digest, globals (>= 0.14.0), listenv (>= 0.8.0), parallel,
parallelly (>= 1.30.0), tools, utils

digest, globals (>= 0.15.0), listenv (>= 0.8.0), parallel,
parallelly (>= 1.30.0), tools, utils

digest, globals (>= 0.15.0), listenv (>= 0.8.0), parallel,
parallelly (>= 1.31.1), tools, utils

digest, globals (>= 0.16.0), listenv (>= 0.8.0), parallel,
parallelly (>= 1.32.1), tools, utils

digest, globals (>= 0.16.1), listenv (>= 0.8.0), parallel,
parallelly (>= 1.32.1), tools, utils

digest, globals (>= 0.8.0), listenv (>= 0.6.0), parallel,
utils
The purpose of this package is to provide a lightweight and
unified Future API for sequential and parallel processing of R
expression via futures.  The simplest way to evaluate an expression
in parallel is to use `x %<-% { expression }` with `plan(multiprocess)`.
This package implements sequential, multicore, multisession, and
cluster futures.  With these, R expressions can be evaluated on the
local machine, on in parallel a set of local machines, or distributed
on a mix of local and remote machines.
Extensions to this package implements additional backends for
processing futures via compute cluster schedulers etc.
Because of its unified API, there is no need to modify code in order
switch from sequential on the local machine to, say, distributed
processing on a remote compute cluster.
Another strength of this package is that global variables and functions
are automatically identified and exported as needed, making it
straightforward to tweak existing code to make use of futures.

digest, globals (>= 0.9.0), listenv (>= 0.6.0), parallel,
utils

digest, globals (>= 0.10.0), listenv (>= 0.6.0), parallel,
utils

digest, globals (>= 0.10.2), listenv (>= 0.6.0), parallel,
utils

digest, globals (>= 0.10.3), listenv (>= 0.6.0), parallel,
utils

digest, globals (>= 0.11.0), listenv (>= 0.6.0), parallel,
utils

digest, globals (>= 0.11.0), listenv (>= 0.7.0), parallel,
utils

digest, globals (>= 0.12.0), listenv (>= 0.7.0), parallel,
utils

Simple parallel and distributed processing using futures
that utilizes the 'BatchJobs' framework,
e.g. 'fit %<-% { glm.fit(x, y) }'.
This package implements the Future API of the 'future' package.