Post Global Financial Crisis derivatives reforms have lifted
    the veil off over-the-counter (OTC) derivative markets. Swap Execution
    Facilities (SEFs) and Swap Data Repositories (SDRs) now publish data
    on swaps that are traded on or reported to those facilities
    (respectively). This package provides you the ability to get this data
    from supported sources.

Provides read and write access to data and metadata from
    the DataONE network <https://www.dataone.org> of data repositories.  
    Each DataONE repository implements a consistent repository application 
    programming interface. Users call methods in R to access these remote 
    repository functions, such as methods to query the metadata catalog, get 
    access to metadata for particular data packages, and read the data objects 
    from the data repository. Users can also insert and update data objects on 
    repositories that support these methods.

Provides a flexible container to transport and manipulate complex
    sets of data. These data may consist of multiple data files and associated
    meta data and ancillary files. Individual data objects have associated system
    level meta data, and data files are linked together using the OAI-ORE standard
    resource map which describes the relationships between the files. The OAI-
    ORE standard is described at <https://www.openarchives.org/ore/>. Data packages
    can be serialized and transported as structured files that have been created
    following the BagIt specification. The BagIt specification is described at
    <https://tools.ietf.org/html/draft-kunze-bagit-08>.

A framework to help construct R data packages in a 
  reproducible manner. Potentially time consuming processing of 
  raw data sets into analysis ready data sets is done in a reproducible 
  manner and decoupled from the usual 'R CMD build' process so that
  data sets can be processed into R objects in the data package and 
  the data package can then be shared, built, and installed by others
  without the need to repeat computationally costly data processing.
  The package maintains data provenance by turning the data processing
  scripts into package vignettes, as well as enforcing documentation 
  and version checking of included data objects. Data packages can be
  version controlled on 'GitHub', and used to share data for manuscripts,
  collaboration and reproducible research.