Apply Function to Elements in Parallel using Futures R package future.apply version 1.20.2 CRAN Task View: HighPerformanceComputing (https://CRAN.R-project.org/view=HighPerformanceComputing) Implementations of apply(), by(), eapply(), lapply(), Map(), .mapply(), 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 <doi:10.32614/RJ-2021-048>. Software Depends: R (>= 3.2.0), future (>= 1.49.0) Imports: globals, parallel, utils Suggests: datasets, stats, tools, listenv, R.rsp, markdown Henrik Bengtsson <henrikb@braju.com> Comprehensive R Archive Network (CRAN) Henrik Bengtsson [aut, cre, cph] (ORCID: <https://orcid.org/0000-0002-7579-5165>), R Core Team [cph, ctb] GPL (>= 2) 2026-02-20 application/tgz https://CRAN.R-project.org/package=future.apply doi:10.32614/CRAN.package.future.apply en-US