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obsSens  

Sensitivity Analysis for Observational Studies
View on CRAN: Click here


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

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

Install by package version:
library("remotes")
install_version("obsSens", "1.4")



Attach the package and use:
library("obsSens")
Maintained by
Greg Snow
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2008-02-21
Latest Update: 2022-04-23
Description:
Observational studies are limited in that there could be an unmeasured variable related to both the response variable and the primary predictor. If this unmeasured variable were included in the analysis it would change the relationship (possibly changing the conclusions). Sensitivity analysis is a way to see how much of a relationship needs to exist with the unmeasured variable before the conclusions change. This package provides tools for doing a sensitivity analysis for regression (linear, logistic, and cox) style models.
How to cite:
Greg Snow (2008). obsSens: Sensitivity Analysis for Observational Studies. R package version 1.4, https://cran.r-project.org/web/packages/obsSens. Accessed 04 Jun. 2026.
Previous versions and publish date:
1.0 (2008-02-21 20:58), 1.1 (2011-02-05 18:08), 1.2 (2011-12-05 20:12), 1.3 (2013-01-18 18:19)
Other packages that cited obsSens R package
View obsSens citation profile
Other R packages that obsSens depends, imports, suggests or enhances
Complete documentation for obsSens
Functions, R codes and Examples using the obsSens R package
Some associated functions: obsSens-package . obsSensCCC . printsSens . 
Some associated R codes: obsSensCCC.R . obsSensCCN.R . obsSensCNN.R . obsSensNCC.R . obsSensNCN.R . obsSensNNN.R . obsSensSCC.R . obsSensSCN.R . obsSensSNN.R . printsSens.R . print.summarysSens.R . summarysSens.R .  Full obsSens package functions and examples
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