Theo

Google Colab

Packages pipe-first API where you chain stages and control who advances. Build complex tournament dynamics with zero dependencies beyond base R — lightweight enough for Shiny internals.

Try the World Cup 2026 simulation:

colab.research.google.com/github/bbthe...

Tournament Generator Create and manage tournament brackets for various competition formats including single elimination, double elimination, round robin, Swiss system, and group-stage-to-knockout tournaments. Provides too...

Started building {bracketeer} because I needed to run a pétanque tournament for friends and couldn't believe this wasn't solved already.

Turns out tournament modeling is harder than it first looks.

bbtheo.github.io/bracketeer/
#rstats

Google Colab

Want to try it without installing anything? This notebook simulates a full 48-team FIFA World Cup 2026 in your browser. 12 groups, 104 matches, custom qualification rules, all the way to a champion.

colab.research.google.com/github/bbtheo/bracketeer/blob/main/notebooks/world_cup_2026.ipynb

GitHub - bbtheo/bracketeer Contribute to bbtheo/bracketeer development by creating an account on GitHub.

Pub quiz. Office ping pong. 10,000 simulated World Cups. A Champions League bracket for your fantasy league.

tournament(teams) |>
swiss("open", rounds = 5) |>
single_elim("playoffs", take = top_n(8))

bracketeer 0.1.0 tournament modeling in base #Rstats
github.com/bbtheo/bracketeer

Not really as it is completely reliant on cuda and the cudf library which are sadly locked to Nvidia gpus.

DuckDB is an amazing tool that I also daily drive! I just wanted to see if I could put my GPU to work and it's been a fun experiment. Works better than I expected.

GitHub - bbtheo/cuplyr: GPU powered dataframes in R GPU powered dataframes in R. Contribute to bbtheo/cuplyr development by creating an account on GitHub.

cuplyr version 0.1.0 is now out!

A GPU-accelerated dplyr backend for R, powered by RAPIDS cuDF.

Write familiar tidyverse code, execute on GPU. Lazy eval with AST optimization.

In my benchmarks 60x faster than dplyr on 50M rows.

github.com/bbtheo/cuplyr
#rstats #cuda #DataScience

tstats-project.org

New post: the design behind secretbase, a 40KB streaming hash package I maintain.

The entire source package is smaller than most hex sticker PNGs.

Hash large objects with constant memory. Powers targets pipelines. Plus CBOR encoding comes to R.

shikokuchuo.net/posts/28-int...

#RStats #OpenSource

The first data science book that has a chapter on monads reproducible-data-science.dev

Learn how to build robust #DataScience pipelines with #RStats, #Python , #Julia and #Nix !

As alway it is quite iteresting when working with C++ dependecies. But I use Pixi as a package manager which has made it relatively painless.

GitHub - bbtheo/cuplyr: GPU powered dataframes in R GPU powered dataframes in R. Contribute to bbtheo/cuplyr development by creating an account on GitHub.

What if your dplyr pipelines ran on GPU?

That's what I built with cuplyr! A CUDA-powered backend for #rstats data manipulation. Looking for testers and feedback!

github.com/bbtheo/cuplyr