Description: Reads the provenance collected by the 'rdtLite' or 'rdt' packages, or
other tools providing compatible PROV JSON output, created by the execution of a
script or a console session, and provides a human-readable summary identifying
the input and output files, the script used (if any), errors and warnings produced,
and the environment in which it was executed.  It can also optionally package all the
files into a zip file.  The exact format of the JSON created by 'rdtLite' and 'rdt'
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>.
Suggests: digest, rdtLite, rdt, testthat

Title: Uses Provenance to Trace File Lineage for One or more R Scripts
Description: 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.
Suggests: digest, knitr, rdt, rdtLite, rmarkdown, testthat

Authors@R: c( person( "Emery", "Boose", role = c("ctb"),
person("Barbara", "Lerner", role = c("aut", "cre"),
person("Sofiya", "Taskova", role = "aut",
person("Miruna", "Oprescu", role = "aut",
person("Nikki", "Hoffler", role = "aut",
person("Marios", "Dardas", role = "aut",
person( "Elizabeth", "Fong", role = "ctb",
person( "Thomas", "Pasquier", role = "ctb",
person( "Aaron", "Ellison", role = "res",
person( "Margo", "Seltzer", role = "res",
Description: 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
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

Title: Statistical Toolbox for Sedimentary Provenance Analysis
Description: Bundles a number of established statistical methods to facilitate the visual interpretation of large datasets in sedimentary geology. Includes functionality for adaptive kernel density estimation, multidimensional scaling, generalised procrustes analysis and individual differences scaling using a variety of dissimilarity measures. Univariate provenance proxies, such as single-grain ages or (isotopic) compositions are compared with the Kolmogorov-Smirnov dissimilarity and Sircombe-Hazelton L2-norm. Categorical provenance proxies, such as mineralogical, petrographic or chemical compositions are compared with the Aitchison and Bray-Curtis distances.