c(
person("Michel", "Lang", NULL, "michellang@gmail.com",
role = c("cre", "aut"), comment = c(ORCID = "0000-0001-9754-0393")),
person("Bernd", "Bischl", NULL, "bernd_bischl@gmx.net", role = "aut"),
person("Dirk", "Surmann", NULL, "surmann@statistik.tu-dortmund.de",
role = "ctb", comment = c(ORCID = "0000-0003-0873-137X"))
)
As a successor of the packages 'BatchJobs' and 'BatchExperiments',
this package provides a parallel implementation of the Map function for high
performance computing systems managed by schedulers 'IBM Spectrum LSF'
(<https://www.ibm.com/us-en/marketplace/hpc-workload-management>),
'OpenLava' (<http://www.openlava.org/>), 'Univa Grid Engine'/'Oracle Grid
Engine' (<http://www.univa.com/>), 'Slurm' (<http://slurm.schedmd.com/>),
'TORQUE/PBS'
(<https://adaptivecomputing.com/cherry-services/torque-resource-manager/>),
or 'Docker Swarm' (<https://docs.docker.com/swarm/>).
A multicore and socket mode allow the parallelization on a local machines,
and multiple machines can be hooked up via SSH to create a makeshift
cluster. Moreover, the package provides an abstraction mechanism to define
large-scale computer experiments in a well-organized and reproducible way.
backports (>= 1.1.2), base64url (>= 1.1), brew, checkmate (>=
1.8.5), data.table (>= 1.11.2), digest (>= 0.6.9), fs (>=
1.2.0), parallel, progress (>= 1.1.1), R6, rappdirs, stats,
stringi, utils, withr (>= 2.0.0)

As a successor of the packages 'BatchJobs' and 'BatchExperiments',
this package provides a parallel implementation of the Map function for high
performance computing systems managed by schedulers 'IBM Spectrum LSF'
(<https://www.ibm.com/products/hpc-workload-management>),
'OpenLava' (<https://www.openlava.org/>), 'Univa Grid Engine'/'Oracle Grid
Engine' (<https://www.univa.com/>), 'Slurm' (<https://slurm.schedmd.com/>),
'TORQUE/PBS'
(<https://adaptivecomputing.com/cherry-services/torque-resource-manager/>),
or 'Docker Swarm' (<https://docs.docker.com/engine/swarm/>).
A multicore and socket mode allow the parallelization on a local machines,
and multiple machines can be hooked up via SSH to create a makeshift
cluster. Moreover, the package provides an abstraction mechanism to define
large-scale computer experiments in a well-organized and reproducible way.
debugme, doMPI, doParallel, e1071, foreach, future,
future.batchtools, knitr, parallelMap, ranger, rmarkdown, Rmpi,
rpart, snow, testthat, tibble