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: 1.3.0
Depends: R (≥ 3.2.0), future (≥ 1.13.0)
Imports: globals (≥ 0.12.4)
Suggests: datasets, stats, tools, listenv (≥ 0.7.0), R.rsp, markdown
Published: 2019-06-18
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


Reference manual: future.apply.pdf
Vignettes: A Future for R: Apply Function to Elements in Parallel
Package source: future.apply_1.3.0.tar.gz
Windows binaries: r-devel: future.apply_1.3.0.zip, r-release: future.apply_1.3.0.zip, r-oldrel: future.apply_1.3.0.zip
OS X binaries: r-release: future.apply_1.3.0.tgz, r-oldrel: future.apply_1.3.0.tgz
Old sources: future.apply archive

Reverse dependencies:

Reverse imports: BAMBI, drtmle, fxtract, genBaRcode, GSODR, kernelboot, origami, phylolm, rangeMapper, robotstxt, RTransferEntropy, sctransform, Seurat, simglm, solitude, sperrorest, steps, TSstudio
Reverse suggests: DeclareDesign, future.BatchJobs, future.batchtools, future.callr, grattan, lgr, merTools, penaltyLearning, stars


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