Yet another Blood Bowl post! This one is to warm up to the upcoming World Cup, analyzing variation in Tournament Roster choices.

Restate my assumptions: If you graph the numbers of any system, patterns emerge. In this post we'll use clustered heatmaps to graph the numbers from the Blood Bowl Fantasy football game, and see what patterns emerge!

This blog post demonstrates that the presence of irrelevant variables can reduce the performance of the Random Forest algorithm (as implemented in R by `ranger()`). The solution is either to tune one of the algorithm's parameters, OR to remove irrelevant features using a procedure called Recursive Feature Elimination (RFE).

This blog post is about the Roche Rapid Antigen Test Nasal. How accurate is it? I tracked down the data mentioned in the kit's leaflet, discuss the whole measurement process and used R to reproduce the sensitivity and specificity of the test.

Here we show how to use Stan and the brms R-package to calculate the posterior predictive distribution of a covariate-adjusted average treatment effect (ATE).

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