doFuture: Use Foreach to Parallelize via the Future Framework

The 'future' package provides a unifying parallelization framework for R that supports many parallel and distributed backends. The 'foreach' package provides a powerful API for iterating over an R expression in parallel. The 'doFuture' package brings the best of the two together. There are two alternative ways to use this package. The first is the traditional 'foreach' approach by registering the 'foreach' adapter 'registerDoFuture()' and so that 'y <- foreach(...) %dopar% { ... }' runs in parallelizes with the 'future' framework. The other alternative is to use 'y <- foreach(...) %dofuture% { ... }', which does not require using 'registerDoFuture()' and has many advantages over '%dopar%'.

Version: 1.0.0
Depends: foreach (≥ 1.5.0), future (≥ 1.32.0)
Imports: future.apply, globals, iterators, parallel, utils
Suggests: doRNG (≥ 1.8.2), markdown, R.rsp
Published: 2023-03-24
Author: Henrik Bengtsson [aut, cre, cph]
Maintainer: Henrik Bengtsson <henrikb at>
License: LGPL-2.1 | LGPL-3 [expanded from: LGPL (≥ 2.1)]
NeedsCompilation: no
Citation: doFuture citation info
Materials: NEWS
In views: HighPerformanceComputing
CRAN checks: doFuture results


Reference manual: doFuture.pdf
Vignettes: doFuture: An Overview on using Foreach to Parallelize via the Future Framework
Foreach Iteration using Futures via %dopar%
Foreach Iteration using Futures via %dofuture%


Package source: doFuture_1.0.0.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): doFuture_1.0.0.tgz, r-oldrel (arm64): doFuture_1.0.0.tgz, r-release (x86_64): doFuture_1.0.0.tgz, r-oldrel (x86_64): doFuture_1.0.0.tgz
Old sources: doFuture archive

Reverse dependencies:

Reverse imports: antaresEditObject, envi, funGp, fxTWAPLS, hwep, kergp, latentcor, LWFBrook90R, multilevelcoda, nebula, pareg, remiod, rpm, SharkDemography, sparrpowR, sphunif, ssdtools, tglkmeans, updog, vmeasur, WeightedCluster
Reverse suggests: bhmbasket, ISAnalytics, kernelshap, ldsr, mikropml, momentuHMM, mslp, oncomsm, progressr, projpred, robust2sls, semPower, sRACIPE


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