Package: DynForest 1.2.0

Anthony Devaux

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:Anthony Devaux [aut, cre], Robin Genuer [aut], Cécile Proust-Lima [aut], Louis Capitaine [aut]

DynForest_1.2.0.tar.gz
DynForest_1.2.0.tar.gz(r-4.5-noble)DynForest_1.2.0.tar.gz(r-4.4-noble)
DynForest_1.2.0.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://cran.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/anthonydevaux/dynforest/issues

Datasets:

3.88 score 8 scripts 180 downloads 7 exports 134 dependencies

Last updated 7 days agofrom:7c7d489e56. Checks:OK: 2. Indexed: no.

TargetResultDate
Doc / VignettesOKOct 24 2024
R-4.5-linuxOKOct 24 2024

Exports:compute_gvimpcompute_ooberrorcompute_vardepthcompute_vimpdynforestget_treeget_treenodes

Dependencies:askpassbackportsbase64encbootbslibcachemcellrangercheckmateclasscliclustercmprskcodetoolscolorspacecpp11crayoncurldata.tableDescToolsdiagramdigestdoParalleldoRNGe1071evaluateExactexpmfansifarverfastmapfontawesomeforeachforeignFormulafsfuturefuture.applyggplot2gldglobalsgluegridExtragtablehighrHmischmshtmlTablehtmltoolshtmlwidgetshttrisobanditeratorsjquerylibjsonliteKernSmoothknitrlabelinglatticelavalcmmlifecyclelistenvlmommagrittrmarqLevAlgMASSMatrixMatrixModelsmemoisemetsmgcvmimemultcompmunsellmvtnormnlmennetnumDerivopensslparallellypbapplypecpillarpkgconfigplotrixpolsplineprettyunitsprodlimprogressprogressrproxyPublishquantregR6randtoolboxrangerrappdirsRColorBrewerRcppRcppArmadilloRcppEigenreadxlrematchriskRegressionrlangrmarkdownrmsrngtoolsrngWELLrootSolverpartrstudioapisandwichsassscalesshapeSparseMSQUAREMstringistringrsurvivalsysTH.datatibbletimeregtinytexutf8vctrsviridisviridisLitewithrxfunyamlzoo

How to use DynForest with categorical outcome?

Rendered fromclassification.Rmdusingknitr::rmarkdownon Oct 24 2024.

Last update: 2024-10-23
Started: 2024-10-23

How to use DynForest with continuous outcome?

Rendered fromregression.Rmdusingknitr::rmarkdownon Oct 24 2024.

Last update: 2024-10-23
Started: 2024-10-23

How to use DynForest with survival outcome?

Rendered fromsurvival.Rmdusingknitr::rmarkdownon Oct 24 2024.

Last update: 2024-10-23
Started: 2024-10-23

Introduction to DynForest methodology

Rendered fromintroduction.Rmdusingknitr::rmarkdownon Oct 24 2024.

Last update: 2024-10-23
Started: 2024-10-23

Overview of DynForest package

Rendered fromoverview.Rmdusingknitr::rmarkdownon Oct 24 2024.

Last update: 2024-10-23
Started: 2024-10-23

Readme and manuals

Help Manual

Help pageTopics
Compute the grouped importance of variables (gVIMP) statisticcompute_gvimp
Compute the Out-Of-Bag error (OOB error)compute_ooberror
Extract characteristics from the trees building processcompute_vardepth
Compute the importance of variables (VIMP) statisticcompute_vimp
data_simu1 datasetdata_simu1
data_simu2 datasetdata_simu2
Random forest with multivariate longitudinal endogenous covariatesdynforest
Extract some information about the split for a tree by userget_tree
Extract nodes identifiers for a given treeget_treenodes
pbc2 datasetpbc2
Plot function in dynforestplot.dynforest plot.dynforestgvimp plot.dynforestpred plot.dynforestvardepth plot.dynforestvimp
Prediction using dynamic random forestspredict.dynforest
Print functionprint.dynforest print.dynforestgvimp print.dynforestoob print.dynforestpred print.dynforestvardepth print.dynforestvimp
Display the summary of dynforestsummary.dynforest summary.dynforestoob