Title: A Parallel-Voting Version of the Support-Vector-Machine
Description: By sampling your data, running the Support-Vector-Machine algorithm on these samples in parallel on your own machine and letting your models vote on a prediction, we return much faster predictions than the regular Support-Vector-Machine and possibly even more accurate predictions.

Title: Automate parallelization of function calls by means of dynamic
Description: Passing a given function name or a call to the
parallelize/parallelize_call functions analyses and executes
the code, if possible in parallel. Parallel code execution can
be performed locally or on remote batch queuing systems.

Description: Utility functions that enhances the 'parallel' packages and that are used for the built-in parallel backends of the 'future' package.  For example, availableCores() gives the number of CPU cores available to your R process as given by relevant R options and environment variables including those set by job schedulers on high-performance compute clusters. If none is set, it will fall back to parallel::detectCores(). Another example is makeClusterPSOCK(), which is backward compatible with parallel::makePSOCKcluster() while doing a better job in setting up a remote cluster workers without the need for configuring the firewall to do port-forwarding to your local computer.

Description: Utility functions that enhance the 'parallel' package and support the built-in parallel backends of the 'future' package.  For example, availableCores() gives the number of CPU cores available to your R process as given by R options and environment variables, including those set by job schedulers on high-performance compute clusters. If none is set, it will fall back to parallel::detectCores(). Another example is makeClusterPSOCK(), which is backward compatible with parallel::makePSOCKcluster() while doing a better job in setting up remote cluster workers without the need for configuring the firewall to do port-forwarding to your local computer.