Download geospatial data available
    from several federated data sources (mainly sources maintained by the
    US Federal government). Currently, the package enables extraction from
    nine datasets: The National Elevation Dataset digital elevation
    models (<https://www.usgs.gov/3d-elevation-program> 1 and 1/3 arc-second; 
    USGS); The National Hydrography Dataset
    (<https://www.usgs.gov/national-hydrography/national-hydrography-dataset>; 
    USGS); The Soil Survey Geographic (SSURGO) database from the National
    Cooperative Soil Survey (<https://websoilsurvey.sc.egov.usda.gov/>; NCSS), 
    which is led by the Natural Resources
    Conservation Service (NRCS) under the USDA; 
    the Global Historical Climatology Network (<https://www.ncei.noaa.gov/products/land-based-station/global-historical-climatology-network-daily>; GHCN), coordinated by National Climatic Data
    Center at NOAA; the Daymet gridded estimates of daily weather
    parameters for North America, version 4, available from the Oak Ridge
    National Laboratory's Distributed Active Archive Center 
    (<https://daymet.ornl.gov/>; DAAC); the
    International Tree Ring Data Bank; the National Land Cover
    Database (<https://www.mrlc.gov/>; NLCD); the Cropland Data Layer from the National Agricultural 
    Statistics Service (<https://www.nass.usda.gov/Research_and_Science/Cropland/SARS1a.php>; NASS); 
    and the PAD-US dataset of protected area boundaries (<https://www.usgs.gov/programs/gap-analysis-project/science/pad-us-data-overview>; USGS).

Access data from the Federal Register API <https://www.federalregister.gov/developers/api/v1>.

Integrate Item Response Theory (IRT) and Federated Learning to estimate traditional IRT models, including the 2-Parameter Logistic (2PL) and the Graded Response Models, with enhanced privacy. It allows for the estimation in a distributed manner without compromising accuracy. A user-friendly 'shiny' application is included.

Provides a flexible set of tools for matching two un-linked data sets. 
    'fedmatch' allows for three ways to match data: exact matches, fuzzy matches, and multi-variable matches. 
    It also allows an easy combination of these three matches via the tier matching function.