[Bayes@Lund] stantargets and Target Markdown for Bayesian model validation

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 stantargets, a new rOpenSci package that generates specialized workflows for Stan models while reducing the required volume of user-side R code, and Target Markdown, an R Markdown interface to transparently communicate the entire process of pipeline construction and prototyping. The example Target Markdown report at https://wlandau.github.io/rmedicine2021-pipeline (source: https://github.com/wlandau/rmedicine2021-pipeline) demonstrates both capabilities in a simulation-based workflow to validate a Bayesian longitudinal linear model common in clinical trial data analysis.