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 a song.” She laughed. But I was deadly … Continue reading →

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’s programs and community. In response, we … Continue reading →

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 Program call closed on February 23, and the response … Continue reading →

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. # list of phrases is generated with the help of LLM. Because ..lazy!!!!phrases_db <- list( … Continue reading →

{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 models for longitudinal clinical trials in … Continue reading →

nnetsauce with and without jax for GPU acceleration

In the new version (0.51.2) of nnetsauce (for Python, but also for R), available on PyPI and for conda, I removed jax and jaxlib (for GPU) from the default version, because jaxlib is heavy. It means that if you want to use GPUs with nnetsauce (as in … Continue reading →

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 putting our community building aside or behind … Continue reading →

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) … Continue reading →

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 systems) is too sparse, claims to automatically … Continue reading →

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 will actually buy. Measure … Continue reading →

Open REDATAM a un anio de su publicacion

pacha.dev/blog window.site_root = '../../..'; window.MathJax = { tex: { inlineMath: [['\\(', '\\)'], ['$', '$']], displayMath: [['\\[', '\\]'], ['$$', '$$']], processEscapes: true, processEnvironments: true, … Continue reading →

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 leadership and foresight as one of our original … Continue reading →

Quantitative Horse Racing with R: Calibration, Backtesting, and Deployment

:root{ --bg:#0b1020; --fg:#e8eefc; --muted:#a9b5d1; --card:#121a2b; --border:#223053; --codebg:#0e162b; --codeborder:#20315a; --accent:#6ea8ff; --accent2:#9be7ff; --good:#55efc4; --warn:#ffeaa7; } *{box-sizing:border-box} body{ … Continue reading →

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 package besides {nnet}, {keras} / {keras3} for the … Continue reading →

Keeping files organised for publication

Getting files ready for publication can be frustrating when you can’t find the right file, or you realize your figures look like 2000s jpeg. Here’s a list of things I wished I’d done (or my students had done) properly earlier in the paper prep process. Save this list somewhere to refer … Continue reading →