Target Markdown and {stantargets} for Bayesian model validation pipelines
Channel: R Consortium
Date: 2021-08-31

Will Landau, Senior Research Scientist, presents on using Target Markdown, a system that has all the convenience of Rmarkdown and {stantargets}. Landau covers the workflows, the dilemma of short runtimes or reproducible results, using the pipeline tool, and other challenges. Landau, also describes an example of the Bayesian longitudinal model for clinical trials, ensuring that the model is implemented correctly through calibration.

Originally presented at R/Medicine 2021 by Will Landau
https://r-medicine.org

Main Sections

0:08 Target markdown and {stantargets} for Bayesian model 
0:57 Repetition: the overlooked bane of long computation 
1:40 Workflows have interconnected steps  
1:57 If you change code or data
2:02 The downstream steps are no longer valid
2:27 Dilemma: short runtimes or reproducible results?
2:56 Let a pipeline tool figure out what to return
3:53 Pipeline tools 
4:26 Challenge
5:16 Extending targets
5:59 Target factories simplify pipeline construction
7:05 Example: Bayesian model for clinical trials
8:12 Interval-based validation study
8:57 Write the pipeline in Target markdown
9:59 One function to simulate prior predictive data 
11:15 Simulations and MCMC with stantargets
12:34 Simple target to convergence diagnostics
13:01 Simple targets for coverage statistics
13:24 Optimal code chunk to run the pipeline
13:49 Optimal code chunks to read the results 
14:39 Coverage is nominal
 
More Resources 

Main Site: https://www.r-consortium.org/ 
News: https://www.r-consortium.org/news 
Blog: https://www.r-consortium.org/news/blog 
Join: https://www.r-consortium.org/about/join 
Twitter: https://twitter.com/Rconsortium 
LinkedIn: https://www.linkedin.com/company/r-consortium/