processpredictR: Process Prediction
Means to predict process flow, such as process outcome, next activity, next time, remaining time, and remaining trace. Off-the-shelf predictive models based on the concept of Transformers are provided, as well as multiple ways to customize the models. This package is partly based on work described in Zaharah A. Bukhsh, Aaqib Saeed, & Remco M. Dijkman. (2021). "ProcessTransformer: Predictive Business Process Monitoring with Transformer Network" <doi:10.48550/arXiv.2104.00721>.
| Version: |
0.1.0 |
| Depends: |
R (≥ 2.10) |
| Imports: |
bupaR, edeaR, dplyr, forcats, magrittr, reticulate, tidyr, tidyselect, purrr, stringr, keras, tensorflow, rlang, data.table, mltools, ggplot2, cli, glue, plotly, progress |
| Suggests: |
knitr, rmarkdown, lubridate, eventdataR |
| Published: |
2023-01-17 |
| DOI: |
10.32614/CRAN.package.processpredictR |
| Author: |
Ivan Esin [aut],
Gert Janssenswillen [cre],
Hasselt University [cph] |
| Maintainer: |
Gert Janssenswillen <gert.janssenswillen at uhasselt.be> |
| License: |
MIT + file LICENSE |
| NeedsCompilation: |
no |
| Materials: |
README |
| CRAN checks: |
processpredictR results |
Documentation:
Downloads:
Linking:
Please use the canonical form
https://CRAN.R-project.org/package=processpredictR
to link to this page.