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AteMeVs  

Average Treatment Effects with Measurement Error and Variable Selection for Confounders
View on CRAN: Click here


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

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

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



Attach the package and use:
library("AteMeVs")
Maintained by
Li-Pang Chen
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2023-09-04
Latest Update: 2023-09-04
Description:
A recent method proposed by Yi and Chen (2023) is used to estimate the average treatment effects using noisy data containing both measurement error and spurious variables. The package 'AteMeVs' contains a set of functions that provide a step-by-step estimation procedure, including the correction of the measurement error effects, variable selection for building the model used to estimate the propensity scores, and estimation of the average treatment effects. The functions contain multiple options for users to implement, including different ways to correct for the measurement error effects, distinct choices of penalty functions to do variable selection, and various regression models to characterize propensity scores.
How to cite:
Li-Pang Chen (2023). AteMeVs: Average Treatment Effects with Measurement Error and Variable Selection for Confounders. R package version 0.1.0, https://cran.r-project.org/web/packages/AteMeVs. Accessed 21 Nov. 2024.
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Complete documentation for AteMeVs
Functions, R codes and Examples using the AteMeVs R package
Some associated functions: AteMeVs-package . DG . EST_ATE . SIMEX_EST . VSE_PS . 
Some associated R codes: DG.R . EST_ATE.R . SIMEX_EST.R . VSE_PS.R .  Full AteMeVs package functions and examples
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