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R/tmle3

Travis-CI Build Status Appveyor Build Status Coverage Status Project Status: Active – The project has reached a stable, usable state and is being actively developed. License: GPL v3 DOI

The Extensible TMLE framework

Author: Jeremy Coyle


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).


Installation

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

remotes::install_github("tlverse/tmle3")

Issues

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


Getting Started

The best place to get started is the β€œFramework Overview” document, which describes the individual components of the tmle3 framework. It may be found at https://tlverse.org/tmle3/articles/framework.html.


Contributions

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


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}
    }

License

Β© 2017-2021 Jeremy R. Coyle

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


References

van der Laan, Mark J, and Sherri Rose. 2011. Targeted Learning: Causal Inference for Observational and Experimental Data. Springer Science & Business Media.

β€”β€”β€”. 2018. Targeted Learning in Data Science: Causal Inference for Complex Longitudinal Studies. Springer Science & Business Media.