Скачать с ютуб Reproducible Computation at Scale in R with {targets} в хорошем качестве

Reproducible Computation at Scale in R with {targets}

The recording from R Lille (R User Group) meetup on the 17th of June, 2021: http://meetup.rlille.fr/events/277902... More Meetups at: http://meetup.rlille.fr/​ Materials / Slides at: https://wlandau.github.io/r-lille-2021/ [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.