Please note: abstracts for R/Medicine 2021 are still under review and talks have yet to be determined. It is still too early to know if this talk will be accepted.

Target Markdown and stantargets for Bayesian model validation pipelines

The targets R package enhances the reproducibility, scale, and maintainability of data science projects in computationally intense fields such as machine learning, Bayesian Statistics, and statistical genomics. Recent breakthroughs in the targets ecosystem make it easy to create ambitious, domain-specific, reproducible data analysis pipelines. Two highlights include Target Markdown, an R Markdown interface to transparently communicate the entire process of pipeline construction and prototyping, and stantargets, a new rOpenSci package that generates specialized workflows for Stan models while reducing the required volume of user-side R code. The index.Rmd R Markdown report in this repository demonstrates both capabilities in a simulation-based workflow to validate a Bayesian longitudinal linear model common in clinical trial data analysis.

Resources

Resource	Link
Slides	https://wlandau.github.io/rmedicine2021-slides/
Slide source	https://github.com/wlandau/rmedicine2021-slides
Pipeline report	https://wlandau.github.io/rmedicine2021-pipeline/
Pipeline source	https://github.com/wlandau/rmedicine2021-pipeline
targets	https://docs.ropensci.org/targets/
Target Markdown	https://books.ropensci.org/targets/markdown.html
stantargets	https://docs.ropensci.org/stantargets/
Stan	https://mc-stan.org/
cmdstanr	https://mc-stan.org/cmdstanr/
posterior	https://mc-stan.org/posterior/

Thanks

• stantargets: Melina Vidoni served as editor and Krzysztof Sakrejda and Matt Warkentin served as reviewers during the rOpenSci software review process.
• Target Markdown: Christophe Dervieux and Yihui Xie provided crucial advice during initial development.
• Richard Payne and Karen Price reviewed this Bayesian model validation project.