Package: processpredictR 0.1.0
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>.
Authors:
processpredictR_0.1.0.tar.gz
processpredictR_0.1.0.zip(r-4.5)processpredictR_0.1.0.zip(r-4.4)processpredictR_0.1.0.zip(r-4.3)
processpredictR_0.1.0.tgz(r-4.5-any)processpredictR_0.1.0.tgz(r-4.4-any)processpredictR_0.1.0.tgz(r-4.3-any)
processpredictR_0.1.0.tar.gz(r-4.5-noble)processpredictR_0.1.0.tar.gz(r-4.4-noble)
processpredictR_0.1.0.tgz(r-4.4-emscripten)processpredictR_0.1.0.tgz(r-4.3-emscripten)
processpredictR.pdf |processpredictR.html✨
processpredictR/json (API)
| # Install 'processpredictR' in R: |
| install.packages('processpredictR', repos = c('https://gertjanssenswillen.r-universe.dev', 'https://cloud.r-project.org')) |
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 2 years agofrom:d7db44aab9. Checks:6 OK, 3 NOTE. Indexed: yes.
| Target | Result | Latest binary |
|---|---|---|
| Doc / Vignettes | OK | Mar 20 2025 |
| R-4.5-win | NOTE | Mar 20 2025 |
| R-4.5-mac | NOTE | Mar 20 2025 |
| R-4.5-linux | NOTE | Mar 20 2025 |
| R-4.4-win | OK | Mar 20 2025 |
| R-4.4-mac | OK | Mar 20 2025 |
| R-4.4-linux | OK | Mar 20 2025 |
| R-4.3-win | OK | Mar 20 2025 |
| R-4.3-mac | OK | Mar 20 2025 |
Exports:%>%compileconfusion_matrixcreate_modelcreate_vocabularyevaluatefitmax_case_lengthnum_outputspredictprepare_examplessplit_train_teststack_layerstokenizevocab_size
Dependencies:askpassbackportsbase64encbslibbupaRcachemclicolorspacecommonmarkconfigcpp11crayoncrosstalkcurldata.tabledigestdplyredeaRevaluateeventdataRfansifarverfastmapfontawesomeforcatsfsgenericsggplot2ggthemesgluegtableherehighrhmshtmltoolshtmlwidgetshttpuvhttrisobandjquerylibjsonlitekerasknitrlabelinglaterlatticelazyevallifecyclelubridatemagrittrMASSMatrixmemoisemgcvmimeminiUImltoolsmunsellnlmeopensslpillarpkgconfigplotlypngprettyunitsprocessxprogresspromisespspurrrR6rappdirsRColorBrewerRcppRcppTOMLreticulaterlangrmarkdownrprojrootrstudioapisassscalesshinyshinyTimesourcetoolsstringistringrsystensorflowtfautographtfrunstibbletidyrtidyselecttimechangetinytexutf8vctrsviridisLitewhiskerwithrxfunxtableyamlzeallotzoo
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Confusion matrix for predictions | confusion_matrix |
| Define transformer model | create_model |
| Create a vocabulary | create_vocabulary |
| Utils | get_vocabulary |
| Calculate the maximum length of a case / number of activities in the longest trace in an event log | max_case_length |
| Calculate number of outputs (target variables) | num_outputs |
| Plot Methods | plot.ppred_predictions |
| ppred_examples_df object | ppred_examples_df |
| ppred_model object | ppred_model |
| ppred_predictions object | ppred_predictions |
| Convert a dataset of type 'log' into a preprocessed format. | prepare_examples |
| Print methods | print.ppred_model |
| processpredictR | processpredictR-package processpredictR |
| Splits the preprocessed 'data.frame'. | split_train_test |
| Stacks a keras layer on top of existing model | stack_layers |
| Tokenize features and target of a processed dataset of class 'ppred_examples_df' | tokenize |
| Calculate the vocabulary size, i.e. the sum of number of activities, outcome labels and padding keys | vocab_size |
