The Extensible TMLE framework
Author: Jeremy Coyle
tmle3 is a general framework that supports the implementation of a
range of Targeted Maximum Likelihood / Minimum Loss-Based Estimation
(TMLE) parameters through exposing a unified interface. The goal is that
the tmle3 framework be as general as the mathematical framework upon
which it’s based.
For a general discussion of the framework of targeted minimum loss-based estimation and the role this methodology plays in statistical and causal inference, the canonical references are van der Laan and Rose (2011) and van der Laan and Rose (2018).
To contribute, install the development version of tmle3 from GitHub
via remotes:
remotes::install_github("tlverse/tmle3")If you encounter any bugs or have any specific feature requests, please file an issue.
The best place to get started is the “Framework Overview” document,
which describes the individual components of the tmle3 framework. It
may be found at https://tlverse.org/tmle3/articles/framework.html.
Contributions are very welcome. Interested contributors should consult our contribution guidelines prior to submitting a pull request.
After using the tmle3 R package, please cite the following:
@software{coyle2021tmle3-rpkg,
author = {Coyle, Jeremy R},
title = {{tmle3}: The Extensible {TMLE} Framework},
year = {2021},
howpublished = {\url{https://github.com/tlverse/tmle3}},
note = {{R} package version 0.2.0},
url = {https://doi.org/10.5281/zenodo.4603358},
doi = {10.5281/zenodo.4603358}
}
© 2017-2021 Jeremy R. Coyle
The contents of this repository are distributed under the GPL-3 license.
See file LICENSE for details.
van der Laan, Mark J, and Sherri Rose. 2011. Targeted Learning: Causal Inference for Observational and Experimental Data. Springer Science & Business Media.
———. 2018. Targeted Learning in Data Science: Causal Inference for Complex Longitudinal Studies. Springer Science & Business Media.