  Over the past years, many applications aim to assess the causal effect of
treatments assigned at the community level, while data are still collected at
the individual level among individuals of the community. In many cases, one
wants to evaluate the effect of a stochastic intervention on the community,
where all communities in the target population receive probabilistically
assigned treatments based on a known specified mechanism (e.g., implementing a
community-level intervention policy that target stochastic changes in the
behavior of a target population of communities). The tmleCommunity package is
recently developed to implement targeted minimum loss-based estimation (TMLE)
of the effect of community-level intervention(s) at a single time point on an
individual-based outcome of interest, including the average causal effect.
Implementations of the inverse-probability-of-treatment-weighting (IPTW) and
the G-computation formula (GCOMP) are also available. The package supports
multivariate arbitrary (i.e., static, dynamic or stochastic) interventions with
a binary or continuous outcome. Besides, it allows user-specified data-adaptive
machine learning algorithms through SuperLearner, sl3 and h2oEnsemble packages.
The usage of the tmleCommunity package, along with a few examples, will be
described in this paper.

Applications;Computation