Displays provenance graphically for provenance collected by the 'rdt' or
  'rdtLite' packages, or other tools providing compatible PROV JSON output. The exact 
  format of the JSON created by 'rdt' and 'rdtLite' is described in 
  <https://github.com/End-to-end-provenance/ExtendedProvJson>.  More information about
  rdtLite and associated tools is available at <https://github.com/End-to-end-provenance/> 
  and Barbara Lerner, Emery Boose, and Luis Perez (2018), Using Introspection to Collect 
  Provenance in R, Informatics, <doi: 10.3390/informatics5010012>.

Provides tools to build quantitative chemometric models and
    applications for near-infrared (NIR) sensors. Chemometric regression
    models are based on partial least squares regression as described by
    Wold (1975) <doi:10.1016/B978-0-12-103950-9.50017-4> and modified
    partial least squares regression as described by Shenk and Westerhaus
    (1991) <doi:10.2135/cropsci1991.0011183X003100020049x>, with further
    discussion by Westerhaus (2014) <doi:10.1255/nirn.1492>.

Functions to measure Alpha, Beta and Gamma Proximity to Irreplaceability.
  The methods for Alpha and Beta irreplaceability were first described in:
    Baisero D., Schuster R. & Plumptre A.J.
    Redefining and Mapping Global Irreplaceability.
    Conservation Biology 2021;1-11.
    <doi:10.1111/cobi.13806>.

Implements optimization techniques for Lasso regression, R.Tibshirani(1996)<doi:10.1111/j.2517-6161.1996.tb02080.x> using Fast Iterative Shrinkage-Thresholding Algorithm (FISTA) and Iterative Shrinkage-Thresholding Algorithm (ISTA) based on proximal operators, A.Beck(2009)<doi:10.1137/080716542>. The package is useful for high-dimensional regression problems and includes cross-validation procedures to select optimal penalty parameters.

Provides an extensible framework for the efficient calculation of auto- and cross-proximities, along with implementations of the most popular ones.

Computes proximity between rows or columns of large matrices efficiently in C++.
    Functions are optimised for large sparse matrices using the Armadillo and Intel TBB libraries.
    Among various built-in similarity/distance measures, computation of correlation,
    cosine similarity, Dice coefficient and Euclidean distance is particularly fast.