The 'Vowpal Wabbit' project is a fast out-of-core learning
system sponsored by Microsoft Research (having started at Yahoo! Research)
and written by John Langford along with a number of contributors. This R
package does not include the distributed computing implementation of the
cluster/ directory of the upstream sources. Use of the software as a network
service is also not directly supported as the aim is a simpler direct call
from R for validation and comparison. Note that this package contains an
embedded older version of 'Vowpal Wabbit'. The package 'rvw' at the GitHub
repo <https://github.com/eddelbuettel/rvw> can provide an alternative using
an external 'Vowpal Wabbit' library installation.
The Boost 'program_options' library
(http://boost.org) is required.

The Boost 'program_options' library
<https://boost.org> is required.

The 'Vowpal Wabbit' project is a fast out-of-core learning
system sponsored by Microsoft Research (having started at Yahoo! Research)
and written by John Langford along with a number of contributors. This R
package does not include the distributed computing implementation of the
cluster/ directory of the upstream sources. Use of the software as a network
service is also not directly supported as the aim is a simpler direct call
from R for validation and comparison. Note that this package contains an
embedded older version of 'Vowpal Wabbit'. The package 'rvw' at the GitHub
repo <https://github.com/rvw-org/rvw-legacy> can provide an alternative using
an external 'Vowpal Wabbit' library installation.

R interface to Vowpal Wabbit fast out-of-core learning
system The Vowpal Wabbit (VW) project is a fast out-of-core
learning system sponsored by Yahoo! Research and written by
John Langford along with a number of contributors.

There are two ways to have a fast learning algorithm: (a) start with a
slow algorithm and speed it up, or (b) build an intrinsically
fast learning algorithm. This project is about approach (b),
and it has reached a state where it may be useful to others as
a platform for research and experimentation.

There are several optimization algorithms available with the baseline
being sparse gradient descent (GD) on a loss function (several
are available). The code should be easily usable. Its only
external dependence is on the Boost library, which is often
installed by default.