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RCTrep  

Validation of Estimates of Treatment Effects in Observational Data
View on CRAN: Click here


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

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

Install by package version:
library("remotes")
install_version("RCTrep", "1.2.0")



Attach the package and use:
library("RCTrep")
Maintained by
Lingjie Shen
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2023-03-29
Latest Update: 2023-11-02
Description:
Validates estimates of (conditional) average treatment effects obtained using observational data by a) making it easy to obtain and visualize estimates derived using a large variety of methods (G-computation, inverse propensity score weighting, etc.), and b) ensuring that estimates are easily compared to a gold standard (i.e., estimates derived from randomized controlled trials). 'RCTrep' offers a generic protocol for treatment effect validation based on four simple steps, namely, set-selection, estimation, diagnosis, and validation. 'RCTrep' provides a simple dashboard to review the obtained results. The validation approach is introduced by Shen, L., Geleijnse, G. and Kaptein, M. (2023) .
How to cite:
Lingjie Shen (2023). RCTrep: Validation of Estimates of Treatment Effects in Observational Data. R package version 1.2.0, https://cran.r-project.org/web/packages/RCTrep. Accessed 13 Mar. 2026.
Previous versions and publish date:
1.0.0 (2023-03-29 10:10), 1.1.0 (2023-08-09 12:20)
Other packages that cited RCTrep R package
View RCTrep citation profile
Other R packages that RCTrep depends, imports, suggests or enhances
Complete documentation for RCTrep
Functions, R codes and Examples using the RCTrep R package
Full RCTrep package functions and examples
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