Automatic differentiation is achieved by using dual numbers without
  providing hand-coded gradient functions. The output value of a mathematical 
  function is returned with the values of its exact first derivative 
  (or gradient). For more details see Baydin, Pearlmutter, Radul, and Siskind
  (2018) <https://jmlr.org/papers/volume18/17-468/17-468.pdf>.

Dual Scaling, developed by Professor Shizuhiko Nishisato (1994, ISBN: 0-9691785-3-6), is a fundamental technique in multivariate analysis used for data scaling and correspondence analysis. Its utility lies in its ability to represent multidimensional data in a lower-dimensional space, making it easier to visualize and understand underlying patterns in complex data. This technique has been implemented to handle various types of data, including Contingency and Frequency data (CF), Multiple-Choice data (MC), Sorting data (SO), Paired-Comparison data (PC), and Rank-Order data (RO), providing users with a powerful tool to explore relationships between variables and observations in various fields, from sociology to ecology, enabling deeper and more efficient analysis of multivariate datasets.

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.