[aut, cre],
Laura Cunico [aut]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
[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 |
| 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: oRus_1.0.0.tgz, r-oldrel: oRus_1.0.0.tgz |
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