The DuckDB project is an embedded analytical data management
    system with support for the Structured Query Language (SQL). This
    package includes all of DuckDB and an R Database Interface (DBI)
    connector.

Provides friendly wrappers for creating 'duckdb'-backed connections
  to tabular datasets ('csv', parquet, etc) on local or remote file systems.
  This mimics the behaviour of "open_dataset" in the 'arrow' package, 
  but in addition to 'S3' file system also generalizes to any list of 'http' URLs.

Programmatic access to the DuckDuckGo Instant Answer API <https://api.duckduckgo.com/api>.

Fast & memory-efficient functions to analyze and manipulate large 
            data sets. It leverages the fast analytical 
            capabilities of 'DuckDB' and its spatial extension (see <https://duckdb.org/community_extensions/extensions/h3>) 
            while maintaining compatibility with R’s spatial data ecosystem to 
            work with spatial vector data.

A drop-in replacement for 'dplyr', powered by 'DuckDB' for
    performance.  Offers convenient utilities for working with in-memory and
    larger-than-memory data while retaining full 'dplyr' compatibility.

Fast and memory-efficient functions to analyze and manipulate large
             spatial datasets. It leverages the fast analytical
             capabilities of ‘DuckDB’ and its spatial extension (see
             <https://duckdb.org/docs/stable/core_extensions/spatial/overview>)
             while maintaining compatibility with R’s spatial data ecosystem to
             work with spatial vector data.

Analyzes non-verbal communication by processing data extracted 
    from video recordings of dyadic interactions. It supports integration 
    with open source tools, currently limited to 'OpenPose' (Cao et al. (2019) 
    <doi:10.1109/TPAMI.2019.2929257>), converting its outputs into CSV format 
    for further analysis. The package includes functions for data pre-processing, 
    visualization, and computation of motion indices such as velocity, acceleration, 
    and jerkiness (Cook et al. (2013) <doi:10.1093/brain/awt208>), facilitating 
    the analysis of non-verbal cues in paired interactions and contributing to 
    research on human communication dynamics.