DESCRIPTION

Package: DoubleML
Type: Package
Title: Double Machine Learning in R
Version: 1.0.0.9000
Authors@R: c(
    person("Philipp", "Bach", email = "philipp.bach@uni-hamburg.de", role=c("aut", "cre")),
    person("Victor", "Chernozhukov", role="aut"),
    person("Malte S.", "Kurz", email = "mkurz-software@gmx.de", role="aut"),
    person("Martin", "Spindler", email="martin.spindler@gmx.de", role="aut"),
    person("Klaassen", "Sven", email="sven.klaassen@uni-hamburg.de", role="aut"))
Description: Implementation of the double/debiased machine learning framework of
    Chernozhukov et al. (2018) <doi:10.1111/ectj.12097> for partially linear
    regression models, partially linear instrumental variable regression models,
    interactive regression models and interactive instrumental variable
    regression models. 'DoubleML' allows estimation of the nuisance parts in
    these models by machine learning methods and computation of the Neyman
    orthogonal score functions. 'DoubleML' is built on top of 'mlr3' and the
    'mlr3' ecosystem. The object-oriented implementation of 'DoubleML' based on
    the 'R6' package is very flexible. More information available in the
    publication in the Journal of Statistical Software: <doi:10.18637/jss.v108.i03>.
License: MIT + file LICENSE
URL: https://docs.doubleml.org/stable/index.html, https://github.com/DoubleML/doubleml-for-r/
BugReports: https://github.com/DoubleML/doubleml-for-r/issues
Encoding: UTF-8
Depends: 
    R (>= 3.5.0)
Imports: 
    R6 (>= 2.4.1),
    data.table (>= 1.12.8),
    stats,
    checkmate,
    mlr3 (>= 0.5.0),
    mlr3tuning (>= 0.3.0),
    mvtnorm,
    utils,
    clusterGeneration,
    readstata13,
    mlr3learners (>= 0.3.0),
    mlr3misc
Roxygen: list(markdown = TRUE, r6 = TRUE)
RoxygenNote: 7.3.1
Suggests: 
    knitr,
    rmarkdown,
    testthat,
    covr,
    patrick (>= 0.1.0),
    paradox (>= 0.4.0),
    dplyr,
    glmnet,
    lgr,
    ranger,
    sandwich,
    AER,
    rpart,
    bbotk,
    mlr3pipelines
VignetteBuilder: knitr
Collate:
    'double_ml.R'
    'double_ml_data.R'
    'double_ml_iivm.R'
    'double_ml_irm.R'
    'double_ml_pliv.R'
    'double_ml_plr.R'
    'helper.R'
    'datasets.R'
    'zzz.R'