Package: DynForest 1.1.3
DynForest: Random Forest with Multivariate Longitudinal Predictors
Based on random forest principle, 'DynForest' is able to include multiple longitudinal predictors to provide individual predictions. Longitudinal predictors are modeled through the random forest. The methodology is fully described for a survival outcome in: Devaux, Helmer, Genuer & Proust-Lima (2023) <doi:10.1177/09622802231206477>.
Authors:
DynForest_1.1.3.tar.gz
DynForest_1.1.3.zip(r-4.5)DynForest_1.1.3.zip(r-4.4)DynForest_1.1.3.zip(r-4.3)
DynForest_1.1.3.tgz(r-4.4-any)DynForest_1.1.3.tgz(r-4.3-any)
DynForest_1.1.3.tar.gz(r-4.5-noble)DynForest_1.1.3.tar.gz(r-4.4-noble)
DynForest_1.1.3.tgz(r-4.4-emscripten)DynForest_1.1.3.tgz(r-4.3-emscripten)
DynForest.pdf |DynForest.html✨
DynForest/json (API)
NEWS
| # Install 'DynForest' in R: |
| install.packages('DynForest', repos = c('https://anthonydevaux.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/anthonydevaux/dynforest/issues
- data_simu1 - Data_simu1 dataset
- data_simu2 - Data_simu1 dataset
- pbc2 - Pbc2 dataset
Last updated 6 months agofrom:e08d41e96d. Checks:OK: 7. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Aug 31 2024 |
| R-4.5-win | OK | Aug 31 2024 |
| R-4.5-linux | OK | Aug 31 2024 |
| R-4.4-win | OK | Aug 31 2024 |
| R-4.4-mac | OK | Aug 31 2024 |
| R-4.3-win | OK | Aug 31 2024 |
| R-4.3-mac | OK | Aug 31 2024 |
Exports:compute_gVIMPcompute_OOBerrorcompute_VIMPDynForestgetTreegetTreeNodesvar_depth
Dependencies:askpassbackportsbase64encbootbslibcachemcellrangercheckmateclasscliclustercmprskcodetoolscolorspacecpp11crayoncurldata.tableDescToolsdiagramdigestdoParalleldoRNGe1071evaluateExactexpmfansifarverfastmapfontawesomeforeachforeignFormulafsfuturefuture.applyggplot2gldglobalsgluegridExtragtablehighrHmischmshtmlTablehtmltoolshtmlwidgetshttrisobanditeratorsjquerylibjsonliteKernSmoothknitrlabelinglatticelavalcmmlifecyclelistenvlmommagrittrmarqLevAlgMASSMatrixMatrixModelsmemoisemetsmgcvmimemultcompmunsellmvtnormnlmennetnumDerivopensslparallellypbapplypecpillarpkgconfigplotrixpolsplineprettyunitsprodlimprogressprogressrproxyPublishquantregR6randtoolboxrangerrappdirsRColorBrewerRcppRcppArmadilloRcppEigenreadxlrematchriskRegressionrlangrmarkdownrmsrngtoolsrngWELLrootSolverpartrstudioapisandwichsassscalesshapeSparseMSQUAREMstringistringrsurvivalsysTH.datatibbletimeregtinytexutf8vctrsviridisviridisLitewithrxfunyamlzoo
How to use DynForest with categorical outcome?
Rendered fromfactor.Rmdusingknitr::rmarkdownon Aug 31 2024.Last update: 2022-10-28
Started: 2022-10-28
How to use DynForest with continuous outcome?
Rendered fromnumeric.Rmdusingknitr::rmarkdownon Aug 31 2024.Last update: 2022-10-28
Started: 2022-10-28
How to use DynForest with survival outcome?
Rendered fromsurv.Rmdusingknitr::rmarkdownon Aug 31 2024.Last update: 2022-11-22
Started: 2022-10-28
Introduction to DynForest methodology
Rendered fromIntroduction.Rmdusingknitr::rmarkdownon Aug 31 2024.Last update: 2022-10-28
Started: 2022-10-28
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Compute the grouped importance of variables (gVIMP) statistic | compute_gVIMP |
| Compute the Out-Of-Bag error (OOB error) | compute_OOBerror |
| Compute the importance of variables (VIMP) statistic | compute_VIMP |
| data_simu1 dataset | data_simu1 |
| data_simu1 dataset | data_simu2 |
| Random forest with multivariate longitudinal endogenous covariates | DynForest |
| Extract some information about the split for a tree by user | getTree |
| Extract nodes identifiers for a given tree | getTreeNodes |
| pbc2 dataset | pbc2 |
| Plot function in DynForest | plot.DynForest plot.DynForestgVIMP plot.DynForestPred plot.DynForestVarDepth plot.DynForestVIMP |
| Prediction using dynamic random forests | predict.DynForest |
| Print function | print.DynForest print.DynForestgVIMP print.DynForestOOB print.DynForestPred print.DynForestVarDepth print.DynForestVIMP |
| Display the summary of DynForest | summary.DynForest summary.DynForestOOB |
| Extract characteristics from the trees building process | var_depth |
