Package: causal.decomp 0.1.0
causal.decomp: Causal Decomposition Analysis
We implement causal decomposition analysis using the methods proposed by Park, Lee, and Qin (2020) and Park, Kang, and Lee (2021+) <arxiv:2109.06940>. This package allows researchers to use the multiple-mediator-imputation, single-mediator-imputation, and product-of-coefficients regression methods to estimate the initial disparity, disparity reduction, and disparity remaining. It also allows to make the inference conditional on baseline covariates. We also implement sensitivity analysis for the causal decomposition analysis using R-squared values as sensitivity parameters (Park, Kang, Lee, and Ma, 2023).
Authors:
causal.decomp_0.1.0.tar.gz
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causal.decomp.pdf |causal.decomp.html✨
causal.decomp/json (API)
# Install 'causal.decomp' in R: |
install.packages('causal.decomp', repos = c('https://syeonkang.r-universe.dev', 'https://cloud.r-project.org')) |
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 2 years agofrom:f7c6799398. Checks:OK: 1 NOTE: 6. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Sep 09 2024 |
R-4.5-win | NOTE | Sep 09 2024 |
R-4.5-linux | NOTE | Sep 09 2024 |
R-4.4-win | NOTE | Sep 09 2024 |
R-4.4-mac | NOTE | Sep 09 2024 |
R-4.3-win | NOTE | Sep 09 2024 |
R-4.3-mac | NOTE | Sep 09 2024 |
Exports:mmipocrsensitivitysmi
Dependencies:backportsbitopsbootcaToolsCBPSchkclicodetoolscolorspacecommonmarkcvAUCdata.tabledigestevaluatefansifarverforeachgamgbmggplot2glmnetgluegplotsgtablegtoolshighrhunspellisobanditeratorsKernSmoothknitrlabelinglatticelifecyclelme4magrittrMASSMatchItMatrixmgcvminqamunsellnlmenloptrnnetnnlsnumDerivpillarpkgconfigPSweightR6RColorBrewerRcppRcppEigenRcppProgressrlangROCRscalesshapespellingSuperLearnerSuppDistssurvivaltibbleutf8vctrsviridisLitewithrxfunxml2yaml