Package: dmlalg
Title: Double Machine Learning Algorithms
Version: 0.0.1
Authors@R: 
    c(person(given = "Corinne",
             family = "Emmenegger",
             role = c("aut", "cre"),
             email = "emmenegger@stat.math.ethz.ch", 
             comment = c(ORCID = "0000-0003-0353-8888")), 
      person(given = "Peter", 
             family = "Buehlmann", 
             role = "ths", 
             email = "buhlmann@stat.math.ethz.ch", 
             comment = c(ORCID = "0000-0002-1782-6015")))
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" <arXiv:2101.12525>. 
    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.
License: GPL (>= 3)
URL: https://gitlab.math.ethz.ch/ecorinne/dmlalg.git
Encoding: UTF-8
Depends: R (>= 4.0.0)
Suggests: testthat (>= 3.0.0)
Imports: glmnet, matrixcalc, stats, splines, randomForest
Config/testthat/edition: 3
NeedsCompilation: no
Packaged: 2021-06-10 12:25:33 UTC; Corinne
Author: Corinne Emmenegger [aut, cre] (<https://orcid.org/0000-0003-0353-8888>),
  Peter Buehlmann [ths] (<https://orcid.org/0000-0002-1782-6015>)
Maintainer: Corinne Emmenegger <emmenegger@stat.math.ethz.ch>
Repository: CRAN
Date/Publication: 2021-06-11 09:10:05 UTC
