Other packages > Find by keyword >

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 05 Aug. 2026.
Previous versions and publish date:
(2026-07-09 06:36), 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
Downloads during the last 30 days

Today's Hot Picks in Authors and Packages

noisyr  
Noise Quantification in High Throughput Sequencing Output
Quantifies and removes technical noise from high-throughput sequencing data. Two approaches are use ...
Download / Learn more Package Citations See dependency  
quickcode  
Quick and Essential 'R' Tricks for Better Scripts
The NOT functions, 'R' tricks and a compilation of some simple quick plus often used 'R' codes to im ...
Download / Learn more Package Citations See dependency  
kernelPSI  
Post-Selection Inference for Nonlinear Variable Selection
Different post-selection inference strategies for kernelselection as described in kernelPSI a Post-S ...
Download / Learn more Package Citations See dependency  
dcov  
A Fast Implementation of Distance Covariance
Efficient methods for computing distance covariance and relevant statistics. See Sz ...
Download / Learn more Package Citations See dependency  
dhReg  
Dynamic Harmonic Regression
Building and forecasting time series data with multiple seasonality using Dynamic Harmonic Regressio ...
Download / Learn more Package Citations See dependency  

28,083

R Packages

239,283

Dependencies

74,457

Author Associations

28,084

Publication Badges

© Copyright since 2022. All right reserved, rpkg.net.  Based in Cambridge, Massachusetts, USA