Research software · Evidence synthesis · Open science · GNU/Linux
Projects · Writing · LinkedIn · X
I build statistical and research tools that make evidence synthesis, biostatistics, and scientific publishing more reproducible.
My work sits mostly at the intersection of R, Python, Bayesian modeling, meta-analysis, research integrity, and GNU/Linux.
- Population-adjusted indirect treatment comparisons and evidence synthesis methods
- R packages and Shiny tools for biostatistics workflows
- Research integrity tooling for citations, retractions, and DOI validation
- Practical automation for statistical programming and open science
| Project | What it does | Stack |
|---|---|---|
| mlumr | Multilevel unanchored meta-regression for disconnected evidence networks | R, Stan, C++ |
| respondeR | Responder analysis for continuous outcomes, with R/Shiny/browser tooling | R, Shiny |
| sysreqR | Preflight checks for R package system requirements on GNU/Linux | R, GNU/Linux |
| citicious | Flags retracted articles and suspicious scholarly citations on academic pages | TypeScript |
| BiostatAgent | Claude Code plugin marketplace for biostatistics workflows in R | Python, R |
| cochraneauthors | Meta-research on authors of Cochrane Reviews | HTML, research methods |
Good research software should be:
- Reproducible enough for another analyst to rerun it.
- Transparent enough for a reviewer to audit it.
- Documented enough for a tired user to understand it.
- Small and boring where possible; cleverness is not a substitute for reliability.
Open science over closed claims. Reproducible code over screenshots. Useful tools over vanity metrics.