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CopSens  

Copula-Based Sensitivity Analysis for Observational Causal Inference
View on CRAN: Click here


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

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

Install by package version:
library("remotes")
install_version("CopSens", "0.1.0")



Attach the package and use:
library("CopSens")
Maintained by
Jiajing Zheng
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2022-05-12
Latest Update: 2025-09-01
Description:
Implements the copula-based sensitivity analysis method, as discussed in Copula-based Sensitivity Analysis for Multi-Treatment Causal Inference with Unobserved Confounding , with Gaussian copula adopted in particular.
How to cite:
Jiajing Zheng (2022). CopSens: Copula-Based Sensitivity Analysis for Observational Causal Inference. R package version 0.1.0, https://cran.r-project.org/web/packages/CopSens. Accessed 25 Jun. 2026.
Previous versions and publish date:
0.1.0 (2022-05-12 10:30)
Other packages that cited CopSens R package
View CopSens citation profile
Other R packages that CopSens depends, imports, suggests or enhances
Complete documentation for CopSens
Functions, R codes and Examples using the CopSens R package
Some associated functions: GaussianT_BinaryY . GaussianT_GaussianY . bcalibrate . cal_rv . cali_mean_ybinary_algm . gcalibrate . get_opt_gamma . mice_est_nulltr . micedata . plot_estimates . 
Some associated R codes: bcalibrate.R . cal_rv.R . cali_mean_ybinary_algm.R . data.R . gcalibrate.R . get_opt_gamma.R . plot_estimates.R .  Full CopSens package functions and examples
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