future 1.3.0 is available on CRAN. With futures, it is easy to write R code once, which the user can choose to evaluate in parallel using whatever resources s/he has available, e.g. a local machine, a set of local machines, a set of remote machines, a high-end compute cluster (via future.BatchJobs and soon also future.batchtools), or in the cloud (e.g. via googleComputeEngineR). Futures makes it easy to harness any resources at hand.
A new version of the future.BatchJobs package has been released and is available on CRAN. With a single change of settings, it allows you to switch from running an analysis sequentially on a local machine to running it in parallel on a compute cluster. Our different futures can easily be resolved on high-performance compute clusters. Requirements The future.BatchJobs package implements the Future API, as defined by the future package, on top of the API provided by the BatchJobs package.