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DynForest: Random Forest with Multivariate Longitudinal Predictors (original) (raw)

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>.

Version: 1.2.0
Depends: R (≥ 4.4.0)
Imports: DescTools, cli, cmprsk, doParallel, doRNG, foreach, ggplot2, lcmm, methods, pbapply, pec, prodlim, stringr, survival, zoo
Suggests: knitr, rmarkdown
Published: 2024-10-23
DOI: 10.32614/CRAN.package.DynForest
Author: Anthony Devaux ORCID iD [aut, cre], Robin Genuer ORCID iD [aut], Cécile Proust-LimaORCID iD [aut], Louis Capitaine ORCID iD [aut]
Maintainer: Anthony Devaux <anthony.devauxbarault at gmail.com>
BugReports: https://github.com/anthonydevaux/DynForest/issues
License: LGPL (≥ 3)
URL: https://github.com/anthonydevaux/DynForest
NeedsCompilation: no
Citation: DynForest citation info
Materials: README, NEWS
CRAN checks: DynForest results

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