Process Prediction
Overview
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.
Install
Health
- OK13 OK2026-08-05
- NOTE12 OK, 1 NOTE2026-08-01
- OK13 OK2026-06-09
- ERROR12 OK, 1 ERROR2026-06-08
- OK14 OK2026-03-10
Documentation
- Help pages
- 19 (2 internal)includes a package overview page
- Examples that run
- 100%5 run in a plain check
- Exported functions with examples
- 42%
- Documented parameters
- 100%
- Return-value docs
- 91%
- Help pages citing references
- 0%
Downloads
Dependencies
Installing processpredictR adds 96 packages (47 with compiled code, 79 MB of source tarballs). Heaviest: stringi 11 MB, ggplot2 6.0 MB, data.table 5.7 MB.
CRAN will archive these unless their check problems are fixed by the date shown. This package needs each one to install.
- mltoolsdeadline passed Oct 12
Code & Tests
People & History
Pick two releases to compare their code metrics.
- RR 4.6.0 released · 2026-04-24
- 0.1.1Latest
- RR 4.5.0 released · 2025-04-11
- RR 4.4.0 released · 2024-04-24
- RR 4.3.0 released · 2023-04-21
- 0.1.02023-01-17
- RR 4.2.0 released · 2022-04-22
Package metadata
- First published
- 2023-01-17
- Total releases
- 2 / 3 yrs
- License
- MIT + file LICENSE OSI
- Minimum R
- ≥ 2.10
- Download size
- 121 KB
Cite
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