Research Workflows

September 7, 2026 | R on kieranhealy.org

I’m teaching MPTC again this semester and have been developing a few workflow slides. Here’s a popular one. The Scientific Process. Now if you’ll excuse me there’s a mandatory webinar I need to click ... [Read more...]

Visualising High-dimensional Data with R workshop

September 5, 2026 | Dariia Mykhailyshyna

Join our workshop on Visualising High-dimensional Data with R, which is a part of our workshops for Ukraine series! Here’s some more info: Title: Visualising High-dimensional Data with R Date: Thursday, October 1st, 10:00 – 12:00 CEST (Rome, Berlin, Paris timezone) Speaker: Dianne Cook is Professor of Statistics at Monash University in ... [Read more...]

Slicing in tidyomics

September 2, 2026 | Michael Love

Introduction The tidyverse verb slice() lets you select observations by position, returning a subset of the data based on integer indices. There are also some related convenience functions, which return observations by: slice_min() and slice_m... [Read more...]

ROC, Paper, Scissor, Shoe

September 1, 2026 | r on Everyday Is A School Day

📊 Working through ROC-AUC from scratch, then poking at its blind spots — low prevalence, calibration, and finally Decision Curve Analysis. Mostly notes to myself on what I learned (and got confused by) along the way. 🤔📈 Motivations We see ROC-AUC so often with classification models, we know the higher the better, but ...
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Skewness-Managed Portfolios: A Practical Guide with R

September 1, 2026 | Selcuk Disci

Introduction Portfolio construction often relies on mean–variance optimization or factor models. Yet, recent research highlights the importance of skewness—the third statistical moment—as a driver of asset returns. Assets with lottery-like payoffs (high positive skewness) tend to be overpriced, while negatively skewed assets are often underpriced. A 66‑page ...
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pilotr: Pilot the study before running it

August 31, 2026 | Pablo Bernabeu

Power calculations can feel reassuringly final. Choose an effect, enter a sample size and read off a percentage. That simplicity disappears in experiments where participants respond to many items. Participants and items both vary, their responses are crossed, and the model usually needs random slopes as well as random intercepts. ...
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