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.

A Future API for Parallel and Distributed Processing using
'batchtools'
Implementation of the Future API on top of the 'batchtools' package.
This allows you to process futures, as defined by the 'future' package,
in parallel out of the box, not only on your local machine or ad-hoc
cluster of machines, but also via high-performance compute ('HPC') job
schedulers such as 'LSF', 'OpenLava', 'Slurm', 'SGE', and 'TORQUE' / 'PBS',
e.g. 'y <- future.apply::future_lapply(files, FUN = process)'.

Implements of the Future API on top of the 'batchtools' package.
This allows you to process futures, as defined by the 'future' package,
in parallel out of the box, not only on your local machine or ad-hoc
cluster of machines, but also via high-performance compute ('HPC') job
schedulers such as 'LSF', 'OpenLava', 'Slurm', 'SGE', and 'TORQUE' / 'PBS',
e.g. 'y <- future_lapply(files, FUN = process)'.

Implementation of the Future API on top of the 'batchtools' package.
This allows you to process futures, as defined by the 'future' package,
in parallel out of the box, not only on your local machine or ad-hoc
cluster of machines, but also via high-performance compute ('HPC') job
schedulers such as 'LSF', 'OpenLava', 'Slurm', 'SGE', and 'TORQUE' / 'PBS',
e.g. 'y <- future_lapply(files, FUN = process)'.

A Future API for Parallel Processing using 'callr'
Implementation of the Future API on top of the 'callr' package.  This allows you to process futures, as defined by the 'future' package, in parallel out of the box, on your local (Linux, macOS, Windows, ...) machine.  Contrary to backends relying on the 'parallel' package (e.g. 'future::multisession'), the 'callr' backend provided here can run more than 125 parallel R processes.