R-bloggers - Part 19

Dealing with correlation in designed field experiments: part I

April 14, 2026 | Andrea Onofri

When we have recorded two traits in different subjects, we can be interested in describing their joint variability, by using the Pearson’s correlation coefficient. That’s ok, altough we have to respect some basic assumptions (e.g. linearity) that ha...

Dealing with correlation in designed field experiments: part II

April 14, 2026 | Andrea Onofri

With field experiments, studying the correlation between the observed traits may not be an easy task. For example, we can consider a genotype experiment, laid out in randomised complete blocks, with 27 wheat genotypes and three replicates, where sev...

Dealing with correlation in designed field experiments: part I

April 14, 2026 | Andrea Onofri

When we have recorded two traits in different subjects, we can be interested in describing their joint variability, by using the Pearson’s correlation coefficient. That’s ok, altough we have to respect some basic assumptions (e.g. linearity) that ha...

Dealing with correlation in designed field experiments: part II

April 14, 2026 | Andrea Onofri

With field experiments, studying the correlation between the observed traits may not be an easy task. For example, we can consider a genotype experiment, laid out in randomised complete blocks, with 27 wheat genotypes and three replicates, where sev...

Dealing with correlation in designed field experiments: part II

April 14, 2026 | Andrea Onofri

With field experiments, studying the correlation between the observed traits may not be an easy task. For example, we can consider a genotype experiment, laid out in randomised complete blocks, with 27 wheat genotypes and three replicates, where sev...

Programming with LLMs in R & Python

April 14, 2026 | The Jumping Rivers Blog

Working with LLMs in Practice Large Language Models are becoming part of everyday data science work. But using them through chat interfaces is only one part of the picture. In this upcoming webinar, we focus on how to work with LLMs programmatica...

Marathon Man II: how to pace a marathon

April 14, 2026 | Stephen Royle

It’s often the way. I posted recently about how to pace a marathon and very quickly received feedback that would’ve improved the original post. Oh well, no going back. This is take two. So, we have a dataset of all runners from the 2025 New York City Marathon. We ...

Do AI coding agents save scientists time?

April 13, 2026 | Seascapemodels

I’m often asked if using AI coding agents saves time. Yes they write code very quickly and can complete entire ecological data analyses. Do agents really help when the deadlines are approaching? Do agents really help when the deadlines are ...

reviser: Analyzing Real-Time Data Revisions in R

April 12, 2026 | rOpenSci

Economic data are rarely static. Gross domestic product (GDP), inflation, employment, and other official statistics arrive as early estimates, then get revised as new source data arrive, seasonal adjustment is updated, or benchmarking changes are appl... [Read more...]

`mlS3` — A Unified S3 Machine Learning Interface in R

April 11, 2026 | T. Moudiki

Introduction to `mlS3` — A Unified S3 Machine Learning Interface in R

Test & Roll: Why Smaller A/B Tests Can Make More Money

April 11, 2026 | Florian Teschner

Short practical advice on A/B testing: Stop sizing tests only for statistical significance - In finite campaigns, your goal is profit, not perfect inference. Treat testing as a trade-off - Every extra test exposure buys learning but ... [Read more...]

Machine Learning Frameworks in R

April 11, 2026 | R'tichoke

R’s ecosystem offers a rich selection of machine learning frameworks, each with distinct design philosophies and strengths. This post is a side-by-side comparison of five ML frameworks in R that provide unified interfaces over multiple algorithms... [Read more...]

New R Package {bdlnm} Released on CRAN: Bayesian Distributed Lag Non-Linear Models in R via INLA

April 10, 2026 | Pau Satorra

CRAN, GitHub TL;DR: {bdlnm} brings Bayesian Distributed Lag Non-Linear Models (B-DLNMs) to R using INLA, allowing to model complex DLNMs, quantify uncertainty, and produce rich visualizations. Background Climate change is increasing exposure to extreme environmental conditions such as heatwaves and air pollution. However, these exposures rarely have immediate effects. ... [Read more...]

TheseusPlot 0.2.0: Visualizing Decomposition of Differences in Rate Metrics

April 9, 2026 | Koji Makiyama

TheseusPlot is an R package that decomposes differences in rate metrics between two groups into contributions from individual subgroups and visualizes the results as a “Theseus Plot”. The package is inspired by the Ship of Theseus thought experi...

Using R to Teach R: Lessons for Software Development

April 9, 2026 | The Jumping Rivers Blog

As we approach the decennial (10-year) anniversary since Jumping Rivers was founded in 2016, it’s a good time to reflect on what we have achieved in that time and share some lessons learned. If you have read our blogs previously then you will be aware that Jumping Rivers is a ...

Hold On Hope: publication lag times at cell biology journals

April 9, 2026 | Stephen Royle

I’ve posted about publication lag times previously. The “lag” refers to the time from submitting a paper and it appearing in a journal. Publication lag times are still a frustration for researchers. Although preprints circumvent the delay in sharing science with others, publication is still king when it comes ...

Developer Engagement and Bioconductor

April 8, 2026 | Nicholas Cooley, PhD

Introduction During the Chan Zuckerberg Institute’s Essential Open Source Software for Science cycle 6 funding round, the Bioconductor Community Manager, Maria Doyle, secured a grant to fund a developer engagement position for Bioconductor, and... [Read more...]

Collaborating between Bioconductor and R-universe on Development of Common Infrastructure

April 7, 2026 | The rOpenSci Team

This article is cross-posted on rOpenSci and R-Consortium blogs. For more than two decades, the Bioconductor project has been a cornerstone of the R ecosystem, providing high-quality, peer-reviewed tools for bioinformatics and computational biol...

Collaborating between Bioconductor and R-universe on Development of Common Infrastructure

April 7, 2026 | rOpenSci

For more than two decades, the Bioconductor project has been a cornerstone of the R ecosystem, providing high-quality, peer-reviewed tools for bioinformatics and computational biology. Its curated repository model, rigorous review standards, and tight...

EM-DAT, the world’s disaster memory, is at risk

April 6, 2026 | R on Stats and R

I do not usually write posts that are calls to action. But sometimes, something important enough comes along that it would feel wrong to stay silent. This is one of those times. What is EM-DAT? EM-DAT, the Emergency Events Database, is the world’s...

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