homicide decline by city.

Scatter plot of 31 U.S. cities (excluding “Total”) showing raw vs. Bayesian MAP–regularized percent change in homicides from 2024 to 2025. Raw changes are on the x-axis, regularized changes on the y-axis. Each point is labeled by city, with small arrows connecting any labels placed more than 10 pixels from their marker. A caption below summarizes the log‐ratio transformation, hierarchical Normal(μ,τ²) model with an Inverse‐Gamma penalty on τ², and the posterior‐mean back-transform to percent change.

Screenshot of Jeff Asher’s Substack titled “Murder is down a lot in big cities.” Below the title is a subtitle: “2025 vs 2024 YTD murders in top 30 most murderous cities.” A table lists four columns—City, YTD 2024, YTD 2025, and % Change—and shows, in descending order of decline, that Denver fell from 29 to 13 homicides (–55.2 %), Birmingham from 61 to 33 (–45.9 %), Cleveland 54→30 (–44.4 %), San Antonio 50→30 (–40.0 %), Las Vegas 54→35 (–35.2 %), Atlanta 50→33 (–34.0 %), Dallas 90→60 (–33.3 %), Indianapolis 70→49 (–30.0 %), and so on.

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Crystal Lewis @cghlewis.bsky.social · 13/05/2025

Thoughtfully planning your data collection efforts has a huge return on investment. As someone who has cleaned data from projects both with and without data management planning, I can safely say it is worth the effort. 40 hours of planning > 80+ hours of cleaning messy data

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Nathaniel B @bechhof.bsky.social · 13/05/2025

There's just something special about running a proper Bayesian MCMC script that I can't replace with anything else after doing it for almost 10 years

A screenshot of a Bayesian statistical model training interface showing NUTS sampler progress. The display includes information about a model with 45 subgroup columns, training on 52,380 of 68,850 rows. The sampling is being performed using PyMC NUTS (JAX-compiled) with 4 chains running in parallel. The table shows the current progress of each chain with metrics including number of draws (ranging from 601-644), step size (0.01), gradient evaluations, sampling speed (approximately 1.14-1.22 draws/s), elapsed time (8:49), and remaining time estimates.

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Julian Reif @julianreif.bsky.social · 10/05/2025

“First, about half the time I reanalyze a study, I find that there are important bugs in the code, or that adding more data makes the mathematical finding go away, or that there’s a compelling alternative explanation for the results.” davidroodman.com/blog/2025/05...

davidroodman.com

Appeal to Me: First Trial of a “Replication Opinion” - David Roodman

My employer, Open Philanthropy, strives to make grants in light of evidence. Of course, many uncertainties in our decision-making are irreducible. No amount of thumbing through peer-reviewed journals ...

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Johanna Rickne @johannarickne.bsky.social · 09/05/2025

7/ A series of errors create an illusion of statistically significant treatment effects of similar size in the five strategies. Examples include changing/adding/removing variables in regressions, using incorrect bandwidths, and changing clustering methods.

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Johanna Rickne @johannarickne.bsky.social · 09/05/2025

1/ The paper claimed that the reform caused a jump in rape by 50–60 percent. This seemed hard to reconcile with the flat time trend in reported rapes around the time of the reform.

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Leslie Root @lesja.bsky.social · 04/04/2025

I need a newsletter because I have so many words inside of me about the "they say they want to raise the birth rate and yet they are decimating maternal and infant health surveillance infrastructure [or whatever else], what a gotcha" thing

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Pam Davis-Kean, PhD @umpamdk.bsky.social · 02/04/2025

@icpsr.bsky.social @umich.edu is hosting DataLumos, which is a crowdsource repository of government data archive.icpsr.umich.edu/datalumos/home

archive.icpsr.umich.edu

A crowd-sourced repository for valuable government data | DATALUMOS

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Saloni @scientificdiscovery.dev · 02/04/2025

Seeing the "shingles vaccine protects from dementia" claim going around again. Unfortunately the main study it's based on is just not convincing. @epiellie.bsky.social has written a great post about why:

medium.com

Shingles vaccine & dementia prevention: too good to be true?

A public peer-review

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Andrew Heiss @andrew.heiss.phd · 03/04/2025

hey hey my constant use of the palmerpenguins data is now relevant for my public policy classes

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Peter Hull @instrumenthull.bsky.social · 28/03/2025

📢New WP w/ @borusyak.bsky.social We derive the most powerful recentered IVs for formula treatments & propose an algorithm for approximating them This approach yields *huge* power gains, relative to conventional simulated IV, when estimating Medicaid crowdout effects Check it out! t.co/UPg2XQBefN

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Nathaniel B @bechhof.bsky.social · 26/03/2025

Standardization and division of labor FTW

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Joram Mutenge @jorammutenge.com · 24/03/2025

Always remove whitespace before doing a unique count of values to get an accurate count. In @pola.rs, there's a function to do just that! #100DaysOfPolars #datasky

Image showing polars code for data analysis.

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NBER @nber.org · 23/03/2025

Healthcare employment in the US has skyrocketed since 1980. Healthcare is a middle-class jobs engine, but "manufacturing-to-meds" transitions are not saving the Rust Belt, from @gottliebecon.bsky.social, @nealemahoney.bsky.social, Kevin Rinz, and Victoria Udalova www.nber.org/papers/w33583

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Crystal Lewis @cghlewis.bsky.social · 18/03/2025

Trying out dplyr::consecutive_id() for a project today. #rstats It generates a unique identifier that increments every time a variable (or combination of variables) changes.

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Nathaniel B @bechhof.bsky.social · 14/03/2025

Let it be known far and wide that @haroldpollack.bsky.social follows up (including custom cup!) when you answer his call for tracking down clean data. He really is the top notch conversationalist you know from his many podcast/media appearances

Nathaniel Bechhofer and Harold Pollack posing for a selfie with the custom cup that Pollack gave as a reward for linking a spreadsheet with 2024 presidential election results by Congressional district.

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Carlisle Rainey 👨‍💻📊📚 @carlislerainey.bsky.social · 10/03/2025

"The Need for Equivalence Testing in Economics" from Jack Fitzgerald (@jackfitzgerald.bsky.social) Preprint: osf.io/preprints/met... We know that "not significant" does not imply evidence for "no effect," but I still see papers make this leap. Good to see more work making this point forcefully!

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Simon Willison @simonwillison.net · 08/03/2025

Here's the handout for my "Cutting-edge web scraping techniques" workshop at #NICAR2025 this morning github.com/simonw/nicar... Plus some extra notes on the custom software I built to support the workshop: simonwillison.net/2025/Mar/8/c...

simonwillison.net

Cutting-edge web scraping techniques at NICAR

Here's the handout for a workshop I presented this morning at [NICAR 2025](https://www.ire.org/training/conferences/nicar-2025/) on web scraping, focusing on lesser know tips and tricks that became po...

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Nathaniel B @bechhof.bsky.social · 08/03/2025

I think this @aoc.bsky.social proposal to have a hard cap of 10% for credit card interest is a very bad idea; the only decent argument for it is a paternalist one that basically assumes people with poor credit should not get credit cards. We can do better helping those people tho in targeted ways.

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Steven Ruggles @stevenruggles.bsky.social · 06/03/2025

In this new paper, I argue that the impending loss of usable ACS public use microdata is an entirely avoidable disaster. If you need to avoid the paywall, email me at ruggles@umn.edu for a reprint. /2 www.pnas.org/doi/10.1073/pnas.2424655122

pnas.org

PNAS

Proceedings of the National Academy of Sciences (PNAS), a peer reviewed journal of the National Academy of Sciences (NAS) - an authoritative source of high-impact, original research that broadly spans...

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Steven Ruggles @stevenruggles.bsky.social · 06/03/2025

In the midst of the unfolding destruction of the scientific enterprise and government more broadly, it is easy to lose sight of threats to research that do not stem from the recent election. /1 www.pnas.org/doi/10.1073/pnas.2424655122

pnas.org

PNAS

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Nathaniel B @bechhof.bsky.social · 03/03/2025

I'm a centrist: I think the Jason Furman caveats about the FedNow forecast are compelling (along with some criticisms floating around about its AR time series assumptions) but I think that the conclusion of serious negative growth for Q1 is correct despite that.

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Saloni @scientificdiscovery.dev · 01/03/2025

Just a little thread on (some) of my favourite charts I've made. 🧵

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apoorva lal @handle.invalid · 19/02/2025

Nice; RIP \text{}

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Center for Health Incentives and Behavioral Economics @pennchibe.bsky.social · 17/02/2025

Sweetened beverage taxes are associated with: 1. Reduced sugary drink purchases 🥤 2. Less sugar intake 3. Improvements in weight status 4. Better oral health 🦷 5. Improved pregnancy and birth outcomes 🤰 ldi.upenn.edu/our-work/res...

ldi.upenn.edu

5 Takeaways from the Evidence on Sweetened Beverage Taxes

In the last decade, eight U.S. cities and dozens of countries have adopted sweetened beverage taxes to generate revenue for community programs and improve health.

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Julia M. Rohrer @dingdingpeng.the100.ci · 17/02/2025

New blog post! Sometimes, when reviewing a manuscript, it's really unclear to me what precisely the authors are trying to do -- which makes it hard to evaluate the work properly. So, here's some advice for how to ensure that readers don't get lost. www.the100.ci/2025/02/17/r...

the100.ci

Reviewer notes: Avoid any ambiguity about analysis aims

For any central statistical analysis that you report in your manuscript, it should be absolutely clear for readers why the analysis is being conducted in the first place – that is, the analysis goal s...

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Sebastian Raschka (rasbt) @rasbt.bsky.social · 16/02/2025

Thanks everyone. Upon popular request, I added a similar doc for the native uv commands (`uv add` etc.): github.com/rasbt/LLMs-f...

github.com

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Sebastian Raschka (rasbt) @rasbt.bsky.social · 15/02/2025

It's 2025, and I’ve finally updated my Python setup guide to use uv + venv instead of conda + pip! Here's my go-to recommendation for uv + venv in Python projects for faster installs, better dependency management: github.com/rasbt/LLMs-f... (Any additional suggestions?)

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Nathaniel B @bechhof.bsky.social · 16/02/2025

Yet another failure for "stereotype accuracy"

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Michael W. Kraus @mwkraus.bsky.social · 16/02/2025

Related to this point, our American Psych paper from last year found that workers overestimated US Black manager representation vs census parity and expected it to continue to increase into future without policy change (all inconsistent with reality). drive.google.com/file/d/1boIo...

Graphs showing that workers overestimated Black v white representation in executive and managerial positions vs actual and vs census parity.

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Damon Kiesow @kiesow.net · 16/02/2025

There are typically four fatal aircraft accidents a week in the U.S., almost always smaller private flights. The major DC accident is continuing to drive a frequency illusion bias.

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Saloni @scientificdiscovery.dev · 03/02/2025

I tend to think people's efforts to "steelman" other people's (bad) arguments are often quite shallow and ignorant themselves. It's more valuable to actually learn about a topic than make guesses at what the charitable version of someone's argument might be, in theory.

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Jan Broder Engler @jbengler.de · 30/01/2025

This is how you can use a log10 axis with data that contains zeros 🤓 #tidyplots #rstats #dataviz #phd

library(tidyplots)

set.seed(42)
y <- runif(500, min = 0, 100)
# add 30 zero values
y[sample(NROW(y), 30)] <- 0
df <- data.frame(x = "cat", y = y)

df |>
tidyplot(x = x, y = y) |>
add_data_points_beeswarm() |>
adjust_y_axis(transform = "pseudo_log", breaks = c(0,1,10,100)) |>
add(ggplot2::guides(y = "axis_logticks"))

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Thomas Lin Pedersen @thomasp85.com · 28/01/2025

If you have used #ggplot2 in the last couple of years you owe a great deal to @teunbrand.bsky.social who is behind most of the new features and fixes. Read about his journey to become a part of the ggplot2 core team here:

tidyverse.org

Joining the ggplot2 team - Tidyverse

I joined the ggplot2 team and would like to share the experience.

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Tanya Shapiro @tanyashapiro.bsky.social · 15/01/2025

Calling all Shiny devs 📣 I'm thrilled to be a part of the ShinyConf Programme Committee. We are on the lookout for interesting talks & workshops to feature in this year's lineup. If you have a cool idea, I encourage you to submit a talk! Deadline is Feb 2nd. More info: www.shinyconf.com

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