Model-agnostic prediction intervals in Python and R: does nnetsauce’s `QuantileRegressor` hold up?
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.
chilemapas is back on CRAN
How to Create a Function in R: Custom Median & ggplot2 Examples
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 (Time Series Forecasting with uncertainty quantification) gets a lot faster to install: most dependencies are now optional
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.
Navigating Challenges in Spatial Machine Learning
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...
Research Workflows
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...]
Analysing seed germination and emergence data with R (a tutorial)
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,...
Visualising High-dimensional Data with R workshop
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...]
Bringing BERT — R functions in Excel — up to R 4.6
BERT lets you call R functions from Excel cells, but its author stopped work in 2018. Bringing it up to R 4.6 — and to working on every R from 3.5 up — turned up a deadlock that froze Excel and two bugs only the build machine could hide.
The post Bringing BERT — R functions ... [Read more...]
R For SEO Part 10: SEO Reporting With Google Sheets & OpenRouter
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
Slicing in tidyomics
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...]
Z-Test in R: Complete Guide with One-Sample & Two-Sample Examples
Complete guide to running one-sample and two-sample Z-tests in R using BSDA::z.test(), the manual pnorm/qnorm method, and how to choose between a Z-test and a t-test for your data.
Read More » [Read more...]
Done with convergence warnings? Publications that used allFit to compare optimisers
Mixed-effects models with the random-effects structure that the design justifies (Barr et al., 2013) can end their fit with a warning from lme4 (Bates, Mächler, et al., 2015) that the optimiser may not have converged. The warning reports that a numerical check exceeded a tolerance, but that alone does not establish ...
How often do researchers say they speculate? Nine literatures compared
Some research topics invite more explicit conjecture than others. Language evolution invited it for long enough that one of its learned societies closed the question down. The statutes that the Société de Linguistique de Paris adopted in 1866 declare, in their second article, that the society admits no communication concerning ...
ROC, Paper, Scissor, Shoe
📊 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 ...
Skewness-Managed Portfolios: A Practical Guide with R
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 ...
depictr: One visual language from first look to final figure
The figures in an analysis are rarely made all at once. An exploratory density plot may survive into a talk, a model adds a forest plot and a reviewer asks for a diagnostic or a Bayesian comparison. When each stage uses different defaults, one analysis acquires several palettes, type sizes ...
Greenhouse gas emissions in context: Raw totals, income groups and human development
National greenhouse gas figures can be reported as totals, per-capita values, sector compositions or associations with measures of development, and each of these descriptions answers a different question. None of them, on its own, measures historical responsibility, establishes causation or determines a fair mitigation burden.
This post uses the 2025 report ... [Read more...]
lexsync: From a word-frequency corpus to an EEG-ready experiment
A high- versus low-frequency word experiment sounds simple until the materials are assembled. The frequency bands must differ, while length and orthographic similarity should not. The selected words then need to be divided across presentation lists without creating a new imbalance. Finally, the same item table must reach the experiment ...
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