DynForest1.2.0 package

Random Forest with Multivariate Longitudinal Predictors

compute_gvimp

Compute the grouped importance of variables (gVIMP) statistic

compute_ooberror

Compute the Out-Of-Bag error (OOB error)

compute_vardepth

Extract characteristics from the trees building process

compute_vimp

Compute the importance of variables (VIMP) statistic

dynforest

Random forest with multivariate longitudinal endogenous covariates

get_tree

Extract some information about the split for a tree by user

get_treenodes

Extract nodes identifiers for a given tree

plot.dynforest

Plot function in dynforest

predict.dynforest

Prediction using dynamic random forests

print.dynforest

Print function

summary.dynforest

Display the summary of dynforest

Download source packageRead PDF manual

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>.

  • Maintainer: Anthony Devaux
  • License: LGPL (>= 3)
  • Last published: 2024-10-23

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