Package: processpredictR
Type: Package
Title: Process Prediction
Version: 0.1.1
Authors@R: c(person("Ivan","Esin", email = "ivan.esin@student.uhasselt", role ="aut"),
      person("Gert","Janssenswillen", email = "gert.janssenswillen@uhasselt.be", role = "cre"),
      person("Hasselt University", role = "cph"))
Description: 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 way 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>.
License: MIT + file LICENSE
Encoding: UTF-8
RoxygenNote: 7.3.3
Imports: bupaR, edeaR, eventdataR, dplyr, forcats, magrittr,
        reticulate, tidyr, tidyselect, purrr, stringr, keras,
        tensorflow, rlang, data.table, mltools, ggplot2, cli, glue,
        plotly
Config/testthat/edition: 3
Depends: R (>= 2.10)
Suggests: knitr, rmarkdown, lubridate
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2026-10-10 08:47:12 UTC; root
Author: Ivan Esin [aut], Gert Janssenswillen [cre], Hasselt University
        [cph]
Maintainer: Gert Janssenswillen <gert.janssenswillen@uhasselt.be>
Config/pak/sysreqs: cmake make libicu-dev libpng-dev libuv1-dev
        libssl-dev python3 zlib1g-dev
Repository: https://gertjanssenswillen.r-universe.dev
Date/Publication: 2025-09-15 14:33:17 UTC
RemoteUrl: https://github.com/cran/processpredictR
RemoteRef: HEAD
RemoteSha: 254ee1a0b7be65aa356e9cb9406c73da67758e66