Stats and R
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🌍 EM-DAT, the world's most trusted global disaster database, maintained by UCLouvain's CRED, is at risk of shutting down after losing USAID funding.
In an era of intensifying climate extremes, reliable data are not a luxury. They are the infrastructure for informed decision-making.
I wrote a short post explaining why EM-DAT matters and how you can help by signing the open letter 👇
https://statsandr.com/blog/em-dat-the-world-s-disaster-memory-is-at-risk/
#OpenData #DisasterRisk #OpenScience -
New post with @joshuamarie: Bayesian Neural Networks in {tidymodels} with {kindling} 🔥
BNNs learn weight distributions instead of fixed values — giving uncertainty estimates alongside predictions, all within a standard {tidymodels} workflow.
👉 https://statsandr.com/blog/bayesian-neural-networks-in-tidymodels-with-kindling/
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🚀 New blog post live!
Together with @joshuamarie, we explore how to do more with neural networks in R using {kindling}, a higher-level interface to {torch} that makes building, training & tuning deep learning models smoother (and tidymodels-friendly)
👉 https://statsandr.com/blog/you-can-do-more-for-neural-networks-in-r-with-kindling/
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📢 Excited to share that our paper “Semi-Markov modeling for disease incidence risk and duration” has been accepted for publication in Biostatistics & Epidemiology!
It explores cancer incidence risk and years of life lost due to cancer using a Semi-Markov illness-death model applied to Belgian Cancer Registry data, with implications for the right to be forgotten in insurance.
🔗 https://www.tandfonline.com/doi/full/10.1080/24709360.2025.2517916