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dmlalg  

Double Machine Learning Algorithms
View on CRAN: Click here


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

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

Install by package version:
library("remotes")
install_version("dmlalg", "1.0.2")



Attach the package and use:
library("dmlalg")
Maintained by
Corinne Emmenegger
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2021-06-11
Latest Update: 2022-02-03
Description:
Implementation of double machine learning (DML) algorithms in R, based on Emmenegger and Buehlmann (2021) "Regularizing Double Machine Learning in Partially Linear Endogenous Models" and Emmenegger and Buehlmann (2021) "Double Machine Learning for Partially Linear Mixed-Effects Models with Repeated Measurements". First part: our goal is to perform inference for the linear parameter in partially linear models with confounding variables. The standard DML estimator of the linear parameter has a two-stage least squares interpretation, which can lead to a large variance and overwide confidence intervals. We apply regularization to reduce the variance of the estimator, which produces narrower confidence intervals that are approximately valid. Nuisance terms can be flexibly estimated with machine learning algorithms. Second part: our goal is to estimate and perform inference for the linear coefficient in a partially linear mixed-effects model with DML. Machine learning algorithms allows us to incorporate more complex interaction structures and high-dimensional variables.
How to cite:
Corinne Emmenegger (2021). dmlalg: Double Machine Learning Algorithms. R package version 1.0.2, https://cran.r-project.org/web/packages/dmlalg. Accessed 10 Mar. 2026.
Previous versions and publish date:
0.0.1 (2021-06-11 11:10), 0.0.2 (2021-06-21 09:30), 1.0.1 (2021-09-03 01:10), 1.0.2 (2022-02-03 13:40)
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