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:
AIPW_0.6.9.1.tar.gz
AIPW_0.6.9.1.zip(r-4.5)AIPW_0.6.9.1.zip(r-4.4)AIPW_0.6.9.1.zip(r-4.3)
AIPW_0.6.9.1.tgz(r-4.4-any)AIPW_0.6.9.1.tgz(r-4.3-any)
AIPW_0.6.9.1.tar.gz(r-4.5-noble)AIPW_0.6.9.1.tar.gz(r-4.4-noble)
AIPW_0.6.9.1.tgz(r-4.4-emscripten)AIPW_0.6.9.1.tgz(r-4.3-emscripten)
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
- eager_sim_obs - Simulated Observational Study
- eager_sim_rct - Simulated Randomized Trial
causal-inferencemachine-learningrobust-estimators
Last updated 2 months agofrom:24d6e279d7. Checks:OK: 1 NOTE: 6. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Nov 02 2024 |
| R-4.5-win | NOTE | Nov 02 2024 |
| R-4.5-linux | NOTE | Nov 02 2024 |
| R-4.4-win | NOTE | Nov 02 2024 |
| R-4.4-mac | NOTE | Nov 02 2024 |
| R-4.3-win | NOTE | Nov 02 2024 |
| R-4.3-mac | NOTE | Nov 02 2024 |
Exports:AIPWAIPW_baseAIPW_nuisAIPW_tmleaipw_wrapperRepeated
Dependencies:bitopscaToolsclicodetoolscolorspacecvAUCdata.tabledigestfansifarverforeachfuturefuture.applygamggplot2globalsgluegplotsgtablegtoolsisobanditeratorsKernSmoothlabelinglatticelifecyclelistenvmagrittrMASSMatrixmgcvmunsellnlmennlsparallellypillarpkgconfigprogressrR6RColorBrewerrlangROCRRsolnpscalesSuperLearnertibbletruncnormutf8vctrsviridisLitewithr
