Forecasting the UEFA Women’s Euro 2025 with enhanced statistical learning

Probabilistic forecasts for the UEFA Women's Euro 2025 are obtained by using a machine learning ensemble that combines statistically-enhanced features and other information about the teams. The favorite is Spain, followed by Germany, France, and England. The UEFA … Continue reading →

Explore Neural Networks Interactively with Quarto Live!

What is Quarto Live? Quarto Live combines Quarto with WebR to enable interactive R code execution directly in the browser allowing for: Running R code directly in the web browser Instant feedback when modifying code examples No installation requirements Easy sharing of interactive … Continue reading →

How to Use R with Excel workshop

Join our workshop on How to Use R with Excel, which is a part of our workshops for Ukraine series!  Here’s some more info:  Title: How to Use R with Excel Date: Thursday, July 31st, 18:00 – 20:00 CET (Rome, Berlin, Paris timezone) Speaker: Alyssa Columbus is a third-year … Continue reading →

Predicting Gold Prices: Backtesting of ML Models

Fitch projects a decline of about 30% in gold in 2026. Easing the trade war and the Israel-Iran conflict may support this idea. We will project how the prices could go by the end of the year. We will use the modeltime.resample package for forecasting … Continue reading →

SAEM PAKEJ, DIFFRINT VERSHUNZ: PAWSIBL?

Concerned cat It started as a simple exchange on Mastodon. Apparently, what is natural for my nix-infused brain may seem far-fetched for non-nixers. Multiple versions of the same package must be possible. But I actually tried this only once, so I decided to document it. Let’s cut to … Continue reading →

Did my eyes just see Python in the pharmaverse?

Hi pharmaverse community! The eagle-eyed among you might have noticed in recent months updated wording on our pharmaverse charter and generally across our website to generalize our scope to “open-source software packages and codebases”. As part of this, we released our first ever … Continue reading →

May 2025 Top 40 New CRAN Packages

One Hundred seventy-six new packages made it to CRAN in May. Here are my Top 40 picks in eighteen categories: Climate Science, Computational Methods, Data, Decision Analysis, Ecology, Epidemiology, Finance, Genomics, Machine Learning, Medicine, Networks, Phylogenetics, Programming, … Continue reading →

ggplot dotplot using R | geom_dotplot, dot plot using ggplot2

Beyond just plotting points on a chart, how can you transform a simple ggplot dotplot into a powerful narrative tool that reveals the hidden stories within your data's distribution, all while avoiding the common pitfalls of visual clutter and misinterpretation? A ggplot dotplot is a powerful … Continue reading →

Counting Digits Quickly

When things run slower than we’d like in R we tend to reach for another, usually compiled, language, and move our code there. What if it “just happened”? What started out as a silly exploration of how to count digits ended up with a race to see which language does it fastest. Maybe some … Continue reading →

rOpenSci News Digest, June 2025

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 Farewell to software review editor Julia Gustavsen This month we say farewell to Software Peer-Review Editor Julia … Continue reading →

Prompting large language models for quality ecological statistics

Prompting large language models for quality ecological statistics Announcing a new pre-print on prompt engineering for ecological statistics.. https://doi.org/10.32942/X2CS80 The pre-print describes the challenges and opportunities of using large language models and agents to do ecological … Continue reading →

R Package Quality: Package Popularity

This is blog two of five: R Package Quality: Validation and beyond! Package Popularity (this one) In our previous post, we introduced the four components that make up a litmus package score: documentation, popularity, code quality, and maintenance. In this post, we’ll look at package … Continue reading →

R Package Quality: Package Popularity

This is blog two of five: R Package Quality: Validation and beyond! Package Popularity (this one) In our previous post, we introduced the four components that make up a litmus package score: documentation, popularity, code quality, and maintenance. In this post, we’ll look at package … Continue reading →

Prompting large language models for quality ecological statistics

Announcing a new pre-print on prompt engineering for ecological statistics.. https://doi.org/10.32942/X2CS80 The pre-print describes the challenges and opportunities of using large language models and agents to do ecological statistics. It then goes onto describe some prompt engineering … Continue reading →

Beyond the Basics: Books to Supercharge Your R Skills

There are a plethora of great books that are widely recommended for the beginner R user, but I many users often find their skills plateauing once they learn just enough to get by on a day-to-day basis. These are some of the books that helped me to break through that plateau and take full … Continue reading →