R-bloggers

R-bloggers is a platform that leverages the content from various documents to provide concise summaries for its audience. The platform serves as a hub for information and insights related to a wide range of topics. By summarizing the content from diverse sources, R-bloggers aims to offer valuable and easily digestible information to its readers. The platform’s summaries are designed to provide a quick overview of the key points and insights covered in the original documents, making it a convenient resource for individuals interested in staying informed about the latest developments in various fields.

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How to Build an Expected Goals (xG) Model in R with worldfootballR

R-bloggers

Expected goals has become one of the most important concepts in modern football analytics. Instead of judging a team only by goals scored, xG helps us estimate the quality of the chances created. In t...

One interface, (Almost) Every Classifier (and Regressor): unifiedml v0.3.0

R-bloggers

News from R package unifiedml, that offers a unified interface to R machine learning models Continue reading: One interface, (Almost) Every Classifier (and Regressor): unifiedml v0.3.0

Edge detection in Python

R-bloggers

Great strides in artificial intelligence development during the last five years produced agents that are now commonplace at work and home. It is humbling to note that virtually all frontier large lang...

Differencing: A Transformation or a Trap?

R-bloggers

1 Introduction Differencing is one of the most common transformations in time series analysis. It is also one of the easiest transformations to misunderstand. In many ARIMA-style workflows, differenci...

New Mentoring Team with Experienced Mentors and New Voices

R-bloggers

Read it in: Español. We are excited to introduce the new team of mentors for the rOpenSci 2026 Champions Program! This year we have eleven individuals committed to open science, bringing together a ri...

Differential Machine Learning with Twin Networks in R: Forecasting Bitcoin with Volatility Proxies

R-bloggers

Introduction Differential Machine Learning (DML), as introduced in the recent arXiv paper (Differential Machine Learning for 0DTE Options with Stochastic Volatility and Jumps), extends supervised lear...

Setting function parameters for debugging

R-bloggers

I tend to write a lot of functions that create specific graphics implemented with ggplot2. Although I try to pick graphic parameters (e.g. colors, text size, etc.) that are reasonable, I will typicall...

JAGS 5.0.0-beta is available

R-bloggers

JAGS 5.0.0-beta is now available from SourceForge. The beta release is for two groups of people: Please send feedback via the JAGS forums or file a bug report The JAGS library The following packages a...

Comparing R’s {targets} and dbt for Data Engineering

R-bloggers

I’m getting more and more into data engineering these days and having used R for a long time, I’m seeing a lot of problems that look nail-shaped to my R-shaped hammer. The available tools to solve tho...

The Magic of In-Context Learning (ICL): When Your Model Already Knows Your Data

R-bloggers

Have you ever looked at a freshly plotted scatter plot and immediately thought, “Ah, this is clearly a logarithmic curve with some heteroskedastic noise,” without running a single line of modeling cod...

Bad Weather and the Subway

R-bloggers

Snow in Inwood, New York. Photograph by the author. Recently I’ve been looking at hourly ridership data from the New York City Subway. Last time we learned that people go to work in the morning and co...

Learning & Exploring Survival Analysis Part 1 – A Note To Myself

R-bloggers

A note to myself on survival analysis — KM curves, log-rank tests & Cox models 🧮 If I wrote it the way I understood it, maybe I’ll actually remember it 🤞 Motivations We see survival analysis or more...