Package: medoutcon 0.2.2
medoutcon: Efficient Natural and Interventional Causal Mediation Analysis
Efficient estimators of interventional (in)direct effects in the presence of mediator-outcome confounding affected by exposure. The effects estimated allow for the impact of the exposure on the outcome through a direct path to be disentangled from that through mediators, even in the presence of intermediate confounders that complicate such a relationship. Currently supported are non-parametric efficient one-step and targeted minimum loss estimators based on the formulation of Díaz, Hejazi, Rudolph, and van der Laan (2020) <doi:10.1093/biomet/asaa085>. Support for efficient estimation of the natural (in)direct effects is also provided, appropriate for settings in which intermediate confounders are absent. The package also supports estimation of these effects when the mediators are measured using outcome-dependent two-phase sampling designs (e.g., case-cohort).
Authors:
medoutcon_0.2.2.tar.gz
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medoutcon_0.2.2.tgz(r-4.4-any)medoutcon_0.2.2.tgz(r-4.3-any)
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medoutcon.pdf |medoutcon.html✨
medoutcon/json (API)
NEWS
# Install 'medoutcon' in R: |
install.packages('medoutcon', repos = c('https://ictml-project.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/nhejazi/medoutcon/issues
causal-inferencecausal-machine-learninginverse-probability-weightsmachine-learningmediation-analysisstochastic-interventionstargeted-learningtreatment-effects
Last updated 8 months agofrom:e2d3fd8a64. Checks:OK: 7. Indexed: no.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Oct 28 2024 |
R-4.5-win | OK | Oct 28 2024 |
R-4.5-linux | OK | Oct 28 2024 |
R-4.4-win | OK | Oct 28 2024 |
R-4.4-mac | OK | Oct 28 2024 |
R-4.3-win | OK | Oct 28 2024 |
R-4.3-mac | OK | Oct 28 2024 |
Exports:medoutcon
Dependencies:abindassertthatbackportsbase64encBBmiscbitopsbslibcachemcaretcaToolscheckmateclasscliclockcodetoolscolorspacecpp11crayondata.tabledelayeddiagramdigestdplyre1071evaluatefansifarverfastmapfontawesomeforeachfsfuturefuture.applygenericsggplot2glm2globalsgluegowergplotsgtablegtoolshardhathighrhmshtmltoolshtmlwidgetsigraphipredisobanditeratorsjquerylibjsonliteKernSmoothknitrlabelinglatticelavalifecyclelistenvlubridatemagrittrMASSMatrixmemoisemgcvmimeModelMetricsmunsellnlmennetnumDerivorigamiparallellypillarpkgconfigplyrprettyunitspROCprodlimprogressprogressrproxypurrrR.methodsS3R.ooR.utilsR6rappdirsrbibutilsRColorBrewerRcppRdpackrecipesreshape2rlangrmarkdownROCRrpartrstackdequesassscalesshapesl3SQUAREMstringistringrsurvivaltibbletidyrtidyselecttimechangetimeDatetinytextzdbutf8uuidvctrsviridisLitevisNetworkwithrxfunyamlzeallot