A powerful and elegant high-level data visualization
  system inspired by Trellis graphics, with an emphasis on
  multivariate data. Lattice is sufficient for typical graphics needs,
  and is also flexible enough to handle most nonstandard requirements.
  See ?Lattice for an introduction.

Dirk Eddelbuettel <edd@debian.org> with contributions 
 by Antoine Lucas, Jarek Tuszynski, Henrik Bengtsson, Simon Urbanek,
 Mario Frasca, Bryan Lewis, Murray Stokely, Hannes Muehleisen,
 Duncan Murdoch, Jim Hester, Wush Wu, Qiang Kou, Thierry Onkelinx, 
 Michel Lang, Viliam Simko, Kurt Hornik and Radford Neal.

Create Compact Hash Digests of R Objects

Implementation of a function 'digest()' for the creation 
 of hash digests of arbitrary R objects (using the 'md5', 'sha-1', 'sha-256', 
 'crc32', 'xxhash' and 'murmurhash' algorithms) permitting easy comparison of R
 language objects, as well as a function 'hmac()' to create hash-based
 message authentication code. Please note that this package is not meant to
 be deployed for cryptographic purposes for which more comprehensive (and
 widely tested) libraries such as 'OpenSSL' should be used.

Provides Foreach Looping Construct for R

c(person("Rich", "Calaway", role="cre", email="richcala@microsoft.com"),
             person("Microsoft", role=c("aut", "cph")),
			 person("Steve", "Weston", role="aut"))

Support for the foreach looping construct.  Foreach is an
        idiom that allows for iterating over elements in a collection,
        without the use of an explicit loop counter.  This package in
        particular is intended to be used for its return value, rather
        than for its side effects.  In that sense, it is similar to the
        standard lapply function, but doesn't require the evaluation
        of a function.  Using foreach without side effects also
        facilitates executing the loop in parallel.

Rich Calaway [cre],
  Microsoft [aut, cph],
  Steve Weston [aut]

Tools for Splitting, Applying and Combining Data

A set of tools that solves a common set of problems: you
    need to break a big problem down into manageable pieces, operate on each
    piece and then put all the pieces back together.  For example, you might
    want to fit a model to each spatial location or time point in your study,
    summarise data by panels or collapse high-dimensional arrays to simpler
    summary statistics. The development of 'plyr' has been generously supported
    by 'Becton Dickinson'.