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README.Rmd
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README.Rmd
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---
output:
rmarkdown::github_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r, echo = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "README-"
)
```
# R/`ctmle3`: modern Super Learning with pipelines
[](https://travis-ci.org/tlverse/ctmle3)
[](https://ci.appveyor.com/project/tlverse/ctmle3)
[](https://codecov.io/github/tlverse/ctmle3?branch=master)
<!-- [](https://www.repostatus.org/#active) -->
[](https://www.gnu.org/licenses/gpl-3.0)
<!-- [](https://doi.org/10.5281/zenodo.1342294) -->
<!-- [](https://gitter.im/ctmle3-Rpkg/Lobby?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge) -->
> A modern implementation of the Collaborative Targeted Maximum Likelihood Estimator (C-TMLE) algorithm for xxxxx
__Authors:__ [Weixin Cai](https://github.com/wilsoncai1992), [Jeremy Coyle](https://github.com/jeremyrcoyle)
---
## What's `ctmle3`?
<!-- `ctmle3` is a modern implementation of the Super Learner algorithm of
@vdl2007super. The Super Learner algorithm performs ensemble learning in one of
two fashions:
1. The _discrete_ Super Learner can be used to select the best prediction
algorithm from among a supplied library of machine learning algorithms
("learners" in the `ctmle3` nomenclature) -- that is, the discrete Super Learner
is the single learning algorithm that minimizes the cross-validated risk with
respect to an appropriate loss function.
2. The _ensemble_ Super Learner can be used to assign weights to a set of
specified learning algorithms (from a user-supplied library of such
algorithms) so as to create a combination of these learners that minimizes
the cross-validated risk with respect to an appropriate loss function. This
notion of weighted combinations has also been referred to as _stacked
regression_ [@breiman1996stacked] and _stacked generalization_
[@wolpert1992stacked].
-->
---
## Installation
<!--
For standard use, we recommend installing the package from
[CRAN](https://cran.r-project.org/) via
```{r cran-installation, eval = FALSE}
install.packages("ctmle3")
```
-->
Install the most recent _stable release_ from GitHub via
[`devtools`](https://www.rstudio.com/products/rpackages/devtools/):
```{r gh-master-installation, eval = FALSE}
devtools::install_github("tlverse/ctmle3")
```
---
## Issues
If you encounter any bugs or have any specific feature requests, please [file an
issue](https://github.com/tlverse/ctmle3/issues).
---
## Examples
<!-- `ctmle3` makes the process of applying screening algorithms, learning algorithms,
combining both types of algorithms into a stacked regression model, and
cross-validating this whole process essentially trivial. The best way to
understand this is to see the `ctmle3` package in action:
-->
---
## Contributions
It is our hope that `ctmle3` will grow to be widely used for creating stacked
regression models and the cross-validation of pipelines that make up such
models, as well as the variety of other applications in which the Super Learner
algorithm plays a role. To that end, contributions are very welcome, though we
ask that interested contributors consult our [contribution
guidelines](https://github.com/tlverse/ctmle3/blob/master/CONTRIBUTING.md)
prior to submitting a pull request.
---
After using the `ctmle3` R package, please cite the following:
@misc{coyle2018ctmle3,
author = {Cai, Weixin and Coyle, Jeremy R},
title = {{ctmle3}: Modern Pipelines for Machine Learning and {Super
Learning}},
year = {2018},
howpublished = {\url{https://github.com/tlverse/ctmle3}},
note = {{R} package version 0.1.0},
}
---
## License
© 2017-2018 [Weixin Cai](https://github.com/wilsoncai1992), [Jeremy R. Coyle](https://github.com/jeremyrcoyle)
The contents of this repository are distributed under the GPL-3 license. See
file `LICENSE` for details.
---
## References