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

c( person( "Emery", "Boose", role = c("ctb"),
email = "boose@fas.harvard.edu"),
person("Barbara", "Lerner", role = c("aut", "cre"),
email = "blerner@mtholyoke.edu"),
person("Sofiya", "Taskova", role = "aut",
email = "sophiataskova@gmail.com"),
person("Miruna", "Oprescu", role = "aut",
email = "aoprescu@college.harvard.edu"),
person("Nikki", "Hoffler", role = "aut",
email = "hoffl22n@mtholyoke.edu"),
person("Marios", "Dardas", role = "aut",
email = "mzdard16@g.holycross.edu"),
person( "Elizabeth", "Fong", role = "ctb",
email = "fong22e@mtholyoke.edu"),
person( "Thomas", "Pasquier", role = "ctb",
email = "thomas.pasquier@bristol.ac.uk"),
person( "Matthew", "Lau", role = "ctb",
email = "matthewklau@fas.harvard.edu"),
person( "Aaron", "Ellison", role = "res",
email = "aellison@fas.harvard.edu"),
person( "Margo", "Seltzer", role = "res",
email = "margo@eecs.harvard.edu")
)
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>.
Emery Boose [ctb],
Barbara Lerner [aut, cre],
Sofiya Taskova [aut],
Miruna Oprescu [aut],
Nikki Hoffler [aut],
Marios Dardas [aut],
Elizabeth Fong [ctb],
Thomas Pasquier [ctb],
Matthew Lau [ctb],
Aaron Ellison [res],
Margo Seltzer [res]

Statistical Toolbox for Sedimentary Provenance Analysis
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.