R-bloggers

There is a quote I really love from Marc Kéry’s book, “Introduction to WinBUGS for Ecologists” (2010, p. 11): “WinBUGS helps free the modeler in you.” And it’s so true! Once you deeply understand a statistical model in all its moving part...

Frank Harrell made this enlightening comment on BlueSky the other day: “At the heart of the failure of the CLT (central limit theorem) is its need for the mean and standard deviation to be independent. With asymmetric distributions they are far fr...

R on Windows made faster

Point predictions tell you what a model *thinks* will happen. They don't tell you how much to trust that number. nnetsauce's `QuantileRegressor` takes any sklearn-compatible regressor and turns it into a full quantile machine by optimizing an offset around its point predictions to minimize the pinball (quantile) loss.

Maps of the Political and Administrative Divisions of Chile

If you've written the same block of R code three times this week — a group summary, a plot, a cleanup step — you don't need another package. You need a function. A custom function is the solution. For example, two functions you'll actually reuse: a cus...

ahead (R) 0.38.1 and its Python wrapper now install in a fraction of the time, by moving almost every heavy modeling dependency from Imports to Suggests and installing them at runtime, only when a function actually needs them.

Spatial machine learning has become a standard tool for producing environmental and geographic prediction maps. It is now relatively (technically) easy to combine field observations with remote sensing, climate, terrain, or other predictor layer...

Germination and emergence assays are pretty straightforward to set up: you grab a sample of seeds, put them in a container under controlled environmental conditions (temperature, humidity, light), and check on them regularly. At each inspection,...

R For SEO Part 10: SEO Reporting With Google Sheets & OpenRouter Welcome back, and we’re finally at the end of my R for SEO series, at least for now. We’ve gone through quite... This post was written by Ben Johnston on Ben Johnston

📊 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 ...