oRus: Operational Research User Stories

A first implementation of automated parsing of user stories, when used to defined functional requirements for operational research mathematical models. It allows reading user stories, splitting them on the who-what-why template, and classifying them according to the parts of the mathematical model that they represent. Also provides semantic grouping of stories, for project management purposes.

Version: 1.0.0
Depends: R (≥ 3.6.0)
Imports: dplyr, stringr, tm, tibble, tidytext, topicmodels, rmarkdown, xlsx, knitr
Suggests: reshape2, qpdf
Published: 2020-07-07
Author: Melina Vidoni ORCID iD [aut, cre], Laura Cunico [aut]
Maintainer: Melina Vidoni <melina.vidoni at rmit.edu.au>
BugReports: https://github.com/melvidoni/oRus/issues
License: GPL-3
URL: https://github.com/melvidoni/oRus
NeedsCompilation: no
Materials: README NEWS
CRAN checks: oRus results

Documentation:

Reference manual: oRus.pdf
Vignettes: How to use oRus?
References
How does oRus Works?

Downloads:

Package source: oRus_1.0.0.tar.gz
Windows binaries: r-devel: oRus_1.0.0.zip, r-release: oRus_1.0.0.zip, r-oldrel: oRus_1.0.0.zip
macOS binaries: r-release (arm64): oRus_1.0.0.tgz, r-release (x86_64): oRus_1.0.0.tgz, r-oldrel: oRus_1.0.0.tgz

Linking:

Please use the canonical form https://CRAN.R-project.org/package=oRus to link to this page.