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
[aut, cre],
Ashley Naimi
[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:
Downloads:
Reverse dependencies:
Linking:
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