R-bloggers

Clinical medicine is by far the largest field in the 2026 Stanford–Elsevier top 2% list: 76,155 of the 249,310 listed authors, 31%, have it…

MICE always felt like magic to me 🪄 So I tried rebuilding it from scratch in R to understand it better. Lots of mistakes and help along the way, but my version ended up close to the mice package. Might be just luck. Still learning, but here are my mes...

Agriculture HRRI v1.0.9: Provides diagnostic functions for integrating longitudinal soil, plant and microbial observations during redox disturbance and recovery. Functions calculate stoichiometric potential oxygen demand, accessible electron ...

Contributor: Pierre-Paul Axisa, Researcher, CBMR, Copenhagen, Denmark. Introduction Testing cell proportional differences from single-cell-resolved count data is central to modern tissue analyses. It becomes a repeated-measures problem as soon as several observations come from the same donor, such as multiple tumour cores in a tissue microarray (TMA) or serial biopsies ...

Ask a large language model a question about your data, and you’ll probably get a confident answer. Ask it again, and you might get a different one. Sometimes, neither answer is correct. That’s a problem if you’re building an AI ...

I recently learned about an interesting way of reducing the dimension of a data set. I’d like to illustrate it with a data set of about 550 historical battles in the UK, extracted from Wikidata using a SPARQL query. No doubt some readers will have seen...

Summary Who this is for: Data scientists and analysts who need to estimate what an intervention does (a call, a discount, a campaign) and want to know when causal ML methods are worth using over LM, GLM, and GBM. The basis is nine simulations w...

It’s really easy to make your own agent. You should do it once, just to understand how this technology works. LLM agents are becoming pervasive, with Meta’s Muse perhaps the first consumer grade agent. They are only going to get more common. If ...

Exploring the integration of caretS3 and ahead for tabular and time series machine learning tasks.

This blog post is about me trying to make a better version of an existing diagram that I think is important, but difficult to read in the version it keeps popping up on my Bluesky feed. Robin J Brooks of the Brookings Institution has been using a usef...

What would happen if we peeled the Earth like an orange? The interrupted Goode homolosine projection gives us something close to that! This equal-area projection preserves relative areas while splitting the world map into lobes. Add a few shades of orange and our world is ready to peel.

Zhenguo Zhang's Blog https://fortune9.netlify.app/2026/10/23/python-simpsons-paradox/ - Introduction Simpson’s paradox occurs when the direction of an association reverses after data from several groups are combined. For example, A can have a higher success rate than B in every subgroup, yet a lower success rate overall. In this ...

Following this previous post and this discussion on Mastodon, I wrote a function that wraps a combination of functions from the ‘terra’ package to project a SpatRaster or SpatVector layer while automatically centering the projection on that layer, to minimize … Continue reading →