An implementation of the decimated two-dimensional complex dual-tree wavelet transform as described in Kingsbury (1999) <doi:10.1098/rsta.1999.0447> and Selesnick et al. (2005) <doi:10.1109/MSP.2005.1550194>. Also includes the undecimated version and spectral bias correction described in Nelson et al. (2018) <doi:10.1007/s11222-017-9784-0>. The code is partly based on the 'dtcwt' Python library.

Create shareable data sets from raw data files that
	     contain protected elements. Relying on master crosswalk
	     files that list restricted variables, package functions
	     warn users about possible violations of data usage
	     agreement and prevent writing protected elements.

Provides an operator for assigning nested components of a list to
  names via a concise pattern matching syntax. This is especially convenient for
  assigning individual names to the multiple values that a function may return
  in the form of a list, and for extracting deeply nested list components.

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 a 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>.

A drop-in replacement for 'dplyr', powered by 'DuckDB' for performance.
    Also defines a set of generics that provide a low-level
    implementer's interface for the high-level user interface of 'dplyr'.

Generates visualizations with Duke’s official suite of colors in a color blind friendly way.

Efficiently create dummies of all factors and character vectors in a data frame. Support is included for learning the categories on one data set (e.g., a training set) and deploying them on another (e.g., a test set).