Hello, R-Bloggers — What This Blog Is About

A short introduction for readers arriving via R-Bloggers — what I write about, who I am, and where to start.

If you’re arriving here from R-Bloggers, welcome. This is a quick orientation.

I’m an applied statistician and ML engineer based in Paris. For the past five years I’ve been embedded in R&D and clinical teams — at L’Oréal, Sanofi, a microbiome biotech, and currently Chanel Parfums Beauté — building data pipelines, statistical models, and ML systems in environments where reproducibility and auditability are not optional. R is my primary tool for statistical work, reporting, and Shiny applications; Python handles the ML side. Most posts here are field notes from that work: things I’ve worked out in production and thought worth documenting, for my future self as much as anyone else.

The R content on this blog is practitioner-focused. You’ll find posts on reproducibility in GxP-regulated environments, statistical methods applied to real formulation and clinical data, and R tooling that has earned its place in my daily workflow. Not beginner tutorials — notes from actual use.

Three posts to start with:

• Building Reproducible Analyses in Pharma with R — how I structure R projects in regulated environments, from renv to audit-ready reporting
• rv: The Modern R Package Manager for Reproducible Workflows — a look at the Rust-backed package manager that is quietly replacing my renv workflow
• Staying Current in a Fast-Moving Field: Why I Built a Resources Catalog — on curation as a practice, and the catalog of 200+ data science resources I maintain

The full blog listing has everything, filtered by category.