In computationally demanding analysis projects,
  statisticians and data scientists asynchronously
  deploy long-running tasks to distributed systems,
  ranging from traditional clusters to cloud services.
  The 'NNG'-powered 'mirai' R package by Gao (2023)
  <doi:10.5281/zenodo.7912722> is a sleek
  and sophisticated scheduler that
  efficiently processes these intense workloads.
  The 'crew' package extends 'mirai' with a unifying
  interface for third-party worker launchers.
  Inspiration also comes from packages.
  'future' by Bengtsson (2021) <doi:10.32614/RJ-2021-048>,
  'rrq' by FitzJohn and Ashton (2023) <https://github.com/mrc-ide/rrq>,
  'clustermq' by Schubert (2019) <doi:10.1093/bioinformatics/btz284>),
  and 'batchtools' by Lang, Bischel, and Surmann (2017)
  <doi:10.21105/joss.00135>.

In computationally demanding analysis projects,
  statisticians and data scientists asynchronously
  deploy long-running tasks to distributed systems,
  ranging from traditional clusters to cloud services.
  The 'crew.aws.batch' package extends the 'mirai'-powered
  'crew' package with a worker launcher plugin for AWS Batch.
  Inspiration also comes from packages 'mirai' by Gao (2023)
  <https://github.com/shikokuchuo/mirai>,
  'future' by Bengtsson (2021) <doi:10.32614/RJ-2021-048>,
  'rrq' by FitzJohn and Ashton (2023) <https://github.com/mrc-ide/rrq>,
  'clustermq' by Schubert (2019) <doi:10.1093/bioinformatics/btz284>),
  and 'batchtools' by Lang, Bischl, and Surmann (2017).
  <doi:10.21105/joss.00135>.

In computationally demanding analysis projects,
  statisticians and data scientists asynchronously
  deploy long-running tasks to distributed systems,
  ranging from traditional clusters to cloud services.
  The 'crew.cluster' package extends the 'mirai'-powered
  'crew' package with worker launcher plugins for traditional
  high-performance computing systems.
  Inspiration also comes from packages 'mirai' by Gao (2023)
  <https://github.com/shikokuchuo/mirai>,
  'future' by Bengtsson (2021) <doi:10.32614/RJ-2021-048>,
  'rrq' by FitzJohn and Ashton (2023) <https://github.com/mrc-ide/rrq>,
  'clustermq' by Schubert (2019) <doi:10.1093/bioinformatics/btz284>),
  and 'batchtools' by Lang, Bischl, and Surmann (2017).
  <doi:10.21105/joss.00135>.