Perform wavelet analysis (orthogonal,translation invariant, tensorial, 1-2-3d transforms, thresholding, block thresholding, linear,...) with applications to data compression or denoising/regression. The core of the code is a port of 'MATLAB' Wavelab toolbox written by D. Donoho, A. Maleki and M. Shahram (<https://statweb.stanford.edu/~wavelab/>).

Code to facilitate simulation and inference when connectivity is defined by underlying random walks. Methods for spatially-correlated pairwise distance data are especially considered. This provides core code to conduct analyses similar to that in Hanks and Hooten (2013) <doi:10.1080/01621459.2012.724647>.

A tool to read and manipulate data generated from 'RiverWare'(TM) 
    <https://www.riverware.org/> simulations. 'RiverWare' and 'RiverSMART' 
    generate data in "rdf", "csv", and "nc" format. This package provides an 
    interface to read, aggregate, and summarize data from one or more 
    simulations in a 'dplyr' pipeline.

Weave and tangle drivers for Sweave extending the
  standard drivers. RweaveExtraLatex and RtangleExtra provide options
  to completely ignore code chunks on weaving, tangling, or both.
  Chunks ignored on weaving are not parsed, yet are written out
  verbatim on tangling. Chunks ignored on tangling may be evaluated as
  usual on weaving, but are completely left out of the tangled
  scripts. The driver RtangleExtra also provides options to control
  the separation between code chunks in the tangled script, and to
  specify the extension of the file name (or remove it entirely) when
  splitting is selected.

An R interface to Weka (Version 3.9.3).
   Weka is a collection of machine learning algorithms for data mining
   tasks written in Java, containing tools for data pre-processing,
   classification, regression, clustering, association rules, and
   visualization.  Package 'RWeka' contains the interface code, the
   Weka jar is in a separate package 'RWekajars'.  For more information
   on Weka see <https://www.cs.waikato.ac.nz/ml/weka/>.