Programmatic interface to the
    Web Service methods provided by the National 'Phenology' Network
    (<https://usanpn.org/>), which includes data on various life history
    events that occur at specific times.

Apply sensitivity analysis for offline policy evaluation, as
    implemented in Jung et al. (2017) <arXiv:1702.04690> based
    on Rosenbaum and Rubin (1983) <http://www.jstor.org/stable/2345524>.

Utility functions to retrieve data from the UK National River
    Flow Archive (<https://nrfa.ceh.ac.uk/>, terms and conditions:
    <https://nrfa.ceh.ac.uk/costs-terms-and-conditions>). The package
    contains R wrappers to the UK NRFA data temporary-API. There are
    functions to retrieve stations falling in a bounding box, to generate
    a map and extracting time series and general information. The package
    is fully described in Vitolo et al (2016) "rnrfa: An R package to
    Retrieve, Filter and Visualize Data from the UK National River Flow
    Archive"
    <https://journal.r-project.org/archive/2016/RJ-2016-036/RJ-2016-036.pdf>.

Routines to interact with the Numerai Machine Learning Tournament
  API <https://numer.ai>. The functionality includes the ability to automatically download the
  current tournament data, submit predictions, and to get information for your
  user.

Creates JavaScript charts with the 'nvd3' library. So far only the multibar chart, the horizontal multibar chart, the line chart and the line chart with focus are available.

This web client interfaces Unpaywall <https://unpaywall.org/products/api>, formerly
    oaDOI, a service finding free full-texts of academic papers by linking DOIs with 
    open access journals and repositories. It provides unified access to various data sources 
    for open access full-text links including Crossref and the Directory of Open Access 
    Journals (DOAJ). API usage is free and no registration is required.

Project road network development based on an existing road 
    network, target locations to be connected by roads and a cost surface. Road 
    projection methods include minimum spanning tree with least cost path 
    (Kruskal's algorithm (1956) <doi:10.2307/2033241>), least cost path 
    (Dijkstra's algorithm (1959) <doi:10.1007/BF01386390>) or snapping. 
    These road network projection methods are ideal for use with land cover
    change projection models.