L’IA travaille. Il vous reste le plus difficile.
Automatiser avec l'IA ne retire pas le travail humain : il reste la vérification et le jugement, là où l'erreur de confiance est la plus probable.
rplatypus rebuilt: deep learning for medical images in R
In October 2020 I wrote about platypus, an R package for object detection and image segmentation: YOLOv3 and U-Net in a few lines of R, on top of the keras and tensorflow packages. People used it. It collected 134 stars and a queue of issues - ...
Roche Open-Source TLG Packages Move to pharmaverse
Written on behalf of the NEST team.
Over the past few months, we have reached an important milestone in our open-source journey: transitioning key clinical reporting and exploratory analysis R packages from the insightsengineering GitHub organ... [Read more...]
Agents in Production: Build Your First AI Agent in R with {ellmer}
This is part one of a five-part series on putting AI agents into production with R.
Each part builds on the same app, a clinical trial safety assistant. The parts are:
From Chatbot to Agent (this post)
Putting the Agent in a Shiny App
Can You Tr...
Carolina Pradier, rOpenSci champion and mentor
We started this article as an interview series with Champions from our program, and we decided to publish for our 15th anniversary series. This is the interview with Carolina Pradier, main maintainer of the eph package.
Carolina Pradier has long worke... [Read more...]
Top-cited scientists in clinical medicine by country: the Stanford–Elsevier top 2% list in R
Clinical medicine is by far the largest field in the 2026 Stanford–Elsevier top 2% list: 76,155 of the 249,310 listed authors, 31%, have it…
Register for the Summer R Workshops in Brisbane, at UQ: Introduction to R and Statistical Modelling
Learn the R skills needed to analyse data confidently, build powerful statistical models, create publication-quality figures, and answer complex research questions with the Centre for Biodiversity and Conservation Science (CBCS) 2027 Summer R Wo... [Read more...]
My Messy Notes of Learning MICE From Scratch
MICE always felt like magic to me 🪄 So I tried rebuilding it from scratch in R to understand it better. Lots of mistakes and help along the way, but my version ended up close to the mice package. Might be just luck. Still learning, but here are my mes...
September 2026 Top 40 New CRAN Packages
Agriculture
HRRI v1.0.9: Provides diagnostic functions for integrating longitudinal soil, plant and microbial observations during redox disturbance and recovery. Functions calculate stoichiometric potential oxygen demand, accessible electron ...
Le prix du jeton s’effondre. Votre facture, non.
Le prix du jeton IA s'effondre, pas votre facture : seul compte le coût d'une tâche réussie, échecs et reprises humaines compris. [Read more...]
Modelling cell-type proportions in repeated-measures designs with sccomp: a TMA case study
Contributor: Pierre-Paul Axisa, Researcher, CBMR, Copenhagen, Denmark.
Introduction
Testing cell proportional differences from single-cell-resolved count data is central to modern tissue analyses. It becomes a repeated-measures problem as soon as several observations come from the same donor, such as multiple tumour cores in a tissue microarray (TMA) or serial biopsies ...
Agents in Production Webinar: From a Chatbot to a Tool You Can Rely On
Ask a large language model a question about your data, and you’ll probably get a confident answer.
Ask it again, and you might get a different one.
Sometimes, neither answer is correct.
That’s a problem if you’re building an AI ...
Characteristic NYC Neighborhood Complaints
New York City month continues here at Kieran Healy Industries, an effort that will eventually produce something properly scholarly in nature. But in the meantime—and with my apologies to the many great urbanists of times past—we can enjoy o... [Read more...]
Operationalizing FAIR and CARE data stewardship principles in WASH systems
Note: This post was originally published by Emmanuel Mhango on LinkedIn on 27 October 2026 (https://www.linkedin.com/pulse/operationalizing-fair-care-data-stewardship-wash-systems-mhango-go7jf/).
Moving beyond data collection toward responsible,... [Read more...]
Battles and space-filling curves
I recently learned about an interesting way of reducing the dimension of a data set. I’d like to illustrate it with a data set of about 550 historical battles in the UK, extracted from Wikidata using a SPARQL query. No doubt some readers will have seen...
When Do Causal ML Models Beat LM / GLM / GBM?
Summary
Who this is for: Data scientists and analysts who need to estimate what an intervention does (a call, a discount, a campaign) and want to know when causal ML methods are worth using over LM, GLM, and GBM. The basis is nine simulations w...
Dogs of New York, Shapes and Sizes Edition
My recent Dogs of New
York, post
got circulated a little. On Friday I got a very nice note from William
Kornblum, an urban and
community studies sociologist who these days is emeritus at CUNY. In the 1980s,
Bill studied the people who use Central Park ... [Read more...]
Make Your Own LLM Agent in R
It’s really easy to make your own agent. You should do it once, just to understand how this technology works.
LLM agents are becoming pervasive, with Meta’s Muse perhaps the first consumer grade agent. They are only going to get more common. If ...
{poorman}: Tips and Tricks for Faster Grouped Operations in base R
Introduction
When learning R, we are always told to vectorise, vectorise, vectorise. This is because R is an interpreted language.
Every time R runs a line of R code, it has to look up each name, match the arguments of each function call and set up... [Read more...]
caretS3 and ahead for Tabular and Time Series Machine Learning
Exploring the integration of caretS3 and ahead for tabular and time series machine learning tasks.
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