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AIPW: Augmented Inverse Probability Weighting

The 'AIPW' pacakge implements the augmented inverse probability weighting, a doubly robust estimator, for average causal effect estimation with user-defined stacked machine learning algorithms. To cite the 'AIPW' package, please use: "Yongqi Zhong, Edward H. Kennedy, Lisa M. Bodnar, Ashley I. Naimi (2021, In Press). AIPW: An R Package for Augmented Inverse Probability Weighted Estimation of Average Causal Effects. American Journal of Epidemiology". Visit: <https://yqzhong7.github.io/AIPW/> for more information.

Version: 0.6.3.2
Depends: R (≥ 2.10)
Imports: stats, utils, R6, SuperLearner, ggplot2, future.apply, progressr, Rsolnp
Suggests: testthat (≥ 2.1.0), knitr, rmarkdown, covr, tmle
Published: 2021-06-11
DOI: 10.32614/CRAN.package.AIPW
Author: Yongqi Zhong ORCID iD [aut, cre], Ashley Naimi ORCID iD [aut], Gabriel Conzuelo [ctb], Edward Kennedy [ctb]
Maintainer: Yongqi Zhong <yq.zhong7 at gmail.com>
BugReports: https://github.com/yqzhong7/AIPW/issues
License: GPL-3
URL: https://github.com/yqzhong7/AIPW
NeedsCompilation: no
Language: es
Citation: AIPW citation info
Materials: README
In views: CausalInference
CRAN checks: AIPW results

Documentation:

Reference manual: AIPW.pdf
Vignettes: Getting Started with AIPW

Downloads:

Package source: AIPW_0.6.3.2.tar.gz
Windows binaries: r-devel: AIPW_0.6.3.2.zip, r-release: AIPW_0.6.3.2.zip, r-oldrel: AIPW_0.6.3.2.zip
macOS binaries: r-release (arm64): AIPW_0.6.3.2.tgz, r-oldrel (arm64): AIPW_0.6.3.2.tgz, r-release (x86_64): AIPW_0.6.3.2.tgz, r-oldrel (x86_64): AIPW_0.6.3.2.tgz

Reverse dependencies:

Reverse imports: RobinCar

Linking:

Please use the canonical form https://CRAN.R-project.org/package=AIPW to link to this page.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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