Description: Implementation of the Future API on top of the 'BatchJobs' package.

Suggests: future.apply, listenv, markdown, R.rsp
e.g. 'y <- future.apply::future_lapply(files, FUN = process)'.
NOTE: The 'BatchJobs' package is deprecated in favor of the 'batchtools'
package. Because of this, it is recommended to use the 'future.batchtools'

Title: Apply Function to Elements in Parallel using Futures
Suggests: listenv (>= 0.6.0), R.rsp, markdown
Description: Implementations of apply(), lapply(), sapply() and friends 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 comes 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.

Suggests: stats, listenv (>= 0.7.0), R.rsp, markdown
person("R Core Team", role = c("cph", "ctb")))
Description: Implementations of lapply(), sapply(), tapply(), vapply() and friends 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.

Suggests: datasets, stats, tools, listenv (>= 0.7.0), R.rsp, markdown
Description: Implementations of apply(), 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.

Imports: globals (>= 0.16.1), parallel, utils
Suggests: datasets, stats, tools, listenv (>= 0.8.0), R.rsp, markdown
Description: 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.