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EScvtmle
View on CRAN: Click
here
Download and install EScvtmle package within the R console
Install from CRAN:
install.packages("EScvtmle")
Install from Github:
library("remotes")
install_github("cran/EScvtmle") Install by package version:
library("remotes")
install_version("EScvtmle", "0.0.2") Attach the package and use:
library("EScvtmle")
Maintained by
Lauren Eyler Dang
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[Scholar Profile | Author Map]
All associated links for this package
First Published: 2022-11-02
Latest Update: 2023-01-05
Description:
The experiment selector cross-validated targeted maximum likelihood estimator (ES-CVTMLE) aims to select the experiment that optimizes the bias-variance tradeoff for estimating a causal average treatment effect (ATE) where different experiments may include a randomized controlled trial (RCT) alone or an RCT combined with real-world data. Using cross-validation, the ES-CVTMLE separates the selection of the optimal experiment from the estimation of the ATE for the chosen experiment. The estimated bias term in the selector is a function of the difference in conditional mean outcome under control for the RCT compared to the combined experiment. In order to help include truly unbiased external data in the analysis, the estimated average treatment effect on a negative control outcome may be added to the bias term in the selector. For more details about this method, please see Dang et al. (2022) .
How to cite:
Lauren Eyler Dang (2022). EScvtmle: Experiment-Selector CV-TMLE for Integration of Observational and RCT Data. R package version 0.0.2, https://cran.r-project.org/web/packages/EScvtmle. Accessed 04 Jun. 2026.
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Complete documentation for EScvtmle
Functions, R codes and Examples using
the EScvtmle R package
Some associated functions: ES.cvtmle . plot.EScvtmle . print.EScvtmle . wash .
Some associated R codes: EScvtmle.R . common_functions.R . support_functions_notxrwd.R . support_functions_txrwd.R . wash.R . Full EScvtmle package functions and examples
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