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.
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