  This article introduces the R package concrete, which implements a recently
developed targeted maximum likelihood estimator (TMLE) for the cause-specific
absolute risks of time-to-event outcomes measured in continuous time.
Cross-validated Super Learner machine learning ensembles are used to estimate
propensity scores and conditional cause-specific hazards, which are then
targeted to produce robust and efficient plug-in estimates of the effects of
static or dynamic interventions on a binary treatment given at baseline
quantified as risk differences or risk ratios. Influence curve-based asymptotic
inference is provided for TMLE estimates and simultaneous confidence bands can
be computed for target estimands spanning multiple multiple times or events. In
this paper we review the one-step continuous-time TMLE methodology as it is
situated in an overarching causal inference workflow, describe its
implementation, and demonstrate the use of the package on the PBC dataset.

Computation