The R package DynForest implements random forests for predicting a categorical or a (multiple causes) time-to-event outcome based on time-fixed and time-dependent predictors. Through the random forests, the time-dependent predictors can be measured with error at subject-specific times, and they can be endogeneous (i.e., impacted by the outcome process). They are modeled internally using flexible linear mixed models (thanks to lcmm package) with time-associations pre-specified by the user. DynForest computes dynamic predictions that take into account all the information from time-fixed and time-dependent predictors. DynForest also provides information about the most predictive variables using variable importance and minimal depth. Variable importance can also be computed on groups of variables. To display the results, several functions are available such as summary and plot functions. This paper aims to guide the user with a step-by-step example of the different functions for fitting random forests within DynForest.

翻译：R包DynForest实现了随机森林，用于基于时间固定和时间依赖预测变量对分类结果或（多原因）生存时间结局进行预测。通过随机森林，时间依赖预测变量可在受试者特定时间点上附带测量误差进行采集，且可为内源性变量（即受结局过程影响）。这些变量内部采用灵活的线性混合模型（借助lcmm包）进行建模，用户可预先指定其时间关联结构。DynForest可计算动态预测值，整合来自时间固定和时间依赖预测变量的全部信息。此外，该软件包通过变量重要性和最小深度提供最具预测性变量的信息，并可计算变量组的变量重要性。结果展示方面，提供了summary和plot等多种函数。本文旨在通过分步示例指导用户使用DynForest中的不同函数来拟合随机森林。