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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 |
DOI: | 10.32614/CRAN.package.oRus |
Author: | Melina Vidoni |
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 |
Reference manual: | oRus.pdf |
Vignettes: |
How to use oRus? References How does oRus Works? |
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-oldrel (arm64): oRus_1.0.0.tgz, r-release (x86_64): oRus_1.0.0.tgz, r-oldrel (x86_64): oRus_1.0.0.tgz |
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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