Uses provenance collected by 'rdtLite' package or comparable tool
    to display information about input files, output files, and exchanged files
    for a single R script or a series of R scripts.

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>.

Functions to measure Alpha and Beta 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>.

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 several built-in similarity/distance measures, computation of correlation,
    cosine similarity and Euclidean distance is particularly fast.

Determine minimal protein set explaining
    peptide spectrum matches. Utility functions for creating fasta amino acid databases with decoys and contaminants.
    Peptide false discovery rate estimation for target decoy search results on psm, precursor, peptide and protein
    level. Computing dynamic swath window sizes based on MS1 or MS2 signal distributions.

Utilize the Bayesian prior and posterior predictive checking
   approach to provide a statistical assessment of replication success 
   and failure. The package is based on the methods proposed in 
   Zhao,Y., Wen X.(2021) <arXiv:2105.03993>.