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 recommended approach is to use 'y <- foreach(...) %dofuture% { ... }', which does not require using 'registerDoFuture()' and has many advantages over '%dopar%'. The alternative is the traditional 'foreach' approach by registering the 'foreach' adapter 'registerDoFuture()' and so that 'y <- foreach(...) %dopar% { ... }' runs in parallelizes with the 'future' framework.

Version: 1.0.1
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: 2023-12-20
Author: Henrik Bengtsson ORCID iD [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_1.0.1.tar.gz
Windows binaries: r-prerel:, r-release:, r-oldrel:
macOS binaries: r-prerel (arm64): doFuture_1.0.1.tgz, r-release (arm64): doFuture_1.0.1.tgz, r-oldrel (arm64): doFuture_1.0.1.tgz, r-prerel (x86_64): doFuture_1.0.1.tgz, r-release (x86_64): doFuture_1.0.1.tgz
Old sources: doFuture archive

Reverse dependencies:

Reverse imports: antaresEditObject, baskexact, basksim, envi, funGp, fxTWAPLS, GeDS, hwep, kergp, latentcor, LWFBrook90R, nebula, pareg, parseRPDR, remiod, rpm, rTwig, SharkDemography, simtrial, skpr, sparrpowR, sphunif, ssdtools, stacks, survstan, tglkmeans, tune, updog, vmeasur, WeightedCluster
Reverse suggests: bhmbasket, ISAnalytics, kernelshap, ldsr, mikropml, momentuHMM, mslp, oncomsm, progressr, projpred, robust2sls, SCdeconR, semPower, sRACIPE


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