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sensitivitymw  

Sensitivity Analysis for Observational Studies Using Weighted M-Statistics
View on CRAN: Click here


Download and install sensitivitymw package within the R console
Install from CRAN:
install.packages("sensitivitymw")

Install from Github:
library("remotes")
install_github("cran/sensitivitymw")

Install by package version:
library("remotes")
install_version("sensitivitymw", "2.1")



Attach the package and use:
library("sensitivitymw")
Maintained by
Paul R. Rosenbaum
[Scholar Profile | Author Map]
First Published: 2014-05-13
Latest Update: 2022-01-04
Description:
Sensitivity analysis for tests, confidence intervals and estimates in matched observational studies with one or more controls using weighted or unweighted Huber-Maritz M-tests (including the permutational t-test). The method is from Rosenbaum (2014) Weighted M-statistics with superior design sensitivity in matched observational studies with multiple controls JASA, 109(507), 1145-1158 .
How to cite:
Paul R. Rosenbaum (2014). sensitivitymw: Sensitivity Analysis for Observational Studies Using Weighted M-Statistics. R package version 2.1, https://cran.r-project.org/web/packages/sensitivitymw. Accessed 09 Apr. 2025.
Previous versions and publish date:
1.0 (2014-05-13 19:55), 1.1 (2014-07-24 08:22)
Other packages that cited sensitivitymw R package
View sensitivitymw citation profile
Other R packages that sensitivitymw depends, imports, suggests or enhances
Complete documentation for sensitivitymw
Functions, R codes and Examples using the sensitivitymw R package
Some associated functions: erpcp . mercury . mscorev . multrnks . newurks . senmw . senmwCI . sensitivitymw-package . separable1k . 
Some associated R codes: mscorev.R . multrnks.R . newurks.R . senmw.R . senmwCI.R . separable1k.R .  Full sensitivitymw package functions and examples
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