Estimates causal effects with panel data using the counterfactual methods. It is suitable for panel or time-series cross-sectional analysis with binary treatments under (hypothetically) baseline randomization.It allows a treatment to switch on and off and limited carryover effects. It supports linear factor models, a generalization of gsynth and the matrix completion method. Implementation details can be found in Liu, Wang and Xu (2022) <arXiv:2107.00856>.

Functions to automate downloading 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 (1 and 1/3 arc-second; USGS); The National Hydrography Dataset
    (USGS); The Soil Survey Geographic (SSURGO) database from the National
    Cooperative Soil Survey (NCSS), which is led by the Natural Resources
    Conservation Service (NRCS) under the USDA; the Global Historical
    Climatology Network (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 (DAAC); the
    International Tree Ring Data Bank; the National Land Cover
    Database (NLCD); the Cropland Data Layer from the National Agricultural 
    Statistics Service; and the PAD-US dataset of protected area boundaries 
    from the 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. For more details, see Biying Zhou, Feng Ji (2024) "'FedIRT': An R package and 'shiny' app for estimating federated item response theory models" <https://github.com/Feng-Ji-Lab/FedIRT/blob/main/paper/paper.pdf>.