future.apply: Apply Function to Elements in Parallel using Futures
Implementations of apply(), by(), 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: |
1.4.0 |
| Depends: |
R (≥ 3.2.0), future (≥ 1.15.1) |
| Imports: |
globals (≥ 0.12.5) |
| Suggests: |
datasets, stats, tools, listenv (≥ 0.8.0), R.rsp, markdown |
| Published: |
2020-01-07 |
| 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 depends: |
dhReg |
| Reverse imports: |
BAMBI, blavaan, ck37r, cSEM, dipsaus, drtmle, forecastML, fxtract, genBaRcode, GSODR, gWQS, gWQSRS, hackeRnews, kernelboot, lightr, mcp, origami, pavo, phylolm, qgcomp, rangeMapper, robotstxt, RTransferEntropy, sctransform, Seurat, simglm, solitude, sperrorest, steps, TSstudio |
| Reverse suggests: |
DeclareDesign, fabletools, future.BatchJobs, future.batchtools, future.callr, glmmboot, grattan, inlinedocs, lgr, merTools, mlr3, mlr3db, PeakSegDisk, penaltyLearning, progressr, stars, tcensReg |
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