Heavy R programming – Heavy News

News and information about the R programming language

New Mentoring Team with Experienced Mentors and New Voices
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 rich diversity of backgrounds and perspectives. The t… Continue reading: New Mentoring Team with Experienced Mentors and New Voices
Differential Machine Learning with Twin Networks in R: Forecasting Bitcoin with Volatility Proxies
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 learning by incorporating not only function values but also their derivatives. In financial contexts, this often means sensitivities such as Greeks. However, when direct derivatives … Continue reading: Differential Machine […]
Setting function parameters for debugging
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 typically define all relevant aesthetics as param… Continue reading: Setting function parameters for debugging
JAGS 5.0.0-beta is available
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 are available: The rjags package In … Continue reading → Continue reading: JAGS 5.0.0-beta is available
Comparing R’s {targets} and dbt for Data Engineering
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 those problems exist for (presumably) very good reasons, so I wanted to … Continue reading: Comparing R’s {targets} and […]
The Magic of In-Context Learning (ICL): When Your Model Already Knows Your Data
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 code? How do you do that? You don’t perform gradient descent in your head. You use your intuition! … Continue reading: The Magic of […]
Bad Weather and the Subway
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 come home in the eve… Continue reading: Bad Weather and the Subway
You Don’t Need to Learn All the Weights on tabular data: The Case for rvflnet (a nonlinear expressive glmnet) on regression, classification and survival analysis
rvflnet is an R package that implements a Random Vector Functional Link (RVFL) network. It is a nonlinear expressive version of glmnet that can be used for regression, classification and survival analysis. Continue reading: You Don’t Need to Learn All the Weights on tabular data: The Case for rvflnet (a nonlinear expressive glmnet) on regression, […]
Learning & Exploring Survival Analysis Part 1 – A Note To Myself
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 generally call… Continue reading: Learning & Exploring Survival Analysis Part 1 – A Note To Myself
Regression Modeling Strategies Short Course 2026, with Frank Harrell; May 14, 15, 18, 19
Frank Harrell’s Regression Modeling Strategies online seminar will take place May 14, 15, 18, and 19. This workshop covers principled strategies for building, validating, and interpreting multivariable regression models for a wide range of outcomes, with emphasis on predictive accuracy, avoiding overfitting, and interpreting estimated effects. It explores spline methods, data reduction, benefits … Continue […]

The 8 Best R Programming Books in 2022

New course aims to train next generation of data scientists

“Ogihara and Bonnell chose to use R because it is attuned to statistical analysis and, by incorporating an increasingly popular collection of tools in R called tidyverse, students can easily learn how to process, wrangle, transform, and model data on their own, too.”