https://github.com/wlandau/nyhackr2020
Presentation on targets at the New York Open Statistical Programming Meetup
https://github.com/wlandau/nyhackr2020
cmdstan cmdstanr drake high-performance-computing pipelines r reproducibility rstats stan targets
Last synced: about 1 month ago
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Presentation on targets at the New York Open Statistical Programming Meetup
- Host: GitHub
- URL: https://github.com/wlandau/nyhackr2020
- Owner: wlandau
- Created: 2020-08-28T22:11:34.000Z (over 4 years ago)
- Default Branch: main
- Last Pushed: 2020-11-14T21:06:23.000Z (over 4 years ago)
- Last Synced: 2025-02-14T13:15:28.560Z (3 months ago)
- Topics: cmdstan, cmdstanr, drake, high-performance-computing, pipelines, r, reproducibility, rstats, stan, targets
- Language: HTML
- Homepage: https://wlandau.github.io/nyhackr2020
- Size: 4.47 MB
- Stars: 2
- Watchers: 3
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Reproducible computation at scale in R with targets
Ambitious workflows in R, such as machine learning analyses, can be difficult to manage. A single round of computation can take several hours to complete, and routine updates to the code and data tend to invalidate hard-earned results. You can enhance the maintainability, hygiene, speed, scale, and reproducibility of such projects with the targets R package. targets resolves the dependency structure of your analysis pipeline, skips tasks that are already up to date, executes the rest with optional distributed computing, and manages data storage for you. It surpasses the permanent limitations of its predecessor, drake, and provides increased efficiency and a smoother user experience. This talk demonstrates how to create and maintain a Bayesian model validation project using targets-powered automation.