future.apply: Apply Function to Elements in Parallel using Futures
Implementations of apply(), eapply(), lapply(), Map(), mapply(), replicate(), sapply(), tapply(), and vapply() that can be resolved using any future-supported backend, e.g. parallel on the local machine or distributed on a compute cluster. These future_*apply() functions come with the same pros and cons as the corresponding base-R *apply() functions but with the additional feature of being able to be processed via the future framework.
| Version: |
1.0.1 |
| Depends: |
R (≥ 3.2.0), future (≥ 1.9.0) |
| Imports: |
globals (≥ 0.12.1) |
| Suggests: |
datasets, stats, tools, listenv (≥ 0.7.0), R.rsp, markdown |
| Published: |
2018-08-26 |
| Author: |
Henrik Bengtsson [aut, cre, cph],
R Core Team [cph, ctb] |
| Maintainer: |
Henrik Bengtsson <henrikb at braju.com> |
| BugReports: |
https://github.com/HenrikBengtsson/future.apply/issues |
| License: |
GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| URL: |
https://github.com/HenrikBengtsson/future.apply |
| NeedsCompilation: |
no |
| Materials: |
NEWS |
| CRAN checks: |
future.apply results |
Downloads:
Reverse dependencies:
| Reverse imports: |
BAMBI, drtmle, kernelboot, origami, phylolm, R.filesets, robotstxt, RTransferEntropy, sperrorest |
| Reverse suggests: |
DeclareDesign, drake, future.BatchJobs, future.batchtools, future.callr, penaltyLearning |
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