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aosmith.rbind.io | ||
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statsandr.com
This article illustrates the main tips, tricks and shortcuts you can use in RStudio and R Markdown to write code more quickly and more efficiently |
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gganimate.com
[AI summary] This guide demonstrates how to create data visualizations and animations using the gganimate R package extension for ggplot2. |
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svmiller.com
Here is a how-to on bootstrapping standard errors in R in a flexible way, using some tidyverse-friendly packages like modelr and purrr. |
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yang-song.net
This blog post focuses on a promising new direction for generative modeling. We can learn score functions (gradients of log probability density functions) on a large number of noise-perturbed data distributions, then generate samples with Langevin-type sampling. The resulting generative models, often called score-based generative models, has several important advantages over existing model families: GAN-level sample quality without adversarial training, flexible model architectures, exact log-likelihood ... |
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