Package: AIPW 0.6.9.1

AIPW: Augmented Inverse Probability Weighting

The 'AIPW' package 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). AIPW: An R Package for Augmented Inverse Probability Weighted Estimation of Average Causal Effects. American Journal of Epidemiology. doi: 10.1093/aje/kwab207". Visit: <https://yqzhong7.github.io/AIPW/> for more information.

Authors:Yongqi Zhong [aut, cre], Ashley Naimi [aut], Gabriel Conzuelo [ctb], Edward Kennedy [ctb]

AIPW_0.6.9.1.tar.gz
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AIPW.pdf |AIPW.html✨
AIPW/json (API)

# Install 'AIPW' in R:
install.packages('AIPW', repos = c('https://yqzhong7.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/yqzhong7/aipw/issues

Datasets:

On CRAN:

Conda:

causal-inferencemachine-learningrobust-estimators

7.65 score 24 stars 1 packages 31 scripts 511 downloads 11 mentions 6 exports 51 dependencies

Last updated 18 days agofrom:1eaba3a386. Checks:1 OK, 8 NOTE. Indexed: yes.

TargetResultLatest binary
Doc / VignettesOKMar 16 2025
R-4.5-winNOTEMar 16 2025
R-4.5-macNOTEMar 16 2025
R-4.5-linuxNOTEMar 16 2025
R-4.4-winNOTEMar 16 2025
R-4.4-macNOTEMar 16 2025
R-4.4-linuxNOTEMar 16 2025
R-4.3-winNOTEMar 16 2025
R-4.3-macNOTEMar 16 2025

Exports:AIPWAIPW_baseAIPW_nuisAIPW_tmleaipw_wrapperRepeated

Dependencies:bitopscaToolsclicodetoolscolorspacecvAUCdata.tabledigestfansifarverforeachfuturefuture.applygamggplot2globalsgluegplotsgtablegtoolsisobanditeratorsKernSmoothlabelinglatticelifecyclelistenvmagrittrMASSMatrixmgcvmunsellnlmennlsparallellypillarpkgconfigprogressrR6RColorBrewerrlangROCRRsolnpscalesSuperLearnertibbletruncnormutf8vctrsviridisLitewithr

Getting Started with AIPW

Rendered fromAIPW.Rmdusingknitr::rmarkdownon Mar 16 2025.

Last update: 2023-11-04
Started: 2020-05-10

Repeated Cross-fitting

Rendered fromRepated_Crossfitting.Rmdusingknitr::rmarkdownon Mar 16 2025.

Last update: 2023-11-04
Started: 2023-11-04