Package: medshift 0.1.4
medshift: Causal mediation analysis for stochastic interventions
Estimators of a parameter arising in the decomposition of the population intervention (in)direct effect of stochastic interventions in causal mediation analysis, including efficient one-step, targeted minimum loss (TML), re-weighting (IPW), and substitution estimators. The parameter estimated constitutes a part of each of the population intervention (in)direct effects. These estimators may be used in assessing population intervention (in)direct effects under stochastic treatment regimes, including incremental propensity score interventions and modified treatment policies. The methodology was first discussed by I Díaz and NS Hejazi (2020) <doi:10.1111/rssb.12362>.
Authors:
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medshift.pdf |medshift.html✨
medshift/json (API)
# Install 'medshift' in R: |
install.packages('medshift', repos = c('https://nhejazi.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/nhejazi/medshift/issues
causal-inferenceinverse-probability-weightsmachine-learningmediation-analysisstochastic-interventionstargeted-learningtreatment-effects
Last updated 3 years agofrom:0ae0572fc5. Checks:OK: 1 WARNING: 6. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Oct 31 2024 |
R-4.5-win | WARNING | Oct 31 2024 |
R-4.5-linux | WARNING | Oct 31 2024 |
R-4.4-win | WARNING | Oct 31 2024 |
R-4.4-mac | WARNING | Oct 31 2024 |
R-4.3-win | WARNING | Oct 31 2024 |
R-4.3-mac | WARNING | Oct 31 2024 |
Exports:LF_ipsimedshiftParam_medshiftpidetest_detmle_medshifttmle3_Spec_medshift
Dependencies:abindassertthatbackportsbase64encBBmiscbitopsbslibcachemcaretcaToolscheckmateclasscliclockcodetoolscolorspacecpp11crayondata.tabledelayeddiagramdigestdplyre1071evaluatefansifarverfastmapfontawesomeforeachfsfuturefuture.applygenericsggplot2globalsgluegowergplotsgtablegtoolshardhathighrhmshtmltoolshtmlwidgetsigraphipredisobanditeratorsjquerylibjsonliteKernSmoothknitrlabelinglatticelavalifecyclelistenvlubridatemagrittrMASSMatrixmemoisemgcvmimeModelMetricsmunsellmvtnormnlmennetnumDerivorigamiparallellypillarpkgconfigplyrprettyunitspROCprodlimprogressprogressrproxypurrrR.methodsS3R.ooR.utilsR6rappdirsrbibutilsRColorBrewerRcppRdpackrecipesreshape2rlangrmarkdownROCRrpartrstackdequesassscalesshapesl3SQUAREMstringistringrsurvivaltibbletidyrtidyselecttimechangetimeDatetinytextmle3tzdbutf8uuidvctrsviridisLitevisNetworkwithrxfunyaml