Reproducible Computation at Scale in R with {targets}, by Will Landau

The {targets} R package enhances the reproducibility, scale, and maintainability of data science projects in computationally intense fields such as machine learning, Bayesian data analysis, and statistical genomics. {targets} resolves the dependency structure of the analysis pipeline, skips tasks that are already up to date, executes the rest with optional distributed computing, and manages data storage. {stantargets} and similar packages extend {targets} to simplify pipeline construction for specialized use cases such as the validation of Bayesian models.

This event (in English) is organised by the R Lille user group.

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