Query the four endpoints of the 'Air and Water Database (AWDB) REST
    API' maintained by the National Water and Climate Center (NWCC) at the 
    United States Department of Agriculture (USDA). Endpoints include data, 
    forecast, reference-data, and metadata. The package is extremely light 
    weight, with 'Rust' via 'extendr' doing most of the heavy lifting to 
    deserialize and flatten deeply nested 'JSON' responses. The AWDB can be 
    found at <https://wcc.sc.egov.usda.gov/awdbRestApi/swagger-ui/index.html>.

Which day a week starts depends heavily on the either the local or
  professional context. This package is designed to be a lightweight solution
  to easily switching between week-based date definitions.

Self-organizing maps (also known as SOM, see Kohonen (2001) <doi:10.1007/978-3-642-56927-2>) are a method for dimensionality reduction and clustering of continuous data. This package introduces interactive (html) graphics for easier analysis of SOM results. It also features an interactive interface, for push-button training and visualization of SOM on numeric, categorical or mixed data, as well as tools to evaluate the quality of SOM.

Collect  your data on digital marketing campaigns from 'Awin' using the 'Windsor.ai' API <https://windsor.ai/api-fields/>.

Make the compiled Java modules of the Amazon Web Services ('AWS') 'SDK' available to be used in downstream R packages interacting with 'AWS'. See <https://aws.amazon.com/sdk-for-java> for more information on the 'AWS' 'SDK' for Java.

Fetching data from Amazon 'Kinesis' Streams using the Java-based 'MultiLangDaemon' interacting with Amazon Web Services ('AWS') for easy stream processing from R. For more information on 'Kinesis', see <https://aws.amazon.com/kinesis>.

We provide a collection of R-functions implementing
    adaptive smoothing procedures in 1D, 2D and 3D. This includes the
    Propagation-Separation Approach to adaptive smoothing,
   the Intersecting Confidence Intervals (ICI), variational approaches and a non-local means filter.
   The package is described in detail in Polzehl J, Papafitsoros K, Tabelow K (2020). 
   Patch-Wise Adaptive Weights Smoothing in R. Journal of Statistical Software, 95(6), 1-27. 
   <doi:10.18637/jss.v095.i06>,
    Usage of the package in MR imaging is illustrated in Polzehl and Tabelow (2023),
   Magnetic Resonance Brain Imaging, 2nd Ed. Appendix A, Springer, Use R! Series.
   <doi:10.1007/978-3-031-38949-8>.