R/tmle3

The Extensible TMLE framework

Author: Jeremy Coyle

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What’s tmle3?

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).

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Installation

To contribute, install the development version of tmle3 from GitHub via remotes:

remotes::install_github("tlverse/tmle3")

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Issues

If you encounter any bugs or have any specific feature requests, please file an issue.

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Getting Started

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.

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Contributions

Contributions are very welcome. Interested contributors should consult our contribution guidelines prior to submitting a pull request.

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Citation

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}
    }

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License

© 2017-2021 Jeremy R. Coyle

The contents of this repository are distributed under the GPL-3 license. See file LICENSE for details.

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References

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.