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, Dufouil, Genuer & Proust-Lima (2022) <doi:10.48550/arXiv.2208.05801>.

Version: 1.1.0
Depends: R (≥ 3.5.0)
Imports: DescTools, cmprsk, doParallel, foreach, ggplot2, lcmm, methods, pbapply, pec, prodlim, stringr, survival, zoo
Suggests: knitr, rmarkdown
Published: 2022-11-19
Author: Anthony Devaux ORCID iD [aut, cre]
Maintainer: Anthony Devaux <anthony.devaux at u-bordeaux.fr>
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


Reference manual: DynForest.pdf
Vignettes: Introduction to 'DynForest' methodology
How to use 'DynForest' with categorical outcome?
How to use 'DynForest' with continuous outcome?
How to use 'DynForest' with survival outcome?


Package source: DynForest_1.1.0.tar.gz
Windows binaries: r-devel: DynForest_1.1.0.zip, r-release: not available, r-oldrel: DynForest_1.1.0.zip
macOS binaries: r-release (arm64): DynForest_1.1.0.tgz, r-oldrel (arm64): DynForest_1.1.0.tgz, r-release (x86_64): DynForest_1.1.0.tgz, r-oldrel (x86_64): DynForest_1.1.0.tgz
Old sources: DynForest archive


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