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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Using R to make an “informational video”

R-bloggers

I volunteer at my local junior AFL club and my role as club registrar grants access to a sports management platform called PlayHQ. It’s a rich source of data so for the end of the 2026 season, I thoug...

Using caretS3 with ahead::ctxthetaf for Time Series Forecasting

R-bloggers

A demonstration of using caretS3 models for time series forecasting with ahead::ctxthetaf. Continue reading: Using caretS3 with ahead::ctxthetaf for Time Series Forecasting

Free AI and Data Courses with 365 Data Science: Unlimited Access until Nov. 20

R-bloggers

Free AI and Data Courses with 365 Data Science: Unlimited Access until Nov. 20 365 Data Science opens its doors this November 100% for free! From November 5 to November 20, 2026, enjoy unlimited acces...

Nuclei, five years later: what a mean Dice hides

R-bloggers

In 2020 I trained a U-Net on the 2018 Data Science Bowl nuclei with the first version of this package, watched a Dice score climb during training, and finished the post with four predicted masks on th...

Postal codes and tessellations

R-bloggers

From five imaginary points to a map of Slovenia: postal code areas with Voronoi, giscoR and sf. Continue reading: Postal codes and tessellations

Environmental modelling in your web browser

R-bloggers

I recall when the R shiny framework first came out. It suddenly got relatively easy for us ecological modellers and environmental statisticians to turn our R code into web apps. This improved accessib...

Shrink stored models with butcher

R-bloggers

Scope Fitted model objects in R often carry more than they need for prediction. A glm object keeps the training data, the fitted values, the residuals, and the environment of its formula. That is fine...

SAS to R: Why R, and Where Your Migration Budget Should Go

R-bloggers

This post is part of our SAS to R series: Part 1: Is SAS still used, and is it worth keeping? Part 2: Why R, and where your migration budget should go (this post) Part 3: coming soon If you’re moving ...

Roche and Appsilon Build on Their Collaboration to Support teal’s Continued Development

R-bloggers

Following the recent migration of the teal framework to pharmaverse, Roche and Appsilon are building on their existing collaboration to support its continued development. Appsilon will continue contri...

Kindness, Curiosity, and Kitties: A rOpenSci 15th Anniversary Interview with Mo

R-bloggers

As part of rOpenSci’s 15th anniversary celebrations, Community Manager Yani sits down with community member Mo to talk about how a first software review turned into years of involvement across R-Unive...

L’IA travaille. Il vous reste le plus difficile.

R-bloggers

Automatiser avec l'IA ne retire pas le travail humain : il reste la vérification et le jugement, là où l'erreur de confiance est la plus probable. Continue reading: L’IA travaille. Il vous reste le pl...

Stratified Modeling for Imbalanced Data

R-bloggers

NOTE: Any updates to this article will be maintained here: https://medley.bryer.org/articles/stratified_glm.html Introduction Statistical modeling when there is substantial imbalance in the dependent ...