References

ACKNOWLEDGEMENTS · SOURCES · TEACHING ECOSYSTEMS

The sources we drew on

This curriculum did not grow in a vacuum. Everything worth keeping in it came from somewhere else first.

The list below is not exhaustive — nothing that tries to cover an entire field ever is — but it names the sources we leaned on hardest and the teachers whose own openly available material shaped the pedagogy. Every entry links to something you can read for free today. Where a book is not open-access we have flagged it, and where a web resource exists in parallel we have linked to that instead.

Comprehensive references

Clinical biostatistics

Frank Harrell — Biostatistics for Biomedical Research

The single most opinionated, densest, most useful reference for applied biostatistics in medicine. Required reading for anyone who wants to stop being surprised by reviewers.

Regression modelling

Frank Harrell — Regression Modeling Strategies

The companion to BBR. Spline fitting, calibration, validation, and the rms package, all in one place.

Course structure

Chi Zhang — MF9130E, University of Oslo

The OCBE introductory course that inspired the layout of this site. A model of how to teach biostatistics transparently and openly, with every lab reproducible from source. See also the GitHub repository.

Statistical learning

Hastie, Tibshirani, Witten, James — ISLR2

Introduction to Statistical Learning with R — the definitive first book on modern ML. The second edition adds deep-learning and survival chapters.

Causal inference

Hernán & Robins — Causal Inference: What If

The canonical modern treatment. G-methods, IV, and the careful statement of the questions that precede them.

Teaching ecosystems

Bergen

Biostats-R

A rich ecosystem of openly licensed courses in R for biosciences, hosted by the University of Bergen.

eR-Biostat

eR-Biostat initiative

Community-maintained R-based materials for applied biostatistics.

UBC Okanagan

BIOL202 — Introduction to Biostatistics

One of the clearest undergraduate introductions freely available online.

Sheffield

Introductory Biostatistics with R

Short, friendly, and opinionated. A good pairing with the first half of Course 1.

Pedagogy and reproducibility

Data-science teaching

Mine Çetinkaya-Rundel — Data Science in a Box

Every contemporary data-science course owes Mine a debt. Teach-the-teacher material at its best.

Quarto

Quarto documentation

The reference manual for the publishing system this site is built on.

Posit Education

Openly licensed teaching resources from the makers of RStudio.

renv

renv: Project Environments for R

The tool that keeps this site reproducible across machines.

Packages used

Every R package loaded by the labs is listed in renv.lock and re-exported by citation() in the session-info block at the foot of each lab. If you reuse any analysis here, cite the package authors alongside this site.

Acknowledgements

Beyond the sources above, this curriculum stands on the shoulders of teachers, tool-builders, colleagues, and reviewers whose contributions are named in full on the dedicated Acknowledgements page — covering didactics and pedagogy, content depth, tooling, and personal thanks.

We are grateful to all the educators above for making their work openly available. The entire point of a site like this one is that the next version of it, wherever and by whomever it is written, will be better.

Cite this curriculum

If this curriculum is useful in your teaching or research, please cite it.

@misc{heller2026courses,
  author       = {Heller, Raban},
  title        = {{\#}courses: A four-course biostatistics programme in R and Quarto},
  year         = {2026},
  publisher    = {CTTIR},
  howpublished = {\url{https://cttir.github.io/courses/}},
  note         = {MIT licence. Source: \url{https://github.com/CTTIR/courses}}
}