The futurize() function turns sequential map-reduce functions such as base::lapply(), purrr::map(), 'foreach::foreach() %do% { ... }' into concurrent alternatives, providing you with a simple, straightforward path to scalable parallel computing via the 'future' ecosystem <doi:10.32614/RJ-2021-048>. By combining this transpiler function with R's native pipe operator, you have a convenient way for speeding up iterative computations with minimal refactoring, e.g. 'lapply(xs, fcn) |> futurize()', 'purrr::map(xs, fcn) |> futurize()', and 'foreach::foreach(x = xs) %do% { fcn(x) } |> futurize()'. Other map-reduce packages that can be "futurized" are 'BiocParallel', 'plyr', 'crossmap', 'pbapply' packages. There is also support for a growing set of domain-specific packages on CRAN (e.g. 'boot', 'caret', 'DiceKriging', 'ez', 'fgsea', 'fwb', 'gamlss', 'glmmTMB', 'glmnet', 'kernelshap', 'lme4', 'metafor', 'mgcv', 'modelsummary', 'parameters', 'partykit', 'pls', 'pvclust', 'riskRegression', 'rugarch', 'sandwich', 'seriation', 'shapr', 'Sim.DiffProc', 'SimDesign', 'stars', 'strucchange', 'SuperLearner', 'tm', 'TSP', and 'vegan') and on Bioconductor (e.g. 'DESeq2', 'GenomicAlignments', 'GSVA', 'Rsamtools', 'scater', 'scuttle', 'SingleCellExperiment', and 'sva').
| Package source: | futurize_1.0.0.tar.gz |
| Windows binaries: | r-devel: futurize_1.0.0.zip, r-release: futurize_1.0.0.zip, r-oldrel: futurize_1.0.0.zip |
| macOS binaries: | r-release (arm64): futurize_1.0.0.tgz, r-oldrel (arm64): futurize_1.0.0.tgz, r-release (x86_64): futurize_1.0.0.tgz, r-oldrel (x86_64): futurize_1.0.0.tgz |
| Old sources: | futurize archive |
| Reverse imports: | futureverse |
| Reverse suggests: | progressify, rtemis |
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