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Reproducible Computation at Scale in R with {targets}

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Mickaël C.
Reproducible Computation at Scale in R with {targets}

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[Abstract]
The {targets} (https://docs.ropensci.org/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} (https://docs.ropensci.org/stantargets/) and similar packages extend {targets} to simplify pipeline construction for specialized use cases such as the validation of Bayesian models.

[Bio]
Will Landau (https://wlandau.github.io/) works at Eli Lilly and Company (https://www.lilly.com/) where he develops methods and tools for clinical statisticians, and he is the creator and maintainer of the {targets} and {drake} R packages. Will earned his PhD in Statistics at Iowa State University in 2016, where his dissertation research applied Bayesian methods, hierarchical models, and GPU computing to the analysis of RNA-seq data.

[About]
This event (in English) is organised by the R Lille user group (Lille, France).
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