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mediationsens
View on CRAN: Click
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Download and install mediationsens package within the R console
Install from CRAN:
install.packages("mediationsens")
Install from Github:
library("remotes")
install_github("cran/mediationsens")
Install by package version:
library("remotes")
install_version("mediationsens", "0.0.3")
Attach the package and use:
library("mediationsens")
Maintained by
Xu Qin
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2020-05-27
Latest Update: 2020-06-14
Description:
Simulation-based sensitivity analysis for causal mediation studies. It numerically and graphically evaluates the sensitivity of causal mediation analysis results
to the presence of unmeasured pretreatment confounding. The proposed method has primary advantages over existing methods.
First, using an unmeasured pretreatment confounder conditional associations with the treatment, mediator, and outcome as
sensitivity parameters, the method enables users to intuitively assess sensitivity in reference to prior knowledge about the
strength of a potential unmeasured pretreatment confounder. Second, the method accurately reflects the influence of unmeasured
pretreatment confounding on the efficiency of estimation of the causal effects. Third, the method can be implemented in
different causal mediation analysis approaches, including regression-based, simulation-based, and propensity score-based
methods. It is applicable to both randomized experiments and observational studies.
How to cite:
Xu Qin (2020). mediationsens: Simulation-Based Sensitivity Analysis for Causal Mediation Studies. R package version 0.0.3, https://cran.r-project.org/web/packages/mediationsens
Other packages that cited mediationsens R package
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Other R packages that mediationsens depends,
imports, suggests or enhances
Functions, R codes and Examples using
the mediationsens R package
Some associated functions: sens . sens.plot .
Some associated R codes: mediationsens.R . Full mediationsens package functions and examples
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