My Presentation at Risk 2026: Lightweight Transfer Learning for Financial Forecasting
My Presentation at Risk 2026: Lightweight Transfer Learning for Financial Forecasting
Pacific island population pyramids (again) by @ellis2013nz
This post is the fifth of a series of seven on population issues in the Pacific, re-generating the charts I used in a keynote speech before the November 2025 meeting of the Pacific Heads of Planning and Statistics in Wellington, New Zealand. The seven ...
Zut, Git ! Porting {saperlipopette} to the terminal with Claude
A while ago, I decided to learn some Rust.
I bought a book, opened it months later and started a small side-project: porting my R package saperlipopette to the terminal by writing a CLI with Rust.
I then lost steam and discarded the project.
Now, teaching more Git with saperlipopette ... [Read more...]
January 2026 Top 40 New CRAN Packages
Two hundred forty-one of the new packages submitted to CRAN in January were still there in mid-February. Here are my Top 40 picks in nineteen categories: Artificial Intelligence, Computational Methods, Data, Dynamical Systems, Ecology, Economics...
Tutorials in Applied Statistics with R (and RStudio)
This note documents my ongoing Tutorials in Applied Statistics with R (and RStudio), which are aimed at first-year undergraduate social scientists.
Three years ago, I published a Data Science with R course that has gone through a few iterations since ... [Read more...]
pharmaverse Pillars
Hi pharmaverse community!
Big news from the council: after our late‑2025 strategy session, we’ve formalized the pharmaverse pillars for 2026 — a friendly framework to give clearer direction, focus effort, and help members plug in where they c...
What was the best year for a saxophone lover to be listening to the Billboard Top 100?
(Tidy Tuesday is a project to supply weekly data sets for R users to practice their coding skills on. You can find full details here.)
I turned to my wife in the car the other day and said, out of nowhere: “You know, I love a bit of sax in ...
Software Review in the Era of AI: What We Are Testing at rOpenSci
The advent of large language models (LLMs) and generative AI tools has changed the nature of software development and of software review.
These developments are complex, challenging, and controversial, but require action to maintain and grow rOpenSci&... [Read more...]
rOpenSci News Digest, February 2026
Dear rOpenSci friends, it’s time for our monthly news roundup! You can read this post on our blog. Now let’s dive into the activity at and around rOpenSci!
rOpenSci HQ
Strong Engagement in the Champions Program Call
The Champions Progr... [Read more...]
Little useless-useful R functions – Typing speed benchmark
Did you ever wonder how fast and with accuracy your typing is? For this instance, we will introduce some random pangrams, code samples and random strings sotrted by level of difficulty. Addition to list of sample strings, we have also…Read more ›
{pmrm} joins openpharma and reaches CRAN
We are delighted to announce that {pmrm} has joined the openpharma GitHub org and is now available on CRAN. This package implements frequentist progression models for repeated measures (PMRMs), a class of continuous-time nonlinear mixed-effects ...
nnetsauce with and without jax for GPU acceleration
How to install nnetsauce with and without jax for GPU acceleration [Read more...]
Our forum is closed but our community is not!
As of this week, we are closing our forum at discuss.ropensci.org.
The forum has become largely inactive in recent years, so we are consolidating our conversations to our other online spaces to reduce maintenance and moderation burden.
We’re not... [Read more...]
Football Betting Model in R (Step-by-Step Guide 2026)
Related (on this site): Install & Use worldfootballR worldfootballR Guide Sports Analytics with R NFL Analytics with R Tennis Analytics with R Boxing Analytics with R Bayesian Sports Analytics (Book/Product) Contents Setup Get match data Feature engineering Model 1: Poisson goals (baseline) Model 2: Dixon–Coles adjustment (improves low scores) From scorelines ... [Read more...]
The Limits of AI: Why Automated Optimization (often) Fails in Smaller Markets
In my last post, I discussed that clicks are a bad metric to optimise against in advertising systems. However, optimising against metrics that are closer to conversion do have their limits.
Basically, if feedback to AI systems (e.g. Ad optimisation sy... [Read more...]
Forget Clicks: Why CTR Is a Terrible Metric for Ad Effectiveness
Short practical advice on ad measurement:
Don’t optimize for CTR alone — The paper shows clicks are a poor proxy for conversions. Across many campaigns, targeting models built on clicks perform no better than random guessing at identifying who... [Read more...]
Open REDATAM a un anio de su publicacion
A un año de la publicación del artículo sobre Open REDATAM, reflexiono sobre el proceso de publicación, la recepción de los usuarios, la revisión en rOpenSci y los desafíos técnicos y personales. [Read more...]
Welcome, Nick Masel
Hi pharmaverse community,
On behalf of the council we are pleased to announce that Nick Masel will be taking over from Sumesh Kalappurakal as the J&J representative on the Pharmaverse Council. Please join us in thanking Sumesh for his lead...
Quantitative Horse Racing with R: Calibration, Backtesting, and Deployment
R DuckDB Parquet Calibration Ranking Bayesian Odds TS Backtesting Racing analytics as an inference-and-decision system Thoroughbred flat racing is not a binary classification problem. It is a multi-competitor outcome process with hierarchy (horse / trainer / jockey / track), time dependence (form cycles, market moves), and decision layers (how you act on probabilities). ... [Read more...]
You can do more for neural networks in R with {kindling}
This post has been written in collaboration with Joshua Marie.
Why this post matters
Neural networks in R are no longer niche. Today, we can choose among:
{nnet} for classic, small-scale neural nets,
{neuralnet} another classic neural nets packag...
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