  Modern applications of survival analysis increasingly involve time-dependent
covariates, which constitute a form of functional data. Learning from
functional data generally involves repeated evaluations of time integrals which
is numerically expensive. In this work we propose a lightweight data
preprocessing step that transforms functional data into nonfunctional data.
Boosting implementations for nonfunctional data can then be used, whereby the
required numerical integration comes for free as part of the training phase. We
use this to develop BoXHED 2.0, a quantum leap over the tree-boosted hazard
package BoXHED 1.0. BoXHED 2.0 extends BoXHED 1.0 to Aalen's multiplicative
intensity model, which covers censoring schemes far beyond right-censoring and
also supports recurrent events data. It is also massively scalable because of
preprocessing and also because it borrows from the core components of XGBoost.
BoXHED 2.0 supports the use of GPUs and multicore CPUs, and is available from
GitHub: www.github.com/BoXHED.
