https://github.com/wlandau/rmedicine2021-slides

Slides for a possible talk at R/Medicine 2021 (submitted and under review, accepted talks not yet determined)
https://github.com/wlandau/rmedicine2021-slides

bayesian bayesian-statistics cmdstanr high-performance-computing markdown pipeline r r-markdown reproducibility stan stantargets statistics targets

Last synced: 13 days ago
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Slides for a possible talk at R/Medicine 2021 (submitted and under review, accepted talks not yet determined)

• Host: GitHub
• URL: https://github.com/wlandau/rmedicine2021-slides
• Owner: wlandau
• License: mit
• Created: 2021-07-02T16:05:51.000Z (over 3 years ago)
• Default Branch: main
• Last Pushed: 2021-08-11T02:26:46.000Z (over 3 years ago)
• Last Synced: 2024-11-05T04:42:22.663Z (2 months ago)
• Topics: bayesian, bayesian-statistics, cmdstanr, high-performance-computing, markdown, pipeline, r, r-markdown, reproducibility, stan, stantargets, statistics, targets
• Language: HTML
• Homepage: https://wlandau.github.io/rmedicine2021-slides/
• Size: 5.98 MB
• Stars: 3
• Watchers: 3
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • Contributing: CONTRIBUTING.md
  • License: LICENSE
  • Code of conduct: CODE_OF_CONDUCT.md

Awesome Lists containing this project

README

        
## 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 at  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 | 

Slide source | 

Pipeline report | 

Pipeline source | 

`targets` | 

Target Markdown | 

`stantargets` |  

Stan | 

`cmdstanr` | 

`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.