  The R package DynForest implements random forests for predicting a
continuous, a categorical or a (multiple causes) time-to-event outcome based on
time-fixed and time-dependent predictors. The main originality of DynForest is
that it handles time-dependent predictors that can be endogeneous (i.e.,
impacted by the outcome process), measured with error and measured at
subject-specific times. At each recursive step of the tree building process,
the time-dependent predictors are internally summarized into individual
features on which the split can be done. This is achieved using flexible linear
mixed models (thanks to the R package lcmm) which specification is
pre-specified by the user. DynForest returns the mean for continuous outcome,
the category with a majority vote for categorical outcome or the cumulative
incidence function over time for survival outcome. DynForest also computes
variable importance and minimal depth to inform on the most predictive
variables or groups of variables. This paper aims to guide the user with
step-by-step examples for fitting random forests using DynForest.
