Package: AIPW 0.6.9

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]

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AIPW.pdf |AIPW.html✨
AIPW/json (API)

Peer review:

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

On CRAN:

causal-inferencemachine-learningrobust-estimators

6 exports 20 stars 3.25 score 51 dependencies 1 dependents 11 mentions 37 scripts 491 downloads

Last updated 11 months agofrom:a3ae8ef59d. Checks:OK: 1 NOTE: 6. Indexed: yes.

Exports:AIPWAIPW_baseAIPW_nuisAIPW_tmleaipw_wrapperRepeated

Dependencies:bitopscaToolsclicodetoolscolorspacecvAUCdata.tabledigestfansifarverforeachfuturefuture.applygamggplot2globalsgluegplotsgtablegtoolsisobanditeratorsKernSmoothlabelinglatticelifecyclelistenvmagrittrMASSMatrixmgcvmunsellnlmennlsparallellypillarpkgconfigprogressrR6RColorBrewerrlangROCRRsolnpscalesSuperLearnertibbletruncnormutf8vctrsviridisLitewithr