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" <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
Author: Ivan Esin [aut], Gert Janssenswillen [cre], Hasselt University [cph]
Maintainer: Gert Janssenswillen <gert.janssenswillen at>
License: MIT + file LICENSE
NeedsCompilation: no
Materials: README
CRAN checks: processpredictR results


Reference manual: processpredictR.pdf
Vignettes: Introduction to processpredictR: workflow


Package source: processpredictR_0.1.0.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): processpredictR_0.1.0.tgz, r-oldrel (arm64): processpredictR_0.1.0.tgz, r-release (x86_64): processpredictR_0.1.0.tgz, r-oldrel (x86_64): processpredictR_0.1.0.tgz


Please use the canonical form to link to this page.