What MuTATE does | The survival outcome naming convention | Fitting a tree | Summarizing the tree | Pruning | Scoring new data | Visualizing the tree | Tuning hyperparameters
Package: MuTATE 0.1.0
MuTATE: Multi-Target Automated Tree Engine (MuTATE)
Recursively partitions datasets on binary splits across multiple targets having different dependent variable types, including categorical, continuous, count, and survival outcomes. This overcomes single-target limitations of traditional decision trees while retaining model interpretability. See Ayton and Trevino (2023) <doi:10.1093/bioinformatics/btad507> and Ayton et al. (2025) <doi:10.1038/s44401-025-00025-4> for details.
Authors:
MuTATE_0.1.0.tar.gz
MuTATE_0.1.0.zip(r-4.7-any)MuTATE_0.1.0.zip(r-4.6-any)MuTATE_0.1.0.zip(r-4.5-any)
MuTATE_0.1.0.tgz(r-4.6-any)MuTATE_0.1.0.tgz(r-4.5-any)
MuTATE_0.1.0.tar.gz(r-4.7-any)MuTATE_0.1.0.tar.gz(r-4.6-any)
MuTATE_0.1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
MuTATE/json (API)
| # Install 'MuTATE' in R: |
| install.packages('MuTATE', repos = c('https://sarahayton.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/sarahayton/mutate/issues
Datasets:
- mutate_example - Synthetic example dataset for MuTATE
Last updated from:2b43070ae5. Checks:9 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel | OK | 299 | ||
| source / vignettes | OK | 254 | ||
| linux-release | OK | 295 | ||
| macos-release | OK | 276 | ||
| macos-oldrel | OK | 241 | ||
| windows-devel | OK | 264 | ||
| windows-release | OK | 193 | ||
| windows-oldrel | OK | 264 | ||
| wasm-release | OK | 143 |
Exports:Cat_EvalCont_EvalCount_EvalCV_TuneCVsplitEvalMTPartMTPartSummaryMTPruneMTSummaryMTTestMultiEvalPlotTreeSplitPrepSurv_Eval
Dependencies:base64enccachemcaretclasscliclockcodetoolscpp11data.tablediagramdigestdplyre1071farverfastmapforeachfuturefuture.applygenericsggforceggplot2ggraphggrepelglobalsgluegowergraphlayoutsgridExtragtablehardhatigraphipredirrisobanditeratorsjsonliteKernSmoothlabelinglatticelavalifecyclelistenvlpSolvelubridatemagrittrMASSMatrixmatrixStatsmemoiseMetricsModelMetricsnlmennetnumDerivparallellypillarpkgconfigplotrixplyrpolyclippROCprodlimprogressrproxypurrrR6RColorBrewerRcppRcppArmadillorecipesreshape2rlangrpartS7scalesshapesparsevctrsSQUAREMstringistringrsurvivalsystemfontstibbletidygraphtidyrtidyselecttimechangetimeDatetweenrtzdbutf8vctrsviridisviridisLitewithr
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| CV_Tune: Cross-Validated Hyperparameter Tuning for MTPart Models | CV_Tune |
| Evaluate the performance of binary classifiers | CVsplitEval |
| Construct multi-target decision trees | MTPart |
| MTPartSummary Function | MTPartSummary |
| MTPrune: Prune a Decision Tree Using Minimal Cost-Complexity Pruning | MTPrune |
| Summarize multiple outcome variables | MTSummary |
| MTTest: Test Multi-Target Tree | MTTest |
| Evaluate features across multiple outcomes and outcome types | Cat_Eval Cont_Eval Count_Eval MultiEval Surv_Eval |
| Synthetic example dataset for MuTATE | mutate_example |
| PlotTree: Plot Decision Tree | PlotTree |
| Prepare data for splitting in a decision tree | SplitPrep |