🎯🎓 Generalized Targeted Learning Framework
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README.md

R/tmle3

Travis-CI Build Status Appveyor Build Status Coverage Status Project Status: WIP – Initial development is in progress, but there has not yet been a stable, usable release suitable for the public. License: GPL v3

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

You can install the development version of tmle3 from GitHub via devtools with

devtools::install_github("tlverse/tmle3")

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 here: https://tlverse.org/tmle3/articles/framework.html.


Issues

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


License

© 2017-2018 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.

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