R-bloggers

Introduction rOpenSci’s R-universe system is an open source platform allowing users to create their own CRAN-like universe of R packages. It is absolutely fantastic. It is particularly useful in one area I research, Mendelian randomization (at ...

Prediction Intervals for Tabular Regression in Python and R via Conformalized TabPFN

Exploring the CovR/S two-component system in Group A Strep 🧫 — from genome annotation with Bakta & BaktFold, to AlphaFold confidence metrics, and a first attempt at protein docking with Haddock3. Learning as we go! 🙌 Motivations ...

Examples of use of R package crossvalidation for Probabilistic Time Series Cross-Validation (measuring coverage and Winkler score)

I came across a post recently by a machine learning engineer who made the bold claim that logistic regression is the worst name for an algorithm ever, or something along those lines1. Many statisticians of the more old-school type seemed to disagree. T...

How long do wars last, on average? If a war such as that currently under way in Iran has lasted 74 days so far, how long do we expect it to last in total? For all sorts of reasons, inquiring minds are interested. Luckily there are some very well curate...

A high can make a regression model look impressively accurate — but this number can be deceptive. If you want to understand why a high is not always a sign of a good model, read on! In the post, Learning Data Science: Modelling Basics, we built a simple model to predict ...

News from R package unifiedml, that offers a unified interface to R machine learning models

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 language models today trace back to a preprint introducing the transformer neural network architecture – a fifteen-page paper that profoundly ...

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, differencing is introduced almost mechanically: i...

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 ...

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 →

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 ...

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! ...