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ciccr  

Causal Inference in Case-Control and Case-Population Studies
View on CRAN: Click here


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

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

Install by package version:
library("remotes")
install_version("ciccr", "0.3.0")



Attach the package and use:
library("ciccr")
Maintained by
Sokbae Lee
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2020-09-18
Latest Update: 2023-10-20
Description:
Estimation and inference methods for causal relative and attributable risk in case-control and case-population studies under the monotone treatment response and monotone treatment selection assumptions. For more details, see the paper by Jun and Lee (2023), "Causal Inference under Outcome-Based Sampling with Monotonicity Assumptions," , accepted for publication in Journal of Business & Economic Statistics.
How to cite:
Sokbae Lee (2020). ciccr: Causal Inference in Case-Control and Case-Population Studies. R package version 0.3.0, https://cran.r-project.org/web/packages/ciccr
Previous versions and publish date:
0.1.0 (2020-09-18 14:00), 0.2.0 (2020-10-29 11:00)
Other packages that cited ciccr R package
View ciccr citation profile
Other R packages that ciccr depends, imports, suggests or enhances
Functions, R codes and Examples using the ciccr R package
Some associated functions: AAA_DML . ACS . ACS_CC . ACS_CP . DZ_CC . FG . FG_CC . FG_CP . avg_AR_logit . avg_RR_logit . cicc_AR . cicc_RR . cicc_plot . ciccr-package . trim_pr . 
Some associated R codes: AAA_DML.R . avg_AR_logit.R . avg_RR_logit.R . cicc_AR.R . cicc_RR.R . cicc_plot.R . ciccr-package.R . data_ACS.R . data_ACS_CC.R . data_ACS_CP.R . data_DZ_CC.R . data_FG.R . data_FG_CC.R . data_FG_CP.R . trim_pr.R .  Full ciccr package functions and examples
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